Spatial profiling of the metabolism-immune axis in ovarian cancer

Ovarian cancer remains a lethal disease marked by profound therapeutic resistance, largely orchestrated by a complex tumor microenvironment (TME) governed by metabolism-immune crosstalk. This review focuses on the spatiotemporal dynamics of the metabolism-immune axis in ovarian cancer progression and resistance, with particular emphasis on how cutting-edge spatial multi-omics technologies reveal previously unrecognized layers of intratumoral heterogeneity and geographic organization that cannot be captured by bulk analyses. Using tools such as MALDI-MSI, GeoMx DSP, and CODEX, these approaches enable high-resolution, spatially resolved mapping of metabolite gradients (e.g., lactate, lipids, kynurenine), immune cell niches, and immunometabolic checkpoints within distinct tumor regions. Such spatial profiling uncovers how metabolic reprogramming-dysregulated glycolysis, lipid metabolism, and glutaminolysis-drives localized immunosuppression and chemoresistance through compartment-specific interactions among tumor cells, cancer-associated fibroblasts (CAFs), adipocytes, and immune populations. These geographically defined insights reshape our understanding of therapeutic failure and highlight precise, location-aware vulnerabilities. Accordingly, we propose spatially informed therapeutic strategies, including regional glycolysis inhibition, glutaminase blockade, lipid pathway interference, and their rational combination with immune checkpoint inhibitors (ICIs), to disrupt pathogenic metabolic-immune circuits and improve immunotherapy outcomes. Looking ahead, advances in vivo spatial imaging, gut microbiota modulation, and AI-powered integrative multi-omics frameworks promise truly personalized treatment of ovarian cancer.

1 Introduction

Ovarian cancer exhibits the highest mortality rate among gynecologic malignancies. Due to nonspecific symptoms, approximately 70% of cases are diagnosed at advanced stages, contributing to poor overall prognosis (Cortez et al., 2018; Colombo et al., 2019). The high lethality and therapeutic challenges stem from profound tumor heterogeneity, a characteristic peritoneal dissemination pattern, and microenvironment-driven drug resistance. Conventional therapeutic paradigms face limitations due to their oversight of dynamic spatiotemporal interactions between tumor and stroma and the evolving metabolism-immune network (Cheng et al., 2009; Hansen et al., 2016; Worzfeld et al., 2017; Kossaï et al., 2018). The current standard-of-care involves cytoreductive surgery combined with platinum-based chemotherapy (Veneziani et al., 2023); however, microscopic residual disease frequently persists post-surgery (Narod, 2016). Following initial platinum therapy, approximately 85% of patients with epithelial ovarian cancer experience recurrence, often progressing to chemoresistant disease (St Laurent and Liu, 2024). Furthermore, PARP inhibitors demonstrate efficacy primarily within the homologous recombination deficiency (HRD)-positive subset (Kaye, 2016). Critically, conventional strategies also neglect the immunosuppressive microenvironment orchestrated by components such as cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs). Chemotherapy itself can paradoxically increase PD-L1 expression, fostering a therapy-induced remodeling of the microenvironment that contributes to immunotherapy failure (Jiménez-Sánchez et al., 2020). As a disease encompassing diverse molecular subtypes, the inherent heterogeneity of ovarian cancer results in variable degrees of intrinsic resistance to conventional platinum chemotherapy. This heterogeneity further drives subtype-specific alterations in the TME, adaptive immune tolerance, and therapeutic susceptibility (Veneziani et al., 2023). Despite this complexity, clinical management remains largely guided by uniform histopathological classification. Consequently, targeted agents directed against aberrations like PI3K/AKT pathway dysregulation or HRD exhibit suboptimal efficacy when applied without consideration of the specific molecular context within ovarian cancer subtypes (Tse et al., 2014).

The dynamic TME ecosystem serves as a central hub driving therapeutic resistance. Comprising heterogeneous cell populations, extracellular matrix, and chemokines, the ovarian cancer TME features core components such as immunosuppressive cells, CAFs, adipocytes, and nascent vascular networks. These elements interact through complex mechanisms to drive immune evasion, chemoresistance, and metastasis (Chap et al., 2024). Infiltrating regulatory T cells (Tregs), M2-polarized TAMs, and myeloid-derived suppressor cells (MDSCs) establish an immunosuppressive barrier by secreting cytokines like IL-10 and TGF-β to inhibit effector T cell function while concurrently inducing immune checkpoint molecules such as PD-1/PD-L1 (Zhu and Paul, 2010; Maimon et al., 2021; Sun et al., 2024; Kumagai et al., 2022). CAFs promote angiogenesis, provide tumor support, enhance cancer cell survival, and critically modulate T cell state and localization within solid tumors (Arpinati et al., 2024). Cancer-associated adipocytes further contribute by secreting adipokines, cytokines, and growth factors that disrupt endocrine signaling to tumor cells (Sabol et al., 2019). Collectively, this intricate signaling crosstalk among diverse TME cell populations drives cancer stem cell maintenance (Nallasamy et al., 2022). Consequently, traditional platinum-based, “one-size-fits-all” therapeutic strategies often fail to achieve desired efficacy due to their disregard for the TME’s spatiotemporal heterogeneity. Physical barriers formed by CAFs impede drug penetration, the immunosuppressive milieu attenuates immunotherapy response, and metabolic reprogramming undermines targeted therapies, culminating in an inability to meet personalized patient needs.

A profound understanding of the spatiotemporal relationships governing the metabolic-immune network is crucial for overcoming therapeutic bottlenecks. The dynamic interplay between metabolism and immunity within the TME significantly influences treatment response and clinical outcomes. While foundational studies established that tumor cells gain proliferative advantage through metabolic reprogramming strategies such as the Warburg effect and glutamine addiction (Koppenol et al., 2011; Altman et al., 2016), recent insights reveal that this metabolic rewiring extends beyond self-sufficiency. Tumor cells actively reshape the immune microenvironment via metabolite exchange, nutrient competition, and signaling crosstalk (Zhong et al., 2022; Morandi et al., 2016). Conversely, aberrant microenvironmental conditions-including hypoxia, low pH, and nutrient deprivation-trigger adaptive responses in tumor cells, such as autophagy, which further modulate cellular metabolism (Cairns et al., 2011). However, conventional research, often limited to in vitro metabolomic analysis at the cellular level, overlooks the three-dimensional spatial compartmentalization of metabolic gradients and intercellular communication between tumor parenchyma and stroma. This review aims to address this limitation by integrating a spatial multi-omics perspective. We focus specifically on spatial transcriptomics, proteomics, and metabolomics technologies, as they enable decoding of the functional spatial network of the metabolic-immune axis across the complete chain from gene expression and protein function to metabolite-mediated cell crosstalk-dynamic processes that cannot be captured by genomic analysis alone. Therefore, integrating spatial metabolomics, single-cell transcriptomics, and multiplexed fluorescence imaging to systematically delineate the spatiotemporal coupling of metabolic enzyme localization, metabolite diffusion boundaries, and immune cell functional states will provide a crucial theoretical foundation. This approach will inform the development of precision interventions targeting metabolic-immune crosstalk, potentially reversing the immunosuppressive TME by alleviating nutrient deprivation and reprogramming the metabolic adaptability of immune cells.

2 Metabolic reprogramming in ovarian cancer: from molecular mechanisms to spatial heterogeneity

2.1 Core hallmarks of metabolic reprogramming

Metabolic reprogramming constitutes a critical driver of malignant progression and therapy resistance in ovarian cancer. Its defining features encompass spatiotemporally dysregulated pathways governing glucose, lipid, and amino acid metabolism. While conventional research has predominantly focused on molecular mechanisms within isolated metabolic pathways, it has largely overlooked the spatial organization (‘spatial heterogeneity’ specifically refers to the diverse distribution patterns of molecular, cellular, and metabolic characteristics across different spatial regions of the TME) of metabolic heterogeneity within the TME. This section systematically dissects the regulatory networks underpinning glycolysis, lipid metabolism, and glutamine dependence. By integrating spatial omics technologies, we delineate the three-dimensional heterogeneity inherent to metabolic reprogramming and elucidate the cooperative oncogenic mechanisms arising from the spatial localization of metabolic enzymes and their interplay with the TME. Collectively, this analysis establishes a theoretical foundation for spatially precise interventions targeting the metabolism-immune axis.

Our dscussion of glycolytic reprogramming centers on key regulatory nodes-HK2, lactate dehydrogenase A (LDHA), and monocarboxylate transporters (MCTs)-based on their established and irreplaceable roles in driving ovarian cancer progression and shaping the immune microenvironment. HK2 is highlighted over other hexokinase isoforms due to its prominent overexpression in cancer, the anti-apoptotic benefit afforded by its mitochondrial localization, and its direct involvement in initiating glycolysis-positioning it as the gatekeeper of glucose entry (Hu et al., 2014). LDHA is underscored as the critical enzyme that converts pyruvate to lactate, directly coupling glycolytic flux to the production of the immunosuppressive metabolite lactate. Its activity is indispensable for maintaining the NAD+/NADH balance necessary for sustained glycolytic activity (Zhang et al., 2022; Sharma et al., 2025). MCTs (particularly MCT1 and MCT4) are highlighted as the primary channels for lactate efflux (from tumor cells) and influx (into stromal or immune cells). Their spatial expression patterns underpin the establishment of a lactate-enriched acidic niche, a hallmark of the immunosuppressive microenvironment. Targeting these transporters disrupts the entire “lactate shuttle” system (Ibacache-Chía et al., 2024; Hatami et al., 2023). This focus on HK2, LDHA, and MCTs enables a mechanistic dissection of how glycolytic flux is initiated, executed, and spatially communicated within the TME, rather than providing a comprehensive but superficial catalog of all glycolytic enzymes.

In ovarian cancer, glycolytic reprogramming exhibits unique clinical correlations. Malignant ascites from high-grade serous ovarian cancer (HGSOC) contains elevated levels of lactate, which is significantly associated with chemoresistance and poor prognosis (Mukherjee et al., 2015; Rashid et al., 2025). Studies have demonstrated that the expression of HK2 and LDHA in ovarian cancer cell lines (e.g., SKOV3, OVCAR3) is directly linked to paclitaxel resistance, while inhibition of LDHA restores chemosensitivity. Furthermore, single-cell RNA sequencing (scRNA-seq) has revealed marked intratumoral heterogeneity in glycolytic activity, with cell subsets displaying strong epithelial-mesenchymal transition (EMT) signatures showing the most robust enrichment of glycolytic pathways (Xiao et al., 2020). Aberrant glucose metabolism, exhibiting pronounced spatial heterogeneity, represents a fundamental characteristic of metabolic reprogramming in ovarian cancer. This dysregulation involves dynamic coordination of the Warburg effect and lactate shuttling (Figure 1). Key enzymes, HK2 and LDHA, act synergistically to drive malignant progression (Hirschhaeuser et al., 2011; Wang and Patti, 2023). HK2, localized to the outer mitochondrial membrane, phosphorylates glucose to glucose-6-phosphate. This reaction not only accelerates glycolysis but also suppresses mitochondrial apoptosis signaling by modulating the mitochondrial permeability transition pore (mPTP), as evidenced in mouse ischemia-reperfusion models, conferring a survival advantage upon tumor cells (Mathupala et al., 2009; Andrienko et al., 2017). Conversely, LDHA catalyzes the conversion of pyruvate to lactate, facilitating NAD⁺ regeneration to sustain glycolysis. Crucially, lactate efflux via MCTs acidifies the extracellular milieu, thereby promoting tumor invasion and immune evasion (Song et al., 2020; Sharma et al., 2022). Spatial metabolomics, particularly mass spectrometry imaging (MSI)-based approaches, offers unique advantages by enabling direct, label-free, and molecularly specific detection of a broad range of metabolites at (sub)micrometer resolution, thereby directly visualizing how metabolic activities are spatially regulated. Furthermore, spatial analysis of candidate drug metabolic responses enables the early identification of detrimental metabolic effects during drug development (Alexandrov, 2023). Applying spatial metabolomics to ovarian cancer research holds transformative potential: it may overcome therapy resistance stemming from metabolic adaptability and facilitate the evaluation of novel therapeutic agents.

Figure 1

Figure 1. Dynamic Coordination of Aberrant Glucose Metabolism and Lactate Shuttling in Ovarian Cancer. HK2, localized on the outer mitochondrial membrane, phosphorylates glucose to accelerate glycolysis. This process occurs even under aerobic conditions, a phenomenon known as the Warburg effect, wherein cancer cells preferentially rely on glycolysis. Therein, LDHA catalyzes the conversion of the resulting pyruvate to lactate, thereby regenerating NAD⁺ to sustain glycolytic flux. Subsequently, the exported lactate is shuttled out of the cell via MCTs. This acidifies the tumor microenvironment, ultimately promoting tumor invasion and immune evasion. HK2, Hexokinase 2; LDHA, Lactate dehydrogenase A; MCTs, monocarboxylate transporters.

Although we have emphasized the synergistic roles of HK2 and LDHA due to their direct links to lactate production and immunosuppression, glycolytic flux is tightly regulated at three irreversible steps. Beyond HK2, PFK-1 and pyruvate kinase (PK) are equally critical nodes. Their regulatory isoforms-PFKFB3 and PKM2-exhibit distinct spatial distribution patterns within the TME, dictating metabolic plasticity and anabolic support.

PFK-1 and PFKFB3 serve as the primary flux-controlling enzymes in glycolysis. PFK-1 activity is potently regulated by PFKFB3, which synthesizes fructose-2,6-bisphosphate (F2,6BP)-the most effective allosteric activator of PFK-1. PFKFB3 is frequently upregulated in cancer and displays spatially heterogeneous expression patterns, often enriched in regions of hypoxia and nutrient stress. Its high activity drives a “glycolytic burst,” diverting carbon flux into the pathway to support biomass synthesis (Yang et al., 2025). Spatial transcriptomics may reveal PFKFB3-high expression niches adjacent to proliferating tumor cells, while its relative absence may mark more quiescent or oxidative regions (Ha et al., 2018). PKM2 is a well-recognized cancer hallmark. Its low catalytic activity and ability to exist as a dimer (in contrast to the highly active tetrameric PKM1) create a metabolic “bottleneck,” allowing upstream glycolytic intermediates to accumulate and be shunted into biosynthetic pathways (e.g., the pentose phosphate pathway for nucleotide synthesis) (Mukherjee et al., 2015; Wa et al., 2023). Critically, the spatial localization and oligomeric state of PKM2 are likely heterogeneous. At the invasive front, it may predominantly exist as a nuclear dimer, functioning as a protein kinase to promote gene expression; in the tumor core, it may be primarily in the metabolically active tetrameric form (Wa et al., 2023). This spatial regulation directly links glycolytic flux to anabolic growth and invasive programs. In summary, the glycolytic landscape of the ovarian cancer TME is collectively shaped by the spatial co-expression and activity of HK2, PFKFB3, and PKM2 (Zhang et al., 2020). HK2 controls entry into the pathway, PFKFB3 governs flux rate, and PKM2 determines the fate of carbon flux-whether toward lactate production or biomass synthesis. This expanded perspective reveals a more complex and hierarchical metabolic control system that future spatial metabolomics studies must dissect to fully understand and target glycolysis in ovarian cancer (Zhang et al., 2022; Wa et al., 2023).

Ovarian cancer displays tissue-specificity in lipid metabolic reprogramming. Endometriosis-associated clear cell ovarian carcinoma (CCOC) is characterized by a distinct lipid accumulation phenotype, whereas high-grade serous ovarian carcinoma (HGSOC) is more dependent on fatty acid oxidation (FAO). Preclinical studies have demonstrated that ovarian cancer stem cells (CSCs) overexpress the fatty acid-binding protein FABP4, sustaining their stemness via enhanced fatty acid uptake (Ha et al., 2018). Critically, LPA is present at abnormally high levels in ovarian cancer ascites, which directly drives tumor progression and immunosuppression through activation of the LPA receptor-STAT3 signaling cascade (Mukherjee et al., 2015; Ha et al., 2018). Within the TME, a core regulatory network centered on lipid metabolic reprogramming maintains dynamic equilibrium by integrating fatty acid uptake, synthesis, and oxidation pathways, thereby driving malignant progression (Röhrig and Schulze, 2016; Jin H. R. et al., 2023). Fatty acid translocase CD36 facilitates the uptake of exogenous lipids by tumor cells from the microenvironment, while fatty acid synthase (FASN) catalyzes the de novo synthesis of endogenous long-chain fatty acids. Together, these processes fulfill the heightened membrane biogenesis demands of rapidly proliferating tumors (Hao et al., 2020; Menendez and Lupu, 2007). Concurrently, CPT1A, the rate-limiting enzyme for mitochondrial β-oxidation, sustains tumor cell survival under nutrient deprivation by regulating FAO to generate energy (Ma L. et al., 2024). Significantly, tumor cells and stromal components-such as CAFs and adipocytes-establish a bidirectional lipid exchange network via exosomes, lipid transfer proteins, and metabolic paracrine signaling. Stromal cells secrete free fatty acids or lipid droplets for tumor cell uptake, while tumor cells reciprocally modulate stromal lipolysis through the release of signaling molecules like lactate and reactive oxygen species (ROS), fostering a pro-tumorigenic metabolic symbiosis (Jin H. R. et al., 2023; Hoy et al., 2021). The application of spatial lipidomics offers a powerful strategy to spatially track distinct lipid species within ovarian cancer. This approach can elucidate how lipid-based TME interactions drive disease progression and therapy resistance, potentially enabling early detection or facilitating large-scale screening of high-risk populations for preventive strategies (Wang G. et al., 2022; Mirretta Barone et al., 2024).

Glutamine metabolism exhibits subtype-specific features in ovarian cancer. Clear CCOC displays high sensitivity to glutamine deprivation, which correlates with the expression level of ASCT2 (SLC1A5). In contrast, high-grade serous ovarian cancer (HGSOC) utilizes glutamine to support the tricarboxylic acid (TCA) cycle through anaplerotic reactions. Studies have shown that the glutaminase inhibitor CB-839 exerts a synergistic anti-tumor effect in BRCA-mutant ovarian cancer models, suggesting that the HRD status may influence glutamine metabolic dependency (Vikramdeo et al., 2025; Hanahan et al., 2025). Therapeutic resistance is closely linked to the metabolic adaptability of tumor cells, with glutamine dependence and amino acid metabolic plasticity serving as key regulatory mechanisms. The primary glutamine transporter ASCT2 (SLC1A5), in concert with GLS1, drives glutaminolysis. This pathway generates α-ketoglutarate (α-KG) to replenish the TCA cycle and maintain redox balance (Du et al., 2022). Studies reveal that chemoresistant tumor cells upregulate ASCT2 to enhance glutamine uptake and utilization, subsequently activating the mTORC1 signaling pathway and promoting nucleotide biosynthesis. This adaptive response mitigates metabolic stress induced by chemotherapeutic agents (Wang et al., 2013). Integrated proteomic and metabolomic analyses have delineated two distinct ovarian cancer subtypes: a low-oxidative phosphorylation (OXPHOS) subtype reliant on glycolysis and a high-OXPHOS subtype. The high-OXPHOS subtype exhibits increased sensitivity to conventional chemotherapy, attributed to its augmented fatty acid oxidation capacity (Gentric et al., 2019). Leveraging single-cell metabolomics holds promise for uncovering latent metabolic subtypes within ovarian cancer and characterizing their specific metabolic dependencies. Combining glutaminolysis or fatty acid oxidation inhibitors with conventional chemotherapeutics represents a potential strategy to overcome chemoresistance in this disease.

2.2 Interplay between metabolic reprogramming and an immunosuppressive microenvironment

Immunosuppression in the ovarian cancer microenvironment is intrinsically governed by metabolic alterations in malignant and stromal compartments. This bidirectional metabolic-immune crosstalk displays significant spatial heterogeneity, with localized enrichment of specific metabolites establishing and maintaining an immune-tolerant microenvironment through distinct molecular mechanisms. Understanding this dynamic provides key insights for deciphering immune evasion and designing spatially informed therapeutic strategies.

Lactate, a quintessential glycolytic byproduct, exemplifies how metabolites transcend metabolic signatures to actively construct immunosuppressive networks (Martinez-Outschoorn et al., 2017; Chen et al., 2025; Certo et al., 2021). Extracellular acidification by lactate impedes CD8⁺ T cell proliferation and cytotoxicity, an effect mediated through metabolic reprogramming associated with glycolytic activity (Kumagai et al., 2022). Furthermore, lactate dynamically calibrates the immunosuppressive function of Tregs based on microenvironmental metabolic cues. Spatial metabolomic profiling demonstrates that Tirzepatide (TZP), a dual GIP/GLP-1 receptor agonist, markedly depletes key glycolytic metabolites in colon cancer tissues, attenuating glycolytic flux. This underscores the role of pyruvate and lactate production in inducing tumor cell nutrient stress (Zhang Y. et al., 2025). Crucially, spatial metabolomics reveals strong histological concordance between lactate and glutamate metabolic zones. This spatial coupling implies a coordinated metabolic program: high glycolytic flux leading to lactate production coincides with increased glutaminolysis demand and may fuel the latter. Glutamate, a key product of glutamine metabolism, can enter the TCA cycle anaplerotically to sustain the production of biosynthetic precursors. Even in the context of truncated metabolism in the form of the Warburg effect (i.e., preferential anaerobic glycolysis), glutamate metabolism compensates for the TCA cycle to support metabolic flexibility (Li et al., 2023; Su et al., 2023). The colocalization of these metabolites highlights regions of intense metabolic activity and active nutrient recycling, which may serve to identify niches of aggressive tumor growth or adaptive resistance. Critically, spatial metabolomics reveals strong histological congruence between lactate and glutamate metabolic regions, and the distinct features of these metabolic niches facilitate the identification of areas associated with aggressive tumor growth or adaptive resistance (Li et al., 2023; de la Cruz-López et al., 2019; Gupta et al., 2017). Extensive regulatory interplay connects glutamine metabolism, lactate handling, glycolysis, and glutathione pathways (Zhu et al., 2023), highlighting the integrated nature of TME metabolism. This coordination reflects adaptive metabolic reprogramming, acidotic pressures, and cellular interactions, potentially unveiling novel targets for ovarian cancer therapy.

Lipid metabolic reprogramming constitutes another critical axis of immune microenvironment regulation, with metabolites dictating macrophage polarization states. AMPK-PPARα signaling activation by lipid species drives TAMs towards an M2 phenotype (Wang et al., 2025). These M2-polarized TAMs enforce immunosuppression via IL-10 and TGF-β secretion coupled with diminished MHC class II expression (Singhal et al., 2019). Polyunsaturated fatty acids (PUFAs) also facilitate M2-like TAM enrichment by suppressing RhoA-YAP1 signaling in ovarian cancer (Wang et al., 2024). Multiplex immunohistochemistry (mIHC) identified p53-deficient CSCs as the primary subset secreting IL-34. IL-34 upregulates CD36-dependent fatty acid oxidation, promoting the polarization of lipid-laden ‘foamy’ TAMs toward an M2 phenotype. These M2-type TAMs subsequently inhibit CD8⁺ T cell cytotoxicity through multifaceted crosstalk mechanisms. Key inhibitory effects include: (i) high expression of co-inhibitory ligands, such as PD-L1, which directly binds to the PD-1 receptor on T cells to deliver intrinsic inhibitory signals; (ii) robust secretion of immunosuppressive cytokines, such as IL-10 and TGF-β, which suppress T cell receptor signaling and effector gene expression; and (iii) creation of a metabolically adverse microenvironment by sequestering essential amino acids (e.g., L-arginine) and producing lactate, collectively impairing T cell metabolic fitness and proliferative capacity. This synergistic process effectively compromises CD8⁺ T cell immunity and facilitates immune evasion (Nian et al., 2024).

In amino acid metabolism, the tryptophan-kynurenine-AhR axis serves as a key regulator of immunosuppression in the ovarian cancer TME. Evidence from ovarian cancer models and patient samples shows elevated expression of IDO1/TDO2 and increased kynurenine levels, which correlate with enhanced Treg abundance and impaired CD8⁺ T cell function. This establishes kynurenine metabolism as a well-recognized component of the immunosuppressive network in ovarian cancer (Hanahan et al., 2025; de la Cruz-López et al., 2019). Within amino acid metabolism, the tryptophan metabolite kynurenine acts as a pivotal regulator, activating the aryl hydrocarbon receptor (AhR) signaling pathway to suppress effector T cell function and promote regulatory T cell (Treg) differentiation. This reveals an additional dimension of metabolic-immune crosstalk. Kynurenine inhibits effector T cell proliferation and enhances Treg generation through AhR activation (Cui et al., 2023; Mezrich et al., 2010; Uyttenhove et al., 2003). While direct spatial multi-omics evidence in ovarian cancer continues to emerge, pioneering studies in other immunologically “hot” and aggressive cancer types provide a compelling conceptual framework. For example, high-throughput digital spatial profiling conducted in TNBC-a malignancy that shares similarities with HGSOC, often exhibiting TP53 mutations, genomic instability, and an immunosuppressive TME-identified enrichment of the rate-limiting metabolic enzyme indoleamine 2,3-dioxygenase 1 (IDO1) in specific compartments, which correlates with patient prognosis. Notably, this study found that IDO1 is co-expressed with PD-L1 in early-stage TNBC, suggesting dynamic microenvironmental crosstalk between IDO1-driven tryptophan catabolism and the development of T cell exhaustion. This co-expression pattern of a key immunometabolic enzyme and a classical immune checkpoint provides critical multi-omics evidence for a conserved immune evasion mechanism highly relevant to ovarian cancer. It exemplifies how spatial technologies can uncover functional niches where metabolic and immunosuppressive pathways converge, offering a testable model for future validation in ovarian cancer specimens (Carter et al., 2023). Notably, IDO1 co-expresses with PD-L1 in early-stage TNBC, suggesting dynamic microenvironmental crosstalk between IDO1-driven tryptophan catabolism (producing kynurenine) and the development of T cell exhaustion phenotypes. This co-expression pattern of metabolic enzymes and immune checkpoints provides crucial multi-omics evidence for deciphering immune evasion mechanisms in ovarian cancer. Furthermore, this suggests that targeting the AhR-IDO1 axis may reverse immunosuppression by remodeling the metabolic landscape. This is supported by ovarian cancer studies, where kynurenine production and AhR activation have been mechanistically linked to the induction of Tregs and suppression of anti-tumor immunity, thereby opening new therapeutic avenues for combination immunotherapy in ovarian cancer (Amobi-McCloud et al., 2021).

In summary, metabolic reprogramming within spatially defined ovarian cancer microenvironments-encompassing dysregulated glycolysis, lipid metabolism, and amino acid catabolism-generates abundant immunosuppressive metabolites, including lactate, specific lipid species, and kynurenine. Collectively, these metabolites establish and sustain an immunosuppressive niche through multiple mechanisms: extracellular acidification, driving M2-polarization of TAMs, impairing effector T cell cytotoxicity, and enhancing Treg activity (Figure 2).

Figure 2

Figure 2. Spatial Interaction Network of the Metabolism-Immune Axis in Ovarian Cancer. Key metabolic pathways, including amino acid metabolism (kynurenine and AhR signaling), carbohydrate metabolism (lactate and extracellular acidification), and lipid metabolism (polyunsaturated fatty acids, CD36, and elevated fatty acid oxidation), are shown to influence immune cell populations. Effector T cells are suppressed by kynurenine-mediated AhR signaling. Lactate from carbohydrate metabolism acidifies the extracellular environment, inhibiting CD8⁺ T cell function and promoting Treg activity. Lipid metabolism drives M2 TAM polarization through RhoA-YAP1 signaling and IL-34 secretion by p53−/− CSCs, further enhancing immunosuppression via IL-10 and TGF-β. This spatial network underscores the dynamic crosstalk driving immune evasion and therapeutic resistance. AhR, Aryl Hydrocarbon Receptor; PUFAs, Polyunsaturated Fatty Acids; Tregs, Regulatory T cells; CSCs, Cancer Stem Cells; TAMs, Tumor-Associated Macrophages.

2.3 Clinical implications of metabolic heterogeneity

The most direct clinical manifestation of metabolic heterogeneity lies in its predictive value for platinum-based chemotherapy sensitivity. This heterogeneity is manifested as a spectrum of glycolytic phenotypes. Tumor cells or regions displaying high aerobic glycolysis-i.e., the Warburg effect, characterized by elevated glucose uptake and lactate production even in the presence of oxygen-exhibit increased glucose uptake and divert pyruvate toward lactate generation. This contrasts with tumor cells with lower glycolytic flux, which may rely more heavily on mitochondrial OXPHOS for energy production. The high glycolytic phenotype not only promotes tumor cell proliferation but also facilitates their evasion of apoptosis (Jin S. et al., 2023). Metabolic imaging (FDG-PET) quantifies tumor glycolytic activity, and radiomic texture features derived from these images offer enhanced characterization of tumor heterogeneity. However, limited spatial resolution may obscure intratumoral heterogeneity (Chicklore et al., 2013; Cook et al., 2018). Integrating FDG-PET with transcriptomic analysis reveals associations between metabolic activity and signaling pathways, enabling prediction of potential therapeutic targets. For instance, transcriptomic profiling of breast cancer (BC) samples stratified by FDG-PET uptake demonstrated strong links between BC metabolism and CXCL8/EGFR signaling activation, driving a shift toward a glycolytic phenotype (Confalonieri et al., 2024). This integrated analytical strategy has demonstrated translational utility. Combining FDG-PET with spatial transcriptomics may thus provide a robust biomarker system to guide precision chemotherapy decisions in ovarian cancer. Therefore, the integration of FDG-PET and spatial transcriptomics holds promise as a powerful biomarker platform for guiding precision chemotherapy strategies in ovarian cancer. This combined analysis enables the stratification of patients into distinct therapeutic subsets. For example, tumors displaying high glycolytic flux (increased FDG uptake) alongside transcriptomic signatures of OXPHOS suppression and upregulated DNA repair pathways may be particularly susceptible to combinations of glycolytic inhibitors (e.g., 2-DG) and PARP inhibitors, leveraging their inherent metabolic vulnerabilities (Sharma et al., 2022; Huang et al., 2025). In contrast, tumors with low FDG-PET avidity and transcriptomic profiles indicative of enhanced FAO and growth factor signaling (e.g., PI3K/AKT pathway) can be targeted combinatorially with FAO inhibitors (e.g., etomoxir) and matched signaling pathway antagonists (Li et al., 2023; Su et al., 2023). Additionally, spatial correlations between metabolic hotspots and immune checkpoint expression can pinpoint regions suitable for combined metabolic-immunotherapeutic strategies, such as the combination of LDHA inhibitors with anti-PD-1/PD-L1 agents (Yang et al., 2025).

Metabolic reprogramming transcends its role as a predictor; it constitutes a fundamental driver of resistance to platinum-based chemotherapy. The metabolic index reflecting glucose-derived lipogenesis and fatty acid uptake directly correlates with cisplatin resistance (Zhang L. et al., 2025). Enhanced fatty acid uptake promotes cisplatin resistance primarily by fueling FAO, which sustains tumor cell survival under cisplatin-induced oxidative stress (Tan et al., 2022). Mimicking in vivo metabolic profiles using patient-derived organoids and pharmacologically inhibiting key metabolic enzymes can significantly restore chemosensitivity (Hu and Lazar, 2022; Gilbert et al., 2024). For instance, the use of tumor organoids has led to the development of anti-LGR4 monoclonal antibodies, which effectively induce ferroptosis in chemoresistant organoids when combined with chemotherapy. LGR4 is a G protein-coupled receptor (GPCR) that has been shown to promote fatty acid metabolism and redox homeostasis in cancer cells. Inhibition of LGR4 disrupts these metabolic processes, thereby rendering cells sensitive to chemotherapy-induced ferroptosis (Pei and Luan, 2023). Such metabolism-targeted interventions represent a promising new avenue to overcome chemotherapy resistance in ovarian cancer, accelerating the translation of precision oncology approaches.

Beyond chemotherapy, the heterogeneity of the tumor metabolic microenvironment significantly modulates the efficacy of ICIs (Kao et al., 2022). Tumor regions with high glycolytic flux are characterized by lactate accumulation and hypoxia, leading to extracellular acidosis and functional exhaustion of tumor-infiltrating lymphocytes (TILs) (Calcinotto et al., 2012; Scharping et al., 2021). PD-1 expression on Tregs further enhances their suppressive activity and diminishes immunotherapy efficacy (Kumagai et al., 2022). Within TAMs, hypoxia-induced REDD1 expression suppresses mTOR signaling, consequently impeding glycolysis; this metabolic constraint curtails the pro-angiogenic activity of TAMs, ameliorating dysfunctional vasculature (Wenes et al., 2016). Hypoxia also upregulates PD-L1 on tumor cells and induces other inhibitory receptors on T cells, establishing multifaceted immunosuppressive barriers (Noman et al., 2014; Do et al., 2013). Moreover, CD8⁺ T cells within highly glycolytic niches exhibit impaired mitochondrial OXPHOS, compromising their capacity for sustained anti-tumor activity (Yu et al., 2020). Nutritional interventions aimed at remodeling the immune microenvironment by targeting immune metabolism and metabolic crosstalk within the TME can potentiate anti-tumor responses (Hirschberger et al., 2021; Geiger et al., 2016). Collectively, these mechanisms suggest that combining metabolic modulators with anti-PD-1 therapy offers a viable strategy to counter chemoresistance in ovarian cancer.

In conclusion, metabolic heterogeneity in ovarian cancer demonstrates significant clinical value-predicting chemotherapy sensitivity, enabling resistance reversal, and augmenting immunotherapy efficacy (Figure 3). Developing biomarkers based on metabolic features and implementing metabolic modulation strategies will establish a new paradigm for precision therapy in ovarian cancer.

Figure 3

Figure 3. Mechanisms of Metabolic Spatial Heterogeneity Impacting Therapeutic Response in Ovarian Cancer. High-glycolysis cancer cells exhibit hypoxia and lactate accumulation, promoting angiogenesis, PD-L1 expression, and fatty acid oxidation, which contribute to chemotherapy and immunotherapy resistance via CXCR8/EGFR signaling and Treg cell-mediated immunosuppression. Enhanced PD-L1 expression and mitochondrial OXPHOS dysfunction in CD8⁺ T cells further exacerbate immunotherapy resistance. In contrast, tumor cell subsets with low glycolytic activity exhibit weaker therapeutic resistance mechanisms and may represent a more treatment-sensitive component within heterogeneous tumors. The spatial distribution of these metabolic features underscores their role in driving differential treatment outcomes and highlights potential targets for overcoming resistance. PD-L1, Programmed Death-Ligand 1; EGFR, Epidermal Growth Factor Receptor; OXPHOS, Oxidative Phosphorylation; Tregs, Regulatory T cells; TAMs, Tumor-Associated Macrophages.

2.4 Spatial omics-driven therapeutic strategies targeting subtype-specific metabolic vulnerabilities in ovarian cancer

The major molecular subtypes of ovarian cancer (stromal, immunoreactive, proliferative, and differentiated) exhibit distinct TME characteristics, which determine their unique metabolic dependencies and therapeutic sensitivities. Spatial multi-omics technologies provide an unprecedented perspective for deciphering these subtype-specific metabolic vulnerabilities and guiding precision therapy.

The stromal subtype is characterized by a dense stroma enriched with CAFs. Spatial metabolomic analysis may reveal that CAFs produce lactate via glycolysis, while adjacent tumor cells potentially utilize this lactate for OXPHOS, forming a putative “metabolic symbiosis.” This finding highlights the therapeutic value of targeting lactate transporters (MCT1/MCT4) (De la Cruz-López et al., 2019; Gupta et al., 2017). Additionally, tumor spheroids of this subtype may rely on macropinocytosis to acquire nutrients in the nutrient-depleted ascites microenvironment, offering an exploratory direction for targeting this pathway.

Although the immunoreactive subtype is marked by abundant immune cell infiltration, these cells are often functionally suppressed. Spatial transcriptomic analysis may uncover that tumor cells and MDSCs in this subtype highly express arginase 1 (ARG1) and IDO1, which may deplete arginine and tryptophan in the TME, leading to T cell dysfunction (Mondanelli et al., 2019). This provides a novel rationale for combining ICIs with metabolic enzyme inhibitors. Meanwhile, the glycolytic activity of tumor cells may generate a lactic acid microenvironment that further impairs T cell function, suggesting that strategies such as MCT inhibitors warrant further investigation (Lopez et al., 2023).

The proliferative subtype is defined by high tumor cell proliferative activity, which may increase the demand for biosynthetic precursors such as nucleic acids, lipids, and amino acids (Martínez-Reyes and Chandel, 2021). Spatial metabolic imaging may demonstrate active serine-glycine metabolic pathways and nucleotide synthesis in this subtype. This metabolic signature indicates potential sensitivity to targeted anabolic interventions, such as PHGDH inhibitors (serine synthesis) and DHODH inhibitors (pyrimidine synthesis).

In summary, spatial multi-omics studies hold great promise for unraveling the metabolic features of different ovarian cancer subtypes and may guide the development of subtype-specific therapeutic strategies, providing new research directions to overcome the limitations of current treatments.

3 Spatial profiling of the ovarian cancer immune microenvironment: from cellular architecture to functional networks

3.1 Spatial architecture of immune cell distribution

Effective regulation of the ovarian cancer immune microenvironment hinges on the precise spatial patterning of immune cells. Cutting-edge spatial profiling technologies-including GeoMx® Digital Spatial Profiler (DSP) and CODEX®-enable comprehensive mapping of immune cell subsets (e.g., T/B lymphocytes, macrophages, CAFs) within distinct anatomical niches: the tumor core, invasive margin, and tertiary lymphoid structures (TLSs). These platforms quantify cellular localization, abundance, and interaction networks, providing a critical topological framework for deciphering heterogeneity in immune responses and mechanisms of therapeutic resistance.

TLSs function as pivotal hubs for anti-tumor immunity, where their spatial distribution and maturation status profoundly impact patient prognosis. In ovarian cancer, TLS formation involves chemokine-mediated lymphocyte recruitment (e.g., CXCL13, CCL19/21) and localized microenvironmental remodeling (Sautès-Fridman et al., 2016). Within TLS germinal centers (GCs), B cell differentiation, antibody secretion, and T cell activation are supported, amplifying anti-tumor immunity (Victora and Nussenzweig, 2022). TLSs foster tumor antigen-specific responses while limiting immunosuppressive cell infiltration, correlating significantly with improved patient survival (Munoz-Erazo et al., 2020; Colbeck et al., 2017; Dieu-Nosjean et al., 2014). Spatial multi-omic analysis of cholangiocarcinoma via GeoMx DSP revealed concentrated clusters of CD19⁺ B cells and CD3⁺ T cells within TLSs. Furthermore, the top differentially upregulated genes between TLSs and TILs were predominantly B cell-associated, suggesting synergistic anti-tumor activity mediated through antigen presentation, co-stimulatory signaling, and cytokine exchange (Chung et al., 2025). GeoMx DSP-based multiplexed proteomic profiling of platinum-refractory (PRF) and platinum-sensitive (PS) advanced high-grade serous ovarian cancer identified elevated apoptosis and anti-tumor immune signatures in PS patients, contrasting with PRF tumors exhibiting AKT1 and WNT dual signaling indicative of immunosuppression (Scalise et al., 2024). Quantifying these spatial interaction networks not only offers novel perspectives for developing TLS-based biomarkers to guide personalized immunotherapies in ovarian cancer but also deepens our understanding of tumor-immune crosstalk within the TME.

Moving beyond the localized effects of TLSs, the immune landscape across distinct functional tumor regions-such as the core versus invasive margin-exhibits profound heterogeneity, sculpting unique immunosuppressive or immunopermissive niches. Significant spatial divergence characterizes the immune microenvironments of the tumor core and invasive front (Wu et al., 2023a). Within the invasive niche, malignant cell-induced stressed stromal cells secrete serum amyloid A (SAA) proteins, recruiting M2-polarized macrophages. A functional triad emerges: highly invasive CXCL6⁺ tumor cells, SAA⁺ stromal cells, and recruited FPR1⁺ M2 macrophages interact synergistically to drive tumor progression (Wu et al., 2023a). Macrophages and NK/T cells accumulate at the tumor-stroma interface, while fibroblasts exhibit pronounced enrichment. This spatial patterning potentially links to hypoxia-driven metabolic reprogramming and HIF-1α-mediated upregulation of immune checkpoint molecules, collectively constraining anti-tumor immunity (Wu et al., 2023a; Ni et al., 2020). CODEX™-based spatial multi-omics imaging delineates immunologically active (“hot”) versus excluded (“cold”) peritumoral niches surrounding spatial subclones and identifies novel immune exhaustion markers (Mo et al., 2024). Concurrently, CODEX integrated with spatial proteomics reveals remarkable plasticity within cancer-CAF subpopulations (Foster et al., 2022). These findings elucidate spatial tumor evolution through local microenvironmental interactions, uncovering patterns of spatial co-localization between M2 macrophages and CAFs within fibrotic regions. This provides a novel biological rationale for targeting immunosuppressive hubs in ovarian cancer.

The ultimate clinical value of immune cell spatial architecture lies in its predictive power for immunotherapy response. This underpins microenvironmental classifications-immune-desert, -inflamed, and -excluded phenotypes-which form the bedrock for precision immunotherapy (Gerard et al., 2021). Immune-desert regions lack significant immune infiltration and correlate with therapy resistance. Inflamed zones display abundant yet functionally constrained immune cells. The excluded phenotype manifests as immune cells confined to stromal cuffs, physically segregated from tumor cells (Hegde et al., 2016; Tumeh et al., 2014; Gajewski et al., 2013). Integrating pan-tissue spatial transcriptomics with single-cell multi-omics identified four conserved CAF spatial subtypes. Notably, s1-CAFs-highly expressing ACTA2 and TGFB1-mediate matrix remodeling and immunosuppression, potentially generating excluded or desert microenvironments that diminish immune checkpoint inhibitor efficacy (Liu et al., 2025). This positions microenvironmental subtyping as a predictive biomarker strategy to guide personalized ovarian cancer therapy.

Furthermore, the spatial architecture of the ovarian cancer immune microenvironment extends beyond solid tumor masses to encompass the critical compartment of malignant ascites. Ascites serves as a dynamic “liquid TME” that facilitates peritoneal dissemination. Although the cellular heterogeneity and lack of inherent spatial structure of malignant ascites present significant research challenges, emerging spatial multi-omics technologies are providing groundbreaking solutions. First, by integrating single-cell data from ascites with spatial transcriptomic and proteomic data from primary tumors and omental tissues, we can trace the “origin” of tumor cells in ascites and identify shed cell clones with high metastatic potential along with their key survival genes. Second, spatial transcriptomic or multiplex immunofluorescence analysis of pelleted ascites cell blocks enables precise dissection of the internal architecture of tumor spheroids, revealing metabolic gradients such as the “hypoxic-necrotic core and proliferative outer shell,” while mapping the scaffold roles of CAFs and mesothelial cells, as well as the spatial distribution of immune cells-for example, whether T cells are excluded, trapped, or entirely absent. Finally, using Laser Capture Microdissection (LCM) or high-resolution DSP, specific micro-regions-from the mesothelial layer and newly attached spheroids to established metastatic lesions on the peritoneum-can be precisely isolated and molecularly decoded. This approach dynamically captures the “cellular dialogue” driving tumor attachment and systematically elucidates the central role of ascites in remodeling the metastatic process in ovarian cancer.

In summary, spatial orchestration of immune cells in ovarian cancer-modulated through TLS activation, regional heterogeneity, ascites-mediated dissemination, and microenvironmental classification-dictates immune responsiveness (Figure 4). Deciphering these spatial features provides a critical theoretical foundation for developing immune-topography-guided therapeutic strategies and prognostic biomarkers.

Figure 4

Figure 4. Spatial Topological Structure of the Ovarian Cancer Immune Microenvironment. The left panel shows local tertiary lymphoid structure (TLS) aggregation observed via GeoMx DSP, indicating regions of enhanced immune activity. The middle panel, captured with CODEX multiplexed imaging and antibody panel, depicts tumor regional heterogeneity, including the invasion front with normal cells, cancer-associated cells, FPR1+ M2 TAMs, and CXCL6+ cancer cells. The right panel presents microenvironment-based analysis of immune response capacity, classifying tumor regions into inflamed (hot tumor), excluded, and desert (cold tumor) phenotypes based on immune infiltration and responsiveness, providing insights for spatially guided immunotherapeutic strategies. TLS, Tertiary Lymphoid Structure; TAMs, Tumor-Associated Macrophages.

3.2 Spatiotemporal regulation of immunometabolic checkpoints

The expression and activity of immune checkpoint molecules (e.g., PD-L1, adenosine pathway components) are not static but are dynamically fine-tuned by the local tumor metabolic microenvironment, giving rise to spatially compartmentalized “immunometabolic checkpoints.” Deconstructing the spatiotemporal regulatory networks through which metabolic signals govern immune checkpoints represents a critical nexus for understanding the spatial heterogeneity of immune evasion in ovarian cancer, overcoming immunotherapy resistance, and devising novel combinatorial strategies.

As the most intensively studied immune checkpoint, PD-L1 expression is subject to intricate regulation by diverse metabolic pathways, with hypoxia-driven glycolysis and lactate metabolism playing pivotal roles. Research demonstrates a strong link between PD-L1 expression on tumor cells and metabolic reprogramming. Specifically, HIF-1α drives aberrant PD-L1 upregulation by activating glycolytic pathways, establishing a synergistic network coupling metabolic signaling to immune evasion. In a GL261 syngeneic glioma model in wild-type C57BL/6 mice, HIF-1α inhibition (PX-478, 30 mg/kg daily) combined with anti-PD-L1 antibody (10 mg/kg twice weekly) synergistically reduced tumor volume by >60%, enhanced CD8⁺ T cell activation (IFN-γ⁺), and improved survival versus monotherapy (n = 5/group, P < 0.01) (Ding et al., 2021). Lactate indirectly sustains stable PD-L1 expression through mechanisms including microenvironmental acidification and activation of G protein-coupled receptors (e.g., GPR81) (Feng et al., 2017). Critically, inhibiting the glycolytic enzyme LDHA or blocking the lactate transporter MCT4 attenuates PD-L1-mediated immunosuppression, thereby bolstering the clinical feasibility of enhancing anti-tumor immune responses (Cui et al., 2025; Babl et al., 2023). This bidirectional immunometabolic crosstalk not only explains primary resistance to ICIs in highly glycolytic tumors but also provides a mechanistic rationale for combination therapies targeting the HIF-1α/PD-L1 axis or lactate metabolism.

Beyond the PD-1/PD-L1 axis, the adenosine signaling pathway represents another crucial spatially defined immunometabolic checkpoint, centered on the spatial remodeling of the extracellular ATP-adenosine metabolic gradient. The adenosine pathway (CD39/CD73/ADO) exerts potent immunosuppression within the TME by regulating extracellular ATP catabolism to adenosine. CD39 and CD73 sequentially catalyze ATP degradation to adenosine, which subsequently triggers immunosuppression via A2a receptor activation (Vijayan et al., 2017; Klysz et al., 2024). Spatial metabolomics provides precise quantification of the ATP-adenosine metabolic gradient within ovarian tumor tissues. This mapping reveals specific immunosuppressive phenotypes spatially correlated with adenosine-enriched niches, offering topographical guidance for targeted interventions. This mapping provides critical topographical guidance for targeted interventions. Small molecule inhibitors or antibodies targeting CD73 can block adenosine generation, reversing the immunosuppressive milieu (Young et al., 2016). Combining these agents with PARP inhibitors-which enhance tumor antigen release by inducing DNA damage-synergistically promotes effector T cell infiltration and functional restoration (Besse et al., 2024). Preclinically, the CD73 inhibitor CB-708 demonstrates enhanced anti-tumor activity when combined with anti-PD-L1 antibodies (Lee et al., 2019), suggesting this combinatorial strategy holds promise for overcoming resistance to existing ICIs in ovarian cancer.

Advances in spatial profiling technologies are driving deeper understanding of the metabolic regulation and spatial heterogeneity of immune checkpoints, offering unprecedented insights into tumor evolution and precision targeting. During tumorigenesis, the levels and spatial distribution of markers for dedifferentiation, proliferation, and immune checkpoints undergo significant remodeling, reflecting dynamic TME evolution and its impact on disease progression (Sheng et al., 2022). Notably, metabolic polarization of immune cells toward the tumor-immune boundary occurs in some tissues, accompanied by increased expression of transporters like CD98 and ASCT2, which influence tumor aggressiveness and therapy resistance (Hartmann et al., 2021; Shimizu et al., 2014; Kaira et al., 2009). Spatial omics technologies, by integrating spatially resolved gene expression, protein localization, and metabolic signatures, provide a comprehensive framework for dissecting ovarian cancer TME heterogeneity, yielding novel perspectives on tumor initiation, progression, and metastasis.

Collectively, elucidating the spatiotemporal metabolic regulation of PD-L1 and the adenosine pathway, particularly through the lens of spatial omics, not only provides a multidimensional theoretical foundation for understanding immune evasion but also paves the way for precision combination strategies that co-target these immunometabolic checkpoints to overcome therapeutic resistance in ovarian cancer.

3.3 Stromal regulation of the metabolic-immune axis

Stromal elements within the ovarian cancer microenvironment-including CAFs, adipocytes, and endothelial cells-transcend structural support to function as pivotal regulators of the metabolic-immune axis. Through secretion of specific metabolites and cytokines, alongside physical barrier formation, they spatiotemporally remodel local metabolic niches, directly and indirectly modulating immune cell functionality and distribution. This orchestration profoundly influences disease progression and therapeutic resistance.

As the most metabolically active stromal component, CAFs architect immunosuppressive metabolic landscapes primarily via secretion of key metabolites like lactate and glutamine. CAFs remodel the TME through lactate and glutamine efflux, establishing metabolic niches conducive to tumor advancement (Pavlides et al., 2009; Francescone et al., 2021). Lactate operates as both an energetic substrate and signaling molecule: its acidification of the microenvironment enhances tumor glycolytic flux and immune evasion, as shown in Pan02 pancreatic xenografts in C57BL/6 mice and NSCLC xenografts tracing 13C-lactate uptake (Martinez-Outschoorn et al., 2017; Chen et al., 2025; Certo et al., 2021), while glutamine provision furnishes carbon/nitrogen sources for tumor cells, fueling nucleotide biosynthesis and redox homeostasis (Altman et al., 2016). Spatial multi-omics profiling of pancreatic cancer following neoadjuvant chemotherapy (NAC) revealed elevated expression of metabolites (e.g., proline, glycine) within the immune-TME interface. Concurrently, significant metabolic reprogramming occurred-downregulation of key glycolytic enzymes alongside upregulation of lipid metabolism, glutaminolysis, and OXPHOS components (Tang et al., 2023). These findings not only elucidate linkages between NAC-induced metabolic reconfiguration and immune microenvironment alterations but also provide a mechanistic foundation for developing precision strategies co-targeting metabolic pathways (e.g., CD36-mediated lipid scavenging) and immunotherapy.

Beyond CAFs, adipocytes-leveraging their distinctive lipid metabolic signature-emerge as significant contributors to immunosuppression within specialized niches like ovarian cancer omental metastases. Recent studies reveal that tumor-associated adipocytes release unsaturated fatty acids that activate the peroxisome proliferator-activated receptor gamma (PPARγ) pathway, driving macrophage polarization toward an anti-inflammatory M2 phenotype. Concurrently, they suppress NF-κB signaling, attenuating pro-inflammatory cytokine secretion, thereby orchestrating localized immunosuppression (Wang et al., 2025; Scirpo et al., 2015). This immunometabolic interplay is particularly pronounced in malignancies prone to omental dissemination, including ovarian cancer (Wang et al., 2024; Tan et al., 2018). MALDI analysis of human omental tumor sections demonstrated heightened phosphatidylcholine abundance in cancer cells proximal to adipocytes, contrasting with distal cells enriched in phosphatidylinositol (PI). This spatially resolved metabolomics confirms adipocyte-mediated phosphatidylcholine elevation in neighboring tumor cells (Mukherjee et al., 2023). Collectively, these findings provide experimental justification for targeting adipocyte metabolism in ovarian cancer immunotherapy strategies.

Endothelial cells spatially navigate immune cell trafficking and positioning through their metabolic state and chemokine secretion, profoundly sculpting regional immune responses. Endothelial metabolism critically regulates vascular functionality and directs lymphocyte delivery/activation (Scharping et al., 2021; Li et al., 2018; Haas et al., 2015; Tyrakis et al., 2016; Riesenberg et al., 2007). In 3D models co-culturing human atherosclerotic plaques with autologous CD8⁺ T cells, T cells preferentially localized near neovessels. CXCL12-overexpressing endothelia within these vessels governed CD8⁺ T cell migration (Parma et al., 2025). Integrated single-cell and spatial transcriptomics in oral squamous cell carcinoma revealed Treg enrichment within high-metabolism CAF domains (Liu Z. et al., 2024). Notably, inflammatory CAFs (iCAFs)-exhibiting elevated pyruvate carboxylase expression-recruit Tregs via CXCL12 secretion, establishing immunosuppressive hubs (Ma et al., 2023; Schwörer et al., 2021). Therefore, endothelial metabolic reprogramming constitutes a promising approach to regulate immune cell infiltration and activation, laying a theoretical foundation for developing novel combined anti-angiogenic/immunotherapeutic strategies in ovarian cancer.

Through metabolite secretion and spatial signaling, CAFs, adipocytes, and endothelial cells collaboratively construct a stromal network governing the metabolic-immune axis. The inherent spatial heterogeneity of this network unveils novel therapeutic targets for stroma-directed immunotherapies.

4 Spatial multi-omics technologies: novel tools for deconvoluting the metabolic-immune axis

The selection of multi-omics technologies discussed in this review-particularly spatial transcriptomics, proteomics, and metabolomics-is based on a core hypothesis: the functional output of the metabolism-immune axis is not merely the sum of its parts, but arises from spatial synergy at the molecular level. While genomics provides a blueprint for potential metabolic perturbations (e.g., mutations in metabolic enzymes), it fails to capture the dynamic nature, post-translational modifications, and microenvironment-dependent characteristics of immuno-metabolic crosstalk. We therefore prioritize technologies that address this gap:

Spatial transcriptomics (e.g., Xenium, Visium): Critical for localizing the expression of metabolic enzymes (e.g., HK2, LDHA), immune checkpoint ligands (e.g., PD-L1), and chemokines within the structural context of the TME, revealing ‘who is where and what they might be doing.'

Spatial proteomics (e.g., GeoMx DSP, CODEX): Since the localization and abundance of proteins-especially metabolic transporters (e.g., MCTs, CD98) and immune cell surface markers (e.g., CD3, CD68)-are often not directly correlated with mRNA levels, spatial proteomics enables direct validation of pathway activity and intercellular interactions.

Spatial metabolomics (e.g., MALDI-MSI, DESI): Represents the ultimate functional readout, providing direct evidence of the biochemical consequences of metabolic reprogramming (e.g., lactate and kynurenine gradients) that directly suppress immune cell function. This level of information cannot be inferred solely from transcriptomic or proteomic data.

These specific technologies were therefore selected to decode functional spatial networks, from gene expression to protein function, and ultimately to metabolite-mediated cellular crosstalk.

4.1 Breakthroughs in spatial metabolomics

The rapid evolution of spatial metabolomics technologies-particularly breakthroughs in high-resolution imaging and in situ metabolite detection-provides revolutionary tools for unbiased, spatially resolved mapping of metabolite distributions within the native tissue microenvironment of ovarian cancer. These advances enable direct visualization of metabolic heterogeneity, dynamic tracking of metabolic flux, and the establishment of spatial correlations between metabolic landscapes, immune phenotypes, and therapeutic responses. Consequently, they offer unprecedented capacity for definitive elucidation of metabolic-immune axis crosstalk networks (Table 1).

Table 1

Table 1. Comparison of spatial multi-omics technologies.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), a cornerstone spatial metabolomics platform, has achieved transformative breakthroughs in circumventing clinical sample limitations and visualizing therapy-resistant metabolite topography. Its application to ovarian cancer provides an indispensable tool for spatially resolving chemotherapy resistance-associated metabolomes. MALDI-MSI enables high-resolution mapping and analysis of lipid distributions directly within tumor sections, revealing dynamic lipid metabolic rewiring during acquired resistance (Berry et al., 2011; Fernández et al., 2011). Studies confirm that ovarian cancer chemoresistance correlates strongly with lipid reprogramming; for instance, lysophosphatidic acid (LPA) functions as a negative regulator of type I interferon (IFN) signaling to confer resistance (Chae et al., 2022). Crucially, MALDI-MSI visually contrasts lipid metabolic heterogeneity between resistant and sensitive tumors, identifying resistance-specific lipid biomarkers (Barré et al., 2018). Furthermore, integrated metabolomic reconstruction of FFPE specimens with MALDI-MSI overcomes traditional fresh-tissue dependency, preserving and reactivating lipid metabolic data-thereby vastly expanding clinically relevant sample resources for ovarian cancer research (Ly et al., 2016). This combined approach furnishes critical technical support for delineating ovarian cancer’s metabolic-immune-therapeutic resistance network.

In contrast to MALDI-MSI, desorption electrospray ionization mass spectrometry (DESI-MS) leverages its matrix-free operation to offer unique advantages for high-resolution molecular spatial analysis of live or intraoperative specimens, particularly in elucidating dynamic lipid-immune phenotype correlations. DESI-MS excels at decoding spatiotemporal relationships between lipid metabolic heterogeneity and immune landscapes within the TME (Razo et al., 2024), enabling correlation of lipid distribution patterns with histopathologically defined tumor regions (Awad et al., 2024). Multi-omics analyses implicate dysregulated phospholipid pathways (e.g., phosphatidylcholine and sphingolipid metabolism) in immunosuppressive niche formation (Ping et al., 2024; Soula et al., 2024), suggesting metabolic reprogramming modulates immune effector functions to drive progression. Integrated metabolomic-lipidomic profiling reveals stark differences in tumor transcriptomes, immune infiltrates, fecal microbiomes, and metabolomes between bevacizumab responders and non-responders (Rosario et al., 2024). These insights pioneer metabolism-based stratification frameworks, simultaneously advancing fundamental understanding of immunometabolic crosstalk and laying a translational foundation for optimizing bevacizumab regimens through rational combination therapies.

Beyond static distribution mapping, the integration of metabolic flux tracing with spatial imaging represents a frontier breakthrough in spatial metabolomics, enabling high spatiotemporal resolution analysis of in vivo metabolic flux dynamics. The synergy between metabolic flux analysis (e.g., ¹³C-glucose tracing) and spatial imaging platforms provides unprecedented tools for dynamically resolving metabolic networks with high spatial and temporal precision. The ¹³C-SpaceM methodology leverages ¹³C-labeled glucose to trace glucose-dependent de novo lipogenesis pathways, achieving single-cell mass spectrometry imaging via ultrahigh-resolution MALDI. By emulating saponification reactions through all-ion fragmentation (AIF) mode, it enables comprehensive co-analysis of fatty acid moieties within major phospholipids including glycerophospholipids (Tang et al., 2023). Ultra-sensitive high-resolution mass detectors deliver precise qualitative and quantitative assessment of fatty acid isotopologues at single-cell resolution (Buglakova et al., 2024). Concurrently, spatial imaging techniques (mass spectrometry imaging, stimulated Raman scattering, or single-cell metabolomics) integrate spatial metabolite distributions to reveal flux heterogeneity across tissue microenvironments and subcellular compartments (Maimó-Barceló et al., 2025; Zhang et al., 2019; Nunes et al., 2024). This multidimensional spatiotemporal profiling empowers researchers to dynamically visualize molecular details of metabolic reprogramming during ovarian carcinogenesis and cellular differentiation-including complex phenomena like metabolic competition within the TME and immunometabolic switching in immune cells.

MALDI-MSI, DESI-MS, and integrated metabolic flux-spatial imaging platforms synergistically deliver multidimensional analytical frameworks for ovarian cancer research-deciphering metabolite topography, lipid metabolic networks, and spatiotemporal dynamics to resolve spatial heterogeneity within the metabolic-immune axis.

4.2 Advances in spatial immunomics

Rapid advancements in spatial immunomics now enable precision mapping and quantitative analysis of immune cell composition, functional states, and interaction networks directly within intact tissue architecture. These technologies overcome the spatial information deficit inherent to conventional single-cell sequencing, providing unprecedented capacity to resolve the complex spatial organization, heterogeneity, and dynamic interplay with metabolic reprogramming and therapeutic responses within the ovarian cancer immune microenvironment.

Within spatial proteomics, the GeoMx® DSP has emerged as a pivotal platform for decoding immune checkpoint spatial heterogeneity through its region-specific, high-plex analytical capabilities. As a next-generation spatial proteomic technology, GeoMx DSP facilitates high-resolution, multiplexed protein expression analysis within morphologically defined tissue regions. This innovative approach precisely localizes distinct functional compartments-including tumor core, immune infiltrates, and stromal zones-via integrated laser microdissection and fluorescent barcoding, while quantifying spatial disparities in key immune checkpoint protein expression (Merritt et al., 2020; Hernandez et al., 2022). When coupled with high-plex proteomics (40+ targets), it achieves multiplexed quantification of >40 antigens at subcellular resolution, constructing multidimensional immune regulatory network maps (Wu et al., 2022). This spatially resolved proteomic strategy delivers critical scientific rationale for optimizing immunotherapy response prediction and developing combination therapies targeting immunosuppressive niches, thereby propelling translational research in precision immuno-oncology for ovarian cancer.

Imaging mass cytometry (e.g., CODEX) and super-resolution spatial transcriptomics (e.g., Xenium) deliver spatially resolved protein or gene expression profiles at single-cell resolution, enabling precise mapping of cellular interactions and functional states. CODEX generates spatially resolved single-cell protein maps (Goltsev et al., 2018), delineating the immune tumor microenvironment in lung cancer and revealing cellular/molecular progression from precancerous lesions to malignancy (Rozenblatt-Rosen et al., 2020). Analysis of tumors from 25 bladder cancer patients via CODEX identified PD-L1/PD-L2-expressing CDH12⁺ epithelial cells co-localizing with CD8⁺ T cells-a spatial configuration potentially underlying clinical responses to checkpoint inhibitors (Gouin et al., 2021). Xenium-based spatial transcriptomics of small intestine tissue mapped tissue-resident memory (TRM) cell distributions across villus tips and crypt bases, uncovering significant transcriptional differences between these niches (Reina-Campos et al., 2025). This study pioneers a paradigm that combines spatial multi-omics with metabolic pathway analysis, uncovering how distinct tissue microenvironments (e.g., intestinal villi and crypts) modulate the metabolic phenotype and functional properties of TRM cells. This serves as a critical point of reference for deciphering T cell adaptation within the TME. While the research focuses on the healthy gut, the methodologies it showcases and the concept of “metabolic checkpoints”-the regulation of T cell function by tissue-specific metabolic niches-provide a conceptual framework and research paradigm for investigating analogous metabolism-centered combination immunotherapeutic approaches in ovarian cancer.

Beyond transcriptional/proteomic profiling, spatial epigenomics (e.g., ATAC-seq) resolves spatial heterogeneity in chromatin accessibility, offering novel insights into epigenetic regulation of immune cell functional states. Spatial ATAC-seq interrogates genome-wide chromatin accessibility landscapes (Buenrostro et al., 2013), mapping regulatory element dynamics to elucidate epigenetic mechanisms in development and pathology. Applied spatially, it discriminates compartment-specific epigenetic signatures in lymphoid organs (Li et al., 2022) and identifies pathogenic chromatin architectures in autoimmunity (e.g., hyperaccessible inflammatory loci in lupus) (Perez et al., 2022; Yu et al., 2021). Comparative analysis of tumor-infiltrating versus peripheral lymphocytes further reveals epigenetic dysregulation during T cell exhaustion following immunotherapy (Sundaram et al., 2024; Satpathy et al., 2019). This methodology holds promise for identifying epigenetic regulators of immune checkpoints to guide targeted therapies for ovarian cancer.

Collectively, GeoMx DSP, CODEX, and spatial ATAC-seq provide multidimensional frameworks for investigating spatial heterogeneity along the metabolism-immunity axis in ovarian cancer through protein mapping, single-cell.

4.3 Integrative multi-omics frameworks

While single-modality spatial omics provide localized insights, deciphering ovarian cancer’s metabolism-immunity network necessitates systematic integration of multimodal spatial data. Emerging computational frameworks are advancing spatial multi-omics beyond mere data superposition toward true information fusion, enabling system-level analysis spanning molecular features, spatial coordinates, and functional interactions. This paradigm shift offers unprecedented opportunities for constructing precision disease models and guiding clinical decision-making.

Next-generation AI algorithms (e.g., SpatialGlue) are redefining integration paradigms through adaptive cross-modal coupling. Unlike traditional methods reliant on preprocessing-stage stitching, SpatialGlue establishes a spatially guided multimodal framework via bidirectional attention mechanisms. This architecture dynamically resolves feature-coordinate interdependencies within a unified computational space-autonomously quantifying modality-specific contributions (e.g., transcriptome vs. proteome) while precisely capturing microenvironmental couplings (e.g., cellular neighborhood topology with gene expression gradients). Such hierarchical attention overcomes fixed-weight fusion limitations, enabling biologically adaptive integration (Long et al., 2024). Applied to ovarian cancer multi-omics (metabolome, transcriptome, proteome), this approach can reconstruct metabolism-immunity interaction networks to systematically resolve dynamic crosstalk between metabolic reprogramming and immune regulation. Deep learning models leveraging these multidimensional features, when integrated with histopathological and spatial molecular data (Chen et al., 2022; Gao et al., 2025), may predict therapy-sensitive niches via graph neural networks and attention mechanisms. This quantifies treatment response heterogeneity across cellular subpopulations while providing an extensible analytical framework for solid tumors.

Integrated multi-omics analyses (genomics, transcriptomics, proteomics, metabolomics, spatial omics) demonstrate significant potential for disease subtyping and clinical translation, offering innovative frameworks for precision oncology in malignancies like ovarian cancer. By consolidating genomic, transcriptomic, proteomic, and metabolomic profiles with spatial transcriptomic/metabolomic data and immune microenvironment features, recent work classified renal cell carcinoma into four distinct immune subtypes-providing actionable strategies for precision therapy (Hu et al., 2024). Clinically, spatial multi-omics elucidates spatial correlations between metabolic heterogeneity and immune infiltration in ovarian tumors (Schweizer et al., 2023; Kader et al., 2024), informing immunophenotyping while enabling precise delineation of target volumes where hypermetabolic zones overlap with immunosuppressive microenvironments. This facilitates optimized radiation dosing during local therapy planning while sparing normal tissues. Complementarily, live-organoid spatiotemporal (LOST) technology employs 3D organoid models coupled with spatial transcriptomics to simulate dynamic tumor microenvironment responses, revealing critical cell-cell communication networks (Ma S. et al., 2024). These advances hold substantial implications for organoid-based precision medicine and drug development.

Despite promise, spatial multi-omics integration faces significant technical and translational hurdles requiring coordinated advances. Current resolution remains constrained by detection sensitivity and physical limits (Si et al., 2025), while dynamic process capture is hampered by destructive sample processing, preventing reconstruction of continuous spatiotemporal processes like cell migration (Liu et al., 2020). Clinically, heterogeneous analytical pipelines impede cross-institutional data integration, and most findings remain mechanistic-lacking validated pathways from spatial biomarker discovery to diagnostic applications (Liu et al., 2021; Vandereyken et al., 2023). Future efforts will fuse next-generation imaging that approaches sub-organelle resolution with single-cell multi-omics, generating three-dimensional molecular maps at the nanoscale. Concomitantly, we will devise in-situ live-cell labelling strategies and temporal modelling algorithms, integrating organoid systems to create a four-dimensional spatial-temporal atlas. These advances will feed directly into an automated spatial-pathology platform, a pan-cancer repository of spatial heterogeneity, and prospective trials that rigorously test the prognostic power of spatial biomarkers.

5 Precision targeting of the metabolic-immune crosstalk

5.1 Combinatorial metabolic and immune modulation

The metabolically driven immunosuppressive landscape in ovarian cancer constitutes a fundamental resistance mechanism to immunotherapies. Strategic targeting of core metabolic circuits-glycolysis, glutamine metabolism, and lipid pathways-to reprogram the immune microenvironment, coupled with immune checkpoint blockade, has emerged as a precision approach to enhance treatment efficacy (Figure 5).

Figure 5

Figure 5. Combined Therapeutic Strategies Targeting the Metabolism-Immune Axis. The left panel presents targeting glycolysis combined with immunotherapy: A glycolysis inhibitor disrupts tumor cell glycolytic metabolism, which not only blocks glucose-dependent PD-L1 upregulation but also directly triggers tumor cell apoptosis through p53 phosphorylation. Concurrently, immunotherapy overcomes residual PD-L1-mediated immune evasion. The middle panel depicts targeting amino acid metabolism (glutamine) combined with immunotherapy: A glutamine metabolism inhibitor induces oxidative stress and mitochondrial dysfunction in tumor cells; this, in turn, feedback-enhances T cell glucose metabolic activation and cytotoxicity, enabling reactivated T cells to effectively attack and eliminate metabolically impaired tumor cells. The right panel shows targeting lipid metabolism combined with immunotherapy: a lipid metabolites inhibitor suppresses STAT6-mediated M2 polarization signaling in tumor-associated macrophages (TAMs), and an anti-CD47 antibody remodels macrophage function to reprogram immunosuppressive M2 TAMs into anti-tumor M1 phenotypes, cooperating with immunotherapy to eliminate tumor cells. PD-L1, Programmed Death-Ligand 1; TAMs, Tumor-Associated Macrophages.

Coordinated glycolysis inhibition and checkpoint disruption exhibits compelling therapeutic synergy. Glut1-targeting agents combined with PD-1 inhibitors significantly restrict tumor progression in preclinical models, as demonstrated in C57BL/6 mice bearing LLC or B16F10 tumors, where BAY-876 plus anti-PD-1 treatment robustly suppressed tumor growth in an immune-dependent manner (Wu et al., 2023b). Cancer cells exploit glycolytic flux to establish immunosuppressive niches (Kao et al., 2022), whereas PD-1/PD-L1/CTLA-4 blockade counteracts this by metabolically reprogramming TILs to restore effector functions (Zappasodi et al., 2021; Parry et al., 2005; Patsoukis et al., 2015; Qorraj et al., 2017). Crucially, glucose availability directly regulates PD-L1 expression to promote immune escape (Guo et al., 2022), while glucose restriction induces p53-dependent apoptosis that amplifies antitumor immunity through antigen release (Wu Y. Q. et al., 2023). Spatial metabolomic platforms (e.g., MALDI-MSI) provide critical insights into combination therapy dynamics via in situ metabolic mapping. Applied to A549 spheroid models, this approach revealed treatment-induced spatial metabolic heterogeneity (e.g., compartmentalized lipid alterations) and established quantitative correlations between drug penetration depth and metabolite gradients (Chen et al., 2021) -informing rational design of combinatorial schedules.

Research reveals that heightened glutaminolysis in tumor cells competitively depletes metabolic resources, profoundly suppressing T cell functionality. Targeting this pathway induces metabolic stress in malignant cells, reducing glutamine-to-glutamate conversion and triggering redox imbalance coupled with mitochondrial stress. Concurrently, it reprograms T cell metabolism to enhance survival, proliferation, and effector responses. Critically, glutaminase inhibition differentially modulates metabolic states across T cell subsets to potentiate antitumor immunity (Lukey et al., 2017; Alkan et al., 2018; Araujo et al., 2017; Johnson et al., 2018). As the most abundant amino acid in tumor metabolism, glutamine targeting synergizes with checkpoint inhibitors (e.g., anti-PD-1) by simultaneously restricting tumor bioenergetics and enhancing T cell glycolytic flux, epigenetic reprogramming, and cytotoxicity (Leone et al., 2019; Su et al., 2020). This dual-action strategy holds clinical promise; rationally designed delivery systems could balance metabolic suppression in tumors with metabolic support in immune cells, opening new therapeutic avenues for ovarian cancer.

Lipid metabolic reprogramming of TAMs polarization represents a pivotal frontier for combination immunotherapy. Distinct from carbohydrate/amino acid interventions, lipid pathway modulation alters TME lipid synthesis/signaling to fundamentally redirect TAM polarization. TAMs chronically internalize lipids via CD36-mediated scavenging, inducing intracellular lipid accumulation. This metabolic rewiring activates fatty acid oxidation- OXPHOS axes to drive mitochondrial reprogramming, ultimately polarizing TAMs toward protumoral M2 phenotypes through STAT6-dependent signaling (Su et al., 2020). Enhanced intracellular lipid peroxidation and membrane lipid efflux further upregulate M2-associated genes while suppressing M1 transcripts (Goossens et al., 2019; Wu et al., 2019). Spatial omics resolves lipid metabolism-TAM polarization heterogeneity through high-resolution mapping of lipid distributions, metabolic enzyme expression, and TAM subpopulation topography. This identifies M2-TAM clusters spatially correlated with dysregulated lipid pathways, informing targeted delivery of lipid-modulating agents combined with checkpoint blockade in ovarian cancer (Nasir et al., 2023).

In summary, the convergence of glycolysis, glutamine, and lipid metabolic axes in ovarian cancer underscores a multifaceted immunosuppressive network amenable to integrated therapeutic intervention. Glycolysis inhibition disrupts tumor bioenergetics and PD-L1-driven evasion, restoring T cell cytotoxicity; glutamine blockade induces tumor redox stress while enhancing T cell metabolic fitness and effector functions; and lipid modulation reprograms TAM polarization from M2 to M1 phenotypes, alleviating protumoral signaling. These pathways intersect through shared mechanisms, such as nutrient competition, mitochondrial dysfunction, and spatial metabolic gradients, which collectively sustain TME immunosuppression. Unified strategies leveraging multi-axis inhibitors (e.g., pan-metabolic agents) combined with checkpoint blockade could synergistically amplify antitumor immunity, as evidenced by preclinical models showing enhanced efficacy when addressing cross-talk between these circuits. Spatial metabolomics further enables precision mapping of these interactions, guiding personalized combinatorial regimens to overcome resistance and improve clinical outcomes in ovarian cancer.

Although preclinical studies have demonstrated substantial potential for targeting tumor metabolism, most metabolism-targeted therapies have failed to meet their primary endpoints in clinical trials, and the underlying causes warrant in-depth reflection. The high heterogeneity and plasticity of tumor metabolism represent major obstacles. Different regions within the same tumor, or even distinct cell subpopulations, may adopt divergent metabolic pathways; this redundancy renders single-pathway inhibition prone to compensatory mechanisms. For instance, inhibition of glycolysis may trigger a compensatory increase in OXPHOS, while blocking glutamine metabolism can activate autophagy or alternative amino acid metabolic pathways. Second, the lack of effective patient stratification biomarkers poses a significant challenge. Most current trials enroll patients who have failed multiple lines of treatment, without screening based on the specific metabolic dependencies of their tumors. Ovarian cancer patients with different molecular subtypes or distinct TME backgrounds may exhibit vastly different responses to the same metabolic intervention. Third, the dual roles of metabolic pathways are often overlooked. Many key metabolites (e.g., lactate, ketone bodies) not only support tumor growth but also play crucial roles in regulating immune cell function. Unselective blocking of these pathways may simultaneously inhibit anti-tumor immunity, thereby attenuating therapeutic efficacy. Additionally, drug delivery barriers cannot be ignored. Abnormal blood supply and interstitial hypertension within tumors may limit the accumulation of drugs in metabolically active hypoxic regions, leading to insufficient target inhibition. These failure experiences suggest that future metabolism-targeted therapies require more precise strategies: identifying specific metabolic dependencies using spatial multi-omics; developing reliable patient stratification biomarkers; and rationally designing combination therapy regimens that account for the integrated effects of metabolic interventions on both tumor cells and the immune microenvironment.

5.2 Development of metabolism-immunity dual-targeting agents

The metabolic-immune interplay within tumor microenvironments offers novel paradigms for bispecific therapeutic development. Integrating spatial metabolomics to resolve metabolic heterogeneity with pharmacokinetic modeling, organoid validation platforms, and multi-omics analytics provides a robust framework for overcoming immunotherapy resistance.

Spatially resolved metabolic landscapes enable molecular stratification for dual-targeting therapies. MALDI-MSI profiling reveals immunosuppressive niche-specific accumulation of lactate, kynurenine, and glutamine. Metabolic subtype-guided interventions include: LDHA inhibitors combined with PD-L1 blockade for glycolytic-dominant tumors (Ju et al., 2022); OXPHOS inhibitors targeting mitochondrial complex I for OXPHOS-dependent subtypes (He et al., 2023); FASN inhibitors with anti-PD-L1 for lipid-metabolism dysregulated cancers (Huang et al., 2024); and dual IDO1/TDO blockade for amino acid-addicted malignancies (Liang et al., 2021).

Precision intervention requires optimization of spatiotemporal drug distribution. Clinical trials comparing rifampicin versus rifapentine in cavitary tuberculosis revealed no superiority despite rifapentine’s lower MIC in murine models. MALDI-MSI-guided PK/PD modeling identified differential cavity penetration kinetics (Rifat et al., 2018), demonstrating how spatial metabolomics can optimize drug scheduling for ovarian cancer combination regimens.

Lactate metabolism orchestrates dual immunoregulatory functions. Tregs exploit lactate to maintain immunosuppression through CTLA-4 upregulation via RNA splicing modulation-a mechanism exploitable by CTLA-4 blockade (Ding et al., 2024); and it establishes acidic microenvironments that promote metastasis while impairing antitumor immunity (Rizwan et al., 2013; Brand et al., 2016). Targeting lactate shuttling (e.g., MCT4 inhibition) with cisplatin demonstrates synergistic therapeutic potential (Chen et al., 2023).

Three-dimensional organoid platforms represent transformative tools for validating metabolic-immune target synergy. As innovative 3D culture systems, organoids uniquely preserve primary tumor heterogeneity and critical signaling pathways, enabling faithful recapitulation of in vivo drug response microenvironments. Patient-derived organoids coupled with high-throughput screening platforms provide robust validation of multi-target efficacy (Roy et al., 2017; Rauth et al., 2021). Integrated spatial transcriptomic-metabolomic analysis quantitatively resolves spatiotemporal drug penetration gradients and pharmacodynamic effects within organoid subregions (Zhou et al., 2025), offering novel solutions to therapy resistance arising from ovarian cancer heterogeneity.

The discovery of novel metabolic immune checkpoints heralds a new era for immunometabolic reprogramming. Translational research focuses on metabolism-enzyme-mediated immunomodulation and clinical intervention potential. Tumor-derived IL4I1 catabolizes tryptophan, activating the aryl hydrocarbon receptor (AhR) to enhance invasiveness and suppress antitumor immunity (Sadik et al., 2020). Both malignant cells and cDC1s overexpress glutamine transporter SLC38A2, facilitating glutamine uptake. Glutamine supplementation enhances cDC1-mediated antitumor responses, potentiating checkpoint blockade or adoptive T cell therapy (Guo et al., 2023). Glutaminolysis inhibitors reverse immunosuppression while directly activating T cells (Leone et al., 2019). Tumor-secreted lyophosphatidylserine suppresses ILC1 antitumor activity via GPR34 receptor, whereas GPR34 antagonism triggers potent ILC1-mediated tumor suppression (Yan et al., 2024). Future work will decode molecular networks linking metabolic reprogramming to immune evasion, leveraging single-cell metabolic flux analysis to design next-generation inhibitors that improve ovarian cancer response rates.

Spatial metabolomic stratification, organoid validation, and novel checkpoint discovery collectively establish an integrated framework from mechanistic insight to clinical translation (Table 2). Advancing this paradigm requires incorporating single-cell metabolic flux analysis and AI predictive modeling to optimize combinatorial sequencing, ultimately enabling precision dual-targeting immunotherapy.

Table 2

Table 2. Clinical trial designs for metabolism-immunity guided precision therapy.

Table 2 summarizes representative clinical trials that illustrate the rationale for co-targeting metabolic pathways and immunotherapy. Notably, as of the writing of this review, the clinical translation of this strategy remains in its early stages, with a marked paucity of trials specifically targeting ovarian cancer. This gap may reflect long-standing challenges in the field of metabolic targeting, such as achieving tumor specificity to avoid on-target systemic toxicity. The trials listed herein, primarily focusing on melanoma, renal cell carcinoma, and non-small cell lung cancer, serve as pioneering “proof-of-concept” studies. Their design-highlighting specific metabolic targets (e.g., IDO1, GLS1), rational combination partners (e.g., anti-PD-1), and corresponding biomarker strategies-provides a critical translational roadmap and source of testable hypotheses for future ovarian cancer-specific clinical research.

5.3 Spatial omics-guided precision therapeutics

Spatial omics-driven personalized therapy overcomes limitations of conventional approaches by resolving spatiotemporal heterogeneity within TME. Mapping regionalized metabolic-immune networks enables spatiotemporally optimized interventions through stimuli-responsive nanocarriers and AI-powered predictive modeling (Dergaa et al., 2024).

Immunotherapy resistance stems fundamentally from spatially driven immune evasion. Despite clinical successes, most patients fail to achieve durable responses. Cytotoxic T lymphocytes (CTLs) eliminate tumor cells via TCR-pMHC recognition followed by perforin/granzyme-mediated apoptosis. Cancers evade this through antigen presentation downregulation (Zaretsky et al., 2016; Sade-Feldman et al., 2017). Glycolysis/Glut1 inhibition enhances CTL cytotoxicity, revealing novel immunotherapeutic targets (Wu et al., 2023b). Spatial heterogeneity critically drives therapeutic resistance, as evidenced by DCE-MRI radiomics predicting treatment response in triple-negative breast cancer (Ramtohul et al., 2024)-a paradigm applicable to ovarian cancer decision-making.

Precision targeting requires intelligent localized delivery systems. Tumor-responsive nanoparticles exploit microenvironmental cues (e.g., disulfide cleavage) for site-specific drug release (Yan et al., 2020; Duan et al., 2017; Zhu et al., 2024). Externally activated systems (light/ultrasound/magnetism/X-ray) trigger payload deployment at irradiated sites (Shen et al., 2017; Zheng et al., 2024). Accumulated nanotherapeutics release agents via passive/active targeting upon external stimulation (Chu et al., 2019; He et al., 2018; Chen et al., 2017), while multistage nanoparticles enable synergistic ROS generation/pH modulation/drug release under combinatorial stimuli (Liu et al., 2017; Sun et al., 2019; Zhao et al., 2020).

Artificial intelligence integrates multimodal spatial data for clinical decision support. Machine learning algorithms decipher nonlinear, high-dimensional relationships across genomics, epigenomics, transcriptomics, proteomics, metabolomics, and radiomics (Esteva et al., 2019; Bera et al., 2019; Bhinder et al., 2021), enabling early detection, molecular subtyping, and treatment response monitoring (Cohen et al., 2018; Capper et al., 2018; Shimizu and Nakayama, 2019; Xu et al., 2022).

Converging nanotechnology with AI-driven modeling establishes an end-to-end framework from mechanistic insight to clinical translation (Figure 6), offering interdisciplinary solutions for spatial precision medicine in treatment-refractory ovarian cancer.

Figure 6

Figure 6. Precision Therapeutic Closed Loop Driven by Spatial Multi-Omics. The process begins with preoperative dynamic contrast-enhanced MRI (DCE-MRI) to characterize intratumoral pharmacokinetic heterogeneity, identifying resistant regions and guiding localized drug delivery. This is followed by intelligent drug administration intervention, utilizing a smart responsive localized drug delivery system to monitor therapeutic response. Finally, multi-platform data from transcriptomics, microbiomics, pathomics, proteomics, and metabolomics are integrated for real-time efficacy monitoring and algorithm iterative updates, forming a continuously optimized precision therapeutic closed loop. Created by Figdraw.

6 Future directions and challenges

Spatial multi-omics technologies, while revolutionizing our understanding of ovarian cancer’s metabolism-immunity axis, present persistent challenges alongside transformative opportunities. Future advances must overcome technical limitations, deepen mechanistic insights, and accelerate clinical translation to realize precision immunometabolic therapeutics.

At the forefront of technology, the development of imaging techniques that can non-invasively capture, in real time, dynamic metabolic processes and immune cell activities within living biological systems is a pivotal breakthrough for deciphering the spatiotemporal dynamics of metabolic-immune interactions. In vivo imaging employs radiographic approaches at the cellular and molecular levels in living organisms, allowing qualitative and quantitative assessment of biological processes and their changes over time. The marked heterogeneity of the tumor metabolic microenvironment severely constrains the effectiveness of metabolism-targeted therapeutics. By applying an in vivo dynamic imaging technique that uses blood oxygen saturation as a marker, it is feasible to clearly visualize tumor vascular structures in deep mouse tissues while quantifying SO₂ levels, thus evaluating tumor metabolic activity and predicting responses to cancer immunotherapy (Fang et al., 2024). Observing molecular signaling changes within tumor cells via in vivo optical molecular imaging to explore mechanisms that facilitate intracellular antigen release and binding to circulating antibodies will aid in advancing the development and application of intracellular antigen-based tumor vaccines (Dai et al., 2022). Using adaptive scanning light-field microscopy (DAOSLIMIT), the movement paths of neutrophils and monocyte-macrophages in mouse livers during acute AILI have been vividly depicted with ultra-high spatiotemporal resolution; when combined with single-cell multi-omics, this has uncovered the immunometabolic landscape of drug-induced liver injury (Chen et al., 2024). The most recent in vivo deep-tissue dynamic metabolic and structural imaging assay leverages an MMF excitation source to power the enhanced dynamic capture of dSLAM microscopy, enabling in vitro tracking of monocyte behaviors and simulation of immune cell recruitment within living vascular networks (Liu K. et al., 2024).

Beyond the tumor’s own microenvironment, the gut microbiota and their metabolites-acting as key systemic modulators-demonstrate significant potential in shaping the tumor immune microenvironment and affecting immunotherapy outcomes, making them a crucial focus for future interventions. The gut microbiota remodels the TME by regulating innate and adaptive immune cell functions, exerting a notable impact on the clinical success of immunotherapy (Jiang et al., 2025). Their metabolites, including short-chain fatty acids, inosine, trimethylamine-N-oxide, and succinate, play key roles in modulating immune responses (Mager et al., 2020; Nomura et al., 2020; Wang H. et al., 2022; Wu et al., 2020). Intratumoral microbiota enhance anti-tumor immunity through mechanisms such as STING pathway activation (Shi et al., 2020), while the gut microbiota not only dictates treatment efficacy but also is closely linked to immune-related adverse events (irAEs) like ICI-associated colitis (Tandon et al., 2018; El Osta et al., 2017), modulating gut microbiota composition via strategies such as fecal microbiota transplantation (FMT), dietary adjustments, and probiotic supplementation (Matson et al., 2018; Goncalves et al., 2020; Huang et al., 2022) provides new therapeutic directions to improve ovarian cancer treatment outcomes and mitigate immune toxicity.

Ultimately, addressing the core challenges in life sciences-limitations of single-omics explanations, obstacles to multi-omics integration, and gaps in clinical translation-relies on systematic innovation and collaboration across technologies, methods, and frameworks. The integration of spatial omics and single-cell sequencing is redefining research frontiers. Advances in data analysis are dismantling dimensional barriers. Refining clinical translation pathways demands bidirectional validation systems. Multi-omics research is trending in three areas: in situ detection technologies overcoming spatiotemporal resolution constraints, AI-driven knowledge graphs integrating public databases, and the pairing of miniaturized detectors with cloud-based analysis platforms to lower research costs. Going forward, developing standardized analytical pipelines, constructing clinical sample repositories and multi-omics databases, and creating modular experimental designs are all valuable goals.

Author contributions

Z-BW: Writing – original draft, Visualization, Supervision, Writing – review and editing. M-HL: Writing – review and editing. PY: Writing – review and editing, Supervision. Y-LW: Writing – review and editing, Supervision. ZY: Writing – review and editing. M-SH: Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was supported by Health Research Project of Hunan Provincial Health Commission (Grant No. 20255405), Hunan Medical Association Medical Research Project (Grant No. HMA202502004), Science and Technology Planning Project of Henan Province (232102310019) and National Natural Science Foundation of China (Grant No. 82204349).

Acknowledgements

We thank Home for Researchers editorial team (www.home-for-researchers.com) for language editing service.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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The author(s) declared that generative AI was not used in the creation of this manuscript.

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References

Alexandrov, T. (2023). Spatial metabolomics: from a niche field towards a driver of innovation. Nat. Metab. 5, 1443–1445. doi:10.1038/s42255-023-00881-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Alkan, H. F., Walter, K. E., Luengo, A., Madreiter-Sokolowski, C. T., Stryeck, S., Lau, A. N., et al. (2018). Cytosolic aspartate availability determines cell survival when glutamine is limiting. Cell. Metab. 28, 706–720.e706. doi:10.1016/j.cmet.2018.07.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Altman, B. J., Stine, Z. E., and Dang, C. V. (2016). From krebs to clinic: glutamine metabolism to cancer therapy. Nat. Rev. Cancer 16, 619–634. doi:10.1038/nrc.2016.71

PubMed Abstract | CrossRef Full Text | Google Scholar

Amobi-McCloud, A., Muthuswamy, R., Battaglia, S., Yu, H., Liu, T., Wang, J., et al. (2021). IDO1 expression in ovarian cancer induces PD-1 in T cells via aryl hydrocarbon receptor activation. Front. Immunology 12, 678999. doi:10.3389/fimmu.2021.678999

PubMed Abstract | CrossRef Full Text | Google Scholar

Andrienko, T. N., Pasdois, P., Pereira, G. C., Ovens, M. J., and Halestrap, A. P. (2017). The role of succinate and ROS in reperfusion injury - a critical appraisal. J. Mol. Cell. Cardiol. 110, 1–14. doi:10.1016/j.yjmcc.2017.06.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Araujo, L., Khim, P., Mkhikian, H., Mortales, C. L., and Demetriou, M. (2017). Glycolysis and glutaminolysis cooperatively control T cell function by limiting metabolite supply to N-glycosylation. Elife 6, e21330. doi:10.7554/eLife.21330

PubMed Abstract | CrossRef Full Text | Google Scholar

Arpinati, L., Carradori, G., and Scherz-Shouval, R. (2024). CAF-Induced physical constraints controlling T cell state and localization in solid tumours. Nat. Rev. Cancer 24, 676–693. doi:10.1038/s41568-024-00740-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Awad, D., Cao, P. H. A., Pulliam, T. L., Spradlin, M., Subramani, E., Tellman, T. V., et al. (2024). Adipose triglyceride lipase is a therapeutic target in advanced prostate cancer that promotes metabolic plasticity. Cancer Res. 84, 703–724. doi:10.1158/0008-5472.Can-23-0555

PubMed Abstract | CrossRef Full Text | Google Scholar

Babl, N., Decking, S. M., Voll, F., Althammer, M., Sala-Hojman, A., Ferretti, R., et al. (2023). MCT4 blockade increases the efficacy of immune checkpoint blockade. J. Immunother. Cancer 11, e007349. doi:10.1136/jitc-2023-007349

PubMed Abstract | CrossRef Full Text | Google Scholar

Barré, F. P. Y., Claes, B. S. R., Dewez, F., Peutz-Kootstra, C., Munch-Petersen, H. F., Grønbæk, K., et al. (2018). Specific lipid and metabolic profiles of R-CHOP-Resistant diffuse large B-Cell lymphoma elucidated by matrix-assisted laser desorption ionization mass spectrometry imaging and in vivo imaging. Anal. Chem. 90, 14198–14206. doi:10.1021/acs.analchem.8b02910

PubMed Abstract | CrossRef Full Text | Google Scholar

Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., and Madabhushi, A. (2019). Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16, 703–715. doi:10.1038/s41571-019-0252-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Berry, K. A., Hankin, J. A., Barkley, R. M., Spraggins, J. M., Caprioli, R. M., and Murphy, R. C. (2011). MALDI imaging of lipid biochemistry in tissues by mass spectrometry. Chem. Rev. 111, 6491–6512. doi:10.1021/cr200280p

PubMed Abstract | CrossRef Full Text | Google Scholar

Besse, B., Pons-Tostivint, E., Park, K., Hartl, S., Forde, P. M., Hochmair, M. J., et al. (2024). Biomarker-directed targeted therapy plus durvalumab in advanced non-small-cell lung cancer: a phase 2 umbrella trial. Nat. Med. 30, 716–729. doi:10.1038/s41591-024-02808-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhinder, B., Gilvary, C., Madhukar, N. S., and Elemento, O. (2021). Artificial intelligence in cancer research and precision medicine. Cancer Discov. 11, 900–915. doi:10.1158/2159-8290.Cd-21-0090

PubMed Abstract | CrossRef Full Text | Google Scholar

Brand, A., Singer, K., Koehl, G. E., Kolitzus, M., Schoenhammer, G., Thiel, A., et al. (2016). LDHA-associated lactic acid production blunts tumor immunosurveillance by T and NK cells. Cell. Metab. 24, 657–671. doi:10.1016/j.cmet.2016.08.011

PubMed Abstract | CrossRef Full Text | Google Scholar

Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., and Greenleaf, W. J. (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-Binding proteins and nucleosome position. Nat. Methods 10, 1213–1218. doi:10.1038/nmeth.2688

PubMed Abstract | CrossRef Full Text | Google Scholar

Buglakova, E., Ekelöf, M., Schwaiger-Haber, M., Schlicker, L., Molenaar, M. R., Mohammed, S., et al. (2024). (13)C-SpaceM: spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer. bioRxiv, 2023.08.18.553810. doi:10.1101/2023.08.18.553810

PubMed Abstract | CrossRef Full Text | Google Scholar

Cairns, R. A., Harris, I. S., and Mak, T. W. (2011). Regulation of cancer cell metabolism. Nat. Rev. Cancer 11, 85–95. doi:10.1038/nrc2981

PubMed Abstract | CrossRef Full Text | Google Scholar

Calcinotto, A., Filipazzi, P., Grioni, M., Iero, M., De Milito, A., Ricupito, A., et al. (2012). Modulation of microenvironment acidity reverses anergy in human and murine tumor-infiltrating T lymphocytes. Cancer Res. 72, 2746–2756. doi:10.1158/0008-5472.Can-11-1272

PubMed Abstract | CrossRef Full Text | Google Scholar

Capper, D., Jones, D. T. W., Sill, M., Hovestadt, V., Schrimpf, D., Sturm, D., et al. (2018). DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474. doi:10.1038/nature26000

PubMed Abstract | CrossRef Full Text | Google Scholar

Carter, J. M., Chumsri, S., Hinerfeld, D. A., Ma, Y., Wang, X., Zahrieh, D., et al. (2023). Distinct spatial immune microlandscapes are independently associated with outcomes in triple-negative breast cancer. Nat. Commun. 14, 2215. doi:10.1038/s41467-023-37806-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Certo, M., Tsai, C. H., Pucino, V., Ho, P. C., and Mauro, C. (2021). Lactate modulation of immune responses in inflammatory versus tumour microenvironments. Nat. Rev. Immunol. 21, 151–161. doi:10.1038/s41577-020-0406-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Chae, C. S., Sandoval, T. A., Hwang, S. M., Park, E. S., Giovanelli, P., Awasthi, D., et al. (2022). Tumor-derived lysophosphatidic acid blunts protective type I interferon responses in ovarian cancer. Cancer Discov. 12, 1904–1921. doi:10.1158/2159-8290.Cd-21-1181

PubMed Abstract | CrossRef Full Text | Google Scholar

Chap, B. S., Rayroux, N., Grimm, A. J., Ghisoni, E., and Dangaj Laniti, D. (2024). Crosstalk of T cells within the ovarian cancer microenvironment. Trends Cancer 10, 1116–1130. doi:10.1016/j.trecan.2024.09.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, Y., Song, G., Dong, Z., Yi, X., Chao, Y., Liang, C., et al. (2017). Drug-loaded mesoporous tantalum oxide nanoparticles for enhanced synergetic chemoradiotherapy with reduced systemic toxicity. Small 13, 1602869. doi:10.1002/smll.201602869

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, Y., Wang, T., Xie, P., Song, Y., Wang, J., and Cai, Z. (2021). Mass spectrometry imaging revealed alterations of lipid metabolites in multicellular tumor spheroids in response to hydroxychloroquine. Anal. Chim. Acta 1184, 339011. doi:10.1016/j.aca.2021.339011

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, R. J., Lu, M. Y., Wang, J., Williamson, D. F. K., Rodig, S. J., Lindeman, N. I., et al. (2022). Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans. Med. Imaging 41, 757–770. doi:10.1109/tmi.2020.3021387

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, W., Tan, Q., and Guo, M. (2023). Tumor cell-derived microparticles packaging monocarboxylate transported inhibitor fluvastatin suppress lung adenocarcinoma via tumor microenvironment remodeling and improve chemotherapy. Chem. Engineering Journal. doi:10.1016/j.cej.2022.138972

CrossRef Full Text | Google Scholar

Chen, S., Lu, Z., Zhao, Y., Xia, L., Liu, C., Zuo, S., et al. (2024). Myeloid-mas signaling modulates pathogenic crosstalk among MYC(+)CD63(+) endothelial cells, MMP12(+) macrophages, and monocytes in acetaminophen-induced liver injury. Adv. Sci. (Weinh) 11, e2306066. doi:10.1002/advs.202306066

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, J., Huang, Z., Chen, Y., Tian, H., Chai, P., Shen, Y., et al. (2025). Lactate and lactylation in cancer. Signal Transduct. Target Ther. 10, 38. doi:10.1038/s41392-024-02082-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Cheng, B., Lu, W., Xiaoyun, W., YaXia, C., and Xie, X. (2009). Extra-abdominal metastases from epithelial ovarian carcinoma: an analysis of 20 cases. Int. J. Gynecol. Cancer 19, 611–614. doi:10.1111/IGC.0b013e3181a416d0

PubMed Abstract | CrossRef Full Text | Google Scholar

Chicklore, S., Goh, V., Siddique, M., Roy, A., Marsden, P. K., and Cook, G. J. R. (2013). Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur. J. Nucl. Med. Mol. Imaging 40, 133–140. doi:10.1007/s00259-012-2247-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Chu, H., Zhao, J., Mi, Y., Di, Z., and Li, L. (2019). NIR-light-mediated spatially selective triggering of anti-tumor immunity via upconversion nanoparticle-based immunodevices. Nat. Commun. 10, 2839. doi:10.1038/s41467-019-10847-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Chung, S. Y., Yeh, Y. C., Huang, C. J., Chiang, N. J., Hsu, D. S. S., Chan, M. H., et al. (2025). Comparative impact of tertiary lymphoid structures and tumor-infiltrating lymphocytes in cholangiocarcinoma. J. Immunother. Cancer 13, e010173. doi:10.1136/jitc-2024-010173

PubMed Abstract | CrossRef Full Text | Google Scholar

Cohen, J. D., Li, L., Wang, Y., Thoburn, C., Afsari, B., Danilova, L., et al. (2018). Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926–930. doi:10.1126/science.aar3247

PubMed Abstract | CrossRef Full Text | Google Scholar

Colbeck, E. J., Ager, A., Gallimore, A., and Jones, G. W. (2017). Tertiary lymphoid structures in cancer: drivers of antitumor immunity, immunosuppression, or bystander sentinels in disease? Front. Immunol. 8, 1830. doi:10.3389/fimmu.2017.01830

PubMed Abstract | CrossRef Full Text | Google Scholar

Colombo, N., Sessa, C., du Bois, A., Ledermann, J., McCluggage, W. G., McNeish, I., et al. (2019). ESMO-ESGO ovarian cancer consensus conference working group. ESMO-ESGO consensus conference recommendations on ovarian cancer: pathology and molecular biology, early and advanced stages, borderline tumours and recurrent disease. Ann. Oncol. 30, 672–705. doi:10.1093/annonc/mdz062

PubMed Abstract | CrossRef Full Text | Google Scholar

Confalonieri, S., Matoskova, B., Pennisi, R., Martino, F., De Mario, A., Miloro, G., et al. (2024). A PET-surrogate signature for the interrogation of the metabolic status of breast cancers. Adv. Sci. (Weinh) 11, e2308255. doi:10.1002/advs.202308255

PubMed Abstract | CrossRef Full Text | Google Scholar

Cook, G. J. R., Azad, G., Owczarczyk, K., Siddique, M., and Goh, V. (2018). Challenges and promises of PET radiomics. Int. J. Radiat. Oncol. Biol. Phys. 102, 1083–1089. doi:10.1016/j.ijrobp.2017.12.268

PubMed Abstract | CrossRef Full Text | Google Scholar

Cortez, A. J., Tudrej, P., Kujawa, K. A., and Lisowska, K. M. (2018). Advances in ovarian cancer therapy. Cancer Chemotherapy Pharmacology 81, 17–38. doi:10.1007/s00280-017-3501-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Cui, J. X., Xu, X. H., He, T., Liu, J. J., Xie, T. Y., Tian, W., et al. (2023). L-kynurenine induces NK cell loss in gastric cancer microenvironment via promoting ferroptosis. J. Exp. Clin. Cancer Res. 42, 52. doi:10.1186/s13046-023-02629-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Cui, Y., Liu, J., Wang, X., Wu, Y., Chang, Y., Hu, X., et al. (2025). Baicalin attenuates the immune escape of oral squamous cell carcinoma by reducing lactate accumulation in tumor microenvironment. J. Adv. Res. 77, 721–732. doi:10.1016/j.jare.2025.01.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Dai, B., Zhang, R., Qi, S., Liu, L., Zhang, X., Deng, D., et al. (2022). Intravital molecular imaging reveals that ROS-caspase-3-GSDME-induced cell punching enhances humoral immunotherapy targeting intracellular tumor antigens. Theranostics 12, 7603–7623. doi:10.7150/thno.75966

PubMed Abstract | CrossRef Full Text | Google Scholar

de la Cruz-López, K. G., Castro-Muñoz, L. J., Reyes-Hernández, D. O., García-Carrancá, A., and Manzo-Merino, J. (2019). Lactate in the regulation of tumor microenvironment and therapeutic approaches. Front. Oncology 9, 1143. doi:10.3389/fonc.2019.01143

CrossRef Full Text | Google Scholar

Dergaa, I., Saad, H. B., El Omri, A., Glenn, J. M., Clark, C. C. T., Washif, J. A., et al. (2024). Using artificial intelligence for exercise prescription in personalised health promotion: a critical evaluation of OpenAI's GPT-4 model. Biol. Sport 41, 221–241. doi:10.5114/biolsport.2024.133661

PubMed Abstract | CrossRef Full Text | Google Scholar

Dieu-Nosjean, M. C., Goc, J., Giraldo, N. A., Sautès-Fridman, C., and Fridman, W. H. (2014). Tertiary lymphoid structures in cancer and beyond. Trends Immunol. 35, 571–580. doi:10.1016/j.it.2014.09.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Ding, X. C., Wang, L. L., Zhang, X. D., Xu, J. L., Li, P. F., Liang, H., et al. (2021). The relationship between expression of PD-L1 and HIF-1α in glioma cells under hypoxia. J. Hematol. Oncol. 14, 92. doi:10.1186/s13045-021-01102-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Ding, R., Yu, X., Hu, Z., Dong, Y., Huang, H., Zhang, Y., et al. (2024). Lactate modulates RNA splicing to promote CTLA-4 expression in tumor-infiltrating regulatory T cells. Immunity 57, 528–540.e526. doi:10.1016/j.immuni.2024.01.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Doedens, A. L., Phan, A. T., Stradner, M. H., Fujimoto, J. K., Nguyen, J. V., Yang, E., et al. (2013). Hypoxia-inducible factors enhance the effector responses of CD8(+) T cells to persistent antigen. Nat. Immunol. 14, 1173–1182. doi:10.1038/ni.2714

PubMed Abstract | CrossRef Full Text | Google Scholar

Du, D., Liu, C., Qin, M., Zhang, X., Xi, T., Yuan, S., et al. (2022). Metabolic dysregulation and emerging therapeutical targets for hepatocellular carcinoma. Acta Pharm. Sin. B 12, 558–580. doi:10.1016/j.apsb.2021.09.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Duan, F., Feng, X., Yang, X., Sun, W., Jin, Y., Liu, H., et al. (2017). A simple and powerful co-delivery system based on pH-responsive metal-organic frameworks for enhanced cancer immunotherapy. Biomaterials 122, 23–33. doi:10.1016/j.biomaterials.2017.01.017

PubMed Abstract | CrossRef Full Text | Google Scholar

El Osta, B., Hu, F., Sadek, R., Chintalapally, R., and Tang, S. C. (2017). Not all immune-checkpoint inhibitors are created equal: meta-Analysis and systematic review of immune-related adverse events in cancer trials. Crit. Rev. Oncol. Hematol. 119, 1–12. doi:10.1016/j.critrevonc.2017.09.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., et al. (2019). A guide to deep learning in healthcare. Nat. Med. 25, 24–29. doi:10.1038/s41591-018-0316-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Fang, Z., Wang, C., Yang, J., Song, Z., Xie, C., Ji, Y., et al. (2024). Oxyhaemoglobin saturation NIR-IIb imaging for assessing cancer metabolism and predicting the response to immunotherapy. Nat. Nanotechnol. 19, 124–130. doi:10.1038/s41565-023-01501-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Feng, J., Yang, H., Zhang, Y., Wei, H., Zhu, Z., Zhu, B., et al. (2017). Tumor cell-derived lactate induces TAZ-Dependent upregulation of PD-L1 through GPR81 in human lung cancer cells. Oncogene 36, 5829–5839. doi:10.1038/onc.2017.188

PubMed Abstract | CrossRef Full Text | Google Scholar

Fernández, J. A., Ochoa, B., Fresnedo, O., Giralt, M. T., and Rodríguez-Puertas, R. (2011). Matrix-assisted laser desorption ionization imaging mass spectrometry in lipidomics. Anal. Bioanal. Chem. 401, 29–51. doi:10.1007/s00216-011-4696-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Foster, D. S., Januszyk, M., Delitto, D., Yost, K. E., Griffin, M., Guo, J., et al. (2022). Multiomic analysis reveals conservation of cancer-associated fibroblast phenotypes across species and tissue of origin. Cancer Cell. 40, 1392–1406.e1397. doi:10.1016/j.ccell.2022.09.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Francescone, R., Barbosa Vendramini-Costa, D., Franco-Barraza, J., Wagner, J., Muir, A., Lau, A. N., et al. (2021). Netrin G1 promotes pancreatic tumorigenesis through cancer-associated fibroblast-driven nutritional support and immunosuppression. Cancer Discov. 11, 446–479. doi:10.1158/2159-8290.Cd-20-0775

PubMed Abstract | CrossRef Full Text | Google Scholar

Gajewski, T. F., Woo, S. R., Zha, Y., Spaapen, R., Zheng, Y., Corrales, L., et al. (2013). Cancer immunotherapy strategies based on overcoming barriers within the tumor microenvironment. Curr. Opin. Immunol. 25, 268–276. doi:10.1016/j.coi.2013.02.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Gao, F., Ding, J., Gai, B., Cai, D., Hu, C., Wang, F. A., et al. (2025). Interpretable multimodal fusion model for bridged histology and genomics survival prediction in pan-cancer. Adv. Sci. (Weinh) 12, e2407060. doi:10.1002/advs.202407060

PubMed Abstract | CrossRef Full Text | Google Scholar

Geiger, R., Rieckmann, J. C., Wolf, T., Basso, C., Feng, Y., Fuhrer, T., et al. (2016). L-Arginine modulates T cell metabolism and enhances survival and anti-tumor activity. Cell. 167, 829–842.e813. doi:10.1016/j.cell.2016.09.031

PubMed Abstract | CrossRef Full Text | Google Scholar

Gentric, G., Kieffer, Y., Mieulet, V., Goundiam, O., Bonneau, C., Nemati, F., et al. (2019). PML-regulated mitochondrial metabolism enhances chemosensitivity in human ovarian cancers. Cell. Metab. 29, 156–173.e110. doi:10.1016/j.cmet.2018.09.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Gerard, C. L., Delyon, J., Wicky, A., Homicsko, K., Cuendet, M. A., and Michielin, O. (2021). Turning tumors from cold to inflamed to improve immunotherapy response. Cancer Treat. Rev. 101, 102227. doi:10.1016/j.ctrv.2021.102227

PubMed Abstract | CrossRef Full Text | Google Scholar

Gilbert, P. M., Hofmann, S., Ng, H. H., Vankelecom, H., and Wells, J. M. (2024). Organoids in endocrine and metabolic research: current and emerging applications. Nat. Rev. Endocrinol. 20, 195–201. doi:10.1038/s41574-023-00933-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Goltsev, Y., Samusik, N., Kennedy-Darling, J., Bhate, S., Hale, M., Vazquez, G., et al. (2018). Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell. 174, 968–981.e915. doi:10.1016/j.cell.2018.07.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Goncalves, R. L. S., Watson, M. A., Wong, H. S., Orr, A. L., and Brand, M. D. (2020). The use of site-specific suppressors to measure the relative contributions of different mitochondrial sites to skeletal muscle superoxide and hydrogen peroxide production. Redox Biol. 28, 101341. doi:10.1016/j.redox.2019.101341

PubMed Abstract | CrossRef Full Text | Google Scholar

Goossens, P., Rodriguez-Vita, J., Etzerodt, A., Masse, M., Rastoin, O., Gouirand, V., et al. (2019). Membrane cholesterol efflux drives tumor-associated macrophage reprogramming and tumor progression. Cell. Metab. 29, 1376–1389.e1374. doi:10.1016/j.cmet.2019.02.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Gouin, K. H., Ing, N., Plummer, J. T., Rosser, C. J., Ben Cheikh, B., Oh, C., et al. (2021). An N-Cadherin 2 expressing epithelial cell subpopulation predicts response to surgery, chemotherapy and immunotherapy in bladder cancer. Nat. Commun. 12, 4906. doi:10.1038/s41467-021-25103-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, D., Tong, Y., Jiang, X., Meng, Y., Jiang, H., Du, L., et al. (2022). Aerobic glycolysis promotes tumor immune evasion by hexokinase2-mediated phosphorylation of IκBα. Cell. Metab. 34, 1312–1324.e1316. doi:10.1016/j.cmet.2022.08.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, C., You, Z., Shi, H., Sun, Y., Du, X., Palacios, G., et al. (2023). SLC38A2 and glutamine signalling in cDC1s dictate anti-tumour immunity. Nature 620, 200–208. doi:10.1038/s41586-023-06299-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Gupta, S., Roy, A., and Dwarakanath, B. S. (2017). Metabolic cooperation and competition in the tumor microenvironment: implications for therapy. Front. Oncology 7, 68. doi:10.3389/fonc.2017.00068

PubMed Abstract | CrossRef Full Text | Google Scholar

Ha, J. H., Radhakrishnan, R., Jayaraman, M., Yan, M., Ward, J. D., Fung, K. M., et al. (2018). LPA induces metabolic reprogramming in ovarian cancer via a pseudohypoxic response. Cancer Research 78, 1923–1934. doi:10.1158/0008-5472.CAN-17-1624

PubMed Abstract | CrossRef Full Text | Google Scholar

Haas, R., Smith, J., Rocher-Ros, V., Nadkarni, S., Montero-Melendez, T., D'Acquisto, F., et al. (2015). Lactate regulates metabolic and pro-inflammatory circuits in control of T cell migration and effector functions. PLoS Biol. 13, e1002202. doi:10.1371/journal.pbio.1002202

PubMed Abstract | CrossRef Full Text | Google Scholar

Hanahan, D., Michielin, O., and Pittet, M. J. (2025). Convergent inducers and effectors of T cell paralysis in the tumour microenvironment. Nat. Rev. Cancer 25, 41–58. doi:10.1038/s41568-024-00761-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Hansen, J. M., Coleman, R. L., and Sood, A. K. (2016). Targeting the tumour microenvironment in ovarian cancer. Eur. Journal Cancer 56, 131–143. doi:10.1016/j.ejca.2015.12.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Hao, J. W., Wang, J., Guo, H., Zhao, Y. Y., Sun, H. H., Li, Y. F., et al. (2020). CD36 facilitates fatty acid uptake by dynamic palmitoylation-regulated endocytosis. Nat. Commun. 11, 4765. doi:10.1038/s41467-020-18565-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Hartmann, F. J., Mrdjen, D., McCaffrey, E., Glass, D. R., Greenwald, N. F., Bharadwaj, A., et al. (2021). Single-cell metabolic profiling of human cytotoxic T cells. Nat. Biotechnol. 39, 186–197. doi:10.1038/s41587-020-0651-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Hatami, H., Sajedi, A., Mir, S. M., and Memar, M. Y. (2023). Importance of lactate dehydrogenase (LDH) and monocarboxylate transporters (MCTs) in cancer cells. Health Sci. Rep. 6, e996. doi:10.1002/hsr2.996

PubMed Abstract | CrossRef Full Text | Google Scholar

He, H., Zhou, J., Liu, Y., Liu, S., Xie, Z., Yu, M., et al. (2018). Near-infrared-light-induced morphology transition of poly(ether amine) nanoparticles for supersensitive drug release. ACS Appl. Mater Interfaces 10, 7413–7421. doi:10.1021/acsami.8b00194

PubMed Abstract | CrossRef Full Text | Google Scholar

He, P., Feng, J., Xia, X., Sun, Y., He, J., Guan, T., et al. (2023). Discovery of a potent and oral available complex I OXPHOS inhibitor that abrogates tumor growth and circumvents MEKi resistance. J. Med. Chem. 66, 6047–6069. doi:10.1021/acs.jmedchem.2c01844

PubMed Abstract | CrossRef Full Text | Google Scholar

Hegde, P. S., Karanikas, V., and Evers, S. (2016). The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition. Clin. Cancer Res. 22, 1865–1874. doi:10.1158/1078-0432.Ccr-15-1507

PubMed Abstract | CrossRef Full Text | Google Scholar

Hernandez, S., Lazcano, R., Serrano, A., Powell, S., Kostousov, L., Mehta, J., et al. (2022). Challenges and opportunities for immunoprofiling using a spatial high-plex technology: the NanoString geoMx(®) digital spatial profiler. Front. Oncol. 12, 890410. doi:10.3389/fonc.2022.890410

PubMed Abstract | CrossRef Full Text | Google Scholar

Hirschberger, S., Strauß, G., Effinger, D., Marstaller, X., Ferstl, A., Müller, M. B., et al. (2021). Very-low-carbohydrate diet enhances human T-cell immunity through immunometabolic reprogramming. EMBO Mol. Med. 13, e14323. doi:10.15252/emmm.202114323

PubMed Abstract | CrossRef Full Text | Google Scholar

Hirschhaeuser, F., Sattler, U. G., and Mueller-Klieser, W. (2011). Lactate: a metabolic key player in cancer. Cancer Res. 71, 6921–6925. doi:10.1158/0008-5472.Can-11-1457

PubMed Abstract | CrossRef Full Text | Google Scholar

Hoy, A. J., Nagarajan, S. R., and Butler, L. M. (2021). Tumour fatty acid metabolism in the context of therapy resistance and obesity. Nat. Rev. Cancer 21, 753–766. doi:10.1038/s41568-021-00388-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Hu, W., and Lazar, M. A. (2022). Modelling metabolic diseases and drug response using stem cells and organoids. Nat. Rev. Endocrinol. 18, 744–759. doi:10.1038/s41574-022-00733-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Hu, J.-w., Sun, P., Zhang, D.-x., Xiong, W.-j., and Mi, J. (2014). Hexokinase 2 regulates G1/S checkpoint through CDK2 in cancer-associated fibroblasts. Cell. Signalling 26, 2210–2216. doi:10.1016/j.cellsig.2014.04.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Hu, J., Wang, S. G., Hou, Y., Chen, Z., Liu, L., Li, R., et al. (2024). Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression. Nat. Genet. 56, 442–457. doi:10.1038/s41588-024-01662-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, J., Liu, D., Wang, Y., Liu, L., Li, J., Yuan, J., et al. (2022). Ginseng polysaccharides alter the gut microbiota and kynurenine/tryptophan ratio, potentiating the antitumour effect of antiprogrammed cell death 1/programmed cell death ligand 1 (anti-PD-1/PD-L1) immunotherapy. Gut 71, 734–745. doi:10.1136/gutjnl-2020-321031

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, J., Tsang, W. Y., Fang, X. N., Zhang, Y., Luo, J., Gong, L. Q., et al. (2024). FASN inhibition decreases MHC-I degradation and synergizes with PD-L1 checkpoint blockade in hepatocellular carcinoma. Cancer Res. 84, 855–871. doi:10.1158/0008-5472.Can-23-0966

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, B., Shen, W., Jia, Y., Qin, L., Wang, H., Sun, Q., et al. (2025). LDHAα, a lactate dehydrogenase A isoform, promotes glycolysis and tumor progression. FEBS J. 292, 2223–2236. doi:10.1111/febs.17374

PubMed Abstract | CrossRef Full Text | Google Scholar

Ibacache-Chía, A. P., Sierralta, J. A., and Schüller, A. (2024). The inhibitory effects of the natural stilbene piceatannol on lactate transport in vitro mediated by monocarboxylate transporters. Mol. Nutr. and Food Res. 68, 2400414. doi:10.1002/mnfr.202400414

PubMed Abstract | CrossRef Full Text | Google Scholar

Jiang, S. S., Kang, Z. R., Chen, Y. X., and Fang, J. Y. (2025). The gut microbiome modulate response to immunotherapy in cancer. Sci. China Life Sci. 68, 381–396. doi:10.1007/s11427-023-2634-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Jiménez-Sánchez, A., Cybulska, P., Mager, K. L., Koplev, S., Cast, O., Couturier, D. L., et al. (2020). Unraveling tumor-immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy. Nat. Genet. 52, 582–593. doi:10.1038/s41588-020-0630-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Jin, H. R., Wang, J., Wang, Z. J., Xi, M. J., Xia, B. H., Deng, K., et al. (2023a). Lipid metabolic reprogramming in tumor microenvironment: from mechanisms to therapeutics. J. Hematol. Oncol. 16, 103. doi:10.1186/s13045-023-01498-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Jin, S., Yin, E., Feng, C., Sun, Y., Yang, T., Yuan, H., et al. (2023b). Regulating tumor glycometabolism and the immune microenvironment by inhibiting lactate dehydrogenase with platinum(iv) complexes. Chem. Sci. 14, 8327–8337. doi:10.1039/d3sc01874a

PubMed Abstract | CrossRef Full Text | Google Scholar

Johnson, M. O., Wolf, M. M., Madden, M. Z., Andrejeva, G., Sugiura, A., Contreras, D. C., et al. (2018). Distinct regulation of Th17 and Th1 cell differentiation by glutaminase-dependent metabolism. Cell. 175, 1780–1795.e1719. doi:10.1016/j.cell.2018.10.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Ju, H., Kim, D., and Oh, Y. K. (2022). Lipid nanoparticle-mediated CRISPR/Cas9 gene editing and metabolic engineering for anticancer immunotherapy. Asian J. Pharm. Sci. 17, 641–652. doi:10.1016/j.ajps.2022.07.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Kader, T., Lin, J. R., Hug, C. B., Coy, S., Chen, Y. A., de Bruijn, I., et al. (2024). Multimodal spatial profiling reveals immune suppression and microenvironment remodeling in fallopian tube precursors to high-grade serous ovarian carcinoma. Cancer Discov. 15, 1180–1202. doi:10.1158/2159-8290.Cd-24-1366

PubMed Abstract | CrossRef Full Text | Google Scholar

Kaira, K., Oriuchi, N., Imai, H., Shimizu, K., Yanagitani, N., Sunaga, N., et al. (2009). Prognostic significance of L-type amino acid transporter 1 (LAT1) and 4F2 heavy chain (CD98) expression in stage I pulmonary adenocarcinoma. Lung Cancer 66, 120–126. doi:10.1016/j.lungcan.2008.12.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Kao, K. C., Vilbois, S., Tsai, C. H., and Ho, P. C. (2022). Metabolic communication in the tumour-immune microenvironment. Nat. Cell. Biol. 24, 1574–1583. doi:10.1038/s41556-022-01002-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Kaye, S. B. (2016). Progress in the treatment of ovarian cancer-lessons from homologous recombination deficiency-the first 10 years. Ann. Oncol. 27 (Suppl. 1), i1–i3. doi:10.1093/annonc/mdw082

PubMed Abstract | CrossRef Full Text | Google Scholar

Klysz, D. D., Fowler, C., Malipatlolla, M., Stuani, L., Freitas, K. A., Chen, Y., et al. (2024). Inosine induces stemness features in CAR-T cells and enhances potency. Cancer Cell. 42, 266–282.e268. doi:10.1016/j.ccell.2024.01.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Koppenol, W. H., Bounds, P. L., and Dang, C. V. (2011). Otto Warburg's contributions to current concepts of cancer metabolism. Nat. Rev. Cancer 11, 325–337. doi:10.1038/nrc3038

PubMed Abstract | CrossRef Full Text | Google Scholar

Kossaï, M., Leary, A., Scoazec, J.-Y., and Genestie, C. (2018). Ovarian cancer: a heterogeneous disease. Pathobiology 85, 41–49. doi:10.1159/000479006

PubMed Abstract | CrossRef Full Text | Google Scholar

Kumagai, S., Koyama, S., Itahashi, K., Tanegashima, T., Lin, Y. T., Togashi, Y., et al. (2022). Lactic acid promotes PD-1 expression in regulatory T cells in highly glycolytic tumor microenvironments. Cancer Cell. 40, 201–218.e209. doi:10.1016/j.ccell.2022.01.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, C. C., Bhupathi, D., Billedeau, R. J., Chen, J., Chen, L., Emberley, E. D., et al. (2019). Reversal of adenosine-mediated immune suppression by CB-708, an orally bioavailable and potent small molecule inhibitor of CD73. Cancer Res. 79, 4134. doi:10.1158/1538-7445.am2019-4134

CrossRef Full Text | Google Scholar

Leone, R. D., Zhao, L., Englert, J. M., Sun, I. M., Oh, M. H., Sun, I. H., et al. (2019). Glutamine blockade induces divergent metabolic programs to overcome tumor immune evasion. Science 366, 1013–1021. doi:10.1126/science.aav2588

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, Y., Patel, S. P., Roszik, J., and Qin, Y. (2018). Hypoxia-driven immunosuppressive metabolites in the tumor microenvironment: new approaches for combinational immunotherapy. Front. Immunol. 9, 1591. doi:10.3389/fimmu.2018.01591

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, Z., Yang, Q., Tang, X., Chen, Y., Wang, S., Qi, X., et al. (2022). Single-cell RNA-Seq and chromatin accessibility profiling decipher the heterogeneity of mouse γδ T cells. Sci. Bull. (Beijing) 67, 408–426. doi:10.1016/j.scib.2021.11.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, F., He, C., Yao, H., Zhao, Y., Ye, X., Zhou, S., et al. (2023). Glutamate from nerve cells promotes perineural invasion in pancreatic cancer by regulating tumor glycolysis through HK2 mRNA-m6A modification. Pharmacol. Res. 187, 106555. doi:10.1016/j.phrs.2022.106555

PubMed Abstract | CrossRef Full Text | Google Scholar

Liang, H., Li, T., Fang, X., Xing, Z., Zhang, S., Shi, L., et al. (2021). IDO1/TDO dual inhibitor RY103 targets Kyn-AhR pathway and exhibits preclinical efficacy on pancreatic cancer. Cancer Lett. 522, 32–43. doi:10.1016/j.canlet.2021.09.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, L., Yi, H., He, H., Pan, H., Cai, L., and Ma, Y. (2017). Tumor associated macrophage-targeted microRNA delivery with dual-responsive polypeptide nanovectors for anti-cancer therapy. Biomaterials 134, 166–179. doi:10.1016/j.biomaterials.2017.04.043

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, Y., Yang, M., Deng, Y., Su, G., Enninful, A., Guo, C. C., et al. (2020). High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell. 183, 1665–1681.e1618. doi:10.1016/j.cell.2020.10.026

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, J., Fan, Z., Zhao, W., and Zhou, X. (2021). Machine intelligence in single-cell data analysis: advances and new challenges. Front. Genet. 12, 655536. doi:10.3389/fgene.2021.655536

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, Z., Zhang, Z., Zhang, Y., Zhou, W., Zhang, X., Peng, C., et al. (2024a). Spatial transcriptomics reveals that metabolic characteristics define the tumor immunosuppression microenvironment via iCAF transformation in oral squamous cell carcinoma. Int. J. Oral Sci. 16, 9. doi:10.1038/s41368-023-00267-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, K., Cao, H., Shashaty, K., Yu, L. Y., Spitz, S., Pramotton, F. M., et al. (2024b). Deep and dynamic metabolic and structural imaging in living tissues. Sci. Adv. 10, eadp2438. doi:10.1126/sciadv.adp2438

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, Y., Sinjab, A., Min, J., Han, G., Paradiso, F., Zhang, Y., et al. (2025). Conserved spatial subtypes and cellular neighborhoods of cancer-associated fibroblasts revealed by single-cell spatial multi-omics. Cancer Cell. 43, 905–924.e6. doi:10.1016/j.ccell.2025.03.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Long, Y., Ang, K. S., Sethi, R., Liao, S., Heng, Y., van Olst, L., et al. (2024). Deciphering spatial domains from spatial multi-omics with SpatialGlue. Nat. Methods 21, 1658–1667. doi:10.1038/s41592-024-02316-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Lopez, E., Karattil, R., Nannini, F., Weng-Kit Cheung, G., Denzler, L., Galvez-Cancino, F., et al. (2023). Inhibition of lactate transport by MCT-1 blockade improves chimeric antigen receptor T-cell therapy against B-cell malignancies. J. Immunother. Cancer 11, e006287. doi:10.1136/jitc-2022-006287

PubMed Abstract | CrossRef Full Text | Google Scholar

Lukey, M. J., Katt, W. P., and Cerione, R. A. (2017). Targeting amino acid metabolism for cancer therapy. Drug Discov. Today 22, 796–804. doi:10.1016/j.drudis.2016.12.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Ly, A., Buck, A., Balluff, B., Sun, N., Gorzolka, K., Feuchtinger, A., et al. (2016). High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue. Nat. Protoc. 11, 1428–1443. doi:10.1038/nprot.2016.081

PubMed Abstract | CrossRef Full Text | Google Scholar

Ma, C., Yang, C., Peng, A., Sun, T., Ji, X., Mi, J., et al. (2023). Pan-cancer spatially resolved single-cell analysis reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment. Mol. Cancer 22, 170. doi:10.1186/s12943-023-01876-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Ma, L., Chen, C., Zhao, C., Li, T., Ma, L., Jiang, J., et al. (2024a). Targeting carnitine palmitoyl transferase 1A (CPT1A) induces ferroptosis and synergizes with immunotherapy in lung cancer. Signal Transduct. Target Ther. 9, 64. doi:10.1038/s41392-024-01772-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Ma, S., Wang, W., Zhou, J., Liao, S., Hai, C., Hou, Y., et al. (2024b). Lamination-based organoid spatially resolved transcriptomics technique for primary lung and liver organoid characterization. Proc. Natl. Acad. Sci. U. S. A. 121, e2408939121. doi:10.1073/pnas.2408939121

PubMed Abstract | CrossRef Full Text | Google Scholar

Mager, L. F., Burkhard, R., Pett, N., Cooke, N. C. A., Brown, K., Ramay, H., et al. (2020). Microbiome-derived inosine modulates response to checkpoint inhibitor immunotherapy. Science 369, 1481–1489. doi:10.1126/science.abc3421

PubMed Abstract | CrossRef Full Text | Google Scholar

Maimó-Barceló, A., Pérez-Romero, K., Rodríguez, R. M., Huergo, C., Calvo, I., Fernández, J. A., et al. (2025). To image or not to image: use of imaging mass spectrometry in biomedical lipidomics. Prog. Lipid Res. 97, 101319. doi:10.1016/j.plipres.2025.101319

PubMed Abstract | CrossRef Full Text | Google Scholar

Maimon, A., Levi-Yahid, V., Ben-Meir, K., Halpern, A., Talmi, Z., Priya, S., et al. (2021). Myeloid cell-derived PROS1 inhibits tumor metastasis by regulating inflammatory and immune responses via IL-10. J. Clin. Investig. 131, e126089. doi:10.1172/jci126089

PubMed Abstract | CrossRef Full Text | Google Scholar

Martinez-Outschoorn, U. E., Peiris-Pagés, M., Pestell, R. G., Sotgia, F., and Lisanti, M. P. (2017). Cancer metabolism: a therapeutic perspective. Nat. Rev. Clin. Oncol. 14, 11–31. doi:10.1038/nrclinonc.2016.60

PubMed Abstract | CrossRef Full Text | Google Scholar

Martínez-Reyes, I., and Chandel, N. S. (2021). Cancer metabolism: looking forward. Nat. Rev. Cancer 21, 669–680. doi:10.1038/s41568-021-00378-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Mathupala, S. P., Ko, Y. H., and Pedersen, P. L. (2009). Hexokinase-2 bound to mitochondria: cancer's stygian link to the “Warburg Effect” and a pivotal target for effective therapy. Semin. Cancer Biol. 19, 17–24. doi:10.1016/j.semcancer.2008.11.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Matson, V., Fessler, J., Bao, R., Chongsuwat, T., Zha, Y., Alegre, M. L., et al. (2018). The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359, 104–108. doi:10.1126/science.aao3290

PubMed Abstract | CrossRef Full Text | Google Scholar

Menendez, J. A., and Lupu, R. (2007). Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nat. Rev. Cancer 7, 763–777. doi:10.1038/nrc2222

PubMed Abstract | CrossRef Full Text | Google Scholar

Merritt, C. R., Ong, G. T., Church, S. E., Barker, K., Danaher, P., Geiss, G., et al. (2020). Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599. doi:10.1038/s41587-020-0472-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Mezrich, J. D., Fechner, J. H., Zhang, X., Johnson, B. P., Burlingham, W. J., and Bradfield, C. A. (2010). An interaction between kynurenine and the aryl hydrocarbon receptor can generate regulatory T cells. J. Immunol. 185, 3190–3198. doi:10.4049/jimmunol.0903670

PubMed Abstract | CrossRef Full Text | Google Scholar

Mirretta Barone, C., Heaver, S. L., Gruber, L., Zundel, F., Vu, D. L., and Ley, R. E. (2024). Spatially resolved lipidomics shows conditional transfer of lipids produced by Bacteroides thetaiotaomicron into the mouse gut. Cell. Host Microbe 32, 1025–1036.e1025. doi:10.1016/j.chom.2024.04.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Mo, C. K., Liu, J., Chen, S., Storrs, E., Targino da Costa, A. L. N., Houston, A., et al. (2024). Tumour evolution and microenvironment interactions in 2D and 3D space. Nature 634, 1178–1186. doi:10.1038/s41586-024-08087-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Mondanelli, G., Iacono, A., Allegrucci, M., Puccetti, P., and Grohmann, U. (2019). Immunoregulatory interplay between arginine and tryptophan metabolism in health and disease. Front. Immunology 10, 1565. doi:10.3389/fimmu.2019.01565

PubMed Abstract | CrossRef Full Text | Google Scholar

Morandi, A., Giannoni, E., and Chiarugi, P. (2016). Nutrient exploitation within the tumor-stroma metabolic crosstalk. Trends Cancer 2, 736–746. doi:10.1016/j.trecan.2016.11.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Mukherjee, A., Ma, Y., Yuan, F., Gong, Y., Fang, Z., Mohamed, E. M., et al. (2015). Lysophosphatidic acid up-regulates hexokinase II and glycolysis to promote proliferation of ovarian cancer cells. Neoplasia 17, 723–734. doi:10.1016/j.neo.2015.09.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Mukherjee, A., Bezwada, D., Greco, F., Zandbergen, M., Shen, T., Chiang, C. Y., et al. (2023). Adipocytes reprogram cancer cell metabolism by diverting glucose towards glycerol-3-phosphate thereby promoting metastasis. Nat. Metab. 5, 1563–1577. doi:10.1038/s42255-023-00879-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Munoz-Erazo, L., Rhodes, J. L., Marion, V. C., and Kemp, R. A. (2020). Tertiary lymphoid structures in cancer - considerations for patient prognosis. Cell. Mol. Immunol. 17, 570–575. doi:10.1038/s41423-020-0457-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Nallasamy, P., Nimmakayala, R. K., Parte, S., Are, A. C., Batra, S. K., and Ponnusamy, M. P. (2022). Tumor microenvironment enriches the stemness features: the architectural event of therapy resistance and metastasis. Mol. Cancer 21, 225. doi:10.1186/s12943-022-01682-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Narod, S. (2016). Can advanced-stage ovarian cancer be cured? Nat. Rev. Clin. Oncol. 13, 255–261. doi:10.1038/nrclinonc.2015.224

PubMed Abstract | CrossRef Full Text | Google Scholar

Nasir, I., McGuinness, C., Poh, A. R., Ernst, M., Darcy, P. K., and Britt, K. L. (2023). Tumor macrophage functional heterogeneity can inform the development of novel cancer therapies. Trends Immunol. 44, 971–985. doi:10.1016/j.it.2023.10.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Ni, J., Wang, X., Stojanovic, A., Zhang, Q., Wincher, M., Bühler, L., et al. (2020). Single-cell RNA sequencing of tumor-infiltrating NK cells reveals that inhibition of transcription factor HIF-1α unleashes NK cell activity. Immunity 52, 1075–1087.e1078. doi:10.1016/j.immuni.2020.05.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Nian, Z., Dou, Y., Shen, Y., Liu, J., Du, X., Jiang, Y., et al. (2024). Interleukin-34-orchestrated tumor-associated macrophage reprogramming is required for tumor immune escape driven by p53 inactivation. Immunity 57, 2344–2361.e2347. doi:10.1016/j.immuni.2024.08.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Noman, M. Z., Desantis, G., Janji, B., Hasmim, M., Karray, S., Dessen, P., et al. (2014). PD-L1 is a novel direct target of HIF-1α, and its blockade under hypoxia enhanced MDSC-Mediated T cell activation. J. Exp. Med. 211, 781–790. doi:10.1084/jem.20131916

PubMed Abstract | CrossRef Full Text | Google Scholar

Nomura, M., Nagatomo, R., Doi, K., Shimizu, J., Baba, K., Saito, T., et al. (2020). Association of short-chain fatty acids in the gut microbiome with clinical response to treatment with nivolumab or pembrolizumab in patients with solid cancer tumors. JAMA Netw. Open 3, e202895. doi:10.1001/jamanetworkopen.2020.2895

PubMed Abstract | CrossRef Full Text | Google Scholar

Nunes, J. B., Ijsselsteijn, M. E., Abdelaal, T., Ursem, R., van der Ploeg, M., Giera, M., et al. (2024). Integration of mass cytometry and mass spectrometry imaging for spatially resolved single-cell metabolic profiling. Nat. Methods 21, 1796–1800. doi:10.1038/s41592-024-02392-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Parma, L., Sachs, N., Sobczak, N., Li, Z., Merchant, K., Pauli, J., et al. (2025). CXCL12 derived from ACKR1(+) intraplaque neovessels mediates CD8(+) T cell recruitment in human atherosclerosis. Circulation 151, 581–584. doi:10.1161/circulationaha.124.072560

PubMed Abstract | CrossRef Full Text | Google Scholar

Parry, R. V., Chemnitz, J. M., Frauwirth, K. A., Lanfranco, A. R., Braunstein, I., Kobayashi, S. V., et al. (2005). CTLA-4 and PD-1 receptors inhibit T-cell activation by distinct mechanisms. Mol. Cell. Biol. 25, 9543–9553. doi:10.1128/mcb.25.21.9543-9553.2005

PubMed Abstract | CrossRef Full Text | Google Scholar

Patsoukis, N., Bardhan, K., Chatterjee, P., Sari, D., Liu, B., Bell, L. N., et al. (2015). PD-1 alters T-cell metabolic reprogramming by inhibiting glycolysis and promoting lipolysis and fatty acid oxidation. Nat. Commun. 6, 6692. doi:10.1038/ncomms7692

PubMed Abstract | CrossRef Full Text | Google Scholar

Pavlides, S., Whitaker-Menezes, D., Castello-Cros, R., Flomenberg, N., Witkiewicz, A. K., Frank, P. G., et al. (2009). The reverse warburg effect: aerobic glycolysis in cancer associated fibroblasts and the tumor stroma. Cell. Cycle 8, 3984–4001. doi:10.4161/cc.8.23.10238

PubMed Abstract | CrossRef Full Text | Google Scholar

Pei, J., and Luan, L. (2023). LGR4 protects PC12 against OGD/R-induced oxidative stress and apoptosis through activation of AKT/GSK3β. Mol. and Cell. Toxicol. 19, 33–39. doi:10.1007/s13273-021-00202-0

CrossRef Full Text | Google Scholar

Perez, R. K., Gordon, M. G., Subramaniam, M., Kim, M. C., Hartoularos, G. C., Targ, S., et al. (2022). Single-cell RNA-Seq reveals cell type-specific molecular and genetic associations to lupus. Science 376, eabf1970. doi:10.1126/science.abf1970

PubMed Abstract | CrossRef Full Text | Google Scholar

Ping, Y., Shan, J., Qin, H., Li, F., Qu, J., Guo, R., et al. (2024). PD-1 signaling limits expression of phospholipid phosphatase 1 and promotes intratumoral CD8(+) T cell ferroptosis. Immunity 57, 2122–2139.e2129. doi:10.1016/j.immuni.2024.08.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Qorraj, M., Bruns, H., Böttcher, M., Weigand, L., Saul, D., Mackensen, A., et al. (2017). The PD-1/PD-L1 axis contributes to immune metabolic dysfunctions of monocytes in chronic lymphocytic leukemia. Leukemia 31, 470–478. doi:10.1038/leu.2016.214

PubMed Abstract | CrossRef Full Text | Google Scholar

Ramtohul, T., Lepagney, V., Bonneau, C., Jin, M., Menet, E., Sauge, J., et al. (2024). Use of pretreatment perfusion MRI-Based intratumoral heterogeneity to predict pathologic response of triple-negative breast cancer to neoadjuvant chemoimmunotherapy. Radiology 312, e240575. doi:10.1148/radiol.240575

PubMed Abstract | CrossRef Full Text | Google Scholar

Rashid, R., MacFawn, I., Nader, N., Coffman, L., and Bruno, T. (2025). Abstract B064: ovarian cancer-induced ascites fluid impacts B cell differentiation fate and function in women with high grade serous ovarian cancer. Cancer Res. 85, B064–B074. doi:10.1158/1538-7445.ovarian25-b064

CrossRef Full Text | Google Scholar

Rauth, S., Karmakar, S., Batra, S. K., and Ponnusamy, M. P. (2021). Recent advances in organoid development and applications in disease modeling. Biochim. Biophys. Acta Rev. Cancer 1875, 188527. doi:10.1016/j.bbcan.2021.188527

PubMed Abstract | CrossRef Full Text | Google Scholar

Razo, I. B., Shea, K., Allen, T. J., Boutin, H., McMahon, A., Lockyer, N., et al. (2024). Accumulation of bioactive lipid species in LPS-induced neuroinflammation models analysed with multi-modal mass spectrometry imaging. Int. J. Mol. Sci. 25, 12032. doi:10.3390/ijms252212032

PubMed Abstract | CrossRef Full Text | Google Scholar

Reina-Campos, M., Monell, A., Ferry, A., Luna, V., Cheung, K. P., Galletti, G., et al. (2025). Tissue-resident memory CD8 T cell diversity is spatiotemporally imprinted. Nature 639, 483–492. doi:10.1038/s41586-024-08466-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Riesenberg, R., Weiler, C., Spring, O., Eder, M., Buchner, A., Popp, T., et al. (2007). Expression of indoleamine 2,3-dioxygenase in tumor endothelial cells correlates with long-term survival of patients with renal cell carcinoma. Clin. Cancer Res. 13, 6993–7002. doi:10.1158/1078-0432.Ccr-07-0942

PubMed Abstract | CrossRef Full Text | Google Scholar

Rifat, D., Prideaux, B., Savic, R. M., Urbanowski, M. E., Parsons, T. L., Luna, B., et al. (2018). Pharmacokinetics of rifapentine and rifampin in a rabbit model of tuberculosis and correlation with clinical trial data. Sci. Transl. Med. 10, eaai7786. doi:10.1126/scitranslmed.aai7786

PubMed Abstract | CrossRef Full Text | Google Scholar

Rizwan, A., Serganova, I., Khanin, R., Karabeber, H., Ni, X., Thakur, S., et al. (2013). Relationships between LDH-A, lactate, and metastases in 4T1 breast tumors. Clin. Cancer Res. 19, 5158–5169. doi:10.1158/1078-0432.Ccr-12-3300

PubMed Abstract | CrossRef Full Text | Google Scholar

Röhrig, F., and Schulze, A. (2016). The multifaceted roles of fatty acid synthesis in cancer. Nat. Rev. Cancer 16, 732–749. doi:10.1038/nrc.2016.89

PubMed Abstract | CrossRef Full Text | Google Scholar

Rosario, S. R., Long, M. D., Chilakapati, S., Gomez, E. C., Battaglia, S., Singh, P. K., et al. (2024). Integrative multi-omics analysis uncovers tumor-immune-gut axis influencing immunotherapy outcomes in ovarian cancer. Nat. Commun. 15, 10609. doi:10.1038/s41467-024-54565-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Roy, P., Canet-Jourdan, C., Annereau, M., Zajac, O., Gelli, M., Broutin, S., et al. (2017). Organoids as preclinical models to improve intraperitoneal chemotherapy effectiveness for colorectal cancer patients with peritoneal metastases: preclinical models to improve HIPEC. Int. J. Pharm. 531, 143–152. doi:10.1016/j.ijpharm.2017.07.084

PubMed Abstract | CrossRef Full Text | Google Scholar

Rozenblatt-Rosen, O., Regev, A., Oberdoerffer, P., Nawy, T., Hupalowska, A., Rood, J. E., et al. (2020). The human tumor atlas network: charting tumor transitions across space and time at single-cell resolution. Cell. 181, 236–249. doi:10.1016/j.cell.2020.03.053

PubMed Abstract | CrossRef Full Text | Google Scholar

Sabol, R. A., Giacomelli, P., Beighley, A., and Bunnell, B. A. (2019). Adipose stem cells and cancer: concise review. Stem Cells 37, 1261–1266. doi:10.1002/stem.3050

PubMed Abstract | CrossRef Full Text | Google Scholar

Sade-Feldman, M., Jiao, Y. J., Chen, J. H., Rooney, M. S., Barzily-Rokni, M., Eliane, J. P., et al. (2017). Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136. doi:10.1038/s41467-017-01062-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Sadik, A., Somarribas Patterson, L. F., Öztürk, S., Mohapatra, S. R., Panitz, V., Secker, P. F., et al. (2020). IL4I1 is a metabolic immune checkpoint that activates the AHR and promotes tumor progression. Cell. 182, 1252–1270.e1234. doi:10.1016/j.cell.2020.07.038

PubMed Abstract | CrossRef Full Text | Google Scholar

Satpathy, A. T., Granja, J. M., Yost, K. E., Qi, Y., Meschi, F., McDermott, G. P., et al. (2019). Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936. doi:10.1038/s41587-019-0206-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Sautès-Fridman, C., Lawand, M., Giraldo, N. A., Kaplon, H., Germain, C., Fridman, W. H., et al. (2016). Tertiary lymphoid structures in cancers: prognostic value, regulation, and manipulation for therapeutic intervention. Front. Immunol. 7, 407. doi:10.3389/fimmu.2016.00407

PubMed Abstract | CrossRef Full Text | Google Scholar

Scalise, C. B., Kincaid, K., Thigpen, H., Moore, J., Dover, B., Norian, L., et al. (2024). A spatial proteomic study of platinum refractory HGSOC implicates dual AKT and WNT activity linked to an immunosuppressive tumor microenvironment. Gynecol. Oncol. 185, 83–94. doi:10.1016/j.ygyno.2024.02.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Scharping, N. E., Rivadeneira, D. B., Menk, A. V., Vignali, P. D. A., Ford, B. R., Rittenhouse, N. L., et al. (2021). Mitochondrial stress induced by continuous stimulation under hypoxia rapidly drives T cell exhaustion. Nat. Immunol. 22, 205–215. doi:10.1038/s41590-020-00834-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Schweizer, L., Krishnan, R., Shimizu, A., Metousis, A., Kenny, H., Mendoza, R., et al. (2023). Spatial proteo-transcriptomic profiling reveals the molecular landscape of borderline ovarian tumors and their invasive progression. medRxiv., 2023.11.13.23298409. doi:10.1101/2023.11.13.23298409

PubMed Abstract | CrossRef Full Text | Google Scholar

Schwörer, S., Pavlova, N. N., Cimino, F. V., King, B., Cai, X., Sizemore, G. M., et al. (2021). Fibroblast pyruvate carboxylase is required for collagen production in the tumour microenvironment. Nat. Metab. 3, 1484–1499. doi:10.1038/s42255-021-00480-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Scirpo, R., Fiorotto, R., Villani, A., Amenduni, M., Spirli, C., and Strazzabosco, M. (2015). Stimulation of nuclear receptor peroxisome proliferator-activated receptor-γ limits NF-κB-dependent inflammation in mouse cystic fibrosis biliary epithelium. Hepatology 62, 1551–1562. doi:10.1002/hep.28000

PubMed Abstract | CrossRef Full Text | Google Scholar

Sharma, D., Singh, M., and Rani, R. (2025). Seminars in cancer biology (Elsevier), 184–195.

Google Scholar

Sharma, D., Singh, M., and Rani, R. (2022). Role of LDH in tumor glycolysis: regulation of LDHA by small molecules for cancer therapeutics. Semin. Cancer Biol. 87, 184–195. doi:10.1016/j.semcancer.2022.11.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Shen, L., Huang, Y., Chen, D., Qiu, F., Ma, C., Jin, X., et al. (2017). pH-Responsive aerobic nanoparticles for effective photodynamic therapy. Theranostics 7, 4537–4550. doi:10.7150/thno.19546

PubMed Abstract | CrossRef Full Text | Google Scholar

Sheng, J., Zhang, J., Wang, L., Tano, V., Tang, J., Wang, X., et al. (2022). Topological analysis of hepatocellular carcinoma tumour microenvironment based on imaging mass cytometry reveals cellular neighbourhood regulated reversely by macrophages with different ontogeny. Gut 71, 1176–1191. doi:10.1136/gutjnl-2021-324339

PubMed Abstract | CrossRef Full Text | Google Scholar

Shi, Y., Zheng, W., Yang, K., Harris, K. G., Ni, K., Xue, L., et al. (2020). Intratumoral accumulation of gut microbiota facilitates CD47-based immunotherapy via STING signaling. J. Exp. Med. 217, e20192282. doi:10.1084/jem.20192282

PubMed Abstract | CrossRef Full Text | Google Scholar

Shimizu, H., and Nakayama, K. I. (2019). A 23 gene-based molecular prognostic score precisely predicts overall survival of breast cancer patients. EBioMedicine 46, 150–159. doi:10.1016/j.ebiom.2019.07.046

PubMed Abstract | CrossRef Full Text | Google Scholar

Shimizu, K., Kaira, K., Tomizawa, Y., Sunaga, N., Kawashima, O., Oriuchi, N., et al. (2014). ASC amino-acid transporter 2 (ASCT2) as a novel prognostic marker in non-small cell lung cancer. Br. J. Cancer 110, 2030–2039. doi:10.1038/bjc.2014.88

PubMed Abstract | CrossRef Full Text | Google Scholar

Si, Y., Lee, J. S., Jun, G., Kang, H. M., and Lee, J. H. (2025). Spatial omics enters the microscopic realm: opportunities and challenges. Trends Genet. 41, 774–787. doi:10.1016/j.tig.2025.05.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Singhal, S., Stadanlick, J., Annunziata, M. J., Rao, A. S., Bhojnagarwala, P. S., O'Brien, S., et al. (2019). Human tumor-associated monocytes/macrophages and their regulation of T cell responses in early-stage lung cancer. Sci. Transl. Med. 11, eaat1500. doi:10.1126/scitranslmed.aat1500

PubMed Abstract | CrossRef Full Text | Google Scholar

Song, W., Li, D., Tao, L., Luo, Q., and Chen, L. (2020). Solute carrier transporters: the metabolic gatekeepers of immune cells. Acta Pharm. Sin. B 10, 61–78. doi:10.1016/j.apsb.2019.12.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Soula, M., Unlu, G., Welch, R., Chudnovskiy, A., Uygur, B., Shah, V., et al. (2024). Glycosphingolipid synthesis mediates immune evasion in KRAS-Driven cancer. Nature 633, 451–458. doi:10.1038/s41586-024-07787-1

PubMed Abstract | CrossRef Full Text | Google Scholar

St Laurent, J., and Liu, J. F. (2024). Treatment approaches for platinum-resistant ovarian cancer. J. Clin. Oncol. 42, 127–133. doi:10.1200/jco.23.01771

PubMed Abstract | CrossRef Full Text | Google Scholar

Su, P., Wang, Q., Bi, E., Ma, X., Liu, L., Yang, M., et al. (2020). Enhanced lipid accumulation and metabolism are required for the differentiation and activation of tumor-associated macrophages. Cancer Res. 80, 1438–1450. doi:10.1158/0008-5472.Can-19-2994

PubMed Abstract | CrossRef Full Text | Google Scholar

Su, W. Y., Tian, L. Y., Guo, L. P., Huang, L. Q., and Gao, W. Y. (2023). PI3K signaling-regulated metabolic reprogramming: from mechanism to application. Biochimica Biophysica Acta (BBA)-Reviews Cancer 1878, 188952. doi:10.1016/j.bbcan.2023.188952

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, X., Zhang, J., Yang, C., Huang, Z., Shi, M., Pan, S., et al. (2019). Dual-responsive size-shrinking nanocluster with hierarchical disassembly capability for improved tumor penetration and therapeutic efficacy. ACS Appl. Mater Interfaces 11, 11865–11875. doi:10.1021/acsami.8b21580

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, J., Corradini, S., Azab, F., Shokeen, M., Muz, B., Miari, K. E., et al. (2024). IL-10R inhibition reprograms tumor-associated macrophages and reverses drug resistance in multiple myeloma. Leukemia 38, 2355–2365. doi:10.1038/s41375-024-02391-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Sundaram, L., Kumar, A., Zatzman, M., Salcedo, A., Ravindra, N., Shams, S., et al. (2024). Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers. Science 385, eadk9217. doi:10.1126/science.adk9217

PubMed Abstract | CrossRef Full Text | Google Scholar

Tan, Y., Lin, K., Zhao, Y., Wu, Q., Chen, D., Wang, J., et al. (2018). Adipocytes fuel gastric cancer omental metastasis via PITPNC1-mediated fatty acid metabolic reprogramming. Theranostics 8, 5452–5468. doi:10.7150/thno.28219

PubMed Abstract | CrossRef Full Text | Google Scholar

Tan, Y., Li, J., Zhao, G., Huang, K. C., Cardenas, H., Wang, Y., et al. (2022). Metabolic reprogramming from glycolysis to fatty acid uptake and beta-oxidation in platinum-resistant cancer cells. Nat. Commun. 13, 4554. doi:10.1038/s41467-022-32101-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Tandon, P., Bourassa-Blanchette, S., Bishay, K., Parlow, S., Laurie, S. A., and McCurdy, J. D. (2018). The risk of diarrhea and colitis in patients with advanced melanoma undergoing immune checkpoint inhibitor therapy: a systematic review and meta-analysis. J. Immunother. 41, 101–108. doi:10.1097/cji.0000000000000213

PubMed Abstract | CrossRef Full Text | Google Scholar

Tang, R., Xu, J., Wang, W., Meng, Q., Shao, C., Zhang, Y., et al. (2023). Targeting neoadjuvant chemotherapy-induced metabolic reprogramming in pancreatic cancer promotes anti-tumor immunity and chemo-response. Cell. Rep. Med. 4, 101234. doi:10.1016/j.xcrm.2023.101234

PubMed Abstract | CrossRef Full Text | Google Scholar

Tse, B. W., Collins, A., Oehler, M. K., Zippelius, A., and Heinzelmann-Schwarz, V. A. (2014). Antibody-based immunotherapy for ovarian cancer: where are we at? Ann. Oncol. 25, 322–331. doi:10.1093/annonc/mdt405

PubMed Abstract | CrossRef Full Text | Google Scholar

Tumeh, P. C., Harview, C. L., Yearley, J. H., Shintaku, I. P., Taylor, E. J. M., Robert, L., et al. (2014). PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571. doi:10.1038/nature13954

PubMed Abstract | CrossRef Full Text | Google Scholar

Tyrakis, P. A., Palazon, A., Macias, D., Lee, K. L., Phan, A. T., Veliça, P., et al. (2016). S-2-hydroxyglutarate regulates CD8(+) T-lymphocyte fate. Nature 540, 236–241. doi:10.1038/nature20165

PubMed Abstract | CrossRef Full Text | Google Scholar

Uyttenhove, C., Pilotte, L., Théate, I., Stroobant, V., Colau, D., Parmentier, N., et al. (2003). Evidence for a tumoral immune resistance mechanism based on tryptophan degradation by indoleamine 2,3-dioxygenase. Nat. Med. 9, 1269–1274. doi:10.1038/nm934

PubMed Abstract | CrossRef Full Text | Google Scholar

Vandereyken, K., Sifrim, A., Thienpont, B., and Voet, T. (2023). Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet. 24, 494–515. doi:10.1038/s41576-023-00580-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Veneziani, A. C., Gonzalez-Ochoa, E., Alqaisi, H., Madariaga, A., Bhat, G., Rouzbahman, M., et al. (2023). Heterogeneity and treatment landscape of ovarian carcinoma. Nat. Rev. Clin. Oncol. 20, 820–842. doi:10.1038/s41571-023-00819-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Victora, G. D., and Nussenzweig, M. C. (2022). Germinal centers. Annu. Rev. Immunol. 40, 413–442. doi:10.1146/annurev-immunol-120419-022408

PubMed Abstract | CrossRef Full Text | Google Scholar

Vijayan, D., Young, A., Teng, M. W. L., and Smyth, M. J. (2017). Targeting immunosuppressive adenosine in cancer. Nat. Rev. Cancer 17, 765. doi:10.1038/nrc.2017.110

PubMed Abstract | CrossRef Full Text | Google Scholar

Vikramdeo, K. S., Miree, O., Anand, S., Sharma, A., Srivastava, S. K., Singh, S., et al. (2025). MYB/AKT3 axis is a key driver of ovarian cancer growth, aggressiveness, and chemoresistance. J. Ovarian Res. 18, 179. doi:10.1186/s13048-025-01761-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, S., Zhuang, Y., Xu, J., Tong, Y., Li, X., and Dong, C. (2023). Advances in the study of hexokinase 2 (HK2) inhibitors. Anti-Cancer Agents Med. Chemistry-Anti-Cancer Agents 23, 736–746. doi:10.2174/1871520623666221020092423

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Y., and Patti, G. J. (2023). The warburg effect: a signature of mitochondrial overload. Trends Cell. Biol. 33, 1014–1020. doi:10.1016/j.tcb.2023.03.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Q., Tiffen, J., Bailey, C. G., Lehman, M. L., Ritchie, W., Fazli, L., et al. (2013). Targeting amino acid transport in metastatic castration-resistant prostate cancer: effects on cell cycle, cell growth, and tumor development. J. Natl. Cancer Inst. 105, 1463–1473. doi:10.1093/jnci/djt241

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, G., Qiu, M., Xing, X., Zhou, J., Yao, H., Li, M., et al. (2022a). Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis. Sci. Transl. Med. 14, eabk2756. doi:10.1126/scitranslmed.abk2756

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, H., Rong, X., Zhao, G., Zhou, Y., Xiao, Y., Ma, D., et al. (2022b). The microbial metabolite trimethylamine N-oxide promotes antitumor immunity in triple-negative breast cancer. Cell. Metab. 34, 581–594.e588. doi:10.1016/j.cmet.2022.02.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, H., Yung, M. M., Xuan, Y., Chen, F., Chan, W., Siu, M. K., et al. (2024). Polyunsaturated fatty acids promote M2-like TAM deposition via dampening RhoA-YAP1 signaling in the ovarian cancer microenvironment. Exp. Hematol. Oncol. 13, 90. doi:10.1186/s40164-024-00558-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, X., Xie, Z., Zhang, J., Chen, Y., Li, Q., Yang, Q., et al. (2025). Interaction between lipid metabolism and macrophage polarization in atherosclerosis. iScience 28, 112168. doi:10.1016/j.isci.2025.112168

PubMed Abstract | CrossRef Full Text | Google Scholar

Wenes, M., Shang, M., Di Matteo, M., Goveia, J., Martín-Pérez, R., Serneels, J., et al. (2016). Macrophage metabolism controls tumor blood vessel morphogenesis and metastasis. Cell. Metab. 24, 701–715. doi:10.1016/j.cmet.2016.09.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Worzfeld, T., Pogge von Strandmann, E., Huber, M., Adhikary, T., Wagner, U., Reinartz, S., et al. (2017). The unique molecular and cellular microenvironment of ovarian cancer. Front. Oncology 7, 24. doi:10.3389/fonc.2017.00024

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, H., Han, Y., Rodriguez Sillke, Y., Deng, H., Siddiqui, S., Treese, C., et al. (2019). Lipid droplet-dependent fatty acid metabolism controls the immune suppressive phenotype of tumor-associated macrophages. EMBO Mol. Med. 11, e10698. doi:10.15252/emmm.201910698

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, J. Y., Huang, T. W., Hsieh, Y. T., Wang, Y. F., Yen, C. C., Lee, G. L., et al. (2020). Cancer-derived succinate promotes macrophage polarization and cancer metastasis via succinate receptor. Mol. Cell. 77, 213–227.e215. doi:10.1016/j.molcel.2019.10.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, Z., Trevino, A. E., Wu, E., Swanson, K., Kim, H. J., D'Angio, H. B., et al. (2022). Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens. Nat. Biomed. Eng. 6, 1435–1448. doi:10.1038/s41551-022-00951-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, L., Yan, J., Bai, Y., Chen, F., Zou, X., Xu, J., et al. (2023a). An invasive zone in human liver cancer identified by Stereo-seq promotes hepatocyte-tumor cell crosstalk, local immunosuppression and tumor progression. Cell. Res. 33, 585–603. doi:10.1038/s41422-023-00831-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, L., Jin, Y., Zhao, X., Tang, K., Zhao, Y., Tong, L., et al. (2023b). Tumor aerobic glycolysis confers immune evasion through modulating sensitivity to T cell-mediated bystander killing via TNF-α. Cell. Metab. 35, 1580–1596.e1589. doi:10.1016/j.cmet.2023.07.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, Y. Q., Zhang, C. S., Xiong, J., Cai, D. Q., Wang, C. Z., Wang, Y., et al. (2023c). Low glucose metabolite 3-phosphoglycerate switches PHGDH from serine synthesis to p53 activation to control cell fate. Cell. Res. 33, 835–850. doi:10.1038/s41422-023-00874-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Xiao, Z., Locasale, J. W., and Dai, Z. (2020). Metabolism in the tumor microenvironment: insights from single-cell analysis. Oncoimmunology 9, 1726556. doi:10.1080/2162402X.2020.1726556

PubMed Abstract | CrossRef Full Text | Google Scholar

Xu, H., Liu, Z., Weng, S., Dang, Q., Ge, X., Zhang, Y., et al. (2022). Artificial intelligence-driven consensus gene signatures for improving bladder cancer clinical outcomes identified by multi-center integration analysis. Mol. Oncol. 16, 4023–4042. doi:10.1002/1878-0261.13313

PubMed Abstract | CrossRef Full Text | Google Scholar

Yan, T., Zhu, S., Hui, W., He, J., Liu, Z., and Cheng, J. (2020). Chitosan based pH-responsive polymeric prodrug vector for enhanced tumor targeted co-delivery of doxorubicin and siRNA. Carbohydr. Polym. 250, 116781. doi:10.1016/j.carbpol.2020.116781

PubMed Abstract | CrossRef Full Text | Google Scholar

Yan, J., Zhang, C., Xu, Y., Huang, Z., Ye, Q., Qian, X., et al. (2024). GPR34 is a metabolic immune checkpoint for ILC1-mediated antitumor immunity. Nat. Immunol. 25, 2057–2067. doi:10.1038/s41590-024-01973-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, Y., Wu, Y., Chen, H., Xu, Z., Lu, R., Zhang, S., et al. (2025). Research progress on the interaction between glucose metabolic reprogramming and lactylation in tumors. Front. Immunol. 16, 1595162. doi:10.3389/fimmu.2025.1595162

PubMed Abstract | CrossRef Full Text | Google Scholar

Young, A., Ngiow, S. F., Barkauskas, D. S., Sult, E., Hay, C., Blake, S. J., et al. (2016). Co-inhibition of CD73 and A2AR adenosine signaling improves anti-tumor immune responses. Cancer Cell. 30, 391–403. doi:10.1016/j.ccell.2016.06.025

PubMed Abstract | CrossRef Full Text | Google Scholar

Yu, Y. R., Imrichova, H., Wang, H., Chao, T., Xiao, Z., Gao, M., et al. (2020). Disturbed mitochondrial dynamics in CD8(+) TILs reinforce T cell exhaustion. Nat. Immunol. 21, 1540–1551. doi:10.1038/s41590-020-0793-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Yu, H., Hong, X., Wu, H., Zheng, F., Zeng, Z., Dai, W., et al. (2021). The chromatin accessibility landscape of peripheral blood mononuclear cells in patients with systemic lupus erythematosus at single-cell resolution. Front. Immunol. 12, 641886. doi:10.3389/fimmu.2021.641886

PubMed Abstract | CrossRef Full Text | Google Scholar

Zappasodi, R., Serganova, I., Cohen, I. J., Maeda, M., Shindo, M., Senbabaoglu, Y., et al. (2021). CTLA-4 blockade drives loss of T(reg) stability in glycolysis-low tumours. Nature 591, 652–658. doi:10.1038/s41586-021-03326-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Zaretsky, J. M., Garcia-Diaz, A., Shin, D. S., Escuin-Ordinas, H., Hugo, W., Hu-Lieskovan, S., et al. (2016). Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829. doi:10.1056/NEJMoa1604958

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, L., Shi, L., Shen, Y., Miao, Y., Wei, M., Qian, N., et al. (2019). Spectral tracing of deuterium for imaging glucose metabolism. Nat. Biomed. Eng. 3, 402–413. doi:10.1038/s41551-019-0393-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, M., Liu, Q., Zhang, M., Cao, C., Liu, X., Zhang, M., et al. (2020). Enhanced antitumor effects of follicle-stimulating hormone receptor-mediated hexokinase-2 depletion on ovarian cancer mediated by a shift in glucose metabolism. J. Nanobiotechnology 18, 161. doi:10.1186/s12951-020-00720-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, W., Wang, C., Hu, X., Lian, Y., Ding, C., and Ming, L. (2022). Inhibition of LDHA suppresses cell proliferation and increases mitochondrial apoptosis via the JNK signaling pathway in cervical cancer cells. Oncol. Reports 47, 1–11. doi:10.3892/or.2022.8288

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, Y., Xie, Y., Xia, S., Ge, X., Li, J., Liu, F., et al. (2025a). The novel dual GIP and GLP-1 receptor agonist tirzepatide attenuates Colon cancer development by regulating glucose metabolism. Adv. Sci. (Weinh) 12, e2411980. doi:10.1002/advs.202411980

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, L., Sun, Y., Lin, Y., Li, H., Huang, Y., Tang, N., et al. (2025b). Cell calcification reverses the chemoresistance of cancer cells via the conversion of glycolipid metabolism. Biomaterials 314, 122886. doi:10.1016/j.biomaterials.2024.122886

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhao, N., Ding, B., Zhang, Y., Klockow, J. L., Lau, K., Chin, F. T., et al. (2020). Reactive oxygen species and enzyme dual-responsive biocompatible drug delivery system for targeted tumor therapy. J. Control Release 324, 330–340. doi:10.1016/j.jconrel.2020.05.031

PubMed Abstract | CrossRef Full Text | Google Scholar

Zheng, K., Song, R., Li, R., Liu, M., Ba, Y., Jiang, W., et al. (2024). Nanomaterials for refining tumor microenvironment and enhancing therapy in head and neck squamous cell carcinoma: a review. Oncol. Transl. Med. 10, 151–161. doi:10.1097/ot9.0000000000000046

CrossRef Full Text | Google Scholar

Zhong, X., He, X., Wang, Y., Hu, Z., Huang, H., Zhao, S., et al. (2022). Warburg effect in colorectal cancer: the emerging roles in tumor microenvironment and therapeutic implications. J. Hematol. Oncol. 15, 160. doi:10.1186/s13045-022-01358-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhou, L., Huang, J., Li, C., Gu, Q., Li, G., Li, Z. A., et al. (2025). Organoids and organs-on-chips: recent advances, applications in drug development, and regulatory challenges. Med 6, 100667. doi:10.1016/j.medj.2025.100667

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhu, J., and Paul, W. E. (2010). Heterogeneity and plasticity of T helper cells. Cell. Res. 20, 4–12. doi:10.1038/cr.2009.138

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhu, Z., Cai, J., Hou, W., Xu, K., Wu, X., Song, Y., et al. (2023). Microbiome and spatially resolved metabolomics analysis reveal the anticancer role of gut Akkermansia muciniphila by crosstalk with intratumoral microbiota and reprogramming tumoral metabolism in mice. Gut Microbes 15, 2166700. doi:10.1080/19490976.2023.2166700

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhu, S., Peng, H., Kong, H., Yan, Q., Xia, K., Wang, L., et al. (2024). Visualization of the hepatic and renal cell uptake and trafficking of tetrahedral DNA origami in tumour. Cell. Prolif. 57, e13643. doi:10.1111/cpr.13643

PubMed Abstract | CrossRef Full Text | Google Scholar