Shuangyu Chen
Wenqian Chen1†
Tinghui Xu
Jiayang Li
Shengliang Qiu- 1The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China
- 2School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with limited responses to immune checkpoint blockade (ICB) therapies in most patients. Increasing evidence indicates that the tumor immune microenvironment (TIME) plays a crucial role in immunotherapy outcomes. Among various metabolic abnormalities in the TIME, dysregulated lipid metabolism has emerged as a critical determinant of immune cell fate, differentiation, and function. In this review, we comprehensively summarize the current understanding of the immune landscape in GC, focusing on how altered lipid metabolism reshapes immune cell populations—including tumor-associated macrophages (TAMs), dendritic cells (DCs), regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and cytotoxic CD8+ T cells. We highlight key metabolic pathways such as fatty acid oxidation(FAO), cholesterol homeostasis, and lipid uptake that impact immune cell activity, contributing to immune evasion and therapeutic resistance. Importantly, we explore emerging therapeutic strategies targeting lipid metabolism, including inhibitors of cluster of differentiation 36 (CD36), fatty acid synthase (FASN), and sterol regulatory element-binding protein 1 (SREBP1) and discuss their synergistic potential when combined with ICB therapies. In conclusion, lipid metabolic reprogramming represents a promising yet underexplored axis in modulating antitumor immunity in GC. Integrating metabolic intervention with immunotherapy holds potential to overcome current treatment limitations and improve clinical outcomes. Future studies incorporating spatial omics and single-cell profiling will be essential to elucidate cell-type specific metabolic dependencies and foster translational breakthroughs.
1 Introduction
According to GLOBOCAN 2022 statistics, in 2022, more than 968,000 new cases of gastric cancer (GC) were added, with nearly 660,000 deaths, ranking fifth globally both in terms of incidence and mortality. The region with the highest incidence rate is East Asia, which imposes a significant burden on cancer (1). Consequently, an urgent exploration and development of new therapeutic approaches has become imperative.
The tumor microenvironment (TME) is a complex system that can inhibit immune responses while promoting tumor progression. The composition of the TME differs across different tumor types, but its defining features include immune cells, stromal cells, vasculature, and extracellular matrix (2, 3). The complexity and dynamic interactions within the TME contribute significantly to the aggressive nature of GC and the development of therapeutic resistance (4). Therefore, understanding the intricate characteristics of the TME, particularly metabolic reprogramming within this milieu, is of substantial clinical importance for developing effective treatments for GC patients.
Metabolic reprogramming is widely recognized as a hallmark of cancer, allowing tumor cells to sustain proliferation, evade immune surveillance, and survive under stressful conditions. Among various metabolic alterations, abnormal lipid metabolism has emerged as a pivotal player in cancer progression, influencing energy metabolism, membrane biosynthesis, and signaling pathways (5–7). Cancer cells undergo significant lipid metabolic reprogramming, including increased lipid uptake, enhanced fatty acid synthesis (FAS), and elevated fatty acid oxidation (FAO). These alterations not only provide essential metabolic substrates but also enable cancer cells to resist oxidative stress, promoting tumor survival and resistance to conventional therapies (8).
Key enzymes involved in lipid metabolism, such as fatty acid synthase (FASN), ATP citrate lyase (ACLY), and stearoyl-CoA desaturase (SCD), are upregulated in GC (9–11), indicating their potential as therapeutic targets. Aberrant lipid metabolic pathways influence the recruitment, differentiation, and function of key immune cell populations including tumor-associated macrophages (TAMs), regulatory T cells (Tregs),and myeloid-derived suppressor cells (MDSCs), dendritic cells (DCs), CD8+ T cells,contributing to an immunosuppressive microenvironment that facilitates tumor progression.
2 Lipid metabolic pathways and molecular mechanisms
A key energy-generating pathway in lipid metabolism is mitochondrial fatty acid β-oxidation, which is mediated by carnitine palmitoyl-transferase 1 (CPT1), especially the isoform CPT1a (12, 13). This enzyme facilitates the transport of long-chain fatty acids to the mitochondria for oxidative breakdown and ATP production, particularly under nutrient-deprived conditions (14). Simultaneously, cancer cells exploit exogenous lipid sources through dietary uptake, with cluster of differentiation 36 (CD36) functioning as a major fatty acid translocase (15). CD36 is frequently overexpressed in malignant cells, contributing to enhanced fatty acid uptake, intracellular lipid accumulation, and increased metabolic plasticity (16–18). This metabolic architecture is tightly regulated by oncogenic signaling cascades, especially the PI3K/Akt/mTOR axis. This axis activates sterol regulatory element-binding protein 1 (SREBP1), a master transcriptional regulator of lipid biosynthesis (19, 20). When SREBP1 is activated, the expression of key enzymes involved in fat production, such as FASN and acetyl CoA carboxylase (ACC), is enhanced. This can promote de novo fat generation and support the promotion of membrane biogenesis and proliferation (21, 22). The uptake of extracellular lipids via CD36 and the lipolysis-stimulated lipoprotein receptor (LSR) is often upregulated in tumors and is also responsive to PI3K/mTOR signaling, reinforcing the lipid supply for cancer progression (23–25). Enzymes like acyl-CoA synthetase long-chain family members (ACSLs) activate imported fatty acids and channel them into biosynthetic and storage pathways, while lipogenesis induced by SREBP1 inhibits ferroptosis and improves tumor cell survival (20, 26). Uptake of lipids by CD36 enhances metastatic potential and contributes to adaptation to the TME (27). Additionally, reorganization of lipid metabolism can alter antigen presentation and inhibit T-cell activation, leading to impairment of immune surveillance (28). Phospholipid remodeling represents another critical branch of lipid metabolism. This metabolic adaptation highlights the key function of lipid metabolism in coordinating cellular bioenergetics with tumor invasiveness and immune escape, laying the mechanistic foundation for its involvement in the formation of an immunosuppressive TME (29).
3 Overview of the immune microenvironment in gastric cancer
TME of GC is composed of various immune cell subsets and non-immune components, and is characterized by prominent immunosuppressive features. Single-cell analyses have revealed a highly heterogeneous pattern of immune cell infiltration within the TME of GC. Immunosuppressive components such as Tregs, MDSCs, and TAMs are widely distributed and are closely associated with ineffective antitumor immune responses (30–33). Tregs suppress CD8+ T cell activity and the antigen presentation process through multiple mechanisms, serving as key regulatory factors in the progression of GC (34, 35). MDSCs exacerbate the immunosuppressive state by secreting inhibitory factors and modulating macrophage polarization (36). Moreover, M2 polarization of TAMs in GC has been shown to be closely associated with immune evasion and poor prognosis (37–39). Another key mechanism underlying the immunosuppressive TME is the upregulation of immune checkpoints, such as PD-L1 and the CD39/CD73 axis, which inhibit T cell effector functions and promote tumor immune evasion (40, 41). Studies have indicated that the TME in GC patients often exhibits a “cold tumor” phenotype—characterized by low immune cell infiltration and weak immune activation—which not only predicts poor prognosis but also correlates with low responsiveness to immunotherapy (42, 43).
Immune infiltration patterns exhibit dynamic changes across different GC subtypes and treatment contexts. Neoadjuvant chemotherapy can significantly remodel the TME by enhancing CD8+T cell infiltration and reducing immunosuppressive cells, highlighting the plasticity of the immune landscape (44, 45). High-throughput analyses and multiplex immunofluorescence have revealed complex interactions among different immune cells within the TME, such as exosome-mediated communication between TAMs and cancer cells (46, 47).
Furthermore, the degree of immune cell infiltration is closely associated with clinical outcomes. For instance, high PD-L1 expression often coexists with an “immune-excluded” infiltration pattern, suggesting that patients may benefit from immune checkpoint inhibitor therapy (48, 49). Key molecular features of the TME significantly shape immune infiltration and immunotherapy responses in GC, highlighting new avenues for enhancing antitumor immunity (50–52). Among these features, spatial metabolic heterogeneity — particularly lipid gradients within the TME — has recently gained attention as a critical factor influencing immune cell behavior.
4 Interactions between aberrant lipid metabolism and immune cells
4.1 TAMs
TAMs, one of the most abundant immune cells in the GC immune microenvironment, exhibit significant metabolic plasticity. Under the stimulation of various cytokines, macrophages can be polarized into two phenotypes with different functions: M1 macrophages, which have pro-inflammatory and tumor-inhibiting effects; And M2 macrophages, which have anti-inflammatory and tumor-promoting effects Their functional state is closely linked to their lipid metabolic program. In gastric cancer, scavenger receptors such as CD36 mediate the endocytosis of fatty acids and cholesterol from the tumor microenvironment, leading to intracellular lipid accumulation and promoting the establishment of a highly immunosuppressive TME (53, 54). This process further activates the peroxisome proliferator-activated receptor γ (PPAR-γ) signaling pathway, upregulating FAO, promoting TAM towards a m2 polarized phenotype, and enhancing its oncogenic function (55, 56). Moreover, lipid uptake promotes enhanced FAO, providing a stable energy supply for M2-polarized TAMs and augmenting their secretion of immunosuppressive factors such as IL-10 and TGF-β (57–59). These alterations collectively contribute to the formation of a microenvironment that favors tumor survival and immune evasion (60, 61). Mechanistically, lipid uptake via CD36 facilitates intracellular fatty acid accumulation, which activates PPAR-γ signaling and upregulates key enzymes of FAO, such as CPT1A.
Further studies have revealed that the metabolic state of TAMs is a key determinant of their spatial distribution and functional heterogeneity. For example, lipid-rich TAMs are predominantly located in hypoxic regions, where they respond to tumor-derived factors such as IL-34 and signals associated with p53 inactivation, exhibiting enhanced immunosuppressive capabilities (62, 63). At the metabolic level, lipid metabolic reprogramming is closely regulated by the TRAF3/STAT6 pathway, which governs key transcriptional programs involved in the polarization process (64). Meanwhile, signaling molecules such as CD40 have been shown to promote the reprogramming of TAMs toward an antitumor phenotype by remodeling fatty acid and glutamine metabolism, highlighting the potential of metabolic interventions in reshaping TAM function (65). Overall, lipid uptake and metabolism determine the fate of TAMs, representing a critical regulatory axis within the GC immune microenvironment and a promising therapeutic target for future treatment strategies (66). These findings highlight the central role of TAM lipid metabolism in promoting immune evasion and progression of gastric cancer.
4.2 Dendritic cells
DCs within the GC immune microenvironment is often markedly suppressed by dysregulated lipid metabolism. In gastric cancer, this metabolic dysfunction contributes to impaired tumor antigen presentation and weakened immune surveillance.The lipid-rich tumor environment leads to lipid accumulation in DCs, particularly the formation of lipid droplets enriched with cholesterol and triglycerides, which significantly impairs their antigen-presenting capacity (67, 68). Lipid overload not only diminishes the expression of major histocompatibility complex (MHC) class I and II molecules but also suppresses the expression of costimulatory molecules such as CD80 and CD86, thereby limiting T cell activation (69, 70). Studies have shown that Epstein-Barr virus–associated GC exacerbates antigen presentation impairment by secreting exosomes that interfere with DC maturation (70). Moreover, tumor-induced lipid metabolic reprogramming can suppress mitochondrial function and glucose metabolism in DCs, driving them toward an immunotolerant phenotype (67, 68). A decline in cross-presentation capacity is another critical defect of lipid-laden DCs, particularly impairing their ability to elicit CD8+ T cell responses (71, 72). Some studies have reported that lipid accumulation hinders the ability of DCs to uptake and process extracellular antigens, thereby weakening their effectiveness in activating tumor-specific T cells (73, 74). Furthermore, Tregs form immunosuppressive complexes with DCs through a CXCR3-mediated chemotactic mechanism, further limiting the ability of DCs to activate CD8+ T cells (75). In recent years, engineered dendritic cell (DC) systems have been developed to bypass the metabolic impairments of natural DCs, offering new avenues for tumor vaccines and targeted immunotherapy (76, 77). Therefore, targeting lipid metabolic regulatory pathways is considered a potential strategy to restore DC immune function and enhance immune responses in gastric cancer (78, 79).
4.3 Tregs and MDSCs
Tregs are abundantly infiltrated in the GC immune microenvironment and rely on lipid metabolism to maintain their stability and immunosuppressive function. Studies have shown that within the tumor environment, Tregs gain an energetic advantage by enhancing FAO, which sustains their Foxp3 expression and suppressive capacity (80, 81). PD-1 deficiency disrupts the metabolic stability of Tregs, suggesting that their metabolic adaptability is a critical factor in the establishment of immune tolerance (80). Moreover, fatty acid-binding protein 5 (FABP5) and the SIRT1–CX3CL1 axis play important roles in regulating lipid metabolism in Tregs, influencing their distribution within the TME and their immunosuppressive capacity (82, 83). In lipid-rich microenvironments, Tregs exhibit enhanced stability and activity, representing one of the major obstacles to the efficacy of immune checkpoint inhibition therapy (84, 85).
Similar to Tregs, MDSCs exhibit potent immunosuppressive properties regulated by lipid metabolism. In high-lipid microenvironments, they sustain their survival through FAS and cholesterol metabolism, while secreting a range of immunosuppressive factors (29, 86). Ginger polysaccharide–induced lipid metabolic disruption can promote apoptosis of MDSCs, indicating that targeting lipid metabolism holds potential for enhancing immune responses (86). Within the GC TME, MDSCs cooperate with Tregs to establish a metabolically coupled immunosuppressive network (87, 88). Recent studies have shown that cancer-associated fibroblasts (CAFs) influence the metabolic activity of MDSCs through CD36 and the secretion of macrophage migration inhibitory factor (MIF), further exacerbating immune evasion (87). In summary, targeting lipid metabolism has emerged as a key strategy for modulating the functions of Tregs and MDSCs and overcoming immune tolerance (29, 85).
4.4 CD8+ T cells
CD8+ T cells are the central effector cells in antitumor immune responses, and their functional state is significantly influenced by dysregulated lipid metabolism within the TME. In the GC microenvironment, fatty acid uptake and cholesterol metabolism reshape the metabolic programming of CD8+ T cells, leading to metabolic imbalance, enhanced exhaustion phenotypes, and reduced cytotoxic function (89). Tumor cells secrete lipid metabolism–regulating factors such as SCD1 and FABP5, which elevate levels of free fatty acids and oxidized lipids in the TME. This induces the accumulation of reactive oxygen species (ROS) in CD8+ T cells, leading to lipid peroxidation and mitochondrial damage (90). This process is accompanied by the upregulation of inhibitory receptors such as PD-1 and TIGIT, ultimately leading to T cell exhaustion and the loss of sustained cytotoxic activity (91). Moreover, excess cholesterol can accumulate in the membranes of CD8+ T cells, disrupting immunological synapse formation and TCR signaling, thereby further suppressing their effector functions (92).
Studies have also indicated that certain lipid metabolic pathways exert bidirectional regulatory effects on CD8+ T cells. Tissue-resident CD8+ T cells rely on FAO to sustain energy supply and long-term survival; however, in the nutrient-deprived and competitive TME, this metabolic dependency may actually constrain the sustained activation of their effector functions (89). Under high-lipid conditions, tumor cells compete with CD8+ T cells for nutritional substrates, leading to energy deprivation in CD8+ T cells. This results in a state of “functional starvation,” characterized by reduced expression of effector molecules such as Granzyme B and IFN-γ (27, 91). Therefore, targeting lipid metabolic pathways—such as CD36 inhibition, FAO blockade, or cholesterol metabolism modulation—is considered a promising strategy to restore CD8+ T cell function and enhance the efficacy of immunotherapy (90, 93) (Figure 1).
Figure 1. Interactions between aberrant lipid metabolism and immune cells.
5 Clinical and therapeutic implications
Lipid metabolic reprogramming is not only a key mechanism in shaping the TME of GC, but also offers multidimensional therapeutic targets for clinical intervention. High expression of key lipid metabolic molecules such as CD36, FASN, and SREBP1 is closely associated with the infiltration of immunosuppressive cells and T-cell exhaustion, and is considered one of the major contributors to immunotherapy resistance (94–96). For instance, Li et al. found that lipid metabolic imbalance can promote symbiotic signaling pathways between CAFs and TAMs, which significantly impairs the efficacy of immune checkpoint inhibitors (ICIs) (97). Emerging lipid-targeted strategies—such as FASN inhibitors, FAO pathway blockers, and cholesterol metabolism modulators—are being actively explored to enhance CD8+T cell function, inhibit TAM polarization, and reduce Treg-mediated immunosuppression (94, 98, 99). Moreover, lipid metabolism–related genes have also been identified as potential predictive biomarkers of immune response. Genes such as RGS2, APOD, and MTTP have demonstrated promising prognostic and therapeutic response prediction value in multiple studies (94, 96, 98).
Combination therapy strategies are emerging as a key approach to overcoming the bottlenecks of immunotherapy in GC Several clinical trials—such as ATTRACTION-2, ATTRACTION-4, KEYNOTE-859, KEYNOTE-061 and CheckMate-649—have validated the efficacy of combining ICIs with chemotherapy (100–104). Combination strategies involving CD36 antagonists or cholesterol synthase inhibitors have significantly enhanced antitumor immune responses in preclinical models (94). Meanwhile, lipid metabolism–based immune subtyping approaches are increasingly being employed to guide the selection of GC patients for immunotherapy (101, 105). In summary, the role of lipid metabolism in precision immunotherapy for GC is becoming increasingly prominent. Existing clinical trials combining immune checkpoint inhibitors with chemotherapy have demonstrated heterogeneous outcomes, which may partially reflect underlying metabolic states of the tumor immune microenvironment (106–110). Aberrant expression of lipid metabolism–related molecules such as FASN, CD36, and SREBP1 has been associated with immune cell exhaustion, Treg enrichment, and impaired dendritic cell function, suggesting their potential value as both therapeutic targets and predictive biomarkers (111–114). Integrating lipidomic analysis into future clinical trial designs may enhance stratification strategies and optimize combination regimens to overcome resistance (Table 1).
Table 1. Clinical trials of immunotherapy-based combination strategies in gastric cancer.
6 Research gaps and future perspectives
Although the role of lipid metabolism in regulating the immune microenvironment of GC has been progressively elucidated, many gaps remain in understanding its mechanistic network. Current research primarily focuses on classical lipid metabolism regulators such as CD36 and FASN, while the roles of non-coding RNAs and RNA modifications (e.g. m6A) in the cross-regulation of lipid metabolism remain largely underexplored (115–117). Moreover, how lipid metabolism specifically affects different immune cell subsets—such as tissue-resident memory T cells (Trm) and progenitor-exhausted T cells (Tpex)—remains insufficiently investigated at the single-cell resolution level (118, 119). Most current mechanistic studies are based on in vitro cell experiments and traditional animal models, with a lack of application of emerging technologies—such as spatial transcriptomics, spatial metabolomics, and single-cell lipidomics—for constructing a “functional lipid map” within the immune microenvironment (120, 121).
In future research, a primary focus should be the expanded systematic screening of lipid metabolism regulators, including transporters, enzymes, and intermediate metabolites, to evaluate their immunological effects (122, 123). Secondly, integrating clinical cohorts to perform lipid metabolic phenotyping and establishing a biomarker system capable of predicting immunotherapy response and resistance risk will be critical for advancing personalized treatment (124–126). Moreover, constructing in vitro microenvironment models—such as organoid–immune cell co-culture systems—or developing novel drug delivery platforms targeting lipid metabolism will help bridge the gap between basic research and clinical application in metabolic immune regulation (127). Building on this foundation, conducting multicenter prospective clinical studies to evaluate the efficacy and safety of lipid metabolism–targeted interventions combined with immunotherapy will be a key pathway toward the clinical translation of metabolism-based immunotherapies (128, 129).
7 Conclusion
Lipid metabolism plays a central regulatory role in the TME of GC. Lipid competition between tumor cells and immune cells not only reshapes energy metabolism patterns but also alters immune cell functional states, inducing immunosuppressive phenotypes such as M2 polarization of TAMs, impaired antigen presentation by DCs, enhanced Treg functionality, and exhaustion of CD8+ T cells (30, 32, 34). Lipid metabolic reprogramming mechanisms—including CD36-mediated lipid uptake, enhanced FAO, and cholesterol accumulation—have been shown to play critical roles in GC progression and immune evasion by regulating immune checkpoint expression, immune cell metabolic adaptation, and the secretion of immunosuppressive factors (40, 48, 101). Targeting lipid metabolic pathways—such as FASN, CPT1A, CD36, or cholesterol metabolism—can enhance immunotherapeutic responses and alleviate the immunosuppressive nature of the TME, demonstrating promising translational potential (123). However, the cell-specific functions of lipid metabolism across different immune cell subsets, its spatial heterogeneity, and the interplay between metabolic and epigenetic regulation axes remain to be further investigated (119, 130, 131). Future research should integrate emerging technologies such as spatial transcriptomics, single-cell lipidomics, and multi-omics analyses, while establishing clinical cohorts to explore predictive biomarkers and novel strategies for metabolism-targeted therapies (127, 132).
Author contributions
SC: Writing – original draft, Writing – review & editing. WC: Writing – original draft. TX: Writing – original draft. JL: Writing – original draft. JY: Writing – original draft. YH: Writing – original draft, Writing – review & editing. SQ: Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Acknowledgments
We utilized the Figdraw online platform (https://www.figdraw.com/) to create the figures in this manuscript and extend our heartfelt thanks for the support and functionality it offers.
Conflict of interest
The authors declare that the research 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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Abbreviations
ACC, acetyl CoA carboxylase; ACLY, ATP citrate lyase; ACSLs, acyl-CoA synthetase long-chain family members; CAFs, cancer-associated fibroblasts; CD36, cluster of differentiation 36; CPT1, carnitine palmitoyl-transferase 1; DCs, dendritic cells; FAO, fatty acid oxidation; FAS, fatty acid synthesis; FASN, fatty acid synthase; FABP5, fatty acid-binding protein 5; GC, gastric cancer; ICIs, immune checkpoint inhibitors; LSR, lipolysis-stimulated lipoprotein receptor; MDSCs, myeloid-derived suppressor cells; MHC, major histocompatibility complex; MIF, migration inhibitory factor; PPAR-γ, peroxisome proliferator-activated receptor γ; ROS, reactive oxygen species; SCD, stearoyl-CoA desaturase; SREBP1, sterol regulatory element-binding protein 1; TAMs, tumor-associated macrophages; TME, tumor microenvironment; Tpex, progenitor-exhausted T cells; Trm, tissue-resident memory T cells; Tregs, regulatory T cells.
References
1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clin. (2024) 74:229–63. doi: 10.3322/caac.21834
2. Anderson NM and Simon MC. The tumor microenvironment. Curr biol: CB. (2020) 30:R921–r5. doi: 10.1016/j.cub.2020.06.081
3. Yasuda T and Wang YA. Gastric cancer immunosuppressive microenvironment heterogeneity: implications for therapy development. Trends cancer. (2024) 10:627–42. doi: 10.1016/j.trecan.2024.03.008
4. Liu Y, Li C, Lu Y, Liu C, and Yang W. Tumor microenvironment-mediated immune tolerance in development and treatment of gastric cancer. Front Immunol. (2022) 13:1016817. doi: 10.3389/fimmu.2022.1016817
5. Snaebjornsson MT, Janaki-Raman S, and Schulze A. Greasing the wheels of the cancer machine: the role of lipid metabolism in cancer. Cell Metab. (2020) 31:62–76. doi: 10.1016/j.cmet.2019.11.010
6. Broadfield LA, Pane AA, Talebi A, Swinnen JV, and Fendt SM. Lipid metabolism in cancer: New perspectives and emerging mechanisms. Dev Cell. (2021) 56:1363–93. doi: 10.1016/j.devcel.2021.04.013
7. Jin HR, Wang J, Wang ZJ, Xi MJ, Xia BH, Deng K, et al. Lipid metabolic reprogramming in tumor microenvironment: from mechanisms to therapeutics. J Hematol Oncol. (2023) 16:103. doi: 10.1186/s13045-023-01498-2
8. Bacci M, Lorito N, Smiriglia A, and Morandi A. Fat and furious: lipid metabolism in antitumoral therapy response and resistance. Trends cancer. (2021) 7:198–213. doi: 10.1016/j.trecan.2020.10.004
9. El-Rifai W, Frierson HF Jr., Moskaluk CA, Harper JC, Petroni GR, Bissonette EA, et al. Genetic differences between adenocarcinomas arising in Barrett’s esophagus and gastric mucosa. Gastroenterology. (2001) 121:592–8. doi: 10.1053/gast.2001.27215
10. Ezzeddini R, Taghikhani M, Somi MH, Samadi N, and Rasaee MJ. Clinical importance of FASN in relation to HIF-1α and SREBP-1c in gastric adenocarcinoma. Life Sci. (2019) 224:169–76. doi: 10.1016/j.lfs.2019.03.056
11. Gao Y, Li J, Xi H, Cui J, Zhang K, Zhang J, et al. Stearoyl-CoA-desaturase-1 regulates gastric cancer stem-like properties and promotes tumour metastasis via Hippo/YAP pathway. Br J cancer. (2020) 122:1837–47. doi: 10.1038/s41416-020-0827-5
12. Wang J, Xiang H, Lu Y, Wu T, and Ji G. The role and therapeutic implication of CPTs in fatty acid oxidation and cancers progression. Am J Cancer Res. (2021) 11:2477–94.
13. Wang C, Zhang C, Li X, Shen J, Xu Y, Shi H, et al. CPT1A-mediated succinylation of S100A10 increases human gastric cancer invasion. J Cell Mol Med. (2019) 23:293–305. doi: 10.1111/jcmm.13920
14. Terry AR and Hay N. Emerging targets in lipid metabolism for cancer therapy. Trends Pharmacol Sci. (2024) 45:537–51. doi: 10.1016/j.tips.2024.04.007
15. Glatz JFC, Heather LC, and Luiken J. CD36 as a gatekeeper of myocardial lipid metabolism and therapeutic target for metabolic disease. Physiol Rev. (2024) 104:727–64. doi: 10.1152/physrev.00011.2023
16. Guerrero-Rodríguez SL, Mata-Cruz C, Pérez-Tapia SM, and Velasco-Velázquez MA. Role of CD36 in cancer progression, stemness, and targeting. Front Cell Dev Biol. (2022) 10:1079076. doi: 10.3389/fcell.2022.1079076
17. Terry AR, Nogueira V, Rho H, Ramakrishnan G, Li J, Kang S, et al. CD36 maintains lipid homeostasis via selective uptake of monounsaturated fatty acids during matrix detachment and tumor progression. Cell Metab. (2023) 35:2060–76.e9. doi: 10.1016/j.cmet.2023.09.012
18. Pascual G, Avgustinova A, Mejetta S, Martín M, Castellanos A, Attolini CS, et al. Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature. (2017) 541:41–5. doi: 10.1038/nature20791
19. Yi J, Zhu J, Wu J, Thompson CB, and Jiang X. Oncogenic activation of PI3K-AKT-mTOR signaling suppresses ferroptosis via SREBP-mediated lipogenesis. Proc Natl Acad Sci United States America. (2020) 117:31189–97. doi: 10.1073/pnas.2017152117
20. Chen M, Li H, Zheng S, Shen J, Chen Y, Li Y, et al. Nobiletin targets SREBP1/ACLY to induce autophagy-dependent cell death of gastric cancer cells through PI3K/Akt/mTOR signaling pathway. Phytomed: Int J phytother phytopharmacol. (2024) 128:155360. doi: 10.1016/j.phymed.2024.155360
21. Chen J, Ye M, Gu D, Yu P, Xu L, Xue B, et al. FTO-induced APOE promotes the Malignant progression of pancreatic neuroendocrine neoplasms through FASN-mediated lipid metabolism. Int J Biol Sci. (2025) 21:1478–96. doi: 10.7150/ijbs.103428
22. Zhou X, Li Y, Yang C, Chen D, Wang T, Liu T, et al. Cordycepin reprogramming lipid metabolism to block metastasis and EMT via ERO1A/mTOR/SREBP1 axis in cholangiocarcinoma. Life Sci. (2023) 327:121698. doi: 10.1016/j.lfs.2023.121698
23. Kawabata K, Takahashi T, Tanaka K, Kurokawa Y, Yamamoto K, Saito T, et al. Lipolysis-stimulated lipoprotein receptor promote lipid uptake and fatty acid oxidation in gastric cancer. Gastric Cancer. (2024) 27:1258–72. doi: 10.1007/s10120-024-01552-z
24. Yang H, Zhao H, Ren Z, Yi X, Zhang Q, Yang Z, et al. Overexpression CPT1A reduces lipid accumulation via PPARα/CD36 axis to suppress the cell proliferation in ccRCC. Acta Biochim Biophys Sinica. (2022) 54:220–31. doi: 10.3724/abbs.2021023
25. Zhao S, Cheng L, Shi Y, Li J, Yun Q, and Yang H. MIEF2 reprograms lipid metabolism to drive progression of ovarian cancer through ROS/AKT/mTOR signaling pathway. Cell Death disease. (2021) 12:18. doi: 10.1038/s41419-020-03336-6
26. Zheng YN, Lou SY, Lu J, Zheng FL, Tang YM, Zhang EJ, et al. Selective PI3Kδ inhibitor TYM-3–98 suppresses AKT/mTOR/SREBP1-mediated lipogenesis and promotes ferroptosis in KRAS-mutant colorectal cancer. Cell Death disease. (2024) 15:474. doi: 10.1038/s41419-024-06848-7
27. Tang Y, Chen Z, Zuo Q, and Kang Y. Regulation of CD8+ T cells by lipid metabolism in cancer progression. Cell Mol Immunol. (2024) 21:1215–30. doi: 10.1038/s41423-024-01224-z
28. Wang Y, Guo Z, Isah AD, Chen S, Ren Y, and Cai H. Lipid metabolism and tumor immunotherapy. Front Cell Dev Biol. (2023) 11:1187989. doi: 10.3389/fcell.2023.1187989
29. Zhang H, Li S, Wang D, Liu S, Xiao T, Gu W, et al. Metabolic reprogramming and immune evasion: the interplay in the tumor microenvironment. biomark Res. (2024) 12:96. doi: 10.1186/s40364-024-00646-1
30. Chen J, Liu K, Luo Y, Kang M, Wang J, Chen G, et al. Single-cell profiling of tumor immune microenvironment reveals immune irresponsiveness in gastric signet-ring cell carcinoma. Gastroenterology. (2023) 165:88–103. doi: 10.1053/j.gastro.2023.03.008
31. Kang B, Camps J, Fan B, Jiang H, Ibrahim MM, Hu X, et al. Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment. Genome Biol. (2022) 23:265. doi: 10.1186/s13059-022-02828-2
32. Li Y, Hu X, Lin R, Zhou G, Zhao L, Zhao D, et al. Single-cell landscape reveals active cell subtypes and their interaction in the tumor microenvironment of gastric cancer. Theranostics. (2022) 12:3818–33. doi: 10.7150/thno.71833
33. Zhou X, Yang J, Lu Y, Ma Y, Meng Y, Li Q, et al. Relationships of tumor differentiation and immune infiltration in gastric cancers revealed by single-cell RNA-seq analyses. Cell Mol Life sciences: CMLS. (2023) 80:57. doi: 10.1007/s00018-023-04702-1
34. Negura I, Pavel-Tanasa M, and Danciu M. Regulatory T cells in gastric cancer: Key controllers from pathogenesis to therapy. Cancer Treat Rev. (2023) 120:102629. doi: 10.1016/j.ctrv.2023.102629
35. Su J, Mao X, Wang L, Chen Z, Wang W, Zhao C, et al. Lactate/GPR81 recruits regulatory T cells by modulating CX3CL1 to promote immune resistance in a highly glycolytic gastric cancer. Oncoimmunology. (2024) 13:2320951. doi: 10.1080/2162402X.2024.2320951
36. Tsutsumi C, Ohuchida K, Katayama N, Yamada Y, Nakamura S, Okuda S, et al. Tumor-infiltrating monocytic myeloid-derived suppressor cells contribute to the development of an immunosuppressive tumor microenvironment in gastric cancer. Gastric Cancer. (2024) 27:248–62. doi: 10.1007/s10120-023-01456-4
37. Zhang C, Wei S, Dai S, Li X, Wang H, Zhang H, et al. The NR_109/FUBP1/c-Myc axis regulates TAM polarization and remodels the tumor microenvironment to promote cancer development. J immunother Cancer. (2023) 11. doi: 10.1136/jitc-2022-006230
38. Li Y, Jiang L, Chen Y, Li Y, Yuan J, Lu J, et al. Specific lineage transition of tumor-associated macrophages elicits immune evasion of ascitic tumor cells in gastric cancer with peritoneal metastasis. Gastric Cancer. (2024) 27:519–38. doi: 10.1007/s10120-024-01486-6
39. He Y, Hong Q, Chen S, Zhou J, and Qiu S. Reprogramming tumor-associated macrophages in gastric cancer: a pathway to enhanced immunotherapy. Front Immunol. (2025) 16:1558091. doi: 10.3389/fimmu.2025.1558091
40. Miao Z, Li J, Wang Y, Shi M, Gu X, Zhang X, et al. Hsa_circ_0136666 stimulates gastric cancer progression and tumor immune escape by regulating the miR-375/PRKDC Axis and PD-L1 phosphorylation. Mol cancer. (2023) 22:205. doi: 10.1186/s12943-023-01883-y
41. Huang T, Ren X, Tang X, Wang Y, Ji R, Guo Q, et al. Current perspectives and trends of CD39-CD73-eAdo/A2aR research in tumor microenvironment: a bibliometric analysis. Front Immunol. (2024) 15:1427380. doi: 10.3389/fimmu.2024.1427380
42. Khan M, Lin J, Wang B, Chen C, Huang Z, Tian Y, et al. A novel necroptosis-related gene index for predicting prognosis and a cold tumor immune microenvironment in stomach adenocarcinoma. Front Immunol. (2022) 13:968165. doi: 10.3389/fimmu.2022.968165
43. Lv K, Sun M, Fang H, Wang J, Lin C, Liu H, et al. Targeting myeloid checkpoint Siglec-10 reactivates antitumor immunity and improves anti-programmed cell death 1 efficacy in gastric cancer. J immunother Cancer. (2023) 11. doi: 10.1136/jitc-2023-007669
44. Xing X, Shi J, Jia Y, Dou Y, Li Z, Dong B, et al. Effect of neoadjuvant chemotherapy on the immune microenvironment in gastric cancer as determined by multiplex immunofluorescence and T cell receptor repertoire analysis. J immunother Cancer. (2022) 10. doi: 10.1136/jitc-2021-003984
45. Huan X, Zou K, Zhang P, Ding H, Luo C, Xiang C, et al. Neoadjuvant chemotherapy is linked to an amended anti-tumorigenic microenvironment in gastric cancer. Int immunopharmacol. (2024) 127:111352. doi: 10.1016/j.intimp.2023.111352
46. Qiu Y, Lu G, Li N, Hu Y, Tan H, and Jiang C. Exosome-mediated communication between gastric cancer cells and macrophages: implications for tumor microenvironment. Front Immunol. (2024) 15:1327281. doi: 10.3389/fimmu.2024.1327281
47. Groen-van Schooten TS, Franco Fernandez R, van Grieken NCT, Bos EN, Seidel J, Saris J, et al. Mapping the complexity and diversity of tertiary lymphoid structures in primary and peritoneal metastatic gastric cancer. J immunother Cancer. (2024) 12. doi: 10.1136/jitc-2024-009243
48. Zhao Q, Yu H, Shi M, Wang X, Fan Z, and Wang Z. Tumor microenvironment characteristics of lipid metabolism reprogramming related to ferroptosis and EndMT influencing prognosis in gastric cancer. Int immunopharmacol. (2024) 137:112433. doi: 10.1016/j.intimp.2024.112433
49. Zhong M, Yu Z, Wu Q, Lu B, Sun P, Zhang X, et al. PCDHGA10 as a potential prognostic biomarker and correlated with immune infiltration in gastric cancer. Front Immunol. (2024) 15:1500478. doi: 10.3389/fimmu.2024.1500478
50. Lin Y, Jing X, Chen Z, Pan X, Xu D, Yu X, et al. Histone deacetylase-mediated tumor microenvironment characteristics and synergistic immunotherapy in gastric cancer. Theranostics. (2023) 13:4574–600. doi: 10.7150/thno.86928
51. Bos J, Groen-van Schooten TS, Brugman CP, Jamaludin FS, van Laarhoven HWM, and Derks S. The tumor immune composition of mismatch repair deficient and Epstein-Barr virus-positive gastric cancer: A systematic review. Cancer Treat Rev. (2024) 127:102737. doi: 10.1016/j.ctrv.2024.102737
52. Li X, Wu N, Wang C, Pei B, Ma X, Xie J, et al. NALCN expression is down-regulated and associated with immune infiltration in gastric cancer. Front Immunol. (2025) 16:1512107. doi: 10.3389/fimmu.2025.1512107
53. Li C, Zhang L, Qiu Z, Deng W, and Wang W. Key molecules of fatty acid metabolism in gastric cancer. Biomolecules. (2022) 12:706. doi: 10.3390/biom12050706
54. Aoki T, Kinoshita J, Munesue S, Hamabe-Horiike T, Yamaguchi T, Nakamura Y, et al. Hypoxia-induced CD36 expression in gastric cancer cells promotes peritoneal metastasis via fatty acid uptake. Ann Surg Oncol. (2023) 30:3125–36. doi: 10.1245/s10434-022-12465-5
55. Liu S, Zhang H, Li Y, Zhang Y, Bian Y, Zeng Y, et al. S100A4 enhances protumor macrophage polarization by control of PPAR-γ-dependent induction of fatty acid oxidation. J immunother Cancer. (2021) 9. doi: 10.1136/jitc-2021-002548
56. Zeng W, Li F, Jin S, Ho PC, Liu PS, and Xie X. Functional polarization of tumor-associated macrophages dictated by metabolic reprogramming. J Exp Clin Cancer research: CR. (2023) 42:245. doi: 10.1186/s13046-023-02832-9
57. Gao J, Liang Y, and Wang L. Shaping polarization of tumor-associated macrophages in cancer immunotherapy. Front Immunol. (2022) 13:888713. doi: 10.3389/fimmu.2022.888713
58. Chen D, Zhang X, Li Z, and Zhu B. Metabolic regulatory crosstalk between tumor microenvironment and tumor-associated macrophages. Theranostics. (2021) 11:1016–30. doi: 10.7150/thno.51777
59. Pan Y, Yu Y, Wang X, and Zhang T. Tumor-associated macrophages in tumor immunity. Front Immunol. (2020) 11:583084. doi: 10.3389/fimmu.2020.583084
60. Li C, Xu X, Wei S, Jiang P, Xue L, and Wang J. Tumor-associated macrophages: potential therapeutic strategies and future prospects in cancer. J immunother Cancer. (2021) 9. doi: 10.1136/jitc-2020-001341
61. Chu X, Tian Y, and Lv C. Decoding the spatiotemporal heterogeneity of tumor-associated macrophages. Mol cancer. (2024) 23:150. doi: 10.1186/s12943-024-02064-1
62. Nian Z, Dou Y, Shen Y, Liu J, Du X, Jiang Y, et al. Interleukin-34-orchestrated tumor-associated macrophage reprogramming is required for tumor immune escape driven by p53 inactivation. Immunity. (2024) 57:2344–61.e7. doi: 10.1016/j.immuni.2024.08.015
63. Liang Y, Bu Q, You W, Zhang R, Xu Z, Gan X, et al. Single-cell analysis reveals hypoxia-induced immunosuppressive microenvironment in intrahepatic cholangiocarcinoma. Biochim Biophys Acta Mol basis disease. (2024) 1870:167276. doi: 10.1016/j.bbadis.2024.167276
64. Shi JH, Liu LN, Song DD, Liu WW, Ling C, Wu FX, et al. TRAF3/STAT6 axis regulates macrophage polarization and tumor progression. Cell Death differentiation. (2023) 30:2005–16. doi: 10.1038/s41418-023-01194-1
65. Liu PS, Chen YT, Li X, Hsueh PC, Tzeng SF, Chen H, et al. CD40 signal rewires fatty acid and glutamine metabolism for stimulating macrophage anti-tumorigenic functions. Nat Immunol. (2023) 24:452–62. doi: 10.1038/s41590-023-01430-3
66. Xiang X, Wang J, Lu D, and Xu X. Targeting tumor-associated macrophages to synergize tumor immunotherapy. Signal transduction targeted Ther. (2021) 6:75. doi: 10.1038/s41392-021-00484-9
67. Sun Z, Zhang L, and Liu L. Reprogramming the lipid metabolism of dendritic cells in tumor immunomodulation and immunotherapy. Biomed pharmacother = Biomed pharmacotherapie. (2023) 167:115574. doi: 10.1016/j.biopha.2023.115574
68. Peng X, He Y, Huang J, Tao Y, and Liu S. Metabolism of dendritic cells in tumor microenvironment: for immunotherapy. Front Immunol. (2021) 12:613492. doi: 10.3389/fimmu.2021.613492
69. Chen J, Duan Y, Che J, and Zhu J. Dysfunction of dendritic cells in tumor microenvironment and immunotherapy. Cancer Commun (London England). (2024) 44:1047–70. doi: 10.1002/cac2.12596
70. Hinata M, Kunita A, Abe H, Morishita Y, Sakuma K, Yamashita H, et al. Exosomes of epstein-barr virus-associated gastric carcinoma suppress dendritic cell maturation. Microorganisms. (2020) 8:1776. doi: 10.3390/microorganisms8111776
71. MacNabb BW, Tumuluru S, Chen X, Godfrey J, Kasal DN, Yu J, et al. Dendritic cells can prime anti-tumor CD8(+) T cell responses through major histocompatibility complex cross-dressing. Immunity. (2022) 55:982–97.e8. doi: 10.1016/j.immuni.2022.04.016
72. Murphy TL and Murphy KM. Dendritic cells in cancer immunology. Cell Mol Immunol. (2022) 19:3–13. doi: 10.1038/s41423-021-00741-5
73. Hilligan KL and Ronchese F. Antigen presentation by dendritic cells and their instruction of CD4+ T helper cell responses. Cell Mol Immunol. (2020) 17:587–99. doi: 10.1038/s41423-020-0465-0
74. Chudnovskiy A, Castro TBR, Nakandakari-Higa S, Cui A, Lin CH, Sade-Feldman M, et al. Proximity-dependent labeling identifies dendritic cells that drive the tumor-specific CD4(+) T cell response. Sci Immunol. (2024) 9:eadq8843. doi: 10.1126/sciimmunol.adq8843
75. Moreno Ayala MA, Campbell TF, Zhang C, Dahan N, Bockman A, Prakash V, et al. CXCR3 expression in regulatory T cells drives interactions with type I dendritic cells in tumors to restrict CD8(+) T cell antitumor immunity. Immunity. (2023) 56:1613–30.e5. doi: 10.1016/j.immuni.2023.06.003
76. Mateus D, Sebastião AI, Frasco MF, Carrascal MA, Falcão A, Gomes CM, et al. Artificial dendritic cells: A new era of promising antitumor immunotherapy. Small (Weinheim an der Bergstrasse Germany). (2023) 19:e2303940. doi: 10.1002/smll.202303940
77. Zheng J, Wang M, Pang L, Wang S, Kong Y, Zhu X, et al. Identification of a novel DEC-205 binding peptide to develop dendritic cell-targeting nanovaccine for cancer immunotherapy. J Controlled release. (2024) 373:568–82. doi: 10.1016/j.jconrel.2024.07.056
78. Marciscano AE and Anandasabapathy N. The role of dendritic cells in cancer and anti-tumor immunity. Semin Immunol. (2021) 52:101481. doi: 10.1016/j.smim.2021.101481
79. Zhou J, Nie RC, Yin YX, Wang Y, Yuan SQ, Zhao ZH, et al. Genomic analysis uncovers the prognostic and immunogenetic feature of pyroptosis in gastric carcinoma: indication for immunotherapy. Front Cell Dev Biol. (2022) 10:906759. doi: 10.3389/fcell.2022.906759
80. Kim MJ, Kim K, Park HJ, Kim GR, Hong KH, Oh JH, et al. Deletion of PD-1 destabilizes the lineage identity and metabolic fitness of tumor-infiltrating regulatory T cells. Nat Immunol. (2023) 24:148–61. doi: 10.1038/s41590-022-01373-1
81. Kumar A, Das JK, Peng HY, Wang L, Ballard DJ, Ren Y, et al. Metabolic fitness of NAC1-deficient Tregs in the tumor microenvironment fuels tumor growth. JCI Insight. (2025) 10. doi: 10.1172/jci.insight.186000
82. Kobayashi S, Wannakul T, Sekino K, Takahashi Y, Kagawa Y, Miyazaki H, et al. Fatty acid-binding protein 5 limits the generation of Foxp3(+) regulatory T cells through regulating plasmacytoid dendritic cell function in the tumor microenvironment. Int J cancer. (2022) 150:152–63. doi: 10.1002/ijc.33777
83. Zi R, Zhao X, Liu L, Wang Y, Zhang R, Bian Z, et al. Metabolic-immune suppression mediated by the SIRT1-CX3CL1 axis induces functional enhancement of regulatory T cells in colorectal carcinoma. Advanced Sci (Weinheim Baden-Wurttemberg Germany). (2025) 12:e2404734. doi: 10.1002/advs.202404734
84. van Hooren L, Handgraaf SM, Kloosterman DJ, Karimi E, van Mil L, Gassama AA, et al. CD103(+) regulatory T cells underlie resistance to radio-immunotherapy and impair CD8(+) T cell activation in glioblastoma. Nat cancer. (2023) 4:665–81. doi: 10.1038/s43018-023-00547-6
85. Yang L, Wang X, Wang S, Shen J, Li Y, Wan S, et al. Targeting lipid metabolism in regulatory T cells for enhancing cancer immunotherapy. Biochim Biophys Acta Rev cancer. (2025) 1880:189259. doi: 10.1016/j.bbcan.2025.189259
86. Song C, Ji Y, Wang W, and Tao N. Ginger polysaccharide promotes myeloid-derived suppressor cell apoptosis by regulating lipid metabolism. Phytother research: PTR. (2023) 37:2894–901. doi: 10.1002/ptr.7784
87. Zhu GQ, Tang Z, Huang R, Qu WF, Fang Y, Yang R, et al. CD36(+) cancer-associated fibroblasts provide immunosuppressive microenvironment for hepatocellular carcinoma via secretion of macrophage migration inhibitory factor. Cell discovery. (2023) 9:25. doi: 10.1038/s41421-023-00529-z
88. Yan Y, Huang L, Liu Y, Yi M, Chu Q, Jiao D, et al. Metabolic profiles of regulatory T cells and their adaptations to the tumor microenvironment: implications for antitumor immunity. J Hematol Oncol. (2022) 15:104. doi: 10.1186/s13045-022-01322-3
89. Wang R, Liu Z, Fan Z, and Zhan H. Lipid metabolism reprogramming of CD8(+) T cell and therapeutic implications in cancer. Cancer letters. (2023) 567:216267. doi: 10.1016/j.canlet.2023.216267
90. Wu D and Chen Y. Lipids for CD8(+) TILs: beneficial or harmful? Front Immunol. (2022) 13:1020422. doi: 10.3389/fimmu.2022.1020422
91. Chen W, Teo JMN, Yau SW, Wong MY, Lok CN, Che CM, et al. Chronic type I interferon signaling promotes lipid-peroxidation-driven terminal CD8(+) T cell exhaustion and curtails anti-PD-1 efficacy. Cell Rep. (2022) 41:111647. doi: 10.1016/j.celrep.2022.111647
92. Zheng M, Zhang W, Chen X, Guo H, Wu H, Xu Y, et al. The impact of lipids on the cancer-immunity cycle and strategies for modulating lipid metabolism to improve cancer immunotherapy. Acta Pharm Sin B. (2023) 13:1488–97. doi: 10.1016/j.apsb.2022.10.027
93. Cui MY, Yi X, Zhu DX, and Wu J. The role of lipid metabolism in gastric cancer. Front Oncol. (2022) 12:916661. doi: 10.3389/fonc.2022.916661
94. Yang S, Sun B, Li W, Yang H, Li N, and Zhang X. Fatty acid metabolism is related to the immune microenvironment changes of gastric cancer and RGS2 is a new tumor biomarker. Front Immunol. (2022) 13:1065927. doi: 10.3389/fimmu.2022.1065927
95. Li Y and Zeng Z. Investigating the dysregulation of genes associated with glucose and lipid metabolism in gastric cancer and their influence on immunity and prognosis. BioFactors (Oxford England). (2025) 51:e2138. doi: 10.1002/biof.2138
96. Wang Z, Chen H, Sun L, Wang X, Xu Y, Tian S, et al. Uncovering the potential of APOD as a biomarker in gastric cancer: A retrospective and multi-center study. Comput Struct Biotechnol J. (2024) 23:1051–64. doi: 10.1016/j.csbj.2024.02.015
97. Li Y, Zheng Y, Huang J, Nie RC, Wu QN, Zuo Z, et al. CAF-macrophage crosstalk in tumour microenvironments governs the response to immune checkpoint blockade in gastric cancer peritoneal metastases. Gut. (2025) 74:350–63. doi: 10.1136/gutjnl-2024-333617
98. Wang W, Gao Y, Liu Y, Xia S, Xu J, Qin L, et al. Pan-cancer analysis reveals MTTP as a prognostic and immunotherapeutic biomarker in human tumors. Front Immunol. (2025) 16:1549965. doi: 10.3389/fimmu.2025.1549965
99. Hou W, Zhao Y, and Zhu H. Predictive biomarkers for immunotherapy in gastric cancer: current status and emerging prospects. Int J Mol Sci. (2023) 24:15321. doi: 10.3390/ijms242015321
100. Kang YK, Morita S, Satoh T, Ryu MH, Chao Y, Kato K, et al. Exploration of predictors of benefit from nivolumab monotherapy for patients with pretreated advanced gastric and gastroesophageal junction cancer: post hoc subanalysis from the ATTRACTION-2 study. Gastric Cancer. (2022) 25:207–17. doi: 10.1007/s10120-021-01230-4
101. Kang YK, Chen LT, Ryu MH, Oh DY, Oh SC, Chung HC, et al. Nivolumab plus chemotherapy versus placebo plus chemotherapy in patients with HER2-negative, untreated, unresectable advanced or recurrent gastric or gastro-oesophageal junction cancer (ATTRACTION-4): a randomised, multicentre, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. (2022) 23:234–47. doi: 10.1016/S1470-2045(21)00692-6
102. Rha SY, Oh DY, Yañez P, Bai Y, Ryu MH, Lee J, et al. Pembrolizumab plus chemotherapy versus placebo plus chemotherapy for HER2-negative advanced gastric cancer (KEYNOTE-859): a multicentre, randomised, double-blind, phase 3 trial. Lancet Oncol. (2023) 24:1181–95. doi: 10.1016/S1470-2045(23)00515-6
103. Gou M, Zhang Y, Wang Z, Qian N, and Dai G. PD-1 inhibitor combined with albumin paclitaxel and apatinib as second-line treatment for patients with metastatic gastric cancer: a single-center, single-arm, phase II study. Investigational New Drugs. (2024) 42:171–8. doi: 10.1007/s10637-024-01425-3
104. Janjigian YY, Shitara K, Moehler M, Garrido M, Salman P, Shen L, et al. First-line nivolumab plus chemotherapy versus chemotherapy alone for advanced gastric, gastro-oesophageal junction, and oesophageal adenocarcinoma (CheckMate 649): a randomised, open-label, phase 3 trial. Lancet (London England). (2021) 398:27–40. doi: 10.1016/S0140-6736(21)00797-2
105. Butterfield LH and Najjar YG. Immunotherapy combination approaches: mechanisms, biomarkers and clinical observations. Nat Rev Immunol. (2024) 24:399–416. doi: 10.1038/s41577-023-00973-8
106. Catenacci DV, Rosales M, Chung HC, HY H, Shen L, Moehler M, et al. MAHOGANY: margetuximab combination in HER2+ unresectable/metastatic gastric/gastroesophageal junction adenocarcinoma. Future Oncol (London England). (2021) 17:1155–64. doi: 10.2217/fon-2020-1007
107. Luke JJ, Bever K, Hodi FS, Taube J, Massey A, Yao D, et al. Rationale and feasibility of a rapid integral biomarker program that informs immune-oncology clinical trials: the ADVISE trial. J immunother Cancer. (2025) 13. doi: 10.1136/jitc-2024-011170
108. Hegewisch-Becker S, Mendez G, Chao J, Nemecek R, Feeney K, Van Cutsem E, et al. First-line nivolumab and relatlimab plus chemotherapy for gastric or gastroesophageal junction adenocarcinoma: the phase II RELATIVITY-060 study. J Clin Oncol. (2024) 42:2080–93. doi: 10.1200/JCO.23.01636
109. Ji Z, Wang X, Xin J, Ma L, Zuo D, Li H, et al. Multiomics reveals tumor microenvironment remodeling in locally advanced gastric and gastroesophageal junction cancer following neoadjuvant immunotherapy and chemotherapy. J immunother Cancer. (2024) 12. doi: 10.1136/jitc-2024-010041
110. Yang W, Zhou M, Li G, Zhou C, Wang L, Xia F, et al. Adjuvant chemoradiotherapy plus PD-1 inhibitor for pN3 gastric cancer: a randomized, multicenter, Phase III trial. Future Oncol (London England). (2024) 20:3389–96. doi: 10.1080/14796694.2024.2421156
111. Janjigian YY, Kawazoe A, Bai Y, Xu J, Lonardi S, Metges JP, et al. Pembrolizumab plus trastuzumab and chemotherapy for HER2-positive gastric or gastro-oesophageal junction adenocarcinoma: interim analyses from the phase 3 KEYNOTE-811 randomised placebo-controlled trial. Lancet (London England). (2023) 402:2197–208. doi: 10.1016/S0140-6736(23)02033-0
112. Bagaev A, Kotlov N, Nomie K, Svekolkin V, Gafurov A, Isaeva O, et al. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell. (2021) 39:845–65.e7. doi: 10.1016/j.ccell.2021.04.014
113. Zeng D, Wu J, Luo H, Li Y, Xiao J, Peng J, et al. Tumor microenvironment evaluation promotes precise checkpoint immunotherapy of advanced gastric cancer. J immunother Cancer. (2021) 9. doi: 10.1136/jitc-2021-002467
114. Li S, Yu W, Xie F, Luo H, Liu Z, Lv W, et al. Neoadjuvant therapy with immune checkpoint blockade, antiangiogenesis, and chemotherapy for locally advanced gastric cancer. Nat Commun. (2023) 14:8. doi: 10.1038/s41467-022-35431-x
115. Shitara K, Ajani JA, Moehler M, Garrido M, Gallardo C, Shen L, et al. Nivolumab plus chemotherapy or ipilimumab in gastro-oesophageal cancer. Nature. (2022) 603:942–8. doi: 10.1038/s41586-022-04508-4
116. Shen L, Zhang Y, Li Z, Zhang X, Gao X, Liu B, et al. First-line cadonilimab plus chemotherapy in HER2-negative advanced gastric or gastroesophageal junction adenocarcinoma: a randomized, double-blind, phase 3 trial. Nat Med. (2025) 31:1163–70. doi: 10.1038/s41591-024-03450-4
117. Chen L and Deng J. Role of non-coding RNA in immune microenvironment and anticancer therapy of gastric cancer. J Mol Med (Berlin Germany). (2022) 100:1703–19. doi: 10.1007/s00109-022-02264-6
118. Jing Q, Yao H, Li H, Yuan C, Hu J, Zhang P, et al. A novel RNA modification prognostic signature for predicting the characteristics of the tumor microenvironment in gastric cancer. Front Oncol. (2023) 13:905139. doi: 10.3389/fonc.2023.905139
119. Yang P, Yang W, Wei Z, Li Y, Yang Y, and Wang J. Novel targets for gastric cancer: The tumor microenvironment (TME), N6-methyladenosine (m6A), pyroptosis, autophagy, ferroptosis and cuproptosis. Biomed pharmacother = Biomed pharmacotherapie. (2023) 163:114883. doi: 10.1016/j.biopha.2023.114883
120. He X, Guan XY, and Li Y. Clinical significance of the tumor microenvironment on immune tolerance in gastric cancer. Front Immunol. (2025) 16:1532605. doi: 10.3389/fimmu.2025.1532605
121. Chang J, Wu H, Wu J, Liu M, Zhang W, Hu Y, et al. Constructing a novel mitochondrial-related gene signature for evaluating the tumor immune microenvironment and predicting survival in stomach adenocarcinoma. J Trans Med. (2023) 21:191. doi: 10.1186/s12967-023-04033-6
122. Su C, Yu R, Hong X, Zhang P, Guo Y, Cai JC, et al. CXCR4 expressed by tumor-infiltrating B cells in gastric cancer related to survival in the tumor microenvironment: an analysis combining single-cell RNA sequencing with bulk RNA sequencing. Int J Mol Sci. (2023) 24:12890. doi: 10.3390/ijms241612890
123. Pang MJ, Burclaff JR, Jin R, Adkins-Threats M, Osaki LH, Han Y, et al. Gastric organoids: progress and remaining challenges. Cell Mol Gastroenterol hepatol. (2022) 13:19–33. doi: 10.1016/j.jcmgh.2021.09.005
124. Bin YL, Hu HS, Tian F, Wen ZH, Yang MF, Wu BH, et al. Metabolic reprogramming in gastric cancer: trojan horse effect. Front Oncol. (2021) 11:745209. doi: 10.3389/fonc.2021.745209
125. Chen X, Zhou B, Wang S, Jiang X, Ping Y, Xia J, et al. Intestinal metaplasia key molecules and UPP1 activation via Helicobacter pylori/NF-kB: drivers of Malignant progression in gastric cancer. Cancer Cell Int. (2024) 24:399. doi: 10.1186/s12935-024-03598-6
126. Shang Z, Ma Z, Wu E, Chen X, Tuo B, Li T, et al. Effect of metabolic reprogramming on the immune microenvironment in gastric cancer. Biomed pharmacother = Biomed pharmacotherapie. (2024) 170:116030. doi: 10.1016/j.biopha.2023.116030
127. Jiang T, Zhang J, Zhao S, Zhang M, Wei Y, Liu X, et al. MCT4: a key player influencing gastric cancer metastasis and participating in the regulation of the metastatic immune microenvironment. J Trans Med. (2025) 23:276. doi: 10.1186/s12967-025-06279-8
128. Liu K, Yuan S, Wang C, and Zhu H. Resistance to immune checkpoint inhibitors in gastric cancer. Front Pharmacol. (2023) 14:1285343. doi: 10.3389/fphar.2023.1285343
129. Liu J, Yuan Q, Guo H, Guan H, Hong Z, and Shang D. Deciphering drug resistance in gastric cancer: Potential mechanisms and future perspectives. Biomed pharmacother = Biomed pharmacotherapie. (2024) 173:116310. doi: 10.1016/j.biopha.2024.116310
130. Zhang Y, Yang Y, Chen Y, Lin W, Chen X, Liu J, et al. PD-L1: Biological mechanism, function, and immunotherapy in gastric cancer. Front Immunol. (2022) 13:1060497. doi: 10.3389/fimmu.2022.1060497
131. Yang T and Guo L. Advancing gastric cancer treatment: nanotechnology innovations and future prospects. Cell Biol toxicol. (2024) 40:101. doi: 10.1007/s10565-024-09943-9
Keywords: gastric cancer, lipid metabolism, tumor immune microenvironment, CD8+ T cells, tumor-associated macrophages, immunotherapy resistance, fatty acid oxidation, immune checkpoint blockade
Citation: Chen S, Chen W, Xu T, Li J, Yu J, He Y and Qiu S (2025) The impact of aberrant lipid metabolism on the immune microenvironment of gastric cancer: a mini review. Front. Immunol. 16:1639823. doi: 10.3389/fimmu.2025.1639823
Received: 02 June 2025; Accepted: 23 July 2025;
Published: 18 August 2025.
Edited by:
Yejun Tan, Hong Kong Polytechnic University, ChinaReviewed by:
Jie Ren, Dalian Medical University, ChinaCopyright © 2025 Chen, Chen, Xu, Li, Yu, He and Qiu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yibo He, aGV5YjIwMTczNjI2QDEyNi5jb20=
†These authors have contributed equally to this work and share first authorship