Metabolic reprogramming and immunosenescence: a new sight for glioma therapy

Gliomas, the most prevalent primary tumor of the central nervous system, are characterized by a poor prognosis and a high recurrence rate. The glioma microenvironment is highly immunosuppressive, which poses a major obstacle to effective immunotherapy. Metabolic reprogramming is a hallmark of glioma, driving tumor progression and therapy resistance. Key alterations include the Warburg effect, increased glutamine dependency, enhanced pentose phosphate pathway activity, and dysregulated lipid metabolism. Immunosenescence, the age-dependent decline in immune function that contributes to disease pathogenesis, encompasses immune dysregulation, senescence-associated secretory phenotype (SASP) accumulation, and epigenetic changes, which together drive immune cell dysfunction and foster an immunosuppressive microenvironment. Meantime, senescent immune cells may change the metabolic microenvironment, whereas metabolic reprogramming also influence immune system. Thus, this small essay is on the purpose of demonstrating the significance and function of metabolic reprogramming and immunosenescence in gliomas, providing evidence of promising therapeutic strategies.

1 Introduction

Gliomas, one of the most common primary brain tumors (for ∼30%), develop from neural stem or progenitor cells carrying tumor-initiating genetic alteration (Weller et al., 2024). According to the fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS) in 2021, gliomas are divided into five major groups: adult-type diffuse gliomas; pediatric-type diffuse low-grade gliomas; pediatric-type diffuse high-grade gliomas; circumscribed astrocytic gliomas; and ependymal tumors based on histological features and distinct molecular biomarkers (Louis et al., 2021). Equally, gliomas are classified into grades 1–4, and the malignant glioblastoma (GBM) belongs to grade 4 with a median overall survival time slightly over a year (Tao et al., 2023; Han et al., 2025). The mutations in the genes for isocitrate dehydrogenase type 1 (IDH1) or histone H3 are typical driver mutations in gliomas, which lead to weighty epigenetic changes, genomic instability, and subsequent acquisition of additional tumor-promoting genetic alterations, or deletions or truncating mutations of tumor suppressor genes (Bunse et al., 2025). Except for genetic factors and external risk factors (exposure to ionizing radiation), Advanced age is associated with gliomas, especially GBM (median age: 66 years) (Han et al., 2024; Weller et al., 2024). Currently, the therapeutic strategies for gliomas are surgery combined with chemotherapy or radiotherapy, and exploring innovative techniques such as immunotherapy and the integration of medical and engineering technology therapy, as well as natural compounds (Lin et al., 2024; Han et al., 2025; Wang et al., 2025). Traditionally, the CNS is an immunologically privileged organ as a result of the presence of the blood-brain barrier (BBB) and the absence of lymphatic vessels. In glioma progression, the BBB becomes impaired, thereby enabling immune cells to transmigrate across the BBB, whereas the interaction between immunocytes and glioma cells led to an immunosuppressive tumor microenvironment (TME), resulting in poor response to immunotherapy (Pu et al., 2025). Additionally, due to the deficient prognosis and high recurrence rate, investigation for promising strategies is significant.

Metabolic reprogramming represents a hallmark of gliomas, facilitating rapid cancer cell proliferation and immune evasion through remodeling of the tumor microenvironment (Figure 1). A classic manifestation of this reprogramming is the Warburg effect, characterized by cancer cells exhibiting extremely high glycolytic rate and lactate production capacity even under aerobic conditions (Ekici et al., 2022). Beyond aerobic glycolysis, the glutamine uptake rate of gliomas is significantly increased, and compared with healthy cells, they require more glutamine storage to maintain survival because glutamine serves as a crucial energy substrate and carbon source for cancer cells (Pavlova and Thompson, 2016). Proliferating cells and tumor cells must also shunt carbon from glycolysis into the pentose phosphate pathway (PPP) for nucleotide synthesis and combating oxidative stress through upregulating PPP-related enzymes and downregulating glycolytic enzymes (Langbein et al., 2006). To meet the high energy demand, tumor cells enhance their ability to take up lipids from the environment and increase lipid biosynthesis (Cortes Ballen et al., 2024). GBM cells also utilize mitochondrial glucose oxidation to promote active growth in vivo (Agnihotri and Zadeh, 2016). In gliomas, metabolic reprogramming fosters multidrug resistance via multidimensional mechanisms: adaptive alterations in energy metabolism, redox homeostasis, stemness regulation, and microenvironment remodeling. For instance, aerobic glycolysis activation promotes HIF-1α signaling, reducing drug penetration and enhancing efflux; glutamine metabolism supports reactive oxygen species (ROS) clearance and epigenetic modification, while lipid metabolism bolsters stemness and immune microenvironment remodeling (Hsu, 2023; D’Aprile et al., 2025; Vijayanathan and Ho, 2025). In-depth analysis of the molecular mechanisms underlying metabolic reprogramming in gliomas and identification of potential therapeutic targets have important theoretical and clinical significance for developing novel therapeutic strategies and improving patient prognosis.

Figure 1

Figure 1. The overview of metabolic reprogramming and immunosenescence in gliomas. The metabolic reprogramming of gliomas mainly includes glucose metabolism, amino acid metabolism, lipid metabolism, and the Krebs cycle. Immunosenescence primarily involves alterations in immune cells, secretion of SASP, and epigenetic modifications. Supported by BioRender (https://app.biorender.com).

With advancing age, the immune system is abnormally activated or disrupted, resulting in remodeling and decline of immune function, named immunosenescence, which was proposed by Roy Walford in the 1960s (Fu et al., 2025). Immunosenescence is primarily driven by bone marrow aging and thymic involution; it can be further exacerbated by DNA damage related to various signaling pathways resulting from diverse endogenous and exogenous factors (Zhang et al., 2024). Immunosenescence is characterized by several key alterations, such as senescent immune cells, thymic involution, disrupted naïve/memory ratio in T and B cells, inflammaging, and metabolic dysregulation, among others (Liu Z. et al., 2023). Thymic involution underlies the imbalance in immune cell proportions, particularly for T cells. Meanwhile, the activity of other immune cells, such as macrophages and natural killer (NK) cells also reduces to accelerate the development of suppressive TME (Nguyen and Cho, 2025). The central pillar of aging is inflammaging, referred to as a systemic state of chronic low-grade inflammation. The senescence-associated secretory phenotype (SASP) of senescent cells, characterized by the secretion of numerous pro-inflammatory factors such as interleukin-1 (IL-1), IL-6, and tumor necrosis factor (TNF) promotes the inflammaging phenotype, and the components of SASP reprogram the TME, suppress CD8⁺ T cell activity, thereby fostering an immunosuppressive TME (Liu Z. et al., 2023; Fu et al., 2025). As a result, targeting immune aging might reverse the suppressive TME and improve the immunotherapy of gliomas.

Thus, this review aims to illustrate the features of metabolic reprogramming and immunosenescence in gliomas, with the ultimate goal of informing the development of potent glioma therapeutics.

2 Metabolic reprogramming of gliomas

Metabolic reprogramming refers to the process by which tumor cells, to meet the demands of rapid proliferation and adapt to the harsh microenvironment, reshape cellular metabolic pathways by regulating the activity of key metabolic enzymes and signaling pathways. Metabolic reprogramming events in gliomas are not only adaptive mechanisms of the tumor that help it continuously meet the growing demand for biosynthetic molecules and energy, but also promote the production of metabolites that are closely associated with multiple oncogenic signaling pathways (Trejo-Solis et al., 2023).

2.1 Warburg effect and PPP

2.1.1 Warburg effect

A hallmark of cancer cell dysregulated metabolism is the “Warburg effect” or aerobic glycolysis, a phenomenon first described by Otto Warburg, in which cells preferentially metabolize glucose to lactate in the cytoplasm despite available oxygen (Callao and Montoya, 1961). This metabolic preference is attributed to the rapid ATP yield afforded by glycolysis and the diversion of glycolytic intermediates into biosynthetic pathways to generate biomass essential for proliferation. Furthermore, lactate production helps maintain cellular redox balance, permitting sustained glycolytic flux. Lactate acidifies the brain microenvironment and, through the action of matrix metalloproteinases (MMPs), degrades the extracellular matrix, thereby fostering tumor invasion (Venneti and Thompson, 2017).

2.1.1.1 Key regulatory factors of Warburg effect

A spectrum of molecules serves as key regulators of glycolysis, including glucose transporter (GLUT), enzymes (HKs, PFK-1, PKs, G6PI, GA3PDH), and transcription factors (HIF-1α, c-Myc, p53). Glycolysis initiates with the uptake of glucose from the extracellular milieu into the cytosol, a process primarily mediated by glucose transporter (GLUT) family proteins. In GBM, the overexpression of GLUT-1 and GLUT-3 was a notable feature, and their elevated levels are associated with poorer patient survival. Notably, studies by Libby et al. demonstrated that GLUT-3 overexpression specifically promoted tumor invasiveness (Libby et al., 2021; Chamarthy and Mekala, 2023). Functionally, mutation of the GLUT-1 CYS207 residue to serine abrogated its palmitoylation and subsequent membrane localization, resulting in suppressing glycolysis, impeding cancer cell proliferation, and inhibiting the tumorigenic potential of GBM (Zhang Z. et al., 2021). The glycolytic process generated substantial lactate, which was predominantly extruded by monocarboxylate transporter 1 (MCT1), particularly in IDH-wildtype gliomas. When knockdown MCT1, the sensitivity of GBM cells to temozolomide (TMZ) markedly increased, and survival was extended (Miranda-Gonçalves et al., 2013; Miranda-Gonçalves et al., 2021). MCT1 was the key proton-coupled lactate exporter in gliomas, essential for sustaining intracellular pH and glycolysis. In IDH1-mutant gliomas, 2-hydroxyglutarate (2-HG) epigenetically suppressed MCT1 expression, thereby impairing lactate efflux (Choi et al., 2012; Fu et al., 2015).

Hexokinase (HK) is a key enzyme in the first step of glycolysis, phosphorylating glucose to glucose-6-phosphate (G-6-P). Hexokinase 2 (HK2) is the most well-characterized isoform in the HK family and serves as the major isoform in brain tumors, such as GBM (Wolf et al., 2011b; Di Magno et al., 2014). Notably, HK2 was preferentially expressed in GBM compared to low-grade astrocytomas and normal brain tissues, and its expression level correlated with patient prognosis (Agnihotri and Zadeh, 2016; Huang et al., 2022). Genetic depletion of HK2 attenuated aerobic glycolysis, restored oxidative phosphorylation, and promoted apoptosis, consequently improving survival in GBM xenograft models (Wolf et al., 2011a; Wolf et al., 2011b). Phosphofructokinase-1 (PFK-1) is involved in a key regulatory and rate-limiting step of glycolysis. 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3)is one of the isoforms of phosphofructokinase-2 (PFK-2). Inhibition of PFKFB3 expression or activity reduced the level of fructose-2,6-bisphosphate (F-2,6-BP) to suppress PFK-1 activity and glycolytic rate, and ultimately inhibited glioma cell proliferation (Yang et al., 2024). Furthermore, studies indicated that transforming growth factor-β1 (TGF-β1) upregulated the mRNA and protein level expression of PFKFB3 in glioma cells to enhance glycolytic flux, glucose uptake, and lactate production via activating p38 mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) signaling pathway (Rodríguez-García et al., 2017).

Pyruvate kinase (PK) is a key enzyme in the payoff phase of glycolysis, mediating the conversion of phosphoenolpyruvate and ADP to pyruvate and adenosine triphosphate (Venneti and Thompson, 2017). In the developing brain, the predominant PKM2 variant facilitates anabolism through its low-activity state, while the glycolytic enzyme Glucose-6-phosphate isomerase (G6PI) exerts multifaceted functions, also acting as an autocrine motility factor (AMF) (Wolf et al., 2011b; Israelsen et al., 2013; Jeffery et al., 2000). Compared to GBM patients without AMF expression, the overall survival rate of GBM patients with AMF overexpression was lower (Tanizaki et al., 2006). Glyceraldehyde-3-phosphate dehydrogenase (GA3PDH) plays an important role in the Warburg effect, catalyzing the conversion of glyceraldehyde-3-phosphate (GA3P) to 1,3-bisphosphoglycerate (1,3BPG) in the glycolytic pathway, and GA3PDH was significantly upregulated in GBM biopsy specimens relative to low-grade gliomas and normal brain tissues (Appelskog et al., 2004; Akram, 2013). Lactate dehydrogenase (LDH) catalyzes the conversion of pyruvate to lactate under anaerobic conditions, while oxidizing reduced nicotinamide adenine dinucleotide (NADH) to its oxidized form (NAD⁺). In GBM, overexpression of the lactate dehydrogenase A (LDHA) isoform was frequently observed, channeling glycolytic flux toward lactate production and thereby promoting tumor cell survival and proliferation (Daniele et al., 2015).

Hypoxia-inducible factors (HIFs) are key regulators of the cellular response to hypoxia in GBM, among which hypoxia-inducible factor 1α (HIF-1α) plays a central role in regulating gene expression when oxygen levels decrease. Besides, HIF-1α is a critical transcription factor that controls the expression of genes related to glucose metabolism. In GBM cells, HIF-1α was stabilized under hypoxic conditions, which in turn led to increased expression of glycolytic enzymes and glucose transporters, thereby promoting the Warburg effect (Venneti and Thompson, 2017; Cortes Ballen et al., 2024). In GBM, glycolytic flux was enhanced through the dysregulation of key metabolic regulators, including p53 and signal transducer and activator of transcription 3 (STAT3). STAT3 activation upregulated the expression of transcription factors such as HIF-1α and c-Myc, which collectively promoted GBM cell proliferation (Yu et al., 2017; Papavassiliou and Papavassiliou, 2022).

2.1.2 Pentose phosphate pathway (PPP)

Beyond enhanced glycolysis, proliferating and tumor cells redirect glycolytic carbon flux into the PPP to support nucleotide biosynthesis and counteract oxidative stress (Agnihotri and Zadeh, 2016). The PPP converts G-6-P into ribose-5-phosphate, which is an essential precursor for nucleotide assembly, and generates reducing equivalents in the form of nicotinamide adenine dinucleotide phosphate (NADPH). Subsequently, ribose-5-phosphate is utilized in purine synthesis through the incorporation of glutamine, glycine, aspartate, carbon dioxide, and tetrahydrofolate, or serves as the backbone for pyrimidine formation along with bicarbonate, aspartate, and glutamine. The phenotypic switch between migration and proliferation in tumor cells was critically regulated by the metabolic shift from the PPP to glycolysis. This shift was driven by synergistic signals from the tumor microenvironment, including mechanics and oxygen tension. On the one hand, elevated matrix stiffness activated integrin-focal adhesion kinase (FAK)-YAP/TAZ signaling, which downregulated key PPP enzymes, thereby diverting metabolic flux toward glycolysis and promoting the proliferative over the migratory phenotype (Romani et al., 2022). On the other hand, under hypoxic conditions, GBM cells suppressed the expression of key PPP enzymes, including glucose-6-phosphate dehydrogenase (G6PD), 6-phosphogluconate dehydrogenase (PGD), and transketolase (TKT), while upregulating glycolytic enzymes such as HK2, 6-phosphofructokinase platelet type (PFKP), aldolase C (ALDOC), PKM2, and LDHA. This metabolic rewiring promoted a phenotypic shift toward enhanced migration at the expense of reduced proliferation (Vijayanathan and Ho, 2025).

2.2 Metabolic reprogramming of amino acids

Amino acids (AAs) serve as fundamental building blocks for proteins and are essential regulators of cellular division, differentiation, and function. Dysregulation of amino acid metabolism is a common feature in numerous pathological conditions, including GBM and other tumors (Ling et al., 2023; Lu et al., 2024).

Glutamine (Gln), the most abundant non-essential amino acid in the human body, serves as a pivotal biosynthetic precursor and a critical source of NADPH and glutathione for maintaining redox homeostasis (Bergström et al., 1974; Curi et al., 2005; Lee et al., 2018). In the brain, the production of the neurotransmitters glutamate and γ-aminobutyric acid (GABA) depends on the neuron-astrocyte metabolic coupling known as the glutamine-glutamate cycle (Bak et al., 2006). Gliomas co-opted this essential pathway, exhibiting markedly enhanced glutamine uptake and dependency to sustain their survival and proliferation, with elevated glutamine concentrations correlating with tumor progression (Ekici et al., 2022; Venneti et al., 2015). This metabolic reprogramming was driven by key regulatory factors: altered glutamate transporter expression [upregulated Glutamate/Aspartate Transporter (GLAST) and downregulated Glutamate Transporter 1 (GLT1)] led to extracellular glutamate accumulation, potentially modulating the tumor microenvironment; oncogenic Myc protein upregulated glutamine transporters and anabolic enzymes; and the balance between glutamine-synthesizing (glutamine synthetase, GS) and glutamine-catabolizing enzymes (glutaminase, GLS) was disrupted, with the oncogenic isoform GLS1 promoting glutamine addiction (McCormick et al., 1990; de la Rosa et al., 2009; Matés et al., 2013; Pavlova and Thompson, 2016; Matés et al., 2019; Matés et al., 2020; Trejo-Solis et al., 2023). Furthermore, glutamine could activate mTOR complex 1 (mTORC1), a key component of the PI3K-Akt-mTOR signaling pathway, which was known to regulate critical cellular processes such as cell growth, proliferation, and survival of gliomas (Obara-Michlewska and Szeliga, 2020; Garcia et al., 2021).

The metabolic pathways of tryptophan (Trp) primarily encompass the kynurenine (Kyn) pathway, the serotonin pathway, and the indole pathway. Oldak et al. reported that levels of Trp metabolites in grade 4 gliomas were significantly higher than those in grade 2–3 gliomas (Oldak et al., 2024). Indoleamine 2,3-dioxygenase 1 (IDO1) was highly expressed in glioma stem cells, and its upregulation was associated with enhanced chemoresistance in GBM cells. Furthermore, the administration of IDO1 antagonists had been shown to improve TMZ cytotoxicity in a mouse glioma model (Hanihara et al., 2016; Ozawa et al., 2020). Overexpression of tryptophan 2,3-dioxygenase 2 (TDO2) in the Kyn pathway within glioma cells augmented Trp metabolism, thereby facilitating the release of biologically active metabolites such as 3-hydroxykynurenine, L-kynurenine, 3-hydroxybenzoic acid, and quinolinic acid. These metabolites promoted the proliferation and tumorigenic potential of glioma cells by activating the aryl hydrocarbon receptor (AhR)/Akt signaling pathway (Zhong et al., 2022). In contrast to the tumor-promoting effects of the kynurenine pathway, melatonin from the serotonin pathway exerted antitumor effects, while activation of the indole pathway enzyme interleukin-4-induced 1 (IL4i1) promoted cancer cell survival (Srivastava et al., 2025). R-2-hydroxyglutarate (R-2-HG), the neomorphic enzymatic product of mutant IDH, could significantly strengthen the activity of TDO2, indirectly activating the kynurenine pathway in monocytes/macrophages. Furthermore, in IDH-mutant gliomas, the extracellular Trp concentration is significantly higher than that in IDH-wildtype tumors and normal brain tissues. This elevated Trp spatially overlapped with the distribution of R-2-HG, forming a “tryptophan-enriched microenvironment” that sustained the activation of key metabolic pathways, ultimately influencing immune response (Friedrich et al., 2021).

GBM cells rewire their amino acid metabolism to fuel tumorigenesis. They primarily imported serine and glycine via the AA transporter alanine serine cysteine transporter 2 (ASCT2) (SLC1A5) to feed the serine-glycine-one-carbon (SGOC) pathway, which was sustained by the conversion of serine to glycine by serine hydroxymethyltransferase (SHMT) and the de novo serine synthesis from 3-phosphoglycerate (3-PG) by phosphoglycerate dehydrogenase (PHGDH) and phosphoserine phosphatase (PSPH). Notably, elevated PSPH and SHMT1 levels correlated with poor patient survival (Srivastava et al., 2025; Chen S. et al., 2022). Concurrently, arginine drove tumor growth, survival, and proliferation by serving as a precursor for nitric oxide, polyamines, and other metabolites, and further promoted invasion by enhancing cellular adhesion (Hou et al., 2022). The upregulation of the cysteine catabolic pathway bolstered antioxidant defense through glutathione synthesis (Noch et al., 2024). In contrast to these pro-tumorigenic pathways, methionine exerted a suppressive effect by inhibiting key signaling proteins like PI3K, thereby curbing GBM cell growth (Palanichamy et al., 2016; Palanichamy and Chakravarti, 2017). In glioma cells, the rewiring of amino acid metabolism (notably the increased uptake of glutamine, serine, and cysteine) underlay a heightened capacity for GSH synthesis (Li and Zhang, 2016; Yang and Stockwell, 2016); together with pathways like NRF2, this established a potent GSH-dependent antioxidant system that managed intrinsic oxidative stress to promote survival and growth while conferring resistance to radiotherapy, chemotherapy, and ferroptosis (Trachootham et al., 2009; Seibt et al., 2019; Fan et al., 2017).

2.3 Metabolic reprogramming of lipids

Beyond their role as energy reserves in the form of triglycerides (simple lipids), lipids are vital structural constituents of cell membranes, primarily as complex lipids like phospholipids and glycolipids, which are also involved in diverse biological signaling and processes (Cortes Ballen et al., 2024). To adapt to hypoxia, tumor cells reprogram their lipid metabolism, enhancing their utilization of fatty acids and boosting lipid production by harnessing both de novo lipogenesis (DNL) from glucose and the uptake of exogenous lipids to sustain growth (Taïb et al., 2019; Jin et al., 2023). Glioma cells expressed fatty acid synthase (FASN), and its expression level showed a positive correlation with the degree of tumor malignancy (Tao et al., 2013). The synthesis of fatty acids originates from the Krebs cycle, where excess citrate is converted to acetyl-CoA by ATP-citrate lyase (ACLY). The resulting acetyl-CoA is then carboxylated to malonyl-CoA by acetyl-CoA carboxylase 1 (ACC1) and subsequently catalyzed by FASN to generate long-chain fatty acids such as palmitic acid (Currie et al., 2013). Acetyl-CoA synthetase short-chain family member 2 (ACSS2) regulated the conversion of acetate to acetyl-CoA for lipid synthesis. ACSS2 was highly expressed in patients with GBM and was associated with poor overall survival (Agnihotri and Zadeh, 2016).

2.4 Krebs cycle

The Krebs cycle acted as a central metabolic hub in glioma cells, supporting their catabolic and anabolic needs. Its maintained activity amidst nutrient restriction and aggressive growth highlighted a key metabolic adaptability that was critical for fulfilling the biosynthetic and bioenergetic demands of malignant progression (Mashimo et al., 2014; Trejo-Solís et al., 2024). Functioning as a key regulatory enzyme in the Krebs cycle and other metabolic processes, IDH had a dual role in glioma. Its wild-type forms (IDH1 in cytoplasm, IDH2 in mitochondria) normally generated α-ketoglutarate (α-KG), but the mutant isoforms (IDH1 R132H and IDH2 R172K), found in most low-grade gliomas, aberrantly produced the oncometabolite 2-HG accompanied by the consumption of NADPH, thereby contributing to tumor development (Hu et al., 2006; Strickland and Stoll, 2017; Trejo-Solís et al., 2024). Due to IDH mutation, the production of NADPH decreased and consumption increased, which led to the levels of NADPH being significantly downregulated to influence the Redox status in gliomas (van Lith et al., 2016). On one hand, the consumption of NADPH compromised the reducing equivalents available for biosynthesis, leading to the accumulation of ROS. On the other hand, NADPH depletion might also impair the regeneration of GSH, collectively influencing antigen presentation and the function of T cells to promote tumor growth (Han S. et al., 2020; Torrisi et al., 2023). 2-HG, which accumulated to approximately 100-fold higher concentrations in patients with IDH1/2 mutations, drove gliomagenesis through multiple interconnected mechanisms (Nam et al., 2014; Bunse et al., 2018). Firstly, it competitively inhibited α-KG-dependent histone demethylases, impairing cellular differentiation (Xu et al., 2011). Secondly, it induced a unique epigenetic state known as the CpG island methylator phenotype (CIMP), characterized by widespread promoter hypermethylation that silenced tumor suppressor genes (Cortes Ballen et al., 2024). Furthermore, 2-HG promoted tumorigenesis by inhibiting the angiogenesis inhibitor endostatin, thereby enhancing glioma vascularization and growth (Liu et al., 2012). IDH-mutant gliomas exhibited a distinct metabolic profile, including the specific expression of glutamate dehydrogenase 2 (GLUD2) to generate α-KG, which was subsequently converted to 2-HG. This pathway fundamentally differentiated them from aggressive, α-KG-producing IDH-wildtype GBM, and underlay the slower progression and enhanced treatment sensitivity characteristic of the mutant subtype (Chen et al., 2014; Han S. et al., 2020).

Dysregulation of key Krebs cycle enzymes contributes to gliomagenesis. Citrate synthase (CS), catalyzing the cycle’s first and irreversible step, was significantly downregulated in GBM compared to low-grade gliomas, suggesting a role in promoting tumor aggressiveness (Fan et al., 2020). Conversely, succinate dehydrogenase (SDH/Complex II), a tumor suppressor with dual roles in the cycle and electron transport chain, was frequently mutated (Rasheed and Tarjan, 2018; Dalla Pozza et al., 2020). These SDH mutations promoted an invasive phenotype through a dual mechanism: the accumulation of succinate inactivated α-KG-dependent enzymes, leading to both the stabilization of HIF-1α and the exacerbation of epigenetic dysregulation via inhibition of histone and DNA demethylases, thereby driving tumor progression (Her and Maher, 2015; Chinopoulos and Seyfried, 2018; Garcia et al., 2021).

3 Characteristics of immunosenescence in the glioma microenvironment

3.1 Characteristics of immune cells in gliomas

Long considered an immunoprivileged zone—implying xenotransplant rejection avoidance and brain tumor immune surveillance evasion—recent studies have confirmed the presence of immune cells in the brain. While the intact BBB limits the access of immune cells to the brain under healthy conditions, it becomes disrupted in contexts like inflammation, tumorigenesis, or post-radiotherapy, permitting the influx of both lymphoid and myeloid cell populations (Bugakova et al., 2024). Tumor-associated Macrophages/microglia (TAMs), myeloid-derived suppressor cells (MDSCs), lymphocytes (CD8 cytotoxic T cells, CD4 regulatory T cells, and B cells, NK cells), and neutrophils, etc., are involved in the composition of the glioma microenvironment (Table 1) (Jayaram and Phillips, 2024).

Table 1

Table 1. Characteristics of major immune cells in the glioma microenvironment.

3.1.1 Tumor-associated macrophages (TAMs)

Microglia, tissue-resident macrophages of the CNS that make up 10%–15% of all glial cells, are a significant innate immune component in the CNS (Lin et al., 2023). Microglia and peripheral macrophages composed TAMs in glioma, accounting for 30% of the cellular architecture in the glioma microenvironment (Song et al., 2025). In GBM, microglia tend to accumulate in the marginal zones, where they contribute to tumor invasion and the regulation of local immune responses. In contrast, peripheral macrophages are mainly clustered in the central regions and perivascular areas; via chemokines, they recruit relevant cells and participate in the formation of an immunosuppressive microenvironment (Zhao et al., 2025).

As is widely known, TAMs are divided into two phenotypes: the M1 phenotype tends to exhibit anti-tumor effects and the M2 phenotype tends to promote tumor progression. The M2 phenotype is further subdivided into M2a, M2b, M2c, and M2d (Mahajan et al., 2023). M1 and M2 macrophages are present at all stages of the tumor and regulate tumor development, invasion, infiltration, angiogenesis and immune response through the secretion and expression of pro-inflammatory such as C-X-C chemokine ligand (CXCL-5), CXCL-9, TNF-α, IL-12 or inhibitory factors like chemokine (C-C motif) ligand (CCL)-17, IL-4, transforming growth factor (TGF)-β, as well as be in a continuum of polarization states and can reciprocally transform in the glioma microenvironment (Tong et al., 2021). To better distinguish M1 and M2 phenotypes, researchers found MRC1, CD163 and CD204/MSR1 as M2-associated markers, and ISG15, CD86, CXCL9, CXCL10, IRF1 and CD40 as M1-associated markers (Cheng et al., 2025). However, macrophage polarization is a plastic and dynamic process, not a simple binary switch between discrete M1 and M2 states. Thus, the observed phenotypes likely indicate a shift toward a predominant state rather than a permanent, exclusive polarization (Ransohoff, 2016). Meanwhile, TAMs might regulate drug metabolism, interact with glioma stem cells (GSCs), facilitate microenvironmental adaptation, and remodel the extracellular matrix to mediate therapeutic resistance (Wang L. J. et al., 2022; Liu and Yu, 2025). Small extracellular vesicles (sEVs) are active players in the continuous exchange of intercellular information in the brain and have become valuable indicators of cancer progression and response to treatment (Tam and Yam, 2025). Interestingly, sEVs from resident macrophages enhanced cell proliferation in a dose-dependent way and promoted cell migration in gliomas (Chen et al., 2025). Recent research investigated that tumor necrosis might lead to tumor microenvironment (TME) restructuring and immunosuppressive TAMs accumulating in perinecrotic regions, resulting in tumor progression in GBM (Li J. B. et al., 2025). Due to the incomplete surgical excision of infiltrative glioma growth, the residual glioma cells increased anti-phagocytosis molecule CD47 binding to the macrophages signal regulatory protein alpha (SIRPα) to evade phagocytosis by macrophages (Ye L. et al., 2023). Based on single-cell transcriptome analysis, researchers found that CD169 (+) macrophages secreted pro-inflammatory chemokines, resulting in T cell and NK cell accumulation, and promoted phagocytosis of apoptotic glioma cells so that strengthening tumor-specific T cell responses (Kim et al., 2022).

Various genes could regulate the phenotype of TAMs to influence gliomas. According to single-cell RNA sequencing (scRNA-seq) analysis, results showed that a plethora of TAMs exhibited M2 polarization and increasing regulatory T cells (Tregs) differentiation in the glioma microenvironment (Zhang S. D. et al., 2025). Furthermore, Lysosomal-associated protein transmembrane 4 A (LAPTM4A) promoted M2 polarization of TAMs to strengthen cell proliferation and invasion, leading to glioma progression. Through constructing LAPTM4A-deficient glioma models, M1 macrophage phenotypes increased, immune activation was strongly and anti-programmed cell death protein-1 (PD-1) therapy became more sensitive (Geng et al., 2025). Researchers found that glioma cell proliferation, migration, and invasion were attenuated, with M1 polarization enhanced through silencing Potassium voltage-gated channel subfamily E regulatory subunit 3 (KCNE3) in macrophages. Further mechanism investigation showed that depleting KCNE3 inhibited Wnt/beta-catenin signaling, up-regulating the secretion of pro-inflammatory cytokines TNF-α, IL-6, and IL-12 (Liu S. Y. et al., 2025). In GBM, enhanced neuronal connectivity regions showed regional immunosuppression with anti-inflammatory TAMs enrichment. Knocking out Thrombospondin-1 (TSP1/Thbs1), suppressing synaptogenesis and glutamatergic neuronal hyperexcitability, enhanced the infiltration of pro-inflammatory TAMs and CD8⁺ T cells and lessened TAM-mediated T cell suppression, to regulate immune response in GBM (Nejo et al., 2025). Nuclear factor erythroid 2-related factor 1 (NFE2L1, also called Nrf1) knockdown contributed to the switch in the TAMs from M2 phenotype to M1 phenotype, resulting in suppression of glioma progression, accompanied by a significant increase of CD8⁺ T cells and anti-PD-1 therapy sensitivity (Zhang Q. et al., 2025). There was a negative correlation between microRNA (miRNA)-192 expression and glioma malignancy, whereas the expression of EGR1/HOXB9, a downstream regulator of miR-192, showed a positive correlation with malignant phenotypes. MiR-192/EGR1-HOXB9 loop could weaken glioma cell stemness, decrease the quantities of M2-phenotype TAMs, with lessen the inhibitory effect on CD8⁺ T cells through mediating immune chemokines. Interestingly, miR-192 also induced immune infiltration via this loop in vivo (Li G. W. et al., 2025). The leukocyte immunoglobulin-like receptor B4 (LILRB4), a pivotal immunoregulatory molecule implicated in cancer progression, showed a negative correlation with survival in GBM. Additionally, knockdown of LILRB4 switched M2 phenotype to M1 phenotype through STAT3/IL10 axis, which might regulate immunotherapy efficacy in gliomas (Pei et al., 2025).

3.1.2 Myeloid-derived suppressor cells (MDSCs)

Myeloid cells, a pivotal part of innate immunity playing an important role in immunotherapy, differentiate into macrophages, granulocytes, and dendritic cells (DCs) in normal. However, under pathological situations, the differentiation of immature myeloid cells in disrupted, resulting in myeloid-derived suppressor cells (MDSCs) developed, proliferated and activated (Khan et al., 2020; Parizi et al., 2021). MDSCs might not only exert pivotal roles in suppressing immune responses within the TME but also regulate the progression of tumor growth, which is related to some small molecules, NK cells, cytokines, inflammatory proteins, and so on (Ge et al., 2021; Jackson et al., 2025). Granulocytic or polymorphonuclear MDSCs (PMN-MDSCs), which are similar to neutrophils in phenotype and morphology, and monocytic MDSCs (M-MDSCs), which are similar to monocytes, compose MDSCs (De Leo et al., 2021). Research data indicated that MDSCs account for approximately 30%–50% of the glioma mass (Parizi et al., 2021). In patients diagnosed with gliomas, a notable elevation of MDSCs has been observed in the peripheral circulation of those with isocitrate dehydrogenase-wildtype (IDH-WT) glioblastoma; furthermore, higher MDSC levels in this subgroup are correlated with a poorer clinical prognosis (Jackson et al., 2025). In GBM, tumor-secreted factors established chemokine gradients to recruit MDSCs to the TME to play a fundamental role. The CXCR2-CXCL5 signaling axis, M-CSF, and IL-34 mediated the accumulation and differentiation of M-MDSCs, and CCL2, CCL3, and hypoxic stress drove the infiltration of PMN-MDSCs (Takacs et al., 2024; Li Y. T. et al., 2025). Based on single-cell RNA sequencing, researchers observed upregulation of genes involved in nuclear factor of activated T cells (NFAT) signaling and cellular response to hypoxia, accompanied by a downregulation of genes responsible for mediating inflammatory responses and regulation of T cell proliferation in PMN-MDSCs. Furthermore, more CXCL2 and less CXCL3 were expressed by PMN-MDSCs in the early stage than the late stage of GBM (Yeo et al., 2022).

A subpopulation of CD4⁺ T cells producing IL-8, myeloid and tumor cells, regulated MDSCs infiltration and angiogenesis, leading to accelerated tumor growth and weakened immune checkpoint blockade (ICB) efficacy (Liu H. et al., 2023). In both mouse and human, M-MDSCs exhibited elevated expression of integrin (31 and dipeptidyl peptidase-4 (DPP-4) relative to PMN-MDSCs. This differential expression pattern was a key component of the enhanced cell adhesion signature characteristic of M-MDSCs. Functional studies demonstrated that blockade of integrin β31 not only abrogated the tumor-promoting phenotypic features of M-MDSCs but also induced significant alterations in the immune cell composition and functional profile of the TME. In parallel, phospho-Extracellular Signal-Regulated Kinase (p-ERK) signaling and their migration towards tumor cells could be inhibited through targeting DPP-4 in M-MDSCs (Bayik et al., 2022). The basic leucine zipper ATF-like transcription factor 2 (BATF2) was involved in the regulation of immune cells and anti-tumor effects, which could inhibit glioma growth and the recruitment of MDSCs, possibly related to regulating SDF-1α/CXCR4 signaling pathway (Zhang X. et al., 2021). The interaction between glioma cells and immune cells might be through the secretion of exosomes, like microRNAs. MiR-1246 was enriched in glioma-derived exosomes and exosomes isolated from the cerebrospinal fluid of glioma patients, which could mediate the differentiation and activation of MDSCs in a manner dependent on dual specificity phosphatase 3 (DUSP3)/ERK (Qiu W. et al., 2021). Meanwhile, miR-1298-5p could strengthen the immunosuppressive effects of MDSCs to promote gliomas (Qi et al., 2022). Besides, the expression levels of other cytokines like sialic acid-binding Ig-like lectin 9 (SIGLEC9), CCR2, CCR5, factor H like protein 1 (FHL-1) and Annexin A2 (ANXA2) could regulate MDSCs infiltration and accumulation (Ma et al., 2021; Zhai et al., 2021; Xu et al., 2022; Pant et al., 2024).

3.1.3 Lymphocytes

Lymphocytes mainly include NK cells, T cells and B cells. Because of the low presence of tumor-infiltrating lymphocytes (TILs), gliomas are named as immune “cold tumors”, accompanied by systemic and local suppressive immune response. NK cells can recognize tumor cells and initiate a cytotoxic cascade, as well as recruit other innate and adaptive immune cells. NK cells are equipped with both stimulatory (or activation) receptors and inhibitory receptors, which enable them to distinguish between healthy cells and aberrant cells through MHC-1 receptor appearance (Read et al., 2024; Wu et al., 2025). NK cells are divided into CD56^bright and CD56^dim subsets. CD56^dim NK cells predominantly mediate cytotoxicity, in contrast to CD56^bright NK cells, which function primarily as secretors of cytokines including IFN-γ and TNF-α (Zhao et al., 2020; Zhang et al., 2024). Based on CGGA databases, the expression of NK cells gene expression was related to WHO grade, IDH1 mutation, MGMT promoter methylation, 1p19q ci-deletion, and tumor subtype. Following screening, UL16 binding proteins (ULBPs), CD70 and BH3-interacting domain death agonist (Bid) were identified to be implicated in the biology and function of NK cells (Li et al., 2022). As GBM progression, NK cells accumulated based on transcriptome analysis. Meanwhile, the proportion of CD3⁺/CD4⁺ helper T cells decreased in GBM patients, and the frequency of NK cells significantly augmented in grade 3 gliomas (Monaco et al., 2022; Lennartz et al., 2023). Glioma-released miR-1983 within exosomes, initiating an innate anti-glioma NK-mediated circuit, was demonstrated. Further research showed that miR-1983 was an endogenous TLR7 ligand; subsequently, secretion of IFN-β, stimulated by TLR7 activation and downstream signaling through MyD88-IRF5/IRF7, stimulated NK cells, leading to glioma eradication (Shah et al., 2021). In glioma models, MDA mediated IKK epsilon/TBK1/IRF3 signaling pathway, subsequently induced the increase of IFN-I, resulting in boosting CD8⁺ T cell and NK cell number and activity (Mu et al., 2023).

T cells comprise CD8⁺ cytotoxic T lymphocytes (CTLs), CD4⁺ Tregs and conventional CD4⁺ T cells, accounting for 1%–5% of gliomas. Upon binding to specific molecules, CD8⁺ T cells differentiated into CTLs capable of releasing perforin- and granzyme-containing granules and CD4⁺ T helper 1 (Th-1) cells secreted pro-inflammatory cytokines such as IL-2, TNF-α and IFN-γ, thereby promoting antitumor activity (Elguindy et al., 2024). A single-cell analysis showed that the infiltration of T cells increased during glioma progression, and the ratio of T cells was higher in recurrent GBM compared to low-grade gliomas and newly diagnosed GBM (Abdelfattah et al., 2022). Through flow cytometry and survival data analysis, it was discovered that the frequency of Th cells and CTLs was negatively correlated with glioma survival, whereas there was a positive relationship between the frequency of gamma delta-T cells and CD56 bright NK cells and survival (Vincze et al., 2024). In gliomas, high cytotoxicity tumor-infiltrating CD8 T cells were correlated with an NK cell-like signature. For example, large numbers of CD8 T cells exhibited expression of both the inhibitory CD161 receptor (KLRB1), which was inactivated in tumor-infiltrating T cells, strengthening their anti-tumor activity in vivo and the activating NKG2C/CD94 receptor (KLRC2 and KLRD1) (Mathewson et al., 2021). The checkpoint and inhibitory receptor HAVCR3 (TIM3) were highly expressed on and BAT 3 was lowly expressed on activated CD4⁺ and CD8⁺ T cells, together with NK cells from GBM patients, related to downregulation of CD69 and IFN gamma, which might be related to glioma immune escape (Zhang Z. et al., 2023; Ahmady et al., 2024). Through CRISPR-Cas9 knockout of TIM3, the abilities of NK cell-mediated growth inhibition of GBM cells were augmented (Morimoto et al., 2021). Another immune checkpoint receptor, TIGIT, is expressed on activated T cells and could inhibit the function of T cells and NK cells to influence GBM (Vincze et al., 2024). ITPRIPL1, CD3 epsilon-inhibitory ligand suppressing the activation of T cells, negatively correlated with CD4⁺ T cells and Th17 cells which could inhibit T cell-mediated immune responses, promoting the development of immunosuppressive TME, thereby promoting tumor progression (Zou et al., 2025).

Tregs, involved in anti-inflammatory and immune tolerance, could secrete inhibitory cytokines TGF-β and IL-10 in TME and exhaust CTLs to regulate immunosuppressive effects, and Forkhead box protein P3 (Foxp3) (+) Tregs played a significant role in the immunosuppressive TME (Xu et al., 2020; Guo et al., 2021). As glioma progression, the ratio of Tregs augmented (from 2.8% to over 40%), verifying the activating effect of GBM with respect to Tregs (Yanysheva et al., 2025). In mouse and human gliomas, tumor cells secreted complement factor H (FH), the upregulation of which was related to the existence of Tregs and the worse prognosis for glioma patients. Furthermore, FH was a kind of Inducible co-stimulator (ICOS) ligand, and the interaction between FH and this immune checkpoint molecule enhanced the survival and functional activity of Tregs, stimulated the release of TGF-β and IL-10, and concurrently inhibited T cell proliferation (Smolag et al., 2025). A recently identified subset of Tregs is T follicular regulatory (Tfr) cells, and both Tfr cells and Tregs exhibited a significant ability to suppress the proliferation of CD8⁺ T cells and their cytotoxic activity against glioma tumor cells. CXCR5 (−) Tregs traditionally showed stronger inhibitory potency on CXCR5 (−) CD8 T cells, whereas Tfr cells exhibited higher suppressive potency on CXCR5 (+) CD8 T cells (Lu et al., 2021).

Tertiary lymphoid structures (TLSs), the intratumoral immune aggregates, are suggested to regulate sustained anti-tumor immune responses. TLSs, which are ectopically formed aggregates of lymphoid and stromal cells consisting of T cell zones with antigen-presenting dendritic cells and B cell zones with germinal centers, support local immune responses through several mechanisms, such as antigen presentation to T cells and differentiation of B cells into plasma cells that secrete tumor-specific antibodies. In gliomas, some TLSs exhibited dynamic immune function features (clonal T and B cell expansion, generation of IgA⁺ and IgG⁺ plasma cells, and dendritic cell-T cell interactions), related to overall survival (Cakmak et al., 2025).

B lymphocytes producing antibodies are key mediators of humoral immunity, and tumor-infiltrating B cells play a key role in cancer immunity (Ma et al., 2024). Like Tregs, regulatory B cells (Bregs) expressed immunosuppressive surface molecules and cytokines and suppressed CD8⁺ T cell proliferation and function, thereby influencing immunotherapy response (De Domenico et al., 2025). Glioma cell-derived placenta growth factor (P1GF) induced the activation of Breg cells, subsequently inhibited the proliferation of CD8⁺ T cells and the release of perforin and granzyme B to influence gliomas (Han et al., 2014). Plasma cells, the primary effector cells of the B-lineage immune system, were aberrantly enriched within the B-cell population that infiltrated GBM. These tumor-infiltrating plasma cells exhibited low levels of somatic hypermutation and were correlated with poor prognosis. Their recruitment into GBM stem cell (GSC) niches is mediated by the CCL2-CCR2 chemokine axis. Furthermore, GSCs acquired pro-proliferative signals through FcγRIIA activation, a pathway engaged by widely used monoclonal antibody-based immune checkpoint inhibitors (Gao et al., 2025).

3.2 Immunosenescence leading immunosuppressive microenvironment

3.2.1 Immune cells

Senescent macrophages showed impaired metabolism, low-grade inflammation, reduced autophagy level, decreased phagocytosis, altered polarization tendency, altered antigen presentation, and altered infiltration and recruitment. Cellular senescence entails cell cycle arrest and lysosomal expansion. These processes are primarily governed by the p16^Ink4a/RB and p53/p21^CIP1 signaling pathways and are mechanistically linked to the elevated activity of senescence-associated beta-galactosidase (Figure 2) (SA-β-Gal) (Li et al., 2024; Fu and Zhou, 2025). Immunosenescence triggers signaling cascades that ultimately converge on the p53/p21^CIP1 and p16^INK4a/pRB pathways, which suppress the cell cycle through dynamic interactions with cyclin-dependent kinases (CDKs); therefore, p16^INK4a and p21^CIP1 are applicable biomarkers for in vivo immunosenescence detection (Liu Z. Q. et al., 2024). Under pH 6.0, the activity of lysosomal β-galactosidase is upregulated in senescent cells, contributing to SA-β-Gal might be used as a biomarker of immunosenescence, and elevated levels of SA-β-Gal are linked to compromised immune cell function (Tsubokawa et al., 2025; Martinez-Zamudio et al., 2021). In the glioma microenvironment, the expression of SA-β-Gal on microglia and macrophages, and senescent macrophages, was related to the upregulation of SA-β-Gal, p16^INK4a, SASP (such as IL-1α, IL-6, CXCL12, FGF, VEGF, MMPs, and TGF-β), and so on (Salam et al., 2023; Li et al., 2024). Furthermore, the partial depletion of p16 ^Ink4a cells led to the modulation of TAMs abundance and activity: upregulation of microglia-related pro-inflammatory genes and downregulation of macrophage-related anti-inflammatory genes (Salam et al., 2023). Glioma-derived IL-6 induced a senescent phenotype in TAMs via the ROS-p38 MAPK pathway. Subsequently, these senescent macrophages produced arginase-1, which suppressed CD3ζ expression and thereby dampened T-cell responses (Li et al., 2024; Shen et al., 2025).

Figure 2

Figure 2. The hallmarks of immunosenescence in gliomas. The senescence of T cells, B cells, and NK cells involves alterations in the levels of molecular markers, which regulate signaling pathways to inhibit immune responses and promote chronic inflammation, among other effects. Senescent immune cells secrete SASP to influence the function of immune cells, thereby fostering the formation of an immunosuppressive microenvironment. Epigenetic changes, such as DNA methylation, histone modifications, and altered expression of non-coding RNAs, further contribute to the establishment of this immune microenvironment. Targeting the key mechanisms of immune senescence can enhance drug sensitivity, suppress inflammation, and extend survival. Red arrows: promoting or up-regulating effects; black arrows: regulating effects; black T-shaped lines: inhibitory effects. Supported by BioRender (https://app.biorender.com).

Senescent T cells are characterized by impaired proliferation, defective signaling pathways, and a reduced ability to differentiate into effector cells (e.g., weakened differentiation of CD8⁺ T cells into cytotoxic T cells) (Nikolich-Zugich, 2014). The hallmark of senescent T cells was the decrease of costimulatory molecule CD28 which might be associated with telomerase inactivation, with upregulated CD8⁺CD28⁻senescent populations with heterogeneous roles in multiple solid and hematogenous tumors. CD8⁺CD28⁻ T cells, similar to Tregs, showed immunosuppressive effects through inducing suppressive receptors to influence Ag-presenting function. Besides, CD57 (HNK-1) is a hallmark of senescent T cells, and CD8⁺ CD28⁻ CD57⁺ populations were expanded in various pathologies, including cancer (e.g., lung cancer, myeloma), HIV, and chronic inflammatory diseases (e.g., diabetes, obesity) (Huff et al., 2019; Nga et al., 2024). Continuous T cell activation also led to the continuous shortening of telomeres and DNA damage, such as exposure to ROS, resulting in T cell senescence. GBM patients with an increased proportion of T cells showing high CD57 and low CD28 expression led to a marked reduction in overall survival (Nafe and Hattingen, 2024). In elderly GBM patients, an upregulation of immune checkpoint genes such as PD-1 ligand-1(PD-L1) and CD80 in T cells drove an immunosuppressive state, which was manifested by a diminished population of cytotoxic CD8⁺ T cells and an expanded population of exhausted CD4⁺ T cells (Wu et al., 2023). Besides, elevated expression of other immune biomarkers, including Tim-3, killer cell lectin-like receptor subfamily G member 1 (KLRG-1), and CD45RA, serves as a well-established marker for the T-cell senescence phenotype (Liu Z. et al., 2023).

Senescent NK cells can be characterized by age-related or pathology-induced changes in their functional capabilities and phenotypic traits. An imbalance in NK cell subset proportions diminished cytotoxic activity. During aging, CD56^bright NK cells decreased, and CD56^dim NK cells underwent continued differentiation, progressively augmenting the expression of CD57, CD16, inhibitory receptor KIR and NKG2A, and downregulation of the activating receptor NKp30, NKp46, and NKG2D, which collectively contributed to the reduction in NK cell cytotoxic function (Qiu et al., 2025). Besides, a decline in degranulation and perforin secretion was accompanied by a shift of NK cells toward a CD56dim subset characterized by enhanced production of pro-inflammatory cytokines, thereby potentially fueling chronic inflammation (Fu et al., 2025).

Age-related changes in B cell composition are a major cause of weakened antibody responses to vaccination and infection in the elderly. Declining levels of IL-7, a cytokine critical for pre-B cell survival, severely constrained B cell output. This was compounded by reduced RAG expression in precursor B cells, which diminished BCR rearrangement, limited diversity, and ultimately led to impaired antigen recognition and humoral immunity (Labrie et al., 2004). Immunosenescence promoted autoimmune progression via age-associated B cells (ABCs). These senescence-dysfunctional ABCs adopted a proinflammatory state, secreting IL-6 and TNF-α while producing auto-antibodies, thereby disrupting immune tolerance. Additionally, they exacerbated chronic inflammation by activating mTOR/NF-κB signaling, which promoted the pathological activation and tissue infiltration of cells like Th17, ultimately accelerating disease (Xiao et al., 2025).

The collective senescence of various immune cells drives tumor progression by fostering an immunosuppressive microenvironment.

3.2.2 SASP

SASP, the core hallmarks of immunosenescence, encompasses a diverse array of factors, including pro-inflammatory cytokines, chemokines, growth factors, ROS, angiogenic factors, and proteases. These components collectively foster a pro-tumorigenic cytokine microenvironment (Qiu et al., 2025). Additionally, the SASP drives tumor progression by enhancing growth, invasion, and immune evasion, which collectively foster an immunosuppressive TME (Fu et al., 2025). The immunosuppressive factors produced by senescent cells, including TGF-β and IL-10, inhibited NK cell function. Specifically, TGF-β downregulated the expression of activating receptors such as NKG2D on NK cells and impaired their production of IFN-γ. In addition to directly suppressing NK cells, TGF-β also promoted the differentiation of regulatory Tregs from effector T cells, further dampening anti-tumor immunity (Gergues et al., 2025). SASP activation was a hallmark of malignant progression in IDH-WT glioma. Patients with high SASP scores exhibited poorer survival, accompanied by a dysregulated immune response and abundant infiltration of M2-phenotype TAMs. Notably, TAMs themselves were a key cellular source of high-level SASP secretion (Liu Y. et al., 2025).

Therapy-induced senescence by temozolomide and radiotherapy, mediated by the DNA damage response and p21^CIP1, activated the NF-κB pathway and was characterized by the secretion of SASP components such as IL-6 and IL-8. In xenograft mouse models, the co-injection of irradiated, senescent primary GBM cells with non-irradiated GBM cells promoted larger and more aggressive tumors compared to the injection of non-irradiated cells alone (Carreno et al., 2021). In addition, radiation-induced senescent astrocytes released TNF-α combing with TNFR1 on the surface of GBM cells to activate the downstream Myc-Max transcription factor complex. Subsequently, CXCL1 transcription promoted by Myc further recruited immunosuppressive cells into glioma TME. Besides, factors within the SASP, such as IL-6 and G-CSF, could activate inflammatory responses in neighboring normal cells through paracrine signaling, thereby amplifying the overall immunosuppressive signaling within the microenvironment (Ji et al., 2024).

3.3 The critical role of epigenetic regulation

Epigenetics involves heritable modifications that regulate gene expression without changing the DNA sequence (Liu Y. et al., 2024). Epigenetic dysregulation—through alterations in DNA methylation, histone modifications, and non-coding RNAs—represents a fundamental hallmark of immunosenescence (Liu Z. Q. et al., 2024).

3.3.1 DNA methylation

DNA methylation is mediated by DNA methyltransferases (DNMTs), which transfer a methyl group (-CH3) from S-adenosyl-L-methionine to the C5 position of cytosine, forming 5-methylcytosine (5mC) and leading to gene silencing. “De novo” methylation by DNMT3 was a critical determinant in guiding post-response T cell fate towards memory versus terminal effector lineages, and inhibition of DNMT3 promoted the formation of memory lymphocytes. Furthermore, the more marked age-related functional decline in CD8⁺ T cells compared to CD4⁺ T cells correlated with hypermethylation of pivotal immune genes, thereby limiting their functional adaptability (Rousseau et al., 2025). In CD4⁺CD28⁻ T cells, loss of DNA methylation primarily occurred outside of cytosine phosphate guanine (CpG) islands, related to protein tyrosine kinase genes, the CD3 complex, and upregulated expression of B cell lymphoma/leukemia type 2 (BCL-2), TYRO protein tyrosine kinase-binding protein (TYROBP), and GzmB (Nga et al., 2024). Variations in DNA methylation patterns might shape the immune landscape of the glioma microenvironment, with potential implications for the response to PD-1/PD-L1 inhibitor therapy (Luo et al., 2022).

3.3.2 Histone modifications

Histone modification is a fundamental epigenetic mechanism that includes a diverse array of chemical alterations (Wang and Yan, 2025). Among these, histone acetylation, phosphorylation, and heterochromatin accumulation show an age-associated increase (Liu Z. et al., 2023). Senescence in lymphocytes, specifically in CD8⁺ CD28⁻ T cells and NKT-like cells, might be correlated with a loss of histone deacetylase 2 (HDAC2), and loss of HDAC1/2 function in mouse podocytes led to the acquisition of a senescent phenotype, characterized by enhanced SA-β-gal activity and lipofuscin deposition (Xiao et al., 2025). In human GBM, high expression of the histone 3 lysine 27 demethylase KDM6B/JMJD3 in intratumoral myeloid cells (including TAMs) promoted an immunosuppressive phenotype, which in turn fuels tumor progression (Riyas Mohamed and Yaqinuddin, 2025). Additionally, histone modification might regulate immune cell infiltration and TGF-β and IL-6 to accelerate the progression of LGG (Wang and Yan, 2025). The upregulation of HDAC3, 5, and 9 in IL-4-induced M2-pheotype TAM during GBM enhanced the secretion of TGF-β and IL-10, ultimately potentiating the immunosuppressive activity of the tumor cells (Chen N. et al., 2022).

3.3.3 Non-coding RNAs

Non-Coding RNAs, such as Long non-coding RNAs (lncRNAs) and miRNAs, function as central epigenetic regulators of immune escape. For instance, the lncRNA AGAP2-AS1 promoted an immunosuppressive microenvironment by sequestering miR-486-3p, which led to upregulated TGF-β1 secretion, subsequent activation of myeloid-derived suppressor cells (MDSCs), and diminished T-cell infiltration (Riyas Mohamed and Yaqinuddin, 2025). Low expression of miR-22 lessened the phagocytic capacity of macrophages, leading to weakened phagocytosis, antigen presentation, and inefficient T cell activation, primarily through targeting HDAC6 and modulating NF-κB signaling (Tu et al., 2022). Another miR-1298-5p was found to strengthen the immunosuppressive effects of MDSCs, resulting in glioma progression (Qi et al., 2022).

4 Interaction between immunosenescence and metabolic reprogramming

Metabolic reprogramming not only directly influences tumor cell proliferation, but the tumor microenvironment it shapes may profoundly influence the function and fate of infiltrating immune cells. In the meantime, immunosenescence may reshape the metabolic microenvironment to influence tumor progression. Thus, neither immunosenescence nor metabolic reprogramming exists in isolation; rather, they interact with each other and collectively influence tumorigenesis and progression.

4.1 Metabolites may drive immune senescence

The efficacy of chemotherapy and immunotherapy might be influenced by metabolites through immunomodulation (Fan et al., 2024). Through key alterations in pathways such as glycolysis, lipid, and amino acid metabolism, glioma cells create an immunosuppressive tumor immune microenvironment. They release abnormal metabolites that cripple the function of antitumor immune cells, thereby facilitating sustained tumor growth by evading immune surveillance (Figure 3) (Xia et al., 2021).

Figure 3

Figure 3. The potential interaction between metabolites and immunosenescence. Metabolic reprogramming can regulate immune cell function by modulating multiple signaling pathways and factors. Conversely, senescent immune cells also influence the metabolic microenvironment by regulating key factors involved in metabolism. Orange arrows: promoting or up-regulating effects; green arrows: regulating effects; orange T-shaped lines: inhibitory effects. Supported by BioRender (https://app.biorender.com).

4.1.1 Glucose metabolism

While various immune cells exhibited heightened glycolysis and lactate production, the resultant lactate accumulation in the tumor microenvironment impaired immune surveillance, protecting tumor cells from NK cells, neutrophils, and MDSCs that infiltrate hypoxic areas (Husain et al., 2013). Lactate in the tumor microenvironment drove immunosuppression by directly inhibiting CD8⁺ T cell function and migration while promoting the accumulation of Tregs and MDSCs, thereby facilitating tumor immune escape (Wang et al., 2023; Yang et al., 2024; Vijayanathan and Ho, 2025).

Within the glycolytic tumor microenvironment, lactate sustained a persistent immunosuppressive state by promoting HIF-1α stabilization in MDSCs, which exacerbated hypoxia, acidified the milieu, and potently inhibited T cell activity (Husain et al., 2013; Noman et al., 2014; Johnston et al., 2019). Furthermore, lactate differentially regulated immune checkpoints by upregulating PD-1 expression on Tregs while suppressing it on effector T cells, a mechanism demonstrated by Kumagai et al. to facilitate tumor immune evasion (Kumagai et al., 2022). Given the crucial role of the mTOR signaling pathway in driving the metabolic switch of T cells from oxidative phosphorylation to glycolysis, a therapeutic strategy that combined mTOR inhibitors with glycolysis inhibitors might disrupt this metabolic reprogramming and enhance anti-tumor efficacy (Abdel-Wahab et al., 2019).

Lactate produced by GBM cells enhanced M2-pheotypes polarization of TAMs and stimulated tumor angiogenesis (Wang et al., 2024). A potential mechanism was that lactate could upregulate the expression of vascular endothelial growth factor (VEGF) and arginase 1 (ARG1) genes through an HIF-1α-mediated pathway, thereby facilitating M2 polarization (Colegio et al., 2014). In the GBM immune microenvironment, Hexokinase 3 (HK3) facilitated immune cell infiltration, with its expression level correlating positively with tumor grade (Li S. et al., 2023; Cortes Ballen et al., 2024). In addition, lactate could activate the HIF-1α pathway, promote the secretion of immunosuppressive factors, induce histone lactylation, and upregulate “do not eat me” signals such as CD47, thereby inhibiting the functions of myeloid cells and NK cells (Keith et al., 2011; Venneti and Thompson, 2017). Separately, TAMs engaged in metabolic crosstalk by releasing IL-1β, which phosphorylated and activated GA3PDH in glioma cells, thereby inducing a glycolytic and proliferative state (Lu et al., 2020). Meanwhile, HIF-1α played a crucial role in orchestrating this metabolic reprogramming in both GBM cells and TAMs (Li Y. et al., 2023).

4.1.2 Amino acid metabolism

Reprogrammed glutamine metabolism fostered competitive interactions between GBM and immune cells. This metabolic competition impaired antitumor immune responses, thereby reinforcing the immunosuppressive features of the tumor microenvironment. Specifically, metabolic predation by GBM restricted glutamine availability to immune cells, compromising their functions and facilitating tumor immune evasion. For instance, glutamine deprivation could impair CD8⁺ T cell function by inducing mitochondrial damage (Wang W. et al., 2022).

The catabolism of Kyn by IDO/TDO suppressed antitumor immunity via a multifaceted mechanism involving Trp depletion and Kyn accumulation. These effects collectively inhibited immune cell function, induced T cell apoptosis and DCs death, promoted Tregs and TAMs recruitment, and activated an AhR-driven positive feedback loop that upregulated PD-1 on CD8⁺ T cells, thereby establishing a profoundly immunosuppressive microenvironment (Zeitler and Murray, 2023; Srivastava et al., 2025). Metabolic reprogramming of specific amino acids within the GBM microenvironment critically orchestrated immunosuppression. The upregulation of serine-glycine-one-carbon pathway enzymes, such as PHGDH in endothelial cells and PSPH/SHMT1 in tumor cells, impaired antitumor T cell infiltration and altered immune cell landscapes by promoting M2 macrophage and resting NK cell recruitment while reducing B cells; notably, PHGDH inhibition could overcome resistance to CAR-T cell therapy (Chen S. et al., 2022; Zhang D. et al., 2023). In parallel, arginine metabolism further suppressed immunity, as tumor-infiltrating dendritic cells (TIDCs) depleted local L-arginine via arginase, directly impairing T cell function (Norian et al., 2009).

4.1.3 Lipid metabolism

The nuclear receptor NR4A2, activated in microglia under oxidative stress, facilitates GBM immune evasion by coordinately upregulating lipogenic pathways to support tumor growth and suppressing MHC-I and antigen presentation machinery to impair immune recognition, thereby creating an immunosuppressive microenvironment (Ye Z. et al., 2023; Cortes Ballen et al., 2024). Targeting squalene monooxygenase (SQLE), a key regulator of cholesterol metabolism in TAMs, represented a promising strategy for disrupting the tumor microenvironment. Pharmacological inhibition of SQLE with terbinafine could alleviate the immunosuppressive tumor microenvironment and enhance the efficacy of immune checkpoint blockade therapy (Ye Z. et al., 2023).

4.1.4 Krebs cycle

2-HG, key regulators in the immune system, reduced the level of signal transducer and activator of transcription 1 (STAT1), CXCL10, and the key factor in recruiting CD8⁺T cells. Consequently, 2-HG decreased CD8⁺T cell infiltration in glioma tumor tissues of syngeneic mice (Reiter-Brennan et al., 2018). Elevated 2-HG production induced by IDH mutations could regulate TAMs and other immune cells, forming an immunosuppressive microenvironment (Virtuoso et al., 2021). Patients with IDH1 and IDH2 mutations exhibited reduced CD8⁺ T cell cytotoxicity and attenuated interferon-γ (IFN-γ) signaling, both of which contributed to the immunosuppressive tumor microenvironment (Notarangelo et al., 2022).

4.2 Immunosenescence may remodel the metabolic microenvironment

By disrupting fundamental nutrient metabolism (glucose, lipids, amino acids) and NAD⁺ homeostasis in immune cells, immunosenescence promoted inflammation and accelerated its own progression. Meanwhile, reduced glycolysis and aberrant mitochondrial energetics jointly impaired T and B cell activation in the process of immunosenescence (Liu Z. Q. et al., 2024). Aging immune cells activated STAT3 and NF-κB pathways and secreted pro-inflammatory SASP such as IL-6, IL-8, TNF-α and CXCL1 to increase GLUT1, FASN and GLS, resulting in enhanced glycolysis, lipogenesis and glutamine dependency (Zhu et al., 2025).

Senescent T cells exhibited markedly reduced lactate dehydrogenase (LDH) activity and proliferative capacity. Specifically, senescent naive CD4⁺ T cells show a decreased extracellular acidification rate (ECAR), accompanied by reductions in metabolites associated with glycolysis and the pentose phosphate pathway. These cells also developed smaller mitochondria with impaired respiratory function and diminished ATP production. This mitochondrial dysfunction might stem from a defect in one-carbon metabolism, which disrupted the biosynthesis of purines and thymidine—essential for T cell proliferation and survival—and consequently impaired effector T cell differentiation (Han et al., 2023). In senescent T cells, AMPK was activated by the low expression of adenosine triphosphate and endogenous DNA damage, which induced constitutive expression of p38. Besides, glucose deprivation and genotoxic stress led to p38 activation, resulting in reduced telomerase activity and inhibited T cell proliferation (Nga et al., 2024). Furthermore, due to constitutive PD-1 signaling in effector CD8⁺ T cells profoundly altering energy metabolism, the T Cell Receptor (TCR) was unresponsive (Fukushima et al., 2018). Senescent B cells underwent a metabolic shift toward glycolysis, leading to substantial lactate production. This lactate, exported via the solute carrier family 5 member 12 (SLC5A12) transporter, promotes an inflammatory phenotype characterized by the secretion of SASP factors and autoantibodies (e.g., anti-dsDNA), and further drives the polarization of CD4⁺ T cells into pro-inflammatory subsets (Llibre et al., 2025). Similarly, senescent B cell subsets displayed increased mitochondrial mass and mitochondrial reactive oxygen species (mtROS), contributing to compromised energy production and abnormal one-carbon metabolism, ultimately impairing the activation of B cells and production of antibody (Goyani et al., 2024).

Given the complexity of the glioma microenvironment, further investigations are warranted to elucidate the impact of immunosenescence on the tumor’s metabolic microenvironment.

5 Therapeutic strategies to target immunosenescence and metabolism

5.1 Targeting metabolic reprogramming

5.1.1 Glycolysis and the PPP

Miyai et al. demonstrated that the H3.3K27M histone acetylation signal induced the expression of GLUT-1 and was associated with enhanced aerobic glycolysis and invasiveness in gliomas (Miyai et al., 2021). Furthermore, inhibiting GLUT-1 expression by silencing the tubulin isotype TUBB4 reduced the proliferation and formation of glioma spheroids (Guda et al., 2019). Glutor, a piperazine derivative, simultaneously targeted GLUT-1, GLUT-2, and GLUT-3 to more comprehensively inhibit tumor glycolysis (Reckzeh et al., 2019). 2-Deoxy-D-glucose (2-DG), a glucose analog, acted as a competitive inhibitor of hexokinase; the phosphorylated form of 2-DG could suppress glycolysis and inhibit GBM growth (Table 2) (Pistollato et al., 2010). Through activation of AMP-activated protein kinase, methylene blue treatment elevated the oxygen consumption rate (OCR) in GBM cell lines, while concurrently reducing the extracellular acidification rate (ECAR) and lactate production to suppress glioma proliferation (Poteet et al., 2013). Studies have shown that co-treatment with the PFKFB3 inhibitor 3PO and the anti-VEGF monoclonal antibody bevacizumab effectively reduced cell proliferation and induced apoptosis in vitro, while also delaying tumor growth and extending survival in vivo (Zhang et al., 2020). R406, a spleen tyrosine kinase (Syk) inhibitor, shifted the metabolic profile of glioma cells from glycolysis toward OXPHOS, leading to increased apoptosis (Sun et al., 2019). Furthermore, acriflavine (ACF), a HIF-1α inhibitor, significantly prolonged the survival of orthotopic GBM models (Cortes Ballen et al., 2024). Targeting these pathways with agents such as dehydroepiandrosterone (DHEA, a G6PD inhibitor) or other non-specific inhibitors (e.g., genistein or imatinib mesylate) might effectively reverse PPP reprogramming in GBM (Cortes Ballen et al., 2024).

Table 2

Table 2. Overview of glioma metabolic reprogramming and targeted therapeutic strategies.

5.1.2 Amino acid metabolism

GLT1 overexpression inhibited glioma proliferation by enhancing glutamate uptake, while the glutamine antagonist JHU-083 suppressed growth and disrupted mTOR signaling—an effect synergistically enhanced when combined with L-asparaginase to deplete both glutamine and asparagine (Yamashita et al., 2021). Complementary approaches included inhibiting glutamine synthetase (GS) with actinomycin D or 5-azacytidine to reduce proliferation, and combining the GLUD1 inhibitor R162 with docetaxel to impede tumor growth in vitro and in vivo (Dranoff et al., 1985; Wang Q. et al., 2022). Furthermore, targeting branched-chain amino acid metabolism via Branched-chain amino acid transaminase 1 (BCAT1) inhibition with curcumin or gabapentin disrupted nucleotide synthesis and redox balance, ultimately suppressing proliferation and inducing apoptosis (Panosyan et al., 2017; Grankvist et al., 2018; Fhu and Ali, 2020).

AT-0174, a dual IDO1/TDO2 inhibitor, improved the TMZ response and prolonged survival in mouse glioma models, with upregulation of CD8⁺T cells and downregulation of CD4⁺Tregs infiltration (Bickerdike et al., 2024). Riess et al. found that arginine depletion could potentiate the cytotoxic effects of cyclin-dependent kinase (CDK) inhibitors on GBM cells by impairing mitochondrial metabolism, inducing autophagy, and blocking the DNA damage response (Riess et al., 2022). Restricting dietary cysteine and methionine improved survival in glioma-bearing mice by reducing tumor cell glutathione and triggering ferroptosis (Upadhyayula et al., 2023).

5.1.3 Lipid metabolism

Inhibition of key enzymes in fatty acid synthesis presents a promising strategy against cancer. ACLY inhibitors (e.g., hydroxycitrate, SB-204990) suppressed tumor growth by disrupting acetyl-CoA production (Zaidi et al., 2012), while the polyketide soraphen specifically targeted acetyl-CoA carboxylase alpha (ACACA) (Corominas-Faja et al., 2014). Furthermore, targeting fatty acid uptake and oxidation via FABP7 inhibition also curbed GBM growth and invasiveness (De Rosa et al., 2012; Hoang-Minh et al., 2018). In the clinical setting, this approach had been validated by the combination of the FASN inhibitor TVB-2640 with bevacizumab, which achieved a 56% overall response rate (ORR), including complete regression in 17% of patients (Kelly et al., 2023).

5.1.4 Krebs cycle

While IDH inhibition demonstrated primary efficacy in low-grade gliomas, its applicability was being explored in GBM. Pharmacologic inhibitors like ivosidenib and vorasidenib effectively penetrated the CNS and suppressed the 2-HG in IDH1-mutant GBM models (Scott et al., 2021; Tejera et al., 2020). Ivosidenib had been approved by the FDA as a standard treatment for recurrent IDH1-mutant grade 2 gliomas. Beyond IDH, additional therapeutic targets related to the Krebs cycle in GBM include glutaminase, which catalyzed the conversion of glutamine to glutamate and supplied α-KG for the Krebs cycle to regulate glioma progression (De Los Santos-Jiménez et al., 2023). Besides, devimistat could decrease the metabolites of the Krebs cycle that induced cell death, and inhibited the proliferation of GBM cells (Nguyen et al., 2022; Anderson et al., 2023).

5.1.5 Nucleotide metabolism

GBM cells exhibited upregulated de novo nucleotide synthesis to meet the demands of rapid proliferation (Cortes Ballen et al., 2024). AMP-activated protein kinase (AMPK) served as a pivotal regulator in this process, and its dysregulation in GBM could perturb nucleotide synthesis by modulating enzymes such as ribonucleotide reductase (RR) and phosphofructokinase (PFK), thereby influencing glioma progression (Han W. et al., 2020). Phosphoinositide 3-kinase-related kinase (PIKK) inhibitors, such as ceralasertib, could inhibit de novo purine synthesis in GBM cells (Wang W. et al., 2021). Targeting purine metabolism can regulate mitochondrial dynamics, thereby enhancing the sensitivity of glioma cells to TMZ (D’Aprile et al., 2025). CAD inhibitors are a class of small molecules that target the multi-enzyme complex carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase; examples such as PALA (N-phosphonacetyl-L-aspartate) represented a potential strategy for inhibiting pyrimidine synthesis in GBMs (Raizer et al., 2010). Similarly, targeting the mitochondrial enzyme Dihydroorotate dehydrogenase (DHODH) with agents like BAY2402234 has demonstrated significant anti-tumor efficacy in GBM xenograft models (Spina et al., 2022). Furthermore, a synergistic approach that concurrently inhibited both the de novo and salvage nucleotide synthesis pathways had been shown to effectively suppress GBM progression, highlighting the promise of combinatorial metabolic inhibition (Laks et al., 2016).

Targeting the multifaceted metabolic reprogramming in gliomas through single or combinatorial inhibition of key pathways in glucose, amino acid, lipid, Krebs cycle, and nucleotide metabolism represents a promising therapeutic strategy.

5.2 Reversing immunosenescence

Since immunosenscence inhibited immune responses in the TME, reducing the senescence of immune cells might improve the efficacy of glioma therapy. The longevity-associated variant (LAV) of the bactericidal/permeability-increasing fold-containing family B member 4 (BPIFB4) improved age-related immune dysfunction and balanced low-grade inflammation in the elderly. LAV-BPIFB4 exerted multiple effects, including the remodeling of the senescent phenotype in GBM cells, enhancement of temozolomide sensitivity, and selective reduction of T cell senescence (Puca et al., 2022). Restoring CD28 expression in human T cells countered replicative senescence by enhancing telomerase activity and proliferative potential. Furthermore, pharmacological inhibition of SRC homology 2 domain-containing phosphatase-1 (SHP-1) improved T cell function in the elderly by augmenting TCR/CD28 signaling (Huff et al., 2019). The elimination of senescent astrocytes using senolytics led to marked inhibition of glioma growth and prolonged survival in mice (Fletcher-Sananikone et al., 2021).

Targeting SASP also might strengthen immune responses. Administration of an IL-7/GM-CSF fusion vaccine to aged GBM-bearing mice triggered thymic regeneration and sustained anti-tumor immunity. This was accompanied by elevated levels of the pro-inflammatory cytokine IL-1β based on global cytokine analysis, driving dendritic cell hyperactivation and leading to an expanded brain-infiltrating T cell population dominated by long-term Th17 effector memory cells (Shireman et al., 2023). Genetic ablation or pharmacological inhibition of IL-6 might moderately increase the infiltration of T cells in GBM and prolong mouse survival (Yang et al., 2021). Administration of a TGF-β2-targeting vaccine augmented adaptive and innate immunity by expanding IFN-γ-producing CD4⁺ and CD8⁺ T cells and activating NK cells, which was associated with elevated activation markers (CD69, NKG2D) and reduced immunosuppressive signals (TGF-β2, PD-1) (Tu et al., 2023). Through a targeted screen, researchers identified a potential SASP inhibitor that markedly suppressed the proliferation and invasion of GBM cells induced by SASP-secreting senescent TAMs. This compound also reduced M2 macrophage polarization and enhanced the efficacy of PD-1 blockade (Liu Y. et al., 2025).

Given the pivotal role of epigenetic dysregulation in immunosenescence, targeting epigenetic mechanisms may represent a strategy to reverse the immunosuppressive state and enhance immune responses. In addition to promoting the expression of the cancer-testis antigen NY-ESO-1 to effectively sensitize glioma cells to T-cell-mediated lysis, DNA methyltransferase inhibitors (DNMTis) also upregulated the expression of MHC-Ⅰ in glioma cells, resulting in enhanced antigen presentation and immune recognition, an effect shared with histone deacetylase inhibitors (HDACis) (Riyas Mohamed and Yaqinuddin, 2025). Besides, HDACis could regulate the efficacy of anti-PD-1 inhibitors against gliomas through increasing PD-L1 expression and immune cytokines and T cell infiltration (Li S. et al., 2025). Targeting miRNAs could regulate Signal transducer and activator of transcription 3 (STAT3) activity and expression to directly or recruiting NK cells suppress M2-phenotype TAM polarization to inhibit gliomas, with a decrease of TGF-β1 and IL-6 (Chen N. et al., 2022).

Currently, relevant clinical trials are underway across various diseases. By employing other therapeutic agents such as miRNA mimics, the regenerative capacity of T cells or B cells can be restored or re-established, thereby enhancing the immune response (Fu et al., 2025). Meanwhile, Clinical trials targeting PD-1 (NCT03718767) and combining HDAC inhibitors with radiotherapy and chemotherapy (NCT03426891) are also underway in gliomas, suggesting that targeting specific molecules involved in immune senescence may represent novel therapeutic approaches. However, owing to the considerable variability among gliomas and the scarcity of relevant clinical trials, further research is required to develop corresponding drugs.

As a result, targeting immunosenescence could be a novel and promising approach in exploring treatments for gliomas.

6 Discussion and therapeutic perspectives

Gliomas represent one of the most frequent primary neoplasms of the central nervous system, comprising approximately 30% of all primary brain tumors and 80% of malignant brain tumors. The current therapeutic arsenal is primarily based on surgical intervention, radiation therapy, and chemotherapy, supplemented by the developing fields of targeted treatment and immunotherapy (Weller et al., 2024). However, the poor prognosis, high recurrence rates, and negative immunotherapy results necessitate the urgent exploration of new treatments.

Metabolic reprogramming, a hallmark of gliomas, enables not only rapid tumor cell proliferation but also immune evasion by remodeling the tumor microenvironment. Glioma cells employ metabolic reprogramming as a dual-purpose strategy. Beyond meeting biosynthetic and bioenergetic demands via the Warburg effect, glutamine dependency, and enhanced lipid synthesis, these alterations actively shape an immunosuppressive niche. By competing for critical nutrients like glucose and glutamine, and by accumulating oncometabolites such as lactate, 2-HG, and kynurenine, tumor cells directly impair the function of cytotoxic CD8⁺ T cells and NK cells, while simultaneously promoting the expansion and activation of immunosuppressive entities, including Tregs, M2-polarized TAMs, and MDSCs (Qiu R. Z. et al., 2021). This metabolic hijacking of the immune landscape suggests that targeting tumor metabolism is not merely a strategy to starve cancer cells but a viable approach to reprogram the TME. The promising preclinical and emerging clinical evidence for inhibitors against key metabolic nodes like GLUTs, IDH, FASN, and DHODH provides a compelling rationale for this approach, potentially breaking the cycle of immune suppression induced by tumor metabolism. Clinical trials combining metabolic therapy protocols with standard treatments are currently underway (NCT04730869) through analysis of metabolic characteristics within the glioma microenvironment. However, the precise efficacy remains unknown at present.

Additionally, dysregulated nucleotide metabolism is a hallmark of cancer. Beyond their fundamental roles in biosynthesis and proliferation, nucleotides are produced via de novo and salvage synthesis. The reprogramming of these pathways in tumors thereby contributes to aggressive proliferation, therapy resistance, and immune escape (Mullen and Singh, 2023). Altered nucleotide metabolism can suppress the anti-tumor activity of immune cells, such as T cells and macrophages, primarily through adenosine signaling (Mullen and Singh, 2023). Moreover, while the link between nucleotide metabolism and immunosenescence remains underexplored, targeting this metabolic axis represents a promising strategy for modulating the immune landscape in glioma. This potential, however, necessitates further investigation.

Concurrently, immunosenescence, an age-dependent decline of immune function, is profoundly correlated with various diseases, including tumors (Liu Z. et al., 2023). Researchers have documented the presence of senescent phenotypes across TAMs, T cells, NK cells, and B cells, characterized by functional exhaustion, telomere attrition, and a potent SASP (Goyani et al., 2024). These senescent immune cells are not merely inert; they become active architects of immunosuppression through the secretion of factors like IL-6, TGF-β, and IL-10. This creates a vicious cycle wherein the senescent TME further paralyzes effective anti-tumor immunity. Consequently, therapeutic strategies aimed at reversing immunosenescence—through the elimination of senescent cells (senolytics), neutralization of the SASP, or epigenetic rejuvenation (e.g., with DNMTis or HDACis)—represent a novel and complementary frontier in glioma therapy. Current research on immunosenescent features within the glioma microenvironment remains limited. Moreover, given the heterogeneous nature of gliomas, further studies are required to definitively characterize immunosenescence in this context. Elucidating the characteristics of immunosenescence within the glioma tumor microenvironment and combining these insights with standard-of-care treatments may offer a promising avenue to improve therapeutic outcomes.

Most critically, a bidirectional crosstalk exists between metabolic reprogramming and immunosenescence. Tumor-derived metabolic stress acts as a primary driver of immune cell senescence. In turn, the intrinsic metabolic alterations in senescent immune cells, such as impaired glycolysis and oxidative phosphorylation, further lock them into a dysfunctional state. This interplay forms a “vicious co-axis” that perpetuates immune evasion. However, as noted in the text, immunosenescence involves a decline in immune function and alterations in immune cell composition and activity, whose mechanisms overlap with those underlying immunosuppression (e.g., abnormal T cell function). In gliomas, a clear consensus is still lacking regarding the specific targets and temporal dynamics through which metabolic reprogramming regulates these two interconnected processes. Therefore, future efforts should focus on elucidating the direct mechanisms by which metabolic reprogramming governs immunosenescence in gliomas to establish a definitive link. This paradigm shift necessitates a move beyond monotherapies towards rational combination strategies. For instance, combining glycolytic inhibitors with interventions that reverse T cell senescence, or pairing immune checkpoint blockade with IDO1/TDO2 inhibition (Bickerdike et al., 2024), could yield synergistic efficacy by simultaneously targeting both the instigator (tumor metabolism) and the consequence (immune dysfunction) of this pathological network.

The BBB is a vital component of the CNS. It maintains a relatively independent internal environment within the CNS, distinct from other bodily systems, thereby ensuring normal physiological functions and a high degree of stability for neural activity. In gliomas, the physical and biochemical barriers of the BBB restrict the delivery of many therapeutic agents to the brain, contributing to suboptimal treatment outcomes for gliomas (Wang Y. H. et al., 2021). This limitation may also pose challenges for emerging therapeutic strategies targeting metabolic reprogramming and immunosenescence.

The future of glioma therapy lies in the development of intelligent combination strategies that integrate conventional radiochemotherapy, emerging immunotherapies, and novel metabolic and senolytic agents. Whether targeting metabolic reprogramming or immunosenescence, effective therapeutic strategies must fully account for the highly dynamic features of the glioma microenvironment. Targeting key metabolic nodes (e.g., GLUT, 2-HG), reversing immunosenescence, restoring immune function, and reshaping the immunosuppressive microenvironment all represent promising new directions. Furthermore, in-depth investigation into the interplay between metabolism and immunosenescence, and the subsequent design of combination therapies based on these insights, may open new avenues for glioma treatment.

In summary, this study elucidates the characteristics of metabolic reprogramming and immunosenescence in gliomas and reveals their potential interplay, thereby providing important insights for the future development of novel therapeutic strategies.

Author contributions

HF: Conceptualization, Writing – original draft, Writing – review and editing. SY: Conceptualization, Writing – original draft. QL: Writing – review and editing. LC: Conceptualization, Writing – review and editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

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.

Generative AI statement

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

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Abdel-Wahab, A. F., Mahmoud, W., and Al-Harizy, R. M. (2019). Targeting glucose metabolism to suppress cancer progression: prospective of anti-glycolytic cancer therapy. Pharmacol. Res. 150, 104511. doi:10.1016/j.phrs.2019.104511

PubMed Abstract | CrossRef Full Text | Google Scholar

Abdelfattah, N., Kumar, P., Wang, C., Leu, J.-S., Flynn, W. F., Gao, R., et al. (2022). Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target. Nat. Commun. 13, 767. doi:10.1038/s41467-022-28372-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Agnihotri, S., and Zadeh, G. (2016). Metabolic reprogramming in glioblastoma: the influence of cancer metabolism on epigenetics and unanswered questions. Neuro Oncol. 18, 160–172. doi:10.1093/neuonc/nov125

PubMed Abstract | CrossRef Full Text | Google Scholar

Ahmady, F., Curpen, P., Perriman, L., Teixeira, A. F., Wu, S. Q., Zhu, H. J., et al. (2024). Reduced T and NK cell activity in glioblastoma patients correlates with TIM-3 and BAT3 dysregulation. Cells 13, 1777. doi:10.3390/cells13211777

PubMed Abstract | CrossRef Full Text | Google Scholar

Akram, M. (2013). Mini-review on glycolysis and cancer. J. Cancer Educ. 28, 454–457. doi:10.1007/s13187-013-0486-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Anderson, R., Pladna, K. M., Schramm, N. J., Wheeler, F. B., Kridel, S., and Pardee, T. S. (2023). Pyruvate dehydrogenase inhibition leads to decreased glycolysis, increased reliance on gluconeogenesis and alternative sources of Acetyl-CoA in acute myeloid leukemia. Cancers (Basel) 15, 484. doi:10.3390/cancers15020484

PubMed Abstract | CrossRef Full Text | Google Scholar

Appelskog, I. B., Ammerpohl, O., Svechnikova, I. G., Lui, W. O., Almqvist, P. M., and Ekström, T. J. (2004). Histone deacetylase inhibitor 4-phenylbutyrate suppresses GAPDH mRNA expression in glioma cells. Int. J. Oncol. 24, 1419–1425.

PubMed Abstract | Google Scholar

Bak, L. K., Schousboe, A., and Waagepetersen, H. S. (2006). The glutamate/GABA-glutamine cycle: aspects of transport, neurotransmitter homeostasis and ammonia transfer. J. Neurochem. 98, 641–653. doi:10.1111/j.1471-4159.2006.03913.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Bayik, D., Bartels, C. F., Lovrenert, K., Watson, D. C., Zhang, D., Kay, K., et al. (2022). Distinct cell adhesion signature defines glioblastoma myeloid-derived suppressor cell subsets. Cancer Res. 82, 4274–4287. doi:10.1158/0008-5472.CAN-21-3840

PubMed Abstract | CrossRef Full Text | Google Scholar

Bergström, J., Fürst, P., Norée, L. O., and Vinnars, E. (1974). Intracellular free amino acid concentration in human muscle tissue. J. Appl. Physiol. 36, 693–697. doi:10.1152/jappl.1974.36.6.693

PubMed Abstract | CrossRef Full Text | Google Scholar

Bickerdike, M. J., Nafia, I., Bessede, A., Chen, C. B., and Wangpaichitr, M. (2024). AT-0174, a novel dual IDO1/TDO2 enzyme inhibitor, synergises with temozolomide to improve survival in an orthotopic mouse model of glioblastoma. BMC Cancer 24, 889. doi:10.1186/s12885-024-12631-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Bugakova, A. S., Chudakova, D. A., Myzina, M. S., Yanysheva, E. P., Ozerskaya, I. V., Soboleva, A. V., et al. (2024). Non-tumor cells within the tumor microenvironment-the “Eminence Grise” of the glioblastoma pathogenesis and potential targets for therapy. Cells 13, 808. doi:10.3390/cells13100808

PubMed Abstract | CrossRef Full Text | Google Scholar

Bunse, L., Pusch, S., Bunse, T., Sahm, F., Sanghvi, K., Friedrich, M., et al. (2018). Suppression of antitumor T cell immunity by the oncometabolite (R)-2-hydroxyglutarate. Nat. Med. 24, 1192–1203. doi:10.1038/s41591-018-0095-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Bunse, L., Bunse, T., Kilian, M., Quintana, F. J., and Platten, M. (2025). The immunology of brain tumors. Sci. Immunol. 10, eads0449. doi:10.1126/sciimmunol.ads0449

PubMed Abstract | CrossRef Full Text | Google Scholar

Cakmak, P., Lun, J. H., Singh, A., Macas, J., Schupp, J., Schuck, J., et al. (2025). Spatial immune profiling defines a subset of human gliomas with functional tertiary lymphoid structures. Immunity 58, 2847–2863.e8. doi:10.1016/j.immuni.2025.09.018

PubMed Abstract | CrossRef Full Text | Google Scholar

Callao, V., and Montoya, E. (1961). Toxohormone-like factor from microorganisms with impaired respiration. Science 134, 2041–2042. doi:10.1126/science.134.3495.2041

PubMed Abstract | CrossRef Full Text | Google Scholar

Carreno, G., Guiho, R., and Martinez-Barbera, J. P. (2021). Cell senescence in neuropathology: a focus on neurodegeneration and tumours. Neuropathology Appl. Neurobiol. 47, 359–378. doi:10.1111/nan.12689

PubMed Abstract | CrossRef Full Text | Google Scholar

Chamarthy, S., and Mekala, J. R. (2023). Functional importance of glucose transporters and chromatin epigenetic factors in Glioblastoma Multiforme (GBM): possible therapeutics. Metab. Brain Dis. 38, 1441–1469. doi:10.1007/s11011-023-01207-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, R., Nishimura, M. C., Kharbanda, S., Peale, F., Deng, Y., Daemen, A., et al. (2014). Hominoid-specific enzyme GLUD2 promotes growth of IDH1R132H glioma. Proc. Natl. Acad. Sci. U. S. A. 111, 14217–14222. doi:10.1073/pnas.1409653111

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, N., Peng, C., and Li, D. (2022). Epigenetic underpinnings of inflammation: a key to unlock the tumor microenvironment in glioblastoma. Front. Immunol. 13, 869307. doi:10.3389/fimmu.2022.869307

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, S., Zhang, S., Feng, W., Li, J., Yuan, Y., Li, W., et al. (2022). Serine and glycine metabolism-related gene expression signature stratifies immune profiles of brain gliomas, and predicts prognosis and responses to immunotherapy. Front. Pharmacol. 13, 1072253. doi:10.3389/fphar.2022.1072253

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, C. H., Hsu, S. Y., Yu, W. J., Chiang, C. S., and Yu, C. F. (2025). Distinct roles of small extracellular vesicles from resident and infiltrating macrophages on glioma growth and mobility. J. Cancer 16, 969–981. doi:10.7150/jca.103595

PubMed Abstract | CrossRef Full Text | Google Scholar

Cheng, H., Yan, Y., Zhang, B., Ma, Z. L., Fu, S. W., Ji, Z., et al. (2025). Single-cell transcriptomics reveals immunosuppressive microenvironment and highlights tumor-promoting macrophage cells in Glioblastoma. Plos One 20, e0312764. doi:10.1371/journal.pone.0312764

PubMed Abstract | CrossRef Full Text | Google Scholar

Chinopoulos, C., and Seyfried, T. N. (2018). Mitochondrial substrate-level phosphorylation as energy source for glioblastoma: review and hypothesis. ASN Neuro 10, 1759091418818261. doi:10.1177/1759091418818261

PubMed Abstract | CrossRef Full Text | Google Scholar

Choi, C., Ganji, S. K., Deberardinis, R. J., Hatanpaa, K. J., Rakheja, D., Kovacs, Z., et al. (2012). 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat. Med. 18, 624–629. doi:10.1038/nm.2682

PubMed Abstract | CrossRef Full Text | Google Scholar

Colegio, O. R., Chu, N. Q., Szabo, A. L., Chu, T., Rhebergen, A. M., Jairam, V., et al. (2014). Functional polarization of tumour-associated macrophages by tumour-derived lactic acid. Nature 513, 559–563. doi:10.1038/nature13490

PubMed Abstract | CrossRef Full Text | Google Scholar

Corominas-Faja, B., Cuyàs, E., Gumuzio, J., Bosch-Barrera, J., Leis, O., Martin Á, G., et al. (2014). Chemical inhibition of acetyl-CoA carboxylase suppresses self-renewal growth of cancer stem cells. Oncotarget 5, 8306–8316. doi:10.18632/oncotarget.2059

PubMed Abstract | CrossRef Full Text | Google Scholar

Cortes Ballen, A. I., Amosu, M., Ravinder, S., Chan, J., Derin, E., Slika, H., et al. (2024). Metabolic reprogramming in glioblastoma multiforme: a review of pathways and therapeutic targets. Cells 13, 1574. doi:10.3390/cells13181574

PubMed Abstract | CrossRef Full Text | Google Scholar

Curi, R., Lagranha, C. J., Doi, S. Q., Sellitti, D. F., Procopio, J., Pithon-Curi, T. C., et al. (2005). Molecular mechanisms of glutamine action. J. Cell Physiol. 204, 392–401. doi:10.1002/jcp.20339

PubMed Abstract | CrossRef Full Text | Google Scholar

Currie, E., Schulze, A., Zechner, R., Walther, T. C., and Farese, R. V., Jr. (2013). Cellular fatty acid metabolism and cancer. Cell Metab. 18, 153–161. doi:10.1016/j.cmet.2013.05.017

PubMed Abstract | CrossRef Full Text | Google Scholar

D'aprile, S., Denaro, S., Gervasi, A., Vicario, N., and Parenti, R. (2025). Targeting metabolic reprogramming in glioblastoma as a new strategy to overcome therapy resistance. Front. Cell Dev. Biol. 13, 1535073. doi:10.3389/fcell.2025.1535073

PubMed Abstract | CrossRef Full Text | Google Scholar

Dalla Pozza, E., Dando, I., Pacchiana, R., Liboi, E., Scupoli, M. T., Donadelli, M., et al. (2020). Regulation of succinate dehydrogenase and role of succinate in cancer. Semin. Cell Dev. Biol. 98, 4–14. doi:10.1016/j.semcdb.2019.04.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Daniele, S., Giacomelli, C., Zappelli, E., Granchi, C., Trincavelli, M. L., Minutolo, F., et al. (2015). Lactate dehydrogenase-A inhibition induces human glioblastoma multiforme stem cell differentiation and death. Sci. Rep. 5, 15556. doi:10.1038/srep15556

PubMed Abstract | CrossRef Full Text | Google Scholar

De Domenico, P., Gagliardi, F., Roncelli, F., Snider, S., and Mortini, P. (2025). Tumor-infiltrating and circulating B cells mediate local and systemic immunomodulatory mechanisms in Glioblastoma. J. Neuro-Oncology 172, 527–548. doi:10.1007/s11060-025-04989-z

PubMed Abstract | CrossRef Full Text | Google Scholar

De La Rosa, V., Campos-Sandoval, J. A., Martín-Rufián, M., Cardona, C., Matés, J. M., Segura, J. A., et al. (2009). A novel glutaminase isoform in mammalian tissues. Neurochem. Int. 55, 76–84. doi:10.1016/j.neuint.2009.02.021

PubMed Abstract | CrossRef Full Text | Google Scholar

De Leo, A., Ugolini, A., and Veglia, F. (2021). Myeloid cells in glioblastoma microenvironment. Cells 10, 18. doi:10.3390/cells10010018

PubMed Abstract | CrossRef Full Text | Google Scholar

De Los Santos-Jiménez, J., Rosales, T., Ko, B., Campos-Sandoval, J. A., Alonso, F. J., Márquez, J., et al. (2023). Metabolic adjustments following glutaminase inhibition by CB-839 in glioblastoma cell lines. Cancers (Basel) 15. doi:10.3390/cancers15020531

CrossRef Full Text | Google Scholar

De Rosa, A., Pellegatta, S., Rossi, M., Tunici, P., Magnoni, L., Speranza, M. C., et al. (2012). A radial glia gene marker, fatty acid binding protein 7 (FABP7), is involved in proliferation and invasion of glioblastoma cells. PLoS One 7, e52113. doi:10.1371/journal.pone.0052113

PubMed Abstract | CrossRef Full Text | Google Scholar

Di Magno, L., Manzi, D., D'amico, D., Coni, S., Macone, A., Infante, P., et al. (2014). Druggable glycolytic requirement for Hedgehog-dependent neuronal and medulloblastoma growth. Cell Cycle 13, 3404–3413. doi:10.4161/15384101.2014.952973

PubMed Abstract | CrossRef Full Text | Google Scholar

Dranoff, G., Elion, G. B., Friedman, H. S., Campbell, G. L., and Bigner, D. D. (1985). Influence of glutamine on the growth of human glioma and medulloblastoma in culture. Cancer Res. 45, 4077–4081.

PubMed Abstract | Google Scholar

D’aprile, S., Denaro, S., Torrisi, F., Longhitano, L., Giallongo, S., Giallongo, C., et al. (2025). Purine metabolism rewiring improves glioblastoma susceptibility to temozolomide treatment. Cell Death and Dis. 16, 336. doi:10.1038/s41419-025-07667-0

CrossRef Full Text | Google Scholar

Ekici, S., Nye, J. A., Neill, S. G., Allen, J. W., Shu, H. K., and Fleischer, C. C. (2022). Glutamine imaging: a new avenue for glioma management. AJNR Am. J. Neuroradiol. 43, 11–18. doi:10.3174/ajnr.A7333

PubMed Abstract | CrossRef Full Text | Google Scholar

Elguindy, M., Young, J. S., Mondal, I., Lu, R. O., and Ho, W. S. (2024). Glioma-immune cell crosstalk in tumor progression. Cancers 16, 308. doi:10.3390/cancers16020308

PubMed Abstract | CrossRef Full Text | Google Scholar

Fan, W., Song, Y., Ren, Z., Cheng, X., Li, P., Song, H., et al. (2020). Glioma cells are resistant to inflammation-induced alterations of mitochondrial dynamics. Int. J. Oncol. 57, 1293–1306. doi:10.3892/ijo.2020.5134

PubMed Abstract | CrossRef Full Text | Google Scholar

Fan, H., Wang, Y., Han, M., Wang, L., Li, X., Kuang, X., et al. (2024). Multi-omics-based investigation of Bifidobacterium’s inhibitory effect on glioma: regulation of tumor and gut microbiota, and MEK/ERK cascade. Front. Microbiol. 15, 1344284. doi:10.3389/fmicb.2024.1344284

PubMed Abstract | CrossRef Full Text | Google Scholar

Fan, Z., Wirth, A. K., Chen, D., Wruck, C. J., Rauh, M., Buchfelder, M., et al. (2017). Nrf2-Keap1 pathway promotes cell proliferation and diminishes ferroptosis. Onc. 6, e371.

PubMed Abstract | CrossRef Full Text | Google Scholar

Fhu, C. W., and Ali, A. (2020). Fatty acid synthase: an emerging target in cancer. Molecules 25. doi:10.3390/molecules25173935

PubMed Abstract | CrossRef Full Text | Google Scholar

Fletcher-Sananikone, E., Kanji, S., Tomimatsu, N., Di Cristofaro, L. F. M., Kollipara, R. K., Saha, D., et al. (2021). Elimination of radiation-induced senescence in the brain tumor microenvironment attenuates glioblastoma recurrence. Cancer Res. 81, 5935–5947. doi:10.1158/0008-5472.CAN-21-0752

PubMed Abstract | CrossRef Full Text | Google Scholar

Friedrich, M., Sankowski, R., Bunse, L., Kilian, M., Green, E., Ramallo Guevara, C., et al. (2021). Tryptophan metabolism drives dynamic immunosuppressive myeloid states in IDH-mutant gliomas. Nat. Cancer 2, 723–740. doi:10.1038/s43018-021-00201-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Fu, T. E., and Zhou, Z. (2025). Senescent cells as a target for anti-aging interventions: from senolytics to immune therapies. J. Transl. Intern. Med. 13, 33–47. doi:10.1515/jtim-2025-0005

PubMed Abstract | CrossRef Full Text | Google Scholar

Fu, X., Chin, R. M., Vergnes, L., Hwang, H., Deng, G., Xing, Y., et al. (2015). 2-Hydroxyglutarate inhibits ATP synthase and mTOR signaling. Cell Metab. 22, 508–515. doi:10.1016/j.cmet.2015.06.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Fu, Y., Wang, B., Alu, A., Hong, W., Lei, H., He, X., et al. (2025). Immunosenescence: signaling pathways, diseases and therapeutic targets. Signal Transduct. Target. Ther. 10, 250. doi:10.1038/s41392-025-02371-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Fukushima, Y., Minato, N., and Hattori, M. (2018). The impact of senescence-associated T cells on immunosenescence and age-related disorders. Inflamm. Regen. 38, 24. doi:10.1186/s41232-018-0082-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Gao, J. C., Gu, D. L., Yang, K. L., Zhang, J. X., Lin, Q. K., Yuan, W., et al. (2025). Infiltrating plasma cells maintain glioblastoma stem cells through IgG-Tumor binding. Cancer Cell 43, 122–143.e8. doi:10.1016/j.ccell.2024.12.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Garcia, J. H., Jain, S., and Aghi, M. K. (2021). Metabolic drivers of invasion in glioblastoma. Front. Cell Dev. Biol. 9, 683276. doi:10.3389/fcell.2021.683276

PubMed Abstract | CrossRef Full Text | Google Scholar

Ge, Y., Cheng, D., Jia, Q., Xiong, H., and Zhang, J. (2021). Mechanisms underlying the role of myeloid-derived suppressor cells in clinical diseases: good or bad. Immune Netw. 21, e21. doi:10.4110/in.2021.21.e21

PubMed Abstract | CrossRef Full Text | Google Scholar

Geng, J., Liang, B., Zhang, Z. H., Shan, F. P., Zhao, X. X., and Yuan, Y. (2025). Loss of LAPTM4A inhibits M2 polarization of tumor-associated macrophages in glioblastoma, promoting immune activation and enhancing anti-PD1 therapy. Commun. Biol. 8, 909. doi:10.1038/s42003-025-08147-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Gergues, M., Bari, R., Koppisetti, S., Gosiewska, A., Kang, L., and Hariri, R. J. (2025). Senescence, NK cells, and cancer: navigating the crossroads of aging and disease. Front. Immunol. 16, 1565278. doi:10.3389/fimmu.2025.1565278

PubMed Abstract | CrossRef Full Text | Google Scholar

Goyani, P., Christodoulou, R., and Vassiliou, E. (2024). Immunosenescence: aging and immune system decline. Vaccines 12, 1314. doi:10.3390/vaccines12121314

PubMed Abstract | CrossRef Full Text | Google Scholar

Grankvist, N., Lagerborg, K. A., Jain, M., and Nilsson, R. (2018). Gabapentin can suppress cell proliferation independent of the cytosolic branched-chain amino acid transferase 1 (BCAT1). Biochemistry 57, 6762–6766. doi:10.1021/acs.biochem.8b01031

PubMed Abstract | CrossRef Full Text | Google Scholar

Guda, M. R., Labak, C. M., Omar, S. I., Asuthkar, S., Airala, S., Tuszynski, J., et al. (2019). GLUT1 and TUBB4 in glioblastoma could be efficacious targets. Cancers (Basel) 11. doi:10.3390/cancers11091308

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, X. Y., Zhang, G. H., Wang, Z. N., Duan, H., Xie, T., Liang, L., et al. (2021). A novel foxp3-related immune prognostic signature for glioblastoma multiforme based on immunogenomic profiling. Aging-Us 13, 3501–3517. doi:10.18632/aging.202282

PubMed Abstract | CrossRef Full Text | Google Scholar

Han, S., Feng, S. Z., Ren, M. L., Ma, E. L., Wang, X. N., Xu, L. S., et al. (2014). Glioma cell-derived placental growth factor induces regulatory B cells. Int. J. Biochem. and Cell Biol. 57, 63–68. doi:10.1016/j.biocel.2014.10.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Han, S., Liu, Y., Cai, S. J., Qian, M., Ding, J., Larion, M., et al. (2020). IDH mutation in glioma: molecular mechanisms and potential therapeutic targets. Br. J. Cancer 122, 1580–1589. doi:10.1038/s41416-020-0814-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Han, W., Shi, J., Cao, J., Dong, B., and Guan, W. (2020). Emerging roles and therapeutic interventions of aerobic glycolysis in Glioma. Onco Targets Ther. 13, 6937–6955. doi:10.2147/OTT.S260376

PubMed Abstract | CrossRef Full Text | Google Scholar

Han, S. J., Georgiev, P., Ringel, A. E., Sharpe, A. H., and Haigis, M. C. (2023). Age-associated remodeling of T cell immunity and metabolism. Cell Metab. 35, 36–55. doi:10.1016/j.cmet.2022.11.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Han, M., Li, S., Fan, H., An, J., Peng, C., and Peng, F. (2024). Regulated cell death in glioma: promising targets for natural small-molecule compounds. Front. Oncol. Volume, 14–2024. doi:10.3389/fonc.2024.1273841

PubMed Abstract | CrossRef Full Text | Google Scholar

Han, M., Yang, J., Chen, P., Li, S., Tang, H., Fan, H., et al. (2025). Isocucurbitacin B inhibits gliomas through the promotion of anoikis by targeting caveolin 1. Cancer Lett. 629, 217873. doi:10.1016/j.canlet.2025.217873

PubMed Abstract | CrossRef Full Text | Google Scholar

Hanihara, M., Kawataki, T., Oh-Oka, K., Mitsuka, K., Nakao, A., and Kinouchi, H. (2016). Synergistic antitumor effect with indoleamine 2,3-dioxygenase inhibition and temozolomide in a murine glioma model. J. Neurosurg. 124, 1594–1601. doi:10.3171/2015.5.JNS141901

PubMed Abstract | CrossRef Full Text | Google Scholar

Her, Y. F., and Maher, L. J., 3rd (2015). Succinate dehydrogenase loss in familial paraganglioma: biochemistry, genetics, and epigenetics. Int. J. Endocrinol. 2015, 296167. doi:10.1155/2015/296167

PubMed Abstract | CrossRef Full Text | Google Scholar

Hoang-Minh, L. B., Siebzehnrubl, F. A., Yang, C., Suzuki-Hatano, S., Dajac, K., Loche, T., et al. (2018). Infiltrative and drug-resistant slow-cycling cells support metabolic heterogeneity in glioblastoma. Embo J. 37. doi:10.15252/embj.201798772

PubMed Abstract | CrossRef Full Text | Google Scholar

Hou, X., Chen, S., Zhang, P., Guo, D., and Wang, B. (2022). Targeted arginine metabolism therapy: a dilemma in Glioma treatment. Front. Oncol. 12, 938847. doi:10.3389/fonc.2022.938847

PubMed Abstract | CrossRef Full Text | Google Scholar

Hsu, T. I. (2023). Editorial: metabolic reprogramming for acquiring therapeutic resistance in glioblastoma. Front. Oncol. 13, 1220063. doi:10.3389/fonc.2023.1220063

PubMed Abstract | CrossRef Full Text | Google Scholar

Hu, G., Lin, A. P., and Mcalister-Henn, L. (2006). Physiological consequences of loss of allosteric activation of yeast NAD+-specific isocitrate dehydrogenase. J. Biol. Chem. 281, 16935–16942. doi:10.1074/jbc.M512281200

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, Y., Ouyang, F., Yang, F., Zhang, N., Zhao, W., Xu, H., et al. (2022). The expression of hexokinase 2 and its hub genes are correlated with the prognosis in glioma. BMC Cancer 22, 900. doi:10.1186/s12885-022-10001-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Huff, W. X., Kwon, J. H., Henriquez, M., Fetcko, K., and Dey, M. (2019). The evolving role of CD8+CD28- immunosenescent T cells in cancer immunology. Int. J. Mol. Sci. 20. doi:10.3390/ijms20112810

PubMed Abstract | CrossRef Full Text | Google Scholar

Husain, Z., Huang, Y., Seth, P., and Sukhatme, V. P. (2013). Tumor-derived lactate modifies antitumor immune response: effect on myeloid-derived suppressor cells and NK cells. J. Immunol. 191, 1486–1495. doi:10.4049/jimmunol.1202702

PubMed Abstract | CrossRef Full Text | Google Scholar

Israelsen, W. J., Dayton, T. L., Davidson, S. M., Fiske, B. P., Hosios, A. M., Bellinger, G., et al. (2013). PKM2 isoform-specific deletion reveals a differential requirement for pyruvate kinase in tumor cells. Cell 155, 397–409. doi:10.1016/j.cell.2013.09.025

PubMed Abstract | CrossRef Full Text | Google Scholar

Jackson, C., Cherry, C., Bom, S., Dykema, A. G., Wang, R., Thompson, E., et al. (2025). Distinct myeloid-derived suppressor cell populations in human glioblastoma. Science 387, eabm5214. doi:10.1126/science.abm5214

PubMed Abstract | CrossRef Full Text | Google Scholar

Jayaram, M. A., and Phillips, J. J. (2024). Role of the microenvironment in glioma pathogenesis. Annu. Rev. Pathology Mech. Dis. 19, 181–201. doi:10.1146/annurev-pathmechdis-051122-110348

PubMed Abstract | CrossRef Full Text | Google Scholar

Jeffery, C. J., Bahnson, B. J., Chien, W., Ringe, D., and Petsko, G. A. (2000). Crystal structure of rabbit phosphoglucose isomerase, a glycolytic enzyme that moonlights as neuroleukin, autocrine motility factor, and differentiation mediator. Biochemistry 39, 955–964. doi:10.1021/bi991604m

PubMed Abstract | CrossRef Full Text | Google Scholar

Ji, J., Ding, K., Cheng, B., Zhang, X., Luo, T., Huang, B., et al. (2024). Radiotherapy-induced astrocyte senescence promotes an immunosuppressive microenvironment in glioblastoma to facilitate tumor regrowth. Adv. Sci. 11, 2304609. doi:10.1002/advs.202304609

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. (2023). 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

Johnston, R. J., Su, L. J., Pinckney, J., Critton, D., Boyer, E., Krishnakumar, A., et al. (2019). VISTA is an acidic pH-selective ligand for PSGL-1. Nature 574, 565–570. doi:10.1038/s41586-019-1674-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Keith, B., Johnson, R. S., and Simon, M. C. (2011). HIF1α and HIF2α: sibling rivalry in hypoxic tumour growth and progression. Nat. Rev. Cancer 12, 9–22. doi:10.1038/nrc3183

PubMed Abstract | CrossRef Full Text | Google Scholar

Kelly, W., Diaz Duque, A. E., Michalek, J., Konkel, B., Caflisch, L., Chen, Y., et al. (2023). Phase II investigation of TVB-2640 (denifanstat) with bevacizumab in patients with first relapse high-grade astrocytoma. Clin. Cancer Res. 29, 2419–2425. doi:10.1158/1078-0432.CCR-22-2807

PubMed Abstract | CrossRef Full Text | Google Scholar

Khan, S., Mittal, S., Mcgee, K., Alfaro-Munoz, K. D., Majd, N., Balasubramaniyan, V., et al. (2020). Role of neutrophils and myeloid-derived suppressor cells in glioma progression and treatment resistance. Int. J. Mol. Sci. 21, 1954. doi:10.3390/ijms21061954

PubMed Abstract | CrossRef Full Text | Google Scholar

Kim, H. J., Park, J. H., Kim, H. C., Kim, C. W., Kang, I., and Lee, H. K. (2022). Blood monocyte-derived CD169+ macrophages contribute to antitumor immunity against glioblastoma. Nat. Commun. 13, 6211. doi:10.1038/s41467-022-34001-5

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

Labrie, J. E., Iii, Sah, A. P., Allman, D. M., Cancro, M. P., and Gerstein, R. M. (2004). Bone marrow microenvironmental changes underlie reduced RAG-mediated recombination and B cell generation in aged mice. J. Exp. Med. 200, 411–423. doi:10.1084/jem.20040845

PubMed Abstract | CrossRef Full Text | Google Scholar

Laks, D. R., Ta, L., Crisman, T. J., Gao, F., Coppola, G., Radu, C. G., et al. (2016). Inhibition of nucleotide synthesis targets brain tumor stem cells in a subset of glioblastoma. Mol. Cancer Ther. 15, 1271–1278. doi:10.1158/1535-7163.MCT-15-0982

PubMed Abstract | CrossRef Full Text | Google Scholar

Langbein, S., Zerilli, M., Zur Hausen, A., Staiger, W., Rensch-Boschert, K., Lukan, N., et al. (2006). Expression of transketolase TKTL1 predicts colon and urothelial cancer patient survival: warburg effect reinterpreted. Br. J. Cancer 94, 578–585. doi:10.1038/sj.bjc.6602962

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, J. S., Adler, L., Karathia, H., Carmel, N., Rabinovich, S., Auslander, N., et al. (2018). Urea cycle dysregulation generates clinically relevant genomic and biochemical signatures. Cell 174, 1559–1570.e1522. doi:10.1016/j.cell.2018.07.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Lennartz, P., Thoelke, D., Dezfouli, A. B., Pilz, M., Lobinger, D., Messner, V., et al. (2023). Biomarkers in adult-type diffuse gliomas: elevated levels of circulating vesicular heat shock protein 70 serve as a biomarker in grade 4 glioblastoma and increase NK cell frequencies in grade 3 glioma. Biomedicines 11. doi:10.3390/biomedicines11123235

CrossRef Full Text | Google Scholar

Li, Z., and Zhang, H. (2016). Reprogramming of glucose, fatty acid and amino acid metabolism for cancer progression. Cell Mol. Life Sci. 73, 377–392. doi:10.1007/s00018-015-2070-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, C., Liu, F., Sun, L., Liu, Z., and Zeng, Y. (2022). Natural killer cell-related gene signature predicts malignancy of glioma and the survival of patients. BMC Cancer 22, 230. doi:10.1186/s12885-022-09230-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, S., Li, Z., Wang, X., Zhong, J., Yu, D., Chen, H., et al. (2023). HK3 stimulates immune cell infiltration to promote glioma deterioration. Cancer Cell Int. 23, 227. doi:10.1186/s12935-023-03039-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, Y., Liu, Y., Xu, J., Chen, D., Wu, T., and Cao, Y. (2023). Macrophage-cancer hybrid membrane-camouflaged nanoplatforms for HIF-1α gene silencing-enhanced sonodynamic therapy of glioblastoma. ACS Appl. Mater Interfaces 15, 31150–31158. doi:10.1021/acsami.3c03001

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, L., Zhang, T., Xiao, M., Lu, Y., and Gao, L. (2024). Brain macrophage senescence in glioma. Seminars Cancer Biol. 104-105, 46–60. doi:10.1016/j.semcancer.2024.07.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, G. W., Jin, Y. P., and Sheng, M. F. (2025). The miR-192/EGR1-HOXB9 loop inhibits immune evasion in glioma by arresting their NSC phenotypes. Int. Immunopharmacol. 152, 114453. doi:10.1016/j.intimp.2025.114453

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, J. B., Shih, L. K., Markwell, S. M., Olson, C. L., Sullivan, D. P., Arvanitis, C., et al. (2025). A clinically relevant model and method to study necrosis as a driving force in glioma restructuring and progression. Proc. Natl. Acad. Sci. U. S. A. 122, e2416024122. doi:10.1073/pnas.2416024122

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, S., Wang, L., Han, M. Y., Fan, H. L., Tang, H. L., Gao, H. L., et al. (2025). Combination of sodium butyrate and immunotherapy in glioma: regulation of immunologically hot and cold tumors via gut microbiota and metabolites. Front. Immunol. 16, 1532528. doi:10.3389/fimmu.2025.1532528

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, Y. T., Chen, Y. H., Cai, K., Qin, Y. J., Wang, X., Zhang, B., et al. (2025). Interactions between glioblastoma and myeloid cells. Front. Cell Dev. Biol. 13, 1632122. doi:10.3389/fcell.2025.1632122

PubMed Abstract | CrossRef Full Text | Google Scholar

Libby, C. J., Gc, S., Benavides, G. A., Fisher, J. L., Williford, S. E., Zhang, S., et al. (2021). A role for GLUT3 in glioblastoma cell invasion that is not recapitulated by GLUT1. Cell Adh Migr. 15, 101–115. doi:10.1080/19336918.2021.1903684

PubMed Abstract | CrossRef Full Text | Google Scholar

Lin, C., Wang, N., and Xu, C. (2023). Glioma-associated microglia/macrophages (GAMs) in glioblastoma: immune function in the tumor microenvironment and implications for immunotherapy. Front. Immunol. 14, 1123853. doi:10.3389/fimmu.2023.1123853

PubMed Abstract | CrossRef Full Text | Google Scholar

Lin, H., Liu, C., Hu, A., Zhang, D., Yang, H., and Mao, Y. (2024). Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives. J. Hematol. and Oncol. 17, 31. doi:10.1186/s13045-024-01544-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Ling, Z. N., Jiang, Y. F., Ru, J. N., Lu, J. H., Ding, B., and Wu, J. (2023). Amino acid metabolism in health and disease. Signal Transduct. Target Ther. 8, 345. doi:10.1038/s41392-023-01569-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, X. B., and Yu, Q. (2025). Advances in tumor-associated macrophage-mediated chemotherapeutic resistance in glioma. Front. Cell Dev. Biol. 13, 1676338. doi:10.3389/fcell.2025.1676338

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, Y., Jiang, W., Liu, J., Zhao, S., Xiong, J., Mao, Y., et al. (2012). IDH1 mutations inhibit multiple α-ketoglutarate-dependent dioxygenase activities in astroglioma. J. Neurooncol 109, 253–260. doi:10.1007/s11060-012-0914-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, H., Zhao, Q., Tan, L., Wu, X., Huang, R., Zuo, Y., et al. (2023). Neutralizing IL-8 potentiates immune checkpoint blockade efficacy for glioma. Cancer Cell 41, 693–710.e698. doi:10.1016/j.ccell.2023.03.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, Z., Liang, Q., Ren, Y., Guo, C., Ge, X., Wang, L., et al. (2023). Immunosenescence: molecular mechanisms and diseases. Signal Transduct. Target. Ther. 8, 200. doi:10.1038/s41392-023-01451-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, Y., Ali, H., Khan, F., Pang, L., and Chen, P. (2024). Epigenetic regulation of tumor-immune symbiosis in glioma. Trends Mol. Med. 30, 429–442. doi:10.1016/j.molmed.2024.02.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, Z. Q., Zuo, L. L., Zhou, Z. K., Liu, S. T., Ba, Y. H., Zuo, A. N., et al. (2024). Targeting immunosenescence for improved tumor immunotherapy. Medcomm 5, e777. doi:10.1002/mco2.777

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, S. Y., Li, Q., Niu, L., Feng, P., Li, W. S., Dang, Y., et al. (2025). KCNE3 facilitates M1 macrophage polarization by suppressing the Wnt/β-Catenin pathway, inhibiting glioma proliferation, migration, and invasion. Mol. Carcinog. 64, 1090–1103. doi:10.1002/mc.23911

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, Y., Feng, Y., Cheng, L., Xu, Y., Wu, A., and Cheng, P. (2025). Profiling with senescence-associated secretory phenotype score identifies GDC-0879 as a small molecule sensitizing glioblastoma to anti-PD1. Cell Death and Dis. 16, 602. doi:10.1038/s41419-025-07915-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Llibre, A., Kucuk, S., Gope, A., Certo, M., and Mauro, C. (2025). Lactate: a key regulator of the immune response. Immunity 58, 535–554. doi:10.1016/j.immuni.2025.02.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Louis, D. N., Perry, A., Wesseling, P., Brat, D. J., Cree, I. A., Figarella-Branger, D., et al. (2021). The 2021 WHO Classification of tumors of the central nervous system: a summary. Neuro-Oncology 23, 1231–1251. doi:10.1093/neuonc/noab106

PubMed Abstract | CrossRef Full Text | Google Scholar

Lu, J., Xu, Z., Duan, H., Ji, H., Zhen, Z., Li, B., et al. (2020). Tumor-associated macrophage interleukin-β promotes glycerol-3-phosphate dehydrogenase activation, glycolysis and tumorigenesis in glioma cells. Cancer Sci. 111, 1979–1990. doi:10.1111/cas.14408

PubMed Abstract | CrossRef Full Text | Google Scholar

Lu, L., Sun, J., Su, H., Luo, S., Chen, J., Qiu, S., et al. (2021). Antitumor CD8 T cell responses in glioma patients are effectively suppressed by T follicular regulatory cells. Exp. Cell Res. 407, 112808. doi:10.1016/j.yexcr.2021.112808

PubMed Abstract | CrossRef Full Text | Google Scholar

Lu, Z., Sun, G. F., He, K. Y., Zhang, Z., Han, X. H., Qu, X. H., et al. (2024). Targeted inhibition of branched-chain amino acid metabolism drives apoptosis of glioblastoma by facilitating ubiquitin degradation of Mfn2 and oxidative stress. Biochim. Biophys. Acta Mol. Basis Dis. 1870, 167220. doi:10.1016/j.bbadis.2024.167220

PubMed Abstract | CrossRef Full Text | Google Scholar

Luo, H., Ye, M., Hu, Y., Wu, M., Cheng, M., Zhu, X., et al. (2022). DNA methylation regulator-mediated modification patterns and tumor microenvironment characterization in glioma. Aging (Albany NY) 14, 7824–7850. doi:10.18632/aging.204291

PubMed Abstract | CrossRef Full Text | Google Scholar

Ma, K., Chen, X., Liu, W., Yang, Y., Chen, S., Sun, J., et al. (2021). ANXA2 is correlated with the molecular features and clinical prognosis of glioma, and acts as a potential marker of immunosuppression. Sci. Rep. 11, 20839. doi:10.1038/s41598-021-00366-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Ma, J. Q., Wu, Y. C., Ma, L. F., Yang, X. P., Zhang, T. C., Song, G. H., et al. (2024). A blueprint for tumor-infiltrating B cells across human cancers. Science 384, eadj4857. doi:10.1126/science.adj4857

PubMed Abstract | CrossRef Full Text | Google Scholar

Mahajan, S., Schmidt, M. H. H., and Schumann, U. (2023). The glioma immune landscape: a double-edged sword for treatment regimens. Cancers 15, 2024. doi:10.3390/cancers15072024

PubMed Abstract | CrossRef Full Text | Google Scholar

Martinez-Zamudio, R. I., Dewald, H. K., Vasilopoulos, T., Gittens-Williams, L., Fitzgerald-Bocarsly, P., and Herbig, U. (2021). Senescence-associated β-galactosidase reveals the abundance of senescent CD8+T cells in aging humans. Aging Cell 20. doi:10.1111/acel.13344

PubMed Abstract | CrossRef Full Text | Google Scholar

Mashimo, T., Pichumani, K., Vemireddy, V., Hatanpaa, K. J., Singh, D. K., Sirasanagandla, S., et al. (2014). Acetate is a bioenergetic substrate for human glioblastoma and brain metastases. Cell 159, 1603–1614. doi:10.1016/j.cell.2014.11.025

PubMed Abstract | CrossRef Full Text | Google Scholar

Matés, J. M., Segura, J. A., Martín-Rufián, M., Campos-Sandoval, J. A., Alonso, F. J., and Márquez, J. (2013). Glutaminase isoenzymes as key regulators in metabolic and oxidative stress against cancer. Curr. Mol. Med. 13, 514–534. doi:10.2174/1566524011313040005

PubMed Abstract | CrossRef Full Text | Google Scholar

Matés, J. M., Campos-Sandoval, J. A., Santos-Jiménez, J. L., and Márquez, J. (2019). Dysregulation of glutaminase and glutamine synthetase in cancer. Cancer Lett. 467, 29–39. doi:10.1016/j.canlet.2019.09.011

PubMed Abstract | CrossRef Full Text | Google Scholar

Matés, J. M., Campos-Sandoval, J. A., De Los Santos-Jiménez, J., Segura, J. A., Alonso, F. J., and Márquez, J. (2020). Metabolic reprogramming of cancer by chemicals that target glutaminase isoenzymes. Curr. Med. Chem. 27, 5317–5339. doi:10.2174/0929867326666190416165004

PubMed Abstract | CrossRef Full Text | Google Scholar

Mathewson, N. D., Ashenberg, O., Tirosh, I., Gritsch, S., Perez, E. M., Marx, S., et al. (2021). Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis. Cell 184, 1281–1298.e26. doi:10.1016/j.cell.2021.01.022

PubMed Abstract | CrossRef Full Text | Google Scholar

Mccormick, D., Mcquaid, S., Mccusker, C., and Allen, I. V. (1990). A study of glutamine synthetase in normal human brain and intracranial tumours. Neuropathol. Appl. Neurobiol. 16, 205–211. doi:10.1111/j.1365-2990.1990.tb01157.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Miranda-Gonçalves, V., Honavar, M., Pinheiro, C., Martinho, O., Pires, M. M., Pinheiro, C., et al. (2013). Monocarboxylate transporters (MCTs) in gliomas: expression and exploitation as therapeutic targets. Neuro Oncol. 15, 172–188. doi:10.1093/neuonc/nos298

PubMed Abstract | CrossRef Full Text | Google Scholar

Miranda-Gonçalves, V., Gonçalves, C. S., Granja, S., Vieira De Castro, J., Reis, R. M., Costa, B. M., et al. (2021). MCT1 is a new prognostic biomarker and its therapeutic inhibition boosts response to temozolomide in human glioblastoma. Cancers (Basel) 13. doi:10.3390/cancers13143468

PubMed Abstract | CrossRef Full Text | Google Scholar

Miyai, M., Kanayama, T., Hyodo, F., Kinoshita, T., Ishihara, T., Okada, H., et al. (2021). Glucose transporter Glut1 controls diffuse invasion phenotype with perineuronal satellitosis in diffuse glioma microenvironment. Neurooncol Adv. 3, vdaa150. doi:10.1093/noajnl/vdaa150

PubMed Abstract | CrossRef Full Text | Google Scholar

Monaco, G., Khavaran, A., Gasull, A. D., Cahueau, J., Diebold, M., Chhatbar, C., et al. (2022). Transcriptome analysis identifies accumulation of natural killer cells with enhanced Lymphotoxin-β expression during glioblastoma progression. Cancers 14, 4915. doi:10.3390/cancers14194915

PubMed Abstract | CrossRef Full Text | Google Scholar

Morimoto, T., Nakazawa, T., Matsuda, R., Nishimura, F., Nakamura, M., Yamada, S., et al. (2021). CRISPR-Cas9-Mediated TIM3 knockout in human natural killer cells enhances growth inhibitory effects on human glioma cells. Int. J. Mol. Sci. 22, 3489. doi:10.3390/ijms22073489

PubMed Abstract | CrossRef Full Text | Google Scholar

Mu, M. L., Niu, W. X., Chu, F., Dong, Q. S., Hu, S. S., and Niu, C. S. (2023). CircSOBP suppresses the progression of glioma by disrupting glycolysis and promoting the MDA5-mediated immune response. Iscience 26, 107897. doi:10.1016/j.isci.2023.107897

PubMed Abstract | CrossRef Full Text | Google Scholar

Mullen, N. J., and Singh, P. K. (2023). Nucleotide metabolism: a pan-cancer metabolic dependency. Nat. Rev. Cancer 23, 275–294. doi:10.1038/s41568-023-00557-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Nafe, R., and Hattingen, E. (2024). Cellular components of the tumor environment in gliomas-what do we know today? Biomedicines 12, 14. doi:10.3390/biomedicines12010014

PubMed Abstract | CrossRef Full Text | Google Scholar

Nam, H., Campodonico, M., Bordbar, A., Hyduke, D. R., Kim, S., Zielinski, D. C., et al. (2014). A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks. PLoS Comput. Biol. 10, e1003837. doi:10.1371/journal.pcbi.1003837

PubMed Abstract | CrossRef Full Text | Google Scholar

Nejo, T., Krishna, S., Yamamichi, A., Lakshmanachetty, S., Jimenez, C., Lee, K. Y., et al. (2025). Glioma-neuronal circuit remodeling induces regional immunosuppression. Nat. Commun. 16, 4770. doi:10.1038/s41467-025-60074-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Nga, H., Nguyen, T. L., and Yi, H. S. (2024). T-Cell senescence in human metabolic diseases. Diabetes and Metabolism J. 48, 864–881. doi:10.4093/dmj.2024.0140

PubMed Abstract | CrossRef Full Text | Google Scholar

Nguyen, T. Q. T., and Cho, K. A. (2025). Targeting immunosenescence and inflammaging: advancing longevity research. Exp. and Mol. Med. 57, 1881–1892. doi:10.1038/s12276-025-01527-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Nguyen, T. T. T., Shang, E., Westhoff, M. A., Karpel-Massler, G., and Siegelin, M. D. (2022). Therapeutic drug-induced metabolic reprogramming in glioblastoma. Cells 11, 2956. doi:10.3390/cells11192956

PubMed Abstract | CrossRef Full Text | Google Scholar

Nikolich-Zugich, J. (2014). Aging of the T cell compartment in mice and humans: from no naive expectations to foggy memories. J. Immunol. 193, 2622–2629. doi:10.4049/jimmunol.1401174

PubMed Abstract | CrossRef Full Text | Google Scholar

Noch, E. K., Palma, L., Yim, I., Bullen, N., Barnett, D., Walsh, A., et al. (2024). Cysteine induces mitochondrial reductive stress in glioblastoma through hydrogen peroxide production. Proc. Natl. Acad. Sci. U. S. A. 121, e2317343121. doi:10.1073/pnas.2317343121

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

Norian, L. A., Rodriguez, P. C., O'mara, L. A., Zabaleta, J., Ochoa, A. C., Cella, M., et al. (2009). Tumor-infiltrating regulatory dendritic cells inhibit CD8+ T cell function via L-arginine metabolism. Cancer Res. 69, 3086–3094. doi:10.1158/0008-5472.CAN-08-2826

PubMed Abstract | CrossRef Full Text | Google Scholar

Notarangelo, G., Spinelli, J. B., Perez, E. M., Baker, G. J., Kurmi, K., Elia, I., et al. (2022). Oncometabolite d-2HG alters T cell metabolism to impair CD8(+) T cell function. Science 377, 1519–1529. doi:10.1126/science.abj5104

PubMed Abstract | CrossRef Full Text | Google Scholar

Obara-Michlewska, M., and Szeliga, M. (2020). Targeting glutamine addiction in gliomas. Cancers (Basel) 12. doi:10.3390/cancers12020310

PubMed Abstract | CrossRef Full Text | Google Scholar

Oldak, L., Basa, A., Milewska, P., Chludzinska-Kasperuk, S., Starosz, A., Grubczak, K., et al. (2024). Preliminary studies on changes in the amount of tryptophan metabolites in human glioma tissues. Anal. Chim. Acta 1327, 343149. doi:10.1016/j.aca.2024.343149

PubMed Abstract | CrossRef Full Text | Google Scholar

Ozawa, Y., Yamamuro, S., Sano, E., Tatsuoka, J., Hanashima, Y., Yoshimura, S., et al. (2020). Indoleamine 2,3-dioxygenase 1 is highly expressed in glioma stem cells. Biochem. Biophys. Res. Commun. 524, 723–729. doi:10.1016/j.bbrc.2020.01.148

PubMed Abstract | CrossRef Full Text | Google Scholar

Palanichamy, K., and Chakravarti, A. (2017). Diagnostic and prognostic significance of methionine uptake and methionine positron emission tomography imaging in gliomas. Front. Oncol. 7, 257. doi:10.3389/fonc.2017.00257

PubMed Abstract | CrossRef Full Text | Google Scholar

Palanichamy, K., Thirumoorthy, K., Kanji, S., Gordon, N., Singh, R., Jacob, J. R., et al. (2016). Methionine and Kynurenine activate oncogenic kinases in glioblastoma, and methionine deprivation compromises proliferation. Clin. Cancer Res. 22, 3513–3523. doi:10.1158/1078-0432.CCR-15-2308

PubMed Abstract | CrossRef Full Text | Google Scholar

Panosyan, E. H., Lin, H. J., Koster, J., and Lasky, J. L., 3rd (2017). In search of druggable targets for GBM amino acid metabolism. BMC Cancer 17, 162. doi:10.1186/s12885-017-3148-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Pant, A., Bergsneider, B. H. L., Srivastava, S., Kim, T., Jain, A., Bom, S., et al. (2024). CCR2 and CCR5 co-inhibition modulates immunosuppressive myeloid milieu in glioma and synergizes with anti-PD-1 therapy. Oncoimmunology 13, 2338965. doi:10.1080/2162402X.2024.2338965

PubMed Abstract | CrossRef Full Text | Google Scholar

Papavassiliou, K. A., and Papavassiliou, A. G. (2022). Transcription factors in glioblastoma - molecular pathogenesis and clinical implications. Biochim. Biophys. Acta Rev. Cancer 1877, 188667. doi:10.1016/j.bbcan.2021.188667

PubMed Abstract | CrossRef Full Text | Google Scholar

Parizi, M. S., Parizi, F. S., Abdolhosseini, S., Vanaei, S., Manzouri, A., and Ebrahimzadeh, F. (2021). Myeloid-derived suppressor cells (MDSCs) in brain cancer: challenges and therapeutic strategies. Inflammopharmacology 29, 1613–1624. doi:10.1007/s10787-021-00878-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Pavlova, N. N., and Thompson, C. B. (2016). The emerging hallmarks of cancer metabolism. Cell Metab. 23, 27–47. doi:10.1016/j.cmet.2015.12.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Pei, J., Li, C., Liu, Q., Miao, F. C., Zheng, Z., Sun, C., et al. (2025). LILRB4 in tumor-associated macrophage regulates macrophage polarization and glioblastoma progression via STAT3/IL10 axis. Gene 971, 149805. doi:10.1016/j.gene.2025.149805

PubMed Abstract | CrossRef Full Text | Google Scholar

Pistollato, F., Abbadi, S., Rampazzo, E., Viola, G., Della Puppa, A., Cavallini, L., et al. (2010). Hypoxia and succinate antagonize 2-deoxyglucose effects on glioblastoma. Biochem. Pharmacol. 80, 1517–1527. doi:10.1016/j.bcp.2010.08.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Poteet, E., Choudhury, G. R., Winters, A., Li, W., Ryou, M. G., Liu, R., et al. (2013). Reversing the Warburg effect as a treatment for glioblastoma. J. Biol. Chem. 288, 9153–9164. doi:10.1074/jbc.M112.440354

PubMed Abstract | CrossRef Full Text | Google Scholar

Pu, T. Y., Sun, J. Z., Ren, G. S., and Li, H. Z. (2025). Neuro-immune crosstalk in cancer: mechanisms and therapeutic implications. Signal Transduct. Target. Ther. 10, 176. doi:10.1038/s41392-025-02241-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Puca, A. A., Lopardo, V., Montella, F., Di Pietro, P., Cesselli, D., Rolle, I. G., et al. (2022). The longevity-associated variant of BPIFB4 reduces senescence in glioma cells and in patients’ lymphocytes favoring chemotherapy efficacy. Cells 11, 294. doi:10.3390/cells11020294

PubMed Abstract | CrossRef Full Text | Google Scholar

Qi, Y., Jin, C., Qiu, W., Zhao, R., Wang, S., Li, B., et al. (2022). The dual role of glioma exosomal microRNAs: glioma eliminates tumor suppressor miR-1298-5p via exosomes to promote immunosuppressive effects of MDSCs. Cell Death and Dis. 13, 426. doi:10.1038/s41419-022-04872-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Qiu, R. Z., Zhong, Y., Li, Q. Q., Li, Y. B., and Fan, H. W. (2021). Metabolic remodeling in glioma immune microenvironment: intercellular interactions distinct from peripheral tumors. Front. Cell Dev. Biol. 9, 693215. doi:10.3389/fcell.2021.693215

PubMed Abstract | CrossRef Full Text | Google Scholar

Qiu, W., Guo, X., Li, B., Wang, J., Qi, Y., Chen, Z., et al. (2021). Exosomal miR-1246 from glioma patient body fluids drives the differentiation and activation of myeloid-derived suppressor cells. Mol. Ther. 29, 3449–3464. doi:10.1016/j.ymthe.2021.06.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Qiu, Z., Li, Z., Zhang, C., Zhao, Q., Liu, Z., Cheng, Q., et al. (2025). NK cell senescence in cancer: from molecular mechanisms to therapeutic opportunities. Aging Disease. doi:10.14336/AD.2025.0053

PubMed Abstract | CrossRef Full Text | Google Scholar

Raizer, J. J., Abrey, L. E., Lassman, A. B., Chang, S. M., Lamborn, K. R., Kuhn, J. G., et al. (2010). A phase II trial of erlotinib in patients with recurrent malignant gliomas and nonprogressive glioblastoma multiforme postradiation therapy. Neuro Oncol. 12, 95–103. doi:10.1093/neuonc/nop015

PubMed Abstract | CrossRef Full Text | Google Scholar

Ransohoff, R. M. (2016). A polarizing question: do M1 and M2 microglia exist? Nat. Neurosci. 19, 987–991. doi:10.1038/nn.4338

PubMed Abstract | CrossRef Full Text | Google Scholar

Rasheed, M., and Tarjan, G. (2018). Succinate dehydrogenase complex: an updated review. Arch. Pathol. Lab. Med. 142, 1564–1570. doi:10.5858/arpa.2017-0285-RS

PubMed Abstract | CrossRef Full Text | Google Scholar

Read, R. D., Tapp, Z. M., Rajappa, P., and Hambardzumyan, D. (2024). Glioblastoma microenvironment-from biology to therapy. Genes and Dev. 38, 360–379. doi:10.1101/gad.351427.123

PubMed Abstract | CrossRef Full Text | Google Scholar

Reckzeh, E. S., Karageorgis, G., Schwalfenberg, M., Ceballos, J., Nowacki, J., Stroet, M. C. M., et al. (2019). Inhibition of glucose transporters and glutaminase synergistically impairs tumor cell growth. Cell Chem. Biol. 26, 1214–1228.e1225. doi:10.1016/j.chembiol.2019.06.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Reiter-Brennan, C., Semmler, L., and Klein, A. (2018). The effects of 2-hydroxyglutarate on the tumorigenesis of gliomas. Contemp. Oncol. Pozn. 22, 215–222. doi:10.5114/wo.2018.82642

PubMed Abstract | CrossRef Full Text | Google Scholar

Riess, C., Del Moral, K., Fiebig, A., Kaps, P., Linke, C., Hinz, B., et al. (2022). Implementation of a combined CDK inhibition and arginine-deprivation approach to target arginine-auxotrophic glioblastoma multiforme cells. Cell Death Dis. 13, 555. doi:10.1038/s41419-022-05006-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Riyas Mohamed, F. R., and Yaqinuddin, A. (2025). Epigenetic reprogramming and antitumor immune responses in gliomas: a systematic review. Med. Oncol. 42, 213. doi:10.1007/s12032-025-02760-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Rodríguez-García, A., Samsó, P., Fontova, P., Simon-Molas, H., Manzano, A., Castaño, E., et al. (2017). TGF-β1 targets Smad, p38 MAPK, and PI3K/Akt signaling pathways to induce PFKFB3 gene expression and glycolysis in glioblastoma cells. Febs J. 284, 3437–3454. doi:10.1111/febs.14201

PubMed Abstract | CrossRef Full Text | Google Scholar

Romani, P., Nirchio, N., Arboit, M., Barbieri, V., Tosi, A., Michielin, F., et al. (2022). Mitochondrial fission links ECM mechanotransduction to metabolic redox homeostasis and metastatic chemotherapy resistance. Nat. Cell Biol. 24, 168–180. doi:10.1038/s41556-022-00843-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Rousseau, L., Hajdu, K. L., and Ping-Chih, H. (2025). Meta-epigenetic shifts in T cell aging and aging-related dysfunction. J. Biomed. Sci. 32, 1–13. doi:10.1186/s12929-025-01146-6

CrossRef Full Text | Google Scholar

Salam, R., Saliou, A., Bielle, F., Bertrand, M., Antoniewski, C., Carpentier, C., et al. (2023). Cellular senescence in malignant cells promotes tumor progression in mouse and patient glioblastoma. Nat. Commun. 14, 441. doi:10.1038/s41467-023-36124-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Scott, A. J., Lyssiotis, C. A., and Wahl, D. R. (2021). Clinical targeting of altered metabolism in high-grade glioma. Cancer J. 27, 386–394. doi:10.1097/PPO.0000000000000550

PubMed Abstract | CrossRef Full Text | Google Scholar

Seibt, T. M., Proneth, B., and Conrad, M. (2019). Role of GPX4 in ferroptosis and its pharmacological implication. Free Radic. Biol. Med. 133, 144–152. doi:10.1016/j.freeradbiomed.2018.09.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Shah, D., Comba, A., Faisal, S. M., Kadiyala, P., Baker, G. J., Alghamri, M. S., et al. (2021). A novel miR1983-TLR7-IFNβ circuit licenses NK cells to kill glioma cells, and is under the control of galectin-1. Oncoimmunology 10, 1939601. doi:10.1080/2162402X.2021.1939601

PubMed Abstract | CrossRef Full Text | Google Scholar

Shen, W. H., Huang, Y. Y., Yang, X. P., Zhang, Y. T., Pan, Y. Y., Xiao, Y., et al. (2025). Senescent macrophages in tumor: phenotypes, roles, and interventions. Cell Death and Dis. 16, 677. doi:10.1038/s41419-025-08000-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Shireman, J. M., Gonugunta, N., Zhao, L., Pattnaik, A., Distler, E., Her, S., et al. (2023). GM-CSF and IL-7 fusion cytokine engineered tumor vaccine generates long-term Th-17 memory cells and increases overall survival in aged syngeneic mouse models of glioblastoma. Aging Cell 22, e13864. doi:10.1111/acel.13864

PubMed Abstract | CrossRef Full Text | Google Scholar

Smolag, K. I., Olszowka, J., Rosberg, R., Johansson, E., Marinko, E., Leandersson, K., et al. (2025). Complement factor H is an ICOS ligand modulating tregs in the glioma microenvironment. Cancer Immunol. Res. 13, 122–138. doi:10.1158/2326-6066.CIR-23-1092

PubMed Abstract | CrossRef Full Text | Google Scholar

Song, K.-W., Lim, M., and Monje, M. (2025). Complex neural-immune interactions shape glioma immunotherapy. Immunity 58, 1140–1160. doi:10.1016/j.immuni.2025.04.017

PubMed Abstract | CrossRef Full Text | Google Scholar

Spina, R., Mills, I., Ahmad, F., Chen, C., Ames, H. M., Winkles, J. A., et al. (2022). DHODH inhibition impedes glioma stem cell proliferation, induces DNA damage, and prolongs survival in orthotopic glioblastoma xenografts. Oncogene 41, 5361–5372. doi:10.1038/s41388-022-02517-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Srivastava, S., Anbiaee, R., Houshyari, M., Sridhar, S. B., Ashique, S., Hussain, S., et al. (2025). Amino acid metabolism in glioblastoma pathogenesis, immune evasion, and treatment resistance. Cancer Cell Int. 25, 89. doi:10.1186/s12935-025-03721-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Strickland, M., and Stoll, E. A. (2017). Metabolic reprogramming in glioma. Front. Cell Dev. Biol. 5, 43. doi:10.3389/fcell.2017.00043

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, S., Xue, D., Chen, Z., Ou-Yang, Y., Zhang, J., Mai, J., et al. (2019). R406 elicits anti-warburg effect via Syk-dependent and -independent mechanisms to trigger apoptosis in glioma stem cells. Cell Death Dis. 10, 358. doi:10.1038/s41419-019-1587-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Taïb, B., Aboussalah, A. M., Moniruzzaman, M., Chen, S., Haughey, N. J., Kim, S. F., et al. (2019). Lipid accumulation and oxidation in glioblastoma multiforme. Sci. Rep. 9, 19593. doi:10.1038/s41598-019-55985-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Takacs, G. P., Garcia, J. S., Hodges, C. A., Kreiger, C. J., Sherman, A., and Harrison, J. K. (2024). CSF1R ligands expressed by murine gliomas promote M-MDSCs to suppress CD8(+) T cells in a NOS-dependent manner. Cancers (Basel) 16, 3055. doi:10.3390/cancers16173055

PubMed Abstract | CrossRef Full Text | Google Scholar

Tam, C. W. L., and Yam, J. W. P. (2025). Harnessing the potential of small extracellular vesicle biomarkers for cancer diagnosis and prognosis with advanced analytical technologies. J. Transl. Intern. Med. 13, 187–200. doi:10.1515/jtim-2025-0019

PubMed Abstract | CrossRef Full Text | Google Scholar

Tanizaki, Y., Sato, Y., Oka, H., Utsuki, S., Kondo, K., Miyajima, Y., et al. (2006). Expression of autocrine motility factor mRNA is a poor prognostic factor in high-grade astrocytoma. Pathol. Int. 56, 510–515. doi:10.1111/j.1440-1827.2006.01999.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Tao, B. B., He, H., Shi, X. H., Wang, C. L., Li, W. Q., Li, B., et al. (2013). Up-regulation of USP2a and FASN in gliomas correlates strongly with glioma grade. J. Clin. Neurosci. 20, 717–720. doi:10.1016/j.jocn.2012.03.050

PubMed Abstract | CrossRef Full Text | Google Scholar

Tao, J. C., Yu, D., Shao, W., Zhou, D. R., Wang, Y., Hou, S. Q., et al. (2023). Interactions between microglia and glioma in tumor microenvironment. Front. Oncol. 13, 1236268. doi:10.3389/fonc.2023.1236268

PubMed Abstract | CrossRef Full Text | Google Scholar

Tejera, D., Kushnirsky, M., Gultekin, S. H., Lu, M., Steelman, L., and De La Fuente, M. I. (2020). Ivosidenib, an IDH1 inhibitor, in a patient with recurrent, IDH1-mutant glioblastoma: a case report from a phase I study. CNS Oncol. 9, Cns62. doi:10.2217/cns-2020-0014

PubMed Abstract | CrossRef Full Text | Google Scholar

Tong, N., He, Z. Q., Ma, Y. J., Wang, Z., Huang, Z. M., Cao, H. H., et al. (2021). Tumor associated macrophages, as the dominant immune cells, are an indispensable target for immunologically cold tumor-glioma therapy? Front. Cell Dev. Biol. 9, 17. doi:10.3389/fcell.2021.706286

PubMed Abstract | CrossRef Full Text | Google Scholar

Torrisi, F., D’aprile, S., Denaro, S., Pavone, A. M., Alberghina, C., Zappalà, A., et al. (2023). Epigenetics and metabolism reprogramming interplay into glioblastoma: novel insights on immunosuppressive mechanisms. Antioxidants 12, 220. doi:10.3390/antiox12020220

PubMed Abstract | CrossRef Full Text | Google Scholar

Trachootham, D., Alexandre, J., and Huang, P. (2009). Targeting cancer cells by ROS-mediated mechanisms: a radical therapeutic approach? Nat. Rev. Drug Discov. 8, 579–591. doi:10.1038/nrd2803

PubMed Abstract | CrossRef Full Text | Google Scholar

Trejo-Solis, C., Silva-Adaya, D., Serrano-García, N., Magaña-Maldonado, R., Jimenez-Farfan, D., Ferreira-Guerrero, E., et al. (2023). Role of glycolytic and glutamine metabolism reprogramming on the proliferation, invasion, and apoptosis resistance through modulation of signaling pathways in glioblastoma. Int. J. Mol. Sci. 24. doi:10.3390/ijms242417633

PubMed Abstract | CrossRef Full Text | Google Scholar

Trejo-Solís, C., Serrano-García, N., Castillo-Rodríguez, R. A., Robledo-Cadena, D. X., Jimenez-Farfan, D., Marín-Hernández, Á., et al. (2024). Metabolic dysregulation of tricarboxylic acid cycle and oxidative phosphorylation in glioblastoma. Rev. Neurosci. 35, 813–838. doi:10.1515/revneuro-2024-0054

PubMed Abstract | CrossRef Full Text | Google Scholar

Tsubokawa, M., Shimizu, Y., Yazaki, M., Shimodan, S., Noguchi, M., Yamazaki, A., et al. (2025). Correlation between the proportion of senescence-associated β-Galactosidase-Stained CD8+T cells and age: a cross-sectional study in Japan. Int. J. Mol. Sci. 26, 8799. doi:10.3390/ijms26188799

PubMed Abstract | CrossRef Full Text | Google Scholar

Tu, J. J., Fang, Y. L., Han, D. F., Tan, X. W., Xu, Z., Jiang, H. F., et al. (2022). MicroRNA-22 represses glioma development via activation of macrophage-mediated innate and adaptive immune responses. Oncogene 41, 2444–2457. doi:10.1038/s41388-022-02236-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Tu, L. Q., Wang, Z., Yang, L., Sun, X. M., Yao, Y. P., Zhang, P., et al. (2023). Incorporation of a TGF-β2-inhibiting oligodeoxynucleotide molecular adjuvant into a tumor cell lysate vaccine to enhance antiglioma immunity in mice. Front. Immunol. 14, 1013342. doi:10.3389/fimmu.2023.1013342

PubMed Abstract | CrossRef Full Text | Google Scholar

Upadhyayula, P. S., Higgins, D. M., Mela, A., Banu, M., Dovas, A., Zandkarimi, F., et al. (2023). Dietary restriction of cysteine and methionine sensitizes gliomas to ferroptosis and induces alterations in energetic metabolism. Nat. Commun. 14, 1187. doi:10.1038/s41467-023-36630-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Van Lith, S. a.M., Navis, A. C., Lenting, K., Verrijp, K., Schepens, J. T. G., Hendriks, W. J. J., et al. (2016). Identification of a novel inactivating mutation in isocitrate dehydrogenase 1 (IDH1-R314C) in a high grade astrocytoma. Sci. Rep. 6, 30486. doi:10.1038/srep30486

PubMed Abstract | CrossRef Full Text | Google Scholar

Venneti, S., and Thompson, C. B. (2017). Metabolic reprogramming in brain tumors. Annu. Rev. Pathol. 12, 515–545. doi:10.1146/annurev-pathol-012615-044329

PubMed Abstract | CrossRef Full Text | Google Scholar

Venneti, S., Dunphy, M. P., Zhang, H., Pitter, K. L., Zanzonico, P., Campos, C., et al. (2015). Glutamine-based PET imaging facilitates enhanced metabolic evaluation of gliomas in vivo. Sci. Transl. Med. 7, 274ra217. doi:10.1126/scitranslmed.aaa1009

PubMed Abstract | CrossRef Full Text | Google Scholar

Vijayanathan, Y., and Ho, I. a.W. (2025). The impact of metabolic rewiring in glioblastoma: the immune landscape and therapeutic strategies. Int. J. Mol. Sci. 26, 669. doi:10.3390/ijms26020669

PubMed Abstract | CrossRef Full Text | Google Scholar

Vincze, S. R., Jaswal, A. P., Frederico, S. C., Nisnboym, M., Li, B., Xiong, Z. J., et al. (2024). ImmunoPET imaging of TIGIT in the glioma microenvironment. Sci. Rep. 14, 5305. doi:10.1038/s41598-024-55296-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Virtuoso, A., Giovannoni, R., De Luca, C., Gargano, F., Cerasuolo, M., Maggio, N., et al. (2021). The glioblastoma microenvironment: morphology, metabolism, and molecular signature of glial dynamics to discover metabolic rewiring sequence. Int. J. Mol. Sci. 22, 3301. doi:10.3390/ijms22073301

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, J., and Yan, S. (2025). Integration of histone modification-based risk signature with drug sensitivity analysis reveals novel therapeutic strategies for lower-grade glioma. Front. Pharmacol. Volume, 15–2024. doi:10.3389/fphar.2024.1523779

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, W., Cui, J., Ma, H., Lu, W., and Huang, J. (2021). Targeting pyrimidine metabolism in the era of precision cancer medicine. Front. Oncol. 11, 684961. doi:10.3389/fonc.2021.684961

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Y. H., Zhang, F. C., Xiong, N. X., Xu, H., Chai, S. S., Wang, H. F., et al. (2021). Remodelling and treatment of the blood-brain barrier in glioma. Cancer Manag. Res. 13, 4217–4232. doi:10.2147/CMAR.S288720

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, L. J., Xue, Y., and Lou, Y. (2022). Tumor-associated macrophages related signature in glioma. Aging (Albany NY) 14, 2720–2735. doi:10.18632/aging.203968

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Q., Wu, M., Li, H., Rao, X., Ao, L., Wang, H., et al. (2022). Therapeutic targeting of glutamate dehydrogenase 1 that links metabolic reprogramming and Snail-mediated epithelial-mesenchymal transition in drug-resistant lung cancer. Pharmacol. Res. 185, 106490. doi:10.1016/j.phrs.2022.106490

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, W., Guo, M. N., Li, N., Pang, D. Q., and Wu, J. H. (2022). Glutamine deprivation impairs function of infiltrating CD8(+) T cells in hepatocellular carcinoma by inducing mitochondrial damage and apoptosis. World J. Gastrointest. Oncol. 14, 1124–1140. doi:10.4251/wjgo.v14.i6.1124

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Z., Dai, Z., Zhang, H., Liang, X., Zhang, X., Wen, Z., et al. (2023). Tumor-secreted lactate contributes to an immunosuppressive microenvironment and affects CD8 T-cell infiltration in glioblastoma. Front. Immunol. 14, 894853. doi:10.3389/fimmu.2023.894853

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, F., Liao, W., Li, C., and Zhu, L. (2024). Silencing BMAL1 promotes M1/M2 polarization through the LDHA/lactate axis to promote GBM sensitivity to bevacizumab. Int. Immunopharmacol. 134, 112187. doi:10.1016/j.intimp.2024.112187

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Y., An, J., Zhou, J., Chang, L., Zhang, Q., and Peng, F. (2025). Hydroxysafflor yellow A: a natural pigment with potential anticancer therapeutic effect. Front. Pharmacol. 15, 1495393. doi:10.3389/fphar.2024.1495393

PubMed Abstract | CrossRef Full Text | Google Scholar

Weller, M., Wen, P. Y., Chang, S. M., Dirven, L., Lim, M., Monje, M., et al. (2024). Glioma. Nat. Rev. Dis. Prim. 10, 33. doi:10.1038/s41572-024-00516-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Wolf, A., Agnihotri, S., Micallef, J., Mukherjee, J., Sabha, N., Cairns, R., et al. (2011a). Hexokinase 2 is a key mediator of aerobic glycolysis and promotes tumor growth in human glioblastoma multiforme. J. Exp. Med. 208, 313–326. doi:10.1084/jem.20101470

PubMed Abstract | CrossRef Full Text | Google Scholar

Wolf, A., Agnihotri, S., Munoz, D., and Guha, A. (2011b). Developmental profile and regulation of the glycolytic enzyme hexokinase 2 in normal brain and glioblastoma multiforme. Neurobiol. Dis. 44, 84–91. doi:10.1016/j.nbd.2011.06.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, S. A., Li, X. W., Hong, F., Chen, Q., Yu, Y. Y., Guo, S. H., et al. (2023). Integrative analysis of single-cell transcriptomics reveals age-associated immune landscape of glioblastoma. Front. Immunol. 14, 1028775. doi:10.3389/fimmu.2023.1028775

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, H., Liu, Q., Wang, F. L., Gao, W. W., Zhou, F., and Zhao, H. K. (2025). Research progress of NK cells in glioblastoma treatment. Oncotargets Ther. 18, 87–106. doi:10.2147/OTT.S486411

PubMed Abstract | CrossRef Full Text | Google Scholar

Xia, L., Oyang, L., Lin, J., Tan, S., Han, Y., Wu, N., et al. (2021). The cancer metabolic reprogramming and immune response. Mol. Cancer 20, 28. doi:10.1186/s12943-021-01316-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Xiao, J., Qin, X., Chen, W., Que, X., Ma, Y., Huang, W., et al. (2025). Immunoinflammation and post-translational modifications in the aging process. J. Transl. Med. 23, 910. doi:10.1186/s12967-025-06892-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Xu, W., Yang, H., Liu, Y., Yang, Y., Wang, P., Kim, S. H., et al. (2011). Oncometabolite 2-hydroxyglutarate is a competitive inhibitor of α-ketoglutarate-dependent dioxygenases. Cancer Cell 19, 17–30. doi:10.1016/j.ccr.2010.12.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Xu, S. C., Tang, L., Li, X. Z., Fan, F., and Liu, Z. X. (2020). Immunotherapy for glioma: current management and future application. Cancer Lett. 476, 1–12. doi:10.1016/j.canlet.2020.02.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Xu, H., Feng, Y., Kong, W., Wang, H., Feng, Y., Zhen, J., et al. (2022). High expression levels of SIGLEC9 indicate poor outcomes of glioma and correlate with immune cell infiltration. Front. Oncol. 12, 878849. doi:10.3389/fonc.2022.878849

PubMed Abstract | CrossRef Full Text | Google Scholar

Yamashita, A. S., Da Costa Rosa, M., Stumpo, V., Rais, R., Slusher, B. S., and Riggins, G. J. (2021). The glutamine antagonist prodrug JHU-083 slows malignant glioma growth and disrupts mTOR signaling. Neurooncol Adv. 3, vdaa149. doi:10.1093/noajnl/vdaa149

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, W. S., and Stockwell, B. R. (2016). Ferroptosis: death by lipid peroxidation. Trends Cell Biol. 26, 165–176. doi:10.1016/j.tcb.2015.10.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, F., He, Z., Duan, H., Zhang, D., Li, J., Yang, H., et al. (2021). Synergistic immunotherapy of glioblastoma by dual targeting of IL-6 and CD40. Nat. Commun. 12, 3424. doi:10.1038/s41467-021-23832-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, L., Li, S., Yu, L., Leng, J., and Li, N. (2024). Targeting glycolysis: exploring a new frontier in glioblastoma therapy. Front. Immunol. 15, 1522392. doi:10.3389/fimmu.2024.1522392

PubMed Abstract | CrossRef Full Text | Google Scholar

Yanysheva, E. P., Melnikov, P. A., Chudakova, D. A., Shirmanova, M. V., Baklaushev, V. P., and Yusubalieva, G. M. (2025). Migration of regulatory T cells to the peritumor microenvironment of experimental glioblastoma. Sovrem. Tehnol. V. Med. 17, 70–78. doi:10.17691/stm2025.17.1.07

PubMed Abstract | CrossRef Full Text | Google Scholar

Ye, L., Lv, W., He, W. C., Li, S. N., Min, Z. Y., Gong, L., et al. (2023). Reduced malignant glioblastoma recurrence post-resection through the anti-CD47 antibody and temozolomide co-embedded in-situ hydrogel system. J. Control. Release 359, 224–233. doi:10.1016/j.jconrel.2023.05.046

PubMed Abstract | CrossRef Full Text | Google Scholar

Ye, Z., Ai, X., Yang, K., Yang, Z., Fei, F., Liao, X., et al. (2023). Targeting microglial metabolic rewiring synergizes with immune-checkpoint blockade therapy for glioblastoma. Cancer Discov. 13, 974–1001. doi:10.1158/2159-8290.CD-22-0455

PubMed Abstract | CrossRef Full Text | Google Scholar

Yeo, A. T., Rawal, S., Delcuze, B., Christofides, A., Atayde, A., Strauss, L., et al. (2022). Single-cell RNA sequencing reveals evolution of immune landscape during glioblastoma progression. Nat. Immunol. 23, 971–984. doi:10.1038/s41590-022-01215-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Yu, L., Chen, X., Sun, X., Wang, L., and Chen, S. (2017). The glycolytic switch in tumors: how many players are involved? J. Cancer 8, 3430–3440. doi:10.7150/jca.21125

PubMed Abstract | CrossRef Full Text | Google Scholar

Zaidi, N., Swinnen, J. V., and Smans, K. (2012). ATP-citrate lyase: a key player in cancer metabolism. Cancer Res. 72, 3709–3714. doi:10.1158/0008-5472.CAN-11-4112

PubMed Abstract | CrossRef Full Text | Google Scholar

Zeitler, L., and Murray, P. J. (2023). IL4i1 and IDO1: oxidases that control a tryptophan metabolic nexus in cancer. J. Biol. Chem. 299, 104827. doi:10.1016/j.jbc.2023.104827

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhai, L., Bell, A., Ladomersky, E., Lauing, K. L., Bollu, L., Nguyen, B., et al. (2021). Tumor cell IDO enhances immune suppression and decreases survival independent of tryptophan metabolism in glioblastoma. Clin. Cancer Res. 27, 6514–6528. doi:10.1158/1078-0432.CCR-21-1392

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, J., Xue, W., Xu, K., Yi, L., Guo, Y., Xie, T., et al. (2020). Dual inhibition of PFKFB3 and VEGF normalizes tumor vasculature, reduces lactate production, and improves chemotherapy in glioblastoma: insights from protein expression profiling and MRI. Theranostics 10, 7245–7259. doi:10.7150/thno.44427

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, X., Liu, Y., Dai, L., Shi, G., Deng, J., Luo, Q., et al. (2021). BATF2 prevents glioblastoma multiforme progression by inhibiting recruitment of myeloid-derived suppressor cells. Oncogene 40, 1516–1530. doi:10.1038/s41388-020-01627-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, Z., Li, X., Yang, F., Chen, C., Liu, P., Ren, Y., et al. (2021). DHHC9-mediated GLUT1 S-palmitoylation promotes glioblastoma glycolysis and tumorigenesis. Nat. Commun. 12, 5872. doi:10.1038/s41467-021-26180-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, D., Li, A. M., Hu, G., Huang, M., Yang, F., Zhang, L., et al. (2023). PHGDH-mediated endothelial metabolism drives glioblastoma resistance to chimeric antigen receptor T cell immunotherapy. Cell Metab. 35, 517–534.e518. doi:10.1016/j.cmet.2023.01.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, Z., Shen, X. L., Tan, Z. L., Mei, Y. R., Lu, T. Z., Ji, Y. L., et al. (2023). Interferon gamma-related gene signature based on anti-tumor immunity predicts glioma patient prognosis. Front. Genet. 13, 1053263. doi:10.3389/fgene.2022.1053263

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, J. Y., Guan, X. J., and Zhong, X. W. (2024). Immunosenescence in digestive system cancers: mechanisms, research advances, and therapeutic strategies. Seminars Cancer Biol. 106, 234–250. doi:10.1016/j.semcancer.2024.10.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, Q., Tian, Q. S., Deng, R. Z., Liu, K. L., Chen, S. Q., Hu, S. F., et al. (2025). Inhibition of NFE2L1 enables the tumor-associated macrophage polarization and enhances Anti-PD1 immunotherapy in Glioma. Cns Neurosci. and Ther. 31, e70488. doi:10.1111/cns.70488

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, S. D., Zhang, L., Guan, N., Feng, X., Lu, M. M., and Wang, Y. Y. (2025). Unveiling the role of TAM-Derived extracellular vesicles in glioma progression through Treg polarization and immune suppression. Oncogene 44, 3364–3385. doi:10.1038/s41388-025-03497-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhao, Y., Zhan, J.-K., and Liu, Y. (2020). A perspective on roles played by immunosenescence in the pathobiology of Alzheimer's disease. Aging Disease 11, 1594–1607. doi:10.14336/AD.2020.0205

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhao, W. W., Zhang, Z., Xie, M. Y., Ding, F., Zheng, X. R., Sun, S. C., et al. (2025). Exploring tumor-associated macrophages in glioblastoma: from diversity to therapy. Npj Precis. Oncol. 9, 126. doi:10.1038/s41698-025-00920-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhong, C., Peng, L., Tao, B., Yin, S., Lyu, L., Ding, H., et al. (2022). TDO2 and tryptophan metabolites promote kynurenine/AhR signals to facilitate glioma progression and immunosuppression. Am. J. Cancer Res. 12, 2558–2575.

PubMed Abstract | Google Scholar

Zhu, J., Shen, K., Su, Q., Yao, G. Z., and Chen, X. C. (2025). Metabolic reprogramming and immunosenescence in colorectal cancer: mechanisms and therapeutic implications. Front. Cell Dev. Biol. 13, 1662464. doi:10.3389/fcell.2025.1662464

PubMed Abstract | CrossRef Full Text | Google Scholar

Zou, X. Y., Ye, W. H., Wu, H., Yang, Y. Y., Liu, C. Q., Wen, H. B., et al. (2025). Immune evasion mechanism mediated by ITPRIPL1 and its prognostic implications in glioma. Brain Behav. 15. doi:10.1002/brb3.70762

CrossRef Full Text | Google Scholar