A pancancer analysis reveals the oncogenic role of glutaminase 1 (GLS1) in tumor metabolism and immune evasion: a bioinformatics analysis

Abstract

Background

Metabolic reprogramming is a significant factor that regulates the function and differentiation of immune cells, thereby influencing the progress of the immune response. The intricate interplay between glutamine metabolism and the immune microenvironment (TME) plays crucial roles in the pathogenesis of cancer. The initiation of glutamine metabolism is facilitated by the enzyme GLS1; however, its oncogenic role remains unclear.

Methods

In this pancancer analysis, we aimed to investigate the potential oncogenic mechanism of GLS1 across various tumor types in The Cancer Genome Atlas (TCGA) dataset.

Results

Our results revealed variable expression levels of GLS1 across different cancer types. Notably, higher expression levels of GLS1 were associated with worse prognosis in patients with kidney renal papillary cell carcinoma (KIRP, HR = 1.01; p < 0.01), liver hepatocellular carcinoma (LIHC, HR = 1.02; p < 0.01), and mesothelioma (MESO, HR = 1.01; p < 0.01). Additionally, we observed distinct associations between GLS1 expression levels and tumor methylation levels, tumor mutation burden (TMB), microsatellite instability (MSI), immune cell infiltration (IFL), and immune scores in several tumor types. We also detected the enrichment of GLS1 in key pathways, such as the mTOR, JAK, and KRAS pathways. Furthermore, we observed associations between GLS1 expression levels and a wide range of immune checkpoint genes, including both immunoinhibitors and immunostimulators, in most tumors.

Conclusions

Our study provides initial insights into the oncogenic roles of GLS1 across different tumors. These findings suggest that GLS1 can serve as a potential biomarker for determining prognosis and designing therapeutic strategies for various tumor types.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03779-3.

Introduction

Metabolic reprogramming is a hallmark of tumor cells, and targeting metabolism holds promise as a therapeutic strategy [1]. Deprivation of specific nutrients induces metabolic reprogramming in cancer cells, enabling sustained proliferation and immune evasion. However, immune cells within the competing nutrient microenvironment face challenges in sustaining anticancer function [2]. Metabolic reprogramming in tumor cells can exacerbate nutrient deficiencies in the TME, which may significantly compromise the anticancer immunity of immune cells [3]. For example, a lack of energy is widely recognized as one of the most important factors limiting the anticancer function of immune cells [4–6]. Currently, accumulating evidence strongly suggests that the function of activated T-cell lymphocytes is highly reliant on cellular energy metabolism [5, 7]. As an example, newly activated effector T cells increase glucose uptake and engage in aerobic glycolysis, facilitating nucleotide synthesis to support rapid proliferation and adenosine triphosphate (ATP) production. This metabolic reprogramming also alleviates translational inhibition to promote cytokine production [8, 9].

Researchers have reported that glutamine serves as an essential nutrient for cells, regardless of whether they are tumor cells or immune cells [10, 11]. The deprivation of glutamine can lead to rapid death in certain cancer cells, which is referred to as the “glutamine addiction” phenomenon [12]. Interestingly, accumulating evidence has demonstrated that intermediary metabolites in cancer cells can regulate various cancer-related phenotypes [13]. For example, α-ketoglutarate (α-KG) is a metabolite of GLS1. α-KG not only contributes to ATP production through the tricarboxylic acid (TCA) cycle but also directly interacts with the demethylase machinery, thereby promoting epigenetic modulation [7]. Numerous studies have provided evidence that increased GLS1 expression is associated with a poor prognosis in patients with tumors [14, 15]. For example, glutamine metabolism can enhance cancer progression by acting on the transcription factor STAT3, which is necessary and sufficient for mediating the proliferative effects of cancer cells [16]. Furthermore, the metabolites produced by GLS1 can also regulate the immune response to tumor immunotherapy [17].

In a recent study, Zou et al. developed a polygenic risk estimation model that links glutamine metabolism to various genomic and immunological characteristics [18]. Daemen A et al. established multiple gene models to predict the efficacy of GLS1 inhibitors in treating patient-derived xenografts of lung cancer [19]. However, few studies have investigated the role of glutamine metabolism in the regulation of the immune microenvironment and epigenetics. In fact, the oncogenic role of GLS1 remains unclear. In this bioinformatic study, we aimed to investigate the expression and alteration status of GLS1, along with its associated cellular pathways, immune status, tumor methylation level, and prognostic correlations across various types of cancer.

Materials and methods

Gene expression data

The normal tissue expression data used in this study were downloaded from the Genotype Tissue Expression project (GTEx, Version 8, released on Sept. 11, 2020, https://www.gtexportal.org/). The GTEx project, which is the largest collection of normal tissue expression data, gathered samples from 54 normal healthy tissue sites from over 1,000 individuals. The GLS1 expression data of 21 types of cancer cell lines were obtained from the Cancer Cell Line Encyclopedia (CCLE, https://sites.broadinstitute.org/ccle/). The GLS1 expression data for various types of cancer and their matched normal tissues were taken from The Cancer Genome Atlas (TCGA) project (http://tcga-data.nci.nih.gov/tcga/).

To determine significant differences in GLS1 expression, we defined an expression difference of more than 1.5-fold with a p value less than 0.001 as significant. For samples without normal tissue or with only a small amount of normal tissue, we used the “Expression Analysis-Box Plots” module from the Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2) web server (http://gepia2.cancer-pku.cn/) to observe the expression differences between these tumor tissues and the corresponding normal tissues from the GTEx project.

Genetic alteration analysis

The genetic alteration information was obtained from the pancancer atlas study module of cBioPortal (https://www.cbioportal.org/). In the present study, the alteration frequency, copy number alteration (CNA), mutation type, and mutation site of GLS1 were analyzed across all tumors in TCGA. The “Comparison” module of the cBioPortal observer revealed the difference in survival between wild-type and mutated GLS1; however, no difference was detected because of the low frequency of GLS1 mutations in the TCGA data.

Protein expression data

The expression analysis of the GLS1 protein was conducted using the Cancer Data Analysis Portal at the University of Alabama at Birmingham (UALCAN) (http://ualcan.path.uab.edu/analysis-prot.html). UALCAN is an interactive web resource that allows for the analysis of total protein and phosphoprotein expression using the Clinical Proteomic Tumor Analysis Consortium dataset (CPTAC). However, since the data for phosphorylated GLS1 were not available in the current version of the UALCAN portal, the analysis of phosphoprotein expression could not be performed in this study.

Survival analysis

The correlations between GLS1 expression and survival across different types of cancers were analyzed via the “Survival Map” module of GEPIA2. The high-expression and low-expression cohorts were determined on the basis of a gene expression threshold (cutoff set at 50%). Additionally, median survival was estimated by the Kaplan‒Meier method, and the hazard ratio (HR) along with 95% confidence intervals (CIs) and log-rank P values were calculated.

Immune analysis

Tumor Immune Estimation Resource 2 (TIMER2) is a comprehensive resource that is specifically designed for the systematic analysis of immune cell infiltration (IFL) within tumors. We used the “immune-gene” module of the TIMER2 web server to explore the associations between GLS1 expression and IFLs in different types of tumors. The correlation between GLS1 expression and the abundance of IFLs, including CD4 + T cells, CD8 + T cells, B cells, dendritic cells, neutrophils, and macrophages, was assessed via gene modules. The associations between GLS1 expression and other immune characteristics, such as chemokines and receptors, were examined by analyzing all types of cancer data from the TCGA through the TISIDB web server (an integrated repository portal for tumor–immune system interactions) available at http://cis.hku.hk/TISIDB.

Previous studies have demonstrated that the ImmuneScore, StromalScore, and ESTIMATEScore are significantly related to the outcomes of several tumors [20, 21]. We therefore examined the correlations between the expression levels of GLS1 and the ImmuneScore, StromalScore, and ESTIMATEScore across all TCGA tumors.

Immune checkpoint inhibitors (ICIs) have been recognized as a groundbreaking advancement in immunotherapy; therefore, we also utilized the TCGA database to investigate the relationship between the expression of GLS1 and several commonly encountered immune checkpoints in our current study.

DNA methylation analysis and gene enrichment analysis

The potential effect of α-KG as a substrate for glutamine decomposition by GLS1 on tumor DNA methylation was investigated. We examined the correlation between GLS1 expression and the expression of four DNA methyltransferases (DNMT1, DNMT2, DNMT3A, and DNMT3B) across various types of tumors. Additionally, the correlations between GLS1 expression and tumor neoantigen, TMB, MSI, and mismatch repair-related genes (MLH1, MSH2, MSH6, PMS2, and EPCAM) were also calculated.

Gene set enrichment analysis (GSEA) was conducted using GSEA 3.0 (http://www.broadinstitute.org/gsea/). The top 100 GLS1-related target genes were obtained from the TCGA dataset via a similar gene detection module to that of GEPIA2. These data were then input into GSEA 3.0 to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Hallmark pathway analysis.

Results

Gene expression

On the basis of the latest version of the GTEx project (Version 8, released on September 11, 2020), we conducted an initial analysis of GLS1 expression levels in various normal tissues. As shown in Supplemental Fig. 1A, GLS1 is expressed in nearly all normal tissues. Specifically, GLS1 expression is highest in blood vessel wall, adrenal gland, kidney, and lung tissues, and lowest in liver, blood cells, and skeletal muscle. We subsequently investigated the expression level of GLS1 in various tumor cell lines on the basis of data from CCLE. Our findings indicate that GLS1 has the highest expression level in kidney tumor cells and the lowest in salivary gland tumor cells, suggesting a potentially complex role in tumorigenesis (Supplemental Fig. 1B).

To further analyze the expression status of GLS1 across multiple types of cancer in the TCGA dataset, we employed the TIMER2 approach. Similar to the results from the CCLE dataset, GLS1 was expressed at higher levels in a variety of tumors. By incorporating the data of normal tissue from the GTEx dataset as a normal control, we evaluated the difference in GLS1 expression levels between tumor tissues and paired normal control tissues. A total of 33 types of tumors were included in our analysis. As shown in Fig. 1A, GLS1 exhibited distinct expression patterns between cancer tissues and normal tissues. Among these, 11 tumors presented greater expression of GLS1 than did normal tissues, whereas the remaining tumors presented the opposite pattern. We hypothesize that GLS1 may play an oncogenic role in the tumorigenesis of tumors with high GLS1 expression, whereas it may also act as a cancer-suppressing gene in tumors with low GLS1 expression during tumorigenesis.

Fig. 1.

A GLS1 gene expression (log2[TPM]) across tumor types and matched normal tissues. Box plots depict expression distribution per cancer type (x-axis abbreviations). Asterisks denote statistical significance (*p < 0.05, **p < 0.01, ***p < 0.001) between tumor and normal groups. B GLS1 genetic alterations in pan-cancer analysis. The bar chart displays the proportion of samples with alterations (mutation, structural variant, amplification, deep deletion, multiple alterations) per cancer type. Genes below the chart represent those with the highest alteration frequencies in each respective cancer. C GLS1 mutation characteristics in the TCGA cohort. Top: Distribution of mutation types (e.g., missense, nonsense, splice site) across the GLS1 gene structure. Bottom: Predicted functional impacts of identified mutations. D GLS1 protein expression in selected tumors and normal tissues. Box plots show expression distribution per cancer type (x-axis labels). Asterisks indicate statistical significance (*p < 0.05, **p < 0.01, ***p < 0.001) between tumor and normal groups

Gene alterations

As shown in Fig. 1B, although more than 22 types of tumors presented GLS1 alterations, the frequency of genetic alterations in GLS1 in various tumor samples was extremely low (0.9%). Among these tumors, GLS1 alteration was most frequently observed in patients with uterine corpus endometrial carcinoma (UCEC). The highest frequency of deep deletion type alterations was observed in patients with bladder urothelial cancer, whereas ovarian cancer (OV) patients presented the highest frequency of amplification type alterations. Among the 77 missense mutations, the most common mutation in GLS1 was the truncating mutation. However, these mutations were sporadic and not repetitive. Figure 1C shows the distribution of post-transcriptional modifications across GLS1, with phosphorylation and acetylation being the most frequent modifications observed. The vast majority of modification sites have no gene alterations. Detailed information regarding the frequency, site and types of GLS1 alterations and posttranscriptional modifications is presented in Fig. 1C.

Protein analysis

We also compared the differences in GLS1 protein expression levels between tumor and normal tissues. Using the CPTAC dataset, we analyzed protein expression data from five types of tumors. As shown in Fig. 1D, the differences in GLS1 protein expression levels were similar to the mRNA differences. Specifically, GLS1 protein expression was found to be greater in colon cancer tissue than in normal tissue. However, in breast invasive carcinoma (BRCA), OV, clear cell renal cell carcinoma (ccRCC), and UCEC, the expression level differences were inverse to those in colon cancer.

Survival analysis

As shown in Fig. 2A, we observed a positive correlation between the expression levels of GLS1 and the stages of KIRP (p = 0.004) and LIHC (p = 0.017). In contrast, GLS1 expression was negatively correlated with OV (p < 0.001) patient stage. Therefore, we hypothesize that GLS1 may be associated with patient survival. As shown in Fig. 2B and C, a higher level of GLS1 was associated with poor overall survival (OS) among patients with KIRP (HR = 1.01; p < 0.01), LIHC (HR = 1.02; p < 0.01) or MESO (HR = 1.01; p < 0.01). Disease-specific survival (DSS) analysis revealed a correlation between the GLS1 expression level and survival in patients with kidney renal clear cell carcinoma (KIRC) (HR = 0.99; p = 0.02), kidney renal papillary cell carcinoma (KIRP) (HR = 1.01; p < 0.01), LIHC (HR = 1.02; p = 0.02), MESO (HR = 1.01; p = 0.03) or prostate adenocarcinoma (PRAD) (HR = 1.12; p = 0.02). Additionally, we observed an association between GLS1 expression and the disease-free interval (DFI) and progression-free interval (PFI) of patients (Supplemental Fig. 2A and B).

Fig. 2.

A GLS1 gene expression stratified by pathological stage (I-IV) in KKIRP, LIHC, and OV. Violin plots illustrate expression distribution within each stage. B Association between GLS1 gene expression and overall survival across cancer types. Each row represents a distinct cancer (left-axis abbreviations). Hazard ratios (HR) with 95% confidence intervals (95% CI) and p-values are shown. C Association between GLS1 gene expression and disease-specific survival across cancer types. Format is identical to Panel B

Immune-associated analysis

Tumor-associated IFLs play key roles in tumorigenesis, tumor progression, and metastasis. Their migration and activation are highly regulated by tumor cells and other elements of the TME. Therefore, on the basis of the data from the TIMER dataset, we investigated the potential relationships between different IFL levels and the expression level of GLS1 in diverse types of cancer in the TCGA database. As shown in Fig. 3A, in most types of cancer, higher GLS1 expression was positively correlated with IFLs, such as adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), BRCA, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and thyroid carcinoma (THCA).

Fig. 3.

A Correlation between GLS1 gene expression and tumor-infiltrating lymphocyte (TIL) fractions across cancers. The heatmap depicts correlation coefficients (vertical axis: immune cell types). Red indicates positive correlation; blue indicates negative correlation. Color intensity reflects coefficient magnitude. B Correlation between GLS1 gene expression and immune scores across cancers. Each subplot shows the relationship (scatter plot) with a regression line. Asterisks denote statistical significance (*p < 0.05, **p < 0.01, ***p < 0.001). C Correlation between GLS1 gene expression and neoantigen burden across cancers. Scatter plots with overlaid density distributions illustrate the relationships. D Correlation between GLS1 gene expression and immune checkpoint gene expression across cancers. The heatmap shows correlation coefficients (axes: checkpoint genes). Triangles indicate direction (up: positive, down: negative); color intensity reflects coefficient magnitude. Asterisks denote statistical significance (*p < 0.05, **p < 0.01, ***p < 0.001)

Currently, several studies have indicated that the tumor immune microenvironment plays an important role in the occurrence and development of tumors [22, 23]. Therefore, using the TIMER dataset and TCGA datasets, we analyzed the potential relationships between immune scores (ImmuneScore, StromalScore, and ESTIMATEScore) and GLS1 expression levels in 33 tumors. We observed a negative correlation between the GLS1 expression level and ImmuneScore in patients with KIRC (r=-0.32, p < 0.001), KIRP (r=-0.13, p = 0.027), brain lower grade glioma (LGG; r=-0.25, p < 0.001), MESO (r=-0.26, p = 0.016), sarcoma (SARC; r=-0.19, p = 0.002), skin cutaneous melanoma (SKCM; r=-0.12, p = 0.011), testicular germ cell tumors (TGCTs; r=-0.20, p = 0.014) and UCEC (r=-0.09, p = 0.037). Moreover, we also observed positive correlations between the GLS1 expression level and ImmuneScore in patients with BLCA (r = 0.30, p < 0.001), BRCA (r = 0.22, p < 0.001), LIHC (r = 0.10, p = 0.047), LUAD (r = 0.26, p < 0.001), LUSC (r = 0.19, p < 0.001), pancreatic adenocarcinoma (PAAD; r = 0.22, p = 0.003), pheochromocytoma and paraganglioma (PCPG; r = 0.22, p = 0.002), PRAD (r = 0.19, p < 0.001) or THCA (r = 0.23, p < 0.001). The conclusions of the StromalScore and ESTIMATEScore analyses were similar. The detailed scatter plots of the mentioned cancers are provided in Supplemental Fig. 3. The most significant results are presented in Fig. 3B. The migration of IFLs is regulated by the antigenicity of cancer. Therefore, we also investigated the correlation between GLS1 expression levels and neoantigen levels in diverse types of cancer. In contrast to most other tumor types, neoantigen counts were negatively correlated with the GLS1 expression level in LIHC patients (r=-0.15, p = 0.031) (Fig. 3C).

We also investigated alterations in immune checkpoint regulatory genes in diverse types of tumors. On the basis of data from the TCGA, we analyzed the correlation between GLS1 expression levels and the expression levels of more than 40 immune checkpoint genes. As shown in Fig. 3D, we found that the expression levels of GLS1 were associated with immune checkpoint expression in most types of cancer (except cholangiocarcinoma (CHOL) and uterine carcinosarcoma (UCS)). For example, GLS1 expression was correlated with PD-L1 expression in patients with BLCA (r = 0.36), BRCA (r = 0.38), LIHC (r = 0.25), LUAD (r = 0.47), PCPG (r = 0.41), or PAAD (r = 0.49). Additionally, we observed that in most types of cancer, the expression of most chemokines and receptors was also correlated with GLS1 expression (Fig. 3E and F). These findings suggest that GLS1 plays a key role in regulating the host immune system. GLS1 not only shapes the tumor immune phenotype but also represents a potential target for immunotherapy.

DNA methylation and methylation-directed mismatch repair (MMR) analysis data

Given that a-ketoglutarate is a metabolite of GLS1 and can be utilized as a substrate for DNA methylation, we investigated the correlation between GLS1 expression levels and methyltransferase levels in a variety of tumors. As shown in Fig. 4A, we observed a positive correlation between GLS1 expression and DNA methylation enzymes (DNMTs) in 15/33 tumor types, except SARC, MESO, LUAD, LGG, kidney chromophobe (KICH), UCS and ACC.

Fig. 4.

A Circular plot of correlations between GLS1 gene expression and DNA methyltransferases (DNMT1, DNMT2, DNMT3A, DNMT3B) across cancers. Each segment represents a cancer type; colored bars depict the GLS1-DNMT relationship. B Heatmap of Pearson correlation coefficients and -log10(p-values) between mismatch repair (MMR) genes (MLH1, MSH2, MSH6, PMS2, EPCAM) and GLS1 expression across cancer types. Color scale indicates correlation strength and significance (redder: stronger positive correlation/lower p-value).C Radial plot of correlation between GLS1 gene expression and tumor mutational burden (TMB). Lines represent correlation coefficients (outward: positive; inward: negative). D Radial plot of correlation between GLS1 gene expression and microsatellite instability (MSI). Red lines/points represent coefficients (prominently marked: 0.51, -0.51). E, F. Gene Set Enrichment Analysis (GSEA) for GLS1 using KEGG pathways, based on gene expression profiles. G, H. GSEA for GLS1 using HALLMARK gene sets, based on gene expression profiles

MMR is widely recognized as a common pathway for maintaining low genome mutation frequencies. High TMB and MSI are consequences of DNA MMR deficiency. Therefore, we examined the correlation between GLS1 expression and the expression of MMR-associated genes (MLH1, MSH2, MSH6, PMS2, and EPCAM). As shown in Fig. 4B, we found a strong correlation between GLS1 expression and MMR-associated genes in most types of tumors, such as LIHC, READ, PRAD, and acute myeloid leukemia (LAML). Interestingly, while we found no correlation between neoantigen counts and GLS1 expression levels in most types of tumors, GLS1 expression levels were associated with patients’ MSI status in most types of cancer. As shown in Fig. 4C, a positive correlation was found between the GLS1 expression level and TMB in patients with LAML and LGG. Moreover, as shown in Fig. 4D, we found negative correlations between GLS1 expression levels and TMB in patients with KICH, LIHC, PAAD, CHOL, uveal melanoma (UVM), or colon adenocarcinoma (COAD). Interestingly, we found a positive correlation between GLS1 expression levels and MSI status in patients with rectum adenocarcinoma (READ), UCEC, and breast invasive carcinoma (BRCA). In contrast, the GLS1 expression level was negatively associated with the MSI status in patients with lymphoid neoplasm diffuse large B-cell lymphoma (DLBC).

Gene set enrichment analysis of GLS1-related partners

Enrichment analyses of GLS1 were separately performed for the KEGG pathway and HALLMARK pathway datasets. As shown in Fig. 4E–H, KEGG pathway analysis revealed that higher GLS1 expression may be involved in the mTOR signaling pathway and leukocyte transendothelial migration. Additionally, GSEA suggested that increased GLS1 expression may play a key role in the IL6-JAK-STAT3 signaling pathway, inflammatory response, and KRAS signaling pathway. Conversely, lower expression of GLS1 may be associated with tyrosine metabolism and fatty acid metabolism.

Discussion

Metabolism reprogramming and immune evasion are common characteristics of tumor cells [1]. Metabolic reprogramming in normal cells of the TME, such as fibroblasts, endothelial cells, and immune cells, is increasingly recognized to regulate tumor progression [24]. For example, owing to the abnormal vasculature of tumors, hypoxia is typically characteristic of the TME [25]. Most cancer cells preferentially utilize glucose to generate ATP, even in the presence of oxygen, which is referred to as the “Warburg effect” [26]. However, glycolysis involves low energy utilization, and the higher glycolysis rate of tumor cells exacerbates the depletion of glucose in other normal cells within the TME. A lack of nutrients induces alternative energy production pathways in all cells, bypassing glycolysis. However, the ability of T cells to mount an anticancer immune response is extremely dependent on energy metabolism. Residing in a nutrient-deficient microenvironment can impose metabolic stress on immune cells, which can impair T-cell activation or support T-cell exhaustion. Importantly, energy shortages may contribute to tumor immune evasion [27].

Recently, researchers have focused on energy metabolism related to alternative sources. Glutamine has been demonstrated to serve as an important energy source for cells and is also the most important source of nitrogen and carbon for cells [11, 28, 29]. Many studies have shown that upregulating the expression of GLS1 enhances tumor progression, whereas downregulating its expression substantially decreases tumor cell proliferation and ATP production capacity [30]. Moreover, the upregulation of GLS1 in tumor cells promotes the metabolism of glutamine, which further exacerbates glutamine deficiency in the TME. Consequently, glutamine deficiency weakens the immune response of immune cells.

Recently, studies have revealed that the metabolites of GLS1 can act as signaling agents, modulating the activity of transcription factors in various cancer cell types. For example, glutamine can indirectly activate the transcription factor STAT3, which plays a crucial role in regulating the proliferative effects of glutamine on cancer cells [16]. Additionally, glutamine metabolites have been shown to activate tumorigenesis signaling pathways. For example, the mammalian target of rapamycin complex 1 (mTORC1)-associated signaling pathway plays a critical role in regulating cell growth and proliferation, whereas α-KG, which hydrolyzes glutamine, is necessary for GTP loading of RagB and subsequent activation of mTORC1 [31]. In this study, we observed higher levels of GLS1 expression in several signaling pathways, including the mTOR- and JAK-STAT3-associated signaling pathways. These findings suggest that GLS1 may regulate tumor initiation and progression through these pathways in diverse tumor types.

The physiological characteristics of each cell are maintained by highly specific transcription networks. In fact, the regulation of specific transcription networks is accomplished through the epigenetic program of chromatin-modifying enzymes, whose activity directly depends on metabolites. Several studies have identified metabolites such as α-KG, acetyl-CoA, S-adenosylmethionine, and lactate, which serve as cofactors for chromatin enzymes to regulate DNA modification [32]. α-KG is also known as 2-oxoglutarate, which is produced from glutamine metabolism [33]. α-KG, an intermediate of glutamine metabolism, can not only provide ATP through the TCA cycle but also present as a substrate for dioxygenases that modify proteins and bases of DNA [32]. Although only a small fraction of the total α-KG is utilized in these reactions, those metabolites are essential for the regulation of epigenetic networks and cell signaling pathways in cells.

DNA methylation involves the addition of a methyl group at the 5-carbon to the cytosine nucleotide. This modification is regulated by writers (DNA methylases), erasers (DNA demethylases), readers, and interpreted proteins. DNA methylation indicates the attachment of a methyl group at the 5-carbon of the cytosine nucleotide. This modification can regulate cellular epigenetics through affecting DNA transcription [31]. DNMTs, which are the key enzymes in mammals, play crucial roles in inducing DNA methylation. Importantly, DNA methylation is not only involved in embryonic development but also affects tumorigenesis [32, 33]. In the present study, we observed a positive correlation between the expression of GLS1 and the levels of DNMTs (DNMT1, DNMT2, DNMT3A, and DNMT3B) in most tumors. Although the mechanism through which GLS1 and its metabolites influence DNMTs and subsequently impact the occurrence and development of tumors remains unclear, these findings suggest that GLS1 may regulate the function of DNMTs, thereby potentially modulating the initiation and progression of numerous tumors [34].

In the present study, we observed a weak correlation between MSI and GLS1 expression in various tumors. Furthermore, higher expression levels of GLS1 were not associated with the number of tumor neoantigens. The inconsistent function of intermediary metabolites in glutamine metabolism may explain this result. For example, the DNA damage response is crucial for sustaining genomic stability and preventing tumorigenesis. According to Mei Kong et al., the consumption of glutamine under energy metabolism stress decreases the expression of α-KG; however, decreased α-KG can hinder the ability to repair DNA alkylation damage by inhibiting AlkB homolog (ALKBH) enzyme activity [35]. Seung Min Jeong et al. reported that mitochondrial glutamine metabolism plays a crucial role in cell death induced by DNA damage. The inhibition of GLS1 increases the production of reactive oxygen species, leading to the transcriptional activation of amphiregulin through the Max-like protein X transcription factor, thereby sensitizing cancer cells to DNA damage [36]. Together with the above findings, the survival benefit of alkylating agent therapy in tumors with high GLS1 expression may be limited. Conversely, combining GLS1 inhibitors may enhance the drug sensitivity of alkylating agents in tumors with high GLS1 expression.

Recently, several clinical trials have provided evidence for the efficacy of immune checkpoint inhibitors (ICIs) in various tumor types [37]. Blocking immune checkpoints potentially enhances the anticancer immunity of the host [37]. In this study, we also revealed that GLS1 has the ability to impact host anticancer immune scores (ImmuneScore, StromalScore, and ESTIMATEScore). We found a positive association between GLS1 expression and IFLs in several cancers, including ACC, BLCA, BRCA, LUAD, LUSC and THCA. These tumors have been identified as having a high potential for survival benefit from immunotherapy [38]. However, clinical trials have shown that the response to immunotherapy varies among these patients. One possible explanation for this variation is that the anticancer functions of IFLs may be weakened in these tumors because of the limited nutrients in the TME. In general, IFLs must compete for nutrients to maintain their anticancer function in the TME [4, 24, 39]. Interestingly, we also observed an association between GLS1 and immune checkpoint expression in most types of cancer. Increased expression of GLS1 in tumor cells can lead to competition for glutamine between tumor cells and IFLs. Notably, a study by Jun-Kyu Byun et al. demonstrated that restricting glutamine metabolism in cancer cells enhances the effectiveness of anti-PD-L1 antibody immunotherapy [17]. These findings suggest that tumors with GLS1 are not only associated with immune scores but also with the expression of immune checkpoint proteins. Therefore, tumor cells may undergo metabolic reprogramming through the upregulation of GLS1 expression, enabling them to evade the immune system. Currently, the combination of immune checkpoint blockade with cancer metabolism therapies has gained attention as a potential therapeutic strategy for enhancing immune cell-mediated antitumor activity [4]. On the basis of these findings, we hypothesize that GLS1 could be a potential biomarker for determining the combination of immune therapy and metabolism therapy in tumors.

Although GLS1 has potential mechanisms for regulating tumor metabolism, epigenetics, and immune evasion, the survival analysis results suggest different conclusions for different tumors. We observed differential expression of GLS1 between tumor tissues and normal tissues in more than 11 types of cancer; however, only 3 types of cancer were correlated with survival. The reason may be explained by the complicated function of GLS1. On the one hand, high GLS1 expression can promote tumor initiation and progression. First, GLS1 can promote the reprogramming of tumor energy metabolism and sustain tumor proliferation. Second, GLS1 can upregulate immune checkpoint proteins and enhance tumor immune evasion. On the other hand, higher expression levels of GLS1 can improve the therapeutic effect on tumors. For example, α-KG can act as a reactant to inhibit DNA alkylation repair, increasing tumor cell sensitivity to alkylating agents [35]. Given the evidence highlighting the complex role of GLS1 in tumor initiation and progression, GLS1 could serve as a potential biomarker for therapeutic strategies. Therefore, patients with high GLS1 expression may benefit from alkylating agents or the inhibition of GLS1 function when combined with immune checkpoint inhibitor therapy. Conversely, patients with low GLS1 expression may benefit from glutamine therapy to enhance the immune response of effector T cells in the TME. Interestingly, CB-839, a selective GLS1 inhibitor, is currently being clinically tested in several tumors, including triple-negative breast, lung, liver, soft tissue, sarcoma, myeloma and colorectal cancer [40–45]. Additionally, several natural compounds similar to CB-839 can regulate glutamine metabolism and regulate tumor progression [46]. We hypothesized that this survival benefit may also be present in other tumors with high GLS1 expression.

Limitations

Several limitations must be acknowledged in this study. First, our findings suggest that GLS1 may serve as a potential biomarker for prognosis and the development of therapeutic strategies across various tumor types. However, this conclusion, derived from analyses of data from the TCGA and GEO datasets, requires further validation through animal and clinical studies. Second, the metabolism of glutamine is also influenced by other enzymes, such as SLC1A5 and SLC7A5. Therefore, additional analyses and validations concerning the impact of these enzymes on tumors may be necessary.

In conclusion, we first conducted comprehensive pancancer analyses examining the correlations between GLS1 expression and DNA methylation, IFLs, TMB, tumor neoantigen, MSI, immune checkpoint protein expression, immune scores and clinical prognosis across various tumors. Our observations revealed that GLS1 affects not only the energy metabolism of cells but also the epigenetics and response of tumors. These results may facilitate the understanding of the oncogenic roles of GLS1.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Conceptualization, Kangsheng Gu and Jianjun Liu; Collection and Assembly of Data, Jianjun Liu; Data Analysis and Interpretation: Jianjun Liu and Shikai Hong; Writing, Jianjun Liu; Visualization: Jianjun Liu; Supervision: Jianjun Liu; Project Administration, Kangsheng Gu;

Funding

National Natural Science Foundation of China, Grant/Award Number: 81802641.

Data availability

The analyzed datasets generated during this study are available in the Supplementary Data files. The original data were derived from the following public resources: The Cancer Genome Atlas (https://portal.gdc.cancer.gov/), Genotype-Tissue Expression project (https://www.gtexportal.org/home/), Cancer Cell Line Encyclopedia (https://sites.broadinstitute.org/ccle/), cBioPortal for Cancer Genomics (https://www.cbioportal.org/), UALCAN (http://ualcan.path.uab.edu/analysis-prot.html) and TISIDB (http://cis.hku.hk/TISIDB/). All datasets used in this study are referenced in the Methods section and are publicly accessible via the provided links. The custom code used for analysis is available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study uses de-identified, publicly available data from TCGA and GEO datasets. The data was originally collected with participant consent for secondary research. Our protocol complies with the database’s Data Use Agreement and focuses on aggregate analysis. No participant re-contact or re-identification will occur. This study does not involve direct participation of human subjects or individuals.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.