Skip to main content
Download PDF

Unveiling the oncogenic role of SLC25A13: a multi-omics pan-cancer analysis reveals its impact on glioma progression

Cancer Cell International volume 25, Article number: 76 (2025) Cite this article

Abstract

SLC25A13, a pivotal component of the mitochondrial aspartate-glutamate carrier, is integral to cellular metabolism and has been linked to various diseases. However, its role in cancer biology remains largely unexplored. In this study, we employed multi-omics data to elucidate the genetic landscape, expression profile, and prognostic value of SLC25A13 in a pan-cancer context. Additionally, we examined the correlation between SLC25A13 and the immune microenvironment across various cancers. By applying multiple machine learning methods, we identified seven core SLC25A13 co-expressed genes and developed a nomogram to predict the prognosis of glioma patients, validating its efficacy across multiple independent datasets. Furthermore, in vitro and in vivo experiments demonstrated that SLC25A13 is significantly overexpressed in glioblastoma tissues compared to paraneoplastic tissues, promoting glioblastoma cell proliferation and migration while inhibiting apoptosis. Collectively, our study positions SLC25A13 as a promising biomarker for cancer prognosis and a potential therapeutic target, particularly in glioma, thereby laying the groundwork for future research into its therapeutic exploitation in cancer.

Introduction

Cancer is one of the leading causes of human mortality worldwide [1]. As a heterogeneous group of diseases, different cancer types exhibit distinct pathogenetic mechanisms, and even within the same cancer type, manifestations can vary significantly [2, 3]. Therefore, integrating multiple cancer types in research is essential for understanding both the heterogeneity and commonality among cancers, which can also facilitate the discovery of new therapeutic targets. Recent advances in sequencing and bioinformatics technologies have enabled pan-cancer studies that integrate data from multiple cancer types, yielding numerous new insights into the mechanisms and characteristics of cancer [4, 5]. Cancer metabolism has emerged as a key focal point in oncological research due to the discovery that metabolic processes within tumor cells differ fundamentally from those in normal cells [6]. These metabolic alterations not only facilitate rapid tumor growth but also present potential avenues for therapeutic intervention. Among the regulators of metabolic pathways, the solute carrier (SLC) superfamily plays a critical role by transporting essential metabolites across cellular membranes [7]. Research on solute carrier proteins has become a hotspot for identifying new cancer therapeutic targets [8, 9].

The solute carrier family 25 (SLC25) comprises 53 members and is the largest solute transporter family in humans [10,11,12]. It is crucial for solute transport across the inner mitochondrial membrane, directly or indirectly influencing the metabolic processes of fats, sugars, and amino acids, thereby playing a significant role in cellular metabolic regulation [10,11,12]. SLC25A13, a member of the SLC25 family and also known as calcium-regulated mitochondrial aspartate/glutamate carrier 2, is primarily responsible for the transport of glutamine and aspartate [11]. It has been implicated in various diseases, including intrahepatic cholestasis [13], hepatocellular carcinoma [14], melanoma [15], and citrin deficiency [16]. While SLC25A13 has been reported to be involved in the metabolic reprogramming of tumor cells and associated with rapid tumor cell proliferation [17], its characterization across different cancers remains unclear, limiting its potential as a therapeutic target. Therefore, integrated pan-cancer studies targeting SLC25A13 could help investigate the heterogeneity of SLC25A13 across different cancer types and provide guidance for therapeutic applications.

In this study, we conducted a detailed analysis of the genetic landscape, expression patterns, and prognostic value of SLC25A13 across multiple tumors by integrating multidimensional pan-cancer data. Additionally, we explored the cancer-promoting role of SLC25A13 in glioma and confirmed through in vitro and in vivo experiments that SLC25A13 promotes the malignant behavior of glioblastoma (GBM) cells, suggesting its potential as a therapeutic target in glioma.

Materials and methods

Genetic landscape and expression pattern of SLC25A13 in pan-cancer

Methylation data and copy number variation data for SLC25A13 were obtained from TCGA database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). The relationship between gene expression and methylation was assessed using Spearman correlation analysis of beta values and \({\text{log}}_{2}(FPKM+1)\). The mutation profile of SLC25A13 across various cancer types was analyzed using cBioPortal (https://www.cbioportal.org/). Gene expression data from The Cancer Genome Atlas (TCGA) pan-cancer, Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and Genotype-Tissue Expression (GTEx) projects were obtained from the UCSC Xena database (https://xenabrowser.net/). These datasets had undergone batch effect removal and normalization. Quantitative protein expression data for pan-cancer studies were acquired from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database (https://proteomics.cancer.gov/programs/cptac).

Analysis of the potential function of SLC25A13 in pan-cancer and its relationship with the immune microenvironment

The correlation between SLC25A13 and tumor mutational burden (TMB) and microsatellite instability (MSI) across various cancers was analyzed using the TCGAplot R package [18]. The immune microenvironment composition in pan-cancer was assessed with the CIBERSORT algorithm, while the ESTIMATE algorithm was employed to calculate the IMMUNE SCORE, STROMAL SCORE, and ESTIMATE SCORE. Spearman correlation coefficients were then calculated between SLC25A13 expression and these scores. Additionally, the protein interaction network of SLC25A13 was constructed using the ComPPI database [19].

Analysis of the potential function and prognostic value of SLC25A13 in glioma

Sequencing data and corresponding clinical information for glioma patients were obtained from the TCGA and CGGA (http://www.cgga.org.cn/) databases. The Rembrandt dataset was acquired from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Quantitative protein data for key pathways were sourced from the TCPA database (https://www.tcpaportal.org/tcpa/). Glioma patients were categorized into high and low SLC25A13 expression groups based on median expression levels, and differentially expressed genes were identified. Gene Set Enrichment Analysis (GSEA) was performed using the R package clusterProfiler [20]. Fourteen tumor functional states defined by CancerSEA [21] were assessed using the R package GSVA [22], and their correlations with SLC25A13 expression were calculated. Patients were further categorized into quartiles (Q1, Q2, Q3, and Q4) based on SLC25A13 expression levels, with Q1 representing the top 25% and Q4 representing the bottom 25% of expression. Immune response and genomic status scores for each group were calculated based on previously reported definitions [23]. Meta-analysis of the results from univariate Cox regression analysis was performed using the inverse variance method, with the log hazard ratio (HR) as the primary measure.

Establishment and evaluation of the nomogram

Through the application of multiple machine learning techniques, seven core genes co-expressed with SLC25A13 were identified. Subsequently, a multivariate Cox analysis model was developed, from which we derived a risk score formula based on the product of gene expression levels and their respective regression coefficients (Risk score \(={\sum }_{n=1}^{7}{exp}_{n}\times {coef}_{n}\)). By integrating this risk score with additional clinicopathological parameters, we constructed a nomogram to predict the prognosis of glioma patients. The nomogram's reliability was validated across multiple datasets. For the analysis of survival and prognostic factors, we utilized the R packages survival and survminer, while the R package regplot was employed to construct the nomogram.

Analysis of single-cell and spatial transcriptome data

The expression of SLC25A13 in the glioma single-cell sequencing datasets was analyzed using data derived from the TISH2 database [24]. Single-cell sequencing data GSE182109, downloaded from the GEO database, was utilized for further analysis. Standard analysis was performed using the R package Seurat [25], and batch effects between samples were removed with the Harmony algorithm [26]. Copy number variation (CNV) was inferred using the R package inferCNV [27] to differentiate between malignant and normal cells. Risk score for each cell were calculated based on previously established formula, and cellular communication was analyzed using the R package CellChat [28]. Metabolic pathway activities of low-risk and high-risk malignant cells were inferred using the R package scMetabolism [29]. Spatial transcriptome data were obtained from 10 × Genomics (https://www.10xgenomics.com/), analyzed with Seurat, and normalized using the sctransform algorithm. The spatial distribution of cell subpopulations was inferred through the inverse convolution algorithm, categorizing regions with high malignant cell content as mixed regions and those with high stromal cell content as normal regions.

Cell culture

U251 and U87 cell lines were cultured in standard DMEM medium (Sigma-Aldrich, St. Louis, MO, USA) supplemented with 10% fetal bovine serum (FBS), 1% penicillin–streptomycin, and 1% glutamine (Gibco). The cells were maintained at 37 °C in a humidified tissue culture incubator with 5% CO2.

Plasmid construction and cell transfection

The SLC25A13 shRNA-Luc sequence was cloned into the pLKO.1-shRNA-Puro vector. Lentiviral infection was packaged using Lipofectamine 3000 (Invitrogen, USA) with psPAX2 and pMD2.G to transfect 293 T cells with U87 and U251 cells according to the manufacturer's protocol. Puromycin (Sigma-Aldrich) was also used for stable transfection.

EdU cell proliferation assay

Cells were cultured in fresh medium containing 10 μM EdU for 3 h, followed by trypsinization to collect the cells. The cells were then fixed with 4% formaldehyde in PBS. After permeabilization with 0.3% Triton X-100, 500 μL of Click-iT® reaction cocktail (ThermoFisher, Waltham, MA, USA) was added to each well according to the manufacturer's instructions. Detection was performed using a flow cytometer. The average fluorescence intensity of the EdU+ population was then analyzed using FlowJo software (version 10, TreeStar). All experiments were conducted with three biological replicates and subjected to statistical analysis.

Cell viability assay

The cells were seeded in 96-well plates at a density of 3,000 cells per well. Afterward, 100 μL of Cell Count Kit-8 (APEBIO, Houston, USA) reagent was added to each well at 0, 24, 48, and 72 h, followed by incubation at 37 °C for 3 h. Absorbance was measured at 450 nm using a Tecan flatbed instrument. Dose–response curves were generated using GraphPad Prism, and cell viability was calculated. All experiments were conducted with three biological replicates and subjected to statistical analysis.

Assessment of cell cycle and apoptosis

To evaluate the cell cycle, cells were stained with 50 μg/mL propidium iodide (PI) containing 20 μg/mL RNase without DNase. The cells were then analyzed using a flow cytometer according to the manufacturer's instructions. Based on DNA content, the G1 phase, S phase (DNA synthesis phase), G2 phase, and M phase (mitosis phase) were determined, and the percentage of cells in each phase was measured. All cellular experiments were conducted in biological triplicates using cryopreserved cells that were thawed independently at different time points. Additionally, cells were double-stained with fluorescein isothiocyanate-conjugated Annexin V (Annexin V-APC) and PI (BD Pharmingen, San Diego, USA), followed by analysis using a flow cytometer.

Cell migration assay

To assess cell migration, a scratch assay was performed. A sterile pipette tip was used to create a scratch in a confluent monolayer of glioma cells. Images of the scratch were captured at 0, 24, and 36 h post-scratch to evaluate the migration of cells into the wound area. All experiments were conducted with three biological replicates and subjected to statistical analysis.

Colony formation assay

The colony formation assay was performed 48 h after transfection with SLC25A13 shRNA. Cells were counted and seeded into six-well plates at a density of 1 × 104 cells per well. The cells were then incubated in a humidified incubator at 37 °C. After an appropriate incubation period, the cells were fixed with 4% paraformaldehyde for 10 min. Subsequently, the colonies were stained with 0.5% crystal violet. Finally, the number of macroscopic colonies was counted under an optical microscope. All experiments were conducted with three biological replicates and subjected to statistical analysis.

Western blot assay

Afterward, the harvested cells were lysed using RIPA buffer (50 mM Tris–HCl, pH 7.5; 150 mM NaCl; 0.1% sodium deoxycholate; 0.1% SDS; 1 mM EDTA, pH 8.0; 1% NP-40) containing a protease and phosphatase inhibitor cocktail (Thermo Fisher) and incubated on ice for 10 min. The cell lysates were then centrifuged at 4 °C using a tabletop centrifuge to remove cellular debris. Equal amounts of protein extracted from different samples were loaded onto a sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE). Following electrophoresis, the proteins were transferred onto a polyvinylidene difluoride (PVDF) membrane. After blocking with 5% bovine serum albumin, the membrane was incubated with primary antibodies and horseradish peroxidase-conjugated secondary antibodies (Proteintech, USA). Detection was performed using the Pierce™ Enhanced Chemiluminescence Substrate Kit (Thermo Fisher Scientific, Waltham, MA, USA) and the ChemiDoc™ Touch Imaging System (Bio-Rad Laboratories, Hercules, CA, USA).

Immunohistochemical assay

Clinical glioma tissue specimens were collected at the Neurosurgery Department of Wuhan Union Hospital. Ethical approval was obtained from the Wuhan Union Hospital. All patients provided written informed consent and did not receive any financial compensation. For immunohistochemistry, 6 μm formalin-fixed paraffin-embedded sections were incubated with an anti-SLC25A13 antibody (1:400, CST). A secondary HRP-conjugated antibody was then applied, followed by visualization using diaminobenzidine (DAB). Pathological assessment was conducted in a blinded manner. Protein expression levels were quantified using a visual grading system based on the extent (percentage of positive tumor cells) and intensity of staining. Under a microscope, protein expression was evaluated by scoring the staining intensity and extent. Staining intensity was rated on a scale of 0–3: 0 = no staining, 1 = weak staining, 2 = moderate staining, and 3 = strong staining. Additionally, the extent of staining was scored based on the percentage of positive-stained cells (0 = 0–5%, 1 = 6–25%, 2 = 26–50%, 3 = 51–75%, 4 = 76–100%). The final score for protein expression was obtained by multiplying the intensity and extent scores.

Xenograft model

Animal experiments were performed per the NIH Guidelines for the Care and Use of Laboratory Animals and approved by the Animal Care Committee of Tongji Medical College. Five-week-old female BALB/c nude mice were used for all xenograft experiments (10 per group). The animals were weighed and randomly divided into two groups, and 5 μL U87-Luc glioma cell suspension (3 × \({10}^{5}\) cells) was injected into the mouse brain using a stereotaxic apparatus at 2 mm lateral and 2 mm anterior to the bregma and at a 3.5 mm depth.

Statistical analysis

Statistical analyses were performed using R software (version 4.4.0) and GraphPad Prism (version 9.5). The Wilcoxon rank-sum test or Student’s t-test was employed to assess differences between two groups. Correlations between variables were evaluated using Spearman's rank correlation coefficient. Survival analyses were conducted using Kaplan–Meier curves, with differences assessed by the log-rank test. To account for multiple comparisons, p-values were adjusted using the Benjamini–Hochberg method. Statistical significance was set at p < 0.05 for all analyses.

Results

Abnormal expression and genetic alterations of SLC25A13 in multiple cancers

By comparing pan-cancer data extracted from the TCGA TARGET, and GTEx projects, we identified abnormal expression of SLC25A13 in human cancers. The analysis revealed that SLC25A13 was significantly upregulated in multiple tumors, including adrenocortical carcinoma (ACC), acute lymphoblastic leukemia (ALL), bladder urothelial carcinoma (BLCA), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), acute myeloid leukemia (LAML), lower grade glioma (LGG), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), stomach and esophageal carcinoma (STES), testicular germ cell tumors (TGCT), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS). Conversely, lower expression levels were observed in cholangiocarcinoma (CHOL), kidney chromophobe (KICH), pan-kidney cohort (KIPAN), kidney renal clear cell carcinoma (KIRC), and thyroid carcinoma (THCA) (Fig. 1A). Using the CPTAC database, we then analyzed the protein expression of SLC25A13 in various tumor types. Our findings showed that SLC25A13 protein levels were significantly elevated in GBM, head and neck squamous cell carcinoma (HNSCC), lung squamous cell carcinoma (LSCC), and LUAD, while significantly decreased in clear cell renal cell carcinoma (CCRCC) and hepatocellular carcinoma (HCC) (Fig. 1B). Further analysis of the relationship between SLC25A13 expression levels and copy number variations (CNVs) revealed a general upward trend in SLC25A13 expression as copy number increased from homozygous deletion to high copy number amplification (Fig. 1C). Additionally, in cancers such as cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), thymoma (THYM), SKCM, and LGG, a negative correlation was observed between SLC25A13 expression and promoter methylation levels (Fig. 1D). Examining the genetic landscape of SLC25A13 across various cancers, we discovered that the primary genetic alteration in SLC25A13 was amplification (Fig. 1E and S1A). These findings suggest that SLC25A13 may play a significant role in the pathogenesis of various cancers, influenced by copy number variations, promoter methylation, and genetic alterations.

Fig. 1
figure 1

Abnormal expression and genetic alterations of SLC25A13 in various cancers. Statistical significance is denoted as follows: ns (not significant, p > 0.05), *(p < 0.05), **(p < 0.01), ***(p < 0.001), and **** (p < 0.0001). A SLC25A13 mRNA expression levels in multiple cancer types compared to normal tissues based on TCGA, TARGET, and GTEx datasets. Differences between the two groups were assessed using the Wilcoxon rank-sum test. B Protein expression levels of SLC25A13 across different cancer types from the CPTAC dataset. Differences between the two groups were assessed using the Wilcoxon rank-sum test. C Association between SLC25A13 expression levels and copy number variations across various cancers. Differences among multiple groups were assessed using the Kruskal–Wallis H test. D Correlation between SLC25A13 expression and promoter methylation levels in multiple cancers. E Genetic alteration of SLC25A13 in various cancers

Full size image

Association of SLC25A13 with TMB, MSI, and immune profiles in multiple cancers

Understanding the correlation between gene expression and oncological markers such as Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI) is crucial for elucidating the biological behavior of tumors, predicting therapeutic responses, and devising personalized treatment plans [30, 31]. Our study highlights the significant positive correlation between SLC25A13 expression and both TMB and MSI across various cancers, indicating its potential role in tumor progression and response to therapy (Fig. 2A and B). To evaluate the prognostic significance of SLC25A13 expression in different cancer types, we stratified patients into high- and low-expression groups based on the median SLC25A13 expression level. Subsequent univariate Cox regression analysis revealed that elevated SLC25A13 expression was associated with poor prognosis in CESC, KICH, LGG, SKCM, and uveal melanoma (UVM), while it served as a favorable prognostic indicator in KIRC, LAML, and liver hepatocellular carcinoma (LIHC) (Fig. 2C).

Fig. 2
figure 2

Correlation of SLC25A13 expression with tumor mutational burden (TMB), microsatellite Instability (MSI), and immune profiles in diverse cancers. Statistical significance is denoted as follows: ns (not significant, p > 0.05), *(p < 0.05), **(p < 0.01), ***(p < 0.001), and **** (p < 0.0001). A Spearman correlation between SLC25A13 expression and TMB across multiple cancer types. B Spearman correlation between SLC25A13 expression and MSI across various cancer types. C Univariate Cox survival analysis showing hazard ratios for overall survival associated with SLC25A13 expression in different cancer types. D Proportion of patients with high and low SLC25A13 expression, stratified by median SLC25A13 expression levels, across different immune subtypes as classified by The Immune Landscape of Cancer. E Spearman correlation between SLC25A13 expression and different immune cell infiltration fractions calculated by the CIBERSORT algorithm in multiple cancers. F Spearman correlation between SLC25A13 expression and various immune suppressive molecules. G Potential protein–protein interactions involving SLC25A13 identified from the ComPPI database. H Spearman correlation between SLC25A13 expression and scores calculated by the ESTIMATE algorithm in multiple cancers

Full size image

Building on the framework of previous research [23] that identifies six immune subtypes—wound healing, IFN-γ dominant, inflammatory, lymphocyte-depleted, immunologically quiet, and TGF-β dominant—our analysis reveals that high SLC25A13 expression is predominantly associated with immune subtypes C1 and C2, while low expression correlates with subtypes C3 and C5 (Fig. 2D). Immunoinfiltration analysis further indicated that SLC25A13 expression correlates with the infiltration of immune cells across multiple cancer types (Fig. 2E). Additionally, our study identifies significant associations between SLC25A13 and various immunomodulatory molecules (Fig. 2F and S2A-B). Our analysis reveals both consistent patterns and tumor-specific heterogeneity in the correlation between SLC25A13 expression and immune parameters. Notably, across most tumor types, SLC25A13 expression shows a negative correlation with T cell infiltration, particularly CD8 + T cells. This inverse relationship suggests that high SLC25A13 expression may be associated with reduced T cell infiltration and impaired immune-mediated tumor cell elimination. Furthermore, we observed consistent positive correlations between SLC25A13 and specific immune checkpoint molecules, particularly KDR and TGFBR1, across various tumor types. This correlation pattern implies potential involvement of SLC25A13 in the regulatory pathways of these immune checkpoint molecules. Together, these findings highlight the distinct immunomodulatory functions of SLC25A13 in different cancer contexts. Utilizing the ComPPI database, we identified proteins that potentially interact with SLC25A13, suggesting intricate networks influencing tumor behavior (Fig. 2G). ESTIMATE analysis revealed a negative correlation of SLC25A13 expression with stromal scores, immune scores, and overall ESTIMATE scores across diverse cancers, underscoring its complex role in modifying the tumor microenvironment (Fig. 2H). Gene Set Enrichment Analysis (GSEA) at the pan-cancer level demonstrated that SLC25A13 was significantly associated with cell cycle-related pathways and various metabolic pathways (Figure S3A).

Differential pathway enrichment and prognostic implications of SLC25A13 in glioma

First, we divided the glioma patients into high and low expression groups based on the median SLC25A13 expression level. Subsequent enrichment analysis of differentially expressed genes using hallmark gene sets revealed significant pathway enrichments in both groups. Notably, in both LGG and GBM, multiple pathways such as Mitotic Spindle, G2M Checkpoint, and E2F Targets were significantly enriched in the high expression group of SLC25A13 (Fig. 3A and S4C). Using the KEGG database, we assessed the activity of various metabolic pathways in the high and low SLC25A13 expression groups. In the high SLC25A13 expression group, certain metabolic pathways, including tyrosine metabolism, were significantly suppressed (Figure S4A and B). To further understand the relevance of SLC25A13 to oncogenic pathways, we analyzed the correlation between SLC25A13 and quantitative data from key oncogenic pathways in the TCPA. The results showed that SLC25A13 was positively correlated with the key oncogenic pathway PI3K/AKT in glioma (Fig. 3B). Further enrichment analysis revealed that SLC25A13 was significantly correlated with pathways involved in cell growth and death, indicating its potential role in tumorigenesis and cancer progression (Figure S5A). Scoring of 14 tumor functional states based on CancerSEA definitions revealed significant positive correlations between SLC25A13 expression and cell cycle, DNA damage repair, EMT, migration, invasion, proliferation, and stemness (Fig. 3C).

Fig. 3
figure 3

Differential pathway enrichment and prognostic implications of SLC25A13 expression in gliomas. Statistical significance is denoted as follows: ns (not significant, p > 0.05), *(p < 0.05), **(p < 0.01), ***(p < 0.001), and **** (p < 0.0001). A Hallmark gene set enrichment analysis for high and low SLC25A13 expression groups in glioblastoma multiforme (GBM). B Spearman correlation of SLC25A13 expression with quantification of functional proteins in TCPA-RPPA database at the pathway level. C Spearman correlation of SLC25A13 with GSVA scores for 14 functional states in gliomas. D Meta-analysis using inverse variance method for SLC25A13-related overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in TCGA LGG cohort. E Association of SLC25A13 expression with different clinicopathologic features in TCGA pan-glioma cohort. Differences between the two groups were assessed using the Wilcoxon rank-sum test. F Correlation between SLC25A13 expression quartiles and immune response scores in gliomas

Full size image

By performing a meta-analysis of four different survival outcome metrics—overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI)—in the TCGA LGG cohort, we confirmed SLC25A13 as a risk factor for patient prognosis (Fig. 3D). Furthermore, Kaplan–Meier survival curve analysis revealed that patients with high SLC25A13 expression in both the pan-glioma and LGG cohorts had significantly worse prognosis (Figure S4D). Additionally, we analyzed the association of SLC25A13 with other clinical features. The results indicated that SLC25A13 expression increased with WHO grade and was higher in IDH wild-type patients, elderly patients, and patients without 1p19q co-deletion (Fig. 3E). This suggests that SLC25A13 may be associated with more malignant clinical features. Moreover, we divided all patients into four quartiles based on gene expression percentiles: Q1 (highest 25%), Q2, Q3, and Q4 (lowest 25%). Analysis of the immune response and genomic status, as proposed by previous studies [23], showed that the CTA score and stromal fraction were higher in the low SLC25A13 expression group (Fig. 3F).

SLC25A13 is preferentially expressed in malignant cells and correlates with drug sensitivity

Using the TISCH2 database, we obtained single-cell resolution expression data for SLC25A13 in gliomas. Our analysis revealed that SLC25A13 was predominantly expressed in oligodendrocytes and malignant cells (Fig. 4A). To further elucidate the role of SLC25A13, we classified cells into SLC25A13-positive and SLC25A13-negative groups. In dataset GSE131928, the proportion of MES-like malignant cells was significantly higher in the SLC25A13-positive group compared to the SLC25A13-negative group (Fig. 4B). Utilizing drug sensitivity data from the PRISM, CTRP, GDSC1, and GDSC2 databases, we analyzed the correlation between SLC25A13 expression and responsiveness to various chemotherapeutic agents. The results indicated that high SLC25A13 expression was associated with resistance to multiple drugs, including temozolomide, a commonly used first-line therapeutic agent for glioma patients (Fig. 4C). To identify potential therapeutic agents that could counteract SLC25A13-mediated tumor promotion, we employed the XSum algorithm to analyze connectivity map (cMAP) drug sensitivity data. Consistent results were obtained in both lower-grade glioma (LGG) and glioblastoma multiforme (GBM) datasets, with the drug STOCK1N-35696 receiving the lowest score (Figure S5B and C). This suggests that STOCK1N-35696 may be a potential therapeutic agent for glioma patients with high SLC25A13 expression. To further confirm the preferential expression of SLC25A13, we first performed deconvolution analysis of spatial transcriptomics data to determine the spatial distribution of cellular subpopulations (Fig. 4D). We then analyzed the relationship between SLC25A13 expression and spatial cellular components. The results indicated that SLC25A13 had higher expression levels in malignant regions and was proportional to the number of malignant cells (Fig. 4E–G). Additionally, analysis of the glioma single-cell datasets from TISCH2 revealed that SLC25A13 expression was positively correlated with malignant cells and negatively correlated with CD8 T cells, but not significantly correlated with M1 macrophages (Fig. 4H–J).

Fig. 4
figure 4

Single-cell and spatial transcriptome resolution of SLC25A13 expression in the glioma tumor microenvironment. A Heatmap showing SLC25A13 expression across various cell types from single-cell resolution data. B Proportion of different cell types in SLC25A13-positive and SLC25A13-negative groups from dataset GSE131928. C Spearman correlation of dose–response curves (area under the curve values) in the CTRP and PRISM databases and IC50 values in the GDSC1 and GDSC2 databases with SLC25A13 expression. D Spatial distribution of different cellular components in glioblastoma. E Spatial distribution of SLC25A13 expression in glioblastoma microenvironment. F Correlation of SLC25A13 expression with the content of different cell types in spatial transcriptome data. G Comparison of SLC25A13 expression levels in spots defined as malignant mixed niche versus normal niche. Differences between the two groups were assessed using the Wilcoxon rank-sum test. H Correlation between SLC25A13 expression and abundance of malignant cells. I Correlation between SLC25A13 expression and presence of CD8 T cells. J Correlation between SLC25A13 expression and presence of M1 tumor-associated macrophages (TAMs)

Full size image

Identification and prognostic evaluation of SLC25A13-related core genes in gliomas

First, we performed a correlation analysis of SLC25A13 in the TCGA pan-glioma cohort and identified the top 150 genes with the highest correlation coefficients as co-expressed genes (Table S1). Interestingly, we found that all 150 co-expressed genes were associated with poor prognosis in the TCGA pan-glioma cohort (Table S2). To further identify the core co-expressed genes, we employed a combination of multiple machine learning approaches (Fig. 5A). Using the C-index as the evaluation criterion, we selected the overlapping genes from the top 15 combinations, ultimately identifying seven core genes: GAS2L3, KIF2C, NDE1, EZH2, SMC4, SLC30A6, and SLC25A13 (Fig. 5B). We obtained risk scores for each sample by performing a multivariate Cox analysis of the seven core genes. Our analysis revealed that the SLC25A13-related risk score is significantly associated with several critical clinical parameters, including age, IDH mutation status, 1p/19q codeletion status, MGMT promoter methylation status, glioma stage, overall survival, and survival status (Fig. 5C). Furthermore, in both TCGA and CGGA datasets, Kaplan–Meier survival curve analysis revealed that patients with high-risk scores had a significantly worse prognosis (Fig. 5D).

Fig. 5
figure 5

Identification and prognostic evaluation of SLC25A13-related risk score in gliomas. A Combination of multiple machine learning methods to screen SLC25A13-related core genes. B Intersection genes for the top 15 machine learning method combinations with the highest C-index. C Association of SLC25A13-related risk score with clinical parameters such as gender, age, IDH mutation status, 1p19q co-deletion status, MGMT promoter methylation status, glioma stage, overall survival, and survival status. D Kaplan–Meier survival curve analysis of high- and low-risk scoring groups in the glioma cohorts from TCGA and CGGA databases

Full size image

In addition, consistent with our findings in glioma cohorts, patients in the high-risk group across ACC, KICH, prostate cancer (PRAD), LIHC, KIRP, LUAD, mesothelioma (MESO), PAAD, and sarcoma (SARC) showed a significantly shorter overall survival compared to those in the low-risk group (Figure S6A-I). Intriguingly, in patients with THYM, those in the high-risk group experienced a longer survival duration compared to their low-risk counterparts (Figure S6J). This distinct survival pattern in THYM patients suggests a potential differential impact of SLC25A13-related risk score in this particular subtype, warranting further investigation. Next, we evaluated the predictive capability of the SLC25A13-related risk score for the prognosis of glioma patients using time-dependent ROC analysis. In the TCGA pan-glioma dataset, the three-year area under the curve (AUC) value for the SLC25A13-related risk score was 0.9 (Figure S6K). In the CGGA dataset, the three-year AUC value was 0.82 (Figure S6L), indicating that the SLC25A13-related risk score has a strong discriminative ability for the prognosis of glioma patients.

Development and evaluation of a prognostic model for glioma patients based on SLC25A13-related risk score

We developed a comprehensive prognostic model for glioma patients that included the SLC25A13-related risk score, age, IDH mutation status, 1p/19q co-deletion status, MGMT promoter methylation status, and glioma stage (Fig. 6A). To evaluate the prognostic significance of these variables, we performed both univariate and multivariate regression analyses. The results indicated that the SLC25A13-related risk score was an independent prognostic factor for glioma patients (Fig. 6B). Additionally, we validated our glioma prognostic model using three independent datasets: TCGA, CGGA693, and CGGA325. Receiver operating characteristic (ROC) curve analysis demonstrated high discriminative ability of the model, with three-year AUC values of 0.84, 0.95, and 0.84 for the TCGA, CGGA693, and CGGA325 datasets, respectively (Fig. 6C–E). Calibration plots, which compare predicted survival probabilities with actual outcomes, indicated a high degree of concordance, suggesting that the model reliably predicts patient survival (Fig. 6F–H). Decision curve analysis (DCA) further showed that the nomogram offers superior clinical net benefit compared to other prognostic factors (Fig. 6I–K). These findings underscore the clinical utility of the SLC25A13-related risk score in improving the prognostication and management of glioma patients.

Fig. 6
figure 6

Development and evaluation of a prognostic model for glioma patients based on SLC25A13-related risk score. A The nomogram constructed based on SLC25A13-related risk scores and other clinicopathologic features. B Univariate and multivariate Cox regression analyses identifying SLC25A13-related risk score as an independent prognostic factor. C–E Receiver Operating Characteristic (ROC) analysis illustrating the model's discrimination ability in predicting glioma patient outcomes across the TCGA, CGGA693, and CGGA325 datasets. F–H Calibration plots comparing the predicted probabilities of survival outcomes with the observed outcomes in glioma patients. I–K Decision curve analysis indicating greater clinical net benefit of the nomogram compared to other prognostic factors

Full size image

Comprehensive single cell analysis of SLC25A13-related risk score in the tumor microenvironment

First, we selected newly diagnosed glioblastoma multiforme (GBM) samples from the single-cell RNA sequencing dataset GSE182109. Subsequently, we analyzed the single-cell data using the Seurat standard workflow, annotating 29,778 cells based on specific markers. These cells were categorized into tumor-associated macrophages (TAMs), malignant cells, T cells, pericytes, oligodendrocytes, and B cells (Fig. 7A and B). Further validation through inferCNV analysis demonstrated significant copy number variation (CNV) events in tumor cells compared to TAMs, notably the amplification of chromosome 7 and the deletion of chromosome 10 (Fig. 7C). This result confirms the accuracy of our annotation of tumor cells. We examined the expression of the seven core genes at single-cell resolution and found that all seven core genes were detected with variable expression in malignant cells (Figure S7A). Subsequently, based on the previously determined formula, we calculated the risk score for each cell, categorizing malignant cells with risk scores in the top 30% as high-risk cells, those in the bottom 30% as low-risk cells, and those in the middle as medium-risk cells (Fig. 7D and E).

Fig. 7
figure 7

Single-cell transcriptomic analysis reveals the implications of SLC25A13-based risk score in the tumor microenvironment. A, B Single-cell clustering and annotation of cell types derived from the GSE182109 dataset. C InferCNV analysis demonstrating significant copy number variation events in tumor cells. D Distribution of SLC25A13-related risk scores across different cell subtypes. E Categorization of malignant cells into high, medium, and low-risk groups based on SLC25A13-related risk scores. F CellChat analysis revealing differential communication patterns among different risk groups. G, H Variation in the strength of the PTPR signaling pathway among malignant cells across different risk groups. I, J CALCR signaling pathway sent by medium-risk malignant cells to pericytes. K The scMetabolism analysis showing significant metabolic differences between high and low-risk malignant cells

Full size image

To explore whether these cells displayed differential interactions with other cellular components in tumor microenvironment, we employed the CellChat algorithm for cell communication analysis. The results revealed intricate cellular communication networks between different cell subpopulations (Fig. 7F and S8A-B). For instance, the PTPR signaling pathway was predominantly initiated by low-risk malignant cells and received by oligodendrocytes, mediated specifically through the LRRC4C-PTPRF receptor-ligand pair (Fig. 7G and H). Similarly, the CALCR signaling pathway was mainly sent by Medium-risk malignant cells to pericytes, facilitated by the DM-CALCR receptor-ligand pair (Fig. 7I and J). Additionally, we analyzed metabolic pathways using scMetabolism, revealing significant metabolic differences between high-risk and low-risk malignant cells (Fig. 7K). This suggests that there is a notable distinction between malignant cells with high-risk scores and those with low-risk scores.

Elevated SLC25A13 expression in glioblastoma tissues verified by multi-source data and experimental analysis

To validate the previous results, we utilized three independent datasets—GSE50161, Rembrandt, and GSE4290—to examine the expression of SLC25A13 in glioma tissues and normal brain tissues. The results confirmed the up-regulation of SLC25A13 expression in tumor tissues (Fig. 8A). In addition, we collected six pairs of surgically resected glioblastoma samples along with their corresponding adjacent non-cancerous tissues. To evaluate the expression levels of SLC25A13, quantitative PCR (qPCR) assays were conducted. The qPCR results demonstrated significantly elevated expression of SLC25A13 in glioma tissues compared to the adjacent non-cancerous tissues (Fig. 8B). This observation was further corroborated by immunohistochemistry (IHC) analysis, which also revealed a high expression of SLC25A13 protein in the tumor samples (Fig. 8C and D). Western blot (WB) analysis of the paired samples provided consistent results, confirming that SLC25A13 levels were markedly higher in the glioma tissues relative to the adjacent non-cancerous tissues (Fig. 8E and F).

Fig. 8
figure 8

Elevated SLC25A13 expression in glioblastoma tissues verified by multi-source data and experimental analysis. Statistical significance is denoted as follows: ns (not significant, p > 0.05), *(p < 0.05), **(p < 0.01), ***(p < 0.001), and ****(p < 0.0001). A SLC25A13 expression in glioblastoma tissues versus non-tumour tissues across GSE50161, Rembrandt, and GSE4290 datasets. Differences between the two groups were assessed using the Wilcoxon rank-sum test. B The qPCR analysis showing elevated SLC25A13 mRNA levels in glioblastoma tissues (n = 6 per group, paired t-test). C, D Immunohistochemical staining indicating stronger SLC25A13 protein expression in glioblastoma tissues compared to paraneoplastic tissue. E, F Western blot assay evaluating SLC25A13 protein expression in tumor tissues compared to adjacent non-cancerous tissues

Full size image

SLC25A13 promotes the malignant behavior of glioblastoma cells

To investigate the role of SLC25A13 in glioma cells, we generated an SLC25A13 knockdown model using shRNA in U87 and U251 cell lines (Figure S9A). EDU staining revealed that the EDU fluorescence intensity in U87 and U251 cells decreased after SLC25A13 knockdown, indicating a reduction in cell proliferation capacity (Fig. 9A and B). The CCK8 assay further confirmed a decrease in the viability of U87 and U251 cells following SLC25A13 knockdown (Fig. 9C). Flow cytometry analysis of cell cycle distribution demonstrated that SLC25A13 knockdown resulted in a decreased G0/G1 phase ratio and an increased G2/M phase ratio, suggesting a G2/M phase block (Fig. 9D). These findings imply that SLC25A13 is essential for proper cell cycle progression in glioma cells. Moreover, the scratch assay indicated reduced migratory capability in both cell lines after SLC25A13 knockdown (Fig. 9E and F), suggesting a role for SLC25A13 in cell motility. An increase in cell apoptosis was also observed (Fig. 9G), indicating increased programmed cell death, which likely contributes to the observed reduction in cell viability and proliferation. Finally, the colony formation assay demonstrated a significant reduction in clonogenic potential following SLC25A13 knockdown (Fig. 9H).

Fig. 9
figure 9

Impact of SLC25A13 knockdown on the functionality of glioblastoma cells. Statistical significance is denoted as follows: ns (not significant, p > 0.05), *(p < 0.05), **(p < 0.01), ***(p < 0.001), and **** (p < 0.0001). Data are presented as mean ± SD of three independent experiments (biological replicates, n = 3). A, B Reduction in EdU staining intensity following SLC25A13 knockdown. C CCK-8 assay confirms reduced cell viability following SLC25A13 knockdown. D Changes in cell cycle distribution with decreased G1 phase percentage and increased G2 phase cells. E, F Diminished migratory capability in glioblastoma cells after SLC25A13 knockdown. G Increased apoptosis levels observed following SLC25A13 knockdown. H Significant reduction in clonogenic potential after SLC25A13 knockdown

Full size image

SLC25A13 promotes the malignant progression of glioblastoma in vivo

To confirm the tumor-promoting effects of SLC25A13 in vivo, we established an in situ tumor model in nude mice. In vivo imaging demonstrated that knockdown of SLC25A13 significantly impaired the tumorigenic capacity of glioma cells (Fig. 10A and S9B). Hematoxylin and eosin (H&E) staining and Ki67 immunohistochemistry of tumor tissue sections further confirmed that SLC25A13 knockdown resulted in a reduced tumor-forming ability (Fig. 10B). Additionally, the knockdown of SLC25A13 prolonged the survival of nude mice and mitigated weight loss compared to the control group, suggesting that the malignant progression of glioma cells was significantly inhibited (Fig. 10C and D).

Fig. 10
figure 10

In vivo experiments validating the tumor-promoting effects of SLC25A13. Statistical significance is denoted as follows: ns (not significant, p > 0.05), *(p < 0.05), **(p < 0.01), ***(p < 0.001), and ****(p < 0.0001). A In vivo imaging experiments illustrating the tumor-forming ability of both control and experimental groups. B Hematoxylin and eosin (H&E) staining and Ki67 staining of tumor tissues from control and experimental groups. C Survival curve analysis of nude mice in control and experimental groups (n = 8 per group). D Body weight of nude mice in control and experimental groups over time (n = 8 per group)

Full size image

Discussion

SLC25A13, a key member of the solute carrier family, has been predominantly studied in the context of non-tumorigenic diseases [32,33,34], with its role in cancer largely underexplored. In this study, we identified the dysregulation of SLC25A13 expression and observed genetic alterations across various cancer types. We further elucidated its relationship with the tumor immune microenvironment and evaluated its prognostic significance. Importantly, our findings provide the first evidence that SLC25A13 plays a role in promoting the malignant progression of glioma, highlighting its potential as a therapeutic target for this aggressive cancer.

With the publication of data from large international pan-cancer studies such as TCGA and CPTAC, significant advancements in our understanding of cancer have emerged. These studies, leveraging extensive cohorts, have yielded numerous new insights into oncogenesis [4, 35]. In our investigation, we first analyzed the expression pattern of the SLC25A13 gene across various cancers using sequencing data from TCGA and normal tissues data from the GTEx project. We further validated these findings with protein quantification data from CPTAC. The widespread dysregulation of SLC25A13 expression observed in these analyses suggests a potentially crucial role in tumorigenesis. Copy number variation and aberrant methylation are critical mechanisms by which tumor cells modulate gene expression to gain survival advantages [36, 37]. We observed consistent aberrations in SLC25A13 expression correlated with copy number amplification and methylation changes, suggesting that alterations in SLC25A13 expression at the genetic level may contribute to malignant transformation. In recent years, immunotherapy has become a focal point in cancer treatment research. However, the variable responses among different cancer patients have limited its universal application [38]. The expression of immune checkpoints and the composition of the tumor immune microenvironment significantly influence immunotherapy efficacy [39]. Tumor mutational burden and microsatellite instability are considered powerful predictors of immunotherapy response [40, 41]. This study investigated the associations between SLC25A13 expression and multiple immune-related parameters, including microsatellite instability, tumor mutational burden, immune cell infiltration patterns, immunophenotype profiles, and immunomodulatory molecule expression. The results revealed both conserved patterns and tumor-specific variations in SLC25A13-mediated immunomodulation across different cancer types, suggesting that targeted regulation of SLC25A13 could potentially enhance immunotherapeutic outcomes in specific tumor types.

Gliomas, the most common primary malignant brain tumors in adults, are classified by the World Health Organization (WHO) into grades I-IV, with glioblastoma (WHO grade IV) being the most aggressive form and having a median survival of approximately 15 months [42, 43]. Given the abnormally high expression of the SLC25A13 gene in gliomas and its association with poorer patient prognosis, we conducted an in-depth analysis of the potential oncogenic role of SLC25A13 in gliomas. Gene set enrichment analysis consistently revealed a significant role for SLC25A13 in the cell cycle of glioma cells. Notably, our bioinformatics analysis results were corroborated by subsequent cell cycle experiments, which confirmed that SLC25A13 knockdown induced a G2/M phase arrest. Moreover, both ex vivo and in vivo experiments consistently demonstrated that SLC25A13 was highly expressed in tumor tissues and facilitated the malignant behavior of glioblastoma cells. These findings suggest that SLC25A13 may serve as a potential therapeutic target in glioblastoma.

The identification of cancer biomarkers has been a critical focus in oncology research, given their importance in early cancer diagnosis, prognosis prediction, and treatment response assessment [44]. In this study, we developed an SLC25A13-associated risk score using multiple machine learning methods and constructed a nomogram by integrating the risk score with other clinical features to guide patient prognosis. Multiple independent datasets validated the reliability of the nomogram for prognostic prediction. Furthermore, our analysis revealed that glioma patients with elevated SLC25A13 expression are more likely to exhibit resistance to temozolomide. Interestingly, STOCK1N-35696 emerged as a potentially effective therapeutic option for this subset of patients. Additionally, higher SLC25A13 expression correlated with more malignant clinicopathologic features of gliomas. These findings suggest that SLC25A13 plays a crucial role in the malignant progression of gliomas and may contribute to treatment resistance. Consequently, we propose that SLC25A13 represents a promising biomarker for predicting tumor aggressiveness and patient outcomes in glioma.

In recent years, rapid advancements in single-cell sequencing and spatial transcriptome technologies have significantly enhanced our understanding of the tumor microenvironment [45, 46]. Motivated by these developments, we first determined the localization of SLC25A13 within the tumor microenvironment, finding a high correlation with malignant cells and a negative correlation with CD8 + T cell infiltration. This suggests that SLC25A13 may play a crucial role in malignant cells and may be associated with immunosuppression. Furthermore, our risk score, derived from the weighted expression of various genes, reflects the expression patterns of multiple genes. We evaluated these patterns at single-cell resolution, categorizing malignant cells into high, medium, or low-risk groups. Notably, low-risk malignant cells exhibited enhanced PTPR signaling activity. Previous studies have shown that PTPRF functions as a tumor suppressor in various cancers [47, 48], supporting our hypothesis that low-risk cells are less malignant than high-risk cells. Medium-risk malignant cells, on the other hand, exhibited stronger CALCR signaling activity, while CALCRL has been reported to play a pro-carcinogenic role in tumors [49, 50]. Additionally, differences in metabolic pathway activity between the two groups of cells further confirm that the risk score effectively distinguishes the heterogeneity of tumor cells. These findings provide a microscopic explanation for the poorer prognosis observed in high-risk patients.

In summary, our study presents, for the first time, a comprehensive pan-cancer analysis of SLC25A13, elucidating its role in promoting the malignant progression of gliomas. These findings suggest that SLC25A13 may serve as a potential therapeutic target in this aggressive malignancy. Moreover, our results support the utility of SLC25A13 as a biomarker for predicting glioma aggressiveness and patient outcomes. The nomogram developed based on the SLC25A13-related risk score offers a valuable tool for prognostic stratification in clinical practice. However, several limitations warrant consideration. The relationship between SLC25A13 and the tumor immune microenvironment requires further experimental validation. Additionally, the molecular mechanisms underlying SLC25A13-mediated promotion of malignant behavior in glioblastoma cells necessitate more in-depth investigation. We anticipate that our work will provide a foundation for future studies aimed at elucidating the specific molecular pathways through which SLC25A13 influences tumor biology.

Conclusions

In conclusion, our study provides a comprehensive analysis of SLC25A13 across various cancer types. We demonstrated for the first time that SLC25A13 is highly expressed in glioma tissues and confirmed through both in vivo and in vitro experiments that SLC25A13 promotes the malignant progression of glioblastoma. Furthermore, our SLC25A13-based nomogram effectively guides glioma patient prognosis. Collectively, our findings suggest that SLC25A13 may serve as a potential therapeutic target for glioma patients, warranting further investigation into its clinical applications.

Data availability

All datasets analyzed in this study are publicly available. The specific sources and accession numbers for each dataset are detailed in the METHODS section.

References

  1. Torre LA, Siegel RL, Ward EM, Jemal A. Global cancer incidence and mortality rates and trends—an update. Cancer Epidemiol Biomarkers Prev. 2016;25:16–27.

    Article  PubMed  Google Scholar 

  2. Lawson DA, Kessenbrock K, Davis RT, Pervolarakis N, Werb Z. Tumour heterogeneity and metastasis at single-cell resolution. Nat Cell Biol. 2018;20:1349–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Junttila MR, de Sauvage FJ. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature. 2013;501:346–54.

    Article  CAS  PubMed  Google Scholar 

  4. Hoadley KA, et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell. 2018;173:291-304.e6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Malta TM, et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell. 2018;173:338-354.e15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Stine ZE, Schug ZT, Salvino JM, Dang CV. Targeting cancer metabolism in the era of precision oncology. Nat Rev Drug Discov. 2022;21:141–62.

    Article  CAS  PubMed  Google Scholar 

  7. Bhutia YD, et al. SLC transporters as a novel class of tumour suppressors: identity, function and molecular mechanisms. Biochem J. 2016;473:1113–24.

    Article  CAS  PubMed  Google Scholar 

  8. Schlessinger A, Zatorski N, Hutchinson K, Colas C. Targeting SLC transporters: small molecules as modulators and therapeutic opportunities. Trends Biochem Sci. 2023;48:801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wu W, Jiang C, Zhu W, Jiang X. Multi-omics analysis reveals the association between specific solute carrier proteins gene expression patterns and the immune suppressive microenvironment in glioma. J Cell Mol Med. 2024;28: e18339.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ruprecht JJ, Kunji ERS. The SLC25 mitochondrial carrier family: structure and mechanism. Trends Biochem Sci. 2020;45:244–58.

    Article  CAS  PubMed  Google Scholar 

  11. Palmieri F. The mitochondrial transporter family SLC25: identification, properties and physiopathology. Mol Aspects Med. 2013;34:465–84.

    Article  CAS  PubMed  Google Scholar 

  12. Kunji ERS, King MS, Ruprecht JJ, Thangaratnarajah C. The SLC25 carrier family: important transport proteins in mitochondrial physiology and pathology. Physiology (Bethesda). 2020;35:302–27.

    CAS  PubMed  Google Scholar 

  13. Fu HY, et al. The mutation spectrum of the SLC25A13 gene in Chinese infants with intrahepatic cholestasis and aminoacidemia. J Gastroenterol. 2011;46:510–8.

    Article  PubMed  Google Scholar 

  14. Chang KW, Chen HL, Chien YH, Chen TC, Yeh CT. SLC25A13 gene mutations in Taiwanese patients with non-viral hepatocellular carcinoma. Mol Genet Metab. 2011;103:293–6.

    Article  CAS  PubMed  Google Scholar 

  15. Lv Y, et al. The overexpression of SLC25A13 predicts poor prognosis and is correlated with immune cell infiltration in patients with skin cutaneous melanoma. Dis Markers. 2022;2022:4091978.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lau NKC, et al. In-house multiplex ligation-dependent probe amplification assay for citrin deficiency: analytical validation and novel exonic deletions in SLC25A13. Pathology. 2021;53:867–74.

    Article  CAS  PubMed  Google Scholar 

  17. Owusu-Ansah M, Guptan N, Alindogan D, Morizono M, Caldovic L. NAGS, CPS1, and SLC25A13 (Citrin) at the crossroads of arginine and pyrimidines metabolism in tumor cells. Int J Mol Sci. 2023;24:6754.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Liao C, Wang X. TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. BMC Bioinformatics. 2023;24:483.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Veres DV, et al. ComPPI: a cellular compartment-specific database for protein-protein interaction network analysis. Nucleic Acids Res. 2015;43:D485–93.

    Article  CAS  PubMed  Google Scholar 

  20. Wu T, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2:100141.

    CAS  PubMed  Google Scholar 

  21. Yuan H, et al. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res. 2019;47:D900-d908.

    Article  CAS  PubMed  Google Scholar 

  22. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Thorsson V, et al. The immune landscape of cancer. Immunity. 2018;48:812-830.e14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Han Y, et al. TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment. Nucleic Acids Res. 2023;51:D1425-d1431.

    Article  PubMed  Google Scholar 

  25. Hao Y, et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol. 2024;42:293–304.

    Article  CAS  PubMed  Google Scholar 

  26. Korsunsky I, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16:1289–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Patel AP, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014;344:1396–401.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jin S, et al. Inference and analysis of cell–cell communication using Cell Chat. Nat Commun. 2021;12:1088.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wu Y, et al. Spatiotemporal immune landscape of colorectal cancer liver metastasis at single-cell level. Cancer Discov. 2022;12:134–53.

    Article  CAS  PubMed  Google Scholar 

  30. Chan TA, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol. 2019;30:44–56.

    Article  CAS  PubMed  Google Scholar 

  31. Li K, Luo H, Huang L, Luo H, Zhu X. Microsatellite instability: a review of what the oncologist should know. Cancer Cell Int. 2020;20:16.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Tavoulari S, Lacabanne D, Thangaratnarajah C, Kunji ERS. Pathogenic variants of the mitochondrial aspartate/glutamate carrier causing citrin deficiency. Trends Endocrinol Metab. 2022;33:539–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Komatsu M, Tanaka N, Kimura T, Yazaki M. Citrin deficiency: clinical and nutritional features. Nutrients. 2023;15:2284.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Hayasaka K. Metabolic basis and treatment of citrin deficiency. J Inherit Metab Dis. 2021;44:110–7.

    Article  CAS  PubMed  Google Scholar 

  35. Cao L, et al. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell. 2021;184:5031-5052.e26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kuiper RP, Ligtenberg MJ, Hoogerbrugge N, Geurts van Kessel A. Germline copy number variation and cancer risk. Curr Opin Genet Dev. 2010;20:282–9.

    Article  CAS  PubMed  Google Scholar 

  37. Nishiyama A, Nakanishi M. Navigating the DNA methylation landscape of cancer. Trends Genet. 2021;37:1012–27.

    Article  CAS  PubMed  Google Scholar 

  38. Zhang Y, Zhang Z. The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications. Cell Mol Immunol. 2020;17:807–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. O’Donnell JS, Teng MWL, Smyth MJ. Cancer immunoediting and resistance to T cell-based immunotherapy. Nat Rev Clin Oncol. 2019;16:151–67.

    Article  CAS  PubMed  Google Scholar 

  40. Hou W, Yi C, Zhu H. Predictive biomarkers of colon cancer immunotherapy: present and future. Front Immunol. 2022;13:1032314.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Jardim DL, Goodman A, de MeloGagliato D, Kurzrock R. The challenges of tumor mutational burden as an immunotherapy biomarker. Cancer Cell. 2021;39:154–73.

    Article  CAS  PubMed  Google Scholar 

  42. Alexander BM, Cloughesy TF. Adult glioblastoma. J Clin Oncol. 2017;35:2402–9.

    Article  CAS  PubMed  Google Scholar 

  43. Lapointe S, Perry A, Butowski NA. Primary brain tumours in adults. Lancet. 2018;392:432–46.

    Article  PubMed  Google Scholar 

  44. Wu L, Qu X. Cancer biomarker detection: recent achievements and challenges. Chem Soc Rev. 2015;44:2963–97.

    Article  CAS  PubMed  Google Scholar 

  45. Ahmed R, et al. Single-Cell RNA sequencing with spatial transcriptomics of cancer tissues. Int J Mol Sci. 2022;23:3042.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lei Y, et al. Applications of single-cell sequencing in cancer research: progress and perspectives. J Hematol Oncol. 2021;14:91.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Bera R, et al. Functional genomics identified a novel protein tyrosine phosphatase receptor type F-mediated growth inhibition in hepatocarcinogenesis. Hepatology. 2014;59:2238–50.

    Article  CAS  PubMed  Google Scholar 

  48. Tian X, Yang C, Yang L, Sun Q, Liu N. PTPRF as a novel tumor suppressor through deactivation of ERK1/2 signaling in gastric adenocarcinoma. Onco Targets Ther. 2018;11:7795–803.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Angenendt L, et al. Calcitonin receptor-like (CALCRL) is a marker of stemness and an independent predictor of outcome in pediatric AML. Blood Adv. 2021;5:4413–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Tang S, et al. CALCRL knockdown suppresses cancer stemness and chemoresistance in acute myeloid leukemia with FLT3-ITD and DNM3TA-R882 double mutations. Drug Dev Res. 2024;85: e22137.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

All authors gratefully acknowledge the following public databases for providing valuable data resources: The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Genotype-Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA).

Funding

Not applicable.

Author information

Author notes

  1. Wenjie Wu, Simin Liu and Huili Ren have contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, China

    Wenjie Wu, Simin Liu, Yuxin Rao & Xiaobing Jiang

  2. Department of Immunology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China

    Keke Wei

  3. Department of Pharmacy, Traditional Chinese and Western Medicine Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Huili Ren

  4. Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Jun Nie

Authors
  1. Wenjie Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Simin Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Huili Ren
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yuxin Rao
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Jun Nie
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Keke Wei
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Xiaobing Jiang
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Conceptualization, Wenjie Wu, Keke Wei, and Xiaobing Jiang; Data curation, Wenjie Wu and Yuxin Rao; Formal analysis, Wenjie Wu, Simin Liu, and Huili Ren; Funding acquisition, Keke Wei and Xiaobing Jiang; Investigation, Wenjie Wu, Simin Liu, Huili Ren, and Yuxin Rao; Methodology, Wenjie Wu, Simin Liu, Huili Ren, and Yuxin Rao; Project administration, Keke Wei and Xiaobing Jiang; Resources, Keke Wei and Xiaobing Jiang; Software, Wenjie Wu and Huili Ren; Supervision, Keke Wei and Xiaobing Jiang; Validation, Simin Liu and Huili Ren; Visualization, Wenjie Wu and Simin Liu; Writing – original draft, Wenjie Wu, Simin Liu, and Huili Ren; Writing – review & editing, Jun Nie, Keke Wei and Xiaobing Jiang. Wenjie Wu, Simin Liu, and Huili Ren contributed equally to this work and therefore jointly share the first authorship.

Corresponding authors

Correspondence to Jun Nie, Keke Wei or Xiaobing Jiang.

Ethics declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. Written informed consent was obtained from all patients or their legal representatives prior to the collection and use of surgical samples in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary Material 1: Figure S1A. Genetic variation patterns of SLC25A13 across different tumor types.

12935_2025_3696_MOESM2_ESM.tif

Supplementary Material 2: Figure S2. Correlation of SLC25A13 with immunomodulatory molecules. (A) Correlation of SLC25A13 with immunostimulatory molecules. (B) Correlation of SLC25A13 with chemokines.

12935_2025_3696_MOESM3_ESM.tif

Supplementary Material 3: Figure S3. Gene Set Enrichment Analysis (GSEA) of the HALLMARK and metabolic pathways in SLC25A13 high and low expression groups. (A) Dot plots illustrating the intensity of different pathway enrichment in the high SLC25A13 expression group.

12935_2025_3696_MOESM4_ESM.tif

Supplementary Material 4: Figure S4. Impact of SLC25A13 expression on metabolic pathway strength in gliomas. (A) Scoring of various metabolic pathways in GBM across high and low SLC25A13 expression groups. (B) Scoring of various metabolic pathways in LGG across high and low SLC25A13 expression groups. (C) Hallmark gene set enrichment analysis for high and low SLC25A13 expression groups in LGG. (D) Survival curve analysis of high and low SLC25A13 expression groups across different grades of gliomas.

12935_2025_3696_MOESM5_ESM.tif

Supplementary Material 5: Figure S5. Pathway enrichment analysis and prediction of drug sensitivity. (A) Enrichment of oncogenic pathways in the SLC25A13 high-expression group in gliomas. (B-C) Identification of potential therapeutic agents for patients with high SLC25A13 expression using the XSum algorithm in LGG and GBM.

12935_2025_3696_MOESM6_ESM.tif

Supplementary Material 6: Figure S6. Broader implications of SLC25A13-related risk score across various cancers. (A-J) Prognostic value of risk scores in multiple cancers. (K-L) Time-dependent ROC curves illustrating the discriminative power of the SLC25A13-related risk score in the TCGA and CGGA glioma cohorts.

12935_2025_3696_MOESM7_ESM.tif

Supplementary Material 7: Figure S7. Expression of core genes in the tumor microenvironment of glioblastoma. (A) Expression of seven core genes across different cellular compositions.

12935_2025_3696_MOESM8_ESM.tif

Supplementary Material 8: Figure S8. Cellular interactions between malignant cells at varying risk levels in the microenvironment. (A) Detailed receptor-ligand pairs involved in signaling from malignant cells at varying risk levels in cellular communication. (B) Detailed receptor-ligand pairs for signals received by malignant cells at varying risk levels in cellular communication.

12935_2025_3696_MOESM9_ESM.tif

Supplementary Material 9: Figure S9. (A) Validation of SLC25A13 knockdown efficiency in U87 and U251 cell lines. (B) Quantitative analysis of in vivo imaging results at different time intervals (n = 5 per group).

Supplementary Material 10.

Supplementary Material 11.

Supplementary Material 12.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, W., Liu, S., Ren, H. et al. Unveiling the oncogenic role of SLC25A13: a multi-omics pan-cancer analysis reveals its impact on glioma progression. Cancer Cell Int 25, 76 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03696-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03696-z

Keywords

Cancer Cell International

ISSN: 1475-2867

Contact us