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Introduction

Gastric cancer (GC) poses a major global health challenge, ranking as one of the most common and deadly malignancies. With ~769,000 annual deaths, the high mortality of GC is largely attributed to its aggressive progression and the tendency for diagnosis at advanced stages (1). Despite substantial advancements in surgical techniques and therapeutic interventions, the overall survival (OS) rate for patients with advanced GC remains poor, with a median survival time of 10–14 months (2). This underscores the urgent need to further elucidate the molecular mechanisms driving GC progression, with the goal of discovering novel therapeutic targets and improving patient outcomes.

Owing to the intrinsic characteristics of most tumors and their reliance on metabolic reprogramming, targeting tumor metabolism has increasingly been recognized as a promising therapeutic approach (3). α-Ketoglutarate (α-KG), a crucial metabolite in cancer metabolism, serves a pivotal role in regulating cellular energy production and epigenetic modifications (4). Within the tricarboxylic acid (TCA) cycle, α-KG acts as an intermediate metabolite that helps maintain cellular energy homeostasis (5). In addition to its metabolic function, α-KG acts as a cofactor for a range of dioxygenases, including ten-eleven translocation (TET) enzymes and JmjC domain-containing histone demethylases, which mediate DNA and histone demethylation, thereby regulating gene expression and cellular differentiation (6,7). Consequently, α-KG has emerged as a potential tumor suppressor by modulating dysregulated metabolic and epigenetic pathways in cancer cells.

Numerous studies have assessed the mechanisms through which α-KG suppresses cancer. In colorectal cancer, glutamine limitation reduces cellular α-KG levels, which enhances Wnt signaling in APC-mutated intestinal organoids. This promotes stemness characteristics, inhibits cellular differentiation, and ultimately leads to adenocarcinoma formation (8). Supplementing exogenous α-KG can reverse the overactivation of Wnt signaling and enhanced stemness caused by low glutamine levels, thereby promoting cellular differentiation and inhibiting tumor growth (8). Regarding the epigenetic regulatory role of α-KG, studies in a 4T1 breast cancer orthotopic mouse model have reported that utilizing an α-KG dehydrogenase (KGDH) inhibitor, AA6, leads to intracellular accumulation of α-KG, which increases the activity of α-KG-dependent epigenetic enzymes (9). This metabolic environment facilitates epigenetic reprogramming, effectively counteracting tumor invasion by inhibiting epithelial-to-mesenchymal transition (EMT). It establishes an α-KG-dependent epigenetic regulatory axis in the TET-microRNA200-Zinc finger E-box-binding homeobox 1/C-terminal-binding protein 1-matrix metalloproteinase 3 pathway, which brought anti-metastatic effects in a breast cancer metastasis mouse model (9). Additionally, in glioma and non-small cell lung cancer, α-KG was reported to inhibit cancer progression through its roles in the TCA cycle and epigenetic regulation (10,11). However, the heterogeneity of different tumor types markedly impacts the mechanisms by which α-KG exerts its effects. The specific impact of α-KG on cell proliferation, apoptosis and the tumor microenvironment in GC remains to be fully elucidated.

Notably, previous studies have revealed that several α-KG-related genes serve as valuable prognostic biomarkers in cancer. Isocitrate dehydrogenase 1 (IDH1) serves a crucial role in malignancies and is considered a marker for liver metastasis (12). Mutant (m)IDH1 produces the carcinogenic metabolite (R)-2-hydroxyglutarate (R-2HG), which promotes cancer by inactivating DNA and histone demethylases (13). Additionally, mutations in IDH1 are a defining feature of a subset of primary gliomas (14). Moreover, research has demonstrated that a single amino acid residue mutation in the active site of IDH1 in gliomas disrupts its ability to catalyze the conversion of isocitrate to α-KG. Instead, it catalyzes the NADPH-dependent conversion of α-KG to R-2HG, and the resulting accumulation of 2-HG is associated with an increased risk for malignant brain tumors (14,15). KGDH, another α-KG-related gene, functions alongside lysine acetyltransferase 2A to execute its succinyltransferase activity. This process promotes glioma growth by succinylating histone H3K79 (16). Therefore, a deeper understanding of α-KG-related genes could aid in identifying potential biomarkers and guide immunotherapy strategies for GC.

The present study aimed to construct a predictive model based on α-KG-related genes to evaluate their utility in forecasting therapy responses and to explore prognostic and immune microenvironment characteristics in GC. Public data from 1,397 patients across The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were analyzed, with the TCGA-stomach adenocarcinoma (STAD) cohort designated for model training and GEO datasets (including GSE26899 and GSE13861) allocated for validation and treatment prediction. By integrating α-KG-related genes, the study employed bioinformatic and statistical approaches to assess associations with survival and prognosis, thereby providing a framework for understanding GC outcomes and immune landscape features. This methodology aimed to support clinical decision-making by linking molecular profiles to potential therapeutic responses.

Materials and methods

Data acquisition

Bulk RNA-sequencing data and associated clinicopathological details for GC samples were sourced from TCGA official data portal (https://gdc portal.nci.nih.gov/). The mRNA and long noncoding RNA transcriptome profiles were obtained via the TCGAbiolinks package (https://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html) (17). Additionally, clinicopathological data and comprehensive expression data were obtained from three validated GC cohorts: GSE15459 (18), GSE84433 (19) and GSE84437 (19). Furthermore, two chemotherapy datasets were used to assess the contribution of the α-KG-related gene index (AKGI) to the prediction of therapy benefits: GSE26899 (20) and GSE13861 (21). Raw count data underwent transcripts per million normalization for standardization.

Identification of the expression and variation levels of α-KG-related genes

To construct the signatures list of α-KG-related genes, genes associated with eight distinct α-KG-related pathways were obtained from trusted scientific databases, including Gene Set Enrichment Analysis (GSEA) gene sets (https://www.gsea-msigdb.org/gsea/index.jsp), Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://www.genome.jp/kegg/) and through manual compilation. After removing duplicates, a total of 508 α-KG-related genes were selected for subsequent analysis. Differential expression analysis was conducted using the ‘limma’ package to identify genes critical to α-KG-related functions (22). Differentially expressed α-KG-related genes between tumor and normal were identified by setting a cutoff value of P<0.05 and |Log2FoldChange (FC)|>1.0. Enrichment analysis was performed using the ‘clusterProfiler’ package (23).

Establishment of a consensus machine learning-driven prognostic signature

To improve the comparability across different cohorts, all data were standardized by first performing Z-score transformation. Cox regression analysis was then performed to identify differentially expressed α-KG-related genes that were significantly associated with patient survival. Genes with a P-value of <0.05 were considered statistically significant and were included for further investigation. To construct an AKGI with high accuracy and generalizability, a combination of 10 machine learning algorithms were used, including supervised principal components (SuperPC), partial least squares regression for Cox (plsRcox), gradient boosting machine (GBM), stepwise Cox, least absolute shrinkage and selection operator (LASSO), Ridge, survival support vector machine (survival-SVM), CoxBoost and elastic net (Enet). By harnessing the distinct advantages of each algorithm, their integration enhanced the overall performance of the prognostic model in predicting STAD outcomes. Among the 10 machine learning algorithms employed in this model, the LASSO, StepCox and CoxBoost algorithms, which are capable of feature selection and dimensionality reduction, were combined with other machine learning methods to construct 115 prognostic signatures.

To identify the best algorithmic combination for constructing the optimal prognostic model, the C-index values was calculated for each model across the GSE15459, GSE84433, GSE84437 and TCGA cohorts. By comparing the average C-index values across these cohorts, the prognostic signature with the highest score was selected as the optimal model for further analysis. The risk score for each patient was then computed using the algorithmic combination selected for the optimal model. To divide patients into high- and low-risk groups, the ‘surv_cutpoint’ function in the ‘survminer’ package was used to determine the optimal cutoff point, which corresponded to the risk score that maximized the distinction in OS time between the two groups.

Performance evaluation of the prognostic model

The optimal predictive model was derived from the training and validation cohorts, risk scores were calculated for each sample using this model, and samples were subsequently classified into high- and low-AKGI groups based on these scores. To evaluate the predictive performance of the constructed signature, Kaplan-Meier survival analysis was performed using the ‘survival’ and ‘survminer’ packages in both the training and validation cohorts. Decision curve analysis (DCA) images were drawn to reflect the clinical benefit of the predictive nomogram model using ‘stdca.R’ package. The discriminative ability of the model between the high- and low-risk groups was assessed by calculating the area under the curve (AUC) in the receiver operating characteristic (ROC) analysis. Furthermore, the ‘timeROC’ and ‘cmprsk’ packages were used to plot time-dependent ROC and decision curves to further gauge the predictive accuracy of the model in the TCGA-STAD cohort.

Construction and performance analysis of the prognostic nomogram

A prognostic nomogram was developed based on independent prognostic factors, including the risk score derived from the signature and key clinical features, using the ‘nomogramEx’ package in the TCGA-STAD cohort. To assess the ability of the nomogram to predict OS, several analyses were performed in the TCGA-STAD cohort. A calibration curve analysis was then performed using the ‘calibrate’ package, allowing for a comparison between the predicted survival probabilities and the actual survival outcomes observed in the cohort.

Comprehensive analysis of immune-omics molecular characterization and immunotherapy response based on AKGI

Several previously published signatures related to tumor microenvironment (TME) cell types, immunotherapy responses, immune suppression and immune exclusion were obtained using the IOBR package. A standardized approach was then used to calculate the enrichment score for each sample, facilitating a thorough analysis of the immunological differences between high- and low-AKGI patients (24). Subsequently, a correlation analysis between AKGI and the enrichment scores of several immune characteristics and biological functions was performed. Using P<0.05 and |r|>0.3 as the selection criteria, items significantly associated with AKGI were identified. Additionally, certain prominent indicators were focused on, such as the stemness (25) and m6A index (26). The distribution of tumor mutational burden (TMB), silent mutations and missense mutations were also compared between the two groups and patients were reclassified based on AKGI. For evaluating the immunotherapy response, the survival of patients was first assessed with delayed response to immunotherapy, followed by the use of the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm (http://tide.dfci.harvard.edu/). to estimate their likelihood of responding to treatment (27). In addition, the predictive role of AKGI was also evaluated using two chemotherapy treatment cohorts, GSE26899 and GSE13861.

In silico analysis to screen potential therapy agents for patients with high AKGI scores

To screen potential therapy agents for patients with high AKGI scores, expression data from human cancer cell lines sourced from the Broad Institute's Cancer Cell Line Encyclopedia (https://sites.broadinstitute.org/ccle) was utilized. This provides extensive molecular profiles across several cancer types and their corresponding cell lines, offering insights into genetic alterations and drug responses. To obtain drug sensitivity data, two comprehensive datasets we used: the Cancer Therapeutics Response Portal (CTRP) v.2.0 (https://portals.broadinstitute.org/ctrp) and the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) Repurposing datasets (19Q4; http://depmap.org/portal/prism/). The area under the dose-response curve (AUC) was used as a key measure of drug sensitivity, where lower AUC values indicated higher drug sensitivity. When evaluating the evidence for candidate drugs, Connectivity Map (CMap) identified potential therapeutic compounds for GC by revealing small molecules whose gene expression signatures inversely correlated with GC-specific transcriptional profiles (connectivity scores: −100 to 0), thereby nominating drug candidates for experimental validation, and PubMed (https://pubmed.ncbi.nlm.nih.gov/) was searched for literature related to the candidates, and all available clinical trials and descriptions involving these drugs were analyzed.

Cell culture and α-KG treatments

GC AGS and MKN74 cells, obtained from Procell Life Science & Technology, Co., Ltd., were used in the current study and cultured in a saturated humidity atmosphere (37°C and 5% CO2). AGS cells were cultured in Ham's F-12 medium (cat. no. PM150810; Pricella®; Elabscience Bionovation Inc.) containing 10% fetal bovine serum (FBS; cat. no. C04001; VivaCell, Shanghai, China) and 100 IU/ml penicillin/ streptomycin antibiotics (P/S; cat. no. 15070063, Thermo Fisher Scientific, Inc.). Meanwhile, RPMI-1640 medium (cat. no. PM150110; Pricella; Elabscience Bionovation Inc.) containing 10% FBS and 100 IU/ml P/S was used to culture MKN74 cells.

Proliferation, wound healing and Transwell assays

A Cell Counting Kit-8 (CCK-8) assay was applied to confirm the cytotoxicity effect of α-KG interventions against the GC progression. Briefly, AGS and MKN74 cells (5×103/well) were plated in 96-well plates (Corning, Inc.) and cultured at 37°C for 24 h. For the α-KG interventions, 2, 4, 8, 10 and 20 mM α-KG was individually supplemented into the culture medium with the cells incubated at 37°C for a further 24 h. In addition, both GC cells treated with the culture medium supplemented with Dulbecco's phosphate-buffered saline (DPBS; cat. no. 14190250; Thermo Fisher Scientific, Inc.) were set as the negative control (NC) group. After the α-KG interventions, 100 µl CCK-8 reagents (cat. no. MA0218; Dalian Meilun Biotech Co., Ltd.) were added to the 96-well plates with the plates further incubated at 37°C for another 4 h. Lastly, the absorbance of 96-well plates at 450 nm was recorded using a microtiter plate reader (Multiskan™; Thermo Fisher Scientific, Inc.), and the inhibiting rates (IR) of α-KG interventions on the proliferation were calculated.

After the optimization of α-KG concentration based on the viability and 50% IR data, a wound healing assay was performed to analyze the effects of α-KG interventions on the migration potentials of GC cells (28). Accordingly, 5×105 AGS or MKN74 cells were seeded in 6-well plates (Corning, Inc.) and further cultured at 37°C for 24 h. Prior to the α-KG intervention, cells were allowed to grow until reaching a high confluence (90–100 %) to ensure consistency across cell lines. A wound across the cell monolayer was prepared by scratching the plates with 200 µl plastic pipette tips, and cellular debris and non-adherent cells were removed by washing with DPBS solution. Cells were subsequently incubated in serum-free medium with α-KG or DPBS at 37°C for 24 h. After treatment, the wound closure was microscopically (TI-S; Nikon Corporation) recorded and further analyzed using ImageJ software (V1.8.0; National Institutes of Health). Furthermore, the effect of α-KG interventions on the invasion potentials of GC cells was assessed using a Transwell invasion assay (29). AGS or MKN74 cells (2×103/well) were collected, resuspended in serum-free medium containing α-KG or DPBS solution and seeded into the upper Transwell chamber precoated with Matrigel at 37°C for 0.5 h (Corning, Inc.). Moreover, the lower chamber was supplemented with 600 µl culture medium containing 10% FBS (cat. no. C04001; Shanghai VivaCell Biosciences, Ltd.). After culturing at 37°C for 24 h, the GC cells in the upper chamber were removed using a cotton swab, and the Transwell chambers were fixed with methanol at room temperature for 10 min, stained with crystal violet solution (cat. no. G1062; Beijing Solarbio Science & Technology Co., Ltd.) at room temperature for 10 min, and microscopically (TI-S; Nikon Corporation) recorded. The number of GC cells that migrated through the Transwell system was quantified using ImageJ software (V1.8.0; National Institutes of Health).

Clonogenicity assay

The potential effect of α-KG treatments on the malignant characteristics of GC cells was assessed using a clonogenicity assay. A total of 1×103 AGS or MKN74 cells were collected, seeded in a 6-well culture plate (Corning, Inc.) and cultured at 37°C for a further 7 days. The culture medium containing α-KG or DPBS solution was replaced daily. Once the visible colonies developed, the colonies were incubated with 4% paraformaldehyde solution (cat. no. P1110, Beijing Solarbio Science & Technology Co., Ltd.) at 37° for 15 min and stained with crystal violet solution at room temperature for 30 min. The number of colonies was further microscopically (TI-S; Nikon Corporation) assessed. Quantification was performed using ImageJ software (V1.8.0; National Institutes of Health), with the colony area threshold adjusted to identify clusters containing >50 cells.

Cell cycle assay

The effect of α-KG treatments on inducing the cell cycle arrest of AGS or MKN74 cells was detected using a Cell Cycle and Apoptosis Analysis Kit (cat. no. MA0334; Dalian Meilun Biotech Co., Ltd.). As previously reported (3), after treatment with α-KG or DPBS, GC cells were collected and fixed with ice-cold 70% ethanol at −20°C. After fixation, cells were stained at room temperature for 15 min with a propidium iodide (PI) solution, which served as the analyte reporter by intercalating with DNA to allow quantification of DNA content. Flow cytometry analysis was performed using a FACSCalibur™ flow cytometer (BD Biosciences). Cell cycle distribution across the G0/G1, S, and G2/M phases was analyzed using FlowJo software (version 10.8.1; BD Biosciences).

Annexin V-FITC/PI staining. The potential effect of α-KG treatments on triggering apoptosis in AGS or MKN74 cells was quantitatively assessed using Annexin V-FITC/PI staining. As per the manufacturer's manual, GC cells of both groups post-treatment were collected and incubated with Annexin V-FITC solution (cat. no. CA1020; Beijing Solarbio Science & Technology Co., Ltd.) in the dark at 37°C for 5 min. After PI solution staining, apoptosis was immediately analysed using a FACSCalibur™ flow cytometer (BD Biosciences, San Jose, CA, USA). Flow cytometric data were analysed using FlowJo software (version 10.8.1; BD Biosciences, http://www.flowjo.com/).

Mitochondrial dysfunction assessment

MitoTracker staining and JC-1 staining were utilized to assess α-KG treatment-induced mitochondrial dysfunctions. For MitoTracker staining to assess mitochondrial activity, GC cells of both groups post-treatment were incubated at 37°C with 200 nM MitoTracker staining regent (cat. no. C1049; Beyotime Institute of Biotechnology) for 30 min and further counterstained with DAPI solution at 37°C for 10 min. The MitoTracker staining of both groups was assessed under a microscope (TI-S; Nikon Corporation), and the MitoTracker staining intensity of both groups was further analyzed using ImageJ software (V1.8.0; National Institutes of Health). For assessing the mitochondrial membrane potential (ΔΨm), GC cells of both groups post-treatment were incubated with 10 µM JC-1 staining solution (cat. no. C2006; Beyotime Institute of Biotechnology) for 20 min, followed by an analysis of JC-1 staining intensity under a microscope (TI-S; Nikon Corporation) using ImageJ software (V1.8.0; National Institutes of Health).

Biochemical assessment

Levels of oxidative stress and ferroptosis-related biomarkers [superoxide dismutase (SOD), malondialdehyde (MDA), reactive oxygen species (ROS) and iron] in GC cells of both groups post-treatment were measured using the following commercial kits according to the manufacturers' protocols: MDA (cat. no. S0131; Beyotime Institute of Biotechnology); ROS (cat. no. S0033; Beyotime Institute of Biotechnology); SOD (cat. no. BC5165; Beijing Solarbio Science & Technology Co., Ltd.); and iron (cat. no. BC5315; Beijing Solarbio Science & Technology Co., Ltd.).

Reverse transcription-quantitative PCR (RT-qPCR)

The anticancer effects of α-KG treatments against GC progression were analyzed using RT-qPCR of apoptosis, oxidative stress, and ferroptosis-related genes. Accordingly, the mRNA of GC cells of both groups post-treatment was extracted using TRIzol™ reagent (cat. no. 12183555; Thermo Fisher Scientific, Inc.) with the synthesis of cDNA performed at 65°C for 10 min using the Prime Script™ RT reagent kit (cat. no. RR047A; Takara Biotechnology Co., Ltd.). For PCR amplification, specific primers targeting genes related to apoptosis (Bax and Bcl-2), oxidative stress [nuclear factor erythroid 2-related factor-2 (Nrf2) and Kelch-like ECH-associated protein 1 (Keap1)] and ferroptosis [glutathione peroxidase 4 (GPX4) and solute carrier family 7 member 11 (SLC7A11)] were designed using the National Center for Biotechnology Information website (https://blast.ncbi.nlm.nih.gov/Blast.cgi) and commercially synthesized by Invitrogen™ (Thermo Fisher Scientific, Inc.). RT-qPCR of each group was then performed using the PikoReal system (Thermo Fisher Scientific, Inc.) with a commercial kit (cat. no. RR820A; Takara Biotechnology Co., Ltd.). The thermocycling conditions used in this study were as follows: An initial denaturation at 9°C for 3 min; followed by 8 cycles of denaturation at 98°C for 15 sec, annealing at 60°C for 15 sec and extension at 72°C for 30 sec; with a final extension at 72°C for 5 min. After RT-qPCR, relative gene expression was calculated using the 2−ΔΔCq method with the ubiquitously expressed GAPDH used as an internal control (30). Primer sequences were as follows: Bax, forward 5′-CCCGAGAGGTCTTTTTCCGAG-3′ and reverse 5′-CCAGCCCATGATGGTTCTGAT-3′; Bcl-2, forward 5′-GGTGGGGTCATGTGTGTGG-3′ and reverse 5′-CGGTTCAGGTACTCAGTCATCC-3′; Nrf2, forward 5′-TTCCCGGTCACATCGAGAG-3′ and reverse 5′-TCCTGTTGCATACCGTCTAAATC-3′; Keap1, forward 5′-GTGTCCATTGAGGGTATCCACC-3′ and reverse 5′-GCTCAGCGAAGTTGGCGAT-3′; GPX4, forward 5′-GAGGCAAGACCGAAGTAAACTAC-3′ and reverse 5′-CCGAACTGGTTACACGGGAA-3′; SLC7A11, forward 5′-TCTCCAAAGGAGGTTACCTGC-3′ and reverse 5′-AGACTCCCCTCAGTAAAGTGAC-3′; and GAPDH, forward 5′-CTGGGCTACACTGAGCACC-3′ and reverse 5′-AAGTGGTCGTTGAGGGCAATG-3′.

Transcriptomic analysis

To further investigate the regulatory mechanism by which α-KG treatment suppresses GC progression, total RNA was extracted from GC cells in both treated and control groups using TRIzol® reagent (cat. no. 15596018; Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. For transcriptomic analysis, polyadenylated mRNA was enriched using oligo(dT) magnetic beads, fragmented into short sequences and reverse transcribed into cDNA using the SuperScript III First-Strand Synthesis System (cat. no. 18080051; Invitrogen; Thermo Fisher Scientific, Inc.) with dNTPs (cat. no. R0192; Thermo Fisher Scientific, Inc.) and random hexamer primers. The reverse transcription temperature protocol was as follows: 2°C for 10 min, 50°C for 50 min and 70°C for 15 min. Double-stranded cDNA was synthesized, end-repaired, A-tailed and ligated to sequencing adapters using the NEBNext Ultra II DNA Library Prep Kit for Illumina (cat. no. E7645S; New England Biolabs, Inc.). The ligation products were purified using AMPure XP beads and amplified by PCR to construct the cDNA library. DNA library quality was assessed using an Agilent 2100 Bioanalyzer with the High Sensitivity DNA Kit (cat. no. 5067-4626; Agilent Technologies, Inc.) to verify integrity, and concentrations were quantified using Qubit 4 Fluorometer (Thermo Fisher Scientific, Inc.). Libraries were sequenced using an Illumina NovaSeq 6000 platform with 150-bp paired-end reads. The sequencing kit used was the NovaSeq 6000 S4 Reagent Kit (300 cycles) (cat. no. 20028312; Illumina, Inc.). The final library was loaded at a concentration of 300 pM, determined using qPCR with the KAPA Library Quantification Kit (cat. no. KK4824; Roche Diagnostics), reported in molar concentration. Raw sequencing reads were processed by trimming low-quality bases and adapter sequences using Trimmomatic (v0.39; http://github.com/usadellab/Trimmomatic). Clean reads were aligned to the human genome reference hg19 using STAR (v2.7.3a). Gene expression levels were quantified as fragments per kilobase of transcript per million mapped reads using featureCounts (v2.0.1). Differentially expressed genes were identified with a threshold of P<0.05 and |log2(fold change)|≥1. Subsequent functional analyses, including Gene Ontology and KEGG pathway enrichment, were performed using the DAVID database (https://david.ncifcrf.gov/). GSEA was also conducted to identify significantly enriched signaling pathways related to α-KG treatment (31–33).

Statistical analysis

In the present study, all statistical analyses were performed in R v.4.1.0 (The R Foundation). Each experiment was conducted in triplicate, unless otherwise specified. Descriptive data were analysed using the Student's t-test and are presented as the mean±standard deviation, unless otherwise indicated. For comparisons, normally distributed variables were analyzed using unpaired Student's t-tests, whilst non-normally distributed variables were assessed using the Wilcoxon rank-sum test. A two-sided Fisher's exact test was performed for the contingency tables. Correlation analyses were performed using Spearman's rank correlation. P<0.05 was considered to indicate a statistically significant difference.

Results

Screening and expression analysis of α-KG-related genes in GC

As shown in Fig. 1A, an in-depth reanalysis of several previously published cohorts was performed to train and validate the predictive model in the present study. This included four bulk RNA sequencing datasets (TCGA-STAD, GSE84437, GSE8433 and GSE15459) as well as two treatment-related cohorts (GSE26899 and GSE13861). In total, a concatenated set of 509 genes (Table SI) derived from eight KEGG pathways associated with α-KG synthesis, metabolism and regulation were analyzed.

Figure 1. Workflow of the study and candidate genes. (A) Schematic diagram of the complete analysis pipeline of the present study. (B) Differential expression of α-KG-related genes in the TCGA cohort. (C) Univariate Cox analysis identified α-KG-related genes significantly associated with the prognosis of patients with stomach adenocarcinoma. (D) Signaling pathways associated with 26 α-KG-related candidate genes. α-KG, α-ketoglutarate; TCGA, The Cancer Genome Atlas.

Based on the TCGA-STAD cohort data, 105 α-KG-related genes were identified that exhibited significant differential expression between tumor and normal samples (adjusted P<0.05 and |log2FC|>1; Fig. 1B and S1). Subsequently, using patient survival data, univariate Cox analysis was performed and 26 prognostic genes were identified from the differentially expressed α-KG-related genes (Fig. 1C and D). These were then used to construct a predictive model.

Construction and evaluation of prognostic models using 115 machine learning algorithms

Using the 26 α-KG-related candidate genes, the AKGI was developed using an ensemble machine learning algorithm. In the TCGA-STAD cohort, the Leave-One-Out Cross Validation strategy was applied to construct 115 predictive models and compute the C-index for each model across all validation cohorts (Fig. 2A). Among the four datasets, the random survival forest (RSF) model demonstrated the highest average C-index of 0.66, establishing it as the most robust AKGI model (Fig. 2A and B). By using RSF we scored the samples, and based on their risk scores, patients were classified into high- and low-AKGI groups according to the optimal cutoff. Using the optimal cutoff value, patients were stratified into high and low AKGI groups (Figs. 2C and S2). Survival analysis revealed that in all four datasets, patients with GC with low AKGI levels had significantly longer OS and disease-free survival compared with those with high AKGI levels (Figs. 2D and E, and S2). These findings highlight the potential of AKGI as a prognostic predictor for patients with GC.

Figure 2. Development of AKGI using integrative machine learning algorithms. (A) AKGI was evaluated using 115 machine learning combinations. The C-index was calculated for each model in the TCGA and Gene Expression Omnibus datasets. The optimal machine learning model was determined based on the mean C-index. (B) Error rate of the data as a function of the classification tree (left panel) and the out-of-bag importance values for the predictors (right panel). (C) Cutoff values calculated for stratifying AKGI groups. (D) Disease-free survival and (E) and OS curves stratified by AKGI in the TCGA dataset. (F) Receiver operating characteristic curves for 1-, 3- and 5-year OS predictions in the TCGA dataset. (G) Calibration plots for 1-, 2- and 3-year OS predictions in the TCGA-STAD cohort. (H) Standardized net benefit. (I) Net reduction in interventions per 100 patients. (J) Comparison of the time-dependent C-index among AKGI, age, sex and pathologic T stage. AKGI, α-ketoglutarate index; C-index, concordance index; TCGA, The Cancer Genome Atlas; OS, overall survival; STAD, stomach adenocarcinoma; T, tumor; AUC, area under the curve; HR, hazard ratio; CI, confidence interval.

Assessment of the AKGI model

To evaluate the independent predictive ability of AKGI, further assessments were performed using multiple approaches. ROC curves based on the TCGA-STAD cohort demonstrated that the AUC values for AKGI were 0.91, 0.93 and 0.92 for predicting 1-, 3- and 5-year survival, respectively (Fig. 2F). Calibration curves confirmed the accuracy of the AKGI prediction model for reflecting actual observations (Fig. 2G).

Subsequently, multivariate Cox regression analysis was performed to evaluate the impact of several clinical factors on the prognosis of patients with GC, including AKGI, sex, age, clinical stage and pathological tumor (T) stage. Based on this analysis, a nomogram was constructed to predict patient outcomes, which indicated that AKGI was a significant prognostic risk factor for patients with GC (P<0.05; Fig. S3), with statistical significance observed at 1-, 2- and 3-year intervals. Additionally, sex, age and pathological T stage were also identified as high-risk factors for GC prognosis.

Moreover, DCA revealed that AKGI provided significantly better clinical outcomes than other indicators for patients with GC, including age, T stage and sex (Fig. 2H and I). Finally, time-dependent C-index analysis further demonstrated that AKGI exhibited superior predictive performance compared with other clinical factors (Fig. 2J).

Biological function analysis of AKGI

To assess the biological functions associated with the AKGI prognostic model score, 501 gene sets and functional scores were collected and a correlation analysis was performed with AKGI (Table SII). The analysis identified 34 gene sets significantly correlated with AKGI (|R|>0.25; P<0.05; Fig. S4). Among these, the mRNA stemness index (mRNAsi) exhibited the strongest negative correlation with AKGI (R=−0.433; Fig. 3A). Notably, another stemness index, the DNA methylation stemness index, demonstrated a weaker correlation with AKGI (R=−0.1; Fig. 3B).

Figure 3. Correlation of AKGI with different biological functions or signaling pathways. Correlation of AKGI with two stemness index, (A) mRNAsi and (B) mDNAsi. Significant negative correlation of (C) ferroptosis, (D) glycosphosphatidylinositol, (E) the cell cycle, (F) DNA replication and (G) pyrimidine metabolism with AKGI. (H) Non-significant negative correlation of ‘Molecular_Cancer_m6A’ with AKGI. Significant positive correlation of (I) chemokines, (J) EMT, (K) M2 macrophages and (L) the TGF-β score with AKGI. AKGI, α-ketoglutarate index; si, stemness index; EMT, epithelial-to-mesenchymal transition.

In addition to mRNAsi, gene sets related to glycosylphosphatidylinositol, ferroptosis, cell cycle, TGF-β signaling, EMT and DNA replication demonstrated significant correlations with AKGI; however, notable m6A scores were not correlated with AKGI (Fig. 3). Furthermore, AKGI demonstrated associations with several immune-related gene sets, including chemokines and macrophage M2 regulation, suggesting that AKGI may be involved in immune modulation and shaping the immune microenvironment in GC (Fig. 3).

Analysis of immune correlation with AKGI

Given the correlation between AKGI and several immune-related markers in GC, the role of AKGI in the immune microenvironment was comprehensively evaluated. Results from multiple immune cell infiltration algorithms demonstrated significantly lower infiltration levels of tumor-associated immune cells, including T, natural killer (NK) and stromal cells, in the high AKGI group, suggesting an immunosuppressive state in this group (Fig. 4A). Additionally, molecular markers associated with immune suppression and rejection, such as those involved in the EMT pathway and TGF-β signaling, were predominantly enriched in the high AKGI group (Fig. 4B). This finding further supports that the high AKGI group exhibits characteristics consistent with an immunosuppressive state.

Figure 4. Tumor microenvironment-related molecular characteristics of patients with high- and low-AKGI scores in The Cancer Genome Atlas-stomach adenocarcinoma cohort. Comparison of (A) immune cell type signature distributions and (B) immune exclusion signature distributions between patient with high- and low-AKGI scores. (C) Distribution of immunotherapy-related biomarkers across patients with high- and low-AKGI scores. (D) Comparison of immunotherapy-related biomarkers between patients with high- and low-AKGI scores. (E) Differences in TMB distribution in patients with high- and low-AKGI scores. Distribution of (F) silent mutation rate and (G) non-silent mutation rates between patients with high- and low-AKGI scores. Survival analysis integrating AKGI with (H) TMB, (I) silent mutation rate and (J) non-silent mutation rate. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001. AKGI, α-ketoglutarate index; TMB, tumor mutational burden.

Gene sets related to malignant tumor treatment and immunotherapy markers were also assessed. The results revealed that markers associated with favorable immunotherapy outcomes were significantly enriched in the low AKGI group compared with the high AKGI group (Fig. 4C and D). TMB was compared between high and low AKGI groups. The results revealed that TMB, silent mutations and missense mutations were significantly higher in the low AKGI group compared with the low AKGI group (Fig. 4E-G), indicating greater immunogenicity in this subgroup.

Further joint analyses of several mutation metrics demonstrated that AKGI could synergize with TMB, silent mutations and missense mutations to predict patient prognosis more effectively. Notably, patients with GC with lower AKGI and higher TMB levels exhibited significantly improved survival outcomes than those with higher AKGI (Fig. 4H-J).

Role of AKGI in GC treatment

To comprehensively evaluate the potential application of AKGI in GC therapy, two treatment cohorts were analyzed, GSE26899 and GSE13861, which provided extensive prognostic and treatment-related data for this patient population. In both cohorts, the 26 genes used to construct the AKGI demonstrated significant differences in expression levels (Fig. 5A). After performing AKG scoring using the RSF model and determining the cut-point for high/low group division (Fig. S5). Prognostic analysis revealed that the high and low AKGI groups had markedly different survival outcomes, with the low AKGI group showing a significantly improved prognosis (Fig. 5B and C). These findings further indicate the utility of low AKGI in assessing therapy outcomes.

Figure 5. Value of AKGI in predicting immunotherapy response in patients with gastric cancer. (A) Heatmap of α-KG-related genes in 2 chemotherapy cohorts. Survival analysis of high- and low-AKGI groups in (B) GSE13861 and (C) GSE26899. (D) TIDE algorithm prediction of the response to immunotherapy in the high- and low-AKGI groups. (E) Subclass mapping algorithm prediction of the response to immunotherapy in the high- and low-AKGI groups. (F) Expression distribution of α-KG-related genes in different immunotherapy response groups. *P<0.05; **P<0.01; ***P<0.001. α-KG, α-ketoglutarate; AKGI, α-KG index; TIDE, Tumor Immune Dysfunction and Exclusion; CTLA4, cytotoxic T-lymphocyte-associated protein 4; PD1, programmed cell death protein 1; FPKM, fragments per kilobase of transcript per million mapped reads; R, response; NR, non-response.

Additionally, the TIDE algorithm was applied to evaluate the responses of patients to immunotherapy. The results revealed that the low AKGI group exhibited a significantly improved response to immunotherapy compared with the high AKGI group (P=0.00179; Fig. 5D). In subgroup mapping analysis of patients with GC receiving immunotherapy, a high AKGI level was significantly associated with an improved response to cytotoxic T-lymphocyte associated protein 4 treatment in comparison with a low AKGI level (Bonferroni-corrected P=0.008; Fig. 5E).

Finally, the expression differences of AKGI-related genes between treatment responder and non-responder groups were assessed. A total of 11 genes demonstrated a significantly higher expression trend in the high AKGI group compared with in the low AKGI group (Fig. 5F), further confirming that AKGI and its associated genes may provide a valuable predictive insight into the efficacy of immunotherapy and chemotherapy for patients with GC.

Drug sensitivity prediction based on AKGI

Given the poor response to therapy in patients with high AKGI, the CTRP and PRISM databases were utilized to identify potential therapeutic drugs for this patient group (Fig. 6A). The algorithm produced meaningful insights through differential drug response analysis between high AKGI (top decile) and low AKGI (bottom decile) groups. Specifically, compounds with lower AUC values in the high AKGI group were identified (log2FC>0.10). To further refine the selection, Spearman correlation analysis was performed between AUC values and AKGI scores, focusing on compounds with a negative correlation coefficient (Spearman r<-0.20 for CTRP and PRISM). As a result, six potential compounds were identified from the CTRP database (Fig. 6B) and four from the PRISM database (Fig. 6C). All 10 compounds exhibited lower AUC values in the high AKGI group compared with in the low AKGI group, indicating a negative correlation with AKGI.

Figure 6. Potential agents for patients with high AKGI. (A) Comprehensive computational pipeline for the screening of potential agents. The correlation and differential analysis of drug sensitivity for potential drugs screened from the (B) Cancer Therapeutics Response Portal and (C) Profiling Relative Inhibition Simultaneously in Mixtures datasets. (D) Identification of the most promising therapeutic agents for patients with high AKGI according to the evidence from multiple sources. (E) RNA expression of the drug target genes of dasatinib and YM-155 between tumor and normal tissues. *P<0.05; **P<0.01; ***P<0.001. AKGI, α-ketoglutarate index; ctrp, cancer therapeutics response portal; AUC, area under the curve; FC, fold change; TPM, transcripts per million; CMap, Connectivity Map.

Although the 10 candidate compounds demonstrated higher drug sensitivity in patients with high AKGI scores, this alone does not confirm their therapeutic efficacy in GC. Therefore, to evaluate their therapeutic potential, four compounds with more comprehensive information were selected from the CMap and PubMed databases for further investigation. First, CMap was used to identify compounds with gene expression patterns opposite to those observed in GC-specific profiles. Specifically, compounds that could downregulate gene expression in tumor tissues after treatment were focused on, countering the elevated expression seen in untreated tumor tissues. A total of two of the selected compounds exhibited CMap scores of <-50, suggesting potential therapeutic effects in GC. Subsequently, an extensive literature review was performed using PubMed to identify clinical trials and supporting evidence regarding the use of the four candidate compounds for GC treatment. To further assess their potential, the fold change differences in mRNA expression levels of the target genes between tumor and normal tissues were calculated. A higher fold change indicated a greater potential for targeting GC (Fig. 6D).

Among the candidate compounds, the drug targets of dasatinib and YM-155 exhibited significant expression differences between tumor and normal tissues (Fig. 6E). Overall, the analysis identified dasatinib and YM-155 as strong candidates with promising therapeutic potential for patients with GC with high AKGI signatures.

Functional analysis of α-KG treatment in GC cells

The aforementioned results demonstrated the prognostic predictive value of AKGI for patients with GC as well as the biological functions of α-KG in GC. To validate these findings, the GC cell lines, MKN74 and AGS, were treated with α-KG and transcriptomic sequencing was performed on the treated cells. Phenotypic analysis revealed that compared with the NC group, α-KG treatment significantly inhibited AGS and MKN74 cell viability (Figs. 7A and S6A), migration (Figs. 7B and S6B), invasion (Figs. 7C and S6C) and colony formation (Figs. 7D and S6D), which is consistent with previous reports highlighting the suppressive effects of α-KG on malignant tumors (8,34,35).

Figure 7. Validation of experimental results of gastric cancer cells treated with α-KG. (A) Viability of AGS cells. Representative and quantitative result of (B) cell migration, (C) invasion and (D) clone formation. (E) Cell cycle distribution and percentage of cells in each cell cycle phase. (F) Representative FACS result of Annexin V/PI staining and quantitative result of apoptosis rate. (G) Expression profiles of α-KG-related genes in α-KG-treated AGS and MKN74 cells. (H) Enrichment of signaling pathways in AKGI high/low groups and α-KG-treated AGS and MKN74 cells. (I) Quantitative result of oxidative stress and ferroptosis-related biomarkers. (J) Reverse transcription-quantitative PCR results of apoptosis, oxidative stress and ferroptosis-related genes. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001. α-KG, α-ketoglutarate; AKGI, α-KG index; NC, negative control; NES, nuclear export signal; SOD, superoxide dismutase; MDA, malondialdehyde; ROS, reactive oxygen species; Nrf2, nuclear factor erythroid 2-related factor-2; Keap1, Kelch-like ECH-associated protein 1; GPX4, glutathione peroxidase 4; SLC7A11, solute carrier family 7 member 11.

Flow cytometry analysis further demonstrated that compared with the NC group, α-KG-treated cells exhibited cell cycle arrest in the G2 and S phases (Figs. 7E and S6E), accompanied by a significant increase in apoptosis levels (Figs. 7F and S6F). This observation aligns with the aforementioned conclusion that AKGI is closely associated with cell cycle regulation and ferroptosis.

Subsequently, the impact of α-KG treatment on AKGI-related gene expression was assessed. Comparative transcriptomic analysis between α-KG-treated cells and the control group revealed significant differential expression of genes within the AKGI model (Fig. 7G). As the RFS model did not yield conclusive gene regression coefficients, single-sample (ss)GSEA for KEGG enrichment analysis was applied on both the transcriptomic data from α-KG-treated cells and the expression profiles of high and low AKGI groups from TCGA. The results demonstrated that the transcriptomic expression patterns of α-KG-treated cells closely resembled those of the low AKGI group, with significant enrichment in pathways such as KRAS signaling, EMT, apoptosis and ROS signaling pathways (Fig. 7H).

Further validation of genes significantly enriched in both the AKGI model and transcriptomic sequencing data was performed using RT-qPCR analysis (Figs. 7J and S6H). These experiments demonstrated that, in α-KG-treated MKN74 and AGS cells, the expression levels of genes involved in apoptosis, ROS-related pathways and ferroptosis signaling were significantly disturbed in the α-KG treatment group with the NC group. In addition, the quantitative result of oxidative stress and ferroptosis-related biomarkers, as well as the RT-qPCR results of oxidative stress and ferroptosis-related genes, further indicated the potential effect of α-KG for inducing oxidative stress and ferroptosis in GC cells (Figs. 7I and S6G). Additionally, mitochondrial activity was assessed in α-KG-treated cells. Mitochondrial activity was significantly reduced following α-KG treatment, in comparison with negative controls, suggesting impaired mitochondrial function, possibly associated with apoptosis or oxidative stress (Fig. S7).

In summary, the aforementioned findings suggest that the inhibitory effects and mechanisms of α-KG on GC cell lines in vitro are closely aligned with the biological processes observed in the low AKGI group. This provides further evidence supporting the molecular mechanisms by which α-KG exerts its role in GC.

Discussion

Integrated machine learning methods have been widely applied to the development of cancer prediction models and target gene screening, achieving excellent predictive outcomes in colorectal cancer, lung adenocarcinoma and GC (36–38). The present study used machine learning methods to systematically evaluate the prognostic significance of α-KG-related genes in GC and analyzed the mechanisms of α-KG in GC based on a prediction model. By integrating machine learning approaches, 26 α-KG-related genes were identified, which were used to construct a prognostic model, AKGI, via RSF. Using nomograms, DCA and AUC curves, it was demonstrated that AKGI has excellent predictive performance. Furthermore, the results revealed a complex and multifaceted relationship between AKGI, the TME and drug sensitivity in GC. These findings underscore the potential clinical application of AKGI in guiding personalized treatment decisions.

As an important factor involved in cellular metabolism, the TCA cycle and epigenetic modifications, α-KG has demonstrated notable inhibitory effects on tumors (4). Based on the constructed AKGI, the results of the present study revealed that AKGI is closely associated with key processes such as stemness, ferroptosis and EMT in GC. This is a finding that is consistent with the reported biological functions of α-KG in other cell types. For example, in naive embryonic stem cells (ESCs), exogenous α-KG maintains ESC self-renewal and regulates the expression of pluripotency-related genes by altering the α-KG/succinate ratio, which influences chromatin modifications such as H3K27me3 and TET-dependent DNA demethylation (39). In diffuse large B-cell lymphoma, treatment with α-KG derivatives promotes oxidative stress in double-hit lymphoma through malate dehydrogenase 1-mediated 2-HG conversion. This process increases ROS, leading to lipid peroxidation, tumor protein p53 activation, ferroptosis, and, ultimately, tumor growth inhibition (40).

Tumor cells possess mechanisms to evade immune surveillance and resist therapeutic drugs, promoting survival and progression (41). The findings of the present study revealed a complex relationship between AKGI and tumor immunity in GC. By exploring AKGI features at the immune-infiltration level using several algorithms, it was demonstrated that the high-AKGI group exhibited lower enrichment of several immune cell types, including T cells, macrophages and NK cells. According to Suzuki et al (42), α-KG can counteract CD8 T-cell dysfunction triggered by glutamine metabolism disruptions, thereby enhancing T-cell functionality. Furthermore, α-KG modulates gene expression patterns in CD8 T cells by participating in the regulation of H3K27 demethylation, thus fine-tuning their activity (42). The regulatory effects of α-KG on NK cells likely occur primarily through the TCA cycle and redox reactions. Although direct evidence linking α-KG to NK cell activity regulation remains elusive, as a key TCA cycle substrate, α-KG may contribute indirectly. For example, when NK cells uptake exogenous pyruvate, it is reduced to lactate to regenerate glycolytic NAD+ and oxidized in the TCA cycle to produce ATP, thereby fueling NK cell effector functions (43). This finding underscores the potential role of α-KG in NK cell modulation.

As T cells and NK cells serve pivotal roles in suppressing tumor growth in GC, with associations demonstrated with improved prognosis and heightened immunotherapy responses, reduced infiltration of these immune cells is associated with unfavorable outcomes and accelerated malignant progression (44). Low infiltration levels of T cells or NK cells have been reported to be associated with a worse prognosis and malignant progression in GC (44). Furthermore, ssGSEA-based correlation analysis revealed that cancer progression-related pathways, including the TGF-β signaling pathway, EMT signaling pathway and cell cycle signaling pathway, were significantly correlated with AKGI. These findings provide further insights into the mechanisms through which AKGI and α-KG influence tumor immunity in GC.

Given the association between AKGI and tumor immunity, the present study further evaluated its significance in immunotherapy using TIDE, immune evasion and TMB analyses. As a key index for evaluating tumor immunotherapy, the results demonstrated that the low-AKGI group exhibited higher TMB levels, potentially contributing to an improved immune response (45). As a tool for evaluating tumor immunotherapy, TIDE provides scores that indicate the likelihood of immune evasion in malignant tumors (46). The elevated TIDE scores observed in the high AKGI group support the concept that immune evasion is associated with high AKGI.

However, in the AKGI model in the present study, the correlation between AKGI and immune infiltration was primarily derived from bulk-seq-based immune-infiltration analysis tools (such as CIBERSORT). Single-cell sequencing offers a more precise depiction of the immune microenvironment in malignant tumors (47). Therefore, further analyses and experimental validation of AKGI and α-KG at the single-cell level in GC are warranted to deepen the understanding of their roles.

As the AKGI model in the present study demonstrated an excellent performance in predicting GC prognosis and serving as a predictive marker for personalized treatment selection, a drug sensitivity analysis was performed that was based on AKGI to identify several potential compounds that could benefit patients with GC. This analysis may facilitate the development of more effective therapeutic strategies for GC. Notably, dasatinib has been reported to exert tumor-suppressive effects in GC (48). Dasatinib, a potent SRC family kinase (SFK) inhibitor, disrupts critical signaling pathways involved in cell proliferation, migration and survival. By inhibiting SFKs, dasatinib affects cell cycle regulation and DNA replication, which are pathways closely linked to AKGI. SFK inhibition also impacts pyrimidine metabolism, crucial for nucleotide biosynthesis, thus interfering with cancer cell metabolism. Additionally, dasatinib may enhance immune modulation, as it influences tumor-associated macrophages and immune checkpoints, complementing AKGI-related immune evasion mechanisms. When used in combination with cisplatin or oxaliplatin, it notably inhibits GC progression. Cadherin 1, bromodomain containing 4 and TNF-related apoptosis-inducing ligand receptor 1 have been identified as therapeutic targets of dasatinib in GC cells (49–51).

Another predicted compound, YM-155, is a small imidazolium-based agent that has specific activity against the survival of cancer cells. It has been reported to inhibit colony formation in GC cells, promote apoptosis and ultimately suppress GC progression (52,53). In a mouse model using patient-derived GC xenografts, injection of YM-155 markedly inhibited cell proliferation, induced apoptosis, reduced cancer stem cell expansion and suppressed xenograft tumor growth in GC cells (54), reinforcing AKGI-regulated pathways. Moreover, YM-155 has demonstrated a favorable safety profile and notable anticancer activity in several Phase I/II clinical trials, particularly in esophageal cancer, prostate cancer and non-Hodgkin lymphoma (55,56). Originally introduced as an imidazolium-based survivin suppressant, YM-155 exhibits potent antitumor effects, especially in hormone-refractory prostate cancer (53). Its therapeutic action was initially attributed to the suppression of survivin expression, which subsequently induced apoptosis. Survivin, a critical regulator of cellular homeostasis, serves a key role in several cellular processes, including the inhibition of apoptosis and regulation of cell division. Beyond its effects on survivin, YM-155 operates through multiple mechanisms, including the modulation of epigenetic regulation, which has also been reported to be associated with the functional role of AKG (57). Specifically, YM-155 influences the expression of genes involved in DNA repair and cellular stress responses, which are vital for maintaining cellular integrity under oncogenic stress (53,58,59). These epigenetic alterations are crucial in cancer therapy, as they can enhance the ability of a tumor to repair DNA damage and manage stress-induced signals.

Through the bioinformatics analysis in the present study, the potential role and biological function of α-KG in GC was demonstrated. To corroborate the bioinformatic observations, two GC cell lines (AGS and MKN74) were treated with α-KG in vitro and cell proliferation, migration and apoptosis were assessed. The results revealed that α-KG treatment mimicked the biological functions observed in the low-AKGI group. Significant enrichment of ferroptosis, cell cycle regulation and ROS signaling pathways was also demonstrated in the α-KG-treated group. These pathways not only show a strong negative association with AKGI, but have also been shown to serve a critical inhibitory role in the progression of GC (60,61).

In conclusion, the results of the present study highlight that α-KG-related genes have significant prognostic value in GC and are potential therapeutic targets. While the findings offer valuable insights into the clinical implications of the AKGI signature, several limitations must be acknowledged. First, the analyses relied heavily on retrospective data, necessitating future studies to validate the clinical relevance of the findings. Additionally, the limited sample size and incomplete clinical information mean that AKGI cannot yet be considered an independent predictor. The combination of machine learning methods with biological validation provides a powerful framework for identifying new biomarkers and therapeutic strategies for cancer. Future research should explore the clinical applicability of α-KG-related prognostic models. Moreover, given the complex nature of GC and its diverse histological phenotypes, comprehensive mechanistic and clinical investigations are required to elucidate the role of α-KG-related genes in different GC subtypes. Further in vivo and in vitro studies are warranted to clarify the mechanisms underlying GC prognosis. Overall, the AKGI model established in the present study provide a novel prognostic predictor and theoretical foundation for the diagnosis, prognosis assessment and mechanistic investigation of α-KG in GC. However, addressing the aforementioned limitations will be essential to strengthen the validity and applicability of the findings.

Supplementary Material

Supporting Data

Supporting Data

Supporting Data

Acknowledgements

Not applicable.

Funding

The present research was supported by the National Natural Science Foundation of China (grant no. 82060567) and Scientific Research Projects of the Inner Mongolian Higher Educational System (grant no. NJZY22674).

Availability of data and materials

The transcriptomic data generated in the present study may be found in the Gene Expression Omnibus under the accession number GSE285448 or at the following URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE285448. All other data generated in the present study may be requested from the corresponding author.

Authors' contributions

FL, GL and LS designed the study. FL, XS and YZ performed data management and statistical analysis, and drafted the manuscript. YZ helped with cohort identification and data management. XS, XM and RZ performed the molecular experiments. GL contributed to the critical revision of the manuscript. FL and GL confirm the authenticity of all the raw data. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The present study was approved by the Ethical Review Committee of Inner Mongolia Medical University and performed according to the Declaration of Helsinki.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References