ArticleDiscover oncology2025
Lactate metabolism-related genes serve as potential biomarkers for predicting gastric cancer progression and immunotherapy.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Efficacy of Neoantigen-Loaded Dendritic Cell Vaccine Immunotherapy in Non-Metastatic Gastric Cancer.Medical sciences (Basel, Switzerland) · 2025Pooled it
- Metabolic immune checkpoints (MICs) in cancer: from molecular mechanisms to challenges and opportunities in clinical translation.Signal transduction and targeted therapy · 2026Review
- The evolving landscape of the Warburg effect in gastric cancer: From molecular mechanisms to targeted therapy.Clinical and translational medicine · 2026Review
- Lactylation in cancer: a new frontier in metabolic-epigenetic regulation and clinical practice.Cell communication and signaling : CCS · 2026Review
- Identify the PANoptosis signature and prognostic model via a multimachine-learning computational framework for bladder urothelial carcinoma.Cancer cell international · 2026Article
- Metabolic reprogramming networks in the gastric cancer tumor microenvironment: an integrated axis of nutrient competition, metabolic crosstalk, and immunosuppression.Frontiers in immunology · 2026Review
- VSIG2 hinders gastric cancer progression by suppressing ANXA2-mediated NF-κB pathway activation.Acta biochimica et biophysica Sinica · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundTargeting lactate metabolism represents a promising therapeutic strategy to enhance anti-tumor immune responses. In this study, we developed a novel model based on lactate metabolism-related genes (LRGs) to predict survival, characterize the immune microenvironment, and assess immunotherapy response in gastric cancer (GC), with the potential to identify new biomarkers.
methodsData sets of GC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. LRGs were sourced from the MSigDB database. Five key prognostic LRGs (MMP11, MMP12, HBB, VSIG2, and SERPINE1) were identified using univariate COX regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Patients were classified into high-risk and low-risk groups based on a median risk score. We conducted prognostic analysis, gene set enrichment analysis (GSEA), immune microenvironment analysis, immunotherapy responsiveness evaluation, and drug screening in these groups.
resultsThe high-risk group exhibited poorer prognosis compared to the low-risk group, as predicted by our nomogram for overall survival. Notably, the high-risk group, marked by higher stromal cell infiltration and RNA stemness scores (RNAss), showed increased susceptibility to immune evasion. In contrast, the low-risk group demonstrated better responses to immunotherapy and greater sensitivity to chemotherapy. Single-cell analysis revealed that SERPINE1 is predominantly positively correlated with immune checkpoint expression, while VSIG2 exhibits a negative correlation.
conclusionsWe have developed and validated a novel lactate metabolism-associated model, providing new insights into the prognosis and immunotherapy of GC patients. The five identified LRGs offer potential as prognostic biomarkers and therapeutic targets in GC.
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