Integrated Analysis of Glycosylation and Inflammation-Related Genes for Prognostic Risk Modeling and Immunotherapy Response Prediction in Gastric Cancer

BackgroundGastric cancer (GC) continues to be among the most commonly identified cancers worldwide. This study integrates glycosylation and inflammation-related gene features for the first time to construct a prognostic model for gastric cancer, providing new theoretical basis for revealing immune escape mechanisms and personalized treatment strategies. MethodsTranscriptomic and clinical data derived from GC samples were meticulously examined, utilizing resources from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Through differential expression analysis, we successfully identified glycosylation and inflammatory-related differentially expressed genes (GANDIRDEGs). To construct a prognostic gene signature, we applied least absolute shrinkage and selection operator (LASSO) analysis in conjunction with Cox regression analysis. Additionally, we performed somatic mutation (SM) along with copy number variation (CNV) analyses, alongside gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, we conducted gene set enrichment analysis (GSEA) along with a comprehensive evaluation of immune infiltration and drug sensitivity. ResultsWe identified and validated a six-gene (INHBA, OLR1, ROS1, EPHA5, TACR1, and IL6) signature, termed GANDIRDEGs, which showed excellent performance in distinguishing overall survival (OS) between high-risk (HR) and low-risk (LR) cohorts. Moreover, we developed a prognostic nomogram utilizing this six-gene signature that provides highly accurate predictions of GC patient outcomes.SM and CNV analyses revealed that MSR1 had the highest mutation rate among the GANDIRDEGs, with a mutation rate of 5%. GO, KEGG, and GSEA revealed significant associations of each pivotal gene with pathways, including cytokine signaling, the inflammatory response, and apoptosis mediated by CDKN1A through TP53, among various biological functions and signal transduction pathways. Our findings offer a novel gene signature, GANDIRDEGs, that correlated with prognosis, immune infiltration, and therapeutic sensitivity in patients with GC. ConclusionThis study establishes a prognostic signature integrating glycosylation and inflammatory pathways in GC, providing valuable insights into the mechanisms of immune evasion and potential personalized treatment approaches.