Molecular clustering and prognostic features based on integrated databases predict survival and immune status in patients with gastric cancer

Background Gastric cancer (GC) remains one of the most common malignancies worldwide with high mortality rates despite advances in treatment approaches. Patients frequently develop drug resistance to current therapies, highlighting the critical need for novel prognostic biomarkers that can enhance survival rates and guide immunotherapy decisions in patients with GC. Methods We conducted a comprehensive bioinformatics analysis using integrated clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. GC cases were categorized into two prognostic-related gene (PRG) clusters, and differentially expressed genes were identified. We established a prognostic model based on 11 key genes, stratified patients into high-risk and low-risk groups, and developed a nomogram model for survival prediction. Expression of selected genes was validated through quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry in clinical samples. Results The identified PRGs and gene clusters strongly associated with patient survival, immune system functions, and cancer-related pathways. Risk scores significantly correlated with immune cell abundance, checkpoint expression, and responses to immunotherapy and chemotherapy. For instance, the area under the curve (AUC) values of patients at 1-year, 3-year, and 5-year survival were all greater than 0.6 in the ROC curves ( p 0.05), which makes our prediction more accurate, and the line graphs predicted a 1-year survival rate exceeding 0.907, a 3-year survival rate exceeding 0.726, and a 5-year survival rate exceeding 0.633; the calibration curves are almost close to the predicted ones ( p 0.05). This implies that patients in the high-risk group demonstrated significantly poorer prognosis. Univariate Cox (UniCox) analysis and multivariate Cox (MultiCox) analysis indicate that CTHRC1 (Collagen Triple Helix Repeat Containing 1), CST6 (Cystatin E/M), and AKR1B1 (Aldo-Keto Reductase Family 1 Member B) are independent prognostic factors, and all are associated with poor survival prognosis (HR 1, p 0.05). Gene set enrichment analysis (GSEA) and single-cell analysis revealed significant enrichment of multiple biological pathways and variability in expression of these genes across different cell types within the tumor microenvironment. qRT-PCR and immunohistochemistry confirmed significant differences in mRNA and protein expression of CTHRC1, CST6, and AKR1B1 between normal and GC tissues ( p 0.05). Conclusion Our research establishes a robust molecular signature for predicting survival of patients with GC and characterizing the tumor immune microenvironment. It aims not only to establish a prognostic model, but also to explore immunobiological functions. The identified prognostic features and key genes (CTHRC1, CST6, and AKR1B1) offer potential as biomarkers and therapeutic targets, potentially guiding more effective personalized treatment strategies for patients with GC.