Developing a Risk Model of Lipid Metabolism-associated Genes: Implications for Clinical Prognosis and Tumor Immune Microenvironment in Gastric Cancer

Background Gastric cancer (GC) remains a significant clinical challenge due to its high mortality rate and complex pathophysiology. Recent advances have suggested that lipid metabolism plays a crucial role in the progression and prognosis of gastric cancer. This study aims to assess the implications of lipid metabolism-associated genes (LMAGs) in the molecular clinical prognosis and tumor immune microenvironment of GC. Methods We analyzed RNA-seq sequencing data from 371 GC transcriptomes in The Cancer Genome Atlas (TCGA) and 433 clinic datasets from the Gene Expression Omnibus (GEO). We selected the top 100 LMAGs for analysis. We applied univariate Cox regression to identify survival-related genes and used consensus clustering to explore molecular subtypes. We also performed copy number variation (CNV) and functional enrichment analysis. Using the LASSO and Cox regression analyses to construct and validate the GC-related prognostic risk model. Results A total of 3911 differentially expressed genes (DEGs) delineated distinct transcriptomic profiles between GC and normal gastric tissues were identified. Among these, 43 LMAGs were significantly associated with survival, most of which were poor prognostic factors. CNV analysis indicated prevalent gains in LMAGs in tumor samples. Consensus clustering revealed two distinct molecular subtypes (ARGcluster A and ARGcluster B) with significant differences in survival outcomes and gene expression profiles. Additionally, immune profiling revealed that ARGcluster A, associated with poorer outcomes, exhibited higher levels of pro-tumorigenic immune cells. Finally, we validated the expression, prognosis, and immune infiltration of fourteen key LMAGs in GC. Conclusion This study underscores the association between LMAGs with the prognosis and tumor immune microenvironment in GC. Further research is needed to validate these findings and explore potential therapeutic targets within the lipid metabolism pathway.