Lipid-Soluble Signaling Molecular Model Predicts Prognosis and Immunotherapy Response in Gastric Cancer

Lipid-soluble signaling molecule-related genes (LSMRGs) are critical in various tumors, but their role in gastric cancer (GC) prognosis and therapy remains unclear. Using transcriptomic data, this study analyzed LSMRG expression patterns, molecular subtypes, prognostic significance, and immune-microenvironment interactions in GC to identify new prognostic biomarkers and support precision medicine approaches. Using GC data from TCGA and GEO, LSMRGs from Genecard were analyzed. Unsupervised clustering defined LSMRG-based subtypes. Differentially expressed LSMRGs were identified by intersecting tumor-normal DEGs. A prognostic risk-score model was built via univariate Cox, LASSO, and multivariate Cox analyses, with model genes validated by qRT-PCR in cell lines. Comprehensive transcriptomic analyses included nomogram development, gene enrichment, immune infiltration, somatic mutations, and drug sensitivity. LSMRG-based clustering identified two patient subtypes with distinct survival and immunotherapy responses. From 83 differentially expressed LSMRGs, a 6-gene prognostic risk-score model was constructed, validated as an independent prognostic factor. Model gene expression was confirmed via qRT-PCR. The risk score accurately predicted 1-, 3-, and 5-year survival. High- and low-risk groups exhibited differential enrichment in pathways including neuroactive ligand-receptor interaction and hormone signaling. The high-risk group had a higher mutation burden, lower immune infiltration, and distinct drug sensitivity profiles compared to the low-risk group. This study delineates the expression landscape of LSMRGs in GC and clarifies their associations with molecular subtypes, prognosis, and immune regulation. The findings provide novel prognostic biomarkers and a molecular basis for targeting lipid-soluble signaling pathways in GC therapy.