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Using transcriptomic data, this study analyzed LSMRG expression patterns, molecular subtypes, prognostic significance, and immune\u2010microenvironment 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\u2010based subtypes. Differentially expressed LSMRGs were identified by intersecting tumor\u2010normal DEGs. A prognostic risk\u2010score model was built via univariate Cox, LASSO, and multivariate Cox analyses, with model genes validated by qRT\u2010PCR in cell lines. Comprehensive transcriptomic analyses included nomogram development, gene enrichment, immune infiltration, somatic mutations, and drug sensitivity. LSMRG\u2010based clustering identified two patient subtypes with distinct survival and immunotherapy responses. From 83 differentially expressed LSMRGs, a 6\u2010gene prognostic risk\u2010score model was constructed, validated as an independent prognostic factor. Model gene expression was confirmed via qRT\u2010PCR. The risk score accurately predicted 1\u2010, 3\u2010, and 5\u2010year survival. High\u2010 and low\u2010risk groups exhibited differential enrichment in pathways including neuroactive ligand\u2010receptor interaction and hormone signaling. The high\u2010risk group had a higher mutation burden, lower immune infiltration, and distinct drug sensitivity profiles compared to the low\u2010risk 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\u2010soluble signaling pathways in GC therapy.<\/jats:p>","DOI":"10.1002\/lipd.70063","type":"journal-article","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T04:30:07Z","timestamp":1778301007000},"page":"625-639","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lipid\u2010Soluble Signaling Molecular Model Predicts Prognosis and Immunotherapy Response in Gastric Cancer"],"prefix":"10.1002","volume":"61","author":[{"given":"Wenwen","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Oncology The Affiliated Hospital of Shaoxing University  Shaoxing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1109-5869","authenticated-orcid":false,"given":"Yanna","family":"Yu","sequence":"additional","affiliation":[{"name":"Clinical Pharmacy Department The 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