Construction of a Lipid Metabolism-Related Gene-Based Prognostic Model for Gastric Cancer and Evaluation of Its Clinical Utility

Background: Gastric cancer remains a lethal malignancy, and lipid metabolic reprogramming is increasingly recognized as a key driver of its progression. However, existing lipid metabolism-related genes (LMGs) signatures lack robust external validation. In this study, we identified differentially expressed LMGs, constructed and externally validated a prognostic model, and evaluated its clinical relevance with respect to the tumor microenvironment, immune escape, and drug sensitivity, while also exploring potential therapeutic agents. Methods: LMGs were obtained from the Gene Set Enrichment Analysis (GSEA) repository. Transcriptome data derived from The Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma cohort were systematically analyzed to identify genes exhibiting significant expression differences. Functional characterization and biological pathway interpretation were subsequently performed based on the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Prognosis-related genes were initially screened using univariable Cox regression analysis, after which a least absolute shrinkage and selection operator approach was employed to construct a risk prediction model. The predictive capacity of the constructed model was systematically evaluated using time-to-event analyses, discrimination assessment based on Receiver Operating Characteristic (ROC) methodology, and multivariable independence testing. Furthermore, a clinically oriented nomogram was established by integrating relevant clinicopathological parameters to improve translational utility. Differences in biological pathway activity, immune-related responses, and susceptibility to chemotherapeutic agents across stratified risk categories were systematically explored using gene set enrichment analysis, the Tumor Immune Dysfunction and Exclusion algorithm, and drug response data derived from the Genomics of Drug Sensitivity in Cancer resource. Results: A total of 179 differential expression lipid metabolism–associated genes were detected. Subsequent functional enrichment analyses revealed that these genes are primarily involved in lipid droplet organization, fatty acid and sphingolipid metabolism, and the peroxisome proliferator-activated receptor (PPAR) signaling pathway. A prognostic model was established based on eleven key genes, and patients stratified into the high-risk subgroup exhibited markedly reduced overall survival compared with the low-risk subgroup in both the training and the GSE15459 validation dataset. The prognostic model showed the area under the ROC curve (AUC) of 0.618, 0.688, and 0.734 for 1-year, 3-year, and 5-year survival intervals, respectively, with a nomogram C-index of 0.6803. Immune characterization combined with GSEA indicated that patients classified as the high-risk subgroup exhibited significant activation of pathways associated with the extracellular matrix and focal adhesion with elevated Tumor Immune Dysfunction and Exclusion (TIDE), T-cell dysfunction, and immune-exclusion scores, whereas low-risk patients showed enrichment in amino acid metabolism and DNA repair pathways, higher microsatellite instability (MSI), and distinct drug sensitivities. Analysis of chemotherapeutic responsiveness indicated that individuals classified as the high-risk subgroup exhibited increased sensitivity to 5-fluorouracil, afatinib, and docetaxel, whereas low-risk patients showed greater sensitivity to dasatinib, AZD1332, and BMS-754807. Conclusion: We developed and validated an 11-gene lipid metabolism–based prognostic model for gastric cancer that demonstrated strong predictive performance and clinical applicability. The signature stratifies patients based on risk, reflects immune escape features and chemotherapy sensitivity, and holds potential as a tool for personalized prognosis and therapeutic decision-making.