Background Dysregulation of lipid metabolism plays important roles in the tumorigenesis and progression of gastric cancer (GC). The present study aimed to establish a prognostic model based on the lipid metabolism-related genes in GC patients. Materials and Methods Two GC datasets from the Gene Expression Atlas, GSE62254 (n=300) and GSE26942 (n=217) were used as training and validation cohorts to establish a risk predictive scoring model. The efficacy of this model was assessed by ROC analysis. The association of the risk predictive scores with patient characteristics and immune cell subtypes were evaluated. A nomogram was constructed based on the risk predictive score model and other prognostic factors. Results A 19-gene risk predictive score model was established based on the expression of lipid metabolism-related genes. The time-dependent ROC analysis revealed that the risk predictive score model is stable and robust. Patients with high risk scores had significantly unfavorable overall survival compared with those with low risk scores in both the training and validation cohorts. A high risk score was associated with aggressive features, including a high tumor grade, an advanced TNM stage, and diffuse type of Lauren classification of GC. Moreover, distinct immune cell subtypes and signaling pathways were found between the high- and low risk score groups. A nomogram containing patients’ age, tumor stage, adjuvant chemotherapy, and the risk predictive score could accurately predict the survival of patients at 1 year, 3 years, and 5 years. Conclusions A novel 19-gene risk predictive score model was developed based on the lipid metabolism-related genes, which could be a potential prognostic indicator and therapeutic targets of GC.
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