Abstract
BACKGROUND: The prognosis of gastric cancer is extremely poor. Metabolic reprogramming involving lipids has been associated with cancer occurrence and progression. AIM: To illustrate fatty acid metabolic mechanisms in gastric cancer, detect core genes, develop a prognostic model, and provide treatment options. METHODS: Raw data from The Cancer Genome Atlas and Gene Expression Omnibus databases were collected and analyzed. Differentially expressed fatty acid metabolism genes were identified and incorporated into a risk model based on least absolute shrinkage and selection operator regression analysis. Then, patients from The Cancer Genome Atlas were assigned to high- and low-risk cohorts according to the mean value of the risk score as the threshold, which was verified in the Gene Expression Omnibus database. Relationships between chemotherapeutic sensitivity and tumor microenvironment features were assessed. RESULTS: An integrated evaluation was performed in this study. Fatty acid metabolism-related genes were used to construct the risk model. Patients classified into the high-risk cohort were considered to be resistant to chemotherapy based on results of the "pRRophetic" R package. Patients in the high-risk cohort were associated with type I/II interferon activation, increased inflammation level, immune cell infiltration, and tumor immune dysfunction based on the exclusion algorithm, indicating the potential benefit of immunotherapy in these patients. CONCLUSION: We constructed a fatty acid-related risk score model to assess the comprehensive fatty acid features in gastric cancer and validated its vital role in prognosis, chemotherapy sensitivity, and immunotherapy.