Abstract
With the deepening research on fatty acid metabolism, people have achieved a preliminary understanding of it in the development and prognosis of tumors. However, few studies are still on the expression pattern and prognostic value of fatty acid metabolism-related genes in gastric cancer (GC). We chose 93 genes relevant to fatty acid metabolism from the Gene Set Enrichment Analysis (GSEA) database. We analyzed differentially expressed genes (DEGs) in The Cancer Genome Atlas (TCGA) patients. Univariate Cox analysis and LASSO regression were used to select the genes most related to prognosis and therefore developed a prognosis model. In addition, a dataset of 76 samples from Gene Expression Omnibus (GEO) selected as a test set to aid in the development of a prognostic model. The prognostic relevance of this model was confirmed using Kaplan-Meier survival analysis, univariate/multivariate Cox analysis, and receiver operating characteristic (ROC) curve. Finally, enrichment analysis and protein-protein interaction (PPI) were used to analyze the functional differences of patients with different risk. Immune infiltration analysis based on CIBERSORT could check the infiltration degree and immune function changes of immune cell subtypes in patients with different risk groups. Overexpression of ELOVL4, ADH4, CPT1C, and ADH1B was linked to poor overall survival (OS) in GC patients, according to our findings. Furthermore, according to prognostic factors, patients with lower risk score tend to have better prognosis than patients with higher risk score. In addition, we also found that the infiltration levels of B cells, dendritic cells, auxiliary T cells, mast cells, neutrophils and tumor-infiltrating lymphocytes in patients with high-risk group were significantly increased, and the type II IFN response of immune cells, CCR and MHC class I receptor functions were significantly enhanced, suggesting that the tumor microenvironment immune activity in patients with high-risk group was active. Four fatty acid metabolism-related genes were discovered to be closely connected to the prognosis of individuals with GC. Through analysis and verification, we believed that this prognostic model was reliable and instructive in the prediction of the prognosis of GC.
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