Abstract
BACKGROUND: Altered lipid metabolism is associated with gastric cancer (GC) progression. Comprehensive analysis to identify critical lipid metabolic drivers for predicting overall survival (OS) is not fully elucidated in GC. Our study aim to explore a novel lipid metabolism-related prognostic marker for GC. METHODS: Transcriptional status and clinical features were obtained from the TCGA-STAD database. The differentially expressed lipid metabolic genes and the risk prognostic model were developed by using bioinformatics and Cox regression analyses. ROC and Kaplan-Meier analysis were established to assess the performance of the risk predictive score model. GSE84437 dataset was used for external validation. Immunochemistry (IHC) was used to examine the expression of CYP19A1 in GC patients. Gene Set Enrichment Analysis (GSEA) was conducted to elucidate the underlying enriched mechanisms. TIMER and CIBERSORT analysis were performed to explore the relationship between CYP19A1 and immune microenvironment. RESULTS: A novel lipid metabolic gene signature (including MTTP, CYP19A1, MYB, SERPINE1), and specifically CYP19A1, might be a promising prognostic factor for GC. Using the validation cohort, ROC curves indicate a good showing of our risk model. Based on the signature yielded a significant difference OS time between the low- and high-risk groups. Cox regression indicates that the signature is an independent prognostic variable. ROC curves present better and reliability predictive accuracy. The IHC data validate that high expression of CYP19A1 was found in GC tissues. GSEA analysis reveals that higher expression of CYP19A1 may significantly up-regulate genes involved in fatty acid metabolism and glycerolipid metabolism. CIBERSORT analysis suggests that CYP19A1 is related to the infiltration of multiple immune cells. CONCLUSION: CYP19A1 could be an independent prognostic factor and a novel metabolic-targeted treatment strategy for gastric cancer.