Lipid metabolism-related gene signatures for predicting the prognosis of lung adenocarcinoma

Background: Lung cancer is one of the most common epithelial malignancies worldwide, accounting for the highest number of new cases and deaths. Metabolism is the sum of chemical reactions that produce energy to keep an organism alive. Several studies have shown that glucose and lipid metabolic disorders are common phenomena related to cancer cell genesis and progression. Methods: We screened the differentially expressed genes (DEGs) of lung adenocarcinoma (LUAD) samples of The Cancer Genome Atlas (TCGA) database, the Gene Set Enrichment Analysis (GSEA), and Gene Card database metabolism-related data, the metabolism-related DEGs of LUAD, as well as the univariate Cox regression analysis genes, for identifying significant outcome-related genes. The least absolute shrinkage and gene selection operator (LASSO) analysis was performed to establish the best risk model. Results: Our study aimed to establish a lipid metabolism-related model for predicting LUAD prognosis. Furthermore, our model's prognosis prediction power was evaluated by survival analysis. This study finally identified 11 differentially expressed genes (DEGs) related to lipid metabolism that were significantly associated with the prognosis of lung adenocarcinoma. It provided a new idea for the treatment of high-risk lung adenocarcinoma patients Conclusions: The constructed clinical prognosis model of lung adenocarcinoma related to lipid metabolism provides a new idea for clinical treatment of lung adenocarcinoma.