Identification of a Novel Lipid Metabolism-Related Gene Signature in Prognosis of Bladder Cancer

Background: Bladder cancer (BLCA) is a destructive cancer with unfavorable prognosis. Mounting studies have demonstrated that lipid metabolism affected the progression and treatment of tumor. Therefore, we aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer. Methods: Lipid metabolism-related genes (LRGs) were acquired from Molecular Signature Database (MSigDB). LRG mRNA expression and clinical data were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify the prognostic gene for predicting overall survival in BLCA. The Kaplan-Meier analysis was performed to assess the prognosis. The function of LRGs was explored via enrichment analysis and single sample Gene Set Enrichment Analysis (ssGSEA). CMAP database was used to find the small molecule drugs for treatment. Results: We successfully constructed and validated a 11-LRG risk model for predicting the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that LRGs were closely related with the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. LRGs were also involved in immune cells infiltration. Five small molecule drugs could be the candidate treatment for BLCA patients based on CMAP dataset. Conclusion: In conclusion, we identified a reliable prognostic biomarker based on11-LRG signature and found five small molecule drugs for BLCA patient treatments.