Development and validation of a novel lipid metabolism-related gene prognostic signature and candidate drugs for patients with bladder cancer

Abstract

Background: Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study 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 the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics. Results: An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset. Conclusions: In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients.