Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning.

Zhan, Yating; Weng, Min; Guo, Yangyang; Lv, Dingfeng; Zhao, Feng; Yan, Zejun; Jiang, Junhui; Xiao, Yanyi; Yao, Lili

Background: Several studies indicate that smoking is one of the major risk factors for bladder cancer. Nicotine and its metabolites, the main components of tobacco, have been found to be strongly linked to the occurrence and progression of bladder cancer. However, the function of nicotine metabolism-related genes (NRGs) in bladder urothelial carcinoma (BLCA) are still unclear. Methods: NRGs were collected from MSigDB to identify the clusters associated with nicotine metabolism. Prognostic differentially expressed genes (DEGs) were filtered via differentially expression analysis and univariate Cox regression analysis. Integrative machine learning combination based on 10 machine learning algorithms was used for the construction of robust signature. Subsequently, the clinical application of signature in terms of prognosis, tumor microenvironment (TME) as well as immunotherapy was comprehensively evaluated. Finally, the biology function of the signature gene was further verified via CCK-8, transwell migration and colony formation. Results: Three clusters associated with nicotine metabolism were discovered with distinct prognosis and immunological patterns. A four gene-signature was developed by random survival forest (RSF) method with highest average Harrell's concordance index (C-index) of 0.763. The signature exhibited a reliable and accurate performance in prognostic prediction across TCGA-train, TCGA-test and GSE32894 cohorts. Furthermore, the signature showed highly correlation with clinical characteristics, TME and immunotherapy responses. Suppression of MKRN1 was found to reduce the migration and proliferation of bladder cancer cell. In addition, enhanced migration and proliferation caused by nicotine was blocked down by loss of MKRN1. Conclusions: The novel nicotine metabolism-related signature may provide valuable insights into clinical prognosis and potential benefits of immunotherapy in bladder cancer patients.

MACHINE learning; CANCER cell proliferation; NICOTINE; BLADDER cancer; TRANSITIONAL cell carcinoma

Frontiers in Immunology, 2024, p1

1664-3224

Academic Journal

10.3389/fimmu.2024.1465638