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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1465638

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

Provisionally accepted
Yating Zhan Yating Zhan 1Min Weng Min Weng 1Yangyang Guo Yangyang Guo 1Dingfeng Lv Dingfeng Lv 1Feng Zhao Feng Zhao 1Zejun Yan Zejun Yan 1Junhui Jiang Junhui Jiang 1Yanyi Xiao Yanyi Xiao 2*
  • 1 Ningbo First Hospital, Ningbo, China
  • 2 The Second Affiliated Hospital of Shanghai University, Wenzhou, China

The final, formatted version of the article will be published soon.

    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 accuracy 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.

    Keywords: Nicotine metabolism, Bladder cancer, machine learning, Prognostic signature, Immunotherapy benefit

    Received: 16 Jul 2024; Accepted: 29 Nov 2024.

    Copyright: © 2024 Zhan, Weng, Guo, Lv, Zhao, Yan, Jiang and Xiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yanyi Xiao, The Second Affiliated Hospital of Shanghai University, Wenzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.