ArticleFrontiers in immunology2024
Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Network toxicology and interpretable modeling identify nicotine-associated neuroendocrine targets and a three-gene prognostic signature in breast cancer.Translational cancer research · 2026Article
- A nine-gene nicotine-metabolism signature predicts prognosis and characterizes the immune landscape in colon adenocarcinoma.Journal of gastrointestinal oncology · 2026Article
- Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling.Metabolites · 2025Review
- Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer.Biology direct · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
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.
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