ArticleJournal of cancer research and clinical oncology2024
Machine learning identifies the role of SMAD6 in the prognosis and drug susceptibility in bladder cancer.
Article in Journal of cancer research and clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification.Discover oncology · 2025Review
- Single-cell RNA sequencing analysis reveals the dynamic changes in the tumor microenvironment during NMIBC recurrence.Apoptosis : an international journal on programmed cell death · 2025Article
- Loss of YTHDC1 mExperimental & molecular medicine · 2025Article
- Development and functional validation of a disulfidoptosis-related gene prognostic model for lung adenocarcinoma based on bioinformatics and experimental validation.Frontiers in immunology · 2025Article
- Machine learning-derived cellular senescence index for predicting prognosis and drug sensitivity in patients with renal cell carcinoma.Frontiers in immunology · 2025Article
- Integrating single-cell transcriptomics to reveal the ferroptosis regulators in the tumor microenvironment that contribute to bladder urothelial carcinoma progression and immunotherapy.Frontiers in immunology · 2024Article
- Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.Frontiers in oncology · 2024Review
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Authors and funding
6 authors.
Funding
Abstract
backgroundBladder cancer (BCa) is among the most prevalent malignant tumors affecting the urinary system. Due to its highly recurrent nature, standard treatments such as surgery often fail to significantly improve patient prognosis. Our research aims to predict prognosis and identify precise therapeutic targets for novel treatment interventions.
methodsWe collected and screened genes related to the TGF-β signaling pathway and performed unsupervised clustering analysis on TCGA-BLCA samples based on these genes. Our analysis revealed two novel subtypes of bladder cancer with completely different biological characteristics, including immune microenvironment, drug sensitivity, and more. Using machine learning classifiers, we identified SMAD6 as a hub gene contributing to these differences and further investigated the role of SMAD6 in bladder cancer in the single-cell transcriptome data. Additionally, we analyzed the relationship between SMAD6 and immune checkpoint genes. Finally, we performed a series of in vitro assays to verify the function of SMAD6 in bladder cancer cell lines.
resultsWe have revealed two novel subtypes of bladder cancer, among which C1 exhibits a worse prognosis, lower drug sensitivity, a more complex tumor microenvironment, and a 'colder' immune microenvironment compared to C2. We identified SMAD6 as a key gene responsible for the differences and further explored its impact on the molecular characteristics of bladder cancer. Through in vitro experiments, we found that SMAD6 promoted the prognosis of BCa patients by inhibiting the proliferation and migration of BCa cells.
conclusionOur study reveals two novel subtypes of BCa and identifies SMAD6 as a highly promising therapeutic target.
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