ArticleMolecular therapy. Nucleic acids2023
Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for muscle-invasive urothelial cancer.
Article in Molecular therapy. Nucleic acids, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed, 75 citations in OpenAlex.
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- AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology.Journal of nanobiotechnology · 2025Review
- A nomogram integrating machine learning-derived CT radiomics and clinical characteristics for prognostic assessment in patients with locally advanced esophageal squamous cell carcinoma treated with definitive chemoradiotherapy with or without immunotherapy.Journal of translational medicine · 2025Article
- Development of a cancer metastasis-associated risk model via multi-machine-learning algorithms for prognostic risk evaluation and clinical application in oral squamous cell carcinoma.Journal of translational medicine · 2025Article
- CER1 as a manganese ion metabolism gene drives gastric cancer progression and therapeutic potential via oxidative stress and tumor microenvironment regulation.Discover oncology · 2025Article
- Comprehensive Characterization of the Immune Microenvironment Based on Nested Resampling Machine Learning Framework Identifies TRAF3 Interacting Protein 3 as a Promising Regulator to Improve the Resistance to Immunotherapy in Glioma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Single-Cell and Bulk RNA Sequencing Highlights Intra-Tumoral Heterogeneity and Malignant Progression Mechanisms in Prostate Cancer.Journal of cellular and molecular medicine · 2025Article
- Integrated multi-omics analysis and machine learning refine molecular subtypes and prognosis in hepatocellular carcinoma through O-linked glycosylation genes.Functional & integrative genomics · 2025Article
- Review
- Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning.Scientific reports · 2025Article
- UBE2D1 as a key biomarker in systemic juvenile idiopathic arthritis: a new perspective on diagnosis and disease activity assessment.Arthritis research & therapy · 2025Article
- Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification.Discover oncology · 2025Review
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Muscle-invasive urothelial cancer (MUC), characterized by high aggressiveness and significant heterogeneity, is currently lacking highly precise individualized treatment options. We used a computational pipeline to synthesize multiomics data from MUC patients using 10 clustering algorithms, which were then combined with 10 machine learning algorithms to identify molecular subgroups of high resolution and develop a robust consensus machine learning-driven signature (CMLS). Through multiomics clustering, we identified three cancer subtypes (CSs) that are related to prognosis, with CS2 exhibiting the most favorable prognostic outcome. Subsequent screening enabled identification of 12 hub genes that constitute a CMLS with robust predictive power for prognosis. The low-CMLS group exhibited a more favorable prognosis and greater responsiveness to immunotherapy and was more likely to exhibit the "hot tumor" phenotype. The high-CMLS group had a poor prognosis and lower likelihood of benefitting from immunotherapy, but dasatinib and romidepsin may serve as promising treatments for them. Comprehensive analysis of multiomics data can offer important insights and further refine the molecular classification of MUC. Identification of CMLS represents a valuable tool for early prediction of patient prognosis and for screening potential candidates likely to benefit from immunotherapy, with broad implications for clinical practice.
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