ArticleCancer cell international2025
Constructing a bladder cancer prognostic model related to exosome using machine learning and identifying THBS1 as a potential target.
Article in Cancer cell international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Nanorobots and Exosomes: Driving the New Frontier for Bladder Tumors through Non-Coding RNAs.Oncology research · 2026Review
- A systematic characterization of amino acid metabolism-related genes reveals molecular subtypes and a prognostic signature in bladder cancer.Frontiers in oncology · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundExosome-mediated molecular processes significantly influence bladder cancer (BCa) development, but clinically applicable exosome-based prognostic systems are still lacking. This research aims to develop an exosome-related prognostic model using machine learning approaches and discover potential therapeutic targets.
methodsWe implemented ten machine learning algorithms with eighty-one combinatorial configurations to analyze BCa transcriptomic data. Model validation incorporated time-dependent receiver operating characteristic analysis, Kaplan-Meier survival curves, nomogram assessment, and Cox regression. Biological mechanisms were explored through immune microenvironment evaluation (CIBERSORT) and functional enrichment analysis (GSEA). Molecular docking using PubChem, PDB structures, and CB-DOCK2 identified potential targets, followed by experimental validation including quantitative reverse transcription PCR (qRT-PCR), Western Blotting (WB), cell proliferation (CCK-8, colony formation), migration (Transwell, wound healing), EMT marker detection, and tail vein injection-based lung metastasis assays in nude mice.
resultsAnalysis revealed 132 differentially expressed genes specific to BCa, which were subsequently refined to 15 prognosis-associated genes through univariate Cox regression. The machine learning-derived prognostic model (MLDPM) outperformed existing clinical indicators, effectively stratifying patients into distinct risk categories. High-risk patients demonstrated characteristics of immune evasion and poorer survival outcomes. Computational docking analysis identified strong molecular interactions between THBS1/MMP9/CXCL12 and standard chemotherapeutic compounds. Importantly, suppression of THBS1 expression significantly inhibited BCa cell migration and invasion.
conclusionThis study establishes the first machine learning-based exosomal prognostic system for BCa and identifies THBS1 as both a potential biomarker and therapeutic target. The combined computational and experimental methodology offers a new approach for personalized BCa treatment strategies.
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