ArticleMolecular oncology2022
Artificial intelligence-driven consensus gene signatures for improving bladder cancer clinical outcomes identified by multi-center integration analysis.
Article in Molecular oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 66 citations in OpenAlex.
- Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology.Journal of translational medicine · 2025Pooled it
- Assessment of Prostate and Bladder Cancer Genomic Biomarkers Using Artificial Intelligence: a Systematic Review.Current urology reports · 2024Pooled it
- Review
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Emerging technologies and clinical translation of urine-based liquid biopsy in urological cancers.Genes & genomics · 2025Review
- Multi-omic characterization of consensus molecular subtype 1 (CMS1) colorectal cancer with dampened immune response improves precision medicine.Molecular oncology · 2025Article
- A prognostic model for gastric cancer constructed by multiple machine learning algorithms.Journal of molecular histology · 2025Article
- Integrative Analysis Reveals the Prognostic Effects of Epigenetic Regulators in Bladder Cancer.Cancer medicine · 2025Article
- Spatial profiling of the metabolism-immune axis in ovarian cancer.Frontiers in pharmacology · 2025Review
- Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024Review
- Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer.BMC cancer · 2024Article
- Machine learning-based identification of a consensus immune-derived gene signature to improve head and neck squamous cell carcinoma therapy and outcome.Frontiers in pharmacology · 2024Article
- Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes.Briefings in bioinformatics · 2023Article
- Multi-center validation of an immune-related lncRNA signature for predicting survival and immune status of patients with renal cell carcinoma: an integrating machine learning-derived study.Journal of cancer research and clinical oncology · 2023Article
- Combining bulk and single-cell RNA-sequencing data to develop an NK cell-related prognostic signature for hepatocellular carcinoma based on an integrated machine learning framework.European journal of medical research · 2023Article
- The Present and Future of Artificial Intelligence in Urological Cancer.Journal of clinical medicine · 2023Review
- Epigenetically regulated gene expression profiles decipher four molecular subtypes with prognostic and therapeutic implications in gastric cancer.Clinical epigenetics · 2023Article
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To accurately predict the prognosis and further improve the clinical outcomes of bladder cancer (BLCA), we leveraged large-scale data to develop and validate a robust signature consisting of small gene sets. Ten machine-learning algorithms were enrolled and subsequently transformed into 76 combinations, which were further performed on eight independent cohorts (n = 1218). We ultimately determined a consensus artificial intelligence-derived gene signature (AIGS) with the best performance among 76 model types. In this model, patients with high AIGS showed a higher risk of mortality, recurrence, and disease progression. AIGS is not only independent of traditional clinical traits [(e.g., American Joint Committee on Cancer (AJCC) stage)] and molecular features (e.g., TP53 mutation) but also demonstrated superior performance to these variables. Comparisons with 58 published signatures also indicated that AIGS possessed the best performance. Additionally, the combination of AIGS and AJCC stage could achieve better performance. Patients with low AIGS scores were sensitive to immunotherapy, whereas patients with high AIGS scores might benefit from seven potential therapeutics: BRD-K45681478, 1S,3R-RSL-3, RITA, U-0126, temsirolimus, MRS-1220, and LY2784544. Additionally, some mutations (TP53 and RB1), copy number variations (7p11.2), and a methylation-driven target were characterized by AIGS-related multi-omics alterations. Overall, AIGS provides an attractive platform to optimize decision-making and surveillance protocol for individual BLCA patients.
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