ArticleBiochemical genetics2024
A Machine Learning Computational Framework Develops a Multiple Programmed Cell Death Index for Improving Clinical Outcomes in Bladder Cancer.
Article in Biochemical genetics, 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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4 citing papers in PubMed.
- Multi-omics identification of a programmed cell death-related signature and potential target P4HB for bladder cancer based on a 101-combination machine learning and experimental validation.Clinical and experimental medicine · 2026Article
- Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation.Scientific reports · 2025Article
- A predictive model based on program cell death genes for prognosis and therapeutic response in early stage hepatocellular carcinoma.Scientific reports · 2025Article
- An efficient graph attention framework enhances bladder cancer prediction.Scientific reports · 2025Article
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5 authors.
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Abstract
Comprehensive action patterns of programmed cell death (PCD) in bladder cancer (BLCA) have not yet been thoroughly investigated. Here, we collected 19 different PCD patterns, including 1911 PCD-related genes, and developed a multiple programmed cell death index (MPCDI) based on a machine learning computational framework. We found that in the TCGA-BLCA training cohort and the independently validated GSE13507 cohort, the patients with high-MPCDI had a worse prognosis, whereas patients with low-MPCDI had a better prognosis. By combining clinical characteristics with the MPCDI, we constructed a nomogram. The C-index demonstrated that the nomogram was significantly more accurate compared to other variables, including MPCDI, age, gender, and clinical stage. The results of the decision curve analysis demonstrated that the nomogram had a better net clinical benefit compared to other clinical variables. Subsequently, we revealed the heterogeneity of BLCA patients, with significant differences in terms of overall immune infiltration abundance, immunotherapeutic response, and drug sensitivity in the two MPCDI groups. Encouragingly, the high-MPCDI patients showed better efficacy for commonly used chemotherapeutic drugs than the low-MPCDI patients, which suggests that MPCDI scores have a guiding role in chemotherapy for BLCA patients. In conclusion, the MPCDI developed and verified in this study is not only an emerging clinical classifier for BLCA patients, but it also serves as a reliable forecaster for both chemotherapy and immunotherapy, which can guide clinical management and clinical decision-making for BLCA patients.
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