ArticleDiscover oncology2025
Development of a machine learning-derived programmed cell death index for prognostic prediction and immune insights in colorectal cancer.
Article in Discover oncology, 2025. 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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Who cites it
4 citing papers in PubMed.
- xNNPCD identifies regulators of programmed cell death by integrating perturbation transcriptomes with cancer dependency profiles.bioRxiv : the preprint server for biology · 2026Article
- Molecular subtyping and prognostic modeling of colon adenocarcinoma based on programmed cell death features: a multi-omics and machine learning study.Frontiers in immunology · 2026Article
- Research on prognostic prediction of colorectal cancer based on multi-dimensional biomarker features and machine learning models.Frontiers in oncology · 2026Article
- Machine Learning-Driven PCDI Classifier for Invasive PitNETs.Current gene therapy · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is a major contributor to cancer-related mortality worldwide, emphasizing the need for improved prognostic tools and therapeutic strategies. Programmed cell death, encompassing diverse modalities, plays a critical role in tumor biology and therapy response. Utilizing machine learning techniques, we developed a novel Programmed Cell Death Index (PCDI) incorporating multiple forms of PCD-related genes to predict outcomes in colorectal cancer CRC patients. The PCDI demonstrated robust prognostic performance, stratifying patients into high- and low-risk groups across multiple cohorts, with high PCDI scores correlating with poor survival, advanced tumor stage, and aggressive pathological features. A nomogram integrating PCDI with clinical variables showed strong predictive accuracy for 1-, 3-, and 5 year survival rates. Functional analysis revealed significant metabolic differences between high- and low-PCDI groups. Immune profiling identified associations between PCDI and immunosuppressive microenvironments, including elevated regulatory T cell levels and reduced PD-L1 expression in high-PCDI patients. Patients with high PCDI exhibited a potential resistance to immune checkpoint inhibitors. These findings emphasize PCDI's potential as a prognostic biomarker and a tool for guiding personalized therapeutic strategies in CRC patients.
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Registered trials
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