ArticleJournal of gastrointestinal oncology2025
Identification of prognostic biomarkers in colorectal cancer through multi-omics profiling of programmed cell death pathways.
Article in Journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Cell death crosstalk in NET-Driven inflammation: mechanisms, disease contexts, and therapeutic perspectives.Biomarker research · 2026Review
- An m6A-programmed cell death signature predicts prognosis and identifies STK25 as a therapeutic target in colon adenocarcinoma.Oncology letters · 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
- A prognostic nomogram for colorectal cancer: integrating blood microbiome and clinical factors.Journal of gastrointestinal oncology · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Programmed cell death (PCD) pathways, including autophagy, ferroptosis, cuproptosis, neutrophil extracellular trap formation (NETosis), and paraptosis, are central to tumor biology and hold potential as therapeutic targets in colorectal cancer (CRC). The aim of this study is to use multi-omics data to analyze the role of PCD in CRC. Methods: We conducted a multi-omics analysis using data from The Cancer Genome Atlas (TCGA), single-cell sequencing, and spatial transcriptomics to investigate the expression profiles, prognostic significance, and functional effects of PCD-associated genes in CRC. Key prognostic genes were identified through gene set variation analysis (GSVA), single-sample gene set enrichment analysis (ssGSEA), univariate Cox regression, and least absolute shrinkage and selection operator (LASSO) regression modeling. Results: Our analysis identified five PCD pathways with significant prognostic relevance in CRC, particularly autophagy and cuproptosis. Pan-cancer analyses highlighted unique and shared expression patterns of PCD-related genes across diverse cancer types, revealing their differential roles in cancer progression. Dynamic network biomarker (DNB) modeling pinpointed stage-specific critical transitions in PCD pathway activity, suggesting temporal variations in pathway influence on tumor progression. Functional assays demonstrated that overexpression of nuclear receptor coactivator 4 ( Conclusions: This study underscores the prognostic significance of PCD pathways in CRC, highlighting their potential as biomarkers and therapeutic targets. By identifying core genes within these pathways and elucidating their temporal effects on tumor progression, we provide a comprehensive foundation for future research into PCD-targeted therapies in CRC, aiming to enhance personalized treatment strategies.
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