ArticleBioImpacts : BI2025
The diagnostic and prognostic value of
Article in BioImpacts : BI, 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.
- Epigenetic and immunological alterations in umbilical cord blood of overweight/obese women with gestational diabetes mellitus: insights into DNA methylation signatures and immune cell dysregulation.BMC pregnancy and childbirth · 2026Article
- MicroRNAs in endometriosis: bioinformatics resources, machine learning strategies, and multi-omics perspectives.Journal of translational medicine · 2026Review
- Self-assembled phenylboronic acid nanomedicine targets sialic acid to synergistically activate ferroptosis via RRM1 suppression and GPX4 Inhibition for precision colon cancer therapy.Journal of nanobiotechnology · 2025Article
- Pan-cancer analysis and oncogenic implications ofJournal of cell communication and signaling · 2025Article
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Authors and funding
19 authors.
Funding
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Abstract
Introduction: Colorectal cancer (CRC) is among the lethal cancers, indicating the need for the identification of novel biomarkers for the detection of patients in earlier stages. RNA and microRNA sequencing were analyzed using bioinformatics and machine learning algorithms to identify differentially expressed genes (DEGs), followed by validation in CRC patients. Methods: The genome-wide RNA sequencing of 631 samples, comprising 398 patients and 233 normal cases was extracted from the Cancer Genome Atlas (TCGA). The DEGs were identified using DESeq package in R. Survival analysis was evaluated using Kaplan-Meier analysis to identify prognostic biomarkers. Predictive biomarkers were determined by machine learning algorithms such as Deep learning, Decision Tree, and Support Vector Machine. The biological pathways, protein-protein interaction (PPI), the co-expression of DEGs, and the correlation between DEGs and clinical data were evaluated. Additionally, the diagnostic markers were assessed with a combioROC package. Finally, the candidate tope score gene was validated by Real-time PCR in CRC patients. Results: The survival analysis revealed five novel prognostic genes, including Conclusion: Machine learning algorithms can be used to Identify key dysregulated genes/miRNAs involved in the pathogenesis of diseases, leading to the detection of patients in earlier stages. Our data also demonstrated the prognostic value of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.