ArticleInternational journal of molecular sciences2025
A Computational Recognition Analysis of Promising Prognostic Biomarkers in Breast, Colon and Lung Cancer Patients.
Article in International journal of molecular sciences, 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.
- The TRAP complex (SSR1-SSR4): mechanistic roles and therapeutic opportunities.Annals of medicine · 2026Review
- Identification of two genes associated with recurrence in Paget's disease and construction of a predictive model.Frontiers in genetics · 2026Article
- Darling (v2.0): Mining disease-related databases for the detection of biomedical entity associations.Computational and structural biotechnology journal · 2025Article
- SNRPE is Associated with ERK/mTOR Signaling Activation and Reduced Autophagy to Promote Lung Adenocarcinoma Cell Proliferation.OncoTargets and therapy · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
Breast, colon, and lung carcinomas are classified as aggressive tumors with poor relapse-free survival (RFS), progression-free survival (PF), and poor hazard ratios (HRs) despite extensive therapy. Therefore, it is essential to identify a gene expression signature that correlates with RFS/PF and HR status in order to predict treatment efficiency. RNA-binding proteins (RBPs) play critical roles in RNA metabolism, including RNA transcription, maturation, and post-translational regulation. However, their involvement in cancer is not yet fully understood. In this study, we used computational bioinformatics to classify the functions and correlations of RBPs in solid cancers. We aimed to identify molecular biomarkers that could help predict disease prognosis and improve the therapeutic efficiency in treated patients. Intersection analysis summarized more than 1659 RBPs across three recently updated RNA databases. Bioinformatics analysis showed that 58 RBPs were common in breast, colon, and lung cancers, with HR values < 1 and >1 and a significant Q-value < 0.0001. RBP gene clusters were identified based on RFS/PF, HR,
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Registered trials
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