ArticleCancer genomics & proteomics
Machine-learning-based Analysis Identifies miRNA Expression Profile for Diagnosis and Prediction of Colorectal Cancer: A Preliminary Study.
Article in Cancer genomics & proteomics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 20 citations in OpenAlex.
- Liquid Biopsy in Colorectal Cancer: Future Perspectives Through the Lens of Artificial Intelligence-A Comprehensive Review of Novel Literature.International journal of molecular sciences · 2026Review
- miRBiT: a rules-based single-sample serum miRNA classifier for pan-cancer detection with multi-cohort validation.Briefings in bioinformatics · 2026Article
- Circulating microRNAs as Biomarkers of Brain Metastases in Lung Cancer: A Pilot Study.Journal of clinical medicine · 2026Article
- Explainable AI for Predicting Mortality Risk in Metastatic Cancer: Retrospective Cohort Study Using the Memorial Sloan Kettering-Metastatic Dataset.JMIR cancer · 2026Article
- Review
- Extracellular vesicles cargo orchestration in colorectal cancer: immune evasion, stromal remodeling, and therapeutic frontiers.Molecular cancer · 2025Review
- Integrating miRNA profiling and machine learning for improved prostate cancer diagnosis.Scientific reports · 2025Article
- Linking microRNA to metabolic reprogramming and gut microbiota in the pathogenesis of colorectal cancer (Review).International journal of molecular medicine · 2025Review
- Construction of a miRNA Panel for Differentiating Lung Adenocarcinoma Brain Metastases and Glioblastoma.Cancers · 2025Article
- Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis.MicroRNA (Shariqah, United Arab Emirates) · 2025Article
- Exosome-Machine Learning Integration in Biomedicine: Advancing Diagnosis and Biomarker Discovery.Current medicinal chemistry · 2025Review
- Revisiting the diagnostic performance of exosomes: harnessing the feasibility of combinatorial exosomal miRNA profiles for colorectal cancer diagnosis.Discover oncology · 2024Article
- Genomic and Transcriptomic Research in the Discovery and Application of Colorectal Cancer Circulating Markers.International journal of molecular sciences · 2023Review
- Easy-to-use machine learning system for the prediction of IDH mutation and 1p/19q codeletion using MRI images of adult-type diffuse gliomas.Brain tumor pathology · 2023Article
- Prognostic Implications of Exosomal miRNA Networks Regulating TGFβ1-induced Epithelial-Mesenchymal Transition Signature in Oral Squamous Cell Carcinoma.Cancer genomics & proteomicsArticle
- Article
- Blood Biomarkers Panels for Screening of Colorectal Cancer and Adenoma on a Machine Learning-Assisted Detection Platform.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe stage of colorectal cancer (CRC) at the day of diagnosis has the greatest influence on survival rate. Thus, for CRC, which is mainly identified as advanced disease, non-invasive, molecular blood or stool tests could boost the diagnosis and lower mortality. Evaluation of miRNA expression levels in serum of patients diagnosed with CRC is a potential tool in early screening. Screening can be supported by machine learning (ML) as a tool for developing a cancer risk predictive model based on genetic data. MATERIALS AND
methodsmiRNA was isolated from the serum of 8 patients diagnosed with CRC and 10 patients from a control group matched for age and sex. The expression of 179 miRNAs was determined using a serum/plasma panel (Exiqon). Determinations were conducted using real-time PCR technique on an Applied Biosystems QuantStudio3 device in 96-well plates. A predictive model was developed through the Azure Machine Learning platform.
resultsA wide panel of 29 up-regulated miRNAs in CRC were identified and divided into two subgroups: 1) miRNAs with significantly higher serum level in cancer patients vs. controls (24 miRNAs) and 2) miRNAs detected only in cancer patients and not in controls (5 miRNAs). Re-analysis of published miRNA profiles of CRC tumours or CRC exosomes revealed that only 2 out of 29 miRNAs were up-regulated in all datasets including ours (miR-34a and miR-25-3p).
conclusionOur research suggests the potential role of overexpressed miRNAs as diagnostic or prognostic biomarkers among CRC patients. Such clustering of miRNAs may be a potential direction for discovering new diagnostic panels of cancer (including CRC), especially using ML. The low correspondence between deregulation of miRNAs in serum and tumour tissue revealed in our study confirms previously published reports.
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