ArticleScientific reports2018
Identifying a miRNA signature for predicting the stage of breast cancer.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.
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Who cites it
78 citing papers in PubMed, 1 synthesis or guideline pooled it, 137 citations in OpenAlex.
- miRNAs as biomarkers for early cancer detection and their application in the development of new diagnostic tools.Biomedical engineering online · 2021Pooled it
- Discriminating Breast Cancer Patients by Spectral Analysis of Arterial Pulse Waveforms.Cardiovascular engineering and technology · 2026Article
- Artificial intelligence-based miRNA analysis for precision oncology: diagnostic and prognostic insights.Frontiers in molecular biosciences · 2026Review
- MicroRNA-592 suppresses breast cancer progression by targeting CYP20A1 to modulate the CHOP pathway.Journal of physiology and biochemistry · 2025Article
- Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies.Cell communication and signaling : CCS · 2025Review
- A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study.Molecular diversity · 2025Article
- microRNAs as Biomarkers of Breast Cancer.International journal of molecular sciences · 2025Review
- MiR-592 Attenuates Tamoxifen Resistance in Breast Cancer Through PIK3CA-Mediated PI3K/AKT/mTOR Signaling Pathway.Applied biochemistry and biotechnology · 2025Article
- Selective arm-usage of pre-miR-1307 dysregulates angiogenesis and affects breast cancer aggressiveness.BMC biology · 2025Article
- Exploring prognostic implications of miRNA signatures and telomere maintenance genes in kidney cancer.Molecular therapy. Oncology · 2024Article
- Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting.Medical oncology (Northwood, London, England) · 2024Review
- MicroRNA-1307-3p contributes to breast cancer progression through PRM2.Thoracic cancer · 2024Article
- Identification of a glycolysis-related miRNA Signature for Predicting Breast cancer Survival.Molecular biotechnology · 2024Article
- CRISPR screens in 3D tumourspheres identified miR-4787-3p as a transcriptional start site miRNA essential for breast tumour-initiating cell growth.Communications biology · 2024Article
- Identification of Gene Expression in Different Stages of Breast Cancer with Machine Learning.Cancers · 2024Article
- Ensemble-based classification using microRNA expression identifies a breast cancer patient subgroup with an ultralow long-term risk of metastases.Cancer medicine · 2024Article
- An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction.NAR genomics and bioinformatics · 2024Article
- Article
- MicroRNA Expression Profile in Early-Stage Breast Cancers.MicroRNA (Shariqah, United Arab Emirates) · 2024Article
- Bioinformatics analysis of the potentially functional circRNA-miRNA-mRNA network in breast cancer.PloS one · 2024Article
18 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a heterogeneous disease and one of the most common cancers among women. Recently, microRNAs (miRNAs) have been used as biomarkers due to their effective role in cancer diagnosis. This study proposes a support vector machine (SVM)-based classifier SVM-BRC to categorize patients with breast cancer into early and advanced stages. SVM-BRC uses an optimal feature selection method, inheritable bi-objective combinatorial genetic algorithm, to identify a miRNA signature which is a small set of informative miRNAs while maximizing prediction accuracy. MiRNA expression profiles of a 386-patient cohort of breast cancer were retrieved from The Cancer Genome Atlas. SVM-BRC identified 34 of 503 miRNAs as a signature and achieved a 10-fold cross-validation mean accuracy, sensitivity, specificity, and Matthews correlation coefficient of 80.38%, 0.79, 0.81, and 0.60, respectively. Functional enrichment of the 10 highest ranked miRNAs was analysed in terms of Kyoto Encyclopedia of Genes and Genomes and Gene Ontology annotations. Kaplan-Meier survival analysis of the highest ranked miRNAs revealed that four miRNAs, hsa-miR-503, hsa-miR-1307, hsa-miR-212 and hsa-miR-592, were significantly associated with the prognosis of patients with breast cancer.
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Registered trials
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