ArticleFrontiers in genetics2021
Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Artificial intelligence in ovarian pathophysiology and management: a systematic review and meta-analysis.Journal of ovarian research · 2026Pooled it
- Breast Cancer Phenotypes in Africa: A Scoping Review and Meta-Analysis.JCO global oncology · 2023Pooled it
- Comparative Analysis of Serum and Tissue miRNA Expression Profiles and Regulatory Pathways in Early-Stage Ovarian Cancer Using Public Databases.International journal of molecular sciences · 2026Article
- Machine Learning-Based Identification of Candidate Serum miRNA Features for Pan-Cancer and Cancer Type Classification.Life (Basel, Switzerland) · 2026Article
- Machine learning prediction for menopause women with low bone mass: a multicenter and retrospective study.Scientific reports · 2026Article
- miR-4787-3p and miR-581 as predictive biomarkers for fibrosis and prognosis in pancreatic cancer.Discover oncology · 2026Article
- Artificial intelligence-based miRNA analysis for precision oncology: diagnostic and prognostic insights.Frontiers in molecular biosciences · 2026Review
- Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies.Cell communication and signaling : CCS · 2025Review
- Decision tree-based machine learning methods for identifying colorectal cancer-associated microRNA signatures and their regulatory networks.Scientific reports · 2025Article
- miR-5100 Overexpression Inhibits Prostate Cancer Progression by Inducing Cell Cycle Arrest and Targeting E2F7.Current issues in molecular biology · 2024Article
- Utilization of miRNAs as Biomarkers for the Diagnosis, Prognosis, and Metastasis in Gynecological Malignancies.International journal of molecular sciences · 2024Review
- Identifying factors related to mortality of hospitalized COVID-19 patients using machine learning methods.Heliyon · 2024Article
- Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review).Oncology reports · 2024Review
- Review
- Article
- Potential miRNA biomarkers and therapeutic targets for early atherosclerotic lesions.Scientific reports · 2023Article
- Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta.Frontiers in digital health · 2023Article
- Qishen Yiqi dropping pills improve cardiomyocyte hypertrophy via the lncRNA TINCR/miR-193b-3p/RORA axis.Journal of thoracic disease · 2022Article
- Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB.Biomedicines · 2022Article
- Unraveling the impact ofFrontiers in cellular and infection microbiology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18-25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.
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Registered trials
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