ArticleFrontiers in genetics2024
In search of the ratio of miRNA expression as robust biomarkers for constructing stable diagnostic models among multi-center data.
Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A high-throughput, end-to-end pipeline for extracellular miRNA biomarker discovery from human biofluids.Cell reports methods · 2026Article
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- Circulating miRNA as diagnostic tools for gynecological diseases and their applications in biosensor development.Analytical and bioanalytical chemistry · 2026Review
- IsomiR stoichiometry changes as disease biomarkers.Molecular therapy. Nucleic acids · 2025Article
- MicroRNAs in long COVID: roles, diagnostic biomarker potential and detection.Human genomics · 2025Review
- Insights into the evolution and regulation of miRNAs from the view of their DNA replication temporal domains.Frontiers in genetics · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
MicroRNAs (miRNAs) are promising biomarkers for the early detection of disease, and many miRNA-based diagnostic models have been constructed to distinguish patients and healthy individuals. To thoroughly utilize the miRNA-profiling data across different sequencing platforms or multiple centers, the models accounting the batch effects were demanded for the generalization of medical application. We conducted transcription factor (TF)-mediated miRNA-miRNA interaction network analysis and adopted the within-sample expression ratios of miRNA pairs as predictive markers. The ratio of the expression values between each miRNA pair turned out to be stable across multiple data sources. A genetic algorithm-based classifier was constructed to quantify risk scores of the probability of disease and discriminate disease states from normal states in discovery, with a validation dataset for COVID-19, renal cell carcinoma, and lung adenocarcinoma. The predictive models based on the expression ratio of interacting miRNA pairs demonstrated good performances in the discovery and validation datasets, and the classifier may be used accurately for the early detection of disease.
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