ArticleFrontiers in genetics2026
A circulating three-miRNA panel (
Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Ovarian cancer (OVCA) remains one of the most lethal gynecological malignancies, primarily due to late-stage diagnosis and the lack of reliable early-detection biomarkers. Circulating microRNAs (miRNAs) have emerged as promising non-invasive biomarkers for cancer detection and prognosis. Objective: This study aimed to computationally identify circulating miRNAs associated with early-stage OVCA using publicly available datasets and bioinformatics workflows. Methods: Differential expression analysis was performed on miRNA-Seq datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Functional enrichment analysis and pathway annotation were performed using miEAA and PANTHER. A random forest-based machine-learning model was developed and optimized for miRNA biomarker classification. Results: Differential expression analysis revealed distinct miRNA signatures between OVCA and other cancer types (BRCA, CESC, UCEC, and COAD), as well as between OVCA and control samples. Stage-specific analysis identified key miRNAs, including Conclusion: This study identifies a panel of circulating miRNAs with significant diagnostic potential for early-stage OVCA. Integration of these miRNAs into clinical workflows could enhance early detection and improve patient outcomes. Further validation using independent cohorts is warranted.
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