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A circulating three-miRNA panel (hsa-miR-29b-3p; hsa-miR-19b-3p; hsa-miR-30e-5p) for early-stage ovarian cancer detection: a machine-learning bioinformatics approach
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DOI：10.3389/fgene.2026.1827636.png)
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Abstract
En 中文
BackgroundOvarian 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.ObjectiveThis study aimed to computationally identify circulating miRNAs associated with early-stage OVCA using publicly available datasets and bioinformatics workflows.MethodsDifferential 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.ResultsDifferential 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 hsa-miR-29b-3p; hsa-miR-19b-3p; and hsa-miR-30e-5p; consistently associated with early-stage OVCA. Functional enrichment analysis highlighted key pathways; including TP53 and VEGFA signaling; central to OVCA pathogenesis. The random forest classifier demonstrated robust performance with an accuracy of 91.67% and an area under the curve (AUC) of 0.991.ConclusionThis 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.
Keywords:
machine learning
biomarkers
microRNA
bioinformatics
early detection
random forest
ovarian cancer
TCGA
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