Frontiers in genetics · 2026
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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El Hosseiny Ahmed, Yagoubi Meriem, Moustafa Ahmed, Amleh Asma
Аннотация
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 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.
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.
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 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.
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.