{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T22:31:05Z","timestamp":1786487465977,"version":"build-2736575974"},"reference-count":14,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Genet."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to computationally identify circulating miRNAs associated with early-stage OVCA using publicly available datasets and bioinformatics workflows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:italic>hsa-miR-29b-3p<\/jats:italic>\n                      ,\n                      <jats:italic>hsa-miR-19b-3p<\/jats:italic>\n                      , and\n                      <jats:italic>hsa-miR-30e-5p<\/jats:italic>\n                      , 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fgene.2026.1827636","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T22:10:31Z","timestamp":1786486231000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["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"],"prefix":"10.3389","volume":"17","author":[{"given":"Ahmed","family":"El Hosseiny","sequence":"first","affiliation":[{"name":"Biotechnology Program, American University in Cairo","place":["New Cairo, Egypt"]},{"name":"Department of Biology, American University in Cairo","place":["New Cairo, Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meriem","family":"Yagoubi","sequence":"additional","affiliation":[{"name":"Biotechnology Program, American University in Cairo","place":["New Cairo, Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Moustafa","sequence":"additional","affiliation":[{"name":"Biotechnology Program, American University in Cairo","place":["New Cairo, Egypt"]},{"name":"Department of Biology, American University in Cairo","place":["New Cairo, Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asma","family":"Amleh","sequence":"additional","affiliation":[{"name":"Biotechnology Program, American University in Cairo","place":["New Cairo, Egypt"]},{"name":"Department of Biology, American University in Cairo","place":["New Cairo, Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"B2","doi-asserted-by":"publisher","first-page":"111258","DOI":"10.1016\/j.biopha.2021.111258","article-title":"Functional enrichment analysis of dysregulated miRNAs in ovarian cancer: focusing on TP53 signaling pathways","volume":"136","author":"Dai","year":"2021","journal-title":"Biomed. and Pharmacother."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1336","DOI":"10.1002\/ijc.35278","article-title":"The GLOBOCAN 2022 cancer estimates: data sources, methods, and a snapshot of the cancer burden worldwide","volume":"156","author":"Filho","year":"2024","journal-title":"Int. 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Guidelines, Move URL a Document Footnote"},{"key":"B13","doi-asserted-by":"publisher","first-page":"356","DOI":"10.18632\/oncotarget.27485","article-title":"Early diagnostic biomarkers for ovarian cancer: insights from circulating miRNAs","volume":"11","author":"Xu","year":"2020","journal-title":"Oncotarget"},{"key":"B14","doi-asserted-by":"publisher","first-page":"10423","DOI":"10.3390\/ijms221910423","article-title":"Stage-specific miRNA biomarkers for ovarian cancer: a bioinformatics analysis","volume":"22","author":"Zhang","year":"2021","journal-title":"Int. J. Mol. Sci."},{"key":"B15","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1186\/s12967-021-02969-2","article-title":"Role of miR-19b-3p in ovarian cancer progression and chemoresistance: a comprehensive review","volume":"19","author":"Zhou","year":"2021","journal-title":"J. Transl. 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