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Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach : application of Boruta

Hamidi, Farzaneh (författare)
Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran.
Gilani, Neda (författare)
Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran.;Tabriz Univ Med Sci, Rd Traff Injury Res Ctr, Tabriz, Iran.
Belaghi, Reza (författare)
Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Uppsala universitet,Statistik, AI och data science,Univ Tabriz, Fac Math Sci, Dept Stat, Tabriz, Iran.;Swedish Agr Univ, Dept Energy & Technol, Uppsala, Sweden.,Institutionen för energi och teknik,Department of Energy and Technology,Uppsala University,University of Tabriz
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Yaghoobi, Hanif (författare)
Univ Tabriz, Sch Nat Sci, Dept Biol Sci, Tabriz, Iran.
Babaei, Esmaeil (författare)
Univ Tabriz, Sch Nat Sci, Dept Biol Sci, Tabriz, Iran.;Univ Tubingen, Interfac Inst Bioinformat & Med Informat IBMI, Tubingen, Germany.
Sarbakhsh, Parvin (författare)
Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran.
Malakouti, Jamileh (författare)
Tabriz Univ Med Sci, Fac Nursing & Midwifery, Dept Midwifery, Tabriz, Iran.
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Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran.;Tabriz Univ Med Sci, Rd Traff Injury Res Ctr, Tabriz, Iran. (creator_code:org_t)
 
Frontiers Media S.A. 2023
2023
Engelska.
Ingår i: FRONTIERS IN DIGITAL HEALTH. - : Frontiers Media S.A.. - 2673-253X. ; 5
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Introduction: In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult.Methods: By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost.Results: Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

artificial intelligence
Boruta
biomarker
feature selection
Gene Expression Omnibus
ovarian cancer
oncology

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