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

Frontiers in Digital Health

Digital Health

Hanif Yaghoobi¹  Esmaeil Babaei²  Reza Arabi Belaghi³  Jamileh Malakouti⁴  Parvin Sarbakhsh⁵  Farzaneh Hamidi⁵  Neda Gilani⁶ 

[1] Department of Biological Sciences, School of Natural Sciences, University of Tabriz, Tabriz, Iran;Department of Biological Sciences, School of Natural Sciences, University of Tabriz, Tabriz, Iran;Interfaculty Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany;Department of Mathematics, Applied Mathematics and Statistics, Uppsala University, Uppsala, Sweden;Department of Statistics, Faculty of Mathematical Science, University of Tabriz, Tabriz, Iran;Department of Energy and Technology, Swedish Agricultural University, Uppsala, Sweden;Department of Midwifery, Faculty of Nursing and Midwifery, Tabriz University of Medical Science, Tabriz, Iran;Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran;Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran;Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran;

关键词: artificial intelligence;    Boruta;    biomarker;    feature selection;    Gene Expression Omnibus;    ovarian cancer;    oncology;   

DOI  :  10.3389/fdgth.2023.1187578

 received in 2023-03-16, accepted in 2023-07-20,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionIn 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.MethodsBy 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.ResultsFour 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.

【 授权许可】

Unknown   
© 2023 Hamidi, Gilani, Arabi Belaghi, Yaghoobi, Babaei, Sarbakhsh and Malakouti.