Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence

Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18–25 nucleotides in length th...

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Autores principales:	Farzaneh Hamidi, Neda Gilani, Reza Arabi Belaghi, Parvin Sarbakhsh, Tuba Edgünlü, Pasqualina Santaguida
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	Biomarker Elasticnet Feature Selection Gene Expression Omnibus (GEO) Lasso Machine Learning Genetics QH426-470
Acceso en línea:	https://doaj.org/article/415ece1b41d74c6f95c7dc62bd466560
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spelling	oai:doaj.org-article:415ece1b41d74c6f95c7dc62bd4665602021-12-01T02:24:50ZExploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence1664-802110.3389/fgene.2021.724785https://doaj.org/article/415ece1b41d74c6f95c7dc62bd4665602021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.724785/fullhttps://doaj.org/toc/1664-8021Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18–25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.Farzaneh HamidiNeda GilaniReza Arabi BelaghiReza Arabi BelaghiParvin SarbakhshTuba EdgünlüPasqualina SantaguidaFrontiers Media S.A.articleBiomarkerElasticnetFeature SelectionGene Expression Omnibus (GEO)LassoMachine LearningGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biomarker Elasticnet Feature Selection Gene Expression Omnibus (GEO) Lasso Machine Learning Genetics QH426-470
spellingShingle	Biomarker Elasticnet Feature Selection Gene Expression Omnibus (GEO) Lasso Machine Learning Genetics QH426-470 Farzaneh Hamidi Neda Gilani Reza Arabi Belaghi Reza Arabi Belaghi Parvin Sarbakhsh Tuba Edgünlü Pasqualina Santaguida Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence
description	Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18–25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.
format	article
author	Farzaneh Hamidi Neda Gilani Reza Arabi Belaghi Reza Arabi Belaghi Parvin Sarbakhsh Tuba Edgünlü Pasqualina Santaguida
author_facet	Farzaneh Hamidi Neda Gilani Reza Arabi Belaghi Reza Arabi Belaghi Parvin Sarbakhsh Tuba Edgünlü Pasqualina Santaguida
author_sort	Farzaneh Hamidi
title	Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence
title_short	Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence
title_full	Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence
title_fullStr	Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence
title_full_unstemmed	Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence
title_sort	exploration of potential mirna biomarkers and prediction for ovarian cancer using artificial intelligence
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/415ece1b41d74c6f95c7dc62bd466560
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Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence

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