Unlocking the potential of microRNAs: machine learning identifies key biomarkers for myocardial infarction diagnosis

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dc.authorscopusid57202831896
dc.authorscopusid48861755500
dc.authorscopusid57729993400
dc.authorscopusid35752987400
dc.authorscopusid57212299393
dc.authorscopusid16553847800
dc.authorscopusid24722480500
dc.contributor.authorSamadishadlou, Mehrdad
dc.contributor.authorRahbarghazi, Reza
dc.contributor.authorPiryaei, Zeynab
dc.contributor.authorEsmaeili, Mahdad
dc.contributor.authorAvcı, Çığır Biray
dc.contributor.authorBani, Farhad
dc.contributor.authorKavousi, Kaveh
dc.date.accessioned2024-08-25T18:52:07Z
dc.date.available2024-08-25T18:52:07Z
dc.date.issued2023
dc.departmentEge Üniversitesien_US
dc.description.abstractBackgroundMicroRNAs (miRNAs) play a crucial role in regulating adaptive and maladaptive responses in cardiovascular diseases, making them attractive targets for potential biomarkers. However, their potential as novel biomarkers for diagnosing cardiovascular diseases requires systematic evaluation.MethodsIn this study, we aimed to identify a key set of miRNA biomarkers using integrated bioinformatics and machine learning analysis. We combined and analyzed three gene expression datasets from the Gene Expression Omnibus (GEO) database, which contains peripheral blood mononuclear cell (PBMC) samples from individuals with myocardial infarction (MI), stable coronary artery disease (CAD), and healthy individuals. Additionally, we selected a set of miRNAs based on their area under the receiver operating characteristic curve (AUC-ROC) for separating the CAD and MI samples. We designed a two-layer architecture for sample classification, in which the first layer isolates healthy samples from unhealthy samples, and the second layer classifies stable CAD and MI samples. We trained different machine learning models using both biomarker sets and evaluated their performance on a test set.ResultsWe identified hsa-miR-21-3p, hsa-miR-186-5p, and hsa-miR-32-3p as the differentially expressed miRNAs, and a set including hsa-miR-186-5p, hsa-miR-21-3p, hsa-miR-197-5p, hsa-miR-29a-5p, and hsa-miR-296-5p as the optimum set of miRNAs selected by their AUC-ROC. Both biomarker sets could distinguish healthy from not-healthy samples with complete accuracy. The best performance for the classification of CAD and MI was achieved with an SVM model trained using the biomarker set selected by AUC-ROC, with an AUC-ROC of 0.96 and an accuracy of 0.94 on the test data.ConclusionsOur study demonstrated that miRNA signatures derived from PBMCs could serve as valuable novel biomarkers for cardiovascular diseases.en_US
dc.description.sponsorshipTabriz University of Medical Sciences [66,372]en_US
dc.description.sponsorshipThis is a report of result from Ph.D. thesis registered in Tabriz University of Medical Sciences with the Number 66,372. This work was extracted from Mehrdad Samadishadlou's thesis titled Developing and manufacturing of a paper-based Nanobiosensor in order to diagnosing myocardial infarction using a set of blood microRNAsen_US
dc.identifier.doi10.1186/s12933-023-01957-7
dc.identifier.issn1475-2840
dc.identifier.issue1en_US
dc.identifier.pmid37697288en_US
dc.identifier.scopus2-s2.0-85170396793en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1186/s12933-023-01957-7
dc.identifier.urihttps://hdl.handle.net/11454/102837
dc.identifier.volume22en_US
dc.identifier.wosWOS:001065066300001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherBmcen_US
dc.relation.ispartofCardiovascular Diabetologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240825_Gen_US
dc.subjectMicroRNAen_US
dc.subjectMachine learningen_US
dc.subjectMyocardial infarctionen_US
dc.subjectBioinformaticsen_US
dc.subjectBiomarkeren_US
dc.titleUnlocking the potential of microRNAs: machine learning identifies key biomarkers for myocardial infarction diagnosisen_US
dc.typeArticleen_US

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