{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T02:19:16Z","timestamp":1785377956182,"version":"3.55.0"},"reference-count":34,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Acute myocardial infarction occurs when blood supply to a particular coronary artery is cut off, causing ischemia or hypoxia and subsequent heart muscle destruction in the vascularized area. With a mortality rate of 17% per year, myocardial infarction (MI) is still one of the top causes of death globally. Numerous studies have been done to identify the genetic risk factors for myocardial infarction, as a positive family history of heart disease is one of the most potent cardiovascular risk factors. The goal of this review is to compile all the information currently accessible in the literature on the genes associated with AMI. We performed a big data analysis of genes associated with acute myocardial infarction, using the following keywords: \u201cmyocardial infarction\u201d, \u201cgenes\u201d, \u201cinvolvement\u201d, \u201cassociation\u201d, and \u201crisk\u201d. The analysis was done using PubMed, Scopus, and Web of Science. Data from the title, abstract, and keywords were exported as text files and imported into an Excel spreadsheet. Its analysis was carried out using the VOSviewer v. 1.6.18 software. Our analysis found 28 genes which are mostly likely associated with an increased risk for AMI, including: PAI-1, CX37, IL18, and others. Also, a correlation was made between the results obtained in the big data analysis and the results of the review. The most important genes increasing the risk for AMI are lymphotoxin-a gene (LTA), LGALS2, LDLR, and APOA5. A deeper understanding of the underlying functional genomic circuits may present new opportunities for research in the future.<\/jats:p>","DOI":"10.3390\/ijms232315008","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T03:49:13Z","timestamp":1669780153000},"page":"15008","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Identification of Risk Genes Associated with Myocardial Infarction\u2014Big Data Analysis and Literature Review"],"prefix":"10.3390","volume":"23","author":[{"given":"Cosmin","family":"Tirdea","sequence":"first","affiliation":[{"name":"Department of Legal Medicine and Bioethics, Faculty of Stomatology, Carol Davila University of Medicine, 050474 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4130-9402","authenticated-orcid":false,"given":"Sorin","family":"Hostiuc","sequence":"additional","affiliation":[{"name":"Department of Legal Medicine and Bioethics, Faculty of Stomatology, Carol Davila University of Medicine, 050474 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Horatiu","family":"Moldovan","sequence":"additional","affiliation":[{"name":"Department of Cardiac Surgery, Faculty of Medicine, Carol Davila University of Medicine, 050474 Bucharest, Romania"},{"name":"Clinical Emergency Hospital Bucharest, 014461 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3207-1025","authenticated-orcid":false,"given":"Alexandru","family":"Scafa-Udriste","sequence":"additional","affiliation":[{"name":"Clinical Emergency Hospital Bucharest, 014461 Bucharest, Romania"},{"name":"Department Cardiology, Faculty of Medicine, Carol Davila University of Medicine, 050474 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"ref_1","first-page":"1555","article-title":"Identification of risk genes associated with myocardial infarction based on the recursive feature elimination algorithm and support vector machine classifier","volume":"17","author":"Yang","year":"2018","journal-title":"Mol. 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