Identification of risk genes associated with myocardial infarction based on the recursive feature elimination algorithm and support vector machine classifier

Yang,Xiaoqiang

doi:10.3892/mmr.2017.8044

Identification of risk genes associated with myocardial infarction based on the recursive feature elimination algorithm and support vector machine classifier

Authors:
- Xiaoqiang Yang
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Affiliations: Department of Cardiology, Yangling Demonstration Zone Hospital, Yangling Demonstration Zone, Xianyang, Shaanxi 712100, P.R. China
Published online on: November 14, 2017 https://doi.org/10.3892/mmr.2017.8044
Pages: 1555-1560
Copyright: © Yang . This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The aim of the present study was to identify risk genes in myocardial infarction. Microarray data GSE34198, containing data from the peripheral blood of 49 myocardial infarction samples and 48 corresponding control samples, were downloaded from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). The DEGs were used to construct a protein‑protein interaction (PPI) network of patient samples, from which the feature genes were identified using the neighboring score method. The recursive feature elimination (RFE) algorithm was employed to select the risk genes among feature genes, which were subsequently applied to perform a support vector machine (SVM) classifier to identify the specific signature in myocardial infarction samples. Another dataset, GSE61144, was also downloaded to verify the efficacy of the classifier. A total of 724 downregulated and 483 upregulated DEGs were screened in patient samples compared with control samples in the GSE34198 dataset. The PPI network of myocardial infarction was comprised of 1,083 nodes (genes) and 46,363 lines (connections). Using the neighborhood scoring method, the top 100 feature genes in myocardial infarction samples were identified as the disease feature genes, which distinguish the myocardial infarction samples from the control samples. The RFE algorithm screened 15 risk genes, which were employed to construct a SVM classifier with an average precision of 88% to the patient sample following visualization by a confusion matrix. The predictive precision of the classifier on another microarray dataset, GSE61144, was 0.92, with an average true positive of 0.9278 and an average false positive of 0.2361. A‑kinase‑anchoring protein 12 (AKAP12) and glycine receptor α2 (GLRA2) were two risk genes in the SVM classifier. Therefore, AKAP12 and GLRA2 exert potential roles in the development of myocardial infarction, potentially by influencing cardiac contractility and protecting against ischemia‑reperfusion injury, which may provide clues in developing potential diagnostic biomarkers or therapeutic targets for myocardial infarction.

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