Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods

Abstract

Background Although the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still missed diagnosis for the absence of any symptoms. The gene expression levels varied during disease development; therefore, a classifier based on gene expression might contribute to CAD diagnosis. This study aimed to construct genetic classification models for CAD using gene expression data, which may provide new insight into the understanding of its pathogenesis. Methods All statistical analysis was completed by R 3.4.4 software. Three raw gene expression datasets (GSE12288, GSE7638 and GSE66360) related to CAD were downloaded from the Gene Expression Omnibus database and included for analysis. Limma package was performed to identify differentially expressed genes (DEGs) between CAD samples and healthy controls. The WGCNA package was conducted to recognize CAD-related gene modules and hub genes, followed by recursive feature elimination analysis to select the optimal features genes (OFGs). The genetic classification models were established using support vector machine (SVM), random forest (RF) and logistic regression (LR), respectively. Further validation and receiver operating characteristic (ROC) curve analysis were conducted to evaluate the classification performance. Results In total, 374 DEGs, eight gene modules, 33 hub genes and 12 OFGs (HTR4, KISS1, CA12, CAMK2B, KLK2, DDC, CNGB1, DERL1, BCL6, LILRA2, HCK, MTF2) were identified. ROC curve analysis showed that the accuracy of SVM, RF and LR were 75.58%, 63.57% and 63.95% in validation; with area under the curve of 0.813 (95% confidence interval, 95% CI 0.761–0.866, P < 0.0001), 0.727 (95% CI 0.665–0.788, P < 0.0001) and 0.783 (95% CI 0.725–0.841, P < 0.0001), respectively. Conclusions In conclusion, this study found 12 gene signatures involved in the pathogenic mechanism of CAD. Among the CAD classifiers constructed by three machine learning methods, the SVM model has the best performance.

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

“…Genes such as HLA-DMB 39 , HLA-B 40 , and GPX1 41 were found to be profoundly expressed in cardiomyopathy. While other biomarkers such as RN7SL2 42 , LILRA2 43 , GAS5 44 , TWF2 45 , EGLN2 46 , SNHG6 47 – 49 , and BRK1 50 have all been previously associated with phenotypic variations linked to CVD, there is limited literature associating protein-coding genes such as RPS28P7 and CTA-363E6.6 to other known CVDs. No direct links were recorded between RN7SL593P and AP003419.11 and known CVDs as well as other non-CVD-related diseases.…”

Exploring the pathogenesis and key genes associated of acute myocardial infarction complicated with Alzheimer’s disease

“…Surprisingly, nevertheless, our research revealed that BCL6 gene expression was up-regulated, which may be connected to negative feedback regulation in individuals with high levels of atherosclerosis. Interestingly, prior research had also discovered increased BCL6 gene expression in individuals suffering from coronary heart disease 40 . Hence, additional mechanisms must be proven through additional tests.…”

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

“…Genes such as HLA-DMB [28], HLA-B [29], and GPX1 [30] were found to be profoundly expressed in cardiomyopathy. While other biomarkers such as RN7SL2 [31], LILRA2 [32], GAS5 [33], TWF2 [34], EGLN2 [35], SNHG6 [36, 37, 38], and BRK1 [39] have all been previously associated with phenotypic variations linked to CVD, there is limited literature associating protein-coding genes such as RPS28P7 and CTA-363E6.6 to other known CVDs. No direct links were recorded between RN7SL593P and AP003419.11 and known CVDs as well as other non-CVD-related diseases.…”