A personalized mRNA signature for predicting hypertrophic cardiomyopathy applying machine learning methods

Abstract

Abstract Hypertrophic cardiomyopathy (HCM) may lead to cardiac dysfunction and sudden death. This study was designed to develop a HCM signature applying bioinformatics and machine learning methods. Data of HCM and normal tissues were obtained from public databases to screen differentially expressed genes (DEGs) using the R software limma package. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed for enrichment analysis of HCM-associated DEGs. Hub genes for HCM were determined using weighted gene co-expression network analysis (WGCNA) together with two machine learning algorithms (SVM-RFE and LASSO). Finally, we introduced a zebrafish model to simulate changes in the hub genes in the HCM and to observe their effects on cardiac disease development. The mRNA expression data from a total of 106 HCM tissues and 39 normal samples were collected and we screened 157 DEGs. Enrichment analysis showed that immune pathways played an important role in the pathogenesis of HCM. Three hub genes (FCN3, MYH6 and RASD1) were identified using WGCNA, SVM-RFE, and LASSO analysis. In a zebrafish model, knockdown of MYH6 and RASD1 resulted in cardiac malformations with reduced ventricular capacity and heart rate, which validated the clinical significance of these genes in the diagnosis of HCM. Based on machine learning algorithms, our study created a signature with potential impact on cardiac function and cardiac quality index for HCM. The current findings had important implications for the early diagnosis and treatment of HCM.

Keywords

• HCM
• Machine learning
• Zebrafish
• Prediction
• Bioinformatics