Identification of Underlying Hub Genes Associated with Hypertrophic Cardiomyopathy by Integrated Bioinformatics Analysis

Abstract

Zetao Ma,1,2,* Xizhi Wang,1,* Qingbo Lv,1 Yingchao Gong,1 Minghong Xia,1 Lenan Zhuang,1 Xue Lu,1 Ying Yang,1 Wenbin Zhang,1 Guosheng Fu,1 Yang Ye,1 Dongwu Lai1 1Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, 310016, People’s Republic of China; 2Department of Cardiology, Zhongshan People’s Hospital, Zhongshan, Guangdong Province, 528403, People’s Republic of China*These authors contributed equally to this workCorrespondence: Dongwu Lai; Yang YeKey Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang Province, 310016, People’s Republic of ChinaTel [phone]Fax [phone]Email [email protected]; [email protected]: Considered as one of the major reasons of sudden cardiac death, hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease. However, effective treatment for HCM is still lacking. Identification of hub gene may be a powerful tool for discovering potential therapeutic targets and candidate biomarkers.Methods: We analysed three gene expression datasets for HCM from the Gene Expression Omnibus. Two of them were merged by “sva” package. The merged dataset was used for analysis while the other dataset was used for validation. Following this, a weighted gene coexpression network analysis (WGCNA) was performed, and the key module most related to HCM was identified. Based on the intramodular connectivity, we identified the potential hub genes. Then, a receiver operating characteristic curve analysis was performed to verify the diagnostic values of hub genes. Finally, we validated changes of hub genes, for genetic transcription and protein expression levels, in datasets of HCM patients and myocardium of transverse aortic constriction (TAC) mice.Results: In the merged dataset, a total of 455 differentially expressed genes (DEGs) were identified from normal and hypertrophic myocardium. In WGCNA, the blue module was identified as the key module and the genes in this module showed a high positive correlation with HCM. Functional enrichment analysis of DEGs and key module revealed that the extracellular matrix, fibrosis, and neurohormone pathways played important roles in HCM. FRZB, COL14A1, CRISPLD1, LUM, and sFRP4 were identified as hub genes in the key module. These genes showed a good predictive value for HCM and were significantly up-regulated in HCM patients and TAC mice. We also found protein expression of LUM and sFRP4 increased in myocardium of TAC mice.Conclusion: This study revealed that five hub genes are involved in the occurrence and development of HCM, and they are potentially to be used as therapeutic targets and biomarkers for HCM.Keywords: hypertrophic cardiomyopathy, HCM, weighted gene coexpression network analysis, WGCNA, hub gene, biomarkers, bioinformatics analysis

Keywords

• hypertrophic cardiomyopathy
• hcm
• weighted gene coexpression network analysis
• wgcna
• hub gene
• biomarkers
• bioinformatics analysis