Basic Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Cardiol. May 26, 2022; 14(5): 282-296
Published online May 26, 2022. doi: 10.4330/wjc.v14.i5.282
Bioinformatics prediction of potential mechanisms and biomarkers underlying dilated cardiomyopathy
Zhou Liu, Ying-Nan Song, Kai-Yuan Chen, Wei-Long Gao, Hong-Jin Chen, Gui-You Liang
Zhou Liu, Gui-You Liang, School of Basic Medical Sciences, Guizhou Medical University, Guiyang 550025, Guizhou Province, China
Zhou Liu, Ying-Nan Song, Kai-Yuan Chen, Wei-Long Gao, Hong-Jin Chen, Gui-You Liang, Translational Medicine Research Center, Guizhou Medical University, Guiyang 550025, Guizhou Province, China
Ying-Nan Song, Hong-Jin Chen, Gui-You Liang, Department of Cardiovascular Surgery, the Affiliated Hospital of Guizhou Medical University, Guiyang 510000, Guizhou Province, China
Author contributions: Liu Z and Song YN designed this study; Chen KY collected the relevant data; Liu Z analyzed the data; Liu Z and Gao WL drafted the manuscript; Chen HJ and Liang GY reviewed and supervised this manuscript; All authors approved the final version of the article.
Supported by National Nature Science Foundation of China, No. 81960051, No. 8217021743, and No. 82160060; Project of High–Level Innovative Talents of Guizhou Province, No. [2016]4034; and Construction Funding from Characteristic Key Laboratory of Guizhou Province, No. [2021]313.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Gui-You Liang, MD, Professor, School of Basic Medical Sciences, Guizhou Medical University, Dangwu, Guian District, Guiyang 550025, Guizhou Province, China. guiyou515@163.com
Received: December 14, 2021
Peer-review started: December 14, 2021
First decision: January 25, 2022
Revised: February 19, 2022
Accepted: April 26, 2022
Article in press: April 26, 2022
Published online: May 26, 2022
Processing time: 155 Days and 6.4 Hours
Abstract
BACKGROUND

Heart failure is a health burden responsible for high morbidity and mortality worldwide, and dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. DCM is a disease of the heart muscle and is characterized by enlargement and dilation of at least one ventricle alongside impaired contractility with left ventricular ejection fraction < 40%. It is also associated with abnormalities in cytoskeletal proteins, mitochondrial ATP transporter, microvasculature, and fibrosis. However, the pathogenesis and potential biomarkers of DCM remain to be investigated.

AIM

To investigate the candidate genes and pathways involved in DCM patients.

METHODS

Two expression datasets (GSE3585 and GSE5406) were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between the DCM patients and healthy individuals were identified using the R package “linear models for microarray data.” The pathways with common DEGs were analyzed via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analyses. Moreover, a protein-protein interaction network (PPI) was constructed to identify the hub genes and modules. The MicroRNA Database was applied to predict the microRNAs (miRNAs) targeting the hub genes. Additionally, immune cell infiltration in DCM was analyzed using CIBERSORT.

RESULTS

In total, 97 DEGs (47 upregulated and 50 downregulated) were identified. GO analysis showed that the DEGs were mainly enriched in “response to growth factor,” “extracellular matrix,” and “extracellular matrix structural constituent.” KEGG pathway analysis indicated that the DEGs were mainly enriched in “protein digestion and absorption” and “interleukin 17 (IL-17) signaling pathway.” The PPI network suggested that collagen type III alpha 1 chain (COL3A1) and COL1A2 contribute to the pathogenesis of DCM. Additionally, visualization of the interactions between miRNAs and the hub genes revealed that hsa-miR-5682 and hsa-miR-4500 interacted with both COL3A1 and COL1A2, and thus these miRNAs might play roles in DCM. Immune cell infiltration analysis revealed that DCM patients had more infiltrated plasma cells and fewer infiltrated B memory cells, T follicular helper cells, and resting dendritic cells.

CONCLUSION

COL1A2 and COL3A1 and their targeting miRNAs, hsa-miR-5682 and hsa-miR-4500, may play critical roles in the pathogenesis of DCM, which are closely related to the IL-17 signaling pathway and acute inflammatory response. These results may provide useful clues for the diagnosis and treatment of DCM.

Keywords: Dilated cardiomyopathy; Bioinformatics; Differentially expressed genes; Function enrichment analysis; Protein-protein interaction network; Immune cell infiltration

Core Tip: As the most common cause of heart failure, the diagnosis and therapy for dilated cardiomyopathy (DCM) are still unsatisfactory because of its indistinct pathogenesis and specific biomarkers. Thus, we comprehensively utilized the microarray data from the Gene Expression Omnibus database to uncover the biomarker and mechanisms underlying DCM development. Collagen type III alpha 1 chain and collagen type I alpha 2 chain, which are regulated by hsa-miR-5682 and hsa-miR-4500, may play critical roles in the pathogenesis of DCM through the acute inflammatory response and interleukin 17 signaling pathway. These biomarkers and mechanisms need to be further studied.