Original Research ARTICLE
Weighted gene co-expression network analysis identifies critical genes in the development of heart failure after acute myocardial infarction
- 1Lanzhou University, China
- 2Heart Center, First Hospital of Lanzhou University, China
- 3Department of Internal Medicine, Baiyin Second People’s Hospital, China
- 4Department of Digestive, Second Affiliated Hospital of Xi'an Jiaotong University, China
- 5The First School of Clinical Medicine, Lanzhou University, China
- 6Department of Cardiology, Gansu Provincial Hospital, China
Background: The development of heart failure (HF) remains a common complication following an acute myocardial infarction (AMI), and is associated with substantial adverse outcomes. However, the specific predictive biomarkers and candidate therapeutic targets for post-infarction HF have not been fully established. We sought to perform a weighted gene co-expression network analysis (WGCNA) to identify key modules, hub genes and possible regulatory targets involved in the development of HF following AMI.
Methods: Differentially expressed genes exhibiting the most (top 25%) variation across samples in a GSE59867 dataset were imported to the WGCNA. Gene Ontology (GO) and pathway enrichment analyses were performed on genes identified in the key module by Metascape. Gene regulatory networks were constructed using the microarray probe reannotation and Enrichr database. Hub genes were screened out from the key module and validated using other datasets.
Results: A total of 5,133 differentially expressed genes and 8 modules were identified between AMI patients who developed HF within 6 months of follow-up and those who did not. Specifically, the blue module was found to be the most significantly related to the development of post-infarction HF. Functional enrichment analysis revealed that the blue module was primarily associated with the inflammatory response, immune system, and apoptosis. Four transcriptional factors, including Spi-1 proto-oncogene (SPI1), zinc finger and BTB domain containing 7A (ZBTB7A), interferon regulatory factor 8 (IRF8), and peroxisome proliferator activated receptor gamma (PPARG), were identified as potential regulators of the expression of genes identified in the blue module. Further, non-coding RNAs, including miR-658 and LINC00537, were identified as having close interactions with genes from the blue module. A total of 4 hub genes (BCL3, HCK, SREBF1, and S100A9) were identified and validated for their predictive value in identifying future HFs.
Conclusions: By using WGCNA, we provide new insights into the underlying molecular mechanism and molecular markers correlated with HF development following an AMI, which may serve to improve risk stratification, therapeutic decisions and prognosis prediction in AMI patients.
Keywords: acute myocardial infarction, Heart Failure, weighted gene co-expression network analysis (WGCNA), Co-expression analysis, biomarker
Received: 07 May 2019;
Accepted: 04 Nov 2019.
Copyright: © 2019 Zhang, Niu, Zhang, Zhang, Hou, Pu, Chu and Bai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mx. Zheng Zhang, Lanzhou University, Lanzhou, China, firstname.lastname@example.org