AUTHOR=Niu Xiaowei , Zhang Jingjing , Zhang Lanlan , Hou Yangfan , Pu Shuangshuang , Chu Aiai , Bai Ming , Zhang Zheng 

TITLE=Weighted Gene Co-Expression Network Analysis Identifies Critical Genes in the Development of Heart Failure After Acute Myocardial Infarction

JOURNAL=Frontiers in Genetics

VOLUME=Volume 10 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.01214

DOI=10.3389/fgene.2019.01214

ISSN=1664-8021

ABSTRACT=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.