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

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: Genes exhibiting the most (top 50%) variation in expression levels across samples in a GSE59867 dataset were imported to the WGCNA. Gene Ontology 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 bioinformatics database. Hub genes were screened out from the key module and validated using other datasets. Results: A total of 10,265 most varied genes and six 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. Seven transcriptional factors, including SPI1, ZBTB7A, IRF8, PPARG, P65, KLF4, and Fos, were identified as potential regulators of the expression of genes identified in the blue module. Further, non-coding RNAs, including miR-142-3p and LINC00537, were identified as having close interactions with genes from the blue module. A total of six hub genes (BCL3, HCK, PPIF, S100A9, SERPINA1, and TBC1D9B) were identified and validated for their predictive value in identifying future HFs. Conclusions: By using the 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.

Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

“…WGCNA has been used to screen genes for different processes in cardiovascular disease such as coronary artery disease [24], congestive HF (CHF) and valvular heart disease (VHD). The use of WGCNA for the discovery of biomarkers related to cardiac arrest after acute myocardial infarction (AMI) has also been reported in a previous study [25] which identified six key genes with a great prognostic value for the progression of HF post-AMI. However, more studies are needed to dissect and decipher the genes involved in left ventricular failure.…”

Weighed Gene Coexpression Network Analysis Screens the Potential Long Noncoding RNAs and Genes Associated with Progression of Coronary Artery Disease

“…Mo et al [ 22 ] used the dataset of GSE59867 as the validation dataset to verify the four gene signatures ( NCF2 , MYO1F , S1PR4 , and FCN1 ) identified by WGCNA in GSE90074. A recent study by Niu et al identified 6 hub genes ( BCL3 , HCK , PPIF , S100A9 , SERPINA1 , and TBC1D9B ) by analyzing GSE59867 using WGCNA [ 23 ]. Though using the same dataset and similar analysis method, our study was different from their study.…”

Biomarkers for Heart Failure Prognosis: Proteins, Genetic Scores and Non-coding RNAs

“…As a step further than GWAS, weighted gene co-expression network analyses (WGCNA) allow functional interpretations of gene network modules (152). In a recent WGCNA analyses, the six hub genes BCL3, HCK, PPIF, S100A9, SERPINA1, and TBC1D9B were identified in HF patients after acute MI and could potentially serve as early prognostic biomarkers for HF (152). These hub genes might be involved in the development of HF by regulating local and systemic inflammatory pathways (152).…”