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

Abstract

Background: Left-sided heart failure (HF) is documented as a key prognostic factor in HF. However, the relative molecular mechanisms underlying left-sided HF is unknown. The purpose of this study is to unearth significant modules, pivotal genes and candidate regulatory components governing the progression of left-sided HF by bioinformatical analysis. Methods: A total of 319 samples in GSE57345 dataset were used for weighted gene correlation network analysis (WGCNA). ClusterProfiler package in R was used to conduct functional enrichment for genes uncovered from the modules of interest. Regulatory networks of genes were built using Cytoscape while Enrichr database was used for identification of transcription factors (TFs). The MCODE plugin was used for identifying hub genes in the modules of interest and their validation was performed based on GSE1869 dataset. Results: A total of six significant modules were identified. Notably, the blue module was confirmed as the most crucially associated with left-sided HF, ischemic heart disease (ISCH) and dilated cardiomyopathy (CMP). Functional enrichment conveyed that genes belonging to this module were mainly those driving the extracellular matrixassociated processes such as extracellular matrix structural constituent and collagen binding. A total of seven transcriptional factors, including Suppressor of Zeste 12 Protein Homolog (SUZ12) and nuclear factor erythroid 2 like 2 (NFE2L2), adrenergic receptor (AR), were identified as possible regulators of coexpression genes identified in the blue module. A total of three key genes (OGN, HTRA1 and MXRA5) were retained after validation of their prognostic value in left-sided HF. The results of functional enrichment confirmed that these key genes were primarily involved in response to transforming growth factor beta and extracellular matrix.

Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods

“…Compared to previous studies, the functional modules and pathways identi ed by WGCNA method were also connected with speci c molecular subgroup of DCM [30][31][32]. We found that the speci c differential expression genes in subgroup 2 were mostly in the black, blue, green and grey WGCNA module.…”

Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods

“…Compared to previous studies, the functional modules and pathways identified by WGCNA method were also connected with specific molecular subgroup of DCM ( Zhou et al, 2020 ; Huang et al, 2021 ; Li et al, 2021 ). We found that the specific differential expression genes in subgroup 2 were mostly in the black, blue, green and grey WGCNA module.…”

Genome-Wide DNA Methylation Profiling of the Failing Human Heart with Mechanical Unloading IdentifiesLINC00881as an Essential Regulator of Calcium Handling in the Cardiomyocyte

“…HTRA1 has been identified as part of a candidate gene signature correlated with cardiomyopathies in a gene correlation network analysis model and its mRNA expression is upregulated 6.9 fold in DCM. 43, 44 PINK1 dependent phosphorylation of FAM65B attenuates ischemia reperfusion injury by suppressing autophagy. 45 The UNC45A gene has been characterized as potential de novo mosaic variant in sporadic cardiomyopathy.…”