Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Abstract

Introduction Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. Methods The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein–protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene—miRNA regulatory network and target gene—TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. Results A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene—miRNA regulatory network and target gene—TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. Conclusion The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

Inhibition of activin A receptor signalling attenuates age-related pathological cardiac remodelling

“…In cardiomyopathy, alternative splicing has been shown to target calcium signalling, sarcomere organisation and apoptosis ( Hu et al, 2017 ; Dlamini et al, 2015 ; Kong et al, 2010 ). These findings are in keeping with our data indicating that splicing-induced functional changes in proteins are important for regulating cardiac muscle architecture ( Kolur et al, 2021 ; D'Amario et al, 2018 ), calcium homeostasis ( Torac et al, 2014 ; Wanitchakool et al, 2017 ), inflammation ( Comerford et al, 2006 ; Wu et al, 2019 ; Morgan and Harris, 2015 ) and circadian rhythmicity ( Young, 2006 ; Wang et al, 2021 ). The numerous changes in response to sActRIIB treatment are likely due to signalling changes downstream of the activin receptor.…”

Exercise-derived peptide protects against pathological cardiac remodeling

“…Moreover, clinical evidence revealed that CCDC80 expression was significantly increased in patients with pulmonary arterial hypertension and heart failure. 40 , 41 , 42 This evidence indicates a close association between CCDC80 and cardiovascular system.…”

Transcriptome analysis uncovers the autophagy‐mediated regulatory patterns of the immune microenvironment in dilated cardiomyopathy

“…The LV free‐wall tissues were harvested at the time of cardiac surgery from subjects with heart failure undergoing transplantation and from unused donor hearts with apparently normal function. The expression matrix was reserved in the gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE141910), 34 and the clinical characteristics of involved samples in GSE141910 were listed in Table . In addition, the validation of autophagy model was performed in another microarray dataset GSE57338 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57338).…”

“…The expression matrix was reserved in the gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/ acc. cgi?acc=GSE14 1910), 34 and the clinical characteristics of involved samples in GSE141910 were listed in Table S1. In addition, the validation of autophagy model was performed in another microarray dataset GSE57338 (https://www.ncbi.nlm.nih.gov/geo/query/ acc.cgi?acc=GSE57338).…”