Identification of biomarkers, pathways, and potential therapeutic targets for heart failure using next-generation sequencing data and bioinformatics analysis

Abstract

Background: Heart failure (HF) is the most common cardiovascular diseases and the leading cause of cardiovascular diseases related deaths. Increasing molecular targets have been discovered for HF prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might aid the diagnosis and treatment of HF. Methods: We searched next-generation sequencing (NGS) dataset (GSE161472) and identified differentially expressed genes (DEGs) by comparing 47 HF samples and 37 normal control samples using limma in R package. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g: Profiler database. The protein–protein interaction (PPI) network was plotted with Human Integrated Protein–Protein Interaction rEference (HiPPIE) and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. Then, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. Results: A total of 930 DEGs, 464 upregulated genes and 466 downregulated genes, were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections, and the citric acid tricarboxylic acid (TCA) cycle and respiratory electron transport. After combining the results of the PPI network miRNA-hub gene regulatory network and TF-hub gene regulatory network, 10 hub genes were selected, including heat shock protein 90 alpha family class A member 1 (HSP90AA1), arrestin beta 2 (ARRB2), myosin heavy chain 9 (MYH9), heat shock protein 90 alpha family class B member 1 (HSP90AB1), filamin A (FLNA), epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), cullin 4A (CUL4A), YEATS domain containing 4 (YEATS4), and lysine acetyltransferase 2B (KAT2B). Conclusions: This discovery-driven study might be useful to provide a novel insight into the diagnosis and treatment of HF. However, more experiments are needed in the future to investigate the functional roles of these genes in HF.

Acute effect of intradialytic aerobic exercise on hemodynamic response, dialysis-related side effects and proteome in patients with kidney failure: a pilot study.

“…Despite the preliminary nature of the data, some changes in the proteome are intriguing and pertinent for potential future therapeutic approaches for renal diseases. OTOA protein, which was associated to blood pressure in this study, has recently been identified as a potential target for a kidney disease in which scar tissue forms on the glomeruli (Ganekal et al, 2023), a kidney structure that is damaged in kidney failure.…”

Real-time prognostic biomarkers for predicting in-hospital mortality and cardiac complications in COVID-19 patients

“…Among the chosen biomarkers, we picked potassium for screening analysis, considering the inherent importance of electrolytes in diagnostics and the clinical role of potassium as a health indicator [12,13,[51][52][53][54][55][56]. Furthermore, several studies have been performed using nextgeneration platforms, showing the differential expression of biomarkers, including omic biomarkers or protein and gene expression of these biomarkers and others related to potassium, in the context of mortality or cardiac complications [57][58][59][60][61][62][63][64][65]. While biomarkers such as troponin and albumin have established correlations with cardiac function, predicting mortality and AA, we opted to focus on potassium as a singular biomarker to test its predictability for different outcomes.…”