A-019 Integrative Analysis of Arrythmogenic Right Ventricular Cardiomyopathy of Potencial Biomarkers Through Bioinformatic Analysis
Authors: Neta A P , Silbiger V N , Marques O C , Hirata T , Hirata M , Luchessi A D 
Published: September 2023

Abstract

Background
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disease usually associated with heterozygous or compound mutations in cardiac desmosomes genes. Pathologically, it is a disease characterized by fibrofatty replacement primarily in the right ventricle of normal cardiac tissue and clinically by sudden cardiac death in healthy young people. Diagnosis is often difficult due to the nonspecific nature of the disease and the broad spectrum of phenotypic variations, being commonly diagnostic post mostem. Non-coding RNAs are biomarkers that regulate gene expression and participate in various pathophysiological processes, becoming an important research field in cardiomyopathology for their potential as non-invasive clinical biomarkers and therapeutic. In this study, we aim to identify miRNAs that may serve as potentially effective targets in diagnosis and treatment as well as the exploration of regulatory miRNAs-mRNAs networks is beneficial for comprehensively understanding the molecular mechanism of ARVC development.

Methods
This study screened RNAs expression profiling studies from the Gene Expression Omnibus (GEO) database. The keyword “arrhythmogenic right ventricular cardiomyopathy” was used to search for suitable datasets that followed the criteria of being a comparable case-control human study, it was selected the GSE29819 and GSE164490 GEO series. The microarray GSE29819 set was analyzed using GEO2R, an interactive web tool disponibility in GEODataset site, resulting in the differential expression genes (DEGs) of this series, while high throughput sequencing GSE164490 set was preprocessing by miARma-Seq v 1.7.5 and for identifying DEGs was used R software v4.2.2 in R Studio (version: 2022.12.0 + 353) and Deseq2 package. The 2 sets used P &lt; 0.05 and |log FC| &gt; 1.5 as the cutoff criteria. An online prediction database, DIANA-microT CDS v 5.0 was used to obtain interactions with high confidence levels between miRNAs and target mRNAs. STRING online database v9 was used to predict protein-protein interactions (PPI) and functional enrichment analysis was conducted using the web-based utility Enrichr to uncover the roles of these molecules.

Results
The integrated analysis of the 2 datasets from GEO sequencing data from ARVC, after a criterium analysis, identify 04 common DEGs: hsa-miR-30d-5p, hsa-miR-200a-3p, hsa-miR-34a-5p and hsa-miR-125a-5p between them. Also, by constructing a PPI network, it was identified 04 genes among the overlapping DEGs, including PIK3R2, RARRES1, SCN2B, and SLCO5A1 (scores ≥0.7). The functional enrichment analyses demonstrated that the DEGs were enriched in some GO biological processes such as regulation of atrial cardiac muscle cell membrane and voltage-gated sodium channel activity involved in cardiac muscle cell action potential.

Conclusion
Our results suggest that these new network gene associations possibly play a potential role in metabolic pathways related to the pathogenesis of ARVC and are good candidates for further functional validation assays to confirm these findings.