RNA-seq driven expression and enrichment analysis to investigate CVD genes with associated phenotypes among high-risk heart failure patients

Abstract

Background Heart failure (HF) is one of the most common complications of cardiovascular diseases (CVDs) and among the leading causes of death in the US. Many other CVDs can lead to increased mortality as well. Investigating the genetic epidemiology and susceptibility to CVDs is a central focus of cardiology and biomedical life sciences. Several studies have explored expression of key CVD genes specially in HF, yet new targets and biomarkers for early diagnosis are still missing to support personalized treatment. Lack of gender-specific cardiac biomarker thresholds in men and women may be the reason for CVD underdiagnosis in women, and potentially increased morbidity and mortality as a result, or conversely, an overdiagnosis in men. In this context, it is important to analyze the expression and enrichment of genes with associated phenotypes and disease-causing variants among high-risk CVD populations. Methods We performed RNA sequencing focusing on key CVD genes with a great number of genetic associations to HF. Peripheral blood samples were collected from a broad age range of adult male and female CVD patients. These patients were clinically diagnosed with CVDs and CMS/HCC HF, as well as including cardiomyopathy, hypertension, obesity, diabetes, asthma, high cholesterol, hernia, chronic kidney, joint pain, dizziness and giddiness, osteopenia of multiple sites, chest pain, osteoarthritis, and other diseases. Results We report RNA-seq driven case–control study to analyze patterns of expression in genes and differentiating the pathways, which differ between healthy and diseased patients. Our in-depth gene expression and enrichment analysis of RNA-seq data from patients with mostly HF and other CVDs on differentially expressed genes and CVD annotated genes revealed 4,885 differentially expressed genes (DEGs) and regulation of 41 genes known for HF and 23 genes related to other CVDs, with 15 DEGs as significantly expressed including four genes already known (FLNA, CST3, LGALS3, and HBA1) for HF and CVDs with the enrichment of many pathways. Furthermore, gender and ethnic group specific analysis showed shared and unique genes between the genders, and among different races. Broadening the scope of the results in clinical settings, we have linked the CVD genes with ICD codes. Conclusions Many pathways were found to be enriched, and gender-specific analysis showed shared and unique genes between the genders. Additional testing of these genes may lead to the development of new clinical tools to improve diagnosis and prognosis of CVD patients.

RNA‐seq‐driven expression analysis to investigate cardiovascular disease genes with associated phenotypes among atrial fibrillation patients

“…Peripheral blood samples were used for RNA extraction, and sequencing was performed using Illumina NovaSeq 6000‐S4 to assess the RNA quality. 2 An efficient data management system (PROMIS‐LCR) with data extraction, transfer and loader system (ETL), created by the authors, 3 was used for patient recruitment and consent tracking as well as dealing with the multi‐omics data, respectively. 4 We also created a publicly available gene‐disease database, PAS‐Gen, which includes over 59 000 protein‐coding and non‐coding genes, and over 90 000 classified gene‐disease associations, to ease the gene‐disease visualization for researchers, medical practitioners and pharmacists.…”

“…First, the transcriptomic data analysis involved the development of an RNA‐seq processing pipeline that contained four operating parts: (I) data pre‐processing, (II) data quality checking, (III) data storage and management and (IV) data visualization (Additional file 1 : High‐resolution figures). 2 The analysis of transcripts per million (TPM) was performed to normalize the RNA‐seq data by using the visualizing genes with disease‐causing variants environment with the findable, accessible, intelligent and reproducible approach (Additional file 4 : AF analysis ‐ gene expression data). It reveals all genes annotated with their associated clinical AF phenotype using gene–disease association.…”

“…It reveals all genes annotated with their associated clinical AF phenotype using gene–disease association. 2 , 5 This expression analysis was expanded to visualize the classification of protein‐ and non‐coding genes in detail as gender‐ and race‐based. First, we looked across the AF‐annotated genes to identify protein‐ and non‐coding genes together and found 71 genes related to AF and relative diseases (Additional file 3 : Complete Gene List).…”

“…Our findings report three highly expressed genes and their associated diseases as AF phenotypes; PF4 : AF; PPBP : AF and MYL4 : AF familial 18, with a similar expression pattern across races and genders. Moreover, when we compared the genes associated with HF from our previous CVD/HF study 2 with those associated with AF, we discovered that two genes ( ACE and MYBPC3 ) were associated with both diseases (HF and AF). These findings are valuable for future research studies as they signify the potential to further investigate these genes for mutations and disease‐specific variants.…”

Investigating variant and expression of CVD genes associated phenotypes among high-risk Heart Failure patients

“…In this manuscript, we report a multifaceted approach involving whole-genome sequencing (WGS) integrated with differential gene expression for HF and other CVD study [16,17].…”