Abstract
In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.
Highlights
The risk of getting cardiovascular disease (CVD) in patients with chronic kidney disease (CKD) is more than without CKD as discussed by Jankowski et al [1]
Evidence-based approach has always been the center of clinical studies, while in-silico approaches focus to produce that potential evidence based on past knowledge, making this integrated process fast and efficient
There is great efforts are ongoing with the aim of reducing CVD residual risk by developing reliable prognostic and predictive biomarkers
Summary
The risk of getting cardiovascular disease (CVD) in patients with chronic kidney disease (CKD) is more than without CKD as discussed by Jankowski et al [1]. The prevalence of CKD has increased in recent decades due to aging which affects about one out of every 10 people [2]. Patients with CKD are more prone to CVD and even death due to the progression of end-stage renal disease (ESRD) [3]. CVD, along with chronic renal disease, remains the leading cause of morbidity and mortality in individuals, in those involving a systemic inflammatory process, such as atherosclerosis [4]. CKD is the 14th leading cause of mortality, with the death rate anticipated to increase to 14 per 100,000 people by 2030 [5]. The molecular description of CKD onset and progression is lacking Based on these findings, the researchers have described CKD as a worldwide epidemic [9]. The present method for prioritizing disease-related genes is based on the “guilt-by-association” assumption, which states that physically and functionally related genes have similar phenotypic effects and are likely to be involved in the same biological pathways [10]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
