Cargando…

Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease

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 t...

Descripción completa

Detalles Bibliográficos
Autores principales: Ahmed, Mohd Murshad, Tazyeen, Safia, Haque, Shafiul, Alsulimani, Ahmad, Ali, Rafat, Sajad, Mohd, Alam, Aftab, Ali, Shahnawaz, Bagabir, Hala Abubaker, Bagabir, Rania Abubaker, Ishrat, Romana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767007/
https://www.ncbi.nlm.nih.gov/pubmed/35071341
http://dx.doi.org/10.3389/fcvm.2021.755321
_version_ 1784634634590486528
author Ahmed, Mohd Murshad
Tazyeen, Safia
Haque, Shafiul
Alsulimani, Ahmad
Ali, Rafat
Sajad, Mohd
Alam, Aftab
Ali, Shahnawaz
Bagabir, Hala Abubaker
Bagabir, Rania Abubaker
Ishrat, Romana
author_facet Ahmed, Mohd Murshad
Tazyeen, Safia
Haque, Shafiul
Alsulimani, Ahmad
Ali, Rafat
Sajad, Mohd
Alam, Aftab
Ali, Shahnawaz
Bagabir, Hala Abubaker
Bagabir, Rania Abubaker
Ishrat, Romana
author_sort Ahmed, Mohd Murshad
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8767007
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-87670072022-01-20 Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease Ahmed, Mohd Murshad Tazyeen, Safia Haque, Shafiul Alsulimani, Ahmad Ali, Rafat Sajad, Mohd Alam, Aftab Ali, Shahnawaz Bagabir, Hala Abubaker Bagabir, Rania Abubaker Ishrat, Romana Front Cardiovasc Med Cardiovascular Medicine 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. Frontiers Media S.A. 2022-01-05 /pmc/articles/PMC8767007/ /pubmed/35071341 http://dx.doi.org/10.3389/fcvm.2021.755321 Text en Copyright © 2022 Ahmed, Tazyeen, Haque, Alsulimani, Ali, Sajad, Alam, Ali, Bagabir, Bagabir and Ishrat. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Ahmed, Mohd Murshad
Tazyeen, Safia
Haque, Shafiul
Alsulimani, Ahmad
Ali, Rafat
Sajad, Mohd
Alam, Aftab
Ali, Shahnawaz
Bagabir, Hala Abubaker
Bagabir, Rania Abubaker
Ishrat, Romana
Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease
title Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease
title_full Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease
title_fullStr Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease
title_full_unstemmed Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease
title_short Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease
title_sort network-based approach and ivi methodologies, a combined data investigation identified probable key genes in cardiovascular disease and chronic kidney disease
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767007/
https://www.ncbi.nlm.nih.gov/pubmed/35071341
http://dx.doi.org/10.3389/fcvm.2021.755321
work_keys_str_mv AT ahmedmohdmurshad networkbasedapproachandivimethodologiesacombineddatainvestigationidentifiedprobablekeygenesincardiovasculardiseaseandchronickidneydisease
AT tazyeensafia networkbasedapproachandivimethodologiesacombineddatainvestigationidentifiedprobablekeygenesincardiovasculardiseaseandchronickidneydisease
AT haqueshafiul networkbasedapproachandivimethodologiesacombineddatainvestigationidentifiedprobablekeygenesincardiovasculardiseaseandchronickidneydisease
AT alsulimaniahmad networkbasedapproachandivimethodologiesacombineddatainvestigationidentifiedprobablekeygenesincardiovasculardiseaseandchronickidneydisease
AT alirafat networkbasedapproachandivimethodologiesacombineddatainvestigationidentifiedprobablekeygenesincardiovasculardiseaseandchronickidneydisease
AT sajadmohd networkbasedapproachandivimethodologiesacombineddatainvestigationidentifiedprobablekeygenesincardiovasculardiseaseandchronickidneydisease
AT alamaftab networkbasedapproachandivimethodologiesacombineddatainvestigationidentifiedprobablekeygenesincardiovasculardiseaseandchronickidneydisease
AT alishahnawaz networkbasedapproachandivimethodologiesacombineddatainvestigationidentifiedprobablekeygenesincardiovasculardiseaseandchronickidneydisease
AT bagabirhalaabubaker networkbasedapproachandivimethodologiesacombineddatainvestigationidentifiedprobablekeygenesincardiovasculardiseaseandchronickidneydisease
AT bagabirraniaabubaker networkbasedapproachandivimethodologiesacombineddatainvestigationidentifiedprobablekeygenesincardiovasculardiseaseandchronickidneydisease
AT ishratromana networkbasedapproachandivimethodologiesacombineddatainvestigationidentifiedprobablekeygenesincardiovasculardiseaseandchronickidneydisease