Identification of pathogenic genes associated with CKD: An integrated bioinformatics approach

Abstract

Chronic kidney disease (CKD) is defined as a persistent abnormality in the structure and function of kidneys and leads to high morbidity and mortality in individuals across the world. Globally, approximately 8%–16% of the population is affected by CKD. Proper screening, staging, diagnosis, and the appropriate management of CKD by primary care clinicians are essential in preventing the adverse outcomes associated with CKD worldwide. In light of this, the identification of biomarkers for the appropriate management of CKD is urgently required. Growing evidence has suggested the role of mRNAs and microRNAs in CKD, however, the gene expression profile of CKD is presently uncertain. The present study aimed to identify diagnostic biomarkers and therapeutic targets for patients with CKD. The human microarray profile datasets, consisting of normal samples and treated samples were analyzed thoroughly to unveil the differentially expressed genes (DEGs). After selection, the interrelationship among DEGs was carried out to identify the overlapping DEGs, which were visualized using the Cytoscape program. Furthermore, the PPI network was constructed from the String database using the selected DEGs. Then, from the PPI network, significant modules and sub-networks were extracted by applying the different centralities methods (closeness, betweenness, stress, etc.) using MCODE, Cytohubba, and Centiserver. After sub-network analysis we identified six overlapped hub genes (RPS5, RPL37A, RPLP0, CXCL8, HLA-A, and ANXA1). Additionally, the enrichment analysis was undertaken on hub genes to determine their significant functions. Furthermore, these six genes were used to find their associated miRNAs and targeted drugs. Finally, two genes CXCL8 and HLA-A were common for Ribavirin drug (the gene-drug interaction), after docking studies HLA-A was selected for further investigation. To conclude our findings, we can say that the identified hub genes and their related miRNAs can serve as potential diagnostic biomarkers and therapeutic targets for CKD treatment strategies.

Identification of immune-related molecular clusters and diagnostic markers in chronic kidney disease based on cluster analysis

“…Thanks to the rise and advancement of bioinformatics analysis and high-throughput sequencing, new approaches will contribute to a better understand the biology of kidney disease through the use of genetic, epigenetic modification and transcriptomic studies, providing new clues and opening up new unknown avenues for the study of disease mechanisms ( Pareek et al, 2011 ; O’seaghdha and Fox, 2011 ). Several bioinformatics studies have made significant advances in the identification of diagnostic biomarkers for CKD ( Ahmed et al, 2022 ; Wang et al, 2022 ). However, purely assessing the differences between CKD cases and normal controls is far from meeting the growing need for its complex pathological features, risk stratification for progressive end-stage renal disease, and high-risk clinical outcomes.…”

Mitochondrial DNA copy number is associated with incident chronic kidney disease and proteinuria in the AIDS linked to the intravenous experience cohort

“…CKD detection efforts have focused on understanding CKD etiology at the genetic 65 , 66 and molecular scales 67 , 68 at the population level, which has implications in biomarker discovery for detection, risk stratification and therapeutics. This is crucial, as early detection and treatment could potentially impact long-term survival and outcomes among patients on the pathway to CKD 69 , 70 .…”