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

2022 ◽  
Vol 8 ◽  
Author(s):  
Mohd Murshad Ahmed ◽  
Safia Tazyeen ◽  
Shafiul Haque ◽  
Ahmad Sulimani ◽  
Rafat Ali ◽  
...  
Keyword(s):  
Seed Gene ◽  
Primary Network ◽  
Gene Expression Microarray Data ◽  
Associated Functions ◽  
Key Genes ◽  
Data Investigation ◽  
Stage Renal Disease ◽  
Spreading Potential ◽  
End Stage ◽  
Evidence Based Therapy

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.

scholarly journals Bioinformatics Analysis Reveals CCR7 As A Potential Biomarker for Predicting CKD Progression

2021 ◽  
Author(s):  
Junju Lai ◽  
Huizhi Shan ◽  
Sini Cui ◽  
Lingfeng Xiao ◽  
Xiaowen Huang ◽  
...  
Keyword(s):  
Immune Response ◽  
Renal Disease ◽  
End Stage Renal Disease ◽  
Curve Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Data Set ◽  
Key Genes ◽  
Stage Renal Disease ◽  
End Stage

Abstract Background Chronic kidney disease (CKD) inevitably progresses to end-stage renal disease if intervention does not occur in time. However, there are limitations in predicting the progression of CKD by solely relying on changes in renal function. A biomarker with high sensitivity and specificity that can predict the progression of CKD early is required. Methods We used the online Gene Expression Omnibus (GEO) microarray dataset GSE45980 to identify differentially expressed genes (DEGs) in patients with progressive and stable CKD. We then performed functional enrichment and protein-protein interaction (PPI) network analysis on DEGs and identified key genes. Finally, the expression patterns of the key genes were verified using the GSE60860 data set, and the receiver operating characteristic curve analysis was performed to clarify their predictive ability of progressive CKD. Ultimately, we verified the expression profiles of these hub genes in an in vitro renal interstitial fibrosis model by RT-PCR and western blot analysis. Results Differential expression analysis identified 50 upregulated genes and 47 downregulated genes. The results of the functional enrichment analysis revealed that the upregulated DEGs were mainly enriched in immune response, inflammatory response, and NF-κB signaling pathways, whereas the downregulated DEGs were mainly related to angiogenesis and the extracellular environment. PPI network and key gene analysis identified CCR7 as the most important gene. CCR7 mainly plays a role in immune response, and its only receptors, CCL19 and CCL21, have also been identified as DEGs. The ROC curve analysis of CCR7, CCL19 and CCL21 found that CCR7 and CCL19 present good disease prediction ability. Conclusion CCR7 may be a stable biomarker for predicting the progression of CKD, and the CCR7-CCL19/CCL21 axis may be a therapeutic target for end-stage renal disease. However, further experiments are needed to explore the relationship between these genes and CKD.

Serum Thiamine Values in End-Stage Renal Disease Patients under Maintenance Hemodialysis

2015 ◽  
Vol 85 (5-6) ◽  
pp. 348-355 ◽  
Author(s):  
Masamitsu Ubukata ◽  
Nobuyuki Amemiya ◽  
Kosaku Nitta ◽  
Takashi Takei
Keyword(s):  
Thiamine Deficiency ◽  
Risk Index ◽  
Nutritional Risk ◽  
Maintenance Hemodialysis ◽  
Dialysis Patients ◽  
Significant Difference ◽  
Nutritional Risk Index ◽  
Serum Aspartate Aminotransferase ◽  
Stage Renal Disease ◽  
End Stage

Abstract. Objective: Hemodialysis patients are prone to malnutrition because of diet or many uremic complications. The objective of this study is to determine whether thiamine deficiency is associated with regular dialysis patients. Methods: To determine whether thiamine deficiency is associated with regular dialysis patients, we measured thiamine in 100 patients undergoing consecutive dialysis. Results: Average thiamine levels were not low in both pre-hemodialysis (50.1 ± 75.9 ng/mL; normal range 24 - 66 ng/mL) and post-hemodialysis (56.4 ± 61.7 ng/mL). In 18 patients, post-hemodialysis levels of thiamine were lower than pre-hemodialysis levels. We divided the patients into two groups, the decrease (Δthiamine/pre thiamine < 0; - 0.13 ± 0.11) group (n = 18) and the increase (Δthiamine/pre thiamine> 0; 0.32 ± 0.21)) group (n = 82). However, there was no significance between the two groups in Kt/V or type of dialyzer. Patients were dichotomized according to median serum thiamine level in pre-hemodialysis into a high-thiamine group (≥ 35.5 ng/mL) and a low-thiamine group (< 35.4 ng/mL), and clinical characteristics were compared between the two groups. The low-thiamine value group (< 35.4 ng/ml; 26.8 ± 5.3 ng/ml) exhibited lower levels of serum aspartate aminotransferase and alanine aminotransferase than the high-thiamine value group (≥ 35.4 ng/ml; 73.5 ± 102.5 ng/ml) although there was no significance in nutritional marker, Alb, geriatric nutritional risk index , protein catabolic rate and creatinine generation rate. Conclusion: In our regular dialysis patients, excluding a few patients, we did not recognize thiamine deficiency and no significant difference in thiamine value between pre and post hemodialysis.

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