Journal of Theoretical & Computational Science

Journal of Theoretical & Computational Science
Open Access

ISSN: 2376-130X

+44 1223 790975


In Silico Based Analysis of CKD Expressions Data in Correlation with Diabetes Mellitus Unveils Biomarker Gene

Mohammed Murshad Ahmed, Safia Tazyeen, Aftab Alam, Anam Farooqui, Shahnawaz Ali, Zubbair Malik and Romana Ishrat

Chronic kidney disease (CKD) is becoming an extensive public health problem worldwide. The current anxiety of disease might be due to the change of the underlying pathogenicity. The aim of our study was to provide a detailed analysis of microarray gene expression data of CKD in correlation with diabetes and identification of biomarker genes. Here, Affymetrix expression arrays were used to identify differentially expressed genes in 22 and 69 samples of CKD and diabetes respectively. It further outlines few of the principal biological alterations observed in the CKD state and depicts specific procedures for conducting quality assessment of Affymetrix Gene chip using GEO datasets (GSE70528, GSE11045) and also illustrates quality control packages to remark the visualization for detailed analysis. We identified 912 differentially expressed genes in CKD and 629 in diabetes. From extensive comparison of CKD with diabetes, we found 80 common genes, of which 29 were found up regulated and 51 down. Further, analysis with NCG of these 80 genes, 10 common genes were found involved in various types of cancer. Thus, the results emphasize the importance of these 10 common differentially expressed genes in considering them as biomarkers for three conditions diabetes, CKD and cancer. Our studies have cataloged differentially expressed genes that may play important role in the pathogenesis of CKD and could serve as biomarkers.