The key candidate genes in tubulointerstitial injury of chronic kidney diseases patients as determined by bioinformatic analysis

Pathologic changes such as renal tubular atrophy and interstitial fibrosis are common in chronic kidney disease (CKD), which in turn, leads to loss of renal function. The aims of present study were to screen critical genes with tubulointerstitial lesion in CKD by weighted gene correlation network analysis (WGCNA). Gene expression data including 169 tubulointerstitial samples of CKD and 21 controls downloaded from Gene Expression Omnibus (GEO) database. Totally 294 differentially expressed genes (DEGs) were screened, including 180 upregulated and 114 downregulated genes. Meanwhile, 90 expression data of tubulointerstitial samples combined with clinic information were applied to explore the potential mechanisms of tubulointerstitial lesion. As a consequence, the blue, brown and yellow modules which included the most DEGs compared to the other modules and exhibited strongly association with eGFR, were significantly enriched in several signalling pathways that have been reported involved in pathogenesis of CKD. Furthermore, it was found that the four genes (PLG, ITGB2, CTSS and CCL5) was one of the DEGs which also be identified as hub genes according to Kwithin. Finally, the Nephroseq online tool showed that the tubulointerstitial expression levels of PLG significantly positively correlated with the estimated glomerular filtration rate (eGFR), while ITGB2, CTSS and CCL5 connected negatively to the eGFR of CKD patients. Taken together, WGCNA is an efficient approach to system biology. By this procedure, the present study enhanced the understanding of the transcriptome status of CKD and might shed a light on the further investigation on the mechanisms of renal tubulointerstitial injury in CKD. Significance of study Traditional molecular biology can only explain the local part of the biological system, and difficult to make comprehensive exploration of the whole biological system in the chronic kidney disease (CKD). In this study, we gave an explicit elucidation of dysregulated protein coding genes by the analysis of microarray datasets in GEO database. We have presented a novel approach using weighted gene correlation network analysis (WGCNA) to explore the DEGs which implicated in CKD process. In this study, we conducted WGCNA to explore the potential mechanisms of renal tubular damage, and provided novel biomarkers associated with the molecular mechanisms underlying renal tubulointerstitial injury in CKD.