Bioinformatic Analysis of Microarray Data of Autosomal Dominant Polycystic Kidney Disease

  Objective      The microarray data of autosomal dominant polycystic kidney disease(ADPKD) was downloaded from the Gene Expression Omnibus (GEO) and analyzed to identify the differential expression genes (DEGs) and to explore the possible signal pathways and protein interaction mechanisms in ADPKD by bioinformatics analysis.

  Methods      Two microarray datasets (GSE7869 and GSE35831)of renal cyst tissue of ADPKD patients and dataset of normal controlled tissue were downloaded and screened from GEO database. The DAVID database and Funrich software were used to analyze biological information and signal pathway analysis, and the STRING database was used to analyze protein interaction mechanisms.

  Results      There were 3970 DEGs in GES7869 and 147 DEGs in GSE35831. There were 28 up-regulated genes in the two groups of DEGs and 24 identical down-regulated genes. Up-regulated of DEGs focused on ion channel-related pathways, enriched in autophagy relted pathways, such as mTOR and PI3K/Akt pathways, growth factors and integrin-related pathways, and down-regulated of DEGs focused on energy metabolism and related signaling pathways.

  Conclusions      Analysis of the 52 DEGs and related enrichment signal pathways of the ADPKD could provide potential biomarkers and directions for the future study of ADPKD. Regulation of renal cell autophagy to delay cystic progression might become a new research focus in ADPKD.