Open Access

Identification of key genes for diabetic kidney disease using biological informatics methods

  • Authors:
    • Fuzhe Ma
    • Tao Sun
    • Meiyan Wu
    • Wanning Wang
    • Zhonggao Xu
  • View Affiliations

  • Published online on: September 29, 2017     https://doi.org/10.3892/mmr.2017.7666
  • Pages:7931-7938
  • Copyright: © Ma et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Diabetic kidney disease (DKD) is a common complication of diabetes, which is characterized by albuminuria, impaired glomerular filtration rate or a combination of the two. The aim of the present study was to identify the potential key genes involved in DKD progression and to subsequently investigate the underlying mechanism involved in DKD development. The array data of GSE30528 including 9 DKD and 13 control samples was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in DKD glomerular and tubular kidney biopsy tissues were compared with normal tissues, and were analyzed using the limma package. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for DEGs using the GO Function software in Bioconductor. The protein‑protein interaction (PPI) network was then constructed using Cytoscape software. A total of 426 genes (115 up‑ and 311 downregulated) were differentially expressed between the DKD and normal tissue samples. The PPI network was constructed with 184 nodes and 335 edges. Vascular endothelial growth factor A (VEGFA), α‑actinin‑4 (ACTN4), proto‑oncogene, Src family tyrosine kinase (FYN), collagen, type 1, α2 (COL1A2) and insulin‑like growth factor 1 (IGF1) were hub proteins. Major histocompatibility complex, class II, DP α1 (HLA‑DPA1) was the common gene enriched in the rheumatoid arthritis and systemic lupus erythematosus pathways, and the immune response was a GO term enriched in module A. VEGFA, ACTN4, FYN, COL1A2, IGF1 and HLA‑DPA1 may be potential key genes associated with the progression of DKD, and immune mechanisms may serve a part in DKD development.

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December 2017
Volume 16 Issue 6

Print ISSN: 1791-2997
Online ISSN:1791-3004

2016 Impact Factor: 1.692
Ranked #19/128 Medicine Research and Experimental
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APA
Ma, F., Sun, T., Wu, M., Wang, W., & Xu, Z. (2017). Identification of key genes for diabetic kidney disease using biological informatics methods. Molecular Medicine Reports, 16, 7931-7938. https://doi.org/10.3892/mmr.2017.7666
MLA
Ma, F., Sun, T., Wu, M., Wang, W., Xu, Z."Identification of key genes for diabetic kidney disease using biological informatics methods". Molecular Medicine Reports 16.6 (2017): 7931-7938.
Chicago
Ma, F., Sun, T., Wu, M., Wang, W., Xu, Z."Identification of key genes for diabetic kidney disease using biological informatics methods". Molecular Medicine Reports 16, no. 6 (2017): 7931-7938. https://doi.org/10.3892/mmr.2017.7666