Identification of hub genes in the crosstalk between type 2 diabetic nephropathy and obesity according to bioinformatics analysis

Adipocyte. 2024 Dec;13(1):2423723. doi: 10.1080/21623945.2024.2423723. Epub 2024 Nov 11.

Abstract

Diabetic nephropathy (DN) and obesity bring a huge burden to society. Obesity plays a crucial role in the progression of type 2 DN, but the pathophysiology remains unclear. Thus, we aimed the explore the association between type 2 DN and obesity using bioinformatics method. The gene expression profiles of type 2 DN (GSE96804) and obesity (GSE94752) were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened with the thresholds defined as |log2FC| ≥1 and P<0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. Subsequently, a protein-protein interaction network was constructed based on the STRING database. Hub genes were identified, and the co-expression network was constructed. Finally, the hub genes were verified in clinical samples of 24 patients by immunohistochemistry. A total of 17 common DEGs were identified. Finally, two overlapping hub genes were identified (CCL18, C1QC). C1QC has been verified in clinical specimens. Using bioinformatics methods, the present study analyzed the common DEGs and the potential pathogenic mechanisms involved in type 2 DN and obesity. C1QC was the hub gene. Further studies are needed to clarify the specific relationships among C1QC, type 2 DN and obesity.

Keywords: Diabetic nephropathy; bioinformatics; differentially expressed genes; hub genes; obesity.

MeSH terms

  • Computational Biology* / methods
  • Databases, Genetic
  • Diabetes Mellitus, Type 2* / genetics
  • Diabetes Mellitus, Type 2* / metabolism
  • Diabetic Nephropathies* / genetics
  • Diabetic Nephropathies* / metabolism
  • Gene Expression Profiling*
  • Gene Ontology
  • Gene Regulatory Networks
  • Humans
  • Obesity* / genetics
  • Obesity* / metabolism
  • Protein Interaction Maps*
  • Transcriptome

Grants and funding

This work was supported by grants from the National Natural Science Foundation of China [82200807 and 82370696], Outstanding Young and Middle-aged Talents Training Program of Zhongnan Hospital of Wuhan University [ZNYQ2022007]. The Research Fund from Medical Sci-Tech Innovation Platform of Zhongnan Hospital, Wuhan University [ZNXKPY2022046, ZNXKPY2022049]. The Translational Medicine and Interdisciplinary Research Joint Fund Project [XKJC202313].