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

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.

Authors

Institutions

Publication Details

Journal
Adipocyte
Published
2024-11-11
DOI
https://doi.org/10.1080/21623945.2024.2423723
Citations
4
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
1.55

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Mei Yang, Shaomin Shi, Xiaoyan Wu, Ke Ding et al.
4 citations
Adipocyte
Liver Disease Diagnosis and Treatment
1.55
article

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

Mei Yang, Shaomin Shi, Xiaoyan Wu, Ke Ding, Lihua Ni, Feng Chen
article en
4 citations

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.

AdipocyteVol. 13(1)
Wuhan University (CN), Hubei University of Arts and Science (CN), Xiangyang Central Hospital (CN), Zhongnan Hospital of Wuhan University (CN)
National Natural Science Foundation of China, Wuhan University, Zhongnan Hospital of Wuhan University
Zero hunger
Openalex Percentile: Top 15%
Liver Disease Diagnosis and Treatment
1.55
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.