| Title: | Integrated analysis identifies key genes underlying the bidirectional association between depression and renal failure |
| Journal: | Scientific Reports |
| Published: | 1 Jul 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40595897/ |
| DOI: | https://doi.org/10.1038/s41598-025-04707-9 |
| URL: | https://www.nature.com/articles/s41598-025-04707-9.pdf |
Publication 16366
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Abstract
Depression is a common psychiatric comorbidity in individuals with end-stage renal disease (ESRD). However, the underlying biological mechanisms and the precise relationship between depression and renal failure remain unclear. While interventions such as cognitive behavioral therapy and exercise have been shown to alleviate symptoms, the interplay between these conditions and their molecular pathways is poorly understood. An integrated analysis was conducted combining bioinformatics approaches and data from the UK Biobank (UKB) cohort. The UKB study revealed a significant association between renal failure and depression. Gene expression data from the Gene Expression Omnibus (GEO) database were analyzed to identify key co-expression modules using Weighted Gene Co-expression Network Analysis (WGCNA). Protein-protein interaction (PPI) networks were constructed using the STRING database, and immune cell infiltration was assessed with the CIBERSORT tool. UKB data confirmed a robust association between renal failure and depression. Bioinformatics analyses highlighted significant enrichment in pathways related to the acute inflammatory response, specific granule lumen, and immune receptor activity. PPI network analysis identified 23 hub genes, including CYP4F2, KCNA3, KISS1R, LILRA5, and ZC3H12D, as key players in the shared pathophysiology of ESRD and depression. Validation studies further emphasized the roles of LILRA5, CYP4F2, and KISS1R in these mechanisms. This study reveals novel insights into the molecular and immune interactions underlying the comorbidity of renal failure and depression. By combining cohort and bioinformatics analyses, we identify potential therapeutic targets and pathways that may inform innovative treatment strategies.</p>
10 Keywords
- Computational Biology
- Depression
- Gene Expression Profiling
- Gene Regulatory Networks
- Humans
- Kidney Failure, Chronic
- Male
- Protein Interaction Maps
- Receptors, Immunologic
- Renal Insufficiency
6 Authors
- Zhengqi Qiu
- Yu Nie
- Xu Lin
- Jun Du
- Juntao Kan
- Emma Yun Zhi Huang
1 Application
| Application ID | Title |
|---|---|
| 406475 | Development and validation of the predict model of healthy aging and longevity based on the lifestyle factors across the life course, genetic factors, and circulating plasma proteins |
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