Integrated analysis identifies key genes underlying the bidirectional association between depression and renal failure

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.

Keywords: Bioinformatics; Depression; End-stage renal disease; Immune system; UK biobank; WGCNA.

Fig. 1

Identify the key modules related to depression and renal failure. (A, B) Heatmaps showing the correlation between the module eigengenes (MEs) and clinical traits of depression and renal failure. Each row represents an ME, and each column represents a clinical trait. Each cell displays the correlation coefficient and its corresponding P-value, illustrating the strength and statistical significance of the relationship between the module and clinical traits. The numbers in each row indicate the number of genes contained within each module.

Fig. 2

Venn diagram of the 42 common genes between depression and renal failure. An analysis of the key modules associated with depression and renal failure has identified common genes.

Fig. 3

GO and KEGG analyses of common genes related to depression and renal failure in selected modules. (A) GO enrichment analysis of genes associated with biological processes (BP), cellular components (CC), and molecular functions (MF). (B) Heatmap illustrating the enrichment of KEGG pathways in the relevant molecules, highlighting the key biological pathways shared between depression and renal failure.

Fig. 4

Protein-protein interaction network analysis of key module genes. Protein-protein interaction (PPI) networks of common genes in depression and renal failure were developed based on the STRING database. The 23 genes with the highest connectivity of MCC were visualized with Cytoscape software. Node color is directly proportional to the MCC of gene connectivity.

Fig. 5

Representative GSEA enrichment plots of DEGs between disease group(depression and renal failure) and normal group expression groups. (A) The first 5 significantly enriched GSEA in depression. (B) The first 5 significantly enriched GSEA in renal failure. (C,D) Visualizing the results of GSEA enrichment analysis in the depression and failure group using ridge plots.

Fig. 6

CIBERSORT analysis of immune infiltration. The fraction of 22 subsets of immune cells in the depression group and renal failure group. Depression(A), renal failure(B). X-axis: each GEO sample; Y-axis: percentage of each kind of immune cell. The violin plot shows the immune infiltration in depression(C) and renal failure(D), where blue represents the control group and red represents the disease group.

Fig. 7

Validation of hub genes. (A) A heat map of hierarchical clustering of DEGs in the GSE76826 dataset. (B) A heat map of hierarchical clustering of DEGs in the GSE66494 dataset. (C) Identification of overlapping genes between differentially expressed genes in GSE76826 and GSE66494.

Fig. 8

Differential expression profiles of hub genes with potential diagnostic ROC curves. (A) Differential expression of CYP4F2, KCNA3, KISS1R, LILRA5, and ZC3H12D between the depressed and normal groups (GSE76826) (B) Differential expression of CYP4F2, KCNA3, KISS1R, LILRA5, and ZC3H12D between the renal failure and normal groups (GSE66494). (C,D) ROC curve showing the diagnostic performance of the signature genes. RF, renal failure.