Bioinformatics analysis of comorbid mechanisms between ischemic stroke and end stage renal disease

Abstract

Ischemic stroke (IS) is a leading global cause of mortality and disability, particularly prominent in patients with end-stage renal disease (ESRD). Despite clinical evidence of their comorbidity, the molecular mechanisms underlying their interaction remain elusive. This study aims to identify shared biomarkers, gene regulatory networks, and therapeutic targets through integrative bioinformatics analyses. Gene expression datasets for IS (GSE16561, GSE22255) and ESRD (GSE37171, GSE142153) were obtained from gene expression omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and differential expression genes (DEGs) analysis identified shared genes and enriched pathways. Protein-protein interaction networks were constructed using STRING with clustering algorithms. Immune cell infiltration analysis was performed via CIBERSORT. Transcriptional regulatory networks were predicted using RcisTarget and miRcode. Key gene expressions were validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in clinical samples. We identified 417 intersecting genes through WGCNA and 1712 shared differentially expressed genes. Among these, seven key genes (MRPL49, MRPS2, MRPS9, MRPS10, MRPS11, MRPS27, TFB1M) demonstrated central roles in mitochondrial function. Immune infiltration analysis revealed significant correlations with T cells and neutrophils. Pathway enrichment implicated these genes in transforming growth factor beta (TGF-β) signaling, p53 pathway, and G2/M checkpoint. Clinical validation confirmed significant downregulation of MRPS9, MRPS10, MRPS11, MRPS27 and TFB1M in comorbid patients. This study systematically elucidates the mitochondrial-immune interaction mechanisms in IS-ESRD comorbidity, highlighting the pivotal role of mitochondrial ribosomal protein (MRP) family genes in regulating cellular energetics and inflammatory responses. These findings provide new foundations for targeted therapies.

Keywords: Bioinformatics analysis; End-stage renal disease; Ischemic stroke; Oxidative stress.

Fig. 1

PCA correction and differential expression analysis. (A) PCA plot of IS datasets (GSE16561 and GSE22255) before Combat correction. (B) PCA plot of IS datasets after Combat correction. (C) Volcano plot of IS differential expression analysis. 3657 DEGs identified (1420 upregulated, 2237 downregulated. (D) Heatmap of top 50 DEGs in IS. Yellow: high expression; green: low expression. (E) PCA plot of ESRD datasets (GSE37171 and GSE142153) before Combat correction. (F) PCA plot of ESRD datasets after Combat correction. (G) Volcano plot of ESRD differential expression analysis. 12659 DEGs identified (7058 upregulated, 5601 downregulated). (H) Heatmap of top 50 DEGs in ESRD. (I) Venn diagram of shared upregulated DEGs between IS and ESRD (594 genes). (J) Venn diagram of shared downregulated DEGs between IS and ESRD (1118 genes).

Fig. 2

Functional enrichment of shared DEGs (A) GO analysis reveals significant enrichment in biological processes such as ncRNA processing and tRNA metabolism. (B) KEGG pathways including DNA replication.

Fig. 3

WGCNA (A) Scale independence and mean connectivity analysis for IS WGCNA. (B) Hierarchical clustering dendrogram of IS genes. Four modules identified: black, blue, gray and pink. (C) Module-trait correlation heatmap for IS. Blue module shows the highest correlation with disease (cor = 0.43, p = 6e-6). (D) Scale independence and mean connectivity analysis for ESRD WGCNA. (E) Hierarchical clustering dendrogram of ESRD genes. Five modules detected: blue, gray, red, turquoise and yellow. (F) Module-trait correlation heatmap for ESRD. Blue module shows the strongest disease association (cor = 0.72, p = 3e-22). (G) Venn diagram of shared genes between IS and ESRD blue modules. Overlapping region contains 417 genes. (H) Intersection of blue module genes and DEGs identifies 237 key genes.

Fig. 4

PPI network and key gene identification (A) PPI network of 237 shared genes constructed via STRING database and visualized in Cytoscape. Node size reflects connectivity, red nodes indicate hub genes. (B) Top-scoring module from MCODE clustering. Key genes (MRPL49, MRPS2, MRPS9, MRPS10, MRPS11, MRPS27 and TFB1M) all belonging to the MRP family.

Fig. 5

Immune cell infiltration in IS (A) The relative proportions of 22 immune cells in the control and disease groups. (B) Pearson correlation between the 22 immune cells, with blue indicating a negative correlation and red indicating a positive correlation. (C) Differences in immune cell counts between the control and disease groups, with blue representing control patients and yellow representing disease patients. (D–J) Correlation between key genes (MRPL49, MRPS2, MRPS9, MRPS10, MRPS11, MRPS27 and TFB1M) and immune cell counts.

Fig. 6

The correlation between key genes and immune factors in IS (A–E) The correlation between key genes (MRPL49, MRPS2, MRPS9, MRPS10, MRPS11, MRPS27 and TFB1M) and chemokines, immunoinhibitors, immunostimulators, MHC and receptors.

Fig. 7

Immune cell infiltration in ESRD (A) The relative proportions of 22 immune cells in control and disease groups. (B) Pearson correlation among the 22 immune cells, with blue indicating a negative correlation and red indicating a positive correlation. (C) Differences in immune cell counts between control and disease groups, with blue representing control patients and yellow representing disease patients. (D–J) Correlation between key genes (MRPL49, MRPS2, MRPS9, MRPS10, MRPS11, MRPS27 and TFB1M) and immune cell counts.

Fig. 8

The correlation between key genes and immune factors in ESRD (A–E) The correlation between key genes (MRPL49, MRPS2, MRPS9, MRPS10, MRPS11, MRPS27 and TFB1M) and chemokines, immunoinhibitors, immunostimulators, MHC and receptors.

Fig. 9

The GSVA of key genes in IS (A–G) GSVA of key genes (MRPL49, MRPS2, MRPS9, MRPS10, MRPS11, MRPS27 and TFB1M) with blue representing pathways linked to high expression genes and green representing pathways linked to low expression genes.

Fig. 10

The GSEA of key genes in IS (A–G) KEGG pathways correlated with key genes (MRPL49, MRPS2, MRPS9, MRPS10, MRPS11, MRPS27 and TFB1M).

Fig. 11

The GSVA of key genes in ESRD (A–G) GSVA of key genes (MRPL49, MRPS2, MRPS9, MRPS10, MRPS11, MRPS27 and TFB1M), with blue representing pathways linked to high expression genes and green representing pathways linked to low expression genes.

Fig. 12

The GSEA of key genes in ESRD (A–G) KEGG pathways correlated with key genes (MRPL49, MRPS2, MRPS9, MRPS10, MRPS11, MRPS27 and TFB1M).

Fig. 13

The transcriptional regulation and miRNA network of key genes (A) Transcriptional regulation of key genes, with red nodes indicating key genes and green nodes indicating transcription factors. (B) Display of all motifs enriched for key genes and their corresponding transcription factors. (C) The miRNA network of key genes, with orange circles representing mRNAs and blue arrows representing miRNAs.

Fig. 14

RT-qPCR validation of key genes. Transcription levels of MRPL49, MRPS2, MRPS9, MRPS10, MRPS11, MRPS27, TFB1M mRNA were analyzed in IS-ESRD patients (n = 10) and healthy controls (n = 10) (*p < 0.05, **p < 0.01, ***p < 0.001; ns not significant). Error bars: SEM; GAPDH as housekeeping gene.