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. 2025;15(1):386-398.
doi: 10.1159/000546299. Epub 2025 May 7.

Uncovering Candidate Genes Associated with Cardiovascular Disease in Patients with Arteriovenous Fistula and End-Stage Renal Disease

Affiliations

Uncovering Candidate Genes Associated with Cardiovascular Disease in Patients with Arteriovenous Fistula and End-Stage Renal Disease

Guoxin Zhang et al. Cardiorenal Med. 2025.

Abstract

Background: The molecular association between end-stage renal disease (ESRD), arteriovenous fistula (AVF) failure, and cardiovascular disease (CVD) remains unclear. This study aimed to investigate their potential relationship.

Methods: Three datasets were downloaded from the public database. AVF-failure-related differentially expressed genes (DEGs), CVD-related DEGs, and ESRD-related DEGs were identified by differential expression analysis and weighted gene co-expression network analysis. Then, AVF-failure-related, CVD-related, and ESRD-related DEGs were overlapped to obtain the hub genes. The diagnostic values of hub genes were evaluated. Finally, the immune infiltration analysis and drug prediction were performed.

Results: A total of four hub genes (ABCC8, ALPI, FGF11, and OBP2A) were identified, and those genes have excellent diagnostic accuracy. Among them, ABCC8, ALPI, and FGF11 showed good sensitivity and specificity. However, compared to the nondiabetic subgroup, the diagnostic ability of these genes was weaker in the diabetic subgroup for distinguishing AVF failure in ESRD patients. Type 17 T helper cells and gamma delta T cells may be associated with CVD caused by ESRD and AVF. A total of 15 drugs associated with hub genes were predicted.

Conclusion: ABCC8, ALPI, and FGF11 could serve as potential diagnostic biomarkers for AVF failure and CVD in HD-treated ESRD patients. Their robustness needs to be validated in larger cohorts and additional subgroups with comorbidities.

Keywords: Arteriovenous fistula; Cardiovascular disease; End-stage renal disease.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1.
Fig. 1.
WGCNA. a The hierarchical clustering tree of all samples in the GSE37171 was displayed, and samples above the red line were discarded. b Scale independence and mean connectivity. c Cluster dendrogram of samples. d Heatmap of the correlation between ME and clinical characteristics of ESRD. e, f Scatter plots of genes in the turquoise (e) and blue (f) modules. ME, module eigengenes.
Fig. 2.
Fig. 2.
Identification of genes related to AVF and CVD. a Identification of DEGs in the GSE233264 dataset. b Identification of DEGs in the GSE97709 dataset. c Venn plot of DEGs in GSE233264 and GSE97709 datasets. d Functional enrichment analysis of 51 intersection genes obtained from Venn using Metascape. TNF, tumor necrosis factor.
Fig. 3.
Fig. 3.
Identification of hub genes. a Venn plot of genes among intersection genes, blue module, and turquoise module. b, c The expression levels of intersection genes in the GSE233264 (b) and GSE97709 (c) datasets.
Fig. 4.
Fig. 4.
Immune cell infiltration analysis. a The correlation between 23 immune cells and four hub genes in healthy controls versus ESRD patients, AVF-matured versus AVF-failed groups, and ESRD patients without CVD versus ESRD patients with CVD. b Venn of intersection immune cells associated with four hub genes in three groups. c Infiltration levels of immune cell types showing significant differences between groups in the GSE37171 dataset. d The immune infiltration level of gamma delta T cells in the GSE233264 dataset. e The immune infiltration level of type 17 T helper cells in the GSE97709 dataset.
Fig. 5.
Fig. 5.
ROC analysis. a ROC curves of ABCC8, ALPI, FGF11, and OBP2A in distinguishing healthy controls from HD-treated ESRD patients. b ROC curves of ABCC8, ALPI, FGF11, and OBP2A in distinguishing AVF-failed from AVF-matured ESRD patients. c ROC curves of ABCC8, ALPI, FGF11, and OBP2A in distinguishing ESRD patients with CVD from those without CVD.
Fig. 6.
Fig. 6.
Drug predictions of hub genes.

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