Screening of biomarkers for diagnosing chronic kidney disease and heart failure with preserved ejection fraction through bioinformatics analysis

Abstract

Background: Previous research has established that chronic kidney disease (CKD) and heart failure with preserved ejection fraction (HFpEF) often coexist. Although we have a preliminary understanding of the potential correlation between HFpEF and CKD, the underlying pathophysiological mechanisms remain unclear. This study aimed to elucidate the molecular mechanisms associated with CKD and HFpEF through bioinformatics analysis.

Methods: Datasets for HFpEF and CKD were obtained from the Gene Expression Omnibus (GEO) database. The R software package "limma" was employed to conduct differential expression analysis. Functional annotation was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We conducted weighted gene co-expression network analysis (WGCNA), correlation analysis with autophagy, ferroptosis, and immune-related processes, as well as transcriptional regulation analysis, immune infiltration analysis, and diagnostic performance evaluation. Finally, the diagnostic potential of the identified hub genes for CKD and HFpEF was assessed using ROC curve analysis (GSE37171).

Results: Differential expression analysis revealed 58 overlapping genes, comprised of 40 up-regulated and 18 down-regulated genes. Both GO and KEGG analyses indicated enriched pathways relevant to both disorders. WGCNA identified 4086 genes associated with CKD. Further comparison with differentially expressed genes (DEGs) identified three hub genes (KLF4, SCD, and SEL1L3) that were linked to autophagy, ferroptosis, and immune processes in both conditions. Additionally, a miRNA-mRNA regulatory network involving 376 miRNAs and 12 transcription factors (TFs) was constructed. ROC curve analysis was performed to evaluate the diagnostic utility of the hub genes for CKD and HFpEF.

Conclusion: This study elucidated shared pathogenic mechanisms and identified diagnostic markers common to both HFpEF and CKD. The identified hub genes show promise as potential tools for early diagnosis and treatment strategies for these conditions.

Keywords: Bioinformatics; Chronic kidney disease (CKD); Heart failure with preserved ejection fraction (HFpEF); Immune infiltration.

Fig. 1

Flow chart depicting the multistep screening strategy used to analyze the bioinformatics data.

Fig. 2

Identification of differentially expressed genes (DEGs). (A) The volcano plot of GSE192886. (B) The volcano plot of GSE175759. (C, D) Venn plots showing common up-regulated and down-regulated DEGs shared by GSE192886 and GSE175759. (E) The top 50 genes with the most remarkable expression changes of GSE192886. (F) The top 50 genes with the most remarkable expression changes of GSE175759.

Fig. 3

Functional enrichment analysis. (A) Gene Ontology (GO) function analysis results on common differentially expressed genes (DEGs). (B)The bubble plots showing the KEGG enrichment analysis results, including biological process, cellular component and molecular function of genes.

Fig. 4

Screening of key module genes in the integrated CKD dataset via WGCNA and identification of CKD key genes through the intersection of key module genes and DEGs. (A) The best β value was found using the scale-free topology model; β = 21 was selected as the soft threshold based on scale independence and average connectedness. (B) A dendrogram of the grouping of CKD genes, where various colors correspond to different modules. (C) The heatmap that shows how the state of CKD and module eigengenes relate to another. The state of CKD and the correlation between the module eigengenes (upper) and p-value (bottom) were displayed. (D) The gene significance of genes in the turquoise module and the membership in the module are correlated. (E) The plot showing correlation between a gene's significance and its participation in the grey module. (F) By using the Venn diagram to show where critical module genes and DEGs overlap, a total of three key genes in CKD were found.

Fig. 5

miRNA-mRNA-TFs network. miRNA networks of hub genes, pink for mRNA, pale green for miRNA and bottle green for TFs (transcription factors annotated to motifs).

Fig. 6

Relationship of hub genes to other genes. (A–C) Correlation of hub genes in CKD with autophagy, ferroptosis and immune factors. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001. (D–F) Correlation of hub genes in HFpEF with autophagy, ferroptosis and immune factors∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001.

Fig. 7

Immune cell infiltration analysis. (A) The heatmap displaying the immune cell proportions of CKD. (B)Violin plot showing immune cells between CKD and control groups. (C) The heatmap displaying the immune cell proportions between HFpEF and control groups. (D)Violin plot showing the comparison of CD8⁺ Tem and memory B-cell between HFpEF and control groups. ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001; ∗∗∗∗P < 0.0001.

Fig. 8

(A)Violin plot showing the comparison of 3 hub genes levels between CKD(GSE37171) and control groups. (B ∼ D)The ROC curve for the diagnostic performance of each candidate biomarker including KLF4(B), SCD(C) and SEL1L3(D),including AUC (area under the curve).