Network pharmacology combined with Mendelian randomization analysis to identify the key targets of renin-angiotensin-aldosterone system inhibitors in the treatment of diabetic nephropathy

Abstract

Background: Diabetic Nephropathy (DN) is one of the microvascular complications of diabetes. The potential targets of renin-angiotensin-aldosterone system (RAAS) inhibitors for the treatment of DN need to be explored.

Methods: The GSE96804 and GSE1009 datasets, 729 RAAS inhibitors-related targets and 6,039 DN-related genes were derived from the public database and overlapped with the differentially expressed genes (DN vs. normal) in GSE96804 to obtain the candidate targets. Next, key targets were screened via the Mendelian randomization analysis and expression analysis. The diagnostic nomogram was constructed and assessed in GSE96804. Additionally, enrichment analysis was conducted and a 'core active ingredient-key target-disease pathway' network was established. Finally, molecular docking was performed.

Results: In total, 60 candidate targets were derived, in which CTSC and PDE5A were screened as the key targets and had a causal association with DN as the protective factors (P < 0.05, OR < 1). Further, a nomogram exhibited pretty prediction efficiency. It is indicated that Benadryl hydrochloride might play a role in the DN by affecting the pathways of 'cytokine cytokine receptor interaction', etc. targeting the CTSC. Moreover, PDE5A might be involved in 'ECM receptor interaction', etc. for the effect of NSAID, captopril, chlordiazepoxide on DN. Molecular docking analysis showed a good binding ability of benadryl hydrochloride and CTSC, NSAID and PDE5A. PTGS2, ITGA4, and ANPEP are causally associated with acute kidney injury.

Conclusion: CTSC and PDE5A were identified as key targets for RAAS inhibitors in the treatment of DN, which might provide some clinical significance in helping to diagnose and treat DN. Among the targets of RAAS inhibitors, PTGS2, ITGA4 and ANPEP have a causal relationship with acute kidney injury, which is worthy of further clinical research.

Keywords: GEO; acute kidney injury; diabetic nephropathy; mendelian randomization; network pharmacology.

Figure 1

Graphical Abstract. The research process is illustrated in the diagram. The upper part represents the data included in the study, the bottom left corner represents the analysis involved in the research, and the bottom right corner represents the research findings.

Figure 2

Screening of differentially expressed genes (DEGs) between normal and DN groups in the GSE96804 dataset. (A) The volcano plot and (B) heatmap for the expression patterns of the DEGs. The heatmap for the enriched (C) Gene Ontology (GO) terms and (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) terms by the DEGs.

Figure 3

Identification and interaction of potential candidate genes in DN. (A) The venn plots of the intersection for DEGs, renin-angiotensin-aldosterone system (RAAS) inhibitors-related targets, and DN-related genes (DRGs). (B, C) The heatmap for the functional enrichment analysis (B:GO; C:KEGG) of the 60 candidate genes. (D) The protein-protein interaction (PPI) network for interaction of candidate genes with confidence score > 0.4.

Figure 4

Mendelian randomization (MR) analysis and expression analysis for selecting two key targets in DN. (A) Boxplots for the expressions levels of six candidate targets with potential causality on DN in the GSE96804 (top) and GSE1009 (bottom) datasets. (B, C) The scatter plot of the Mendelian randomization (MR) analysis for relationship of two key targets (B:CTSC; C:PDE5A) and DN. (D, E) Forest plots of the MR analysis for diagnostic significance of two key targets (D:CTSC; E:PDE5A) on DN. (F, G) Funnel plots of the MR analysis for two key targets (F:CTSC; G:PDE5A) on DN. (H, I) Leave-one-out analysis of the MR analysis for sensitivity analyses of two key targets (H:CTSC; I:PDE5A) on DN.

Figure 5

Evaluation for clinical utilize of CTSC and PDE5A via a nomogram. (A) Nomogram for predicting the risk of DN. (B) Receiver operator characteristic (ROC) curve evaluating the predictive accuracy of nomogram in GSE96804. (C) Calibration curves of nomogram for comparing predicted (the horizontal coordinate) and actual (the vertical coordinate) probability of DN. The 45-degree line represents the ideal prediction. (D) Decision curve analysis (DCA) for the GSE96804 dataset.

Figure 6

Functionality, active ingredients and disease pathway targeting two key targets. Gene set enrichment analysis (GSEA) of (A) CTSC and (B) PDE5A. (C) The core active ingredient-key target-disease pathway network. Pink is the key target gene, blue is the disease pathway, and green is the core active ingredient.

Figure 7

Regulatory networks of two key targets. (A) The transcription factors (TF)-key targets network. Pink is the target gene, and yellow is TF. (B) The competitive endogenous RNA (ceRNA) network targeting key targets. Pink is the key target gene, green is lncRNA, and blue is miRNA.

Figure 8

Molecular docking results of two key targets. (A) CTSC. (B) PDE5A.