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. 2024 Jan 8:14:1328757.
doi: 10.3389/fimmu.2023.1328757. eCollection 2023.

Integrative analysis of potential diagnostic markers and therapeutic targets for glomerulus-associated diabetic nephropathy based on cellular senescence

Donglin Sun #  1 , Shuqi Wei #  2 , Dandan Wang #  3 , Min Zeng  4 , Yihao Mo  4 , Huafeng Li  4 , Caixing Liang  4 , Lu Li  5 , Jun Wei Zhang  4 , Li Wang  4
Affiliations

Integrative analysis of potential diagnostic markers and therapeutic targets for glomerulus-associated diabetic nephropathy based on cellular senescence

Donglin Sun et al. Front Immunol. .

Abstract

Introduction: Diabetic nephropathy (DN), distinguished by detrimental changes in the renal glomeruli, is regarded as the leading cause of death from end-stage renal disease among diabetics. Cellular senescence plays a paramount role, profoundly affecting the onset and progression of chronic kidney disease (CKD) and acute kidney injuries. This study was designed to delve deeply into the pathological mechanisms between glomerulus-associated DN and cellular senescence.

Methods: Glomerulus-associated DN datasets and cellular senescence-related genes were acquired from the Gene Expression Omnibus (GEO) and CellAge database respectively. By integrating bioinformatics and machine learning methodologies including the LASSO regression analysis and Random Forest, we screened out four signature genes. The receiver operating characteristic (ROC) curve was performed to evaluate the diagnostic performance of the selected genes. Rigorous experimental validations were subsequently conducted in the mouse model to corroborate the identification of three signature genes, namely LOX, FOXD1 and GJA1. Molecular docking with chlorogenic acids (CGA) was further established not only to validate LOX, FOXD1 and GJA1 as diagnostic markers but also reveal their potential therapeutic effects.

Results and discussion: In conclusion, our findings pinpointed three diagnostic markers of glomerulus-associated DN on the basis of cellular senescence. These markers could not only predict an increased risk of DN progression but also present promising therapeutic targets, potentially ushering in innovative treatments for DN in the elderly population.

Keywords: cellular senescence; diabetic nephropathy; diagnostic marker; glomeruli; molecular docking; therapeutic targets.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The flowchart of this study.
Figure 2
Figure 2
The identification and the enrichment analysis of DEGs. (A) Volcano plot of DEGs between the DN and control groups. Red dots represent upregulated genes and blue dots represent downregulated genes. (B) Heatmap of DEGs. The horizontal axis shows a sample type, and the vertical axis displays the difference in expression between genomes. (C) Gene set enrichment analysis for DEGs. (D) GSEA ridgeplot that showed NES value of top GO terms.
Figure 3
Figure 3
The identification and mRNA expression analysis of DESRGs. (A) Overlapping genes in DEGs and aging-related genes. (B) The difference in the mRNA expression profiling of DESRGs between the DN and control groups. *P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 4
Figure 4
The heatmap and correlation analysis of DESRGs. (A) Heatmap to visualize the expressions of DESRGs. (B) The correlation network plot of DESRGs in the DN group. (C) The correlation heatmap of DESRGs in the DN group. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.
Figure 5
Figure 5
the GO and KEGG analysis of DESRGs and the PPI network construction. (A) Biological processes (BPs), cellular components (CCs), and molecular functions (MFs) associated with DESRGs. (B) KEGG pathways enriched by DESRGs. (C) Chord plot of enriched genes, GO terms and KEGG pathways. (D) PPI network of cellular senescence-related DEGs.
Figure 6
Figure 6
The selection of feature genes by applying machine learning algorithms. (A) The coefficient from LASSO regression analysis. Different colors represent different genes. (B) Feature genes screened from LASSO algorithm. (C) Accuracy of RF algorithm. (D) Importance ranking of top 20 genes screened by RF algorithm. Average accuracy decreased in importance and Gini index. (E) Venn diagram of the intersection of screening results among LASSO and RF.
Figure 7
Figure 7
ROC curves estimating the diagnostic performance of the feature genes. (A–D) ROC curves of FOXD1, LOX, GJA1 and BTG3 in training cohort GSE30122. (E–H) ROC curves of FOXD1, LOX, GJA1 and BTG3 in test cohort GSE1009. (I–L) ROC curves of FOXD1, LOX, GJA1 and BTG3 in test cohort GSE104948.
Figure 8
Figure 8
The validation of selected feature genes in DN mouse experiments. (A) Quantitative real-time PCR (q-PCR) analysis of mRNA expression levels of the identified genes in homogenized kidney tissues from the DN mouse model (n = 9-15). Statistical analysis was performed using the t-test to determine significant differences between the control and DN groups. (B) Correlative scatterplots illustrating the relationship between the identified key genes in the kidney and the mRNA expression levels of Kim-1, determined by Spearman rank correlation (R). (C) Western blot analysis of protein expression levels of the relevant genes in homogenized kidney tissues from the DN mouse model (n = 4). Statistical analyses were conducted using the Student’s t-test to identify significant differences between the control and DN groups.
Figure 9
Figure 9
Molecular docking results of CGA interaction with GJA1, LOX and FOXD1. (A, B) Molecular docking conformation of CGA interaction with GJA1. (C, D) Molecular docking conformation of CGA interaction with LOX. (E, F) Molecular docking conformation of CGA interaction with FOXD1.
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