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. 2023 Sep 1:14:1206154.
doi: 10.3389/fendo.2023.1206154. eCollection 2023.

Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning

Jiaming Su  1   2 , Jing Peng  1 , Lin Wang  1   2 , Huidi Xie  1   2 , Ying Zhou  1   2 , Haimin Chen  1   2 , Yang Shi  1   2 , Yan Guo  1   2 , Yicheng Zheng  1   2 , Yuxin Guo  1   2 , Zhaoxi Dong  1   2 , Xianhui Zhang  1   2 , Hongfang Liu  1   2
Affiliations

Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning

Jiaming Su et al. Front Endocrinol (Lausanne). .

Abstract

Backgrounds: Diabetes nephropathy (DN) is a growing public health concern worldwide. Renal dysfunction impairment in DN is intimately linked to ER stress and its related signaling pathways. Nonetheless, the underlying mechanism and biomarkers for this function of ER stress in the DN remain unknown.

Methods: Microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database, and ER stress-related genes (ERSRGs) were downloaded from the MSigDB and GeneCards database. We identified hub ERSRGs for DN progression by intersecting ERSRGs with differentially expressed genes and significant genes in WGCNA, followed by a functional analysis. After analyzing hub ERSRGs with three machine learning techniques and taking the intersection, we did external validation as well as developed a DN diagnostic model based on the characteristic genes. Immune infiltration was performed using CIBERSORT. Moreover, patients with DN were then categorized using a consensus clustering approach. Eventually, the candidate ERSRGs-specific small-molecule compounds were defined by CMap.

Results: Several biological pathways driving pathological injury of DN and disordered levels of immune infiltration were revealed in the DN microarray datasets and strongly related to deregulated ERSRGs by bioinformatics multi-chip integration. Moreover, CDKN1B, EGR1, FKBP5, GDF15, and MARCKS were identified as ER stress signature genes associated with DN by machine learning algorithms, demonstrating their potential as DN biomarkers.

Conclusions: Our research sheds fresh light on the function of ER stress in DN pathophysiology and the development of early diagnostic and ER stress-related treatment targets in patients with DN.

Keywords: WGCNA (weighted gene co-expression network analysis); diabetic nephropathy; endoplasmic reticulum stress; immune cell infiltration; machine learning; molecular subtypes.

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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
Identification of DEGs in glomeruli of DN patients. (A) Gene expression profiles without the removal of the batch effect. (B) Gene expression profiles with removal of batch effect. (C) Active GO functional enrichment of gene expression matrix in DN. (D) Active KEGG pathway of gene expression matrix in DN.
Figure 2
Figure 2
Construction of WGCNA co-expression network. (A) Sample clustering dendrogram with tree leaves corresponding to individual samples. (B) The screen of the best soft thresholds. Six was considered the best soft threshold. (C) The merging of similar modules. (D) Correlations between different modules and clinical traits. Red represents a positive correlation, and blue represents a negative correlation. (E) Clustering dendrogram of module feature genes. (F) Collinear heat map of module feature genes. Red color indicates a high correlation, blue color indicates opposite results. (G) The significance of genes related to DN in the brown, black, green and salmon module (a dot represents the genes in the module).
Figure 3
Figure 3
Identification of hub ERSRGs in the modules. (A) Venn diagram. (B) Heat map of hub ERSRGs. (C) GO enrichment analysis of hub ERSRGs. (D) KEGG enrichment analysis of hub ERSRGs.
Figure 4
Figure 4
The selection of characteristic genes of DN via machine learning algorithm. (A) Twenty-one characteristic genes of LASSO. (B) Nine characteristic genes of SVM-RFE. (C) Top twenty characteristic genes of RF. (D) Visualization of intersecting genes. (E) GBDT to verified the selected intersecting genes reliability.
Figure 5
Figure 5
Validation of characteristic genes in the gene chip datasets. (A) Correlation analysis of characteristic genes. (B) Representative violin plots present the expression of characteristic genes in the multi-chip dataset. (C) Representative violin plots present the expression of characteristic genes in tubulointerstitium dataset (GSE104954). (D) Representative violin plots present the expression of characteristic genes in kidney biopsy dataset (GSE142025). (E) Prediction model of nomogram. (F) The ROC curves for evaluating the diagnostic performance. *p-value < 0.05; **p-value < 0.01; ***p-value < 0.001.
Figure 6
Figure 6
Immune cell infiltration analysis. (A) Distribution of 22 kinds of immune cells in tissues of DN and control groups. (B) Correlation diagram between immune cells. (C) Expression of immune cells in DN and control groups. (D) Immune cells correlation with the expression of characteristic genes.
Figure 7
Figure 7
Identification of ER stress-associated molecular patterns in DN. (A) Consensus clustering matrix when k = 2. (B) Representative CDF curves when k = 2 to 6. (C) Relative alterations in CDF delta area curves. (D) Consensus score in each subtype when k = 2 to 6. (E) PCA analysis demonstrates that the DN patients are classified into two distinct subtypes.
Figure 8
Figure 8
The different immune characteristics and molecular mechanisms between two subtypes. (A) Vio plots showing the mRNA expression of characteristic genes in two ER stress subtypes. *p-value < 0.05; **p-value < 0.01; ***p-value < 0.001. (B) Vio plots demonstrating the infiltration levels of immune cell components in two ER stress subtypes. (C) Differences in enriched biological functions between ER stress subtypes ranked by t value of GSVA score. (D) Differences in the enriched hallmark pathways between ER stress subtypes ranked by t value of GSVA score.
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