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. 2023 Dec 7:14:1288699.
doi: 10.3389/fimmu.2023.1288699. eCollection 2023.

Identification of driver genes in lupus nephritis based on comprehensive bioinformatics and machine learning

Zheng Wang  1 , Danni Hu  1 , Guangchang Pei  1 , Rui Zeng  1   2   3   4 , Ying Yao  1   5
Affiliations

Identification of driver genes in lupus nephritis based on comprehensive bioinformatics and machine learning

Zheng Wang et al. Front Immunol. .

Abstract

Background: Lupus nephritis (LN) is a common and severe glomerulonephritis that often occurs as an organ manifestation of systemic lupus erythematosus (SLE). However, the complex pathological mechanisms associated with LN have hindered the progress of targeted therapies.

Methods: We analyzed glomerular tissues from 133 patients with LN and 51 normal controls using data obtained from the GEO database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Weighted gene co-expression network analysis (WGCNA) was utilized to identify key gene modules. The least absolute shrinkage and selection operator (LASSO) and random forest were used to identify hub genes. We also analyzed immune cell infiltration using CIBERSORT. Additionally, we investigated the relationships between hub genes and clinicopathological features, as well as examined the distribution and expression of hub genes in the kidney.

Results: A total of 270 DEGs were identified in LN. Using weighted gene co-expression network analysis (WGCNA), we clustered these DEGs into 14 modules. Among them, the turquoise module displayed a significant correlation with LN (cor=0.88, p<0.0001). Machine learning techniques identified four hub genes, namely CD53 (AUC=0.995), TGFBI (AUC=0.997), MS4A6A (AUC=0.994), and HERC6 (AUC=0.999), which are involved in inflammation response and immune activation. CIBERSORT analysis suggested that these hub genes may contribute to immune cell infiltration. Furthermore, these hub genes exhibited strong correlations with the classification, renal function, and proteinuria of LN. Interestingly, the highest hub gene expression score was observed in macrophages.

Conclusion: CD53, TGFBI, MS4A6A, and HERC6 have emerged as promising candidate driver genes for LN. These hub genes hold the potential to offer valuable insights into the molecular diagnosis and treatment of LN.

Keywords: Lupus nephritis; WGCNA; bioinformatics; immune infiltration; machine learning.

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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
Flow chart of the research study.
Figure 2
Figure 2
Identification of the DEGs in lupus nephritis. (A) The principal component analysis (PCA) showing the distribution of samples in patients with lupus nephritis and normal controls. (B) The heatmap illustrating the top 1000 genes with the highest standard deviation changes among individuals diagnosed with lupus nephritis and normal controls. (C) The volcano showing the expression of DEGs between lupus nephritis and normal controls. (D) The bar graph illustrating the significantly upregulated KEGG pathways in lupus nephritis compared to the normal controls. (E) The lollipop graph illustrating the upregulated(right) and downregulated(left) GO terms in lupus nephritis compared to the normal controls.
Figure 3
Figure 3
Identification of candidate hub genes based the WGCNA analysis. (A) The soft threshold power(left) and mean connectivity(right) of WGCNA network. (B) The cluster dendrogram of WGCNA network. (C) The dot plot showing the top enriched reactome pathways among different modules. (D) The heatmap depicting the relationship between the modules and clinical traits, specifically lupus nephritis and normal controls. (E) The bar chart illustrating the gene significance among different modules in lupus nephritis. (F) The scatter plot between gene significance (GS) and module members (MM) in turquoise module. (G) The venn diagram of the intersection of DEGs, turquoise module genes.
Figure 4
Figure 4
Identification of final hub genes by lasso regression analysis and random forest analysis. (A) Path diagram of the LASSO coefficients for the hub genes associated with lupus nephritis in training group. (B) LASSO regression cross-validation curve. Optimal λ values were determined using 10-fold cross-validation in training group. (C) The error rate confidence intervals for random forest mode in training group. (D) The lollipop graph illustrating the relative importance of genes in the random forest model within training group. (E) The venn diagram of the intersection of LASSO and random forest signature genes. (F) Expression levels of four hub genes in lupus nephritis patients compared with normal controls in training group. (G) ROC analysis of four hub genes in training group (H) Expression levels of four hub genes in lupus nephritis patients compared with normal controls in validation group. (I). ROC analysis of four hub genes in validation group.
Figure 5
Figure 5
The GSEA of hub genes in lupus nephritis. (A) The GSEA of CD53 in lupus nephritis. (B) The GSEA of TGFBI in lupus nephritis. (C) The GSEA of MS4A6A in lupus nephritis. (D) The GSEA of HERC6 in lupus nephritis.
Figure 6
Figure 6
The immune cell infiltration association with hub genes. (A) The immune cell infiltration between lupus nehritis and normal controls. (B) The association between CD53 and different immune cell infiltration in lupus nephritis. (C) The association between TGFBI and different immune cell infiltration in lupus nephritis. (D) The association between MA4A6A and different immune cell infiltration in lupus nephritis. (E) The association between HERC6 and different immune cell infiltration in lupus nephritis. * p < 0.05;** P < 0.01;*** P < 0.0001.
Figure 7
Figure 7
Relationships between the expression of hub gene and pathological classification, stage of chronic kidney disease(CKD), and proteinuria. (A) The scatter plots depicting the relationship between the expression level of the CD53 and three variables: pathological classification (eft), stage of CDK(center), and proteinuria (right). (B) The scatter plots depicting the relationship between the expression level of the TGFBI and three variables: pathological classification (left), stage of CKD (center), and proteinuria (right). (C) The scatter plots depicting the relationship between the expression level of the MS4A6A and three variables: pathological classification (left), stage of CKD (center), and proteinuria (right). (D) The scatter plots depicting the relationship between the expression level of the HERC6 and three variables: pathological classification (left), stage of CKD (center), and proteinuria (right). * p < 0.05;** P < 0.01.
Figure 8
Figure 8
Distribution and expression of hub genes based on the single-cell RNA sequencing data. (A) t-SNE plot showing the 22 identified cell clusters. (B) Featureplot, bar plot and dot plot showing the distribution and expression of CD53. (C) Featureplot, bar plot and dot plot showing the distribution and expression of TGFBI. (D) Featureplot, bar plot and dot plot showing the distribution and expression of MS4A6A. (E) Featureplot, bar plot and dot plot showing the distribution and expression of HERC6. (F) Violin plots showing combined expression scores of hub genes.
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