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. 2025 Apr 11:16:1551450.
doi: 10.3389/fgene.2025.1551450. eCollection 2025.

Identification of cellular senescence-related genes as biomarkers for lupus nephritis based on bioinformatics

Wei Chen  1   2 , Xiaofang Wang  1   3 , Guoshun Huang  1   2 , Qin Sheng  4 , Enchao Zhou  1   2
Affiliations

Identification of cellular senescence-related genes as biomarkers for lupus nephritis based on bioinformatics

Wei Chen et al. Front Genet. .

Abstract

Background: Lupus nephritis (LN) is one of the most common and severe complications of systemic lupus erythematosus with unclear pathogenesis. The most accurate diagnosis criterion of LN is still renal biopsy and nowadays treatment strategies of LN are far from satisfactory. Cellular senescence is defined as the permanent cell cycle arrest marked by senescence-associated secretory phenotype (SASP), which has been proved to accelerate the mobility and mortality of patients with LN. The study is aimed to identify cellular senescence-related genes for LN.

Methods: Genes related to cellular senescence and LN were obtained from the MSigDB genetic database and GEO database respectively. Through differential gene analysis, Weighted Gene Go-expression Network Analysis (WGCNA) and machine learning algorithms, hub cellular senescence-related differentially expressed genes (CS-DEGs) were identified. By external validation, hub CS-DEGs were further filtered and the remaining genes were identified as biomarkers. We explored their potential physiopathologic function through GSEA.

Results: We obtained 432 genes related to cellular senescence, 1,208 differentially expressed genes (DEGs) and 840 genes in the key gene module related to LN, which were intersected with each other for CS-DEGs. Subsequent Machine learning algorithms screened out six hub CS-DEGs and finally three hub CS-DEGs, ALOX5, PTGER2 and PRKCB passed through external validation, which were identified as biomarkers. The three biomarkers were enriched in "B Cell receptor signaling pathway" and "NF-kappa B signaling pathway" based on GESA results.

Conclusion: This study explored the potential relationship between cellular senescence and LN, and identified three biomarkers ALOX5, PTGER2, and PRKCB playing key roles in LN, which will provide new insights for the diagnosis and treatment of LN.

Keywords: biomarker; cellular senescence; lupus nephritis; machine learning; weighted gene Co-expression network (WGCNA).

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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 flow diagram of the study.
FIGURE 2
FIGURE 2
Identification of DEGs in LN. (A) PCA scatter plot of the training set before batch correction. (B) PCA scatter plot of the training set after batch correction. (C) Volcano plot of DEGs between LN and normal groups. (D) Heatmap of the top 50 DEGs.
FIGURE 3
FIGURE 3
Identification of gene modules related to LN by WGCNA. (A) Sample clustering dendrogram of 137 samples in the training set with three outliers eliminated. (B, C) The scale-free fit index and the mean connectivity for different soft-thresholding powers (β). (D) Dendrogram of genes clustered via the dissimilarity measure (1-TOM) and hierarchical clustering. (E) Heatmap of the correlation between module genes and the disease status of LN. (F) Scatter plot of gene significance (GS) versus module membership (MM) of the turquoise module.
FIGURE 4
FIGURE 4
The identification and function analysis of CS-DEGs. (A) Venn diagram showing 20 CS-DEGs in LN that overlapped DEGs, key module genes, and CSGs. (B) Chromosome localization circles of CS-DEGs. (C) PPI network of CS-DEGs. (D) GO results (E) KEGG analysis results.
FIGURE 5
FIGURE 5
Screening hub CS-DEGs by machine learning. (A) LASSO regression of 10 hub genes. (B) Cross validation of parameter selection in LASSO regression. (C) The important feature selection graph obtained by SVM-RFE algorithm. (D) RF algorithm illustrating the relationship between the number of trees and error rate. (E) Ranking of genes based on their relative importance through the RF algorithm. (F) Venn diagram showing the hub genes shared by SVM-RFE, LASSO and RF algorithms.
FIGURE 6
FIGURE 6
Validation of six hub CS-DEGs. (A, B) ROC curves of the six hub genes in the training set and the external validation set. ALOX5, PRKCB, and PTGER2 demonstrated strong diagnostic values for LN in the external validation set (AUC> 0.75). (C, D) The expression pattern of the six hub genes in the external validation set.
FIGURE 7
FIGURE 7
GSEA and immune filtration analysis results. (A–C) Single-gene GSEA of biomarkers (ALOX5, PTGER2, and PRKCB). (D) Abundances of twenty-one immune cells differed significantly in LN. (E) Heatmap of correlation analysis of biomarkers and twenty-one kinds of immune cells (F–H) Lollipop plots of correlation analysis of biomarkers and twenty-one kinds of immune cells.
FIGURE 8
FIGURE 8
(A) “TF-miRNA-gene” network presenting the regulatory mechanisms of biomarkers. (B) The relationship between biomarkers and drugs predicted from the public database.
FIGURE 9
FIGURE 9
Relationships between the expression of biomarkers and stage of chronic kidney disease (CKD), proteinuria and LN pathological classification. (A) Relationship between the expression of ALOX5 and three variables: stage of CKD, proteinuria and pathological classification. (B) Relationship between the expression of PTGER2 and three variables: stage of CKD, proteinuria and pathological classification. (C) Relationship between the expression of PRKCB and three variables: stage of CKD, proteinuria and pathological classification.
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