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ORIGINAL RESEARCH article
Front. Genet.
Sec. Genetics of Common and Rare Diseases
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1551450
Identification of Cellular Senescence-related genes as biomarkers for lupus nephritis based on bioinformatics
Provisionally accepted- 1 Nanjing University of Chinese Medicine, Nanjing, China
- 2 Suzhou Hospital of Integrated Traditional Chinese and Western Medicine, Suzhou, Liaoning Province, China
- 3 Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu Province, China
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 Goexpression Network Analysis (WGCNA) and machine learning algorithms, hub cellular senescencerelated 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.We obtained 432 genes related to cellular senescence, 1208 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 6 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.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: Lupus Nephritis, cellular senescence, biomarker, Weighted Gene Co-Expression Network (WGCNA), machine learning
Received: 25 Dec 2024; Accepted: 01 Apr 2025.
Copyright: © 2025 Chen, Wang, Huang, Sheng and Enchao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhou Enchao, Nanjing University of Chinese Medicine, Nanjing, China
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