Identification and validation of key genes involved in cellular senescence in multiple sclerosis using bioinformatics

doi:10.5246/jcps.2025.06.040

Abstract

Abstract:

Multiple sclerosis (MS) is a neurodegenerative disease, with aging being a significant risk factor that increases neural susceptibility to damage and reduces resilience. Cellular senescence (CS), a critical biological process of aging, also plays a pivotal role in MS pathogenesis. This study investigated the role of CS in MS by bioinformatics analyses, identifying key genes and potential therapeutic drugs. In differential gene expression (DEG) analysis, we identified 565 DEGs, comprising 166 upregulated and 399 downregulated genes (P < 0.05, |LogFC| > 1.5). Gene Set Enrichment Analysis (GSEA) revealed that these DEGs were enriched in pathways related to ribosomes, CS, and MAPK signaling. Weighted gene co-expression network analysis (WGCNA) identified the turquoise module, consisting of 164 genes, as having the strongest correlation with MS (R² = 0.54, P = 1e^–14). KEGG pathway analysis indicated that this module was most enriched in autophagy, Salmonella infection, and apoptosis pathways. Intersecting the DEGs, WGCNA key module genes, and 1381 CS-associated genes, we identified 49 key genes involved in MS. Machine learning algorithms further pinpointed ATF7IP, ATR, BCL10, CTNNB1, PDCD1, PIK3CA, TNFSF13, MSH3, HTR2A, and ALPL as MS hub genes, which were validated using the GSE13732 testing set. Seven candidate gene-related drugs were identified from DrugBank and the Comparative Toxicogenomics Database (CTD). Molecular docking results indicated that the binding energies for ATF7IP, ATR, BCL10, HTR2A, and PDCD10 with these drugs ranged from –2.444 to –6.523 Kcal/mol.

Key words: Multiple sclerosis, Cellular senescence, WGCNA, Molecular docking

Supporting: /attached/file/20250705/20250705004417_62.pdf

Xiaoqin Zhang, Qiuhong Qin, Xiaojie Li, Jiangang Yang, Jibin Ma, Jianping Ren. Identification and validation of key genes involved in cellular senescence in multiple sclerosis using bioinformatics[J]. Journal of Chinese Pharmaceutical Sciences, 2025, 34(6): 530-542.

Figures/Tables 6

References 22

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