基于生物信息学探究细胞衰老参与多发性硬化关键基因的筛选及验证

doi:10.5246/jcps.2025.06.040

中国药学(英文版) ›› 2025, Vol. 34 ›› Issue (6): 530-542.DOI: 10.5246/jcps.2025.06.040

基于生物信息学探究细胞衰老参与多发性硬化关键基因的筛选及验证

张晓琴¹^,³^,^#, 秦湫红²^,³^,^#, 李晓杰²^,³, 杨建刚²^,³, 麻纪斌²^,³, 任建平¹^,²^,³^,^*()

^1. 陕西中医药研究所(陕西医药信息中心), 陕西咸阳 712000
^2. 陕西医药控股医药研究院有限公司, 陕西西安 710075
^3. 陕西省中药与天然药物研发重点实验室, 陕西西安 710075

收稿日期:2025-03-16 修回日期:2025-04-23 接受日期:2025-04-30 出版日期:2025-07-03 发布日期:2025-07-03
通讯作者: 任建平

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

Xiaoqin Zhang¹^,³^,^#, Qiuhong Qin²^,³^,^#, Xiaojie Li²^,³, Jiangang Yang²^,³, Jibin Ma²^,³, Jianping Ren¹^,²^,³^,^*()

¹ Shaanxi Chinese Medicine Institute Shaanxi Pharmaceutical Information Centre, Xianyang 712000, Shaanxi, China
² Medicine Research Institute of Shaanxi Pharmaceutical Holoding Cooperation, Xi’an 710075, Shaanxi, China
³ Shaanxi Key Laboratory for Chinese Medicine and Natural Medicine Research and Development, Xi’an 710075, Shaanxi, China

Received:2025-03-16 Revised:2025-04-23 Accepted:2025-04-30 Online:2025-07-03 Published:2025-07-03
Contact: Jianping Ren
About author:
^# Xiaoqin Zhang and Qiuhong Qin contributed equally to this work.
Supported by:
The Key R&D Plan of Xianyang Construction of Xianyang City’s in vitro rapid diagnostic reagent technology integration and pilot scale shared service platform (Grant No. 2021ZDYF-SF-0012).

摘要/Abstract

摘要：

多发性硬化症(MS)是一种神经退行性疾病, 疾病发展的最重要风险因素之一是衰老, 衰老会增加神经对损伤的易感性, 降低其恢复力。此外, 细胞衰老(CS)是衰老的一个重要生物学过程。本研究通过生物信息学分析探讨细胞衰老参与多发性硬化症的发病机制, 筛选出关键基因及相关治疗药物。在DEG分析中, 共获得565个DEG, 其中166个上调基因和399个下调基因(P < 0.05, |LogFC| > 1.5), 基于GSEA分析的结果表明, DEG富含核糖体、细胞衰老和MAPK信号通路。WGCNA分析显示, 绿松石模块(164个基因)与MS的相关性最强(R² = 0.54, P = 1e^–14), KEGG分析显示绿松石模式在自噬途径、沙门氏菌感染途径和细胞凋亡途径中富集最多。将DEG和WGCNA关键模块基因与1381个细胞衰老相关基因取交集, 获得49个关键基因。随后, 使用机器学习算法识别ATF7IP、ATR、BCL10、CTNNB1、PDCD1、PIK3CA、TNFSF13、MSH3、HTR2A和ALPL为细胞衰老参与MS关键基因, 并使用测试集GSE13732进行验证。从DrugBank和CTD收集了7种候选基因相关药物。分子对接结果表明, ATF7IP、ATR、BCL10、HTR2A和PDCD10与7种药物的最小结合能为–2.444 Kcal/mol, 最大结合能为–6.523 Kcal/mol。

关键词: 多发性硬化症, 细胞衰老, WGCNA, 分子对接

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

张晓琴, 秦湫红, 李晓杰, 杨建刚, 麻纪斌, 任建平. 基于生物信息学探究细胞衰老参与多发性硬化关键基因的筛选及验证[J]. 中国药学(英文版), 2025, 34(6): 530-542.

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.

图/表 6

Figure 1. Data filtering and preprocessing of DEGs. The PCA plot shows the distribution of data before (A) and after (B) removing batch effects. The expression patterns of DEGs (166 upregulated and 399 downregulated) are described in volcanic maps (C) and Heat map plots (D), respectively. (E) GO term enrichment analysis of DEGs. (F) GSEA of DEGs. HC, health controls; MS, multiple sclerosis.

Figure 2. Gene co-expression network construction and module identification. (A) Analysis of scale-free fitting index analysis and mean connectivity for various soft threshold capabilities. (B) Heat map of the correlation between module characteristic genes and clinical features of MS. (C) Scatter plot of characteristic genes in the turquoise module. (D) GO analysis of turquoise modules (BP, CC, and MF). (E) KEGG analysis of turquoise module.

Figure 3. Construction of a diagnostic model using the hub CS-related DEGs. (A) Identification of key DEGs related to CS using Venn diagrams. (B, C) LASSO regression model. (D, E) RF model. (F) Using nomogram to display diagnostic prediction model. (G) ROC curves showing the diagnostic ability of the 10 hub CS-related DEGs in the training and testing datasets. (H) The relative expression of 10 selected genes in the training set is shown in a boxplot. (I–R) The ROC curves of the MS signature genes in the training set.

Figure 4. Immune cell infiltration analysis of 22 types of immune cells and their correlation with key genes. The histogram (A) and heatmap (B) show the estimated proportions of 22 immune cells in the healthy and diseased groups. (C) The correlation evaluation of 22 immune cells, with red representing positive correlation and blue representing negative correlation. (D–M) The lollipop chart displays the correlation between sample characteristic genes and immune cells.

Figure 5. Hub gene-drug network (yellow represents hub genes, green represents the top eight drugs in the degree value, while blue represents other drugs). Seven relevant drugs from DrugBank and CTD.

Table 1. Molecular docking binding energy results (Kcal/mol).

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基于生物信息学探究细胞衰老参与多发性硬化关键基因的筛选及验证

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

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