中华中医药学刊

基于生物信息学探讨原发性干燥综合征和特发性肺间质纤维化的共病机制及潜在中药预测

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叶晓滨 ¹^,² , 罗芳涛 ³

中华中医药学刊 | 国家项目点击 2026,44(5): 119-124

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中华中医药学刊 |国家项目点击 2026 , 44 (5) : 119 -124

基于生物信息学探讨原发性干燥综合征和特发性肺间质纤维化的共病机制及潜在中药预测

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叶晓滨¹^,², 罗芳涛³

作者信息

^1．厦门大学附属中山医院，福建厦门 361004

^2．厦门市思明区筼筜街道社区卫生服务中心，福建厦门 361012

^3．厦门医学院附属第二医院，福建厦门 361021

通讯作者:

罗芳涛（1964-），男，福建厦门人，主任医师，学士，研究方向：内分泌与骨质疏松等。E-mail：4283298@qq.com。

作者简介:

叶晓滨（1982-），男，福建厦门人，主管中药师，学士，研究方向：中药学。

Exploring Comorbidity Mechanism and Potential Traditional Chinese Medicine Prediction of Primary Sjogren's Syndrome and Idiopathic Pulmonary Interstitial Fibrosis Based on Bioinformatics

Xiaobin YE¹^,², Fangtao LUO³

Affiliations

^1. Zhongshan Hospital Affiliated to Xiamen University, Xiamen 361004, Fujian, China

^2. Community Health Service Center of Tongdang Street, Siming District, Xiamen 361012, Fujian, China

^3. The Second Affiliated Hospital of Xiamen Medical College, Xiamen 361021, Fujian, China

出版时间: 2026-05-10 doi: 10.13193/j.issn.1673-7717.2026.05.023

文章导航

摘要

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目的

通过生物信息学方法获得原发性干燥综合征（Primary Sjögren＇s syndrome，pSS）-特发性肺间质纤维化（Idiopathic pulmonary fibrosis，IPF）的差异基因及所涉及途径，匹配预测治疗pSS-IPF的潜在中药。

方法

从GEO数据库分别下载训练数据集和验证集的基因芯片数据，确定pSS-IPF样本中的差异基因、蛋白互作网络，并应用CytoHubba插件鉴定关键基因，MCODE插件聚类分析并挖掘疾病相关功能模块，利用TRRUST数据库预测共同差异表达基因的转录因子。对关键基因的GO功能和KEGG通路进行分析，阐明关键基因的生物学过程。同时采用验证集差异基因表达情况对前面分析的结果进行验证。最后利用Coremine Medical数据库预测对关键基因具有显著调控作用的中药。

结果

从GEO数据库获得pSS-IPF差异表达基因136个，导入STRING数据库进行检索得到127个节点和474条边组。MCODE插件聚类分析并挖掘5个疾病相关功能模块，CytoHubba插件寻找关系网络数据中的25个Hub基因，上传至GeneMANIA数据库，得出MX1、IFI44L、ISG15、RSAD2等与核心基因最密切的10个基因，然后对以上基因进行GO注释和KEGG通路分析。富集分析结果显示，内质网蛋白质加工、病毒感染相关信号通路、脂质与动脉粥样硬化、氨基酸的生物合成和细胞质DNA传感途径信号通路等通路对pSS-IPF的发病至关重要。经TRRUST分析获得IRF1、SP1和E2F1以上3个主要的转录因子，还有CASP7、TNFSF10、HSPA5和PHGDH受转录因子调控的基因。采用COREMINE预测关键基因和通路，结果显示人参、菟丝子、雷公藤、苦参、枸杞、甘草、冬虫夏草、人参叶是治疗pSS-IPF的核心中药。

结论

本研究明确了pSS-IPF的核心基因及基因所富集的通路，探讨了pSS-IPF主要涉及的生物学过程，并采用验证数据集验证了Hub基因，为pSS-IPF治疗的新靶点选择提供了支撑，预测了针对核心基因的治疗中药，为pSS-IPF的治疗和研究提供了依据。

关键词

生物信息学 / pSS / IPF / 差异基因分析 / 中药预测

Abstract

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Objective

To obtain the differential genes of primary Sjögren's syndrome (pSS) -idiopathic pulmonary fibrosis (IPF) and related pathways by bioinformatics methods, and to match and predict the potential Chinese medicines for treating pSS-IPF.

Methods

The gene chip data of training data set and validation data set were downloaded from GEO database, and the interaction networks of differential genes and proteins in pSS-IPF samples were determined. The key genes were identified by CytoHubba plug-in, and the disease-related functional modules were clustered and analyzed by MCODE plug-in. The transcription factors of common differentially expressed genes were predicted by TRRUST database. The GO function and KEGG pathway of key genes were analyzed to clarify the biological process of key genes. At the same time, the results of the previous analysis were verified by the differential gene expression in the verification set. Finally, the Coremine Medical database was used to predict the traditional Chinese medicine with significant regulatory effect on key genes.

Results

A total of 136 differentially expressed genes of pSS-IPF were obtained from GEO database, and 127 nodes and 474 edge groups were obtained by importing them into STRING database. The MCODE plug-in performs cluster analysis on the PPI network and mines five disease-related functional modules. The CytoHubba plug-in searched 25 Hub genes in the relational network data and uploaded them to the GeneMANIA database, and obtained 10 genes which were most closely related to Hub genes, such as MX1, IFI44L, ISG15 and RSAD2. Then, the Hub genes were annotated by GO and analyzed by KEGG pathway. The results of enrichment analysis showed that the protein processing of endoplasmic reticulum, signal pathway related to virus infection, lipid and atherosclerosis, amino acid biosynthesis and cytoplasmic DNA sensing pathway were very important for the pathogenesis of pSS-IPF. TRRUST analysis showed that IRF1, SP1 and E2F1 were the three main transcription factors, and CASP7, TNFSF10, HSPA5 and PHGDH were also regulated by transcription factors. COREMINE was used to predict the key genes and pathways. The results showed that Renshen (Ginseng Radix et Rhizoma), Tusizi (Cuscutae Semen), Leigongteng (Radix et Rhizoma Tripterygii), Kushen (Sophorae Flavescentis Radix), Gouqi (Lycii Fructus), Gancao (Glycyrrhizae Radix et Rhizoma), Dongchongxiacao (Ophiocordyceps) and Renshenye (Ginseng Folium) were the core Chinese medicines for pSS-IPF.

Conclusion

In this study, the core genes and gene-enriched pathways of pSS-IPF were identified, the biological processes involved in pSS-IPF were discussed, and the Hub gene was verified by the verification data set, which provided the support for the selection of new targets for pSS-IPF treatment, predicted the therapeutic Chinese medicine for Hub gene, and provided a basis for the treatment and research of pSS-IPF.

Key words

bioinformatics / pSS / IPF / differential gene analysis / traditional Chinese medicine prediction

引用本文

叶晓滨, 罗芳涛. 基于生物信息学探讨原发性干燥综合征和特发性肺间质纤维化的共病机制及潜在中药预测. 中华中医药学刊, 2026 , 44 (5) : 119 -124 . DOI: 10.13193/j.issn.1673-7717.2026.05.023

Xiaobin YE, Fangtao LUO. Exploring Comorbidity Mechanism and Potential Traditional Chinese Medicine Prediction of Primary Sjogren's Syndrome and Idiopathic Pulmonary Interstitial Fibrosis Based on Bioinformatics[J]. Chinese Archives of Traditional Chinese Medicine, 2026 , 44 (5) : 119 -124 . DOI: 10.13193/j.issn.1673-7717.2026.05.023

基金

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国家重点研发计划项目(2021 YFC3312218)
成都中医药大学大学生科研实践创新项目(ky-2023002)
成都中医药大学临床医学院研究生科研创新实践项目(CXZD2021007)

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2026年第44卷第5期

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文章信息

doi: 10.13193/j.issn.1673-7717.2026.05.023

首发时间：2026-10-14
出版时间：2026-05-10

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基金

国家重点研发计划项目(2021 YFC3312218)

成都中医药大学大学生科研实践创新项目(ky-2023002)

成都中医药大学临床医学院研究生科研创新实践项目(CXZD2021007)

作者信息

^1．厦门大学附属中山医院，福建厦门 361004

^2．厦门市思明区筼筜街道社区卫生服务中心，福建厦门 361012

^3．厦门医学院附属第二医院，福建厦门 361021

通讯作者:

罗芳涛（1964-），男，福建厦门人，主任医师，学士，研究方向：内分泌与骨质疏松等。E-mail：4283298@qq.com。

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https://castjournals.cast.org.cn/joweb/zhzyyxk/CN/10.13193/j.issn.1673-7717.2026.05.023

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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