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干燥综合征和包涵体肌炎之间潜在的共同分子机制：生物信息学分析和验证。

Potential common molecular mechanisms between Sjögren syndrome and inclusion body myositis: a bioinformatic analysis and validation.

机构信息

Department of Neurology, Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.

Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Provincial People's Hospital, Chengdu, China.

出版信息

Front Immunol. 2023 Apr 21;14:1161476. doi: 10.3389/fimmu.2023.1161476. eCollection 2023.

DOI:10.3389/fimmu.2023.1161476
PMID:37153570
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10160489/
Abstract

BACKGROUND

Inclusion body myositis (IBM) is a slowly progressive inflammatory myopathy that typically affects the quadriceps and finger flexors. Sjögren's syndrome (SS), an autoimmune disorder characterized by lymphocytic infiltration of exocrine glands has been reported to share common genetic and autoimmune pathways with IBM. However, the exact mechanism underlying their commonality remains unclear. In this study, we investigated the common pathological mechanisms involved in both SS and IBM using a bioinformatic approach.

METHODS

IBM and SS gene expression profiles were obtained from the Gene Expression Omnibus (GEO). SS and IBM coexpression modules were identified using weighted gene coexpression network analysis (WGCNA), and differentially expressed gene (DEG) analysis was applied to identify their shared DEGs. The hidden biological pathways were revealed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Furthermore, protein-protein interaction (PPI) networks, cluster analyses, and hub shared gene identification were conducted. The expression of hub genes was validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR). We then analyzed immune cell abundance patterns in SS and IBM using single-sample gene set enrichment analysis (ssGSEA) and investigated their association with hub genes. Finally, NetworkAnalyst was used to construct a common transcription factor (TF)-gene network.

RESULTS

Using WGCNA, we found that 172 intersecting genes were closely related to viral infection and antigen processing/presentation. Based on DEG analysis, 29 shared genes were found to be upregulated and enriched in similar biological pathways. By intersecting the top 20 potential hub genes from the WGCNA and DEG sets, three shared hub genes (, , and ) were derived and validated to be active transcripts, which all exhibited diagnostic values for SS and IBM. Furthermore, ssGSEA showed similar infiltration profiles in IBM and SS, and the hub genes were positively correlated with the abundance of immune cells. Ultimately, two TFs (HDGF and WRNIP1) were identified as possible key TFs.

CONCLUSION

Our study identified that IBM shares common immunologic and transcriptional pathways with SS, such as viral infection and antigen processing/presentation. Furthermore, both IBM and SS have almost identical immune infiltration microenvironments, indicating similar immune responses may contribute to their association.

摘要

背景

包涵体肌炎（IBM）是一种进展缓慢的炎症性肌病，通常影响股四头肌和手指屈肌。干燥综合征（SS）是一种自身免疫性疾病，其特征是外分泌腺的淋巴细胞浸润，已被报道与 IBM 具有共同的遗传和自身免疫途径。然而，其共同性的确切机制仍不清楚。在这项研究中，我们使用生物信息学方法研究了 SS 和 IBM 共同涉及的共同病理机制。

方法

从基因表达综合数据库（GEO）中获得 IBM 和 SS 的基因表达谱。使用加权基因共表达网络分析（WGCNA）鉴定 SS 和 IBM 的共表达模块，并应用差异表达基因（DEG）分析鉴定其共同的 DEG。使用基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析揭示隐藏的生物学途径。此外，进行了蛋白质-蛋白质相互作用（PPI）网络、聚类分析和枢纽共享基因鉴定。通过逆转录定量聚合酶链反应（RT-qPCR）验证枢纽基因的表达。然后，我们使用单样本基因集富集分析（ssGSEA）分析 SS 和 IBM 中的免疫细胞丰度模式，并研究它们与枢纽基因的关联。最后，使用 NetworkAnalyst 构建共同转录因子（TF）-基因网络。

结果

使用 WGCNA，我们发现 172 个相交基因与病毒感染和抗原加工/呈递密切相关。基于 DEG 分析，发现 29 个共同基因上调，并在相似的生物学途径中富集。通过相交 WGCNA 和 DEG 集合中的前 20 个潜在枢纽基因，得出并验证了三个共同的枢纽基因（、和）是活跃的转录物，它们都对 SS 和 IBM 具有诊断价值。此外，ssGSEA 显示 IBM 和 SS 中具有相似的浸润模式，枢纽基因与免疫细胞的丰度呈正相关。最终，确定了两个转录因子（HDGF 和 WRNIP1）作为可能的关键转录因子。

结论

我们的研究表明，IBM 与 SS 具有共同的免疫和转录途径，如病毒感染和抗原加工/呈递。此外，IBM 和 SS 具有几乎相同的免疫浸润微环境，表明类似的免疫反应可能导致它们之间的关联。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/26272ac513d7/fimmu-14-1161476-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/6cf6ea8e87cf/fimmu-14-1161476-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/9611fd3122b8/fimmu-14-1161476-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/a247c54ef2cb/fimmu-14-1161476-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/9b3f9a1d31fe/fimmu-14-1161476-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/eef69f5572b2/fimmu-14-1161476-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/483844ef5d64/fimmu-14-1161476-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/26272ac513d7/fimmu-14-1161476-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/6cf6ea8e87cf/fimmu-14-1161476-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/4f07a81e6b63/fimmu-14-1161476-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/9611fd3122b8/fimmu-14-1161476-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/a247c54ef2cb/fimmu-14-1161476-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/9b3f9a1d31fe/fimmu-14-1161476-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/eef69f5572b2/fimmu-14-1161476-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/483844ef5d64/fimmu-14-1161476-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f439/10160489/26272ac513d7/fimmu-14-1161476-g008.jpg

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