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一项基于转录组学的荟萃分析确定了结节病的跨组织特征。

A transcriptomics-based meta-analysis identifies a cross-tissue signature for sarcoidosis.

作者信息

Jiang Yale, Jiang Dingyuan, Costabel Ulrich, Dai Huaping, Wang Chen

机构信息

Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, China.

Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.

出版信息

Front Med (Lausanne). 2022 Sep 20;9:960266. doi: 10.3389/fmed.2022.960266. eCollection 2022.

Abstract

Sarcoidosis is a granulomatous disease of unknown etiology, immunologically characterized by a Th1 immune response. Transcriptome-wide expression studies in various types of sarcoid tissues contributed to better understanding of disease mechanisms. We performed a systematic database search on Gene Expression Omnibus (GEO) and utilized transcriptomic data from blood and sarcoidosis-affected tissues in a meta-analysis to identify a cross-tissue, cross-platform signature. Datasets were further separated into training and testing sets for development of a diagnostic classifier for sarcoidosis. A total of 690 differentially expressed genes were identified in the analysis among various tissues. 29 of the genes were robustly associated with sarcoidosis in the meta-analysis both in blood and in lung-associated tissues. Top genes included ( = 3.11 × 10), ( = 5.56 × 10), and ( = 1.11 × 10). Pathway enrichment analysis revealed activated IFN-γ, IL-1, and IL-18, autophagy, and viral infection response. IL-17 was observed to be enriched in peripheral blood specific signature genes. A 16-gene classifier achieved excellent performance in the independent validation data (AUC 0.711-0.964). This study provides a cross-tissue meta-analysis for expression profiles of sarcoidosis and identifies a diagnostic classifier that potentially can complement more invasive procedures.

摘要

结节病是一种病因不明的肉芽肿性疾病,在免疫学上以Th1免疫反应为特征。对各种类型结节病组织进行的全转录组表达研究有助于更好地理解疾病机制。我们在基因表达综合数据库(GEO)上进行了系统的数据库搜索,并在一项荟萃分析中利用来自血液和受结节病影响组织的转录组数据来识别跨组织、跨平台的特征。数据集被进一步分为训练集和测试集,以开发结节病的诊断分类器。在分析中,共在各种组织中鉴定出690个差异表达基因。在荟萃分析中,其中29个基因在血液和肺相关组织中均与结节病密切相关。排名靠前的基因包括(= 3.11 × 10)、(= 5.56 × 10)和(= 1.11 × 10)。通路富集分析显示IFN-γ、IL-1和IL-18、自噬以及病毒感染反应被激活。观察到IL-17在周围血特异性特征基因中富集。一个16基因分类器在独立验证数据中表现出色(AUC为0.711 - 0.964)。本研究为结节病的表达谱提供了一项跨组织荟萃分析,并鉴定出一个诊断分类器,其可能可以补充更具侵入性的检查方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/406c/9530451/5e9a49b85197/fmed-09-960266-g001.jpg

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