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基于免疫基因和免疫细胞浸润的胆道闭锁免疫特征

Immune Characteristics in Biliary Atresia Based on Immune Genes and Immune Cell Infiltration.

作者信息

Yang Chenyu, Xing Huiwu, Tan Bingqian, Zhang Mingman

机构信息

Department of Hepatobiliary Surgery Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.

Chongqing Higher Institution Engineering Research Center of Children's Medical Big Data Intelligent Application, Chongqing, China.

出版信息

Front Pediatr. 2022 May 23;10:902571. doi: 10.3389/fped.2022.902571. eCollection 2022.

DOI:10.3389/fped.2022.902571
PMID:35676907
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9168997/
Abstract

BACKGROUND

Biliary atresia (BA) is a serious biliary disease in infancy. Jaundice is the most visual and prominent symptom, and it mainly involves bile duct cells leading to the loss of intrahepatic and extrahepatic bile ducts. If left untreated, it will eventually progress to liver cirrhosis. The pathogenesis of BA is not clear, and it is now generally accepted that BA is an autoimmune disease. However, few studies have revealed the infiltration of immune cells in the liver of BA from a global perspective. We used liver tissue sequencing data to predict the infiltration and relative content of immune cells in BA.

METHODS

The BA datasets GSE46960, GSE15235, and GSE84044, and patient information were downloaded from the Gene Expression Omnibus (GEO) database. After batch normalization, the differentially expressed immune genes (DE-IGs) in BA liver, normal liver, and hepatitis B liver were analyzed with the cut-off value of |logfold change (logFC)| >1 and false discovery rate (FDR) <0.05. CIBERSORT software was used to predict the proportions of 22 immune cells in all samples of the datasets.

RESULTS

73 DE-IGs have been screened out between BA and normal tissue; among them, 20 genes were highly expressed and another 53 were expressed at a low level. A total of 30 DE-IGs existed between inflammation and fibrosis livers of BA, and all of them were expressed at low levels in fibrosis livers of BA. In GO term analysis, these DE-IGs were mainly associated with the MHC protein complex, cytokine, chemokine activity, and MHC-II receptor activity. In KEGG pathway analysis, the DE-IGs were mainly enriched in pathways of Th1 and Th2 cell differentiation, Th17 cell differentiation, IL-17 signaling pathway, Toll-like receptor signaling pathway, TNF signaling pathway, and autoimmune diseases. There were significant differences in immune infiltration among different pathological types of BA, and there were also obvious differences in immune infiltration of hepatitis B as a disease control of BA.

CONCLUSION

Based on immune genes and immune cell infiltration, this study reveals the immune characteristics of BA from a global point of view, which provides a new perspective for understanding the pathogenesis of BA and provides a direction for the diagnosis and treatment of BA.

摘要

背景

胆道闭锁(BA)是一种严重的婴儿期胆道疾病。黄疸是最明显和突出的症状,主要累及胆管细胞,导致肝内和肝外胆管丧失。若不治疗,最终会发展为肝硬化。BA的发病机制尚不清楚,目前普遍认为BA是一种自身免疫性疾病。然而,从全局角度揭示BA肝脏中免疫细胞浸润情况的研究较少。我们利用肝脏组织测序数据预测BA中免疫细胞的浸润情况及相对含量。

方法

从基因表达综合数据库(GEO)下载BA数据集GSE46960、GSE15235和GSE84044以及患者信息。经批次标准化后,分析BA肝脏、正常肝脏和乙型肝炎肝脏中差异表达的免疫基因(DE-IGs),截断值为|log倍变化(logFC)|>1且错误发现率(FDR)<0.05。使用CIBERSORT软件预测数据集中所有样本中22种免疫细胞的比例。

结果

已筛选出BA与正常组织之间的73个DE-IGs;其中,20个基因高表达,另外53个基因低表达。BA的炎症性肝脏和纤维化肝脏之间共有30个DE-IGs,且在BA的纤维化肝脏中均低表达。在基因本体(GO)术语分析中,这些DE-IGs主要与MHC蛋白复合物、细胞因子、趋化因子活性和MHC-II受体活性相关。在京都基因与基因组百科全书(KEGG)通路分析中,DE-IGs主要富集于Th1和Th2细胞分化、Th17细胞分化、IL-17信号通路、Toll样受体信号通路、TNF信号通路和自身免疫性疾病通路。BA不同病理类型之间的免疫浸润存在显著差异,作为BA疾病对照的乙型肝炎的免疫浸润也存在明显差异。

结论

本研究基于免疫基因和免疫细胞浸润情况,从全局角度揭示了BA的免疫特征,为理解BA的发病机制提供了新视角,为BA的诊断和治疗提供了方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/df309eb08e5d/fped-10-902571-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/dca1373058db/fped-10-902571-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/4625a77af549/fped-10-902571-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/e2970a9a875a/fped-10-902571-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/b4571ce9f0bd/fped-10-902571-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/18c7df708498/fped-10-902571-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/df309eb08e5d/fped-10-902571-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/dca1373058db/fped-10-902571-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/4625a77af549/fped-10-902571-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/e2970a9a875a/fped-10-902571-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/b4571ce9f0bd/fped-10-902571-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/18c7df708498/fped-10-902571-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05d6/9168997/df309eb08e5d/fped-10-902571-g0010.jpg

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