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基于生物信息学分析的溃疡性结肠炎差异肠道黏膜转录组学生物标志物的鉴定。

Identification of Differential Intestinal Mucosa Transcriptomic Biomarkers for Ulcerative Colitis by Bioinformatics Analysis.

机构信息

Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Road, 430022 Wuhan, China.

Hubei Key Laboratory of Nature Medicinal Chemistry and Resource Evaluation, Tongji Medical College, Huazhong University of Science and Technology, 430030 Wuhan, China.

出版信息

Dis Markers. 2020 Oct 21;2020:8876565. doi: 10.1155/2020/8876565. eCollection 2020.

DOI:10.1155/2020/8876565
PMID:33144895
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7596466/
Abstract

BACKGROUND

Ulcerative colitis (UC) is a complicated disease caused by the interaction between genetic and environmental factors that affect mucosal homeostasis and triggers inappropriate immune response. The purpose of the study was to identify significant biomarkers with potential therapeutic targets and the underlying mechanisms.

METHODS

The gene expression profiles of GSE48958, GSE73661, and GSE59071 are from the GEO database. Differentially expressed genes (DEGs) were screened by the GEO2R tool. Next, the Database for Annotation, Visualization and Integrated Discovery (DAVID) was applied to analyze gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Then, protein-protein interaction (PPI) was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING).

RESULTS

There were a total of 128 common DEGs genes, including 86 upregulated genes enriched in extracellular space, regulation of inflammatory response, chemokine-mediated signaling pathway, response to lipopolysaccharide, and cell proliferation, while 42 downregulated genes enriched in the integral component of the membrane, the integral component of the plasma membrane, apical plasma membrane, symporter activity, and chloride channel activity. The KEGG pathway analysis results demonstrated that DEGs were particularly enriched in cytokine-cytokine receptor interaction, TNF signaling pathway, chemokine signaling pathway, pertussis, and rheumatoid arthritis. 18 central modules of the PPI networks were selected with Cytotype MCODE. Furthermore, 18 genes were found to significantly enrich in the extracellular space, inflammatory response, chemokine-mediated signaling pathway, TNF signaling pathway, regulation of cell proliferation, and immune response via reanalysis of DAVID.

CONCLUSION

The study identified DEGs, key target genes, functional pathways, and pathway analysis of UC, which may provide potential molecular targets and diagnostic biomarkers for UC.

摘要

背景

溃疡性结肠炎(UC)是一种复杂的疾病,由遗传和环境因素相互作用引起,影响黏膜稳态并引发不适当的免疫反应。本研究旨在确定具有潜在治疗靶点和潜在机制的显著生物标志物。

方法

从 GEO 数据库中选择 GSE48958、GSE73661 和 GSE59071 基因表达谱。使用 GEO2R 工具筛选差异表达基因(DEGs)。然后,使用数据库注释、可视化和综合发现(DAVID)分析基因本体论(GO)和京都基因与基因组百科全书(KEGG)途径。然后,使用 Cytoscape 与搜索工具检索相互作用基因(STRING)可视化蛋白质-蛋白质相互作用(PPI)。

结果

共筛选出 128 个共同的 DEGs 基因,包括 86 个上调基因,这些基因在细胞外空间、炎症反应调节、趋化因子介导的信号通路、脂多糖反应和细胞增殖中富集,而 42 个下调基因在膜的整体成分、质膜的整体成分、顶端质膜、协同转运蛋白活性和氯通道活性中富集。KEGG 途径分析结果表明,DEGs 特别富集在细胞因子-细胞因子受体相互作用、TNF 信号通路、趋化因子信号通路、百日咳和类风湿关节炎中。使用 Cytotype MCODE 从 PPI 网络中选择了 18 个中心模块。此外,通过重新分析 DAVID,发现 18 个基因在细胞外空间、炎症反应、趋化因子介导的信号通路、TNF 信号通路、细胞增殖调节和免疫反应中显著富集。

结论

本研究确定了 UC 的 DEGs、关键靶基因、功能途径和途径分析,这可能为 UC 提供潜在的分子靶标和诊断生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/da6b81eb0795/DM2020-8876565.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/621a3ec3002a/DM2020-8876565.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/32d0b5ef83e0/DM2020-8876565.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/71e407b6c3cb/DM2020-8876565.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/7aa4a985902e/DM2020-8876565.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/f88a6105eb6a/DM2020-8876565.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/da6b81eb0795/DM2020-8876565.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/621a3ec3002a/DM2020-8876565.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/32d0b5ef83e0/DM2020-8876565.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/71e407b6c3cb/DM2020-8876565.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/7aa4a985902e/DM2020-8876565.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/f88a6105eb6a/DM2020-8876565.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e539/7596466/da6b81eb0795/DM2020-8876565.006.jpg

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