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基于大样本患者的生物信息学分析对胆管癌进行全面的转录组学研究。

A comprehensive transcriptomic landscape of cholangiocarcinoma based on bioinformatics analysis from large cohort of patients.

机构信息

Department of Hepatobiliary Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250021, Shandong, China.

Department of Hepatobiliary Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.

出版信息

Sci Rep. 2021 Jul 1;11(1):13713. doi: 10.1038/s41598-021-93250-4.

DOI:10.1038/s41598-021-93250-4
PMID:34211100
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8249535/
Abstract

Cholangiocarcinoma (CCA) is a group of malignancies emerging in the biliary tree and is associated with a poor patient prognosis. Although the anatomical location is the only worldwide accepted classification basis, it still has bias. The current study integrates the whole-genome expression data from several big cohorts in the literature, to screen and provide a comprehensive bioinformatic analysis, in order to better classify molecular subtypes and explore an underlying cluster mechanism related to anatomy and geographical regions. Differentially expressed protein-coding genes (DEGs) were identified for CCA as well as subtypes. Biological function enrichment analysis-Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis-was applied and identified different DEGs enriched signaling pathways in CCA subtypes. A co-expression network was presented by Weighted gene co-expression network analysis package and modules related to specific phenotypes were identified. Combined with DEGs, hub genes in the given module were demonstrated through protein-protein interaction network analysis. Finally, DEGs which significantly related to patient overall survival and disease-free survival time were selected, including ARHGAP21, SCP2, UBIAD1, TJP2, RAP1A and HDAC9.

摘要

胆管癌(CCA)是一组发生在胆道系统的恶性肿瘤,患者预后较差。尽管解剖位置是目前全球唯一被接受的分类依据,但仍存在一定的局限性。本研究整合了文献中几个大型队列的全基因组表达数据,进行筛选并提供全面的生物信息学分析,以更好地对分子亚型进行分类,并探索与解剖和地理区域相关的潜在聚类机制。鉴定出 CCA 及亚型的差异表达蛋白编码基因(DEGs)。通过京都基因与基因组百科全书通路富集分析进行生物学功能富集分析,鉴定出 CCA 亚型中不同的 DEGs 富集信号通路。通过加权基因共表达网络分析包呈现共表达网络,并鉴定出与特定表型相关的模块。结合 DEGs,通过蛋白质-蛋白质相互作用网络分析展示给定模块中的枢纽基因。最后,选择与患者总生存期和无病生存期显著相关的 DEGs,包括 ARHGAP21、SCP2、UBIAD1、TJP2、RAP1A 和 HDAC9。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a51d/8249535/0743e2c32249/41598_2021_93250_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a51d/8249535/4bf4689f717c/41598_2021_93250_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a51d/8249535/b472b641bf89/41598_2021_93250_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a51d/8249535/377562c4686c/41598_2021_93250_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a51d/8249535/691910325ee7/41598_2021_93250_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a51d/8249535/0743e2c32249/41598_2021_93250_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a51d/8249535/4bf4689f717c/41598_2021_93250_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a51d/8249535/2617d3e302e1/41598_2021_93250_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a51d/8249535/b472b641bf89/41598_2021_93250_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a51d/8249535/377562c4686c/41598_2021_93250_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a51d/8249535/691910325ee7/41598_2021_93250_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a51d/8249535/0743e2c32249/41598_2021_93250_Fig6_HTML.jpg

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