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表观遗传变异分析助力胃癌生物标志物的发现。

Epigenetic Variation Analysis Leads to Biomarker Discovery in Gastric Adenocarcinoma.

作者信息

Zhang Yan, Guo Dianjing

机构信息

State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong, Shatin, China.

出版信息

Front Genet. 2020 Dec 8;11:551787. doi: 10.3389/fgene.2020.551787. eCollection 2020.

DOI:10.3389/fgene.2020.551787
PMID:33363566
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7753064/
Abstract

As one of the most common malignant tumors worldwide, gastric adenocarcinoma (GC) and its prognosis are still poorly understood. Various genetic and epigenetic factors have been indicated in GC carcinogenesis. However, a comprehensive and in-depth investigation of epigenetic alteration in gastric cancer is still missing. In this study, we systematically investigated some key epigenetic features in GC, including DNA methylation and five core histone modifications. Data from The Cancer Genome Atlas Program and other studies (Gene Expression Omnibus) were collected, analyzed, and validated with multivariate statistical analysis methods. The landscape of epi-modifications in gastric cancer was described. Chromatin state transition analysis showed a histone marker shift in gastric cancer genome by employing a Hidden-Markov-Model based approach, indicated that histone marks tend to label different sets of genes in GC compared to control. An additive effect of these epigenetic marks was observed by integrated analysis with gene expression data, suggesting epigenetic modifications may cooperatively regulate gene expression. However, the effect of DNA methylation was found more significant without the presence of the five histone modifications in our study. By constructing a PPI network, key genes to distinguish GC from normal samples were identified, and distinct patterns of oncogenic pathways in GC were revealed. Some of these genes can also serve as potential biomarkers to classify various GC molecular subtypes. Our results provide important insights into the epigenetic regulation in gastric cancer and other cancers in general. This study describes the aberrant epigenetic variation pattern in GC and provides potential direction for epigenetic biomarker discovery.

摘要

作为全球最常见的恶性肿瘤之一,胃腺癌(GC)及其预后仍未得到充分了解。多种遗传和表观遗传因素已被指出与GC的致癌作用有关。然而,对胃癌表观遗传改变的全面深入研究仍然缺失。在本研究中,我们系统地研究了GC中的一些关键表观遗传特征,包括DNA甲基化和五种核心组蛋白修饰。收集了来自癌症基因组图谱计划和其他研究(基因表达综合数据库)的数据,并使用多变量统计分析方法进行分析和验证。描述了胃癌中表观修饰的格局。染色质状态转换分析通过基于隐马尔可夫模型的方法显示了胃癌基因组中的组蛋白标记转移,表明与对照相比,组蛋白标记在GC中倾向于标记不同的基因集。通过与基因表达数据的综合分析观察到这些表观遗传标记的累加效应,表明表观遗传修饰可能协同调节基因表达。然而,在我们的研究中发现,在没有五种组蛋白修饰的情况下,DNA甲基化的影响更为显著。通过构建蛋白质-蛋白质相互作用网络,确定了区分GC与正常样本的关键基因,并揭示了GC中致癌途径的不同模式。其中一些基因还可作为潜在的生物标志物来分类各种GC分子亚型。我们的结果为胃癌及其他癌症的表观遗传调控提供了重要见解。本研究描述了GC中异常的表观遗传变异模式,并为表观遗传生物标志物的发现提供了潜在方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7ca/7753064/714102424586/fgene-11-551787-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7ca/7753064/e24532a843d0/fgene-11-551787-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7ca/7753064/d0ff78b54fbe/fgene-11-551787-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7ca/7753064/1b9adca77288/fgene-11-551787-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7ca/7753064/7746d58f692e/fgene-11-551787-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7ca/7753064/714102424586/fgene-11-551787-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7ca/7753064/e24532a843d0/fgene-11-551787-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7ca/7753064/d0ff78b54fbe/fgene-11-551787-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7ca/7753064/1b9adca77288/fgene-11-551787-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7ca/7753064/7746d58f692e/fgene-11-551787-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7ca/7753064/714102424586/fgene-11-551787-g005.jpg

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Mutational landscape of gastric cancer and clinical application of genomic profiling based on target next-generation sequencing.
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Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.Metascape 为系统水平数据集的分析提供了面向生物学家的资源。
Nat Commun. 2019 Apr 3;10(1):1523. doi: 10.1038/s41467-019-09234-6.
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STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.STRING v11:具有增强覆盖范围的蛋白质-蛋白质相互作用网络,支持在全基因组实验数据集的功能发现。
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