100篇泛癌研究文献解读之原位癌症和转移癌症的区别

为了分析不同类型、组织起源肿瘤的共性、差异以及新课题。TCGA于2012年10月26日-27日在圣克鲁兹,加州举行的会议中发起了泛癌计划。参考:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6000284/ 为此我也录制了系列视频教程在:TCGA知识图谱视频教程(B站和YouTube直达)

发表于普通杂志:Mol Cancer Res. 2019 Feb; 文章是:Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across The Cancer Genome Atlas. 系统性的研究了TCGA数据库的11种癌症的 4,473 primary tumor samples and 395 tumor metastasis samples ,发现不同癌症的 转移和原位癌的表达差异都很大,不同癌症有一些overlap情况,当然除了比较mRNA-seq数据,还有miRNAs,RPPA, DNA methylation 的数据的比较探索。还利用了 Gene expression data (TPM values) from GTEx Analysis version 7 数据库,也有一些GEO数据库的,比如GSE110590。

文献解读属于100篇泛癌研究文献系列,首发于:http://www.bio-info-trainee.com/4132.html

差异表达

样本量如此悬殊,作者居然也做了差异分析

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作者采用了多种统计学算法来寻找差异基因:

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不同癌症的上下调基因的overlap情况如下:

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不同癌症的上下调基因集的overlap情况:

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TCGA数据库和GEO数据库的比较

如下:

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蛋白质芯片数据的泛癌比较

RPPA proteomic data involved 218 features and four cancer types (BRCA, PCPG, SKCM, and THCA) with metastasis profiles.

下面是其中一个例子,蛋白和编码其的基因都是显著差异

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miRNA表达数据的泛癌比较

For each cancer type examined, the correlations with metastasis for RPPA (Reverse Phase Protein Array) and microRNA features represented in TCGA. Also included are mRNA:microRNA pairings, as defined by both a previously identified miRNA-target interaction (as cataloged by miRTarBase Release 7.0) and significant differential expression in metastasis (FDR<0.1) for both mRNA and microRNA, in opposite directions from each other (mRNA up:microRNA down or mRNA down:microRNA up).

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DNA甲基化芯片数据的泛癌比较

For each cancer type, top metastasis-associated DNA methylation CpG Island features, selected using Pearson’s correlation (logit-transformed values) with Storey and Tibshirini estimate of False Discovery Rate (FDR) of <10%. Differential mRNA statistics (metastasis versus primary) corresponding to the associated genes are also included.

主要关注:CpG Islands (by Illumina 450K array, 150K CpG Island probes)

图展示差异甲基化位点和差异表达基因的overlap情况,如下;

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定下 metastasis signature

这里并没有使用 miRNAs,RPPA, DNA methylation 的数据,就是纯粹的mRNA-seq数据来获得的 metastasis signature

A set of 821 genes were found significant (FDR < 10%) with same direction of change for two or more cancer types

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生存分析说明临床意义

比较奇怪的是,这里并没有展示作者自己的821个基因的metastasis signature 在TCGA的生存分析效果,反而是用前列腺癌的GEO数据。

The TCGA-derived prostate cancer metastasis signature in particular could define a subset of aggressive primary prostate cancer.

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补充材料

  • Supplementary Information - Supplementary Figures and Description of Data Files
  • Table S1 - TCGA cancer cases and molecular profiles examined in this study.
  • Table S2 - For all genes represented in TCGA RNA-seq datasets, the mRNA-level correlations with metastasis for each cancer type.
  • Table S3 - For each cancer type, top metastasis-associated mRNA features, selected using Pearson's correlation on log-transformed data with Storey and Tibshirini estimate of False Discovery Rate (FDR) of <10%.
  • Table S4 - Gene Ontology (GO) term associations for the top metastasis-associated genes for each cancer type.
  • Table S5 - For each cancer type examined, the correlations with metastasis for RPPA (Reverse Phase Protein Array) and microRNA features represented in TCGA.
  • Table S6 - For each cancer type, top metastasis-associated DNA methylation CpG Island features, selected using Pearson's correlation (logit-transformed values) with Storey and Tibshirini estimate of False Discovery Rate (FDR) of <10%. Differential mRNA statistics (metastasis versus primary) corresponding to the associated genes are also included.

后记

从流程图来看,本研究并不复杂,也很容易复现出来, 关键是如何提出还有如何挑选数据集。

本文献解读属于100篇泛癌研究文献系列,首发于:http://www.bio-info-trainee.com/4132.html

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