100篇泛癌研究文献解读之原位癌症和转移癌症的区别

Posted on by

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
image-20190507131612495
作者采用了多种统计学算法来寻找差异基因：
image-20190507134000442
不同癌症的上下调基因的overlap情况如下：
image-20190507131743142
不同癌症的上下调基因集的overlap情况：
image-20190507131912907

TCGA数据库和GEO数据库的比较

如下：
image-20190507131953584

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
image-20190507134548815

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).
image-20190507134502856

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；
image-20190507134712920

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
image-20190507134357603

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.
image-20190507134750368

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

​

Comments are closed.