GSE35982, BMC, 2012
TCGA, BMC, 2012
(GSE11016,GSE12105,GSE16441,GSE23085), BMC, 2013
TCGA, Nat Commun, 2015
TCGA, Molecular Oncology, 2015
GEO, International journal of cancer, 2012
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0137476
TCGA, PLOS ONE, 2014
TCGA, PLOS ONE, 2014
(GSE 4271, GSE4412), PLOS ONE, 2014
TCGA, PLOS ONE, 2014
首先指明一个现象,多种癌症里面,尤其是HNSCC中,STAT3基因的第705个酪氨酸(Y,tyrosine)的磷酸化导致它被过度激活。
接着针对STAT3基因,抓取HNSCC相关的数据:Mutation, mRNA expression, promoter methylation, and copy number alteration data were extracted from TCGA and examined in the context of pSTAT3(Y705) protein expression.
进而从表达量的相关性找到了1279 相关genes。
TCGA,2015 PLOS ONE
从题目就知道他们做了什么分析:In this study, the miRNA profiles in 327 HCC patients, including 327 tumor and 43 adjacent non-tumor tissues, from The Cancer Genome Atlas (TCGA) Liver hepatocellular carcinoma (LIHC) were analyzed.
找到了差异表达的miRNA(Differentially expressed miRNAs (DEmiRNA) ),然后用miRNA pathway analysis工具miRPath做了功能分析。当然,生存分析也必不可少啦。
TCGA,2012 PLOS ONE
这个稍微有点不同,作者用自己的2009年发表的iCluster工具来灌水,下载了TCGA glioblastoma (GBM) 的3种数据,iCluster was applied using 1,599 copy number features, 1,515 DNA methylation features, and 1,740 expression features。
用iCluster把GBM分成了3个亚型。
再与前人单纯的用表达数据,或者甲基化数据的分类做了比较,还跟PCA比较,必须是自己的好呀。
TCGA,2016 PLOS ONE
用的是公共数据,用了多种数据来把样本分类,自己开发了一个简单粗糙的分类方法。
https://www.biomedcentral.com/search?query=TCGA
http://journals.plos.org/plosone/search?filterJournals=PLoSONE&q=tcga
http://www.nature.com/search?journal=srep&q=tcga
http://www.impactjournals.com/oncotarget/index.php journal=oncotarget&page=search&op=advanced