只要是按照primary site来定义的肿瘤都是可以进行分子分型

Jimmy

按照primary site来定义肿瘤本来就过时了！
那么现在有了那么多二代测序数据，我粗略的算了一下，仅仅是分子分型，可以灌水(6+6)*5*33篇文章。
其中6+6是tcga的数据类型，5是目前用的多的几个统计学方法，33是TCGA的肿瘤类型数目。

比如说HCC的一篇文章：http://journals.plos.org/plosone ... ournal.pone.0165457
用cluster of cluster这个统计学方法对6种数据的整合，对HCC进行分子分型

Hepatocellular carcinoma is one of the most heterogeneous cancers, as reflected by its multiple grades and difficulty to subtype.
那么就可以整合TCGA里面的HCC样本已有的opy number variation, DNA methylation, mRNA, and miRNA data 进行分型。

当然，不一定需要整合那么多数据，可以就选择miRNA或者mRNA找一下signature也行，反正是灌水嘛，运气好就可以碰到一些得过且过的编辑还有审稿人。比如：

• Identifying microRNA/mRNA dysregulations in ovarian cancer
  

TCGA,BMC,2012

Jimmy

再比如这个：http://journals.plos.org/plosone ... ournal.pone.0128628  文章，A Seven-microRNA Expression Signature Predicts Survival in Hepatocellular Carcinoma

也是非常简单：
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.

The associations of the differentially expressed miRNAs with patient survival and other clinical characteristics were examined with t-test and Cox proportional regression model.

Finally, a tumor-specific miRNA signature was generated and examined with Kaplan–Meier survival, univariate\multivariate Cox regression analyses and KEGG pathway analysis.
用公共的miRNA表达矩阵，找差异，然后把差异结果拿起来做生存分析富集分析即可

Jimmy

Integrative Subtype Discovery in Glioblastoma Using iCluster ：http://journals.plos.org/plosone ... ournal.pone.0035236

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。还跟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