纯数据挖掘的ceRNA貌似快成为下一个meta了

自从2011年ceRNA问世以来，关于ceRNA的文章出现了爆炸性的增长，无论是湿的，还是干的，特别是基于TCGA数据库构建的。我们找了一下相关的文章，TCGA中做能做的癌症类型貌似都被做了。不妨我们可以看一下：

1、Comprehensive analysis of lncRNA-associated ceRNA network in colorectal cancer（https://doi.org/10.1016/j.bbrc.2018.11.151）

2、Reconstruction and analysis of the aberrant lncRNA-miRNA-mRNA network based on competitive endogenous RNA in CESC (DOI: 10.1002/jcb.26850)

3、Construction and analysis of mRNA, miRNA,lncRNA, and TF regulatory networks reveal the key genes associated with prostate cancer（https://doi.org/10.1371/journal.pone.0198055）

4、Comprehensive analysis of a long noncoding RNA-associated competing endogenous RNA network in colorectal cancer（http://dx.doi.org/10.2147/OTT.S158309）

5、Comprehensive analysis of competitive endogenous RNAs network associated with head and neck squamous cell carcinoma

（DOI:10.1038/s41598-018-28957-y）

6、Comprehensive analysis of long noncoding RNA-associated competing

endogenous RNA network in cholangiocarcinoma（https://doi.org/10.1016/j.bbrc.2018.10.186）

7、Identification of ceRNA network based on a RNA‑seq shows 

prognostic lncRNA biomarkers in human lung adenocarcinoma（DOI: 10.3892/ol.2018.9336）

8、Prediction of candidate RNA signatures for recurrent ovarian cancer prognosis by the construction of an integrated competing endogenous RNA network（DOI: 10.3892/or.2018.6707）

9、Identification of Six Potentially Long Noncoding RNAs as

Biomarkers Involved Competitive Endogenous RNA in Clear

Cell Renal Cell Carcinoma（https://doi.org/10.1155/2018/9303486）

。。。。

剩下的我们就不列出来了，实在太多了。

其实利用TCGA ceRNA这个套路也是非常简单的

1、下载TCGA的mRNA,miRNA，lncRNA

2、分别对mRNA,miRNA，lncRNA做差异分析

3、构建lncRNA-miRNA网络（网站预测，然后筛选出差异的lncRNA,miRNA）

4、构建miRNA-mRNA网络（网站预测，然后筛选出差异的miRNA,mRNA)

5、构建ceRNA