%0 Journal Article
%T Combination of meta-analysis and graph clustering to identify prognostic markers of ESCC
%A Gao
%A Hongyun
%A Wang
%A Lishan
%A Cui
%A Shitao
%A Wang
%A Mingsong
%J Genetics and Molecular Biology
%D 2012
%I Sociedade Brasileira de Gen¨¦tica
%R 10.1590/S1415-47572012000300021
%X esophageal squamous cell carcinoma (escc) is one of the most malignant gastrointestinal cancers and occurs at a high frequency rate in china and other asian countries. recently, several molecular markers were identified for predicting escc. notwithstanding, additional prognostic markers, with a clear understanding of their underlying roles, are still required. through bioinformatics, a graph-clustering method by dpclus was used to detect co-expressed modules. the aim was to identify a set of discriminating genes that could be used for predicting escc through graph-clustering and go-term analysis. the results showed that cxcl12, cyp2c9, tgm3, mal, s100a9, emp-1 and sprr3 were highly associated with escc development. in our study, all their predicted roles were in line with previous reports, whereby the assumption that a combination of meta-analysis, graph-clustering and go-term analysis is effective for both identifying differentially expressed genes, and reflecting on their functions in escc.
%K esophageal squamous cell carcinoma
%K meta-analysis
%K graph clustering.
%U http://www.scielo.br/scielo.php?script=sci_abstract&pid=S1415-47572012000300021&lng=en&nrm=iso&tlng=en