Combination of meta-analysis and graph clustering to identify prognostic markers of ESCC

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.