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

Abstract

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.

Keywords

• esophageal squamous cell carcinoma
• meta-analysis
• graph clustering