Combination of meta-analysis and graph clustering to identify prognostic markers of ESCC
AUTOR(ES)
Gao, Hongyun, Wang, Lishan, Cui, Shitao, Wang, Mingsong
FONTE
Genetics and Molecular Biology
DATA DE PUBLICAÇÃO
2012
RESUMO
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.
Documentos Relacionados
- Prognostic value of the neutrophil to lymphocyte ratio in lung cancer: A meta-analysis
- Prognostic role of neutrophil-to-lymphocyte ratio to laryngeal squamous cell carcinoma: a meta-analysis
- Clinicopathological and prognostic significance of COX-2 in glioma patients: a meta-analysis
- Meta-analysis - perineural invasion as prognostic factor in rectal cancer
- Prognostic role of vascular endothelial growth factor in hepatocellular carcinoma: systematic review and meta-analysis