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| Wu,Wei; Huang,Bo; Yan,Yan; Zhong,Zhi-Qiang. |
| Gene networks have been broadly used to predict gene functions based on guilt by association (GBA) principle. Thus, in order to better understand the molecular mechanisms of esophageal squamous cell carcinoma (ESCC), our study was designed to use a network-based GBA method to identify the optimal gene functions for ESCC. To identify genomic bio-signatures for ESCC, microarray data of GSE20347 were first downloaded from a public functional genomics data repository of Gene Expression Omnibus database. Then, differentially expressed genes (DEGs) between ESCC patients and controls were identified using the LIMMA method. Afterwards, construction of differential co-expression network (DCN) was performed relying on DEGs, followed by gene ontology (GO) enrichment... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Esophageal squamous cell carcinoma; Gene oncology; Guilt by association; Differentially expressed genes; Area under the curve. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2018000600601 |
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| Gao,Hongyun; Wang,Lishan; Cui,Shitao; Wang,Mingsong. |
| 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... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Esophageal squamous cell carcinoma; Meta-analysis; Graph clustering. |
| Ano: 2012 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572012000300021 |
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