Exploration of gene functions for esophageal squamous cell carcinoma using network-based guilt by association principle
Braz. j. med. biol. res
;
51(6): e6801, 2018. tab, graf
Article
in English
| LILACS
| ID: biblio-889107
ABSTRACT
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 analysis based on a known confirmed database and DEGs. Eventually, the optimal gene functions were predicted using GBA algorithm based on the area under the curve (AUC) for each GO term. Overall, 43 DEGs and 67 GO terms were gained for subsequent analysis. GBA predictions demonstrated that 13 GO functions with AUC>0.7 had a good classification ability. Significantly, 6 out of 13 GO terms yielded AUC>0.8, which were determined as the optimal gene functions. Interestingly, there were two GO categories with AUC>0.9, which included cell cycle checkpoint (AUC=0.91648), and mitotic sister chromatid segregation (AUC=0.91597). Our findings highlight the clinical implications of cell cycle checkpoint and mitotic sister chromatid segregation in ESCC progression and provide the molecular foundation for developing therapeutic targets.
Full text:
Available
Index:
LILACS (Americas)
Main subject:
Esophageal Neoplasms
/
Carcinoma, Squamous Cell
/
Gene Expression Regulation, Neoplastic
/
Computational Biology
/
Gene Expression Profiling
/
Gene Regulatory Networks
Type of study:
Prognostic study
/
Risk factors
Limits:
Humans
Language:
English
Journal:
Braz. j. med. biol. res
Journal subject:
Biology
/
Medicine
Year:
2018
Type:
Article
Affiliation country:
China
Institution/Affiliation country:
Daqing Oilfield General Hospital/CN
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