Identification of candidate target genes for endometrial cancer, such as ANO1, using weighted gene co-expression network analysis.

ABSTRACT: Network-based systems biology has become an important method for analysis of high-throughput gene expression data and gene function mining. The aim of the present study was to implement a weighted gene co-expression network analysis to screen genes that were significantly correlated with the clinical phenotype of endometrial cancer based on data from The Cancer Genome Atlas. By using the function 'pickSoftThreshold' in R software, the optimum soft thresholding power was determined to be 4. Subsequently, a total of 2,414 expressed genes were identified among 19,791 genes from 506 samples, which were divided into 24 modules according to the different expression patterns. After analyzing the correlation between the gene expression in these 24 modules and the clinical phenotype of endometrial

SUBMITTER: Wang F 

PROVIDER: S-EPMC6307379 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature