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

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 cancer, the anoctamin 1 (ANO1) gene was selected for further analysis. The Chi-squared test indicated that ANO1 was significantly associated with age (P=0.047), histological type (P<0.001), clinical stage (P<0.001), pathological grade (P<0.001) and positive peritoneal washing (P=0.001) of endometrial carcinoma. Kaplan-Meier survival analysis revealed that a high level of ANO1 was significantly associated with a good prognosis for endometrial cancer patients. Univariate and multivariate Cox regression analysis indicated that ANO1 is an independent prognostic factor in endometrial cancer. Further characterization of the most relevant module containing ANO1 with the database for annotation, visualization and integrated discovery tool suggested that ANO1 is involved in various pathways, including metabolic pathways. The present study suggests that ANO1 may be a potential marker for good prognosis in endometrial cancer.