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 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.

Construction of T cell exhaustion model for predicting survival and immunotherapy effect of bladder cancer based on WGCNA

“…According to the soft threshold and the scale-free fitting index, it was found that there was no intersection point between power and the horizontal line (correlation 0.8) ( Figure 2B ). Referring to the previously reported method ( 26 ), the estimated value of powerEstimate equal to 5 is selected as the β value. It can be seen that k is negatively correlated with p(k) (correlation coefficient 0.89>0.8) ( Figure 2C ), indicating that the selected β value is suitable for the establishment of a gene scale-free network.…”

Upregulation of microRNA miR-141-3p and its prospective targets in endometrial carcinoma: a comprehensive study

“…Endometrial carcinoma (EC), also known as uterine corpus endometrial carcinoma (UCEC), is a malignant tumor that originates from the epithelium in the endometrium. As one of the most common gynecological malignant tumors, EC threatens women's health and is also the third greatest cause of cancer-related death in women [1][2][3][4]. Although the statistics are incomplete, an estimated 65,620 new cases and 12,590 deaths have occurred globally in 2020 [5].…”

Identification of a MicroRNA Signature Associated With Lymph Node Metastasis in Endometrial Endometrioid Cancer

“…Recently, WGCNA analysis is widely used to construct the modules of co-expression genes that relate to prognosis or other clinical outcomes. For instance, researchers found that Prostaglandin D2 Synthase (PTGDS) predicted poor survival, while ANO1 might be a potential marker for good prognosis in endometrial cancer by WGCNA ( Wang et al, 2019 ; Zou et al, 2020 ). Besides, an increasing number of research apply the machine learning into the biomedical field.…”