A seven-m6A regulator-related CpG site-based prognostic signature for endometrial carcinoma

• Published in: Medicine (Baltimore) Vol. 100; no. 29; p. e26648
• Main Authors: Zhang, Xiang, Pang, Xuecheng, Huang, Yue, Qian, Sumin
• Format: Journal Article
• Language: English
• Published: United States Lippincott Williams & Wilkins 23-07-2021
• Subjects: Adenosine - analogs & derivatives; Adenosine - genetics; Adenosine - metabolism; Biomarkers, Tumor - genetics; Biomarkers, Tumor - metabolism; China; Clinical Trial/Experimental Study; Endometrial Neoplasms - genetics; Endometrial Neoplasms - metabolism; Endometrial Neoplasms - mortality; Female; Gene Expression Regulation, Neoplastic; Humans; Methylation; Middle Aged; Nomograms; Predictive Value of Tests; Prognosis; Proportional Hazards Models; RNA - genetics; Survival Analysis; China
• ISSN: 0025-7974; 1536-5964
• DOI: 10.1097/MD.0000000000026648

Endometrial carcinoma (EC) has become a common gynecologic malignancy with a high mortality. The m6A regulators have been identified to be closely associated with multiple human cancers including EC. However, the CpG methylation signature related to m6A regulators in EC remains unclear.
The methylation profiles of EC patients including cancer samples and adjacent normal samples were obtained from The Cancer Genome Atlas (TCGA) database. The CpG sites in 20 m6A regulators were identified. Univariate Cox regression and LASSO Cox regression analysis were used to screen key CpG sites which were located at m6A regulators and significantly related to the prognosis of EC. The predictive model for EC prognosis was constructed, and multivariate Cox regression analysis was applied to explore whether the risk score derived from the model could function as an independent signature for EC prognosis. Meanwhile, a nomogram model was constructed by combing the independent prognostic signatures for prediction of the long-term survival in EC patients.
A total of 396 CpG sites located at 20 m6A regulators were identified. A specific predictive model for EC prognosis based on 7 optimal CpG sites was constructed, which presented good performance in prognosis prediction of EC patients. Moreover, risk score was determined to be an independent signature both in the training set and validation set. By bringing in three independent prognostic factors (age, risk score, and TNM stage), the nomogram was constructed and could effectively predict the 3- and 5-year survival rates of EC patients.
Our study suggested that the CpG sites located at m6A regulators might be considered as potential prognostic signatures for EC patients.