Background: Endometrial cancer (EC) is the sixth most common cancer in women globally. It has been found that the expression levels of m6A regulators can be potentially used for prognostic stratification in some cancers, but the role of m6A regulators in EC prognosis remains unclear. Methods: The data of 584 EC samples were downloaded from The Cancer Genome Atlas and the mRNA expression profiles of 20 m6A regulators were analyzed, followed by functional enrichment analysis, immune infiltration analysis, and least absolute shrinkage and selection operator method-COX regression analysis. Results: The mRNA expression levels of 20 m6A regulators were significantly different between cancer samples across different grades. The 548 EC samples could be clearly divided into 2 clusters. Kaplan-Meier survival analysis proved that these two groups had highly different overall survival probabilities. Besides, the univariate regression analysis further reserved eight genes related to overall survival from the 20 m6A regulators. We established a prognostic signature including two genes, that is, IGF2BP1 and YTHDF3, that showed a strong ability for stratifying prognostically different EC patients. We identified 3239 differentially expressed genes between the high- and low-risk groups, involving in multiple biological processes and signaling pathways. Meanwhile, 6 differentially infiltrated immune cell types between the high- and low-risk groups could effectively distinguish the high- and low-risk EC groups. The expressions of immune checkpoints were different between high- and low-risk EC patients. Conclusion: We first report the prognostic role of m6A regulators in EC, which should contribute to a better understanding of the underlying mechanisms of EC pathogenesis and progression.

Background Endometrial carcinoma (EC) has become a common gynecologic malignancy with a high mortality. The m 6 A regulators have been identified to be closely associated with multiple human cancers including EC. However, the CpG methylation signature related to m 6 A regulators in EC remains unclear.Method 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 m 6 A regulators were identified. Univariate Cox regression and LASSO Cox regression analysis were used to screen key CpG sites which were located at m 6 A 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.Results A total of 396 CpG sites located at 20 m 6 A 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. ConclusionOur study suggested that the CpG sites located at m 6 A regulators might be considered as potential prognostic signatures for EC patients.

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Cuproptosis is a new modality of cell death regulation that is currently considered as a new cancer treatment strategy. However, cuproptosis-related lncRNAs (CRLs) have an unclear relationship with endometrial cancer (EC). In this study, a total of 906 CRLs were identified, and 7 specific cuproptosis-related lncRNAs (AL807761.3, AF131215.7, AC008073.2, AC009229.1, CDKN2A.DT, LINC01615, LINC01166) were selected to conduct a risk model. Patients were divided into high- and low-risk groups according to the median of risk score. The prognosis of the high-risk group was worse than that of the low-risk group, and the predictive accuracy was high (AUC = 0.781), indicating the good reliability and specificity of our risk model. According to Gene Set Variation Analysis (GSVA) and GSEA, both metabolism and cytoskeleton have CRL participation. In addition, we found that the CRLs-related scores were associated with the ESTIMATE score. Stratified survival analysis also revealed that the risk signature have has a high prediction accuracy among people with different clinicopathological characteristics. Further in vitro experimental validation indicated that LINC01615 may promote the invasion of EC cells during progression. The efficient risk model based on seven CRLs has a high prognostic accuracy, and LINC01615 may act as a novel biomarker and therapeutic target for EC patients.

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