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Correlation between Differential Expression of m6A and Prognosis of Uterine Corpus Endometrial Carcinoma

: UCEC, known as Uterine corpus endometrial carcinoma, is one of the most common types of gynecologic malignancy worldwide. Notwithstanding great focus has been put on the treatments of UCEC recently, both the incidence rate and mortality rate of UCEC are still increasing. The 5-year overall survival (OS) rate in early-stage UCEC ranges from 74 to 91%. Chemotherapy and hormone therapy are viable treatment options for patients with metastasis or recurrence. However, not all patients benefit from these. For advanced stage III or IV disease, the 5-year OS rates are 57–66% and 20–26%, respectively. The most common form of post transcriptional RNA modification, N6-methyladenine (m6A) has attracted increasing interest in cancer pathogenesis and progression. The differential expression of m6A could be an important clue in the area of prognosis. Thus, we aimed to identify the correlations between m6A expressions and prognosis of UCEC, and build a prognostic gene signature in UCEC. In this study, firstly, we filtrated and analysed the gene expression in RNA sequence and the matched clinical information of UCEC patients from The Cancer Genome Atlas (TCGA) database. Second, we determined that several m6A regulatory genes had a significant negative impact on patient survival. By using the Statistical Product and Service Solutions (SPSS) and R-studio, we built both a univariate Cox regression model and a multivariate Cox regression model. In the end, we discovered these four m6A gene expressions that had a significant association with the UCEC patient survival data: VIRMA, METTL14, HNRNPC and FTO. Whereas the multivariate Cox regression model’s analysis suggested that risk score might be an independent prognostic indicator for the overall survival of patients with UCEC (p-value ¡0.05). In conclusion, m6A regulator could be an effective and reliable biomarker for future UCEC prognosis prediction and it deserves further research.