A newly defined risk signature, consisting of three m 6 A RNA methylation regulators, predicts the prognosis of ovarian cancer

TLDR: Wang et al. as mentioned in this paper analyzed the expression of m6A RNA methylation regulators in ovarian cancer and their correlation with prognosis remain elusive, and they demonstrated that the 18 central m6a RNA methylization regulators were expressed differently between ovarian cancer (OC) and normal tissues.

Abstract: N6-methyladenosine (m6A) RNA methylation, involved in cancer initiation and progression, is dynamically regulated by the m6A RNA methylation regulators. However, the expression of m6A RNA methylation regulators in ovarian cancer and their correlation with prognosis remain elusive. Here, we demonstrated that the 18 central m6A RNA methylation regulators were expressed differently between ovarian cancer (OC) and normal tissues. By applying consensus clustering, all ovarian cancer patient cases can be divided into three subgroups (cluster1/2/3) based on overall expression levels of all 18 m6A RNA methylation regulators. We systematically analyzed the prognostic value of transcription levels of 18 m6A RNA methylation regulators in ovarian cancer and found that insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1), vir like m6A methyltransferase associated (VIRMA), and zinc finger CCCH-type containing 13 (ZC3H13) yield the highest scores for predicting the prognosis of ovarian cancer. Accordingly, we derived a risk signature consisting of transcription levels of these three selected m6A RNA methylation regulators as an independent prognostic marker for OC and validated our findings with data derived from a different ovarian cancer cohort. Moreover, by the Gene Set Enrichment Analysis (GSEA), we demonstrated that the three selected regulators were all correlated with pathways in cancer and WNT signaling pathways. In conclusion, m6A RNA methylation regulators are vital participants in ovarian cancer pathology; and IGF2BP1, VIRMA, and ZC3H13 mRNA levels are valuable factors for prognosis prediction and treatment strategy development.