The pan-cancer analysis of the two types of uterine cancer uncovered clinical and prognostic associations with m6A RNA methylation regulators. (24th March 2021) - Zou, Zhilin
Abstract : IGF2BP1 was enriched in multiple GO and KEGG pathways in two types of uterine cancer. The effectiveness of the constructed prognostic model was verified by ROC curves. Abstract : The role of m6A RNA methylation modification in uterine cancer has not been studied until now. We explored the relationship between m6A regulators and clinical characteristics and prognosis in uterine corpus endometrial carcinoma (UCEC) and uterine carcinosarcoma (UCS) with the data from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). We found that several regulators were up-regulated or down-regulated in the two types of cancer, and identified two cluster subgroups with statistically significant differences in pathological grade, age and survival rate. Multivariate Cox regression analysis showed that methyltransferase-like 16 (METTL16) had a low hazard ratio in UCEC. We used several regulators to construct a risk signature and divided tumor patients into high-risk and low-risk groups, and found that the high-risk group had significantly lower survival rates. Independent prognostic analysis showed that the insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1) was a pan-prognostic regulator of uterine cancer. This result was further verified in the Gene Expression Omnibus (GEO) database. Based on above results, we conducted gene-ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to further reveal a potential mechanism for m6A RNA methylation regulators. We found that IGF2BP1 was enriched in gene expression (GO:0010467), poly(A) RNA binding (GO:0044822) and RNA binding (GO:0003723) pathways. KEGG analysis showed that IGF2BP1 was enriched in microRNAs in the cancer (hsa05206) pathway. Our study systematically elucidated the relationship between m6A RNA methylation regulators and uterine cancer and constructed the risk signature that can predict the prognosis and clinicopathological characteristics of uterine cancer.