Research Paper Volume 12, Issue 18 pp 18453—18475

A newly defined risk signature, consisting of three m6A RNA methylation regulators, predicts the prognosis of ovarian cancer

Lili Fan1, *, , Ying Lin2, *, , Han Lei1, , Guang Shu3, , Liuer He3, , Zhipeng Yan4, , Hai Rihan3, , Gang Yin1, ,

  • 1 Department of Pathology, Xiangya Hospital, School of Basic Medical Sciences, Central South University, Changsha, Hunan Province, China
  • 2 Department of Immunology, School of Basic Medical Sciences, Central South University, Changsha, Hunan Province, China
  • 3 School of Basic Medical Sciences, Central South University, Changsha, Hunan Province, China
  • 4 Hunan Cancer Hospital, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan Province, China
* Equal contribution

Received: January 11, 2020       Accepted: July 20, 2020       Published: September 20, 2020      

https://doi.org/10.18632/aging.103811
How to Cite

Copyright: © 2020 Fan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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.

Abbreviations

m6A: N6-methyladenosine; OC: ovarian cancer; IGF2BP1: insulin-like growth factor 2 mRNA binding protein 1; VIRMA: vir like m6A methyltransferase associated; ZC3H13: zinc finger CCCH-type containing 13; GSEA: Gene Set Enrichment Analysis; m1A: N1-methyladenosine; m5C: 5-methylcytosine; UTR: untranslated region; MTC: methyltransferase complex; YTH: YT521-B homology; eIF3: eukaryotic initiation factor 3; IGF2BPs: Insulin-like growth factor 2 mRNA-binding proteins; HNRNP: heterogeneous nuclear ribonucleoprotein; EMT: epithelial to mesenchymal transition; PARPi: poly ADP-ribose polymerase inhibitors; TCGA: The Cancer Genome Atlas; GTEx: Genotype-Tissue Expression; CDKs: cyclin dependent kinases; POLR2: RNA polymerase II; MED: mediator; PCA: principal component analysis; OS: overall survival; CDF: cumulative distribution function; LASSO: the least absolute shrinkage and selection operator; ROC: receiver operating characteristic; AUC: area under the curve; CPTAC: Clinical Proteomic Tumor Analysis Consortium; OV: ovarian serous cystadenocarcinoma; CCLE: Cancer Cell Line Encyclopedia; KEGG: Kyoto Encyclopedia of Genes and Genomes; GEO: Gene Expression Omnibus; PPI: protein-protein interactions; STRING: Search Tool for the Retrieval of Interacting Genes.