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Systematic Description of the Expression and Prognostic Value of M6A Regulators in Human Ovarian Cancer

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Systematic Description of the Expression and Prognostic Value of M6A Regulators in Human Ovarian Cancer Yuwei Chen Fujian Medical University Fujian Cancer Hospital Yang Sun ( [email protected] ) Fujian Cancer Hospital https://orcid.org/0000-0003-4224-8832 Xinbei Chen Fujian Medical University Fujian Cancer Hospital Siming Li Fujian Medical University Fujian Cancer Hospital Hongmei Xia Fujian Cancer Hospital Research Keywords: m6A regulators, m6A, ovarian cancer, Prognostic values, bioinformatics analysis. Posted Date: December 22nd, 2020 DOI: https://doi.org/10.21203/rs.3.rs-131682/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Systematic description of the expression and prognostic value of m6A regulators in human ovarian cancer 1 Yuwei Chen1, Yang Sun1*, Xinbei Chen1, Siming Li1, Hongmei Xia1 2 * Correspondence: 3 Yang Sun 4 [email protected] 5 Total words：5253 6 Total Figures and Tables: 11 Figures and 3 Tables 7 Keywords: m6A regulators, m6A, ovarian cancer, Prognostic values, 8 bioinformatics analysis. 9 Abstract 10 Background: N6-methyladenine (m6A) methylation, known as a kind of RNA 11 methylation regulator, has become a hotspot for research in the life sciences in recent 12 years. The existing studies revealed that m6A regulators helped to regulate the 13 progression of several malignant tumors. However, the expression mode and 14 prognostic value of m6A regulators in ovarian cancer have not been fully elucidated. 15 Methods: ONCOMINE, GEPIA, Human protein atlas, Kaplan-meier, UALCAN, 16 cBioPortal, GeneMANIA, DAVID, Sring, and Metascape were utilized in this study. 17 Results: In this study, through a comprehensive use of multiple database systems, 18 m6A regulators-induced mRNA and protein expression levels and their prognostic 19 value in the epithelial ovarian cancer of various histological types were analyzed. 20 Moreover, the interaction, epigenetic changes and functional enrichment of m6A 21 regulators in ovarian cancer were also discussed. By analyzing the transcription levels 22 and survival curves of m6A regulators in serous and endometrioid ovarian cancers, it 23 was found that FTO, YTHDF1, YTHDF2 and IGF2BP1 could be used as the 24 therapeutic target for serous ovarian cancer. YTHDC2 and IGF2BP2 could serve as 25 endometrioid ovarian cancer’s therapeutic target and potential prognostic biomarker, 26 respectively. 27 Conclusions: Our results may provide novel insights for the selection of therapeutic 28 targets and prognostic biomarkers for serous and endometrioid ovarian cancers. 29 1 Background 1 30 Ovarian cancer, one of the most common malignant tumors in female genitalia, was 31 identified with an ever-increasing incidence and high mortality(1). According to 32 cancer statistics in 2020, about 21,750 patients were diagnosed with ovarian cancer, 33 and 13,940 ovarian cancer-related deaths occurred in the United States (2). Although 34 the combination of cytoreductive surgery and neoadjuvant chemotherapy had 35 increased the survival time of such patients (3), the 5-year overall survival rate (OS) 36 of patients with ovarian cancers was still less than 30% (4). In recent years, many 37 researchers have shifted their attention to some biomarkers, such as Cancer antigen- 38 125 (CA-125), HE4, osteopontin, mesothelin, and E2FS family members, and some 39 progress has been made (4-6). 40 However, there is still a long way to go, and more therapeutic targets and prognostic 41 markers need to be identified. 42 Epigenetic modification refers to reversible and heritable changes in gene function 43 without variations in genomic DNA sequence. Aside from DNA methylation and 44 histone acetylation, N6-methyladenine (m6A) was also one of the most common and 45 abundant epigenetic modifications (7). Studies showed that N6-methyladenine was 46 involved in translation control, RNA splicing defects, and many cancer types (8). 47 RNA methylation was dynamically regulated by three types of regulators, including 48 methyltransferases ("writers"), RNA-binding proteins ("readers"), and demethylases 49 ("erasers") (9). Abnormal regulation of RNA m6A modification was closely related to 50 the development of central nervous system diseases, embryonic dysplasia, and tumor 51 radio-resistance(10-12). Cumulative evidence indicated that abnormal changes of m6A 52 regulators played an important role in the occurrence and development of malignant 53 tumors(13-16). In recent years, their role in tumor biology and cancer stem cells has 54 been increasingly highlighted in the field of tumorigenesis and potential biological 55 target screening. However, the underlying mechanisms and unique functions of N6- 56 methyladenine in regulating epithelial ovarian cancer have not been fully elucidated. 57 With the rapid development of methylation sequencing technology and the 58 establishment of various databases, comprehensive analysis of RNA methylation has 59 become possible. In this study, based on major open databases, the relationship 60 between 14 widely reported N6-methyladenine regulators and epithelial ovarian 61 cancer progression and prognosis was analyzed comprehensively. 62 2 Methods 63 2.1 Oncomine dataset 64 Oncomine (www.oncomine.org) is the largest cancer gene chip database and 65 integrated data-mining platform globally, aiming at mining cancer gene information 2 66 (17, 18). Data were extracted to evaluate the expression of m6A regulators in ovarian 67 cancer. The thresholds were restricted as follows: P-value = 0.05; fold-change = 2; 68 and data type, mRNA. Then, the expressions of these m6A regulators in clinical 69 cancer specimens and normal controls were systematically compared. 70 2.2 GEPIA dataset 71 GEPIA (http://gepia.cancer-pku.cn/index.html) is an analysis tool developed by 72 Peking University. With RNA sequence expression data of 9,736 tumor samples and 73 8,587 normal tissue samples, GEPIA has extended the quantification of gene 74 expression from gene level to transcriptional level and could support the analysis and 75 comparison of specific cancer subtypes. It provides many functions such as 76 tumor/normal differential expression analysis, profiling according to cancer types or 77 pathological stages, patient survival analysis, similar gene detection, correlation 78 analysis, and dimensionality reduction analysis (19). Correlations between the 79 expression level of m6A regulators and clinical characteristics in ovarian cancer were 80 assessed according to the GEPIA dataset. 81 2.3 Human protein atlas dataset 82 The Human Protein Atlas (HPA) (https://www.proteinatlas.org/) is a freely accessed 83 website which aims to study the expression of proteins in human tissues and cells 84 (20). By taking advantage of the website, the protein expressions of m6A regulators in 85 ovarian cancer and normal tissues were analyzed by means of immunohistochemistry 86 images. 87 2.4 The Kaplan Meier plotter analysis 88 The prognostic value of m6A regulators in mRNA expression was evaluated using an 89 online database. Kaplan Mayer plotter (www.kmplot.com), which incorporates 90 microarray gene expression data and survival information derived from 91 comprehensive TCGA gene expression-related database and cancer biomedical 92 information grid, especially 1,816 clinical primary cancer patients' gene expression 93 data and survival information (21). To analyze the overall survival (OS), progression- 94 free survival (PFS), and post-progression survival (PPS) of patients with ovarian 95 cancer, patient samples were split into two groups by median expression (high versus 96 low expression) and assessed by a Kaplan-Meier survival plot, with the hazard ratio 97 (HR) with 95% confidence intervals (CIs) and log-rank p-value. Only the JetSet best 98 probe set was chosen to obtain Kaplan-Meier plots. 99 2.5 The cBioPortal for Cancer Genomics 3 100 The cBioPortal (http://www.cbioportal.org/） can be utilized for the visualization, 101 analysis, and download of large-scale cancer genome datasets. Based on the TCGA 102 database, genetic alterations (amplification, deep deletion, and missense mutations) 103 and copy number variances of m6A regulators were obtained from cBioPortal (22), 104 and mRNA expression z-scores (RNA Seq V2 RSEM) were assessed using the 105 cBioPortal for Cancer Genomics database and TCGA. 106 2.6 UALCAN dataset 107 UALCAN (http://ualcan.path.uab.edu/analysis.html) is a comprehensive, user- 108 friendly, and interactive web resource for cancer analysis based on the Cancer 109 Genome Atlas (TCGA) and MET500 cohort data (23). In our study, with the help of 110 this website, the top 20 genes co-expressed with each m6A regulator in ovarian cancer 111 were downloaded. 112 2.7 Functional Enrichment Analysis 113 David (https://david.ncifcrf.gov/home.jsp)is a comprehensive functional annotation 114 website to help researchers better clarify the biological function of submitted 115 genes(24). In this study, GO annotation analysis and the KEGG pathway 116 (https://www.kegg.jp/kegg/pathway.html) enrichment analysis were performed to 117 predict pathways and BPs of m6A regulators by using DAVID. 118 2.8 GeneMANIA dataset 119 GeneMANIA (http://www.genemania.org) is a powerful website that provides 120 information on protein and genetic interactions, pathways, co-expression, and co- 121 localization (25, 26). 122 2.9 String dataset 123 The main role of String (https://string-db.org/) is to collect, score, and integrate all 124 publicly available protein-protein
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  • A Plant-Like Mechanism Coupling M6a Reading to Polyadenylation

    A Plant-Like Mechanism Coupling M6a Reading to Polyadenylation

    RESEARCH ARTICLE A plant-like mechanism coupling m6A reading to polyadenylation safeguards transcriptome integrity and developmental gene partitioning in Toxoplasma Dayana C Farhat1, Matthew W Bowler2, Guillaume Communie3, Dominique Pontier4, Lucid Belmudes5, Caroline Mas6, Charlotte Corrao1, Yohann Coute´ 5, Alexandre Bougdour1, Thierry Lagrange4, Mohamed-Ali Hakimi1†*, Christopher Swale1†* 1IAB,Team Host-Pathogen Interactions & Immunity to Infection, INSERMU1209, CNRSUMR5309, Grenoble Alpes University, Grenoble, France; 2European Molecular Biology Laboratory, Grenoble, France; 3Institut Laue-Langevin, Grenoble, France; 4Laboratoire Genome et Developpement des Plantes (LGDP), UMR5096, Centre National de la Recherche Scientifique (CNRS), Universitede Perpignan via Domitia (UPVD), Perpignan, France; 5Univ. Grenoble Alpes, INSERM, CEA, UMR BioSante´ U1292, CNRS, CEA, Grenoble, France; 6Integrated Structural Biology Grenoble (ISBG) CNRS, CEA, Universite´ Grenoble Alpes, EMBL, Grenoble, France *For correspondence: [email protected] Abstract Correct 3’end processing of mRNAs is one of the regulatory cornerstones of gene (M-AH); expression. In a parasite that must adapt to the regulatory requirements of its multi-host life style, christopher.swale@univ-grenoble- there is a need to adopt additional means to partition the distinct transcriptional signatures of the alpes.fr (CS) closely and tandemly arranged stage-specific genes. In this study, we report our findings in T. †These authors contributed gondii of an m6A-dependent 3’end polyadenylation serving as a transcriptional barrier at these loci. equally to this work We identify the core polyadenylation complex within T. gondii and establish CPSF4 as a reader for Competing interests: The m6A-modified mRNAs, via a YTH domain within its C-terminus, a feature which is shared with authors declare that no plants.