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

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

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

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 interaction (PPI) data sources (27). For the purpose 125 of this study, a PPI network analysis of differentially expressed m6A regulators was 126 conducted to explore the interactions among them with STRING.

127 2.10 Metascape dataset

128 Metascape (http://metascape.org), known as a reliable publicly-available database, is 129 mainly used for gene and protein function annotation and enrichment pathway 130 analysis(28). In this study, the protein function enrichment of m6A regulators and 131 genes associated with it in ovarian cancer were analyzed by capitalizing on this 132 database.

133 3 Results

134 3.1 Aberrant expression of m6A RNA methylation regulators in patients with 135 ovarian cancer

136 After the completion of the literature review, a catalog of 14 genes principally 137 functioning as regulators of RNA methylation (Fig.1) were curated, including three 138 writers, two erasers, and nine readers. The m6A RNA methylation regulators in 139 cancers were compared with those in normal tissue samples using ONCOMINE 140 databases (Fig.2). ONCOMINE analysis revealed that the mRNA expressions of 141 WTAP, METTL3, YTHDC2, and YTHDF3 were downregulated in patients with 142 ovarian cancer, while the mRNA levels of IGF2BP2 and IGF2BP3 were 143 overregulated in ovarian cancer. Next, the mRNA expression levels of m6A regulators 144 in two types of epithelial ovarian cancer were dissected. WTAP was significantly 145 downregulated in patients with serous ovarian cancer in 2 datasets. In Lu's dataset 146 (29), WTAP was decreased in serous ovarian carcinoma compared with that in the 147 normal samples, with a fold change of 1.214 and p-value of 0.018. In Hendrix's 148 dataset (30), WTAP was down-expressed in serous ovarian carcinoma with a fold 149 change of 1.037 and the p-value of 6.34E-4. For METTL3, lower expression of 150 METTL3 was observed in the Adib dataset(31), the mRNA expression of METTL3 in 151 serous ovarian carcinoma was found with a fold change of 1.521 and p-value of 0.010. 152 But in Lu's dataset (29), the expression level of METTL3 was significantly elevated in 153 endometrioid ovarian adenocarcinoma (fold change=1.210 and p-value=0.002). The 154 mRNA levels of YTHDC2 in serous ovarian carcinoma (fold change=1.122 and p- 155 value=0.004) were significantly lower than those in the normal samples in Lu's 156 dataset (29). YTHDF1 was found to increase in endometrioid ovarian cancer in Lu's 157 dataset (29) with a fold change of 1.259 and a p-value of 0.014. In Hendrix’s dataset 158 (30), YTHDF2 and EIF3 were overexpressed in endometrioid ovarian cancer 159 compared with those in the normal samples (fold change=1.266, p-value=7.21E-8; 160 fold change=1.108, p-value=0.001). In the Cancer Genome Atlas data, YTHDF3 was 161 found down-expressed in serous ovarian carcinoma (fold change=1.713 and p- 162 value=4.004E-6). On the contrary, IGF2BP2 and IGF2BP3 were found overly 163 expressed in serous ovarian cancer. For IGF2BP2, Yoshihara's dataset displayed 164 increased expression in serous ovarian tumor tissues with a fold change of 6.470 and 165 p-value of 8.71E-11 (32). Additionally, higher expressions of IGF2BP3 were 166 observed in 3 datasets. Yoshihara et al. (32) showed that IGF2BP3 was increased in 167 ovarian carcinoma (fold change = 14.314 and p–value=3.11E-10) compared to that in 168 normal samples. Lu et al. (29) reported that IGF2BP3 was overexpressed in serous 169 ovarian carcinoma (fold change=2.459 and p-value=2.14E-4), while Hendrix et al. 170 (30) concluded that increased expression of IGF2BP3 was found in serous ovarian 171 carcinoma compared to normal samples (fold change=1.330 and p-value=1.68E-5).

172 In addition, no significant differences in METTL14, ALKBH5, YTHDC1, and 173 IGF2BP1 mRNA expression were detected between ovarian cancer and normal 174 controls, according to ONCOMINE analysis. Nevertheless, the transcription levels of 175 FTO were also slightly lower than that in normal ovarian tissues with a p-value of no 176 more than 0.05 (Table 1).

177 The transcription expressions of m6A regulators between serous ovarian cancer and 178 normal tissues were also compared by using the GEPIA dataset (Fig.3). The results 179 indicated that METTL3, YTHDC1, and YTHDC2 were significantly downregulated 180 in tumor tissues, while IGF2BP2 and IGF2BP3 mRNA levels were upregulated. The 181 expressions of m6A regulators between endometrioid ovarian cancer and normal 182 controls were shown in Fig.4 It is found that METTL3, YTHDF1, YTHDF2, and 183 EIF3 were significantly elevated in tumor tissues, while the expressions of the 184 remaining regulators were not significantly different in the two groups.

185 In order to have a deeper exploration of the protein expression level of m6A 186 regulators in different types of ovarian cancer, immunohistochemistry staining images 187 from the HPA (Fig.5) were explored. The results showed that WTAP and YTHDC1 188 proteins were moderately expressed in normal ovarian tissues and endometrioid 189 ovarian cancer, but insufficiently expressed in serous ovarian cancer. Low protein 190 expressions of METTL14 were observed in normal and ovarian cancer tissues. FTO 191 proteins were not expressed in serous ovarian cancer tissues, while low protein 192 expressions of FTO were detected in normal ovarian tissues and endometrioid ovarian 193 cancer. ALKBH5 proteins were highly expressed in normal ovarian tissues, and 194 moderately expressed in ovarian cancer tissues. YTHDF2 proteins were not expressed 195 in normal and serous ovarian cancer tissues but mildly expressed in endometrioid 196 ovarian cancer tissues. EIF3 proteins were moderately expressed in normal and serous 197 ovarian cancer tissues, but highly expressed in endometrioid ovarian cancer tissues. 198 IGF2BP2 proteins were found with low expressions in normal and endometrioid 199 ovarian cancer tissues, but with high expressions in serous ovarian cancer tissues. 200 IGF2BP3 proteins were not expressed in normal and endometrioid ovarian cancer 201 tissues, but highly expressed in serous ovarian cancer tissues. Furthermore, YTHDC2 202 and IGF2BP1 proteins were not expressed in normal and ovarian cancer tissues. 203 Interestingly, METTL3, YTHDF1, and YTHDF3 data were absent in this database. In 204 conclusion, compared with normal tissues, it is found that the protein expressions of 205 WTAP, FTO and YTHDC1 were lower in serous ovarian cancer tissues, and 206 ALKBH5 experienced down expression in both types of ovarian cancer. However, 207 high protein expressions of YTHDF2 and EIF3 were observed in endometrioid 208 ovarian cancer, while IGF2BP2 and IGF2BP3 were on the increase in serous ovarian 209 cancer tissues.

210

211 Fig.1 Proportions of readers, writers and erasers in the m6A regulators.

212

213 Fig. 2 The mRNA Expression levels of m6A regulators in different cancers.

214

215

216

217

218 Table 1 Comparison of m6A regulators expression between different types of ovarian 219 cancer and ovarian tissues.

Type Gene Types of ovarian cancer vs. normaL Fold change P -value t -Test Ref Writers WTAP Ovarian serous carcinoma vs. normal 1.214 0.018 2.626 Lu Ovarian serous carcinoma vs. normal 1.037 6.34E -4 3.685 Hendrix METTL3 Ovarian serous carcinoma vs. normal 1.521 0.010 3.077 Adib Ovarian Endometrioid cancer vs. normal 1.210 0.002 4.084 Lu METTL14 NA NA NA NA NA Erasers FTO Ovarian serous carcinoma vs. normal -1.066 0.921 -1.446 Welsh ALKBH5 NA NA NA NA NA Readers YTHDC1 NA NA NA NA NA YTHDC2 Ovarian serous carcinoma vs. normal 1.122 0.004 3.466 Lu YTHDF1 Ovarian Endometrioid cancer vs. normal 1.259 0.014 2.803 Lu YTHDF2 Ovarian Endometrioid cancer vs. normal 1.266 7.21E-8 17.806 Hendrix YTHDF3 Ovarian serous carcinoma vs. normal 1.713 4.00E-6 10.017 TCGA EIF3 Ovarian Endometrioid cancer vs. normal 1.108 0.001 4.291 Hendrix IGF2BP1 NA NA NA NA NA IGF2BP2 Ovarian serous carcinoma vs. normal 6.470 8.71E-11 10.907 Yoshihara IGF2BP3 Ovarian serous carcinoma vs. normal 14.314 3.11E-10 8.261 Yoshihara Ovarian serous carcinoma vs. normal 2.459 2.14E-4 4.251 Lu Ovarian serous carcinoma vs. normal 1.330 1.68E-5 4.981 Hendrix 220

221

222 METTL3 YTHDC1 YTHDC2

223

224

225 IGF2BP2 IGF2BP3

226

227 Fig.3 The expression of m6A regulators serous ovarian cancer tissues. 228 229 230 231 232 233 METTL3 p-value=0.002 YTHDF1 p-value=0.014

234

235 YTHDF2 p-value=7.21E-8 EIF3 p-value=0.001

236 237 238 Fig. 4 The expression of m6A regulators in normal and Endometrioid ovarian cancer 239 tissues (1: Normal; 2: Tumor).

240 Normal Serous Endometrioid

WTAP

241

242 Staining: medium Staining: low Staining: medium

243 Normal Serous Endometrioid

METTL14

244

245 Staining: low Staining: low Staining: low

246 Normal Serous Endometrioid

FTO

247

248 Staining: low Staining: not detected Staining: low

249

250 Normal Serous Endometrioid

ALKBH5

251

252 Staining: high Staining: medium Staining: medium

253 Normal Serous Endometrioid

YTHDC1

254

255 Staining: medium Staining: low Staining: medium

256 Normal Serous Endometrioid

YTHDC2

257

258 Staining: not detected Staining: not detected Staining: not detected

259 Normal Serous Endometrioid

YTHDF2

260

261 Staining: not detected Staining: not detected Staining: low 11

262 Normal Serous Endometrioid

EIF3

263

264 Staining: medium Staining: medium Staining: high

265 Normal Serous Endometrioid

IGF2BP1

266

267 Staining: not detected Staining: not detected Staining: not detected

268 Normal Serous Endometrioid

IGF2BP2

269

270 Staining: low Staining: high Staining: low

271 Normal Serous Endometrioid

IGF2BP3

272

273 Staining: not detected Staining: high Staining: not detected 12

274 Fig. 5 Protein expression levels of the m6A regulators in serous and endometrioid 275 ovarian cancer.

276 3.2 Prognostic Values of m6A RNA methylation regulators in Patients with 277 Ovarian Cancer

278 Kaplan–Meier plotter was adopted to evaluate the prognostic significance of the m6A 279 regulators in ovarian cancer with different histology subtypes, including serous and 280 endometrioid ones. It was found that low expressions of WTAP, FTO, YTHDF1, 281 YTHDF2 and IGF2BP1 were associated with better OS, and patients with decreased 282 expression of FTO, YTHDC1, YTHDF1, YTHDF2 and IGF2BP1 indicated a better 283 PFS in ovarian cancer of serous type (Fig.6).

284 In endometrioid ovarian cancer, the high expression of YTHDC2 contributed to 285 better PFS, but increased IGF2BP2 expression was associated with poor PFS. The 286 remaining m6A regulators had no significant correlation with OS and PFS in ovarian 287 cancer (Fig.7).

288 Only statistically significant (p<0.05) survival curves were shown in Fig.6 and 289 Fig.7. More details were presented in Table 2.

290

OS OS

291

PFS PFS

292

OS PFS

293

OS PFS

294

OS PFS

295

296 Fig.6 OS and PFS survival curves of m6A regulators in serous ovarian cancer 297 using the Kaplan–Meier plotter database.

PFS PFS

298

299 Fig.7 PFS survival curves of m6A regulators in endometrioid ovarian cancer 300 using the Kaplan–Meier plotter database.

301

302

303

304

305

306

307

308 Table 2 Prognostic value of m6A regulators in different pathological subtypes of 309 ovarian cancer.

Gene symbol Histology PFS OS PPS Cases HR 95%CI p -value Cases HR 95%CI p -valueCases HR 95%CI p -value WTAPOverall1436 1.13 0.99-1.28 0.0631657 1.22 1.07-1.38 0.0028782 1.16 0.98-1.37 0.09 203137_at Serous1232 1.05 0.91-1.21 0.511232 1.19 1.03-1.39 0.0231232 1.1 0.92-1.3 0.29 Endometrioid 62 0.49 0.18-1.31 0.1562 0.67 0.11-4.03 0.6662 - - - METTL3Overall1436 1.18 1.04-1.34 0.0111657 0.94 0.82-1.07 0.32782 0.97 0.82-1.15 0.71 209265_s_at Serous1232 0.95 0.82-1.1 0.471232 0.91 0.78-1.06 0.251232 0.99 0.83-1.17 0.87 Endometrioid 62 1.91 0.74-4.95 0.17 62 - - -62 - - - METTL14 Overall1436 0.85 0.71-1.03 0.0991657 0.88 0.71-1.07 0.2782 0.96 0.76-1.22 0.73 227601_at Serous1232 0.91 0.74-1.12 0.381232 0.86 0.68-1.08 0.191232 0.92 0.72-1.18 0.51 Endometrioid 62 - - -62 0.28 0.03-2.72 0.2462 - - - FTO Overall1436 1.16 1.02-1.32 0.0191657 1.14 1.01-1.3 0.04782 1.17 0.99-1.38 0.071 209702_at Serous1232 1.17 1.02-1.36 0.0281232 1.44 1.21-1.7 2.8e-051232 1.1 0.92-1.3 0.3 Endometrioid 62 1.95 0.73-5.21 0.1762 3.04 0.34-27.21 0.362 - - - ALKBH5 Overall1436 1.06 0.88-1.27 0.571657 1.14 0.93-1.4 0.2782 1.07 0.85-1.36 0.56 234302_s_at Serous1232 1.08 0.88-1.33 0.451232 1.1 0.88-1.38 0.381232 1.06 0.83-1.36 0.64 Endometrioid 62 0.47 0.16-1.39 0.1662 3.01 0.31-29 0.3262 - - - YTHDC1 Overall1436 1.14 1.01-1.3 0.0361436 1.14 1.01-1.3 0.036782 1.07 0.91-1.27 0.4 212455_at Serous1232 1.18 1.02-1.36 0.0281232 1.18 0.86-1.17 0.951232 1.08 0.91-1.28 0.4 Endometrioid 62 0.78 0.31-1.97 0.5962 0.78 0.31-1.97 0.5962 - - - YTHDC2 Overall1436 1.13 1-1.28 0.061657 0.96 0.84-1.09 0.49782 1.04 0.88-1.23 0.64 213077_at Serous1232 1.04 0.9-1.2 0.621232 0.98 0.84-1.14 0.811232 1.04 0.87-1.23 0.68 Endometrioid 62 0.39 0.14-1.03 0.04962 0.44 0.07-2.63 0.3562 - - - YTHDF1Overall1436 1.29 1.14-1.46 6.4e-05 1657 1.18 1.04-1.35 0.01782 1.08 0.91-1.27 0.39 221741_s_at Serous1232 1.29 1.12-1.49 5e-041232 1.29 1.11-1.5 0.00111232 1.1 093-1.31 0.27 Endometrioid 62 1.76 0.68-4.54 0.2462 1.65 0.28-9.9 0.5862 - - - YTHDF2Overall1436 1.31 1.16-1.49 2.3e-05 1657 1.17 1.03-1.33 0.017782 1.12 0.95-1.33 0.18 217812_at Serous1232 1.31 1.13-1.51 0.00028 1232 1.23 1.06-1.43 0.00781232 1.11 0.94-1.32 0.23 Endometrioid 62 0.7 0.27-1.82 0.4762 4.31 0.48-38.65 0.1562 - - - YTHDF3Overall1436 1.18 1.04-1.34 0.011657 1.12 0.98-1.27 0.084782 1 0.84-1.18 0.97 221749_at Serous1232 1.1 0.95-1.27 0.21232 1.04 0.89-1.21 0.61232 0.97 0.81-1.15 0.7 Endometrioid 62 0.76 0.29-1.96 0.5762 1.6 0.27-9.6 0.662 - - - EIF3 Overall1436 1.22 1.07-1.38 0.00241657 1 0.88-1.14 0.99782 1.16 0.98-1.38 0.078 200595_s_at Serous1232 1.12 0.97-1.29 0.131232 1.1 0.94-1.28 0.231232 1.12 0.94-1.33 0.19 Endometrioid 62 0.66 0.26-1.71 0.3962 3.39 0.38-30.33 0.2562 - - - IGF2BP1 Overall1436 1.51 1.25-1.82 1.6e-05 1657 1.09 0.89-1.33 0.41782 1.14 0.9-1.45 0.27 227377_at Serous1232 1.55 1.26-1.9 3e-051232 1.25 1-1.57 0.0481232 1.15 0.9-1.47 0.28 Endometrioid 62 0.63 0.21-1.88 0.462 2305214361.99 0-lnf 0.0262 - - - IGF2BP2 Overall1436 1.09 0.96-1.24 0.161657 1.08 0.95-1.22 0.26782 1.04 0.88-1.23 0.65 218847_at Serous1232 1.04 0.9-1.2 0.581232 1.1 0.95-1.28 0.211232 1.04 0.88-1.24 0.63 Endometrioid 62 3.03 1.13-8.11 0.02162 1.99 0.33-11.9 0.4462 - - - IGF2BP3 Overall1436 1.02 0.9-1.16 0.711657 1.1 0.97-1.25 0.15782 1.11 0.94-1.31 0.23 203820_s_at Serous1232 1 0.87-1.15 0.991232 1.13 0.97-1.32 0.111232 1.14 0.96-1.36 0.13 Endometrioid 62 0.8 0.32-2.04 0.6462 0.69 0.12-4.15 0.6962 - - -

310

311 3.3 Co-expression and interaction analyses of m6A RNA methylation 312 regulators in patients with ovarian cancer

313 The correlations of m6A regulators with each other were calculated by analyzing their 314 mRNA expressions (RNA Seq V2 RSEM) via the online tool cBioPortal for ovarian 315 cancer (TCGA, Provisional). The Pearson's correction analysis indicated significant 316 positive correlations in the following m6A regulators: WTAP with METTL14, 317 ALKBH5, YTHDC1 and YTHDF2; METTL3 with YTHDC2 and EIF3; METTL14 318 with WTAP, FTO, YTHDC1, YTHDF2 and EIF3; FTO with METTL14, EIF3 and 16

319 IGF2BP3; ALKBH5 with WTAP and YTHDF2; YTHDC1 with WTAP, METTL14, 320 YTHDF2 and EIF3; YTHDC2 with METTL3, FTO, YTHDF1, EIF3 and IGF2BP3; 321 YTHDF1 with YTHDC2 and IGF2BP3; YTHDF2 with WTAP, METTL14, ALKBH5 322 and YTHDC1; YTHDF3 with IGF2BP1; EIF3 with METTL3, METTL14, FTO, 323 YTHDC1, YTHDC2 and IGF2BP2; IGF2BP1 with YTHDF3; IGF2BP2 with EIF3; 324 and IGF2BP3 with FTO, YTHDC2 and YTHDF1. In addition, significant negative 325 correlations were noted for WTAP with FTO, YTHDC2 and IGF2BP3; METTL3 326 with YTHDF3 and IGF2BP1; METTL14 with YTHDF1 and IGF2BP1; FTO with 327 WTAP; YTHDC1 with YTHDF1 and IGF2BP1; YTHDC2 with WTAP and 328 IGF2BP1; YTHDF1 with METTL14 and YTHDC1; YTHDF3 with METTL3; 329 IGF2BP1 with METTL3, METTL14, YTHDC1 and YTHDC2; and IGF2BP3 with 330 WTAP (Fig.8a).

331 GeneMANIA was used to conduct a correlation analysis of m6A regulators at the 332 gene level (Fig.8b). The results showed relationships in shared protein domains 333 between METTL3, METTL14, YTHDC1, YTHDC2, YTHDF1, YTHDF2 and 334 YTHDF3, as well as IGF2BP1, IGF2BP2 and IGF2BP3. Physical interactions were 335 found between WTAP, METTL3 and METTL14; EIF3A(=EIF3) and YTHDF3; 336 IGF2BP1 with IGF2BP2 and IGF2BP3. Further, relationships in co-expression were 337 noted between WTAP, YTHDF2 and YTHDF3; YTHDF2, EIF3A and IGF2BP1; 338 YTHDC1 with YTHDC2 and YTHDF3; YTHDF1 with FTO and YTHDF3, as well 339 as IGF2BP1 and IGF2BP3.

340 Interactions of m6A regulators at the protein expression level were identified by 341 using STRING (Fig.8c). WTAP was shown to interact with METTL3 and METTL14 342 in gene co-occurrence, text-mining, and protein homology. Additionally, relationships 343 were noticed between METTL14 with YTHDC1, YTHDC2, YTHDF1, YTHDF2 and 344 YTHDF3 in text-mining, and protein homology. And FTO was only found to interact 345 with METTL3, METTL14 and ALKBH5 in text-mining.

346 a

347

348 b c

349

350 Fig.8 Co-expression and interaction of m6A regulators at the gene and protein 351 levels in ovarian cancer patients. a Spearman's correlation analysis of m6A regulators. 352 b Gene–gene interaction network among m6A regulators in the GeneMANIA dataset. 353 c Protein–protein interaction network among m6A regulators in the STRING dataset.

354 3.4 m6A RNA methylation regulators' genetic alteration in ovarian cancer 355 patients

356 The m6A regulators alterations in ovarian cancer were investigated by using the online 357 tool cBioPortal. Based on three ovarian cancer databases (TCGA Firehouse Legacy, 358 TCGA PanCancer Atlas, TCGA Nature 2011), the analysis results showed that the 359 percentages of m6A regulators genetic alterations were 51.97% (302/583), 39.21% 360 (228/584), 36.4% (117/489), respectively (Fig.9a). The percentages of genetic 361 alterations in m6A regulators for ovarian cancer varied from 1.3 to 21% for individual 362 genes based on three TCGA datasets (WTAP, 2.8%; METTL3, 4%; METTL14, 1.5%; 363 FTO, 1.7%; ALKBH5, 1.3%; YTHDC1, 2.4%; YTHDC2, 3%; YTHDF1, 10%; 364 YTHDF2, 1.5%; YTHDF3, 5%; EIF3A, 2%; IGF2BP1, 2%; IGF2BP2, 21%; 365 IGF2BP3, 3%) (Fig.9b). Interestingly, the results of the Kaplan-Meier plotter and log- 366 rank test indicated m6A RNA methylation regulators' genetic alterations correlated 367 with better overall survival (OS) only in the TCGA Firehouse Legacy dataset 368 (p=0.0497, as shown in Fig.9c), but no significant difference in the other two 369 databases was discovered. In addition, there was no significant difference found for 370 Disease-Free Survival (DFS) in any dataset (p=0.194, as shown in Fig.9d).

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383 c

384 Fig.9 Alteration frequency of m6A regulators in ovarian cancer. a m6A regulators 385 genetic alteration in TCGA Firehouse Legacy, TCGA PanCancer Atlas, TCGA 386 Nature 2011. b Alteration frequency of m6A regulators based on the TCGA 387 Firehouse Legacy, TCGA PanCancer Atlas, TCGA Nature 2011. c Kaplan– 388 Meier plots comparing OS in cases with/without m6A regulators genetic 6 389 alterations. d Kaplan–Meier plots comparing DFS in cases with/without m A 390 regulators genetic alterations.

391 3.5 Functional enrichment analysis of m6A RNA methylation regulators and 392 positively correlative genes in ovarian cancer

393 The strongly related genes of m6A regulators in ovarian cancer were downloaded 394 from the UALCAN dataset, and the Top 20 related genes of each m6A RNA regulator 395 were selected for subsequent analysis. The detailed results were shown in Table 3. 396 The functions of m6A regulators and the genes positively correlated with regulators 397 were predicted by analyzing gene ontology (GO) and Kyoto Encyclopedia of Genes 398 and Genomes (KEGG) in the Database for Annotation, Visualization, and Integrated 399 Discovery (DAVID). The top 20 GO enrichment items were based on three aspects: 400 Firstly, Biological Process (6 items): transcription, DNA-templated; regulation of 401 transcription, DNA-templated; negative regulation of transcription from RNA 402 polymerase II promoter; positive regulation of transcription, DNA-templated; 403 negative regulation of transcription, DNA-templated; protein transport. Secondly, 404 Cellular Component (7 items): protein binding; poly(A) RNA binding; DNA binding; 405 ATP binding; RNA binding; nucleic acid binding; nucleotide binding. Thirdly, 406 molecular function group (7 items): Nucleus; Cytoplasm; nucleoplasm; cytosol; 407 nucleolus; nuclear speck; perinuclear region of cytoplasm (Fig.10a). The most 408 significant KEGG signaling pathways were Wnt signaling pathway, Ubiquitin

409 mediated proteolysis, Spliceosome, RNA transport, RNA degradation, Ribosome 410 biogenesis in eukaryotes, and GnRH signaling pathway (Fig.10b). Among these 411 pathways, Wnt and GnRH signaling pathways were related to multiple tumor 412 development and were involved in the tumorigenesis and pathogenesis of ovarian 413 cancer. Then, protein-protein interaction enrichment analysis was performed to better 414 understand the roles of m6A regulators and their associated genes in ovarian cancer 415 (Fig.10c and Fig.10d). The results showed that biological function was mainly related 416 to RNA splicing, mRNA processing, regulation of mRNA-based metabolic process, 417 macromolecule methylation, transcriptional regulation by TP53, etc.

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435 Table 3 Top 20 genes associated with each m6A regulators found in ovarian 436 cancer.

Type Gene Correlation genes

Writers WTAP TCP1 MRPL18 TBP KATNA1 PCMT1 IGF2R MAP3K4 PPIL4 SERAC1 FGFR1OP FBXO30

NUP43 C6orf35 VTA1 HBS1L ZBTB2 C6orf120 FAM120B PDCD2 TAB2

METTL3 SUPT16H TOX4 RAB2B CHD8 PARP2 METT11D1 APEX1 C14orf176 TTC5 TMEM55B

CCNB1IP1 PRMT5 OSGEP ZNF219 FLJ10357 TEP1 OXA1L ZNF429 ZNF430 HAUS4

METTL14 SEC24B KIAA1109 AP1AR LARP7 BBS7 INTS12 PHF17 SPATA5 ELF2 MFSD8 EXOSC9 TBCK

CLCN3 GSTCD ALPK1 ANKRD50 PAPSS1 USP38 TMEM184C GAR1

Erasers FTO RBL2 ZFP90 RS PRY1 CHD9 ATXN1L BBS2 ZNF319 DYNC1LI2 N4BP1 DDX19A NUDT21

SLC38A7 GLG1 OGFOD1 CNOT1 PAPD5 TERF2 ARL2BP SNTB2 CSNK2A2

ALKBH5 SMCR8 C17orf39 NCOR1 TOP3A PRPSAP2 USP22 ELAC2 ZNF287 MED9 ZNF18 MAP2K4

ZNF624 FLCN DRG2 SMCR7 EPN2 MAPK7 LMLN LLGL1 EAF1

Readers YTHDC1 CENPC1 TARDBP BCLAF1 BPTF ZNF638 ZC3H11A UBA6 HNRNPH3 RC3H1 MBD5 ZNF192

ZC3H4 HNRPDL HNRNPU CELF1 SAFB HNRNPD PUM2 REST FOXO3B

YTHDC2 WDR36 APC DCP2 CHD1 CEP120 AFF4 PGGT1B DMXL1 RAPGEF6 TCERG1 PPIP5K2 TTC37

HMGXB3 CSNK1G3 HARS2 PRRC1 SRFBP1 DDX46 PHAX MTX3

YTHDF1 C20orf11 LSM14B C20orf20 OSBPL2 TAF4 RAB22A DIDO1 ARFGAP1 TPD52L2 GMEB2

SS18L1 PCMTD2 CSTF1 DNAJC5 SPATA2 CABLES2 ZGPAT RAE1 STAU1 TH1L

YTHDF2 SFRS4 PUM1 GMEB1 ARID1A PPP1R8 EPB41 EYA3 CCDC21 HNRNPR EIF4G3 SFRS13A

RCC2 SFPQ KHDRBS1 TARDBP FAM76A PDIK1L FBXO42 PHACTR4 USP48

YTHDF3 RAB2A VCPIP1 RB1CC1 NCOA2 KIAA1429 ARFGEF1 UBE2W STAU2 ASPH VPS13B RBM12 B

IMPAD1 WWP1 DPY19L4 TCEA1 ITCH WAC INTS8 NSMAF ARFGEF2

EIF3 -

IGF2BP1 NUDT11 ST6GALNAC3 HMGA2 ST8SIA2 LRRC4B GPC2 TIMELESS CADM4 SCUBE3

DENND5B C19orf61 HIC2 MGC87042 FIGNL2 ZNF428 STRAP ALDH1A2 UHRF1 TOX2

EXOC3L2

IGF2BP2 SENP2 CBX2 GPC3 ACVR2B RHOBTB1 CACNA2D2 BAHCC1 PLCB1 PCDHGA7 C12orf76

C14orf135 C5orf13 TRA2B TBCCD1 MECOM RCOR2 ZKSCAN2 CBX4 FBN3 FAM171A2

IGF2BP3 KLHL7 NUPL2 CBX3 TRA2A PLEKHA8 KIAA0895 KBTBD2 C6orf120 LSM5 RP9P RP9 7 -Sep

FAM126A C7orf31 SCRN1 CDCA7L CCDC126 HNRNPA2B1 CENPO YKT6 437

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443 a

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449 Fig.10 Enrichment analysis of m6A regulators and its related genes and proteins in 450 patients with ovarian cancer. a Top 20 Gene Ontology (GO) enrichment. b Top 9 Kyoto 451 Encyclopedia of Genes and Genomes (KEGG) enrichment. c Protein–protein 452 interaction (PPI) network. d Functional enrichment analysis of MCODE components.

453 4 Discussion

454 The function of RNA methylation was dynamically and reversibly regulated by 455 methyltransferase, demethylase and methylated reading protein (33). RNA 456 methylation played an important role in the development and progression of diseases 457 by regulating RNA splicing, translation, nuclear export and stability (3, 15, 16). Many 458 studies have found that abnormal expression of RNA methylation regulators was a 459 common epigenetic change in malignant tumors, which not only weakened tumor 460 proliferation and differentiation but also affected tumor prognosis (34-37). However, 461 the expression pattern, prognostic value and potential biological function of RNA 462 methylation regulatory factors in malignant ovarian tumors are still unclear. 463 Therefore, an in-depth analysis of the expression, prognosis, genetic alteration, co- 464 expression and related biological functions and signal pathways of RNA methylation 465 regulators in ovarian cancer was carried out in this study.

466 The expression of RNA methylation regulators at the RNA and protein levels and 467 the relationship with tumor stage in ovarian cancer were firstly analyzed. Compared 468 with normal ovarian tissue, 3 significantly down-regulated genes (METTL3, 469 YTHDC1, YTHDC2) and 2 significantly up-regulated genes (IGF2BP2 and 470 IGF2BP3) were identified at mRNA levels in ovarian cancer of serous type. In 471 endometrioid ovarian cancer, it is found that the mRNA levels of METTL3, 472 YTHDF1, YTHDF2 and EIF3 were significantly upregulated. ALKBH5 protein

473 expression levels were decreased in both types of ovarian cancer, but the protein 474 expression levels of WTAP, FTO and YTHDC1 were only decreased in serous 475 ovarian cancer than those in normal ovarian tissues. However, compared with normal 476 ovarian tissues, the protein expression levels of YTHDF2 and EIF3, IGF2BP1 and 477 IGF2BP2 were higher in endometrioid ovarian cancer and serous ovarian cancer, 478 respectively. In addition, as the tumor progressed, the expressions of WTAP, METL3, 479 METL14, ALKBH5, YTHDC1, YTHDC2, and YTHDF3 were significantly reduced.

480 Structural biology revealed that WTAP did not have methyltransferase activity, but 481 it interacted with METTL3-14 complex to affect m6A methyltransferase activity and 482 methylation in vivo accurate location (38). WTAP, an oncogene, played an important 483 role in the progression and prognosis of many tumors (39-41). Yu et al.(42) confirmed 484 that WTAP could be used as an independent prognostic factor in high-grade serous 485 ovarian cancer. In this study, it is found that high expression of WTAP was 486 significantly associated with worse OS in serous ovarian cancer. In our report, 487 patients with high FTO, YTHDF1, YTHDF2 and IGF2BP1 expressions had worse OS 488 and PFS in serous ovarian cancer. Patients with elevated YTHDC1 mRNA 489 experienced poor PFS in serous ovarian cancer. In endometrioid ovarian cancer, the 490 high mRNA expression level of YTHDC2 was significantly associated with 491 satisfactory PFS, while the increased IGF2BP2 mRNA expression level suggested 492 worse PFS. Although many studies have confirmed that m6A RNA methylation 493 regulators display prognostic value in a variety of cancers (37, 43-47), previous 494 studies on the expression level and prognostic value of m6A RNA methylation 495 regulators in ovarian cancer are very rare.

496 To clarify the genetic alteration, function and molecular mechanism of m6A 497 regulators, the cBioPortal dataset was adopted to calculate the genetic alteration 498 frequency of m6A regulators in ovarian cancer. The difference in a single gene based 499 on the TCGA provisional data set was found to be within 1.3% and 21% based on 500 three TCGA datasets (TCGA Firehouse Legacy, TCGA PanCancer Atlas, TCGA 501 Nature 2011). In the TCGA Firehouse Legacy dataset, gene alteration group revealed 502 better OS than non- alteration group, which might be related to the loss of 503 carcinogenic function after oncogene alteration. Genes co-expressed with each m6A 504 regulators were found from the CALCAN dataset in ovarian cancer, and then =the top 505 20 genes with the strongest association with each of m6A regulators were selected for 506 GO functional clustering and KEGG pathway enrichment. As expected, it was found 507 that the possible functions of these genes were related to Wnt and GnRH signaling 508 pathways, and cumulative studies have reported that these pathways are involved in 509 ovarian cancer tumorigenesis (48, 49).

510 Our research shows the expression level and biological function of m6A regulators 511 in ovarian cancer, which provides more feasible options for the treatment of ovarian 512 cancer. However, there're still many limitations to be overcome, and in vivo and in 513 vitro experiments need to be performed to verify them.

514 5 Conclusions

515 In this review, the mRNA and protein expressions of m6A regulators and their 516 prognostic value in ovarian cancer were summarized. Moreover, the m6A regulators 517 interaction, genetic alteration and functional pathway enrichment in ovarian cancer 518 were analyzed. The results indicated that the mRNA expression levels of METTL3, 519 YTHDC1 and YTHDC2 were significantly downregulated, whereas IGF2BP2 and 520 IGF2BP3 significantly increased; the expressions of WTAP, FTO, ALKBH5 and 521 YTHDC1 protein were decreased, while IGF2BP2 and IGF2BP3 protein were more 522 highly expressed in serous ovarian cancer compared with normal ovarian tissues. In 523 endometrioid ovarian cancer, the mRNA expression levels of METTL3, YTHDF1, 524 YTHDF2 and EIF3 were significantly increased, and the protein expression levels of 525 YTHDF2 and EIF3 were upregulated, but ALKBH5 protein expression was decreased 526 compared with the control group. According to the survival curve, the increased 527 expressions of FTO, YTHDF1, YTHDF2 and IGF2BP1 indicated worse OS and PFS 528 in serous ovarian cancer. High mRNA expression of YTHDC2 was associated with 529 better PFS, but high expression of IGF2BP2 led to worse PFS in endometrial ovarian 530 cancer. These results proved that FTO, YTHDF1, YTHDF2 and IGF2BP1 could serve 531 as therapeutic targets for serous ovarian cancer. IGF2BP2 could be used as a 532 therapeutic target and YTHDC2 could work as a potential prognostic biomarker in 533 endometrioid ovarian cancer. Our findings will provide valuable clues for people to 534 understand the initiation, prognosis and pathogenesis of ovarian cancer, and bring 535 more possibilities for the treatment of ovarian cancer.

536 6 Abbreviations

537 OS: overall survival; PFS: progression-free survival; PPS: post-progression 538 survival; HR: hazard ratio; CIS: confidence intervals; TCGA: The Cancer 539 Genome Atlas; KEGG: Kyoto Encyclopedia of Genes and Genomes.

540 7 Authors’ contributions

541 Y Chen designed the experiment. Y Chen, X Chen, S Li and H Xia performed the 542 analysis and interpreted the data. Y Chen discussed the results and wrote the 543 paper. Y Sun revised the manuscript and provided funding. All authors 544 contributed to manuscript revision, read, and approved the submitted version.

545 8 Ethics approval and consent to participate 26

546 Not applicable

547 9 Consent for publication

548 Not applicable

549 10 Availability of data and materials

550 Publicly available datasets were analyzed in this study. This data can be found here: 551 Oncomine (https://www.Oncomine.org). GEPIA (http://gepia.cancer-pku.cn/). 552 The Human Protein Atlas (HPA) (https://www.proteinatlas.org/). KM Plotter 553 (http://kmplot.com/analysis/). cBioPortal (http://www.cbioportal.org/). UALCAN 554 (http://ualcan.path.uab.edu/analysis.html).

555 11 Competing interests

556 The authors declare that they have no competing interests.

557 12 Funding

558 This study is supported by grant from the National Natural Science Foundation of 559 China (No.81873045).

560 13 Acknowledgements

561 Not applicable

562 14 Author details

563 1Department of Gynecology, Fujian Cancer Hospital, Affiliated Cancer Hospital 564 of Fujian Medical University, Fuzhou350014, China.

565 15 ORCID iDS

566 Yuwei Chen: https://orcid.org/0000-0003-4224-8832

567 Yang Sun: https://orcid.org/0000-0003-3271-1491

568 Xinbei Chen: https://orcid.org/0000-0002-4247-1148

569 16 Reference

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Figures

Figure 1

Proportions of readers, writers and erasers in the m6A regulators.

Figure 2

The mRNA Expression levels of m6A regulators in different cancers. Figure 3

The expression of m6A regulators serous ovarian cancer tissues. Figure 4

The expression of m6A regulators in normal and Endometrioid ovarian cancer tissues (1: Normal; 2: Tumor). Figure 5

Protein expression levels of the m6A regulators in serous and endometrioid ovarian cancer. Figure 6

OS and PFS survival curves of m6A regulators in serous ovarian cancer using the Kaplan–Meier plotter database. Figure 7

PFS survival curves of m6A regulators in endometrioid ovarian cancer using the Kaplan–Meier plotter database. Figure 8

Co-expression and interaction of m6A regulators at the gene and protein levels in ovarian cancer patients. a Spearman's correlation analysis of m6A regulators. b Gene–gene interaction network among m6A regulators in the GeneMANIA dataset. c Protein–protein interaction network among m6A regulators in the STRING dataset. Figure 9

Alteration frequency of m6A regulators in ovarian cancer. a m6A regulators genetic alteration in TCGA Firehouse Legacy, TCGA PanCancer Atlas, TCGA Nature 2011. b Alteration frequency of m6A regulators based on the TCGA Firehouse Legacy, TCGA PanCancer Atlas, TCGA Nature 2011. c Kaplan–Meier plots comparing OS in cases with/without m6A regulators genetic alterations. d Kaplan–Meier plots comparing DFS in cases with/without m6A regulators genetic alterations. Figure 10

Enrichment analysis of m6A regulators and its related genes and proteins in patients with ovarian cancer. a Top 20 Gene Ontology (GO) enrichment. b Top 9 Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. c Protein–protein interaction (PPI) network. d Functional enrichment analysis of MCODE components.