bioRxiv preprint doi: https://doi.org/10.1101/615237; this version posted April 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

1 Integrated analysis of the miRNA-mRNA network associat-

2 ed with LMP1 gene in nasopharyngeal carcinoma 3 Short title: A co-analysis of EBV-associated genes in NPC 4 5 Autho list:Yang Yanga, Wen Liua, Yan Zhanga,b, Shuo Wua, Bing Luoa* 6 7 Affilation: 8 a: Department of Pathogenic Biology, Qingdao University Medical College, 38 Dengzhou Road, 9 Qingdao, 266071, China 10 b: Department of Clinical Laboratory, Central Hospital of Zibo, 54 Gongqingtuan Road, ZiBo, 11 255036, China 12 13 *Corresponding author: Bing Luo 14 Affilation: Department of Pathogenic Biology, Qingdao University Medical College, 38 Dengzhou 15 Road, Qingdao, 266071, China. Tel: 86-532-8299108. 16

17 Abstract: Epstein-Barr virus oncogenic latent membrane protein 1 (LMP1) has been known to

18 play important roles in nasopharyngeal carcinoma (NPC). LMP1 gene also induced a variety of

19 microRNAs (miRNAs) which bear pivotal roles in regulation of mRNAs expression. However,

20 little was known about the global change of mRNAs and miRNAs induced by LMP1 gene in NPC.

21 In our study, one NPC tissue microarray profile and two LMP1-associated microarray expression

22 profiles data were downloaded from the Gene Expression Omnibus database. A protein-protein

23 interaction network was constructed by using bioinformatics platform Gene-Cloud of Biotech-

24 nology Information (GCBI). 78 differentially expressed miRNAs and 3322 differentially ex-

25 pressed genes were identified in order to generate a macroscopic network between miRNAs and

26 mRNAs associated with LMP1 gene. In addition, two significant models were generated to illus-

27 trate the expression tendency. Our study provided a way to reveal the interaction between

28 miRNAs and mRNAs in LMP1 axis, bringing insights into the pathogenesis of NPC.

29 30 Keywords: latent membrane protein 1; Epstein-Barr virus; micro RNA; nasopharyngeal carcino- 31 ma 32

1 bioRxiv preprint doi: https://doi.org/10.1101/615237; this version posted April 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

33 Introduction

34 Nasopharyngeal carcinoma is a primary cancer arising from the nasopharynx. The disease 35 commonly distributes in certain regions of East Asia and Africa, especially in Guangdong prov- 36 ince of China [1]. It is generally considered that genetic susceptibility, Epstein-Barr virus (EBV) 37 infection and environmental factors were involved in the pathogenesis of NPC. Linkage analysis 38 showed that several susceptibility loci were related to the etiology of NPC in China [2,3]. Con- 39 sumption of salt-preserved fish may also play an important role in NPC [4]. 40 EBV is an enveloped, double-stranded human DNA herpesvirus. It infects more than 90% of 41 the global adult people being for life and in most carriers the infection is usually asymptomatic. 42 Nevertheless, EBV has oncogenic properties for several malignancies including Hodgkin's disease, 43 Burkitt's lymphoma, as well as undifferentiated NPC [5]. The consistent detection of virus in NPC 44 cases and the continuous expression of virus genes indicate that EBV is crucial for the malignant 45 growth. The circulating cell free EBV DNA is well-recognized biomarker in undifferentiated NPC 46 [6]. EBV latently infects NPC cells by expressing multiple virus genes including EBV nuclear 47 antigens 1 (EBNA1), the latent membrane proteins (LMP1) , 2A, and 2B and viral noncoding 48 RNAs. Among them, LMP1 oncoprotein may play a critical role in the initiation and progression 49 of NPC as well as the invasion and metastasis [7-9]. LMP1 is a 66kDa integral membrane protein 50 consisting of a cytoplasmic amino-terminus, six transmembrane domains and a large cytoplasmic 51 C-terminal tail comprising three regions: transformation effector site (TES)/C-terminal activating 52 regions (CTAR) 1, 2 and 3. CTAR1, CTAR2 are responsible for recruiting cellular signaling mol- 53 ecules of the tumor necrosis factor receptor associated factor (TRAF) family and TRAF-associated 54 death domain protein (TRADD) and the activation of the nuclear factor kap- 55 pa-light-chain-enhancer of activated B cells (NF-κB) and c-Jun N-terminal protein kinase (JNK) 56 /DNA-binding activity of activator protein-1 (AP-1) signaling [10,11]. 57 It is well recognized that the many small non-coding microRNAs (miRNAs) play pivotal role 58 in regulation the homeostasis of mRNAs expression in physiological and pathological process [12]. 59 MiRNAs can bind to the specific regions in the target mRNAs, thus leading to the degradation or 60 repression of the mRNAs [13]. As a multifunctional gene, LMP1 was proved to modulate many 61 kinds of miRNAs in B cells and epithelial cells [14-16]. Most of these studies have focused on the 62 interaction of single miRNA with the carcinoma cells, thus a landscape of interaction between 63 miRNAs and mRNAs was needed to illustrate the global change induced by LMP1 in NPC. 64 In the present study, we investigated three expression profiles involving LMP1-associated 65 miRNAs and mRNAs data and NPC tissue samples from the Gene Expression Omnibus (GEO) 66 database (http://www.ncbi.nlm.nih.gov/geo/). We analyzed the differentially expressed genes 67 (DEGs) and miRNAs (DE-miRNAs) and performed the pathway analysis. The protein-protein 68 interaction (PPI) network and the mRNA-miRNA interaction network were constructed to obtain 69 the key genes and miRNAs in NPC. 70 71 Materials and Methods 72 Data Collection 73 Gene expression profiles were downloaded as raw signals from GEO. The dataset GSE12452 74 comprised 31 NPC tissues and 10 normal nasopharyngeal tissues. The dataset GSE29297 involved 75 a series of LMP1 TES2-stimulated HEK-293 cell line samples at different time points. The dataset

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76 GSE26596 valuated the miRNAs expression in NPC cell line TW03 transfected with LMP1. 77 TW03 was an EBV-negative cell line derived from a lymphoepitheliomatous undifferentiated car- 78 cinoma in Taiwan province [17]. The sequences of the LMP1 gene encoded by different EBV 79 strains has been shown to have a degree of variation in NPC. The LMP1 vector used in dataset 80 GSE29596 was as described [18]. Vector encoding LMP1 TES2 (amino acids 351-386) was used 81 in GSE29297. Both of the two vector covered part of C-terminal activating region. 82 Differencial Expression Analysis 83 An online platform Gene-Cloud of Biotechnology Information (GCBI) was used to inter- 84 preted, normalized, log2 scaled the datasets GSE12452 and GSE29297. This platform integrated 85 with biology, computer science, medicine, informatics, mathematics and other disciplines. Micro- 86 array probe signals with absolute value of fold change (FC)>1.2, P-value<0.05, and false discov- 87 ery rate (FDR) <0.05 were considered to be statistically differential [19,20]. Co-expression net- 88 works were constructed based upon contribution degrees and models of Series Test of Cluster 89 (STC) were established by random permutation scheme (http://college.gcbi.com.cn/helpme). 90 GSE26596 was analysed via the R-programming language-based dataset analysis tool GEO2R 91 (http://www.ncbi.nlm.nih.gov/geo/geo2r/). This interactive tool was allowed to screen different 92 expression miRNAs between TW03 cell line and TW03 cell line transfected with LMP1 in dataset 93 GSE26596 [21]. 94 GO and pathway analysis 95 The functional annotation of the DEGs and DE-miRNAs were performed by GCBI platform. 96 Gene Ontology (GO) (http://www.geneontology.org) classification was used to analysis the DEGs 97 function including biological processes, molecular function, and cellular component [22]. The 98 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was utilized to 99 explore the pathway of DEGs. 100 Gene regulation network and miRNA-mRNA network 101 Micro RNA target genes were predicted mainly based on the bioinformatic platform 102 miRWalk 2.0 [23]. The platform integrates information from 12 miRNA-target databases, includ- 103 ing prediction datasets and validated information: MiRWalk, MicroT4, miRanda, miRBridge, 104 miRDB, miRMap, miRNAMap, PICTAR2, PITA, RNA22, RNAhybrid, Targetscan. The 3' un- 105 translated regions for the target genes were used as the primary base-pairing regions of miRNAs 106 [24]. The miRNA-gene pairs that were common in at least five databases and met with P<0.05 107 were regard as reliable. The miRNA target mRNAs were then compared with the genes in STC 108 models and the overlapped genes were filtered for further analysis. The LMP1 associated 109 miRNA-mRNA interactomes was further visualized using the Cytoscape 3.5.1 platform [25]. In 110 addition, DEGs of GSE29297 and GSE12452 were used to construct a protein-protein interaction 111 networks based on GCBI platform. The details of the network construction algorithm were de- 112 scribed on GCBI platform. 113 Results 114 Identification of DEGs and DE-miRNAs associated with LMP1 115 In the present study, the mRNA profiling dataset GSE29297 was analyzed on GCBI platform. 116 Samples in the dataset were divided into five groups according to the TES2 stimulation period. 117 3109 probes for 2787 DEGs were identified after background correction and normalization (P 118 value<0.05, Q value<0.05, Figure 1). 41 samples (including 31 NPC samples and 10 normal tis- 119 sues) in the dataset GSE12452 were divided into two groups using GCBI tools. 2566 probes for

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120 1815 genes were up-regulated and 2021 probes for 1507 genes were down-regulated. The criteria 121 were set to adjust P value <0.05 and FC>1.5 (Figure 2). 372 DEGs were up-regulated and 245 122 DEGs were down-regulated in both datasets GSE29297 and GSE12452. For dataset GSE26596, 123 72 miRNAs were up-regulated, and 6 miRNAs were down-regulated (adjust P value<0.05, 124 FC>1.5). 125 Models of Series Test of Cluster 126 The expression tendency of gene clusters of dataset GSE29297 was calculated on GCBI 127 platform. Among the 20 expression STC models constructed, 15 models were estimated as statis- 128 tical significant (all P values<0.05, Figure 3). Notably, a descending expression model STC32 129 including 1358 probes for 1070 genes and an ascending model STC49 including 1422 genes were 130 identified as significantly enriched, which may be affected by the LMP1 associated up-regulated 131 miRNAs. 132 Prediction of miRNA-target genes 133 The potentially targeted mRNAs were predicted using the integrated bioinformatic tool 134 miRWalk 2.0. Among the 78 DE-miRNAs, 11 miRNAs showed up-regulated expression with at 135 least 20 fold change, including hsa-miR-134, hsa-miR-7, hsa-miR-432 and miR-515 family such 136 as hsa-miR-516-3p, hsa-miR-520b, hsa-miR-520d, hsa-miR-520e, hsa-miR-523. While only 6 137 miRNAs down-regulated mildly, including the hsa-miR-15a, hsa-miR-107, mmu-miR-140*, 138 hsa-miR-149, hsa-miR-194, hsa-miR-103. The target genes of several miRNAs were later ob- 139 served with the STC models. 140 Significant functions and pathway enrichment analysis. 141 Gene ontology and KEGG pathway enrichment analysis were performed on the DEGs to re- 142 veal their biological significance in NPC on GCBI platform. 143 The results showed that the DEGs in dataset GSE29297 were significantly involved in the 144 molecular functions of protein binding, zinc ion binding and DNA binding and sequence-specific 145 DNA binding transcription factor activity. In terms of biological processes, the DEGs were mainly 146 enriched in the process of positive and negative regulation of transcription. Mitogen-activated 147 protein kinase (MAPK) signaling pathway, phosphatidylinositol-3-kinase-Serine/threoninekinase 148 (PI3K-AKT) signaling pathway and pathways of human T-cell leukemia virus type 1 (HTLV-I) 149 and hepatitis B were predominately significant in the KEGG pathway analysis. 150 GO enrichment analysis revealed that the DEGs of dataset GSE12452 were significantly en- 151 riched in protein and ATP binding, DNA and RNA binding as well as mitotic cell cycle. KEGG 152 pathway analysis showed that the DEGs were significantly enriched in the metabolic pathway, the 153 PI3K signaling pathway and pathways associated with cell cycle and RNA transport. 154 Network Analysis 155 The protein-protein coexpression network showed that a number of genes presented higher 156 node degree including exportin 1(XPO1), eukaryotic translation initiation factor 3(EIF3E), tran- 157 scription elongation regulator 1(TCERG1), anaphase promoting complex subunit 5 (ANAPC5), 158 cytochrome c, somatic (CYCS) (Figure 4). The pathway relation network showed that MAPK 159 signaling pathway, pathway of cell cycle and apoptosis and p53 signaling pathway were signifi- 160 cant-enriched downstream pathways, while the pathway in cancer regulated significantly upstream 161 (Figure 5). 162 The time-associated DEGs in STC32 and STC49 models and the up-regulated miRNAs iden- 163 tified in GSE26596 were used to generate a LMP1-specific miRNA-RNA interacting network. Top

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164 5 miRNAs were chosen as core miRNAs to built the network (Figure 6). 165 Discussion

166 NPC featured an EBV type latency which was characterized by the expression of LMP1 167 [7]. High level miRNAs such as the BamHI-A rightward transcripts (BARTs), with the combina- 168 tion of the human noncoding RNAs induced by the virus, may affect the process of the 169 oncogenesis. However, a variety of ncRNAs may hinder our understanding about the LMP1 axis. 170 This integrated study aimed to seek a clue about the interaction between LMP1 and its induced 171 miRNAs in the universe of the signal network. 172 A co-expression analysis of the datasets GSE12452 and GSE29297 displayed a 36-hub genes 173 network. Among these hub genes, the major export receptors of mRNA (XPO1) gene was pre- 174 dicted to co-express with 22 genes in NPC cell and the expression level was 1.8 fold up-regulated 175 (P<0.01). The gene can be regulated by hsa-miR-155 and hsa-miR-373*, which were upregulated 176 in GSE26596. This dataset comprised the expression profile of EBV-negative NPC cell line TW03 177 and the LMP1-transfected TW03 cell line. We proposed that the up-regulated expression levels of 178 miRNAs act as a response to attenuate the up regulated mRNAs such as the XPO1. 179 An overlapped analysis of the datasets GSE12452 and GSE29297 revealed that the DEGs 180 were significantly involved in the PI3K-AKT signaling pathway. Studies have shown that LMP1 181 can activate the PI3K-AKT pathway and evade tumor suppressor responses in many ways. LMP1 182 over-expression led to upregulated level of mi-155-Ubiquilin1 axis to activate PI3K-AKT pathway 183 in NPC cells, which promoted the radioresistence of NPC [26]. In diffuse large B-cell lymphoma 184 the PI3K-AKT signaling pathway was significantly activated by the overexpression of miR-155 in 185 DHL16 cells, while in OCL-Ly3 cells knockdown of miR-155 attenuate the AKT activity [27]. 186 Correspondingly, in GSE26596, miRNA-155 was 12.73 fold upregulated (adjust P<0.01), which 187 indicating that the PI3K-AKT signaling pathway may possibly be regulated by LMP1 via 188 miR-155 in NPC. 189 Analysis showed that most genes in GSE29297 were enriched in two STC expression models 190 in LMP1 TES2-stimulated HEK-293 cell line, named STC32 and STC49. The ascendant expres- 191 sion model STC49 indicated that 1787 probes for 1422 genes were up-regulated in pace with the 192 TES2 stimulation. Pathway analysis showed that these genes were primarily enriched in the pro- 193 cesses such as ubiquitin mediated proteolysis and transforming growth factor-β signaling pathway. 194 It has been proved that LMP1 can regulate ubiquitination by the activation of Interferon regulatory 195 factor 7 [28]. While in the down-regulated gene model STC32, the genes were enriched in the 196 process of mesenchymal to epithelial transition and cell communication. Several studies have 197 shown that LMP1 can promote the progress of epithelial to mesenchymal transition, thus contrib- 198 utes to the metastatic nature of NPC [29,30]. 199 Integrate analysis revealed a miR-515 family including hsa-miR-520e, hsa-miR-523, 200 hsa-miR-520b, hsa-miR-516-3p and hsa-miR-520d was significantly up-regulated. The targets of 201 miR-515 family target genes were predicted to cover large number of genes in the down-regulated 202 model STC32. However, as shown in figure.4, more putative interactions were in the ascending 203 model STC49, hinting that the up-regulation of miRNAs may be a restrain means or a remedial 204 measure to counteract the up-regulation of particular mRNAs. For example, analysis of miRNA 205 hsa-miR-520e targeted genes showed that the down-regulated genes were primarily enriched in 206 glypican pathway, while the up-regulated genes were mainly enriched in vascular endothelial

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207 growth factor signaling pathway, hinting that hsa-miR-520e may play a negative role in balancing 208 the abnormal increasement of specific miRNAs. Another study showed that miR-520 acted as a 209 tumor-suppressive factor by direct targeting of transcription factor P65 and thus inhibit the NF-κB 210 signaling to reduce the expression of the pro-inflammatory cytokines [31]. 211 Recently, it has been reported that hsa-miR-134 played crucial roles in abundant and compli- 212 cated pathways, including KRAS signal pathway, Notch pathway and EGFR pathway [32,33]. In 213 dataset GSE2927, both up-regulated genes such as SMAD6, MYCN, PRLR, CYTH3, BMP3, 214 PDE5A, and down-regulated genes such as BCL2, TGFB2, FOXP2, PKD2, TAF4B, and TSN1 215 were predicted to be targets of miR-134. As many studies showed, miR-134 not only functions as 216 a tumor repressor, but also acts as a cancer promoter. High expression of miR-134 contributes to 217 head and neck carcinogenesis by targeting the WW Domain-Containing Oxidoreductase (WWOX) 218 gene [34]. However, overexpression of miR-134 can inhibit the cell cycle progression of human 219 human ovarian cancer stem cells and decrease the tumorigenicity in nude mice [35]. These studies 220 hinting that a disregulation of miR-134 may participate in the development of NPC. 221 Nevertheless, our study had its points which were not rigorous enough, for example, the da- 222 taset GSE29297 used was limited in only one cite TES2, the DEGs and the DE-miRNAs involved 223 in our study might associate with the sample sorts, the pathological stage and the condition of cell 224 lines. In addition, more experiments were necessary to validate our results. 225 Conclusion 226 In conclusion, by taking advantage of bioinformatic tools and GEO profiles, a protein-protein 227 network and a miRNA-mRNA interaction network were constructed. Our result revealed another 228 layer of gene regulation network in the LMP1-associated gene expression axis, which would pro- 229 vide a better understanding of the interaction of mRNAs and miRNAs. The interaction relation- 230 ship may open a way to explore the potential use of miRNA in NPC. 231 Acknowledgments

232 This research was supported by the National Natural Science Foundation of China (NSFC 233 81571995) and the Natural Science Foundation of Shandong Province (ZR2015HM069). The au- 234 thors appreciated Linlin Wang for providing an account of GCBI tools. 235 Authors’ contributions

236 BL and QS conceived the project and designed experiments. WL, YZ, SW and YY participated in 237 performance of the experiments and the drafting of the manuscript. All authors read and approved 238 the final manuscript. 239 Ethics, consent and permissions

240 Competing interests The authors declared that they have no competing interests. 241 Ethical approval The study was approved by Ethics Committee of Qingdao University Medical 242 College. 243 Consent to publish All the datasets used in the study were obtained from GEO public database, 244 which were equipped with informed consent.

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245 Reference

246 [1] MA Wei K-R, Zheng R-S, Zhang S-W, Liang Z-H, Li Z-M, Chen W-Q et al. Nasopha- 247 ryngeal carcinoma incidence and mortality in China 2013. Chin J Cancer. 2017;36:90. 248 [2] Feng, B.J., Huang, W., Shugart, Y.Y, et al. Genome-wide scan for familial nasopharyn- 249 geal carcinoma reveals evidence of linkage to chromosome 4. Nat Genet. 2002; 31(4): 395. 250 DOI:10.1038/ng932. 251 [3] Xiong, W., Zeng, Z.Y., Xia, J.H., Xia, K., Shen, S.R., Li, X.L., Hu, D.X., Tan, C., Xiang, 252 J.J., Zhou, J., Deng, H., Fan, S.Q., Li, W.F., Wang, R., Zhou, M., Zhu, S.G., Lü, H.B., Qian, J., 253 Zhang, B.C., Wang, J.R., Ma, J., Xiao, B.Y., Huang, H., Zhang, Q.H., Zhou, Y.H., Luo, X.M., 254 Zhou, H.D., Yang, Y.X., Dai, H.P., Feng, G.Y., Pan, Q., Wu, L.Q., He, L., Li, G.Y., et al. A suscep- 255 tibility locus at chromosome 3p21 linked to familial nasopharyngeal carcinoma. Cancer 256 Res.2004;64(6):1972-4. DOI:10.1158/0008-5472.CAN-03-3253. 257 [4] Ren, Z.F., Liu, W.S., Qin, H.D., Xu, Y.F., Yu, D.D., Feng, Q.S., Chen, L.Z., Shu, X.O., 258 Zeng, Y.X., Jia, W.H.,et al. Effect of family history of cancers and environmental factors on risk of 259 nasopharyngeal carcinoma in Guangdong, China. Cancer Epidemiol. 2010;34(4):419-24. DOI: 260 10.1016/j.canep.2010.04.011. 261 [5] Taylor, G.S., Long, H.M., Brooks, J.M., Rickinson, A.B., Hislop, A.D., et al. The immu- 262 nology of Epstein-Barr virus-induced disease. Annu Rev Immunol. 2015;33:787-821. 263 DOI:10.1146/annurev-immunol-032414-112326. 264 [6] Lo, Y.M., Chan, L.Y., Lo, K.W., Leung, S.F., Zhang, J., Chan, A.T., Lee, J.C., Hjelm, 265 N.M., Johnson, P.J., Huang, D.P., et al. Quantitative analysis of cell-free Epstein-Barr virus DNA 266 in plasma of patients with nasopharyngeal carcinoma. Cancer Res.,1999;59(6):1188-91. 267 [7] Yoshizaki, T., Kondo, S., Wakisaka, N., Murono, S., Endo, K., Sugimoto, H., Nakanishi, 268 S., Tsuji, A., Ito, M., et al, Pathogenic role of Epstein-Barr virus latent membrane protein-1 in the 269 development of nasopharyngeal carcinoma. Cancer Lett. 2013;337(1):1-7. 270 DOI:10.1016/j.canlet.2013.05.018. 271 [8] Horikawa, T., Yoshizaki, T., Sheen T.S., Lee, S.Y., Furukawa, M., et al. Association of la- 272 tent membrane protein 1 and matrix metalloproteinase 9 with metastasis in nasopharyngeal carci- 273 noma. Cancer. 2000;89(4):715-23. 274 [9] Lee, D.C., Chua, D.T., Wei, W.I., Sham, J.S., Lau, A.S.,et al. Induction of matrix 275 metalloproteinases by Epstein-Barr virus latent membrane protein 1 isolated from nasopharyngeal 276 carcinoma. Biomed Pharmacother. 2007;61(9):520-6. DOI:10.1016/j.biopha.2007.08.007. 277 [10] Ersing, I., Bernhardt, K., Gewurz, B.E., et al. NF-κB and IRF7 Pathway Activation by 278 Epstein-Barr Virus Latent Membrane Protein 1. Viruses. 2013;5(6):1587-606. 279 DOI:10.3390/v5061587. 280 [11] Devergne, O., Hatzivassiliou, E., Izumi, K.M., Kaye, K.M., Kleijnen, M.F., Kieff, E., 281 Mosialos, G., et al. Association of TRAF1, TRAF2, and TRAF3 with an Epstein-Barr virus LMP1 282 domain important for B-lymphocyte transformation: role in NF-kappaB activation. Mol Cell Biol. 283 1996;16(12):7098-108. 284 [12] Hammond S M. An overview of microRNAs. Adv Drug Deliv Rev. 2015;87:3-14. DOI: 285 10.1016/j.addr.2015.05.001. 286 [13] Hammond, S. M. Dicing and slicing: the core machinery of the RNA interference path- 287 way. FEBS Lett. 2005;579(26):5822-9. DOI:10.1016/j.febslet.2005.08.079.

7 bioRxiv preprint doi: https://doi.org/10.1101/615237; this version posted April 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

288 [14] Li, G., Wu, Z., Peng, Y., Liu, X., Lu, J., Wang, L., Pan, Q., He, M.L., Li, X.P., et al. 289 MicroRNA-10b induced by Epstein-Barr virus-encoded latent membrane protein-1 promotes the 290 metastasis of human nasopharyngeal carcinoma cells. Cancer Lett.2010;299(1):29-36. 291 DOI:10.1016/j.canlet.2010.07.021. 292 [15] Du, Z.M., Hu, L.F., Wang, H.Y., Yan, L.X., Zeng, Y.X., Shao, J.Y., Ernberg, I., et al. 293 Upregulation of MiR-155 in nasopharyngeal carcinoma is partly driven by LMP1 and LMP2A and 294 downregulates a negative prognostic marker JMJD1A. Plos One. 2011;6(4):e19137. 295 DOI:10.1371/journal.pone.0019137. 296 [16] Xia, H., Ng, S.S., Jiang, S., Cheung, W.K., Sze, J., Bian, X.W., Kung, H.F., Lin, M.C., et 297 al. miR-200a-mediated downregulation of ZEB2 and CTNNB1 differentially inhibits nasopha- 298 ryngeal carcinoma cell growth, migration and invasion. Biochem Biophys Res Commun. 299 2010;391(1):535-41. 300 [17] Teramoto N, Maeda A, Kobayashi K, et al. Epstein-Barr virus infection to Epstein-Barr 301 virus-negative nasopharyngeal carcinoma cell line TW03 enhances its tumorigenicity. Laboratory 302 investigation, 2000;80(3):303-312. 303 [18].Hu LF, Zabarovsky ER, Chen F, Cao SL, Ernberg I, Klein G, Winberg G., et al. Isolation 304 and sequencing of the Epstein-Barr virus BNLF-1 gene (LMP1) from a Chinese nasopharyngeal 305 carcinoma. J Gen Virol. 1991;72(Pt10):2399Ð2409. 306 [19] Tusher, V.G., Tibshirani, R., Chu, G., et al. Significance analysis of microarrays applied 307 to the ionizing radiation response. Proc Natl Acad Sci U S A. 2011;98(9):5116-21. 308 DOI:10.1073/pnas.091062498. 309 [20] Reiner, A., Yekutieli, D., Benjamini, Y., et al. Identifying differentially expressed genes 310 using false discovery rate controlling procedures. Bioinformatics. 2003,19(3):368-75. 311 [21] Barrett, T., Wilhite, S.E., Ledoux, P., Evangelista, C., Kim, I.F., Tomashevsky, M., Mar- 312 shall, K.A., Phillippy, K.H., Sherman, P.M., Holko, M., Yefanov, A., Lee, H., Zhang, N., Robert- 313 son, C.L., Serova, N., Davis, S., Soboleva, A., et al. NCBI GEO: archive for functional genomics 314 data sets--update. Nucleic Acids Res. 2013;41 (Database issue), D991-5. 315 DOI:10.1093/nar/gks1193. 316 [22]Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., 317 Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., 318 Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., Sherlock, G., et al. Gene 319 ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 320 2000;25(1):25-9. DOI:10.1038/75556. 321 [23] Dweep, Harsh., N., Gretz., et al. miRWalk2.0: a comprehensive atlas of mi- 322 croRNA-target interactions. Nat Methods.2015;12(8):697. DOI:10.1038/nmeth.3485. 323 [24] Rehmsmeier, M., Steffen, P., Hochsmann, M., Giegerich, R., et al. Fast and effective 324 prediction of microRNA/target duplexes. RNA. 2004;10(10):1507-17. 325 [25] Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., 326 Schwikowski, B., Ideker, T., et al. Cytoscape: a software environment for integrated models of 327 biomolecular interaction networks. Genome Res. 2003;13(11):2498-504. 328 DOI:10.1101/gr.1239303. 329 [26] Yang, F., Liu, Q., Hu, C.M., et al. Epstein-Barr virus-encoded LMP1 increases miR-155 330 expression, which promotes radioresistance of nasopharyngeal carcinoma via suppressing 331 UBQLN1. Eur Rev Med Pharmacol Sci. 2015;19(23):4507-15.

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332 [27] Huang, X., Shen, Y., Liu, M., Bi, C., Jiang, C., Iqbal, J., McKeithan, T.W., Chan, W.C., 333 Ding, S.J., Fu, K., et al. Quantitative proteomics reveals that miR-155 regulates the PI3K-AKT 334 pathway in diffuse large B-cell lymphoma. Am J Pathol.2012;181(1):26-33. 335 DOI:10.1016/j.ajpath.2012.03.013. 336 [28] Ning, S., Pagano, J.S., Barber, G.N., et al. IRF7: activation, regulation, modification and 337 function. Genes Immun.2011;12(6):399-414. DOI:10.1038/gene.2011.21. 338 [29] Wasil, L.R., Shair, K.H., et al. Epstein-Barr virus LMP1 induces focal adhesions and ep- 339 ithelial cell migration through effects on integrin-α5 and N-cadherin. Oncogenesis.2015;4:e171. 340 DOI:10.1038/oncsis.2015.31. 341 [30] Sides, M.D., Klingsberg, R.C., Shan, B., Gordon, K.A., Nguyen, H.T., Lin, Z., 342 Takahashi,T., Flemington, E.K., Lasky, J.A., et al. The Epstein-Barr virus latent membrane protein 343 1 and transforming growth factor--β1 synergistically induce epithelial--mesenchymal transition in 344 lung epithelial cells. Am J Respir Cell Mol Biol. 2011;44(6):852-62. 345 DOI:10.1165/rcmb.2009-0232OC. 346 [31] Keklikoglou, I., Koerner, C., Schmidt, C., Zhang, J.D., Heckmann, D., Shavinskaya, A., 347 Allgayer, H., Gückel, B., Fehm, T., Schneeweiss, A., Sahin, O., Wiemann, S., Tschulena, U., et al. 348 MicroRNA-520/373 family functions as a tumor suppressor in estrogen receptor negative breast 349 cancer by targeting NF- jB and TGF-b signaling pathways. Oncogene.2012;31(37):4150-63. 350 DOI:10.1038/onc.2011.571. 351 [32] Liu, Y., Zhang, M., Qian, J., Bao, M., Meng, X., Zhang, S., Zhang, L., Zhao, R., Li, S., 352 Cao, Q., Li, P., Ju, X., Lu, Q., Li, J., Shao, P., Qin, C., Yin, C., et al. miR-134 functions as a tumor 353 suppressor in cell proliferation and epithelial-to-mesenchymal Transition by targeting KRAS in 354 renal cell carcinoma cells. DNA Cell Biol. 2015;34(6):429-36. 355 [33] Gao, Y., Liu, T., Huang, Y., et al. MicroRNA-134 suppresses endometrial cancer stem 356 cells by targeting POGLUT1 and Notch pathway proteins. FEBS Lett. 2015;589(2):207-14. 357 DOI:10.1016/j.febslet.2014.12.002. 358 [34] Ekizoglu, S., Bulut, P., Karaman, E., Kilic, E., Buyru, N., et al. Epigenetic and genetic 359 alterations affect the WWOX gene in head and neck squamous cell carcinoma. PLoS One.2015; 360 10(1):e0115353. DOI:10.1371/journal.pone.0115353. 361 [35] Chang, C., Liu, T., Huang, Y., Qin,W., Yang, H., Chen, J.,et al. MicroRNA-134-3p is a 362 novel potential inhibitor of human ovarian cancer stem cells by targeting RAB27A. Gene. 363 2017;605:99-107. DOI:10.1016/j.gene.2016.12.030.

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364 Figure legends 365 366 Figure 1. Heat map showing the differential expression pattern of genes in dataset 367 GSE29297. The x-axis represents samples and the y-axis represents the genes. The 368 bar on the top indicates the log2-scaled expression level, up-regulated genes were 369 presented in red, while down-regulated in green. 370 371 Figure 2. Heat map showing the differential expression pattern of genes in dataset 372 GSE12452. The x-axis represents samples and the y-axis represents the genes. The 373 bar on the top indicates the log2-scaled expression level, up-regulated genes were 374 presented in red, while down-regulated in green. 375 376 Figure 3. STC models. The profile represent the series test of cluster (STC), profile 377 number represent the STC models. P-value: adjusted P-value represents the signifi- 378 cant levels of the genes in a certain model compared with theoretical genes. The dia- 379 gram was present in red lines as the P-value of the model<0.05, otherwise in blue 380 lines. Numbers in horizontal ordinate were relative periods of the LMP1-TES2 gene 381 stimulation on the HEK293 cell line. The ordinate numbers represent relative grades 382 of expression levels. 383 384 Figure 4. Gene co-expression network of datasets GSE29297 and GSE12452.The size 385 of the node represents the degree of the gene. The positive correlation coefficient was 386 presented in red line, the negative was in blue. 387 388 Figure 5. Pathway relation network of datasets GSE29297 and GSE12452. The arrow 389 in the network directed to the downstream pathways. The size of the node represents 390 the degree of pathway. 391 392 Figure 6. Interaction network between top 5 up-regulated miRNAs in GSE26596 with 393 expression models STC32 and STC49. Up-regulated genes were filled in Red dia- 394 mond, and down-regulated genes green ellipse. The miRNAs were filled in blue dia- 395 mond. These genes were regarded as targets of the miRNAs in predicted and validate 396 databases as described. 397

10 bioRxiv preprint doi: https://doi.org/10.1101/615237; this version posted April 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/615237; this version posted April 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/615237; this version posted April 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/615237; this version posted April 29, 2019. The copyright holder for this preprint (which was not Figure 4 certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under GSNaCC-BY-NC-ND 4.0 International license. SERPINB1 PPP2R5C DNAH1 MLLT4 ENO1 PER2 OAS3 HEMK1 TJP2 U2AF2 AGK CALM1 EIF3E HSPD1 CYCS ATP1B1 PAK2 PTK2 TCERG1 XPO1 CD44 ANAPC5 ABCC4 CDC23 RANBP2 U2SURP SOS1 PNN CUL2 GLS PRPF40A SEH1L LAMP2 PTEN

PIK3C2A bioRxiv preprint doi: https://doi.org/10.1101/615237; this version posted April 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Leukocyte transendothelial migration

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ELMO2

PHF20L1