Integrated Analysis of Circular RNA Associated Cerna Network Reveals Potential CircRNA Biomarkers in Human Breast Cancer

Integrated Analysis of Circular RNA Associated Cerna Network Reveals Potential CircRNA Biomarkers in Human Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Primary research Integrated Analysis of Circular RNA Associated Cerna Network Reveals Potential CircRNA Biomarkers in Human Breast Cancer Hui Shen, Huan Pan, Jianju Lu, Jianfen Shen, Longsheng Xu, Han Sheng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-90865/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background There is increasing evidence that circular RNA (circRNA) is closely related to tumorigenesis and cancer progression. circRNA has been identified as a sponge of microRNA (miRNA) in a competitive endogenous RNA (ceRNA) network and is involved in the regulation of mRNA expression. However, the roles of cancer specific circRNAs in circRNA-related ceRNA network of breast cancer (BRCA) are still unclear. This study aims to construct a ceRNA network associated with circRNA and to explore new therapeutic and prognostic targets and biomarkers for breast cancer. Methods We downloaded the circRNA expression profile of BRCA from Gene Expression Omnibus (GEO) microarray datasets and downloaded the miRNA and mRNA expression profiles of BRCA from The Cancer Genome Atlas (TCGA) database, these data were included in the study for comprehensive analysis. Differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs) and differentially expressed circRNAs (DEcircRNAs) were identified and a competitive endogenous RNA (ceRNA) regulatory network was constructed based on circRNA–miRNA pairs and miRNA–mRNA pairs. Gene ontology and pathway enrichment analysis were performed on mRNAs regulated by circRNAs in ceRNA networks. Survival analysis and correlation analysis of all mRNAs and miRNAs in the ceRNA network were performed. The STRING search tool was used to predict the interaction between proteins, and the hub genes were screened by the MCODE plugin in Cytoscape. Results A total of 72 DEcircRNAs, 158 DEmiRNAs and 2762 DE mRNAs were identified. The constructed ceRNA network contains 60 circRNA-miRNA pairs and 140 miRNA-mRNA pairs, including 40 circRNAs, 30 miRNAs and 100 mRNAs. Functional enrichment indicated that DEmRNAs regulated by DEcircRNAs in ceRNA networks were significantly enriched in PI3K-Akt signaling pathway, MicroRNAs in cancer and Proteoglycans in cancer. Survival analysis and correlation analysis of all mRNAs and miRNAs in the ceRNA network showed that a total of 13 mRNAs and 6 miRNAs were significantly associated with overall survival, and 48 miRNA-mRNA interaction pairs had a significant negative correlation. A PPI network was established and 21 hub genes were determined from the network. After comprehensive analysis, four potential ceRNA regulatory axes were constructed based on three circRNAs, two miRNAs, and three mRNAs. Conclusions This study provides an effective bioinformatics basis for further understanding the molecular mechanisms and predictions of breast cancer. A better understanding of the circRNA-related ceRNA network in BRCA will help identify potential biomarkers for diagnosis and prognosis. Cancer Biology circRNA ceRNA network biomarker carcinogenesis bioinformatics breast cancer The Cancer Genome Atlas Gene Expression Omnibus Figures Figure 1 Figure 1 Figure 2 Figure 2 Figure 3 Figure 3 Figure 4 Figure 4 Figure 5 Figure 5 Figure 6 Figure 6 Figure 7 Figure 7 Figure 8 Figure 8 Figure 9 Figure 9 Background Breast cancer is one of the most common cancer among women worldwide [1], with strong invasiveness and metastasis, and the incidence and mortality of breast cancer continue to increase [2]. Currently, treatments for breast cancer include surgery, radiation therapy, endocrine therapy, chemotherapy, and biotargeted therapy. However, the recurrence rate and drug resistance of some patients are still high, and the therapeutic effect and prognosis of breast cancer have not been satisfactory. Therefore, the molecular pathogenesis of breast cancer needs to be further understood, and the identification of new candidate therapeutic targets and biomarkers is urgently needed for breast cancer treatment. An in-depth study of the molecular mechanism of tumors based on bioinformatics analysis has exploited an important method to tumor research. It can not only explore the molecular pathogenesis of tumors in depth, but also identify new biomarkers for tumor pathogenesis and prognosis [3]. In the past few decades, 70%-90% of the transcribed human genome has been identified. Related data indicate that protein-coding genes account for only about 2% of the human genome, and non-coding RNAs make up the majority of the human transcriptome [4]. Non-coding RNAs are a large class of RNA molecules that do not encode proteins, but which serve regulatory roles, mainly includes: circular RNAs (circRNAs), microRNAs (miRNAs), long nocoding RNAs (lncRNAs) and small nuclear RNAs. The competitive endogenous RNA (ceRNA) hypothesis reveals a new mechanism for interaction between RNAs. The main idea of the ceRNA hypothesis is that multiple types of RNA transcripts communicate with each other by competing for binding to shared miRNA-binding sites (miRNA response elements or MREs) [5]. It has been reported that circRNAs contain multiple miRNA-binding sites that bind to miRNAs, which are seen as miRNA sponges that result in inhibition of miRNAs activity and regulation of expression of their downstream target genes [6, 7]. CircRNA is a class of covalently closed single-stranded circular RNA molecules without free 5 or 3 end which makes them well expressed and more stable than their linear counterparts. CircRNA is abundant in eukaryotic cells, highly conserved, structurally stable, and has certain tissue, time and disease specificity. Due to these characteristics, circRNA has become a new hotspot of research [8]. A vast number of circRNAs have been discovered in a variety of cancers and they are activated in inhibiting tumor progression or promoting tumorigenesis. For example, circ-MTO1 can inhibit the progression of liver cancer cells [9]. circ-LARP4 can inhibit cell proliferation and invasion of gastric cancer cells by sponging miR-424-5p and regulating the expression of LATS1 [10]. Circ-FBXW7 suppresses the development of gliomas, and its expression is positively correlated with the overall survival of patients with glioblastoma [11]. The hsa_circ_001783 regulates the progression of breast cancer (in vitro) by sponging miR-200c-3p to regulate ZEB1/2 and ETS1 and is associated with poor clinical outcomes in breast cancer patients [12]. In the current study, we collected the expression profiles of circRNA, miRNA and mRNA from BRCA tissues and adjacent normal mammary gland tissues from the Gene Expression Omnibus (GEO) database and the The Cancer Genome Atlas (TCGA) database. We performed a comprehensive analysis of these expression profiles to identify differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed circRNAs (DEcircRNAs). After predicting sponging of miRNAs by circRNA and miRNA target genes, we constructed a circRNA-miRNA-mRNA network. To investigate the main functional pathways involved in the development of breast cancer in this ceRNA network, DEmRNAs of the ceRNA network were assessed by gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, and we have established a protein-protein interaction network, this study will try to better understand the pathogenesis of BRCA. Finally, we performed an overall survival analysis of miRNAs and mRNAs in ceRNA networks to identify prognostic biomarkers associated with breast cancer. Through this study, we can not only further understand the molecular mechanism of breast cancer development, but also provide potential circRNA, miRNA and mRNA biomarkers for the early diagnosis, treatment and prognosis of breast cancer. Methods Expression profiling in The Cancer Genome Atlas and Gene Expression Omnibus The mRNA and miRNA sequence data of breast cancer were extracted from the TCGA database ( https://portal.gdc.cancer.gov/ ). All file data were downloded using the GDC Data Transfer Tool (Provided by GDC Apps) ( https://tcga-data.nci.nih.gov/ ). The mRNA profiles contained 1097 BRCA tissues and 114 adjacent normal tissues, and the miRNA profiles contained 1092 BRCA tissues and 105 adjacent normal tissues. The exclusion criteria were set as follows: samples without clinical data and samples without complete information of stage and overall survival period. The circRNA expression profiles of BRCA were downloaded from GEO database ( http://www.ncbi.nlm.nih.gov/geo ) by searching keywords (("breast neoplasms" [MeSH Terms] OR breast cancer [All Fields]) AND circRNA [All Fields]) AND ("Homo sapiens" [Organism] AND ("Non-coding RNA profiling by array" [Filter] OR "Non-coding RNA profiling by high throughput sequencing" [Filter])). We selected data according to the following criteria: selected datasets should be circRNA transcriptome data of the whole genome, these data were derived from tumor tissues and adjacent normal tissues of patients with BRCA, and datasets were standardized or raw datasets. The GSE101123 dataset met the screening requirements and was used in this study. The dataset included 3 normal mammary gland tissues and 8 BRCA tissues. These expression profiles do not require ethical approval or informed consent due to we used the publicly available data from TCGA and GEO. Identification of differentially expressed mRNAs, miRNA, circRNA in breast cancer compared to adjacent tissues Firstly, the difficultly detected mRNAs/miRNAs, which with read count value=0 in more than 50% samples, were filtered and deleted. To obtain the differentially expressed mRNAs (DEmRNAs) and miRNAs (DEmiRNAs) between normal tissues and BRCA, the count data were processed with the Bioconductor package edge R [13] in software. All RNA expression levels were standardized to the sample mean. The P value was corrected with a false discovery rate (FDR). The threshold for the expression of DEmRNAs and DEmiRNAs was FDR1. Additionally, the differently expressed circRNAs (DEcircRNAs) were screened using Limma package, the threshold for the expression of DEcircRNAs was P value1. Construction of the ceRNA regulatory network The Circular RNA Interactome (CircInteractome) ( https://circinteractome.nia.nih.gov/ ) and Cancer-Specific CircRNA (CSCD) ( http://gb.whu.edu.cn/CSCD/ ) were used to predict miRNA binding sites (MREs). These miRNAs were considered as potential target miRNAs of the DEcircRNAs. These target miRNAs were further screened by DEmiRNA based on the TCGA. Interactions between miRNA and mRNA were predicted based on the Targetscan [14], miRTarBase [15], and miRDB [16] databases. Only mRNAs recognized by all three database were considered as candidate mRNAs, and were intersected with DEmRNAs to screen the DEmRNAs targeted by DEmiRNAs. The circRNA-miRNA-mRNA regulatory network was contructed using a combination of circRNA-miRNA pairs and miRNA-mRNA pairs. Finally, the network was visualized and mapped using Cytoscape v3.7.0 [17]. Figure 1 shows a flow chart for the development of the ceRNA network. Gene ontology and pathway enrichment analysis of DEGs in the ceRNA network To assess the function of differentially expressed genes (DEGs) in the ceRNA network in tumorigenesis, we performed Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using the clusterProfiler package [18] of R software. P -value<0.01 was set as the cut-off criterion. Survival analysis and correlation analysis of DEmiRNAs and DEmRNAs in ceRNA networks Each sample in the TCGA was independent of each other and it contained all sample information such as gene expression, prognosis and survival time. We obtained clinical information from breast cancer patients from the TCGA database and combined the expression data of DEmiRNAs and DE mRNAs with clinical data from patients. We used Survival package of R to perform survival analysis of DEmiRNAs and DE mRNAs in the ceRNA network with P < 0.05 as the threshold. In addition, DEmiRNAs and DEmRNAs with significant overall survival were identified as prognostic biomarkers. In the TCGA-BRCA dataset, the vast majority of samples were present in both miRNA and mRNA expression profiles, and samples that were only present in one expression profile were deleted. Correlation analysis between the interacting miRNA and mRNA in the ceRNA network was performed using R software, with r<-0.3, P <0.001 as the threshold. The miRNA-mRNA pair that satisfies the condition is considered to have a strong negative correlation. Construction PPI network and module analysis To assess the interactions between the DEGs in the ceRNA network, we constructed a protein-protein interaction (PPI) network using the Search Tool for the Retrieval of Interacting Genes (STRING, http://string.embl.de/) online tool. We used the MCODE plugin to screen modules of hub genes from the PPI network. The interaction network was visualized using Cytoscape software. Quantitative real-time PCR validation Ten pairs of breast cancer tissues and corresponding adjacent non-tumor tissues from BRCA patients were obtained from Department of Breast Disease, The First Affiliated Hospital of Jiaxing University. The study was approved by the ethics committee and written informed consent was obtained from all patients. In this ceRNA network, we randomly selected six circRNAs, miRNAs and mRNAs respectively, and verified the reliability and validity of the prediction results in BRCA patients using qRT-PCR. Total RNA was isolated using Trizol reagent (Invitrogen, USA) according to the manufacturer's protocol, and RNA purity was detected by NanoDrop 2000 spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Based on SuperReal PreMix Plus (Invitrogen, USA) in StepOneplus Real-time PCR Detection System (Applied Biosystems, Foster City, CA, USA), the qRT-PCR reactions were performed. The relative gene expression was calculated by 2 - △△ Ct . The humanβ-actin and human U6 was used as endogenous controls for mRNA and miRNA expression in analysis, respectively. The human GAPDH was used as endogenous controls for circRNA expression in analysis. Results Identification of differentially expressed RNAs in breast cancer Compared to adjacent tissues, a total of 2762 DEmRNAs (1118 upregulated and 1644 downregulated miRNAs) and 158 DEmiRNAs (71 upregulated and 87 downregulated miRNAs) were identified in BRCA with FDR1. A total of 72 DEcircRNAs (51 upregulated and 21 downregulated circRNAs) were obtained in BRCA compared to adjacent tissues with P value1. The RNAs hierarchical clustering analyses are presented in Figure 2, and it was demonstrated that the expression levels of these three type of RNAs were significantly differentiated compared with the normal tissues. Finally, volcano plots were generated, and differences between the normal and tumor groups were identified (Figure 2). Table 1, 2 and 3 show the top 10 up and down regulation of DEmRNAs, DEmiRNAs and DEcircRNAs in BRCA, respectively. Table 1 Top 10 upregulation and downregulation DEmRNAs in BRCA. mRNA logFC P value FDR Regulation COL10A1 6.870373 1.01E-60 1.67E-59 Up CST1 6.616865 1.52E-56 2.15E-55 Up MMP13 6.354082 8.94E-57 1.27E-55 Up IBSP 5.863152 3.64E-104 1.91E-102 Up MMP11 5.781753 2.35E-84 7.56E-83 Up COL11A1 5.337783 1.91E-37 1.39E-36 Up MMP1 5.235857 1.55E-39 1.21E-38 Up PLPP4 5.184921 2.61E-88 9.12E-87 Up SLC24A2 4.98746 2.18E-73 5.43E-72 Up COMP 4.598021 7.01E-40 5.58E-39 Up ADH1B -6.86393 2.57E-114 1.69E-112 Down TUSC5 -6.81912 1.32E-161 3.10E-159 Down ADIPOQ -6.79919 3.51E-120 2.65E-118 Down CIDEC -6.63588 3.51E-174 1.19E-171 Down SCARA5 -6.48854 3.00E-239 1.53E-235 Down FABP4 -6.14086 1.66E-110 1.01E-108 Down PLIN1 -6.00856 4.59E-146 6.21E-144 Down GPD1 -5.99957 5.75E-169 1.57E-166 Down AQP7 -5.81861 1.75E-206 1.91E-203 Down PLIN4 -5.79315 4.21E-115 2.84E-113 Down Table 2 Top 10 upregulation and downregulation DEmiRNAs in BRCA. miRNA logFC P value FDR Regulation hsa-miR-592 3.424794 2.41E-76 6.71E-75 Up hsa-miR-1307-5p 3.197336 1.96E-88 6.73E-87 Up hsa-miR-96-5p 3.155099 3.09E-90 1.25E-88 Up hsa-miR-141-3p 3.087332 4.21E-97 1.88E-95 Up hsa-miR-429 2.994871 1.10E-70 2.32E-69 Up hsa-miR-190b 2.963382 6.87E-26 2.73E-25 Up hsa-miR-183-5p 2.924208 6.80E-108 5.05E-106 Up hsa-miR-200a-3p 2.638975 4.00E-63 6.60E-62 Up hsa-miR-184 2.553121 1.13E-13 2.68E-13 Up hsa-miR-200a-5p 2.512744 2.55E-65 4.53E-64 Up hsa-miR-486-5p -4.09846 1.82E-115 1.62E-113 Down hsa-miR-139-3p -3.60464 2.43E-132 5.41E-130 Down hsa-miR-204-5p -3.53102 4.25E-102 2.70E-100 Down hsa-miR-139-5p -3.19493 2.46E-156 1.10E-153 Down hsa-miR-451a -3.07530 1.49E-76 4.42E-75 Down hsa-miR-5683 -2.96346 7.12E-51 7.73E-50 Down hsa-miR-144-5p -2.79822 2.63E-71 5.84E-70 Down hsa-miR-1247-3p -2.73934 1.42E-45 1.27E-44 Down hsa-miR-452-5p -2.48651 2.97E-54 3.67E-53 Down hsa-miR-145-5p -2.45593 1.41E-123 1.57E-121 Down Table 3 Top 10 upregulation and downregulation DEcircRNAs in BRCA. circRNA Alias logFC P value Regulation hsa_circRNA_001846 hsa_circ_0000520 3.800033 0.0004347 Up hsa_circRNA_000167 hsa_circ_0000518 3.742085 0.0016513 Up hsa_circRNA_002172 hsa_circ_0000514 3.365067 0.0042528 Up hsa_circRNA_002144 hsa_circ_0000511 2.995608 0.0044046 Up hsa_circRNA_000166 hsa_circ_0000512 2.883005 0.0044287 Up hsa_circRNA_000585 hsa_circ_0000515 2.788805 0.0012788 Up hsa_circRNA_001678 hsa_circ_0000517 2.6775 0.0001633 Up hsa_circRNA_101967 hsa_circ_0041732 2.335477 0.002966 Up hsa_circRNA_101233 hsa_circ_0008784 1.958382 0.0035194 Up hsa_circRNA_002178 hsa_circ_0000519 1.90691 0.00011 Up hsa_circRNA_102049 hsa_circ_0043278 -3.924372 0.0099265 Down hsa_circRNA_102619 hsa_circ_0000977 -3.646793 0.0080463 Down hsa_circRNA_102051 hsa_circ_0006220 -3.398483 0.0091516 Down hsa_circRNA_103345 hsa_circ_0065173 -2.507172 0.0025211 Down hsa_circRNA_102651 hsa_circ_0008911 -2.179549 0.001517 Down hsa_circRNA_001153 hsa_circ_0001455 -2.059051 0.0016539 Down hsa_circRNA_104653 hsa_circ_0008303 -1.776765 0.0009081 Down hsa_circRNA_101381 hsa_circ_0004781 -1.771974 0.0029019 Down hsa_circRNA_100685 hsa_circ_0020080 -1.714172 0.0034233 Down hsa_circRNA_000554 hsa_circ_0000376 -1.615084 0.0001321 Down Construction of ceRNA regulatory network in BRCA To elucidate the regulatory mechanism of BRCA, a circRNA-miRNA-mRNA related ceRNA network of BRCA was developed according the above results. First, we searched for the target miRNAs of the 72 DEcircRNAs in the CircIteractome and CSCD databases, and found 295 interactive circRNAs-miRNAs pairs after intersecting with the DEmiRNAs. The circRNA-miRNA relationship pairs were screened according to a negative regulatory pattern, and positively co-expressed circRNA-miRNA pairs were discarded. The results showed that 162 interactive circRNA-miRNA pairs were screened, of which 72 DEmiRNAs were confirmed to interact with 59 DEcircRNAs. Following this, we predicted that 1626 mRNAs were targeted by these 72 DEmiRNAs in all three target predicting databases (TargetScan, miRTarBase and miRDB). these 1626 target mRNAs intersected with the 2762 DEmRNAs, and target mRNAs not contained in DEmRNAs was excluded, resulting in a total of 327 interactive miRNA-mRNA pairs. At the same time, we also screened miRNA-mRNA pairs based on negative regulatory patterns and discarded positively co-expressing pairs. The results showed that eventually 30 DEmiRNAs and 100 DEmRNAs formed 140 interactive miRNA-mRNA pairs. The circRNA-miRNA and miRNA –mRNA relationship pairs (Tables 4 and 5) were combined into the ceRNA network following the pattern of negative regulation. Finally, we constructed the ceRNA regulatory network of BRCA comprised of 200 edges among 40 DEcircRNAs, 30 DEmiRNAs and 100 DEmRNAs. The ceRNA network in BRCA was visualized using Cytoscape software (Figure 3). Table 4 Interaction between circRNA and miRNA in the ceRNA network. circRNA miRNA hsa_circ_0000069 hsa-miR-193a-5p hsa_circ_0000376 hsa-miR-142-5p hsa_circ_0000511 hsa-miR-296-5p hsa_circ_0000512 hsa-miR-296-5p hsa_circ_0000514 hsa-miR-296-5p hsa_circ_0000515 hsa-miR-204-5p, hsa-miR-296-5p hsa_circ_0000517 hsa-miR-193a-5p, hsa-miR-296-5p hsa_circ_0000519 hsa-miR-204-5p, hsa-miR-296-5p hsa_circ_0000520 hsa-miR-296-5p hsa_circ_0001455 hsa-miR-142-5p hsa_circ_0001806 hsa-miR-139-5p hsa_circ_0002702 hsa-miR-100-5p, hsa-miR-99a-5p, hsa-miR-296-5p hsa_circ_0003528 hsa-miR-224-5p hsa_circ_0003645 hsa-miR-335-5p hsa_circ_0004313 hsa-miR-365a-3p, hsa-miR-365b-3p hsa_circ_0004315 hsa-miR-145-5p, hsa-miR-195-5p, hsa-miR-497-5p, hsa-miR-218-5p hsa_circ_0004538 hsa-miR-503-5p, hsa-miR-7-5p hsa_circ_0005273 hsa-miR-328-3p hsa_circ_0005397 hsa-miR-10b-5p, hsa-miR-1-3p hsa_circ_0005699 hsa-miR-143-3p hsa_circ_0006220 hsa-miR-342-3p hsa_circ_0006758 hsa-miR-224-5p, hsa-miR-1-3p hsa_circ_0008365 hsa-miR-328-3p hsa_circ_0008784 hsa-miR-193a-5p, hsa-miR-139-5p hsa_circ_0014624 hsa-miR-7-5p hsa_circ_0016201 hsa-miR-33a-5p, hsa-miR-33b-5p hsa_circ_0020080 hsa-miR-7-5p hsa_circ_0022587 hsa-miR-193a-5p hsa_circ_0025388 hsa-miR-139-5p, hsa-miR-144-3p, hsa-miR-335-5p hsa_circ_0028190 hsa-miR-451a hsa_circ_0031724 hsa-miR-143-3p hsa_circ_0041732 hsa-miR-193a-5p hsa_circ_0041821 hsa-miR-144-3p hsa_circ_0049998 hsa-miR-205-5p, hsa-miR-145-5p hsa_circ_0054021 hsa-miR-141-3p, hsa-miR-200a-3p hsa_circ_0058753 hsa-miR-218-5p hsa_circ_0069104 hsa-miR-451a, hsa-miR-296-5p hsa_circ_0082564 hsa-miR-205-5p hsa_circ_0084429 hsa-miR-129-5p, hsa-miR-224-5p hsa_circ_0084443 hsa-miR-129-5p Table 5 Interaction between miRNA and mRNA in the ceRNA network. miRNA mRNA hsa-miR-100-5p FGFR3 hsa-miR-10b-5p GATA3, SDC1 hsa-miR-129-5p CBX4, COL1A1 hsa-miR-139-5p TPD52, ZNF367 hsa-miR-1-3p ADAM12, AP1S1, CERS2, E2F5, FAM102A, FN1, RIMS4, SLC25A22, GPR137C, TRPS1 hsa-miR-141-3p EPHA2,QKI, STAT5A, USP53, YAP1, ZEB2 hsa-miR-142-5p CREBRF, FIGN, SLITRK4, TNS1, ZBTB20 hsa-miR-143-3p COL1A1, ERBB3, LIMK1, SERPINE1, TTYH3 hsa-miR-144-3p EZH2, KPNA2, NACC1, SIX4 hsa-miR-145-5p ABHD17C, ABRACL, RTKN, SERPINE1, SOX11, TPM3 hsa-miR-193a-5p NUP210 hsa-miR-195-5p CBX2, CBX4, CCNE1, CDC25A, CDCA4, CEP55, CHEK1, CLSPN, E2F7, ENTPD7, HMGA1, KIF23, MYB, RASEF, RET, SLC25A22, ZNF367 hsa-miR-200a-3p DLC1, EPHA2, HGF, QKI, THRB, USP53, YAP1, ZEB2 hsa-miR-204-5p AP1S1, EZR, RUNX2 hsa-miR-205-5p CENPF, ERBB3, EZR, LPCAT1, PARD6B, RUNX2 hsa-miR-218-5p BCL9, CDH2, CNTNAP2, FBN2, FBXO41, LMNB1, RET, NACC1, PRLR, RUNX2, TPD52, TTYH3 hsa-miR-224-5p DIO1 hsa-miR-296-5p HMGA1 hsa-miR-328-3p H2AFX hsa-miR-335-5p CDH11 hsa-miR-33a-5p ABCA1, ARID5B, DSC3, GAS1, ZC3H12C hsa-miR-33b-5p ABCA1, ARID5B, GAS1 hsa-miR-342-3p FIGN, ID4, MBNL3 hsa-miR-365a-3p MCOLN2, SIX4 hsa-miR-365b-3p MCOLN2, SIX4 hsa-miR-451a CDKN2D, MIF hsa-miR-497-5p ANLN, CBX2, CBX4, CCNE1, CDC25A, CDCA4, CEP55, CHEK1, CLSPN, E2F7, KIF23, RASEF, ZNF367 hsa-miR-503-5p AKT3, CCND2, CYP26B1, FGF2, PIK3R1, RECK hsa-miR-7-5p EGFR, FNDC4, IRS1, IRS2, KLF4, RBMS3, RRAS2, SNCA, SOCS2 hsa-miR-99a-5p FGFR3 Functional annotation of the DEGs in the ceRNA network In order to better understand the potential functional significance of differentially expressed genes in the ceRNA network, we performed GO and KEGG functional enrichment analysis. In the GO analysis we identified a total of 162 enriched GO terms (FDR<0.01). The top 8 significantly enriched GO terms in the biological process (BP), cellular components (CC) and molecular function (MF) are shown in Figure 4. The biological processes of these differentially expressed genes were primarily associated with regulated by protein kinase B signaling, phosphatidylinositol phosphorylation, protein kinase B signaling and lipid phosphorylation. Meanwhile, the genes related to cellular components were mostly involved in nuclear transcription factor complex, focal adhesion, cell-substrate adherens junction and cell-substrate junction. In terms of molecular function, these differential genes were mostly enriched in phosphatidylinositol-4,5-bisphosphate 3-kinase activity, phosphatidylinositol bisphosphate kinase activity, phosphatidylinositol 3-kinase activity and 1-phosphatidylinositol-3-kinase activity. Additionally, KEGG signal pathway analysis showed that 24 signal pathways were significantly enriched (FDR<0.01). The top 15 significantly enriched pathways are shown in Figure 5. Among these pathways, the ‘PI3K-Akt signaling pathway’, ‘MicroRNAs in cancer’, ‘Proteoglycans in cancer’, ‘Cellular senescence’, ‘FoxO signaling pathway’, ‘Central carbon metabolism in cancer’ and ‘Cell cycle’ are closely correlated with the carcinogenesis and development of BRCA. Prognostic characteristics of RNAs in the ceRNA regulatory network Survival analysis based on Survival package of R found that 13 mRNAs （CCNE1, TPD52, SDC1, ANLN, ZNF367, SOX11, IRS2, EZR, DSC3, CCND2, KPNA2, CBX2 and CEP55）among the 100 DEmRNAs in the ceRNA network were closely associated with the overall survival of breast cancer patients. The low expression of CCNE1, TPD52, SDC1, ANLN, ZNF367, SOX11, EZR, KPNA2, CBX2 and CEP55 was associated with high survival, whereas for IRS2, DSC3 and CCND2, high expression was associated with high survival. Six miRNAs (hsa-miR-204-5p, hsa-miR-335-5p, hsa-miR-100-5p, hsa-miR-195-5p, hsa-miR-328-3p and hsa-miR-342-3p) of 30 DEmiRNAs were associated with prognosis. High expression of hsa-miR-204-5p, hsa-miR-335-5p, hsa-miR-100-5p, hsa-miR-195-5p and hsa-miR-342-3p indicated long survival time, while high expression of hsa-miR-328-3p indicated a relatively short survival time. Survival analysis results are shown in Table 6 and Figure 6. Notably, based on the ceRNA network, we found that the hsa_circ_0004315-hsa-miR195-5p axis was associated with four mRNAs associated with breast cancer prognosis. Table 6 Prognostic value of the differentially expressed mRNAs and miRNAs. Name HR (95% Cl) P value CCNE1 1.606 (1.169-2.208) 0.0038 TPD52 1.578 (1.148-2.169) 0.0053 SDC1 1.516 (1.102-2.086) 0.0102 ANLN 1.489 (1.083-2.046) 0.0154 ZNF367 1.475 (1.073-2.025) 0.0176 SOX11 1.443 (1.050-1.983) 0.0244 IRS2 0.705 (0.512-0.971) 0.0299 EZR 1.418 (1.030-1.950) 0.0311 DSC3 0.718 (0.521-0.988) 0.0398 CCND2 0.718 (0.522-0.987) 0.0400 KPNA2 1.395 (1.015-1.917) 0.0410 CBX2 1.391 (1.012-1.912) 0.0433 CEP55 1.384 (1.007-1.902) 0.0474 hsa-miR-195-5p 0.629 (0.455-0.870) 0.0046 hsa-miR-204-5p 0.648 (0.469-0.894) 0.0086 hsa-miR-335-5p 0.664 (0.481-0.916) 0.0134 hsa-miR-342-3p 0.696 (0.504-0.961) 0.0280 hsa-miR-100-5p 0.704 (0.509-0.972) 0.0323 hsa-miR-328-3p 1.410 (1.021-1.947) 0.0356 Interaction between miRNA and mRNA from the ceRNA network According to ceRNA theory, circRNA could indirectly affect mRNA through miRNA. At the expression level, miRNA was negatively correlated with circRNA and mRNA. In order to verify that the network we built was consistent with ceRNA theory, we needed to perform correlation analysis on different kinds of RNA. The expression information of circRNA in this study was from the GSE101123 dataset, while the expression information of miRNA and mRNA were from the TCGA dataset. Since the expression information of RNAs in the correlation analysis must be from the same sample, this study could only analyze the correlation between the expression levels of miRNA and mRNA. We performed a correlation analysis of miRNA-mRNA pairs in the ceRNA network based on R software, and the results showed that there were 48 miRNA-mRNA pairs with strong negative correlation (r<-0.3, P <0.001) (Table 7). For instance, hsa-miR-141-3p negatively correlated with ZEB2 (r=-0.599, P <0.001) and QKI (r=-0.535, P <0.001), hsa-miR-195-5p negatively correlated with CEP55 (r=-0.547, P <0.001) and CLSPN (r=-0.525, P <0.001), hsa-miR-200a-3p negatively correlated with ZEB2 (r=-0.520, P <0.001) as well as QKI (r=-0.513, P =0.001) (Figure 7). Table 7 Correlation analysis of the relationship between miRNA and mRNA. miRNA mRNA R P _vlaue hsa-miR-141-3p ZEB2 -0.59875 0 hsa-miR-195-5p CEP55 -0.54744 0 hsa-miR-141-3p QKI -0.53509 0 hsa-miR-195-5p CLSPN -0.52524 0 hsa-miR-200a-3p ZEB2 -0.52049 0 hsa-miR-200a-3p QKI -0.51254 0 hsa-miR-195-5p HMGA1 -0.49915 0 hsa-miR-497-5p CEP55 -0.49525 0 hsa-miR-195-5p CHEK1 -0.49341 0 hsa-miR-195-5p CDC25A -0.49147 0 hsa-miR-195-5p CCNE1 -0.48761 0 hsa-miR-497-5p ANLN -0.45814 0 hsa-miR-139-5p TPD52 -0.44566 0 hsa-miR-195-5p E2F7 -0.44235 0 hsa-miR-139-5p ZNF367 -0.43672 0 hsa-miR-497-5p CLSPN -0.43102 0 hsa-miR-145-5p TPM3 -0.42875 0 hsa-miR-195-5p CBX2 -0.42701 0 hsa-miR-145-5p RTKN -0.42632 0 hsa-miR-195-5p ZNF367 -0.42269 0 hsa-miR-200a-3p DLC1 -0.4224 0 hsa-miR-497-5p CCNE1 -0.42016 0 hsa-miR-7-5p RBMS3 -0.41862 0 hsa-miR-497-5p CDC25A -0.4139 0 hsa-miR-342-3p ID4 -0.4063 0 hsa-miR-497-5p CHEK1 -0.40444 0 hsa-miR-141-3p STAT5A -0.39451 0 hsa-miR-218-5p LMNB1 -0.3945 0 hsa-miR-141-3p YAP1 -0.3803 0 hsa-miR-497-5p CBX2 -0.37969 0 hsa-miR-200a-3p HGF -0.37823 0 hsa-miR-497-5p E2F7 -0.3743 0 hsa-miR-218-5p TPD52 -0.37324 0 hsa-miR-195-5p CDCA4 -0.3683 0 hsa-miR-204-5p AP1S1 -0.36607 0 hsa-miR-497-5p CDCA4 -0.36528 0 hsa-miR-204-5p EZR -0.35209 0 hsa-miR-497-5p ZNF367 -0.34916 0 hsa-miR-7-5p SNCA -0.33958 0 hsa-miR-145-5p ABRACL -0.33926 0 hsa-miR-365b-3p MCOLN2 -0.33306 0 hsa-miR-365a-3p MCOLN2 -0.33285 0 hsa-miR-141-3p USP53 -0.31765 0 hsa-miR-200a-3p YAP1 -0.3142 0 hsa-miR-145-5p ABHD17C -0.3107 0 hsa-miR-33b-5p GAS1 -0.3097 0 hsa-miR-195-5p ENTPD7 -0.30541 0 hsa-miR-33b-5p ARID5B -0.3005 0 Construction of PPI network and module analysis The STRING database was used to unveil the interrelationships between the DEmRNAs in the ceRNA network by constructing PPI network. This PPI network involves a total of 75 nodes and 283 edges. Visualization was performed with Cytoscape (Figure 8A). In order to identify hub genes in the process of BRCA carcinogenesis, the MCODE plugin in Cytoscape was used to identify the core subnetwork in the PPI network. Two core sub-networks were obtained, including 21 genes and 49 edges (Figure 8B). We used these 21 genes as potential hub genes. Quantitative real-time PCR validation Finally, we randomly selected four DEcircRNAs, DEmiRNAs and DEmRNAs respectively in the ceRNA network to verify the reliability and validity of the above analysis results. These results showed that CCNE1, CEP55, ANLN, hsa-miR-592, hsa-miR-141-3p, hsa_circ_0000069, hsa_circ_0000518 and has_circ_0000520 were up-regulated in BRCA tumor tissues compared to adjacent non-tumor tissues, while ADIPOQ, hsa-miR-195-5p, hsa-miR-204-5p and has_circ_0000977 were down-regulated in BRCA tumor tissues (Figure 9). The results of qRT-PCR validation from new breast cancer patients were consistent with the above bioinformatics results, indicating that our bioinformatics analysis was credible. Discussion Abnormal expression of circRNA has been widely observed in various diseases. Studies have shown that dysregulated circRNA plays a key role in the important biological properties of cancer [19]. However, only a few studies have described the profile of circRNA in BRCA by microarray analysis. The constructed BRCA-related circRNA-associated ceRNA network provides important hints for detecting the key RNAs of ceRNA-mediated gene regulatory network in the initiation and development of BRCA. We obtained BRCA mRNA, miRNA expression profile and circRNA expression profile from TCGA database and GEO database respectively. After statistical analysis, 2762 DEmRNAs, 158 DEmiRNAs and 72 DEcircRNAs were identified. Next, we screened the circRNAs-miRNAs interaction pairs through CircIteractome and CSCD databases, screened the miRNA-mRNA interaction pairs by TargetScan, miRTarBase and miRDB databases, and then took the intersection, and finally constructed a specific circRNA-miRNA-mRNA ceRNA regulatory network. We have found that specific circRNAs in this ceRNA network, such as hsa_circ_0000376, hsa_circ_0000069, hsa_circ_0000520 and hsa_circ_0008365, have also been reported as potential diagnostic markers in certain cancers. Hsa_circ_0000376 is highly expressed in gastric cancer tissues [20], and hsa_circ_0000069 is up-regulated in colorectal cancer tissues, which can promote the proliferation, migration and invasion of tumor cells [21]. Hsa_circ_0000520 was up-regulated in breast cancer and cell lines (T47D, MCF-7, MDA-MB-231, BT549 and SKBR3), and hsa_circ_0000520 high expression was associated with poor overall survival[22]. Hsa_circ_0008365 (Circ-SERPINE2) is a novel proliferative promoter that can regulate YWHAZ through sponge miR-375 to promote the development of gastric cancer [23]. To understand the potential functional significance of differentially expressed mRNA in ceRNA networks, we performed GO analysis and KEGG analysis. It was worth noting that KEGG analysis found that some enriched signaling pathways were closely related to the development of cancer, such as 'PI3K-Akt signaling pathway' [24], 'MicroRNAs in cancer', 'Proteoglycans in cancer', 'Cellular senescence', ' FoxO signaling pathway' [25], 'Central carbon metabolism in cancer' and 'Cell cycle' [26]. Functionally annotated results also indicate that circRNAs that regulate these key mRNAs may play an important role in the initiation and development of BRCA and pathways associated with cancer genes. In order to further identify the key genes involved in the regulatory network, we established a PPI network and screened two core sub-networks through the MCODE plug-in, which contained 21 genes, which will be used as potential hub genes. At the same time, we analyzed the relationship between DEmRNAs and DEmiRNAs in ceRNA networks and overall survival of breast cancer patients, and found that 13 mRNAs (CCNE1, TPD52, SDC1, ANLN, ZNF367, SOX11, IRS2, EZR, DSC3, CCND2, KPNA2, CBX2 and CEP55) and 6 miRNAs (hsa-miR-204-5p, hsa-miR-335-5p, hsa-miR-100-5p, hsa-miR-195-5p, hsa-miR-328-3p and hsa-miR-342-3p) are significantly associated with the prognosis of breast cancer patients. Most of these mRNA molecules related to patient survival are thought to be related to the molecular pathogenesis of various tumors, and were closely related to the occurrence, development, proliferation, metastasis and prognosis of cancer [27-31]. For example, the DNA copy number of TPD52 is amplified in prostate cancer cells, and the level of TPD52 protein may be regulated by androgen. Studies have shown that genomic amplification and dysregulation of TPD52 caused by androgen induction may play a role in the progression of prostate cancer [28]. Gui X found that SDC1 is overexpressed in breast cancer and may be a potential prognostic indicator for breast cancer [29]. It has been reported that the upregulation of ANLN is a common feature in the carcinogenesis of lung tissue， ANLN can play a key role in the development of human lung cancer by activating RHOA and participating in the phosphoinositide 3-kinase/AKT pathway， the expression of ANLN is also associated with low survival in patients with NSCLC [30]. CEP55 is a determinant of mitosis in breast cancer cells [31]. By immunohistochemical analysis, it was found that EZR is up-regulated in breast cancer and can be used as a potential marker for overall survival of breast cancer [32]. It is well known that miRNAs regulate about 60% of human genes and mediate a variety of biological pathways, including pathways critical for tumorigenesis. Here, we found that microRNAs associated with BRCA overall survival in ceRNA networks have been reported to play an important role in tumorigenesis, development, prognosis, and drug resistance. Extracellular vesicles containing miR-335-5p can downgrade the growth and invasion of liver cancer in vitro and in vivo， the exosome miR-335-5p can be used as a novel therapeutic strategy for hepatocellular carcinoma [33]. NABAVI N identified miR-100-5p as one of the key molecular components in the initiation and evolution of androgen ablation therapy resistance in prostate cancer [34]. A research team reported that miR-328-3p is up-regulated in ovarian cancer stem cell (CSC), and high expression of miR-328-3p can directly target DNA damage-binding protein 2 to maintain CSC properties， inhibition of miR-328-3p is a new strategy to effectively eliminate CSC [35]. Enhanced expression of miR-342-3p synergizes with miR-205-5p to inhibit E2F1, thereby reducing tumor chemoresistance [36]. Published studies have shown that hsa-miR-204-5p can be used to predict the prognosis of patients with clear renal cell carcinoma, lung adenocarcinoma and other cancers [37, 38]. Hsa-miR-204-5p directly targets FOXA1 to regulate tumor cell infiltration and metastasis [39], and can affect tumor angiogenesis by interfering with the expression of ANGPT1/TGFBR2 [40]. hsa-miR-195-5p can affect the development of colorectal cancer by inhibiting the Hippo-YAP pathway [41], meanwhile, hsa-miR-195-5p can be a potential diagnostic and prognostic target in breast cancer [42]. We performed a correlation analysis between the expression levels of miRNAs and mRNAs from the same sample in the TCGA database. The results indicate that there are 43 pairs of interconnected miRNAs and mRNAs with a significant negative correlation in the constructed miRNA-mRNA interaction pairs. These links have also been found in some reports. For example, Luo Q found that overexpression of hsa-miR-195-5p can reduce the expression level of CCNE1 and targeting this miRNA may provide a new strategy for the diagnosis and treatment of breast cancer [42]. In addition, reports on circRNA found that circAGFG1 can act as a sponge of hsa-miR-195-5p, which promotes the progression of triple-negative breast cancer by regulating the expression of CCNE1 [43]. These results also indirectly reflect the feasibility of using bioinformatics to construct regulatory networks. Here, we identified four ceRNA regulatory axes, indicating competitive regulatory relationships of three circRNAs with the three genes in BRCA. However, given that these results are based solely on bioinformatics models, further in-depth studies are critical to verifying the possible role of these four axes in BRCA. Conclusions In this study, we identified aberrant expressed key RNAs by analyzing the RNAs expression profiles of BRCA in public databases. These specific circRNA, miRNA and mRNA molecules may be helpful in the discovery of sensitive biomarkers in BRCA. Importantly, we have constructed a circRNA-miRNA-mRNA ceRNA network that will be used to elucidate the unknown ceRNA regulatory axes in BRCA. Our findings provide novel insights into an in-depth understanding of circRNA-related ceRNA networks in breast cancer as well as potential diagnostic and prognostic biomarkers. Declarations Authors’ contributions Hui Shen and Huan Pan carried out data analysis. Ming Yao participated in study design and data collection. All authors drafted the final manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. Competing interests The authors declare that they have no competing interests. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Consent for publication Not applicable. Ethics approval and consent to participate This study was approved by the ethics committee of The First Affiliated Hospital of Jiaxing University. Funding This study was supported, in part, by grants from Zhejiang Provincial Natural Science Foundation of China (LY20H090020, LQ19H090007, LGF20H090021); the Science and Technology Project of Jiaxing City (2020AY30010, 2019AD32251, 2018AD32095, 2018AY32012); Zhejiang Provincial Medical Scientific Research Foundation of China (2020KY948, 2020358554); Zhejiang Provincial Medical and Health General Research Program of China (2019KY687); the Construction Project of Anesthesiology Discipline Special Disease Center in Zhejiang North Region (201524); the Key Medical Subjects Established by Zhejiang Province and Jiaxing City Jointly Pain Medicine (2019-ss-ttyx); the Construction Project of Key Laboratory of Nerve and Pain Medicine in Jiaxing City; 2019 Jiaxing Key Discipiline of Medicine-Clinical Laboratory Diagnostics (Innovation Subject) (2019-cx-03). 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Yao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACxgYwJcHAwN588MEHAxs7ErTwHEs2nFGQlkyCfRI+asI8Hw5BzcADmGfkGH74mGORJx/Bw8ZsY3CAmYH98NENeB3Wc8ZYcuY2iWLD273HHucY3OFj4ElLu4FXS3uPgTTvNonEjXPOpRvnGDxjZpDgMcOvpZnH+DdYy4wcM2kLg8OMDQS1tPeYgW2ZLwHUwkCUlp5jZZZAvyRuAAVyj0FaMhshvxjOSN584+O2usT57cCo/PHHxo6f/fAx/FoaOAzADIMDUBE2fMpBQJ6B/QGE0UBI6SgYBaNgFIxYAABlv03SJA3OsgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4226-8473","institution":"The First Affiliated Hospital of Jiaxing University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Yao","suffix":""}],"badges":[],"createdAt":"2020-10-10 20:50:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-90865/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-90865/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":2977784,"identity":"bbcfe814-537b-4409-b717-22b900c99372","added_by":"auto","created_at":"2020-10-14 15:59:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1055626,"visible":true,"origin":"","legend":"Flow chart of comprehensive bioinformatics analysis in the construction of competing endogenous RNA (ceRNA) regulatory network.","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/c3bcc643291b2d54abdac5b8.png"},{"id":2977774,"identity":"d64a42d5-9b7e-4756-ac43-511d5d864251","added_by":"auto","created_at":"2020-10-14 15:59:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1055626,"visible":true,"origin":"","legend":"Flow chart of comprehensive bioinformatics analysis in the construction of competing endogenous RNA (ceRNA) regulatory network.","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/57ebeab398ba6714d1fbbfee.png"},{"id":2977785,"identity":"c8ef8c92-d9df-4c9d-91ff-58be1b92fac1","added_by":"auto","created_at":"2020-10-14 15:59:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10006700,"visible":true,"origin":"","legend":"Heatmap and volcano diagrams of breast cancer-related differentially expressed mRNAs, miRNAs and circRNAs. A, mRNA; B, miRNA; C, circRNA. The color from blue to red shows a trend from low expression to high expression. The red dot represents upregulated mRNA, miRNA and circRNA, the green dot represents downregulated mRNA, miRNA and circRNA.","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/a64c85bf2d67329cf79c8261.png"},{"id":2977775,"identity":"4b002534-424f-4f88-a779-94bdebb7e48e","added_by":"auto","created_at":"2020-10-14 15:59:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10006700,"visible":true,"origin":"","legend":"Heatmap and volcano diagrams of breast cancer-related differentially expressed mRNAs, miRNAs and circRNAs. A, mRNA; B, miRNA; C, circRNA. The color from blue to red shows a trend from low expression to high expression. The red dot represents upregulated mRNA, miRNA and circRNA, the green dot represents downregulated mRNA, miRNA and circRNA.","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/04465fae4e367667987ded9e.png"},{"id":2977786,"identity":"175033f0-ac6e-411e-aded-ee4487c1a88b","added_by":"auto","created_at":"2020-10-14 15:59:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":853254,"visible":true,"origin":"","legend":"Competing endogenous RNA (ceRNA) (DEcircRNA-DEmiRNA-DEmRNA) regulatory network. The v nodes, round rectangle nodes and elliptical nodes indicate DEcircRNAs, DEmiRNAs and DEmRNAs, respectively. Red and blue represent upregulation and downregulation, respectively. Green borders surrounding the nodes indicate prognostic significance. Purple edges indicate good negative correlation between RNAs.","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/70e6207980c930a7f4d40847.png"},{"id":2977776,"identity":"56596197-71bd-4655-9937-f1c72a08c591","added_by":"auto","created_at":"2020-10-14 15:59:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":853254,"visible":true,"origin":"","legend":"Competing endogenous RNA (ceRNA) (DEcircRNA-DEmiRNA-DEmRNA) regulatory network. The v nodes, round rectangle nodes and elliptical nodes indicate DEcircRNAs, DEmiRNAs and DEmRNAs, respectively. Red and blue represent upregulation and downregulation, respectively. Green borders surrounding the nodes indicate prognostic significance. Purple edges indicate good negative correlation between RNAs.","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/6ec6986a833eaff1c59231d4.png"},{"id":2977787,"identity":"a9206552-49bb-4b9b-973c-f6ed0c2024ce","added_by":"auto","created_at":"2020-10-14 15:59:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":232011,"visible":true,"origin":"","legend":"Significantly enriched Gene Ontology (GO) terms of differentially expressed mRNAs in ceRNA regulatory network. BP, biological process; CC, cellular component; MF, molecular function. The x-axis shows counts of host genes enrich in GO terms and the y-axis shows GO terms. The color scale represented p.adjust.","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/45d8edab4b8c297090cdff7f.png"},{"id":2977777,"identity":"8749f893-56b3-41fd-8830-e46791f333a2","added_by":"auto","created_at":"2020-10-14 15:59:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":232011,"visible":true,"origin":"","legend":"Significantly enriched Gene Ontology (GO) terms of differentially expressed mRNAs in ceRNA regulatory network. BP, biological process; CC, cellular component; MF, molecular function. The x-axis shows counts of host genes enrich in GO terms and the y-axis shows GO terms. The color scale represented p.adjust.","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/ebde3d984bf128bd3176a091.png"},{"id":2977788,"identity":"ce9e1966-a6a5-43e5-8797-8defa32a7277","added_by":"auto","created_at":"2020-10-14 15:59:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":158188,"visible":true,"origin":"","legend":"Significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of differentially expressed mRNAs in ceRNA regulatory network. The x-axis shows counts of host genes enrich in KEGG pathways and the y-axis shows KEGG pathways. The color scale represented p.adjust.","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/3c4882563adf16975afdb16b.png"},{"id":2977778,"identity":"646711c7-eeb1-4c69-a253-4ac9c11082f5","added_by":"auto","created_at":"2020-10-14 15:59:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":158188,"visible":true,"origin":"","legend":"Significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of differentially expressed mRNAs in ceRNA regulatory network. The x-axis shows counts of host genes enrich in KEGG pathways and the y-axis shows KEGG pathways. The color scale represented p.adjust.","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/f460e5b870bcee8b1c632808.png"},{"id":2977789,"identity":"2a66378c-1a21-4214-b8b9-92d24ab2b6fb","added_by":"auto","created_at":"2020-10-14 15:59:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3454932,"visible":true,"origin":"","legend":"Kaplan-Meier survival curves of differentially expressed miRNAs (DEmiRNAs) and differentially expressed mRNAs (DEmRNAs) in the competing endogenous RNA (ceRNA) network that are significantly associated with overall survival in breast cancer.","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/9ab074d2b7b305c6b0e5b78c.png"},{"id":2977779,"identity":"af793e2a-f685-4762-845f-1152e900d249","added_by":"auto","created_at":"2020-10-14 15:59:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3454932,"visible":true,"origin":"","legend":"Kaplan-Meier survival curves of differentially expressed miRNAs (DEmiRNAs) and differentially expressed mRNAs (DEmRNAs) in the competing endogenous RNA (ceRNA) network that are significantly associated with overall survival in breast cancer.","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/ced865eb7628bfa5964991d6.png"},{"id":2977790,"identity":"9a92de7a-1c97-4dce-b1b2-9d12b79c0d90","added_by":"auto","created_at":"2020-10-14 15:59:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2252358,"visible":true,"origin":"","legend":"Pearson's correlation analysis between the expression level of the interacting miRNA and mRNA in the ceRNA network.","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/5ea5927013164f53468d4715.png"},{"id":2977780,"identity":"db237066-309d-45a2-bc56-da337e7d8f1d","added_by":"auto","created_at":"2020-10-14 15:59:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2252358,"visible":true,"origin":"","legend":"Pearson's correlation analysis between the expression level of the interacting miRNA and mRNA in the ceRNA network.","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/ebf3beb8848b174ca0b95cd9.png"},{"id":2977791,"identity":"ed876508-d05a-4240-9218-ceff01ef3f58","added_by":"auto","created_at":"2020-10-14 15:59:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":961872,"visible":true,"origin":"","legend":"Identification of hub genes from the PPI network with the MCODE algorithm. A. PPI network construction. B. Two core subnets with 21 hub genes. Red nodes represent the upregulated genes, blue nodes represent downregulated genes; PPI, protein-protein interaction.","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/f429db0b19f8a5d3d1e00250.png"},{"id":2977781,"identity":"21b3f4e3-2900-4f3e-9d6a-4ebc6d1ec2e3","added_by":"auto","created_at":"2020-10-14 15:59:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":961872,"visible":true,"origin":"","legend":"Identification of hub genes from the PPI network with the MCODE algorithm. A. PPI network construction. B. Two core subnets with 21 hub genes. Red nodes represent the upregulated genes, blue nodes represent downregulated genes; PPI, protein-protein interaction.","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/53ea6e41f01e0143ff37853d.png"},{"id":2977792,"identity":"75292cca-a75a-4fed-ae3c-261728429621","added_by":"auto","created_at":"2020-10-14 15:59:37","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":29610,"visible":true,"origin":"","legend":"qRT-PCR validation of the DEmRNAs, DEmiRNAs and DEcircRNAs in breast cancer. The x-axis represents the DEmRNAs/DEmiRNAs/DEcircRNAs and the y-axis represents log2(fold change).","description":"","filename":"Onlinefloatimage9.Png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/0564accf73226deb302599ff.Png"},{"id":2977782,"identity":"a678eeee-eb34-4d35-977d-c3d56ab412d5","added_by":"auto","created_at":"2020-10-14 15:59:32","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":29610,"visible":true,"origin":"","legend":"qRT-PCR validation of the DEmRNAs, DEmiRNAs and DEcircRNAs in breast cancer. The x-axis represents the DEmRNAs/DEmiRNAs/DEcircRNAs and the y-axis represents log2(fold change).","description":"","filename":"Onlinefloatimage9.Png","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/bb6740d2315e33257c50432b.Png"},{"id":13603588,"identity":"1f868712-7645-459d-8f6b-06faff1fe13d","added_by":"auto","created_at":"2021-09-17 05:56:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6120755,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-90865/v1/1b1077e4-1801-47df-91dd-cf1bc0538cc1.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eIntegrated Analysis of Circular RNA Associated Cerna Network Reveals Potential CircRNA Biomarkers in Human Breast Cancer\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer is one of the most common cancer among women worldwide [1], with strong invasiveness and metastasis, and the incidence and mortality of breast cancer continue to increase [2]. Currently, treatments for breast cancer include surgery, radiation therapy, endocrine therapy, chemotherapy, and biotargeted therapy. However, the recurrence rate and drug resistance of some patients are still high, and the therapeutic effect and prognosis of breast cancer have not been satisfactory. Therefore, the molecular pathogenesis of breast cancer needs to be further understood, and the identification of new candidate therapeutic targets and biomarkers is urgently needed for breast cancer treatment. An in-depth study of the molecular mechanism of tumors based on bioinformatics analysis has exploited an important method to tumor research. It can not only explore the molecular pathogenesis of tumors in depth, but also identify new biomarkers for tumor pathogenesis and prognosis [3].\u003c/p\u003e\n\u003cp\u003eIn the past few decades, 70%-90% of the transcribed human genome has been identified. Related data indicate that protein-coding genes account for only about 2% of the human genome, and non-coding RNAs make up the majority of the human transcriptome [4]. Non-coding RNAs are a large class of RNA molecules that do not encode proteins, but which serve regulatory roles, mainly includes: circular RNAs (circRNAs), microRNAs (miRNAs), long nocoding RNAs (lncRNAs) and small nuclear RNAs. The competitive endogenous RNA (ceRNA) hypothesis reveals a new mechanism for interaction between RNAs. The main idea of the ceRNA hypothesis is that multiple types of RNA transcripts communicate with each other by competing for binding to shared miRNA-binding sites (miRNA response elements or MREs) [5]. It has been reported that circRNAs contain multiple miRNA-binding sites that bind to miRNAs, which are seen as miRNA sponges that result in inhibition of miRNAs activity and regulation of expression of their downstream target genes [6, 7].\u003c/p\u003e\n\u003cp\u003eCircRNA is a class of covalently closed single-stranded circular RNA molecules without free 5 or 3 end which makes them well expressed and more stable than their linear counterparts. CircRNA is abundant in eukaryotic cells, highly conserved, structurally stable, and has certain tissue, time and disease specificity. Due to these characteristics, circRNA has become a new hotspot of research [8]. A vast number of circRNAs have been discovered in a variety of cancers and they are activated in inhibiting tumor progression or promoting tumorigenesis. For example, circ-MTO1 can inhibit the progression of liver cancer cells [9]. circ-LARP4 can inhibit cell proliferation and invasion of gastric cancer cells by sponging miR-424-5p and regulating the expression of LATS1 [10]. Circ-FBXW7 suppresses the development of gliomas, and its expression is positively correlated with the overall survival of patients with glioblastoma [11]. The hsa_circ_001783 regulates the progression of breast cancer (in vitro) by sponging miR-200c-3p to regulate ZEB1/2 and ETS1 and is associated with poor clinical outcomes in breast cancer patients [12].\u003c/p\u003e\n\u003cp\u003eIn the current study, we collected the expression profiles of circRNA, miRNA and mRNA from BRCA tissues and adjacent normal mammary gland tissues from the Gene Expression Omnibus (GEO) database and the The Cancer Genome Atlas (TCGA) database. We performed a comprehensive analysis of these expression profiles to identify differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed circRNAs (DEcircRNAs). After predicting sponging of miRNAs by circRNA and miRNA target genes, we constructed a circRNA-miRNA-mRNA network. To investigate the main functional pathways involved in the development of breast cancer in this ceRNA network, DEmRNAs of the ceRNA network were assessed by gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, and we have established a protein-protein interaction network, this study will try to better understand the pathogenesis of BRCA. Finally, we performed an overall survival analysis of miRNAs and mRNAs in ceRNA networks to identify prognostic biomarkers associated with breast cancer. Through this study, we can not only further understand the molecular mechanism of breast cancer development, but also provide potential circRNA, miRNA and mRNA biomarkers for the early diagnosis, treatment and prognosis of breast cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eExpression profiling in The Cancer Genome Atlas and Gene Expression Omnibus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mRNA and miRNA sequence data of breast cancer were extracted from the TCGA database (\u003ca href=\"https://portal.gdc.cancer.gov/\"\u003ehttps://portal.gdc.cancer.gov/\u003c/a\u003e). All file data were downloded using the GDC Data Transfer Tool (Provided by GDC Apps) (\u003ca href=\"https://tcga-data.nci.nih.gov/\"\u003ehttps://tcga-data.nci.nih.gov/\u003c/a\u003e). The mRNA profiles contained 1097 BRCA tissues and 114 adjacent normal tissues, and the miRNA profiles contained 1092 BRCA tissues and 105 adjacent normal tissues. The exclusion criteria were set as follows: samples without clinical data and samples without complete information of stage and overall survival period.\u003c/p\u003e\n\u003cp\u003eThe circRNA expression profiles of BRCA were downloaded from GEO database (\u003ca href=\"http://www.ncbi.nlm.nih.gov/geo\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/a\u003e) by searching keywords ((\"breast neoplasms\" [MeSH Terms] OR breast cancer [All Fields]) AND circRNA [All Fields]) AND (\"Homo sapiens\" [Organism] AND (\"Non-coding RNA profiling by array\" [Filter] OR \"Non-coding RNA profiling by high throughput sequencing\" [Filter])). We selected data according to the following criteria: selected datasets should be circRNA transcriptome data of the whole genome, these data were derived from tumor tissues and adjacent normal tissues of patients with BRCA, and datasets were standardized or raw datasets. The GSE101123 dataset met the screening requirements and was used in this study. The dataset included 3 normal mammary gland tissues and 8 BRCA tissues. These expression profiles do not require ethical approval or informed consent due to we used the publicly available data from TCGA and GEO.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed mRNAs, miRNA, circRNA in breast cancer compared to adjacent tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, the difficultly detected mRNAs/miRNAs, which with read count value=0 in more than 50% samples, were filtered and deleted. To obtain the differentially expressed mRNAs (DEmRNAs) and miRNAs (DEmiRNAs) between normal tissues and BRCA, the count data were processed with the Bioconductor package edge R [13] in software. All RNA expression levels were standardized to the sample mean. The \u003cem\u003eP\u003c/em\u003e value was corrected with a false discovery rate (FDR). The threshold for the expression of DEmRNAs and DEmiRNAs was FDR\u0026lt;0.01 and |log\u003csub\u003e2\u003c/sub\u003efold change|\u0026gt;1. Additionally, the differently expressed circRNAs (DEcircRNAs) were screened using Limma package, the threshold for the expression of DEcircRNAs was \u003cem\u003eP\u003c/em\u003e value\u0026lt;0.01 and |log\u003csub\u003e2\u003c/sub\u003efold change|\u0026gt;1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the ceRNA regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Circular RNA Interactome (CircInteractome) (\u003ca href=\"https://circinteractome.nia.nih.gov/\"\u003ehttps://circinteractome.nia.nih.gov/\u003c/a\u003e) and Cancer-Specific CircRNA (CSCD) (\u003ca href=\"http://gb.whu.edu.cn/CSCD/\"\u003ehttp://gb.whu.edu.cn/CSCD/\u003c/a\u003e) were used to predict miRNA binding sites (MREs). These miRNAs were considered as potential target miRNAs of the DEcircRNAs. These target miRNAs were further screened by DEmiRNA based on the TCGA.\u003c/p\u003e\n\u003cp\u003eInteractions between miRNA and mRNA were predicted based on the Targetscan [14], miRTarBase [15], and miRDB [16] databases. Only mRNAs recognized by all three database were considered as candidate mRNAs, and were intersected with DEmRNAs to screen the DEmRNAs targeted by DEmiRNAs. The circRNA-miRNA-mRNA regulatory network was contructed using a combination of circRNA-miRNA pairs and miRNA-mRNA pairs. Finally, the network was visualized and mapped using Cytoscape v3.7.0 [17]. Figure 1 shows a flow chart for the development of the ceRNA network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene ontology and pathway enrichment analysis of DEGs in the ceRNA network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the function of differentially expressed genes (DEGs) in the ceRNA network in tumorigenesis, we performed Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using the clusterProfiler package [18] of R software. \u003cem\u003eP\u003c/em\u003e-value\u0026lt;0.01 was set as the cut-off criterion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival analysis and correlation analysis of DEmiRNAs and DEmRNAs in ceRNA networks \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach sample in the TCGA was independent of each other and it contained all sample information such as gene expression, prognosis and survival time. We obtained clinical information from breast cancer patients from the TCGA database and combined the expression data of DEmiRNAs and DE mRNAs with clinical data from patients. We used Survival package of R to perform survival analysis of DEmiRNAs and DE mRNAs in the ceRNA network with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 as the threshold. In addition, DEmiRNAs and DEmRNAs with significant overall survival were identified as prognostic biomarkers.\u003c/p\u003e\n\u003cp\u003eIn the TCGA-BRCA dataset, the vast majority of samples were present in both miRNA and mRNA expression profiles, and samples that were only present in one expression profile were deleted. Correlation analysis between the interacting miRNA and mRNA in the ceRNA network was performed using R software, with r\u0026lt;-0.3, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 as the threshold. The miRNA-mRNA pair that satisfies the condition is considered to have a strong negative correlation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction PPI network and module analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the interactions between the DEGs in the ceRNA network, we constructed a protein-protein interaction (PPI) network using the Search Tool for the Retrieval of Interacting Genes (STRING, http://string.embl.de/) online tool. We used the MCODE plugin to screen modules of hub genes from the PPI network. The interaction network was visualized using Cytoscape software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative real-time PCR validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTen pairs of breast cancer tissues and corresponding adjacent non-tumor tissues from BRCA patients were obtained from Department of Breast Disease, The First Affiliated Hospital of Jiaxing University. The study was approved by the ethics committee and written informed consent was obtained from all patients. In this ceRNA network, we randomly selected six circRNAs, miRNAs and mRNAs respectively, and verified the reliability and validity of the prediction results in BRCA patients using qRT-PCR. Total RNA was isolated using Trizol reagent (Invitrogen, USA) according to the manufacturer's protocol, and RNA purity was detected by NanoDrop 2000 spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Based on SuperReal PreMix Plus (Invitrogen, USA) in StepOneplus Real-time PCR Detection System (Applied Biosystems, Foster City, CA, USA), the qRT-PCR reactions were performed. The relative gene expression was calculated by 2\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e△△\u003c/sup\u003e\u003csup\u003eCt\u003c/sup\u003e. The human\u0026beta;-actin and human U6 was used as endogenous controls for mRNA and miRNA expression in analysis, respectively. The human GAPDH was used as endogenous controls for circRNA expression in analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed RNAs in breast cancer \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared to adjacent tissues, a total of 2762 DEmRNAs (1118 upregulated and 1644 downregulated miRNAs) and 158 DEmiRNAs (71 upregulated and 87 downregulated miRNAs) were identified in BRCA with FDR\u0026lt;0.01 and |log\u003csub\u003e2\u003c/sub\u003efold change|\u0026gt;1. A total of 72 DEcircRNAs (51 upregulated and 21 downregulated circRNAs) were obtained in BRCA compared to adjacent tissues with \u003cem\u003eP\u003c/em\u003e value\u0026lt;0.01, |log\u003csub\u003e2\u003c/sub\u003efold change|\u0026gt;1. The RNAs hierarchical clustering analyses are presented in Figure 2, and it was demonstrated that the expression levels of these three type of RNAs were significantly differentiated compared with the normal tissues. Finally, volcano plots were generated, and differences between the normal and tumor groups were identified (Figure 2). Table 1, 2 and 3 show the top 10 up and down regulation of DEmRNAs, DEmiRNAs and DEcircRNAs in BRCA, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Top 10 upregulation and downregulation DEmRNAs in BRCA.\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003e\u003cstrong\u003emRNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003elogFC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u003cstrong\u003eFDR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eCOL10A1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e6.870373\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.01E-60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.67E-59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eCST1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e6.616865\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.52E-56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e2.15E-55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eMMP13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e6.354082\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e8.94E-57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.27E-55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eIBSP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5.863152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3.64E-104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.91E-102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eMMP11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5.781753\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.35E-84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e7.56E-83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eCOL11A1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5.337783\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.91E-37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.39E-36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eMMP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5.235857\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.55E-39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.21E-38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ePLPP4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5.184921\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.61E-88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e9.12E-87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eSLC24A2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e4.98746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.18E-73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e5.43E-72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eCOMP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e4.598021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e7.01E-40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e5.58E-39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eADH1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-6.86393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.57E-114\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.69E-112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eTUSC5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-6.81912\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.32E-161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e3.10E-159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eADIPOQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-6.79919\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3.51E-120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e2.65E-118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eCIDEC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-6.63588\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3.51E-174\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.19E-171\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eSCARA5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-6.48854\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3.00E-239\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.53E-235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eFABP4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-6.14086\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.66E-110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.01E-108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ePLIN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-6.00856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e4.59E-146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e6.21E-144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eGPD1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-5.99957\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5.75E-169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.57E-166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003eAQP7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-5.81861\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.75E-206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.91E-203\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ePLIN4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-5.79315\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e4.21E-115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e2.84E-113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Top 10 upregulation and downregulation DEmiRNAs in BRCA.\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003e\u003cstrong\u003emiRNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003elogFC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u003cstrong\u003eFDR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-592\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3.424794\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.41E-76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e6.71E-75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-1307-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3.197336\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.96E-88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e6.73E-87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-96-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3.155099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3.09E-90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.25E-88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-141-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3.087332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e4.21E-97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.88E-95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-429\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.994871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.10E-70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e2.32E-69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-190b\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.963382\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e6.87E-26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e2.73E-25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-183-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.924208\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e6.80E-108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e5.05E-106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-200a-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.638975\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e4.00E-63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e6.60E-62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.553121\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.13E-13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e2.68E-13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-200a-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.512744\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.55E-65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e4.53E-64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-486-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-4.09846\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.82E-115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.62E-113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-139-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-3.60464\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.43E-132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e5.41E-130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-204-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-3.53102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e4.25E-102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e2.70E-100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-139-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-3.19493\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.46E-156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.10E-153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-451a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-3.07530\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.49E-76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e4.42E-75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-5683\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-2.96346\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e7.12E-51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e7.73E-50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-144-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-2.79822\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.63E-71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e5.84E-70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-1247-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-2.73934\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.42E-45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.27E-44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-452-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-2.48651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.97E-54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e3.67E-53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"149\"\u003e\n\u003cp\u003ehsa-miR-145-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-2.45593\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.41E-123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e1.57E-121\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Top 10 upregulation and downregulation DEcircRNAs in BRCA.\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003e\u003cstrong\u003ecircRNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003e\u003cstrong\u003eAlias\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003elogFC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_001846\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0000520\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e3.800033\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0004347\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_000167\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0000518\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e3.742085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0016513\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_002172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0000514\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e3.365067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0042528\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_002144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0000511\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e2.995608\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0044046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_000166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0000512\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e2.883005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0044287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_000585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0000515\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e2.788805\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0012788\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_001678\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0000517\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e2.6775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0001633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_101967\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0041732\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e2.335477\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.002966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_101233\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0008784\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e1.958382\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0035194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_002178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0000519\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e1.90691\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.00011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eUp\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_102049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0043278\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-3.924372\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0099265\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_102619\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0000977\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-3.646793\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0080463\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_102051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0006220\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-3.398483\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0091516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_103345\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0065173\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-2.507172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0025211\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_102651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0008911\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-2.179549\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.001517\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_001153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0001455\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-2.059051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0016539\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_104653\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0008303\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-1.776765\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0009081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_101381\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0004781\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-1.771974\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0029019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_100685\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0020080\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-1.714172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0034233\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"168\"\u003e\n\u003cp\u003ehsa_circRNA_000554\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003ehsa_circ_0000376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-1.615084\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"86\"\u003e\n\u003cp\u003e0.0001321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eDown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of ceRNA regulatory network in BRCA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the regulatory mechanism of BRCA, a circRNA-miRNA-mRNA related ceRNA network of BRCA was developed according the above results. First, we searched for the target miRNAs of the 72 DEcircRNAs in the CircIteractome and CSCD databases, and found 295 interactive circRNAs-miRNAs pairs after intersecting with the DEmiRNAs. The circRNA-miRNA relationship pairs were screened according to a negative regulatory pattern, and positively co-expressed circRNA-miRNA pairs were discarded. The results showed that 162 interactive circRNA-miRNA pairs were screened, of which 72 DEmiRNAs were confirmed to interact with 59 DEcircRNAs. Following this, we predicted that 1626 mRNAs were targeted by these 72 DEmiRNAs in all three target predicting databases (TargetScan, miRTarBase and miRDB). these 1626 target mRNAs intersected with the 2762 DEmRNAs, and target mRNAs not contained in DEmRNAs was excluded, resulting in a total of 327 interactive miRNA-mRNA pairs. At the same time, we also screened miRNA-mRNA pairs based on negative regulatory patterns and discarded positively co-expressing pairs. The results showed that eventually 30 DEmiRNAs and 100 DEmRNAs formed 140 interactive miRNA-mRNA pairs. The circRNA-miRNA and miRNA \u0026ndash;mRNA relationship pairs (Tables 4 and 5) were combined into the ceRNA network following the pattern of negative regulation. Finally, we constructed the ceRNA regulatory network of BRCA comprised of 200 edges among 40 DEcircRNAs, 30 DEmiRNAs and 100 DEmRNAs. The ceRNA network in BRCA was visualized using Cytoscape software (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Interaction between circRNA and miRNA in the ceRNA network.\u003c/p\u003e\n\u003ctable border=\"1\" width=\"565\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003e\u003cstrong\u003ecircRNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003e\u003cstrong\u003emiRNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0000069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-193a-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0000376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-142-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0000511\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-296-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0000512\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-296-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0000514\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-296-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0000515\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-204-5p, hsa-miR-296-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0000517\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-193a-5p, hsa-miR-296-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0000519\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-204-5p, hsa-miR-296-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0000520\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-296-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0001455\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-142-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0001806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-139-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0002702\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-100-5p, hsa-miR-99a-5p, hsa-miR-296-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0003528\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-224-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0003645\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-335-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0004313\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-365a-3p, hsa-miR-365b-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0004315\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-145-5p, hsa-miR-195-5p, hsa-miR-497-5p, hsa-miR-218-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0004538\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-503-5p, hsa-miR-7-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0005273\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-328-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0005397\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-10b-5p, hsa-miR-1-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0005699\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-143-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0006220\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-342-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0006758\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-224-5p, hsa-miR-1-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0008365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-328-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0008784\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-193a-5p, hsa-miR-139-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0014624\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-7-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0016201\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-33a-5p, hsa-miR-33b-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0020080\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-7-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0022587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-193a-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0025388\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-139-5p, hsa-miR-144-3p, hsa-miR-335-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0028190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-451a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0031724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-143-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0041732\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-193a-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0041821\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-144-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0049998\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-205-5p, hsa-miR-145-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0054021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-141-3p, hsa-miR-200a-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0058753\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-218-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0069104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-451a, hsa-miR-296-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0082564\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-205-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0084429\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-129-5p, hsa-miR-224-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"177\"\u003e\n\u003cp\u003ehsa_circ_0084443\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"387\"\u003e\n\u003cp\u003ehsa-miR-129-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e Interaction between miRNA and mRNA in the ceRNA network.\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003e\u003cstrong\u003emiRNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003e\u003cstrong\u003emRNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-100-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eFGFR3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-10b-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eGATA3, SDC1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-129-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eCBX4, COL1A1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-139-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eTPD52, ZNF367\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-1-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eADAM12, AP1S1, CERS2, E2F5, FAM102A, FN1, RIMS4, SLC25A22, GPR137C, TRPS1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-141-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eEPHA2,QKI, STAT5A, USP53, YAP1, ZEB2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-142-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eCREBRF, FIGN, SLITRK4, TNS1, ZBTB20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-143-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eCOL1A1, ERBB3, LIMK1, SERPINE1, TTYH3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-144-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eEZH2, KPNA2, NACC1, SIX4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-145-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eABHD17C, ABRACL, RTKN, SERPINE1, SOX11, TPM3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-193a-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eNUP210\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eCBX2, CBX4, CCNE1, CDC25A, CDCA4, CEP55, CHEK1, CLSPN, E2F7, ENTPD7, HMGA1, KIF23, MYB, RASEF, RET, SLC25A22, ZNF367\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-200a-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eDLC1, EPHA2, HGF, QKI, THRB, USP53, YAP1, ZEB2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-204-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eAP1S1, EZR, RUNX2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-205-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eCENPF, ERBB3, EZR, LPCAT1, PARD6B, RUNX2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-218-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eBCL9, CDH2, CNTNAP2, FBN2, FBXO41, LMNB1, RET,\u0026nbsp; NACC1, PRLR, RUNX2, TPD52, TTYH3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-224-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eDIO1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-296-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eHMGA1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-328-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eH2AFX\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-335-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eCDH11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-33a-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eABCA1, ARID5B, DSC3, GAS1, ZC3H12C\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-33b-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eABCA1, ARID5B, GAS1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-342-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eFIGN, ID4, MBNL3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-365a-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eMCOLN2, SIX4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-365b-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eMCOLN2, SIX4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-451a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eCDKN2D, MIF\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eANLN, CBX2, CBX4, CCNE1, CDC25A, CDCA4, CEP55, CHEK1, CLSPN, E2F7, KIF23, RASEF, ZNF367\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-503-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eAKT3, CCND2, CYP26B1, FGF2, PIK3R1, RECK\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-7-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eEGFR, FNDC4, IRS1, IRS2, KLF4, RBMS3, RRAS2, SNCA, SOCS2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"158\"\u003e\n\u003cp\u003ehsa-miR-99a-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"410\"\u003e\n\u003cp\u003eFGFR3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional annotation of the DEGs in the ceRNA network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to better understand the potential functional significance of differentially expressed genes in the ceRNA network, we performed GO and KEGG functional enrichment analysis. In the GO analysis we identified a total of 162 enriched GO terms (FDR\u0026lt;0.01). The top 8 significantly enriched GO terms in the biological process (BP), cellular components (CC) and molecular function (MF) are shown in Figure 4. The biological processes of these differentially expressed genes were primarily associated with regulated by protein kinase B signaling, phosphatidylinositol phosphorylation, protein kinase B signaling and lipid phosphorylation. Meanwhile, the genes related to cellular components were mostly involved in nuclear transcription factor complex, focal adhesion, cell-substrate adherens junction and cell-substrate junction. In terms of molecular function, these differential genes were mostly enriched in phosphatidylinositol-4,5-bisphosphate 3-kinase activity, phosphatidylinositol bisphosphate kinase activity, phosphatidylinositol 3-kinase activity and 1-phosphatidylinositol-3-kinase activity.\u003c/p\u003e\n\u003cp\u003eAdditionally, KEGG signal pathway analysis showed that 24 signal pathways were significantly enriched (FDR\u0026lt;0.01). The top 15 significantly enriched pathways are shown in Figure 5. Among these pathways, the \u0026lsquo;PI3K-Akt signaling pathway\u0026rsquo;, \u0026lsquo;MicroRNAs in cancer\u0026rsquo;, \u0026lsquo;Proteoglycans in cancer\u0026rsquo;, \u0026lsquo;Cellular senescence\u0026rsquo;, \u0026lsquo;FoxO signaling pathway\u0026rsquo;, \u0026lsquo;Central carbon metabolism in cancer\u0026rsquo; and \u0026lsquo;Cell cycle\u0026rsquo; are closely correlated with the carcinogenesis and development of BRCA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic characteristics of RNAs in the ceRNA regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvival analysis based on Survival package of R found that 13 mRNAs （CCNE1, TPD52, SDC1, ANLN, ZNF367, SOX11, IRS2, EZR, DSC3, CCND2, KPNA2, CBX2 and CEP55）among the 100 DEmRNAs in the ceRNA network were closely associated with the overall survival of breast cancer patients. The low expression of CCNE1, TPD52, SDC1, ANLN, ZNF367, SOX11, EZR, KPNA2, CBX2 and CEP55 was associated with high survival, whereas for IRS2, DSC3 and CCND2, high expression was associated with high survival. Six miRNAs (hsa-miR-204-5p, hsa-miR-335-5p, hsa-miR-100-5p, hsa-miR-195-5p, hsa-miR-328-3p and hsa-miR-342-3p) of 30 DEmiRNAs were associated with prognosis. High expression of hsa-miR-204-5p, hsa-miR-335-5p, hsa-miR-100-5p, hsa-miR-195-5p and hsa-miR-342-3p indicated long survival time, while high expression of hsa-miR-328-3p indicated a relatively short survival time. Survival analysis results are shown in Table 6 and Figure 6. Notably, based on the ceRNA network, we found that the hsa_circ_0004315-hsa-miR195-5p axis was associated with four mRNAs associated with breast cancer prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 \u003c/strong\u003ePrognostic value of the differentially expressed mRNAs and miRNAs.\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR (95% Cl)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eCCNE1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e1.606 (1.169-2.208)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0038\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eTPD52\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e1.578 (1.148-2.169)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0053\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eSDC1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e1.516 (1.102-2.086)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0102\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eANLN\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e1.489 (1.083-2.046)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0154\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eZNF367\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e1.475 (1.073-2.025)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0176\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eSOX11\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e1.443 (1.050-1.983)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0244\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eIRS2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.705 (0.512-0.971)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0299\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eEZR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e1.418 (1.030-1.950)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0311\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eDSC3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.718 (0.521-0.988)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0398\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eCCND2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.718 (0.522-0.987)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0400\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eKPNA2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e1.395 (1.015-1.917)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0410\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eCBX2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e1.391 (1.012-1.912)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0433\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eCEP55\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e1.384 (1.007-1.902)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0474\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003ehsa-miR-195-5p\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.629 (0.455-0.870)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0046\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003ehsa-miR-204-5p\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.648 (0.469-0.894)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0086\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003ehsa-miR-335-5p\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.664 (0.481-0.916)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0134\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003ehsa-miR-342-3p\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.696 (0.504-0.961)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0280\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003ehsa-miR-100-5p\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.704 (0.509-0.972)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0323\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003ehsa-miR-328-3p\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e1.410 (1.021-1.947)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e0.0356\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction between miRNA and mRNA from the ceRNA network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to ceRNA theory, circRNA could indirectly affect mRNA through miRNA. At the expression level, miRNA was negatively correlated with circRNA and mRNA. In order to verify that the network we built was consistent with ceRNA theory, we needed to perform correlation analysis on different kinds of RNA. The expression information of circRNA in this study was from the GSE101123 dataset, while the expression information of miRNA and mRNA were from the TCGA dataset. Since the expression information of RNAs in the correlation analysis must be from the same sample, this study could only analyze the correlation between the expression levels of miRNA and mRNA. We performed a correlation analysis of miRNA-mRNA pairs in the ceRNA network based on R software, and the results showed that there were 48 miRNA-mRNA pairs with strong negative correlation (r\u0026lt;-0.3, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) (Table 7). For instance, hsa-miR-141-3p negatively correlated with ZEB2 (r=-0.599, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and QKI (r=-0.535, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), hsa-miR-195-5p negatively correlated with CEP55 (r=-0.547, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and CLSPN (r=-0.525, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), hsa-miR-200a-3p negatively correlated with ZEB2 (r=-0.520, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) as well as QKI (r=-0.513, \u003cem\u003eP\u003c/em\u003e=0.001) (Figure 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7 \u003c/strong\u003eCorrelation analysis of the relationship between miRNA and mRNA.\u003c/p\u003e\n\u003ctable border=\"1\" width=\"568\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003e\u003cstrong\u003emiRNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u003cstrong\u003emRNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e_vlaue\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-141-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eZEB2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.59875\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCEP55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.54744\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-141-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eQKI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.53509\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCLSPN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.52524\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-200a-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eZEB2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.52049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-200a-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eQKI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.51254\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eHMGA1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.49915\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCEP55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.49525\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCHEK1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.49341\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCDC25A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.49147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCCNE1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.48761\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eANLN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.45814\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-139-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eTPD52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.44566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eE2F7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.44235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-139-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eZNF367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.43672\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCLSPN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.43102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-145-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eTPM3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.42875\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCBX2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.42701\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-145-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eRTKN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.42632\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eZNF367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.42269\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-200a-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eDLC1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.4224\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCCNE1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.42016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-7-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eRBMS3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.41862\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCDC25A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.4139\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-342-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eID4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.4063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCHEK1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.40444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-141-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eSTAT5A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.39451\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-218-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eLMNB1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.3945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-141-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eYAP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.3803\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCBX2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.37969\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-200a-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eHGF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.37823\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eE2F7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.3743\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-218-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eTPD52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.37324\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCDCA4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.3683\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-204-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eAP1S1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.36607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCDCA4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.36528\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-204-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eEZR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.35209\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eZNF367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.34916\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-7-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eSNCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.33958\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-145-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eABRACL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.33926\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-365b-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eMCOLN2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.33306\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-365a-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eMCOLN2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.33285\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-141-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eUSP53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.31765\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-200a-3p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eYAP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.3142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-145-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eABHD17C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.3107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-33b-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eGAS1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.3097\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-195-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eENTPD7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.30541\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"196\"\u003e\n\u003cp\u003ehsa-miR-33b-5p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eARID5B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-0.3005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of PPI network and module analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe STRING database was used to unveil the interrelationships between the DEmRNAs in the ceRNA network by constructing PPI network. This PPI network involves a total of 75 nodes and 283 edges. Visualization was performed with Cytoscape (Figure 8A). In order to identify hub genes in the process of BRCA carcinogenesis, the MCODE plugin in Cytoscape was used to identify the core subnetwork in the PPI network. Two core sub-networks were obtained, including 21 genes and 49 edges (Figure 8B). We used these 21 genes as potential hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative real-time PCR validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, we randomly selected four DEcircRNAs, DEmiRNAs and DEmRNAs respectively in the ceRNA network to verify the reliability and validity of the above analysis results. These results showed that CCNE1, CEP55, ANLN, hsa-miR-592, hsa-miR-141-3p, hsa_circ_0000069, hsa_circ_0000518 and has_circ_0000520 were up-regulated in BRCA tumor tissues compared to adjacent non-tumor tissues, while ADIPOQ, hsa-miR-195-5p, hsa-miR-204-5p and has_circ_0000977 were down-regulated in BRCA tumor tissues (Figure 9). The results of qRT-PCR validation from new breast cancer patients were consistent with the above bioinformatics results, indicating that our bioinformatics analysis was credible.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAbnormal expression of circRNA has been widely observed in various diseases. Studies have shown that dysregulated circRNA plays a key role in the important biological properties of cancer [19]. However, only a few studies have described the profile of circRNA in BRCA by microarray analysis. The constructed BRCA-related circRNA-associated ceRNA network provides important hints for detecting the key RNAs of ceRNA-mediated gene regulatory network in the initiation and development of BRCA.\u003c/p\u003e\n\u003cp\u003eWe obtained BRCA mRNA, miRNA expression profile and circRNA expression profile from TCGA database and GEO database respectively. After statistical analysis, 2762 DEmRNAs, 158 DEmiRNAs and 72 DEcircRNAs were identified. Next, we screened the circRNAs-miRNAs interaction pairs through CircIteractome and CSCD databases, screened the miRNA-mRNA interaction pairs by TargetScan, miRTarBase and miRDB databases, and then took the intersection, and finally constructed a specific circRNA-miRNA-mRNA ceRNA regulatory network. We have found that specific circRNAs in this ceRNA network, such as hsa_circ_0000376, hsa_circ_0000069, hsa_circ_0000520 and hsa_circ_0008365, have also been reported as potential diagnostic markers in certain cancers. Hsa_circ_0000376 is highly expressed in gastric cancer tissues [20], and hsa_circ_0000069 is up-regulated in colorectal cancer tissues, which can promote the proliferation, migration and invasion of tumor cells [21]. Hsa_circ_0000520 was up-regulated in breast cancer and cell lines (T47D, MCF-7, MDA-MB-231, BT549 and SKBR3), and hsa_circ_0000520 high expression was associated with poor overall survival[22]. Hsa_circ_0008365 (Circ-SERPINE2) is a novel proliferative promoter that can regulate YWHAZ through sponge miR-375 to promote the development of gastric cancer [23]. To understand the potential functional significance of differentially expressed mRNA in ceRNA networks, we performed GO analysis and KEGG analysis. It was worth noting that KEGG analysis found that some enriched signaling pathways were closely related to the development of cancer, such as 'PI3K-Akt signaling pathway' [24], 'MicroRNAs in cancer', 'Proteoglycans in cancer', 'Cellular senescence', ' FoxO signaling pathway' [25], 'Central carbon metabolism in cancer' and 'Cell cycle' [26]. Functionally annotated results also indicate that circRNAs that regulate these key mRNAs may play an important role in the initiation and development of BRCA and pathways associated with cancer genes.\u003c/p\u003e\n\u003cp\u003eIn order to further identify the key genes involved in the regulatory network, we established a PPI network and screened two core sub-networks through the MCODE plug-in, which contained 21 genes, which will be used as potential hub genes. At the same time, we analyzed the relationship between DEmRNAs and DEmiRNAs in ceRNA networks and overall survival of breast cancer patients, and found that 13 mRNAs (CCNE1, TPD52, SDC1, ANLN, ZNF367, SOX11, IRS2, EZR, DSC3, CCND2, KPNA2, CBX2 and CEP55) and 6 miRNAs (hsa-miR-204-5p, hsa-miR-335-5p, hsa-miR-100-5p, hsa-miR-195-5p, hsa-miR-328-3p and hsa-miR-342-3p) are significantly associated with the prognosis of breast cancer patients. Most of these mRNA molecules related to patient survival are thought to be related to the molecular pathogenesis of various tumors, and were closely related to the occurrence, development, proliferation, metastasis and prognosis of cancer [27-31]. For example, the DNA copy number of TPD52 is amplified in prostate cancer cells, and the level of TPD52 protein may be regulated by androgen. Studies have shown that genomic amplification and dysregulation of TPD52 caused by androgen induction may play a role in the progression of prostate cancer [28]. Gui X found that SDC1 is overexpressed in breast cancer and may be a potential prognostic indicator for breast cancer [29]. It has been reported that the upregulation of ANLN is a common feature in the carcinogenesis of lung tissue， ANLN can play a key role in the development of human lung cancer by activating RHOA and participating in the phosphoinositide 3-kinase/AKT pathway， the expression of ANLN is also associated with low survival in patients with NSCLC [30]. CEP55 is a determinant of mitosis in breast cancer cells [31]. By immunohistochemical analysis, it was found that EZR is up-regulated in breast cancer and can be used as a potential marker for overall survival of breast cancer [32].\u003c/p\u003e\n\u003cp\u003eIt is well known that miRNAs regulate about 60% of human genes and mediate a variety of biological pathways, including pathways critical for tumorigenesis. Here, we found that microRNAs associated with BRCA overall survival in ceRNA networks have been reported to play an important role in tumorigenesis, development, prognosis, and drug resistance. Extracellular vesicles containing miR-335-5p can downgrade the growth and invasion of liver cancer in vitro and in vivo， the exosome miR-335-5p can be used as a novel therapeutic strategy for hepatocellular carcinoma [33]. NABAVI N identified miR-100-5p as one of the key molecular components in the initiation and evolution of androgen ablation therapy resistance in prostate cancer [34]. A research team reported that miR-328-3p is up-regulated in ovarian cancer stem cell (CSC), and high expression of miR-328-3p can directly target DNA damage-binding protein 2 to maintain CSC properties， inhibition of miR-328-3p is a new strategy to effectively eliminate CSC [35]. Enhanced expression of miR-342-3p synergizes with miR-205-5p to inhibit E2F1, thereby reducing tumor chemoresistance [36]. Published studies have shown that hsa-miR-204-5p can be used to predict the prognosis of patients with clear renal cell carcinoma, lung adenocarcinoma and other cancers [37, 38]. Hsa-miR-204-5p directly targets FOXA1 to regulate tumor cell infiltration and metastasis [39], and can affect tumor angiogenesis by interfering with the expression of ANGPT1/TGFBR2 [40]. hsa-miR-195-5p can affect the development of colorectal cancer by inhibiting the Hippo-YAP pathway [41], meanwhile, hsa-miR-195-5p can be a potential diagnostic and prognostic target in breast cancer [42].\u003c/p\u003e\n\u003cp\u003eWe performed a correlation analysis between the expression levels of miRNAs and mRNAs from the same sample in the TCGA database. The results indicate that there are 43 pairs of interconnected miRNAs and mRNAs with a significant negative correlation in the constructed miRNA-mRNA interaction pairs. These links have also been found in some reports. For example, Luo Q found that overexpression of hsa-miR-195-5p can reduce the expression level of CCNE1 and targeting this miRNA may provide a new strategy for the diagnosis and treatment of breast cancer [42]. In addition, reports on circRNA found that circAGFG1 can act as a sponge of hsa-miR-195-5p, which promotes the progression of triple-negative breast cancer by regulating the expression of CCNE1 [43]. These results also indirectly reflect the feasibility of using bioinformatics to construct regulatory networks.\u003c/p\u003e\n\u003cp\u003eHere, we identified four ceRNA regulatory axes, indicating competitive regulatory relationships of three circRNAs with the three genes in BRCA. However, given that these results are based solely on bioinformatics models, further in-depth studies are critical to verifying the possible role of these four axes in BRCA.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we identified aberrant expressed key RNAs by analyzing the RNAs expression profiles of BRCA in public databases. These specific circRNA, miRNA and mRNA molecules may be helpful in the discovery of sensitive biomarkers in BRCA. Importantly, we have constructed a circRNA-miRNA-mRNA ceRNA network that will be used to elucidate the unknown ceRNA regulatory axes in BRCA. Our findings provide novel insights into an in-depth understanding of circRNA-related ceRNA networks in breast cancer as well as potential diagnostic and prognostic biomarkers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHui Shen and Huan Pan carried out data analysis. Ming Yao participated in study design and data collection. All authors drafted the final manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committee of The First Affiliated Hospital of Jiaxing University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported, in part, by grants from Zhejiang Provincial Natural Science Foundation of China (LY20H090020, LQ19H090007, LGF20H090021); the Science and Technology Project of Jiaxing City (2020AY30010, 2019AD32251, 2018AD32095, 2018AY32012); Zhejiang Provincial Medical Scientific Research Foundation of China (2020KY948, 2020358554); Zhejiang Provincial Medical and Health General Research Program of China (2019KY687); the Construction Project of Anesthesiology Discipline Special Disease Center in Zhejiang North Region (201524); the Key Medical Subjects Established by Zhejiang Province and Jiaxing City Jointly Pain Medicine (2019-ss-ttyx); the Construction Project of Key Laboratory of Nerve and Pain Medicine in Jiaxing City; 2019 Jiaxing Key Discipiline of Medicine-Clinical Laboratory Diagnostics (Innovation Subject) (2019-cx-03).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1 \u003c/sup\u003eDepartment of Central Laboratory, The First Affiliated Hospital of Jiaxing University, Jiaxing 314000, China; \u003csup\u003e2 \u003c/sup\u003eDepartment of Breast Disease, The First Affiliated Hospital of Jiaxing University, Jiaxing 314000, China; \u003csup\u003e3\u003c/sup\u003e Department of Nursing, The First Affiliated Hospital of Jiaxing University, Jiaxing 314000, China; \u003csup\u003e4\u003c/sup\u003e Department of Anesthesiology and Pain Medicine, The First Affiliated Hospital of Jiaxing University, Jiaxing 314000, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Jemal A: \u003cstrong\u003eCancer Statistics, 2017\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2017, 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\u003cstrong\u003e18\u003c/strong\u003e(1):4\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"circRNA, ceRNA network, biomarker, carcinogenesis, bioinformatics, breast cancer, The Cancer Genome Atlas, Gene Expression Omnibus","lastPublishedDoi":"10.21203/rs.3.rs-90865/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-90865/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\u003cp\u003eThere is increasing evidence that circular RNA (circRNA) is closely related to tumorigenesis and cancer progression. circRNA has been identified as a sponge of microRNA (miRNA) in a competitive endogenous RNA (ceRNA) network and is involved in the regulation of mRNA expression. However, the roles of cancer specific circRNAs in circRNA-related ceRNA network of breast cancer (BRCA) are still unclear. This study aims to construct a ceRNA network associated with circRNA and to explore new therapeutic and prognostic targets and biomarkers for breast cancer.\u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003cp\u003eWe downloaded the circRNA expression profile of BRCA from Gene Expression Omnibus (GEO) microarray datasets and downloaded the miRNA and mRNA expression profiles of BRCA from The Cancer Genome Atlas (TCGA) database, these data were included in the study for comprehensive analysis. Differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs) and differentially expressed circRNAs (DEcircRNAs) were identified and a competitive endogenous RNA (ceRNA) regulatory network was constructed based on circRNA–miRNA pairs and miRNA–mRNA pairs. Gene ontology and pathway enrichment analysis were performed on mRNAs regulated by circRNAs in ceRNA networks. Survival analysis and correlation analysis of all mRNAs and miRNAs in the ceRNA network were performed. The STRING search tool was used to predict the interaction between proteins, and the hub genes were screened by the MCODE plugin in Cytoscape.\u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eA total of 72 DEcircRNAs, 158 DEmiRNAs and 2762 DE mRNAs were identified. The constructed ceRNA network contains 60 circRNA-miRNA pairs and 140 miRNA-mRNA pairs, including 40 circRNAs, 30 miRNAs and 100 mRNAs. Functional enrichment indicated that DEmRNAs regulated by DEcircRNAs in ceRNA networks were significantly enriched in PI3K-Akt signaling pathway, MicroRNAs in cancer and Proteoglycans in cancer. Survival analysis and correlation analysis of all mRNAs and miRNAs in the ceRNA network showed that a total of 13 mRNAs and 6 miRNAs were significantly associated with overall survival, and 48 miRNA-mRNA interaction pairs had a significant negative correlation. A PPI network was established and 21 hub genes were determined from the network. After comprehensive analysis, four potential ceRNA regulatory axes were constructed based on three circRNAs, two miRNAs, and three mRNAs.\u003c/p\u003e\u003cp\u003eConclusions\u003c/p\u003e\u003cp\u003eThis study provides an effective bioinformatics basis for further understanding the molecular mechanisms and predictions of breast cancer. A better understanding of the circRNA-related ceRNA network in BRCA will help identify potential biomarkers for diagnosis and prognosis.\u003c/p\u003e","manuscriptTitle":"Integrated Analysis of Circular RNA Associated Cerna Network Reveals Potential CircRNA Biomarkers in Human Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-10-14 15:59:28","doi":"10.21203/rs.3.rs-90865/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fec5d635-698c-4520-94ba-e0230050e196","owner":[],"postedDate":"October 14th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":775261,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2020-10-14T15:59:31+00:00","versionOfRecord":[],"versionCreatedAt":"2020-10-14 15:59:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-90865","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-90865","identity":"rs-90865","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}