Systematic Analyses of the Expression, Function, and Prognostic Value of CCNBs in Breast Caner

Background Cyclin B (CCNB) family plays key roles in the cell cycle, cell division and proliferation. Three members of CCNB family have been identied, including CCNB1, CCNB2 and CCNB3. Many studies have explored the roles of CCNBs in the tumorigenesis and pathogenesis of different types of cancer. However, the expression level, function, and prognostic value of CCNBs in breast caner (BC) are still unclear. Methods We explored the specic alterations of CCNBs in BC and predicted their prognostic value for BC patients. Oncomine, Gene Expression Proling Interactive Analysis (GEPIA), Kaplan-Meier plotter, cBioPortal, STRING, Database for Annotation,Visualization and Integrated Discovery (DAVID) databases were used for above analyses. Results We found that CCNB1 amd CCNB2 were signicantly overexpressed in BC compared with normal samples, but not CCNB3. Survival analysis showed that upregulated CCNB1 and CCNB2 expression levels were associated with poor prognosis of BC patients, while high CCNB3 expression was related to good prognosis for BC patients. Furthermore, gene oncology (GO) enrichment analysis was performed to reveal the functions of CCNBs and the interacted genes related to CCNBs. In addition, hsa-miR-139-5p and has-miR-944 were identied to be potentially involved in the regulation of CCNB1. Conclusion Our study suggests that CCNB1, CCNB2 are potential targets of precise therapy for BC patients and that CCNB3 is a novel biomarker for the good prognosis of BC patients.


Introduction
Breast cancer (BC) is the most common cancer and the most common cause of cancer-related death among women [1][2][3][4]. E cient tools for preventive intervention, therapeutic evaluation, and prognostic prediction are lacking [5]. Traditional histopathological classi cation does not provide su cient predictive value, and therefore, in recent studies, researchers have gradually identi ed biomarkers indicating intrinsic features of tumors at the molecular level [6][7][8][9]. Elucidation of the genes associated with breast cancer play a key role in the develcopment of BC treatment and prognostic prediction.
The Cyclin B (CCNB) family is a speci c group that mainly functions in the cell cycle pathway, regulating cyclin-dependent kinase 1 (CDK1) enzyme activity through binding and degradation. CCNB synthesis occurs in the late S phase and G2 phase [10][11][12]. CDKs are Ser/Thr kinase systems involved in the cell cycle. Active CDK1-CCNB-Cks phosphorylate critical substrates during mitosis, leading to dramatic recombination and eventual separation of sister chromatids on mitotic spindles. Once separated, expression related to chromatids must return to a low level so that nuclear membrane recombination, spindle disassembly, and cytokinesis can occur [13].
At present, three members of the CCNB family have been identi ed: CCNB1, CCNB2 and CCNB3. CCNBs were reported to play key roles in human BC. High CCNB1 expression has been reported in many tumors, including colorectal cancer [14], lung cancer [15], ovarian cancer [16], liver cancer [17]. High CCNB2 expression is related to bladder cancer [18] and non-small cell lung cancer [19]. BCOR-CCNB3 gene fusion was also shown to be closely related to round cell sarcomas [20,21]. Moreover, many drugs and upstream genes block the G2/M transition by inhibiting the action of CCNB1 and CCNB2, thus affecting cancer-related processes [15,22,23].
Although it has been reported that CCNB family play key roles in many cancers, its bioinformatics analysis in BC has not been systematically explored. In our study, the expression level, functions, and prognostic value of CCNBs in BC were analyzed by online databases and demonstrated based on a series of cell experiments. Our study suggests that CCNB1 and CCNB2 are potential biomarkers and precise therapy targets for BC patients and CCNB3 is a novel biomarker for good prognosis of breast cancer. Our study contributes to enriching the acknowledge of CCNBs in human BC.

Ethics Statement
All analyses and protocols in this study strictly comply with the requirements of the Ethics Committee and Institutional Review Board of Qingdao University. Datasets were sourced from publicly available databases on the web and were consistent with the appropriate requirements.

Oncomine analysis
Oncomine [24] (http://www.oncomine.org), which integrates RNA and DNA data from open databases such as GEO and TCGA, was used to explore the mRNA expression of CCNB family genes between BC and normal samples. Student's t-test was applied to generate a P value for comparison, and the criteria were as follows: P = 0.01 and fold change = 2.

CCLE analysis
The Cancer Cell Line Encyclopedia [25] (CCLE, https://portals.broadinstitute.org/ccle) is an open database containing data from nearly a thousand cancer patients' cell lines, which can be used to study genomic data. The CCNBs expression levels in various cancer cell lines were analysed by CCLE.

Kaplan-Meier Plotter
Kaplan-Meier plotter [26] (http://kmplot.com/analysis/) is an online tool to generate survival plots that can be used to assess the correlation between the expression levels of genes and the clinical outcomes of BC patients. Survival curves, which included HR and log-rank P value, were used to show the prognostic value of CCNB1, CCNB2 and CCNB3 based on the Kaplan-Meier plotter.

STRING analysis
The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) [27]. (https://string-db.org) is a database that collects and integrates protein-protein interaction information and predicts further interactions. The protein-protein interaction network of CCNBs was established by STRING.

GEPIA Dataset
Gene Expression Pro ling Interactive Analysis (GEPIA) [28](http://gepia.cancer-pku.cn/) is a newly developed interactive web server for analyzing the RNA sequencing expression data from TCGA and GTEx projects using a standard processing pipeline. We used the GEPIA database to analyse the expression levels of CCNBs in BC tissues compared with normal samples.

cBioPortal analysis
TheBreast Invasive Carcinoma database (TCGA, PanCancer Atlas) was selected to establish the Breast Cancer Genome Atlas of CCNBs by the cBioPortal [29] (http://www.cBioPortal.org/) database. The selected genomic pro les contained the following: mutations, putative copy-number alterations from DNA and mRNA expression data (microarray) and z-scores relative to diploid samples.

GO enrichment analysis
Database for Annotation, Visualization and Integrates Discovery (DAVID) [30] (https://david,ncifcrf.gov/), an integration and analysis tool, was used to analyze the CCNBs and interacted genes to identify Gene Ontology (GO) terms and visualize genes on KEGG pathway maps.

ENCORI analysis
The Encyclopedia of RNA Interactomes (ENCORI) (http://starbase.sysu.edu.cn/index.php) [31] database can be used to explore the potential relationships, scuh as miRNA-mRNA, miRNA-ncRNA. In our study, ENCORI was used to predict miRNAs that regulated CCNBs and showed the miRNA expression level in breast cancer. The options used in our study were as follows: CLIP data: medium stringency (≥3); Degradome data: with or without data; Pan-Cancer: 1 cancer type.
Cell culture and qPCR detection BC cell line MDA-MB-231 was stored in the laboratory and MCF-10A (human normal breast epithelial tissue cell) was acquired from Biotechnology Co., Ltd. Shanghai enzyme research. MDA-MB-231 cells were cultured in culture medium comprising DMEM supplemented with 10% fetal bovine serum (Tianhang Biotechnology Co., Ltd., China) while MCF-10A cells were cultured in MCF-10 cultural solution (CC-Y1607M from EK-Bioscience) and both were maintained at 37 °C in a 5% CO2-incubator.
Total RNA of TNBC and MCF-10A was isolated using RNA extraction kit (R0032, Beyotime Biotechnology, China). BeyoFast™ SYBR Green qPCR Mix(2x) kit (Beyotime Biotechnology, China) was utilized for all gene qRT-PCR assays according to the manufacturer's protocol. Quantitative RT-PCR was performed on a real-time PCR system using QuantStudio3 by Theme Fisher. PCR cycles were run as follows: predenaturation for 2 min at 95°C followed by 40 cycles of 15 s at 95°C, 30 s at 56°C and 30 s at 72℃.

Statistical Analysis
Student's t-test was used to analyse the statistical difference of the CCNBs expression levels in BC and normal sampes. The analysis of qRT-PCR was used to show the relative expression level of CCNBs in human BC and normal breast cells by using 2(-ΔΔC(T)) method. Using Kaplan-Meier plotter, the survival curves, including Log-rank test and hazard ratio (HR), of different subtypes of BC patients with different expression levels of CCNBs were drawn. P <0.05 was considered to be statistically signi cant.

Transcriptional Levels of CCNBs in BC
At present, three members have been identi ed in the CCNB family, including CCNB1, CCNB2 and CCNB3.
However, it is not clearly known that the roles of CCNBs in BC. The Oncomine database was used to analyse the mRNA expression levels of CCNBs in BC patients and those in normal samples. As shown in Fig. 1a, 445 CCNB1 analyses, 415 CCNB2 analyses and 249 CCNB3 analyses were performed in the Oncomine database. Our results showed that CCNB1 (upregulated in 14 datasets) and CCNB2(upregulated in 25 datasets) were signi cantly overexpressed in BC, but not CCNB3.
Overexpression of CCNB1 and CCNB2 were closely related to BC Furthermore, a meta-analysis was performed to show that the expression levels of CCNB1 and CCNB2 were signi cantly increased in BC (supplement material 1). The results demonstrated that the CCNB1 expression level was signi cantly upregulated in BC (P value = 4.79E-8) and the mRNA expression level of CCNB2 was also prominently increased in BC (P value = 6.39E-5), indicating that upregulation of CCNB1 and CCNB2 were closely associated with BC.
In addition, qRT-PCR analysis con rmed that CCNBs mRNA was up-regulated at the cellular level. Compared with MCF-10A cell line, the average expression of CCNB1 mRNA in MDA-MB-231 cell line was 9.97 (Fig. 2a), and CCNB2 expression was 8.35 (Fig. 2b), while CCNB3 expression has no difference between 2 cell lines (Fig. 2c). These results indicate that CCNBs may be a special factor of breast cancer.
In order to verify that CCNB1 and CCNB2 were overexpressed in BC compared with normal samples, but not CCNB3, a series of analyses were performed. We used the Cancer Cell Line Encyclopedia (CCLE) database to showed the mRNA transcription levels of CCNB1, CCNB2 and CCNB3 in BC. As shown in supplement material 2, the mRNA transcription levels of CCNB1 and CCNB2 in BC cell line and BC tissue were signi cantly up-regulated. However, we did not observe that the increased CCNB3 mRNA transcription level in BC cell line and BC tissue .
High expression of CCNB1 and CCNB2 were associated with poor prognosis for BC patients, while high expression of CCNB3 was related to good prognosis for BC patients We also found that high mRNA expression level of CCNB3 was signi cantly associated with longer RFS in all BC patients (HR = 0.66, p = 2.4e-07). In particular, analysis of different BC subtypes showed that high CCNB3 mRNA expression was signi cantly related to ER-positive (HR = 0.74, p = 0.046), Luminal A (HR = 0.76, p = 0.026), Luminal B (HR = 0.64, p = 0.0051) and Lymphnode-negative (HR = 0.68, p = 0.049) (Fig. 3).

The predictive function of CCNBs and their frequently interacted genes in patients with BC
To detect the expression pattern and mutation of CCNBs, the cBioPortal database was used to analyze the alterations of CCNBs in BC patients. As shown in supplement material 5A, CCNBs mRNA expression levels were altered in 184 (17%) of queried 1108 BC patients. In addition, the correlations of the mRNA expressions of CCNBs in BC was analysed by the Gene Expression Pro ling Interactive Analysis (GEPIA) database. We found that there were signi cant positive correlations between following CCNBs: CCNB1 with CCNB2 (R = 0.75), CCNB1 with CCNB3 (R = 0.086) and CCNB2 with CCNB3 (R = 0.13) (supplement material 5B).
We established a protein-protein interaction network of CCNBs, and 50 most frequently altered genes were also included in it (Fig. 4a). Furthermore, the functions of CCNBs and the interacted genes associated with CCNBs were predicted by gene oncology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses in the Database for Annotation, Visualization and Integrated Discovery (DAVID). Using GO enrichment analysis, we predicted the potential functions of target genes through the GO terms, including biological process (BP), cellular component (CC), and molecular function (MF) (Fig. 4b). We found that GO and GO:0030332 (cyclin binding) were the MFs that were signi cantly controlled by CCNBs alterations.
The pathways related to the altered functions of CCNBs and frequently altered interaction genes were de ned by KEGG analysis. KEGG analysis revealed pathways associated with the altered functions of CCNBs in BC, such as cell cycle and p53 signaling pathway (supplement material 6), which involved in the tumorigenesis and pathogenesis of BC.
Identi cation of regulatory miRNA and their roles on prognostic value in breast cancer patients As shown in Table 1-2(supplementary material), 42 miRNAs regulating CCNB1 and 5 miRNAs regulating CCNB2 were predicted by the ENCORI database. We did not nd any miRNA that met the criteria to regulate CCNB3. Among them, 22 miRNA-CCNB1 pairs and 3 miRNA-CCNB2 pairs were negatively correlated. However, only 2 miRNA-CCNB1 pairs were decreased expression in BC compared with normal samples and predicted poor prognosis for patients with BC. As shown in Fig. 6a and 6b, the high expression of hsa-miR-139-5p and hsa-miR-944 in BC were related to longer overall survival in BC patients. Therefore, hsa-miR-139-5p and has-miR-944 may target CCNB1 to exert anti-tumor function in BC (Fig. 6c).

Discussion
It has been reported that BC is the most common type of cancer in women [33,34]. Classic clinical prognostic biomarkers, including PR, HER2 and ER, have positive roles in precision therapy in BC patients [35]. However, the current limitations of tumor markers are sensitivity and speci city, which are determined by the unique heterogeneity of various tumors. Therefore, we need valuable novel biomarkers as prognostic predictors for cancer patients to effectively increase the prognosis and precise therapy effects of cancer patients. Fortunately, some open bioinformatics databases have been established by biomedical researchers, such as Oncomine [36], CCLE [25], cBioPortal [29], and some potential novel biomarkers were identi ed as valuable prognostic predictors and therapeutic targets [37][38][39].
It has been reported that the expression levels of CCNBs in many types of cancer are dysregulated, such as colorectal cancer, lung cancer, ovarian cancer, liver cancer, bladder cancer, round cell sarcomas and BC [14][15][16][17][18][19][20][21]40]. Although key roles of CCNBs in tumor occurrence, progression, distant metastasis and predicting the prognosis of tumor patients have been partially reported, the detailed biological information of CCNBs in BC has not been systematically explored. Bioinformatics analyses of CCNBs in BC were performed based on online databases to systematically explore the expression levels, functions and prognostic value of CCNBs in BC to enrich the acknowledge of CCNBs in BC.
We found that CCNB1 and CCNB2 were signi cantly overexpressed in BC compared with normal samples (Fig. 1). It has been reported that the expression level of CCNB1 and CCNB2 are increased in BC and predicts poor prognosis of BC patients [40][41][42]. However, the prognostic value of CCNBs in BC patients, including the survival rate of all BC patients and BC subgroups, still needs to be further clari ed. Using Kaplan-Meier plotter, we further clari ed the correlation between the high CCNB1 and CCNB2 expression in BC and poor prognosis of BC patients, including the relapse free survival of all BC patients and BC subgroups (PR+, PR-, basal-like, ER+, ER-, Her2+, Her2-, Luminal A, Luminal B, Lymphnode + and Lymphnode-subgroups) (supplement material 1). Alterations and mutations of CCNBs in BC and the correlation of CCNB family members were also been shown (supplement material 5). We established a protein-protein interaction network of CCNBs and frequently altered genes. Furthermore, the functions of CCNBs and the interacted genes associated with CCNBs were predicted (Fig. 4). Our results indicated that cell cycle was the main pathway which CCNBs involved in the tumorigenesis and pathogenesis of BC. Korgun et al. reported that CCNB1 had an important role in the G2/M transition [43]. Gustafsson N et al. con rmed that downregulation of CCNB1 expression affected the G2/M phase transition [44]. Liu et al. demonstrated that acetylated MORC2 reduced the H3T11P expression and repressed the expression of its downstream gene CCNB1, which sensitized BC cells to DNA-damaging chemotherapy by arresting BC cells at the G2/M transition [45]. CCNB2 also plays an important role in BC. Acid ceramidase (ASAH1) can upregulated the CCNB2 expression and promote BC cell proliferation [46]. Meng et al. reported that PTTG1 was overexpressed in BC and might target CCNB2, resulting in more BC cells distributed in S phase [47]. To date, there is no report revealing the expression level of CCNB3 in BC and predicting the prognostic value for BC patients. In our study, we found that high CCNB3 expression was related to good prognosis of BC patients. Our study suggests that CCNB3 is a novel biomarker for good pronosis of BC patients.
According to previous reports, the expression of miRNA is closely associated with the occurrence, progerssion and therapy of various tumors, indicating that these miRNAs may become potential diagnostic, prognostic biomarkers for cancer [48]. Using ENCORI database, miRNAs (including has-miR-139-5p and has-miR-944) that negatively regulated CCNB1 in BC were identi ed. It has been reported that has-miR-139-5p can inhibit the proliferation of BC [49]. Flores-Pérez et al. reported that has-miR-944 could target SIAH1 and PTP4A1 to suppress BC cells migration [50]. We established the has-miR-139-5p-CCNB1 and has-miR-944-CCNB1 regulatory network that may be potentially prognostic biomarkers and therapeutic targets in BC.

Conclusion
In this study, the expression level, function, and prognostic value of CCNBs were systematically explored using online databases. We con rmed that CCNB1 and CCNB2 expression were signi cantly increased in BC compared with normal samples, but not CCNB3. Our result show that high expression of CCNB1 and CCNB2 were associated with poor prognosis of BC patients. High CCNB3 expression was related to good prognosis of BC patients. We suggest that CCNB1 and CCNB2 are potential biomarkers and precise therapy targets for BC patients and CCNB3 is a novel biomarker for good prognosis of BC patients.    Protein-protein interaction network and gene oncology enrichment analysis of CCNBs in breast cancer. a

Declarations
The protein-protein interaction network of CCNBs and frequently altered interaction genes was established based on STRING database. b The potential functions of CCNBs and interaction genes signi cantly related to CCNBs were predicted through the DAVID database, including biological process (BP), cellular component (CC), molecular function (MF), and KEGG pathways.