CDC73 promotes breast cancer through impairing MAPK1 ubiquitination and activating mTOR signaling pathway 

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CDC73 (Human cell division cycle 73), a nuclear protein, participates transcription regulation and its functions are controversial in malignancies. CDC73 has been reported to be upregulated in breast cancer. The underlying mechanism, however, has not been fully illuminated. In breast cancer, CDC73 could promote the proliferation of tumor cells, and the expression of CDC73 was related to poor prognosis in patients. Here, we found that CBL, an E3 ubiquitin ligase, could interact with CDC73 and promote MAPK1 ubiquitination and degradation of this protein. In addition, silencing MAPK1 led to a suppression of breast cancer cell growth in vitro and in vivo , and even abolished the promoting effects of CDC73 overexpression. We also found that mTOR pathway played a role in CDC73-mediated breast cancer. mTOR pathway inhibitor reversed cell phenotypes induced by CDC73 overexpression. Our study revealed the underlying mechanism of CDC73 in breast cancer: it promoted MAPK1 ubiquitination and degradation so that affected MAPK1 level and subsequently led to tumor progression, providing a novel therapeutic strategy to combat cancer. Breast cancer CDC73 MAPK1 ubiquitination mTOR signaling pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Breast cancer is estimated to be the second leading cause of cancer death in women, after lung cancer, according to Cancer statistics, 2022 [ 1 ]. The mortality of breast cancer has been decreasing year by year due to earlier diagnosis, through increased awareness and mammography screening, as well as improvements in treatment [ 1 ]. Despite tremendous progress in tumor diagnostic and therapeutic strategies, the prognosis of patients suffering from breast cancer is still poor due to the metastasis and heterogeneity of the cancer [ 1 – 3 ]. Hence, it is important to develop novel targets and more potential therapeutic strategies in this deadly disease. Human cell division cycle 73 (CDC73), akas parafibromin, is a nuclear protein, which is encoded by CDC73 gene located on chromosome 1q31.2 [ 4 – 6 ]. CDC73 binds to β-catenin to generate the RNA polymerase-associated factor 1 complex (Paf1C), eventually regulating transcription [ 4 , 7 , 8 ]. Paf1C is composed of five subunits (Cdc73, Paf1, Ctr9, Leo1 and Rtf1) in yeast, which affects levels of many RNAs via participating in chromatin transcription and genomic regulation, implying its functions in development and human diseases [ 9 , 10 ]. CDC73 was originally identified as a tumor suppressor, implicated in the hyperparathyroidism-jaw tumor syndrome and sporadic parathyroid carcinoma [ 5 , 11 , 12 ]. Later, its oncogenic properties were found in other cases [ 13 – 16 ]. Furthermore, the clinicopathological and prognostic significances of CDC73 expression in breast cancer have been unveiled [ 17 ]. It is involved in tumorigenesis and progression of breast cancer via an uncertain mechanism. In this work, we identified CDC73’s promoting role in breast cancer. The expression of CDC73 increased in breast cancer tissues and cells, which was related to a poor prognosis in patients. Functional investigations showed that CDC73 downregulation suppressed breast cancer cell growth in vitro . Subsequent studies demonstrated that CDC73 mediated breast cancer via targeting MAPK1 and activating mTOR pathway, and that CDC73 could regulate MAPK1 expression via affecting MAPK1 ubiquitination. Functionally, downregulating MAPK1 or mTOR inhibitor could reverse the effects of CDC73 overexpression on breast cancer development. Our results indicated that CDC73 might be a promising treatment target for breast cancer. Materials and Methods Ethical statement This study obtained ethical support from Zhengzhou university life science ethics review committee. Tissues collection, cell lines and mice culture A tissue microarray (TMA) including 40 breast cancer tissues and 42 normal tissues was employed. All the patients who provided the tissues signed informed consent. Mammary epithelial cell HBL-100 and three breast cancer cell lines MDA-MB-231, BT-549 and MCF-7 were purchased from American type culture collection (ATCC) (https://www.atcc.org/). HBL-100 and MDA-MB-231 cells were cultured in 1640+10%FBS and Leibovitz’s L-15 (PM151010)+10% FBS (164210-500)+1% P/S (PB180120), respectively. BT-549 and MCF-7 were grown in DMEM+10% FBS. The cells were incubated in a 37℃ incubator with 5% CO 2. The four-week-old female BALB-c nude mice were purchased from Jiangsu Jicui Yaokang Biotechnology Co., Ltd, which were kept in cage (5 mice/cage); temperature: 22‑25˚C; humidity: 50-60%; 12 h light/dark cycle. Adequate water and food supplies ensured that mice could get them freely. Bioinformatics analysis In this study, CDC73 mRNA levels in breast cancer and normal tissues were investigated based on TCGA data through GEPIA 2 website (http://gepia2.cancer-pku.cn/#analysis). The ubiquitin E3 ligases of MAPK1 were predicted through the Ubibrowser website (http://ubibrowser.bio-it.cn/ubibrowser/home/index). Immunohistochemical staining (IHC) IHC was used to observe the expression of antibodies in the lesion sites. The slides from TMA were deparaffinized with xylene for 3 times. Then, the slides were subjected to antigen repair using EDTA solution and block using 3% H 2 O 2 . After that, primary and secondary antibodies were added, subsequent DAB and Hematoxylin were applied to visualize the expression patterns of antibodies in the healthy and lesion sites. IHC scoring included four categories: negative (0), positive (1-4), ++ positive (5-8), or +++ positive (9-12), based on the staining intensity (varied from weak to strong) and staining extent scores. Finally, the high and moderate expression parameters were determined by the median of IHC experimental scores of all tissues. The details of antibodies were listed as follows: CDC73 (1:500, abcam, #ab223840), Ki67 (1:100, abcam, #ab16667), Goat Anti-Rabbit IgG H&L (HRP) (1:400, abcam, #ab97080). Plasmid construction and lentivirus infection Using CDC73 and MAPK1 genes as template, the RNA interference target sequence of CDC73 (TAGGTCTTTGTCTGAAGCTAT) and MAPK1 (GTTCGAGTAGCTATCAAGAAA) were designed and corresponding shRNA lentiviral vector was constructed. CDC73 overexpression plasmids (LV-013) were generated through pMD2.G and pSPAX2 vectors. 2×10 5 MDA-MB-231 and BT-549 cells in logarithmic growth phase were infected under ENI.S+Polybrene using 40 μL 1×108 TU/mL lentivirus, then were maintained in their corresponding medium. The infection efficiencies were evaluated by microscopic fluorescence. RNA extraction and Real-time quantitative PCR (qRT-PCR) The total RNA of cells was extracted according to the manufacturer’s instruction of TRIzol reagent (Sigma, St. Louis, MO, USA), which was subsequently synthesis cDNA. 10 μL qRT-PCR system was conducted with SYBR Green Mastermixs Kit (Vazyme, Nanjing, Jiangsu, China). The relative expression of mRNA was calculated based on 2-△△Ct method. The primer sequences (5′-3′) were presented in Table S1 . Western blot assay and co-immunoprecipitation (Co-IP) The cells were lysed in 1× Lysis Buffer lysis (Cell Signal Technology, Danvers, MA) and the total proteins were segregated by 10% SDS-PAGE. Then the proteins were transferred onto PVDF membranes, the membranes were blocked with TBST solution containing 5% skim milk at room temperature for 1 h and incubated with primary and secondary antibodies. After that, the membranes were washed with TBST for three times, 10 min each time. Finally, the ECL+plusTM Western blot system kit was used for color rendering and X-ray imaging was captured. In terms of Co-IP, the protein of MDA-MB-231 cells were collected and pulled down using IgG or Anti-CBL. Finally, western blot was performed using CBL and CDC73 antibodies. The details of antibodies were listed as follows: CDC73 (1:1000, Abcam, #ab223840), MAPK1 (1:1000, abcam, #ab32537), mTOR (1:2000, Proteintech, #28273-1-AP), p-mTOR (1:1000, Proteintech, #67778-1-Ig), GAPDH (1:3000, Bioworld, AP0063), Goat Anti-Rabbit (1:3000, Beyotime, # A0208), Goat Anti-Mouse (1:3000, Beyotime, A0216). Celigo cell counting assay After infection, the cells were inoculated in a 96-well plate at the density of 2000 cells/well. The number of cell was counted for continuous 5 days using Celigo. Finally, the data were statistically analyzed to plot the cell proliferation curve. CCK8 assay After infection, the cells were inoculated in a 96-well plate with 5000 cells per well for culturing and then were treated using mTOR inhibitor (AZD8055, 100nm, #SC0042-25mg) for 24 h. Each group contained five plates to determine continuous 5-day cell growth. Before termination of culture, 10 μL CCK-8 reagent was supplemented into the 96-well plate. After 4 h, the 96-well plate was placed on an oscillator and oscillated for 2-5 min. The OD value was detected by microplate reader at 450 nm. Wound healing assay After infection, the cells were cultured in a 96-well plate (7×104 cells/well). Then, the cells were incubated in an incubator with 5% CO 2 at 37°C. The images were graphed by a microscope at indicated time. The migration rate of cells was evaluated based on the scratch images. Transwell assay After infection, the cells were prepared at the density of 4×105 cells/mL and loaded into the upper chamber incubated in serum-free medium. Then, the upper chamber was transferred to the lower chamber containing medium with 30% FBS and incubated for 72 h. After that, 400 µL Giemsa was added for cell staining and the cell migration ability was quantified. Cell apoptosis Lentivirus-infected cells were cultured in 6-well plates (2 mL/well) for 5 days. 10 μL Annexin V-APC was added for staining 10-15 min at room temperature in the dark. The cell apoptosis level was measured by using FACSCalibur (BD Biosciences, San Jose, CA, USA). PrimeView human gene expression array Total RNA was extracted as described previously. The quality and integrity of RNA were determined by a Nanodrop 2000 (Thremo Fisher Scientific, Waltham, MA, USA) and Agilent 2100 and Agilent RNA 6000 Nano Kits (Agilent, Santa Clara, CA, USA). Referring to the manufacturer’s instructions, RNA sequencing was performed using Affymetrix human GeneChip PrimeView and the data were scanned by an Affymetrix Scanner 3000 (Affymetrix, Santa Clara, CA, USA). The statistical significance of the raw data was completed using a Welch t-test with Benjamini-Hochberg FDR (|fold change| ≥ 1.3 and FDR 0 is considered valuable. Analysis of protein degradation To analyze protein degradation, CDC73-depleted/CBL-overexpressed MDA-MB-231 and BT-549 cells were treated with 50 μg /mL cycloheximide (CHX) and harvested at indicated time points. The cell lysate was subjected to immunoblotting. To analyze the protein ubiquitination of MAPK1, cells were co-transfected with shCDC73/CBL and ubiquitin. At 24 h after infection, the proteasome inhibitor MG132 (10 μM) was added and the cells were incubated for 6 h. The MAPK1 or IgG antibodies were added to the cell lysate before incubation overnight at 4°C. The ubiquitin was detected using an ubiquitin antibody (1:2000, CST, #3936S). The construction of tumor xenograft model 1×107 MDA-MB-231 cells infected indicated lentiviruses were subcutaneously injected into the axilla of the animal's right forelimb, each group containing 4 mice. The tumor volume was calculated at indicated time according to the following formula: tumor volume=π/6×L×W×W, where L is tumor length and W is tumor width. After 32 days, the mice were sacrificed and the tumors were removed for weighing and photographing and finally frozen in liquid nitrogen and stored at −80°C. Statistical analysis All the experiments were in triplicate. Data in this study were analyzed by GraphPad Prism 8 (San Diego, CA, USA) and SPSS 19.0 (IBM, SPSS, Chicago, IL, USA), and presented as the mean ± SD. Student’s t-test and one-way ANOVA were used to analyze the statistical significance. The Spearman correlation analysis and Mann-Whitney U analysis were used to assess the relationship between the expression of CDC73 and clinicopathological characteristics of breast cancer patients. Kaplan-Meier survival analysis was performed to reveal the relationship between CDC73 expression and breast cancer patients’ overall survival. Results CDC73 expression is increased in human breast cancer tissues and is associated with poor prognosis To determine the carcinogenic roles of CDC73 in breast cancer, we performed IHC analysis on a breast cancer TMA using CDC73 antibody. The results of staining visualized that tumor tissues harbored elevated CDC73 especially in patient tissues with higher pathological stage ( Figure 1A ). Compared with normal ones, CDC73 was highly expressed in breast cancer tissues with a statistical significance ( P < 0.001, Table 1 ). In addition, downregulation of CDC73 in breast cancer patients is closely linked to a favorable prognosis ( P < 0.05, Figure 1B ). Moreover, the link between CDC73 expression and patients’ clinicopathological parameters was established. As summarized in Table 2 and Table 3 , CDC73 expression positively correlated with tumor infiltrate ( P < 0.01), lymphatic metastasis ( P < 0.05), AJCC stage ( P < 0.01) as well as tumor size ( P < 0.001). On the other hand, we applied publicly available human patient-derived data from TCGA database to assess the expression level of CDC73 in human breast cancer. The result shows that the mRNA level of CDC73 is significantly higher in breast cancer than that in normal breast tissues ( Figure 1C ). However, we found that there were no close link between CDC73 levels and the prognosis of breast cancer patients according to the Kaplan-Meier plotter, which might be due to the small sample size ( Figure 1D ). In vitro functional analysis of the knockdown of CDC73 In this section, we aimed to investigate in vitro functional roles of the knockdown of CDC73 in breast cancer cells. First, overexpression of CDC73 mRNA in breast cancer cell lines relative to human mammary epithelial cells, HBL-100 was verified by qRT-PCR ( Figure 2A ). Then, MDA-MB-231 and BT-549 cells, with high and low endogenous CDC73 expression, respectively, were infected with shRNA targeting CDC73. qRT-PCR and western blot assays verified the status of infection ( Figure 2B and 2C ). Subsequently, Celigo cell counting assay was employed to assess the changes in MDA-MB-231 and BT-549 cell growth after knocking down CDC73. The data showed that the proliferation of CDC73-depleted cells was significantly suppressed ( Figure 2D ), which was highlighted via colony formation experiment ( Figure 2E ). In addition to cell viability, we also detected cell motility via wound-healing and transwell assays. As presented by the layout of F and G in Figure 2 , the motility of CDC73-depleted cells was weakened, implying an arrest of cell migration caused by CDC73 downregulation. As we all know, cell apoptosis is involved in tumor development. Thus, flow cytometry was conducted, which revealed markedly enhanced levels of apoptosis in cells infected with shCDC73 as compared with those infected with shCtrl ( Figure 2H ). Taken together, silencing CDC73 attenuating breast cancer cell proliferation and migration while ameliorating cell apoptosis. MAPK1 is a downstream target of CDC73 To uncover how CDC73 regulates breast cancer development, a genechip primeview human patharray TM was performed in MDA-MB-231 cells with shCDC73 and shCtrl. A total of 3760 differentially expressed genes (DEGs) were present in shCDC73 group compared with shCtrl group. Among them, 1662 were upregulated and 2098 were downregulated ( Figure 3A ). We also constructed a network between CDC73 and several classic signaling pathway such as PI3K/AKT signaling, p53 signaling, ATM signaling, mTOR signaling and NF-kB signaling ( Figure 3B ). Then we picked up three downregulated genes ATG13, INSR and MAPK1, with relatively higher fold changes, for further verification. Only MAPK1 mRNA levels were decreased in CDC73-depleted MDA-MB-231 and BT-549 cells ( Figure S1A ). Immediately, we examined the response of MAPK1 protein levels to CDC73 depletion, followed by an obvious reduction of MAPK1 ( Figure S1B ). Besides, MAPK1 was also found to be abundant in breast cancer cell lines ( Figure S1C ). These results together suggested that MAPK1 might be a downstream target of CDC73 regulating breast cancer. Here, a question raised as to how CDC73 affects MAPK1. By chance, we found that CBL was an interacting protein of CDC73, which could endogenously bind to CDC73 ( Figure 3C ). More interestingly, CBL is an E3 ligase of MAPK1 using ubibrowser website (http://ubibrowser.bio-it.cn/ubibrowser/strict/networkview/networkview/name/P28482/jobId/ubibrowse-I2022-11-04-97604-1667525967) ( Figure 3D ). We thus inferred that CDC73 may regulate the expression of MAPK1 through CBL. To verify this hypothesis, the half-life of MAPK1 protein was investigated in CDC73-depleted and CBL-overexpressed MDA-MB-231 cells following CHX treatment. The findings indicated that both silencing CDC73 and overexpressing CBL shortened the half-life of MAPK1 protein ( Figure 3E ), which were reversed after MG132 addition, indicating that MAPK1 was degraded through the ubiquitin proteasome system (UPS) ( Figure 3F ). Additional ubiquitin assay demonstrated that silencing CDC73 and elevating CBL increased the level of MAPK1 ubiquitination ( Figure 3G ). Collectively, CDC73 stabilized MAPK1 level through blocking its ubiquitination. In vitro and in vivo functional analysis of CDC73 and MAPK1 Having identified MAPK1 as downstream target of CDC73, this section aimed to examine the effects of CDC73 and MAPK1 on breast cancer development in vitro and in vivo , we therewithal constructed MDA-MB-231 and BT-549 cell models with merely overexpressing CDC73, merely silencing MAPK1, and simultaneously silencing MAPK1 and overexpressing CDC73. In contrast to the above data, forced CDC73 expression enhanced proliferation and migration of MDA-MB-231 and BT-549 cells, while arresting cell apoptosis. Furthermore, silencing MAPK1 achieved the same effects on breast cancer cells as knocking down CDC73. More notably, MAPK1 knockdown inhibited the proliferation and migration of CDC73-overexpressing cells. Then, flow cytometry experiments showed that MAPK1 inhibition accelerated cell apoptosis of CDC73 overexpressing cells ( Figure 4A-4D ). On the other hand, the above cell models were subcutaneously injected into nude mice to generate xenograft tumor models. We measured L and W of tumors at indicated time to calculate tumor volume. As expected, CDC73-forced expression increased tumor volume. The same observations were made for tumor weight. Besides, the increased expression of Ki67 in the tumors was verified by western blot. On the other hand, depleting MAPK1 impaired the growth of xenografts and reversed the malignant phenotypes of xenografts with CDC73-overexpressing MDA-MB-231 cells ( Figure 4E-4H ). These data indicated that CDC73 and MAPK1 favor the development of breast cancer, both in vitro and in vivo . CDC73 regulates breast cancer through mTOR pathway Finally, we sought to gain insights on the downstream pathway involved in CDC73-induced breast cancer. As described previously ( Figure 3B ), a network visualized the link between CDC73 and mTOR pathway. We thus speculated that CDC73 might regulate breast cancer cell events through mTOR pathway. Subsequently, western blot analysis in CDC73-overexpressed MDA-MB-231 and BT-549 cells showed an increase in CDC73 and p-mTOR protein levels, which was reversed after mTOR inhibitor treatment ( Figure 5A ). However, total levels of mTOR was not always obviously changed. Then, CCK8 assay showed that mTOR inhibitor disrupted the potential of cells to proliferate ( Figure 5B ), while expediting cell apoptosis ( Figure 5C ). These data demonstrated that CDC73 influences breast cancer through mTOR pathway. Discussion In this study, overexpressed CDC73 was found in breast cancer tissues and cells. Besides, elevated CDC73 positively correlated with multiple pathological parameters eg. tumor infiltrate, lymphatic metastasis, AJCC stage as well as tumor size. Knocking down CDC73 in breast cancer cells suppressed cell events related to tumor development. Thus, CDC73 was identified as a tumor promoter in breast cancer development. Furthermore, we explored the downstream mechanism of CDC73 action and focused on MAPK1. Mitogen activating protein (MAP) kinase signaling cascade Ras-Raf-MEK-MAPK signaling pathway, an evolutionarily conserved signaling pathway [18], has been reported to be involved in a variety of cellular and physiological processes related to life activities [19, 20]. Once extracellular stimulators including cytokines, neurotransmitters and hormones bind to transmembrane receptors, Ras-GDP in the plasma membrane is activated and converted into Ras-GTP. Then, a homodimer or heterodimer consisting of Rafs were generated by Ras-GTP [21]. The Raf enzymes subsequently catalyze the phosphorylation and activation of MEK, where MEK activates MAPK [22, 23]. Finally, MAPK is activated to catalyze many cytoplasmic and nuclear substrates, such as transcription factors and regulatory molecules [18, 24]. In summary, this signaling pathway controls cell growth, cell proliferation, cell survival, differentiation, immune response, metabolism, nervous system function, and transcription through a series of phosphorylation reactions. Thus, it is responsible for tumor development [25, 26]. As one of the key components of this pathway, MAPK has been intensively studied and identified as oncogenes [27, 28]. In this study, by combining in vitro and in vivo experiments, we analyzed the carcinogenic roles of CDC73 and MAPK1 in breast cancer. We found high expression of MAPK1 in breast cancer cells, and that its knockdown inhibited breast cancer cell viability and migration but promoted apoptosis, indicating the carcinogenic role of MAPK1 in breast cancer. Moreover, by infection of MAPK1 overexpression vector in breast cancer cells with the presence of CDC73, the results revealed that MAPK1 was responsible for the function of CDC73 in breast cancer. The mechanism of CDC73 regulating MAPK1 was also uncovered. The process of ubiquitination involves a cascade of three enzymes, the third of which is an E3 ligase that transfers the ubiquitin from an E2 ubiquitin conjugating enzyme to specific substrates [29]. Here, we found CBL, an interacting protein of CDC73, is an E3 ligase enzyme of MAPK1. Thus, we hypothesized that the UPS might play a role in MAPK1 protein degradation. Using protein synthesis inhibitor CHX, we found the half-life of MAPK1 was shortened after knocking down CDC73. Additionally, the protein level of MAPK1 was elevated in CDC73-depleted MDA-MB-231 cells following MG132 treatment, indicating that BECN1 is degraded through the UPS. Further study demonstrated that silencing CDC73 enhanced MPAK1 ubiquitination, thereby downregulating MAPK1 protein patterns. On the other hand, mTOR signaling pathway has been reported to be involved in regulating cell proliferation and apoptosis, thereby mediating cancer initiation [30] and progression as well as frequently being regarding as a therapeutic target [31-34]. More notably, mTOR signaling pathway has been widely discussed in breast cancer. For instance, Lu et al. reported that UBE2C affects breast cancer proliferation through AKT/mTOR signaling pathway [35]. Homoharringtonine (HHT), a natural alkaloid derived from the cephalotaxus, suppressed breast cancer cell growth and promoted apoptosis by mTOR signaling pathway [36]. Consistent with their findings, this current study showed that the phosphorylation levels of mTOR were increased following CDC73 upregulation, while no influences on its total protein levels. Sequent cell functional experiments evidenced that mTOR inhibitor could disrupted the potential of cells to proliferate, while expediting cell apoptosis. These data demonstrated that CDC73 influences breast cancer through mTOR pathway. In conclusion, our research provided evidence of CDC73 tumor promotor in breast cancer, indicating that CDC73 is a potential therapeutic target in breast cancer. Abbreviations CDC73: Human cell division cycle 73; Paf1C: polymerase-associated factor 1 complex; ATCC: American type culture collection; qRT-PCR: Real-time quantitative PCR; Co-IP: co-immunoprecipitation; IPA: Ingenuity Pathway Analysis; CHX: cycloheximide; DEGs: differentially expressed genes; UPS: ubiquitin proteasome system; HHT: Homoharringtonine. Declarations Ethics approval and consent to participate This study obtained ethical support from Zhengzhou university life science ethics review committee. Consent for publication All the patients who provided the tissues signed informed consent. Availability of data and materials The data generated in this study are available within the article and its supplementary data files. Competing interests The authors declare that they have no conflict of interest. Funding This study was conducted with support from National Natural Science Foundation of China (No. 82002806) and Natural Science Foundation of Anhui Province (No. 2008085MH295). Authors' contributions Haige Zhang, Yu Tang, Xiaozhi Zhang and Jing Pei designed this research. Ya Gao, Mingming Du, Erhu Pan, Guopeng Sang and Chang Liu operated experiments. Fangfang Pei, Mingliang Sun, Zhifan Ruan and Yubo Pan participated in the processing and analysis of the data. Haige Zhang completed the manuscript which was reviewed by Yu Tang, Xiaozhi Zhang and Jing Pei. All authors have confirmed the submission of this manuscript. Acknowledgements None. References Siegel RL, Miller KD, Fuchs HE, Jemal A: Cancer statistics, 2022. CA Cancer J Clin 2022, 72(1):7–33. Fahad Ullah M: Breast Cancer: Current Perspectives on the Disease Status. Adv Exp Med Biol 2019, 1152:51–64. 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Chin Med J (Engl) 2021, 134(20):2465–2474. Wang LB, Wang DN, Wu LG, Cao J, Tian JH, Liu R, Ma R, Yu JJ, Wang J, Huang Q et al : Homoharringtonine inhibited breast cancer cells growth via miR-18a-3p/AKT/mTOR signaling pathway. Int J Biol Sci 2021, 17(4):995–1009. Tables Table 1. Expression patterns of CDC73 in breast cancer tissues and para-carcinoma tissues revealed in immunohistochemistry analysis. CDC73 expression Tumor tissue Para-carcinoma tissue P value Cases Percentage Cases Percentage 0.008 Low 23 57.5% 42 100.0% High 17 42.5% 0 - Table 2. Relationship between CDC73 expression and tumor characteristics in patients with breast cancer. Features No. of patients CDC73 expression P value low high All patients 40 23 17 Age (years) 0.497 ≤51 21 11 10 >51 19 12 7 Grade 0.510 II 6 4 2 III 29 15 14 AJCC stage 0.010 1 12 11 1 2 23 10 13 3 1 1 0 4 1 0 1 Tumor infiltrate 0.003 1 15 13 2 2 20 9 11 3 1 0 1 4 1 0 1 lymphatic metastasis (N) 0.036 0 27 19 8 1 8 2 6 3 2 1 1 Tumor size 0.003 ≤2cm 14 12 2 >2cm 14 4 10 Table 3. Relationship between CDC73 expression and tumor characteristics in patients with breast cancer. CDC73 Tumor infiltrate Spearman correlation 0.495 Signification (double-tailed) 0.002 N 37 lymphatic metastasis (N) Spearman correlation 0.349 Signification (double-tailed) 0.034 N 37 AJCC stage Spearman correlation 0.427 Signification (double-tailed) 0.008 N 37 Tumor size Spearman correlation 0.577 Signification (double-tailed) 0.001 N 28 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3141760","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":216151310,"identity":"029e7f0e-c21f-4327-b7ce-98a5d24874a2","order_by":0,"name":"Haige Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYDACCSBmbGDgYWBgPnAg8Z8NAxsJWtgSHzxgSyNeCxDwGBs+YDtM2F3ys5ufPfy6w05G3r3BTCKB57w9n3TzA4YfFdtwamGcc8zcWPZMMo/hmQNpEgkStxPbZI4ZMPacuY1TC7NEgpm0ZBszj+GMhGMSCQa3E9iAJDNjG24tbBLp34Ba6nkM5z9sk0hIOGcPFPmAVwuPRI6Z5Me2wzzyEszMBgkHDjC2SeTgt0VCIqdMmrHtOI8BTxrjg8SG5ESgloKD+PwiPyN9m+TPtmp7+fbzHw7+bLCzB4psfPCjArcWcBAA45HB4ACSyAHsChGA8QfIugZCykbBKBgFo2DEAgCK21TcqBRodwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Science \u0026 Technology","correspondingAuthor":true,"prefix":"","firstName":"Haige","middleName":"","lastName":"Zhang","suffix":""},{"id":216151313,"identity":"ebf22453-6b78-4ef7-85ae-777f450a9aa8","order_by":1,"name":"Yu Tang","email":"","orcid":"","institution":"Cancer Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Tang","suffix":""},{"id":216151316,"identity":"3e3eee26-4932-432d-868f-0a56b7489dd9","order_by":2,"name":"Ya Gao","email":"","orcid":"","institution":"University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Ya","middleName":"","lastName":"Gao","suffix":""},{"id":216151317,"identity":"ce8b1534-9583-43f4-abf2-9750b237177e","order_by":3,"name":"Mingming Du","email":"","orcid":"","institution":"University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Mingming","middleName":"","lastName":"Du","suffix":""},{"id":216151320,"identity":"9849cf46-e732-4c2a-83a4-2ef6c2a8da33","order_by":4,"name":"Erhu Pan","email":"","orcid":"","institution":"University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Erhu","middleName":"","lastName":"Pan","suffix":""},{"id":216151323,"identity":"ba1a5b33-85de-40b1-b109-ee96b1203a91","order_by":5,"name":"Fangfang Pei","email":"","orcid":"","institution":"University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Pei","suffix":""},{"id":216151325,"identity":"8f05f501-6f06-4b13-bb0d-2108a12d715a","order_by":6,"name":"Mingliang Sun","email":"","orcid":"","institution":"University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Mingliang","middleName":"","lastName":"Sun","suffix":""},{"id":216151329,"identity":"39ab976e-51b3-45ea-9c26-11bba9a111e7","order_by":7,"name":"Guopeng Sang","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guopeng","middleName":"","lastName":"Sang","suffix":""},{"id":216151330,"identity":"c185ccc9-3894-4a78-93bd-5cc4742b3286","order_by":8,"name":"Chang Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Liu","suffix":""},{"id":216151332,"identity":"85a4553e-ac37-42b9-b81e-cb1b71100cfe","order_by":9,"name":"Zhifan Ruan","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhifan","middleName":"","lastName":"Ruan","suffix":""},{"id":216151333,"identity":"0d1a9fba-1580-41ad-8294-5e09041b6154","order_by":10,"name":"Yubo Pan","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yubo","middleName":"","lastName":"Pan","suffix":""},{"id":216151336,"identity":"769aacef-7dd9-429e-af12-877128ea9fd6","order_by":11,"name":"Xiaozhi Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaozhi","middleName":"","lastName":"Zhang","suffix":""},{"id":216151339,"identity":"7e7a3794-a1ed-444d-80f9-2ab2c1f1cd84","order_by":12,"name":"Jing Pei","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Pei","suffix":""}],"badges":[],"createdAt":"2023-07-05 09:14:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3141760/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3141760/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39858074,"identity":"5564b9ec-2cac-4954-9d14-5839ec1ab282","added_by":"auto","created_at":"2023-07-11 14:23:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3169722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCDC73 expression is increased in human breast cancer tissues and is associated with poor prognosis. \u003c/strong\u003e(A) Protein patterns of CDC73 in breast cancer and normal tissues based on IHC staining. (B) The link between CDC73 high/low expression and patients’ overall survival. (C) The mRNA level of CDC73 was revealed in breast cancer and normal samples from TCGA database. (D) High CDC73 mRNA levels implied a poor prognosis.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3141760/v1/dad4196c347d19a9ea60a65c.png"},{"id":39858070,"identity":"45b44f4f-533d-4874-ad22-d323db1122ca","added_by":"auto","created_at":"2023-07-11 14:23:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1155614,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e functional analysis of the knockdown of CDC73. (A) \u003c/strong\u003eThe mRNA level of CDC73 in mammary epithelial cell HBL-100 and three breast cancer cell lines. (B, C) The protein (B) and mRNA (C) levels of CDC73 in BT-549 and MDA-MB-231 cells were assessed after shCtrl and shCDC73 infection. (D, E) After shCtrl and shCDC73 infection, BT-549 and MDA-MB-231 cell proliferation were detected through CCK8 assay (D) and colony formation assay (E). (F, G) After shCtrl and shCDC73 infection, the abilities of BT-549 and MDA-MB-231 cell migration were observed through wound-healing assay (F) and transwell assay (G). (H) After shCtrl and shCDC73 infection, BT-549 and MDA-MB-231 cell apoptosis were analyzed through flow cytometry. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3141760/v1/46efe9cb2336546b694aa309.png"},{"id":39858069,"identity":"679818dc-1c76-441c-b34c-04fd9fce8280","added_by":"auto","created_at":"2023-07-11 14:23:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1022352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMAPK1 is a downstream target of CDC73.\u003c/strong\u003e (A) The DEGs in MDA-MB-231 cells with shCDC73 and shCtrl by a genechip primeview human patharray\u003csup\u003eTM\u003c/sup\u003e. (B) The network between CDC73 and several signaling pathways. (C) The endogenous interaction between CDC73 and CBL was revealed via Co-IP experiment. (D) The E3 ubiquitin ligases of MAPK1. (E) The half-life of MAPK1 was detected in CDC73-depleted and CBL-overexpressed MDA-MB-231 cells following CHX treatment. (F) The protein levels of MAPK1 were analyzed in CDC73-depleted and CBL-overexpressed MDA-MB-231 cells after MG132 treatment. (G) The ubiquitination of MAPK1 was detected in CDC73-depleted and CBL-overexpressed MDA-MB-231 cells.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3141760/v1/e184741204c5c9610161e675.png"},{"id":39858075,"identity":"1a846c17-c77e-4eb7-a753-71a9b2e48a1e","added_by":"auto","created_at":"2023-07-11 14:23:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6490210,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003efunctional analysis of CDC73 and MAPK1.\u003c/strong\u003e (A-D) The cell proliferation (A), migration (B, C) and apoptosis (D) were detected in BT-549 and MDA-MB-231 from NC (OE+KD), CDC73+NC (KD), shMAPK1+NC (OE) and CDC73+shMAPK1 groups. (E, F) The volume (E) and weight (F) of tumor derived from MDA-MB-231 cells with indicated lentiviruseswere measured. (G) The photos of tumor were collected. (H) Ki67 antibody was stained in tumor tissues from different xenografts. NC (OE+KD): Control; CDC73+NC (KD): CDC73 overexpression; shMAPK1+NC (OE): MAPK1 downregulation; CDC73+shMAPK1: CDC73 overexpression and MAPK1 downregulation\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3141760/v1/2d46577d52988d09b8a6153c.png"},{"id":39859460,"identity":"b5b48f9d-2df8-4a9a-b915-808a84cb532c","added_by":"auto","created_at":"2023-07-11 14:31:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":542685,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCDC73 regulates breast cancer through mTOR pathway.\u003c/strong\u003e (A) The protein levels of MAPK1, mTOR and p-mTOR were quantified in CDC73-overexpressed BT-549 and MDA-MB-231 cells after mTOR inhibitor treatment. (B, C) The proliferation and migration of CDC73-overexpressed BT-549 and MDA-MB-231 cells were analyzed after mTOR inhibitor treatment.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3141760/v1/3a0fb4c37bd3de5da364e285.png"},{"id":40034752,"identity":"5d4f046a-a912-43ef-bb83-fadc338ddeea","added_by":"auto","created_at":"2023-07-14 13:44:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10651325,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3141760/v1/c63df748-3812-45cb-8458-9c0312fdfaf2.pdf"},{"id":39858071,"identity":"93a125e5-c02a-492f-85e2-c2b34a7792a4","added_by":"auto","created_at":"2023-07-11 14:23:28","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1665268,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3141760/v1/487334e3302d4d787b65b350.tif"},{"id":39858073,"identity":"6fdf0f90-d8f6-40e4-82f9-c2c6fe2f5ac3","added_by":"auto","created_at":"2023-07-11 14:23:28","extension":"pptx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1274432,"visible":true,"origin":"","legend":"","description":"","filename":"Originaldata.pptx","url":"https://assets-eu.researchsquare.com/files/rs-3141760/v1/450b67dd49dc1e4d2796332f.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"CDC73 promotes breast cancer through impairing MAPK1 ubiquitination and activating mTOR signaling pathway ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is estimated to be the second leading cause of cancer death in women, after lung cancer, according to Cancer statistics, 2022 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The mortality of breast cancer has been decreasing year by year due to earlier diagnosis, through increased awareness and mammography screening, as well as improvements in treatment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite tremendous progress in tumor diagnostic and therapeutic strategies, the prognosis of patients suffering from breast cancer is still poor due to the metastasis and heterogeneity of the cancer [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Hence, it is important to develop novel targets and more potential therapeutic strategies in this deadly disease.\u003c/p\u003e \u003cp\u003eHuman cell division cycle 73 (CDC73), akas parafibromin, is a nuclear protein, which is encoded by CDC73 gene located on chromosome 1q31.2 [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CDC73 binds to β-catenin to generate the RNA polymerase-associated factor 1 complex (Paf1C), eventually regulating transcription [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Paf1C is composed of five subunits (Cdc73, Paf1, Ctr9, Leo1 and Rtf1) in yeast, which affects levels of many RNAs via participating in chromatin transcription and genomic regulation, implying its functions in development and human diseases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. CDC73 was originally identified as a tumor suppressor, implicated in the hyperparathyroidism-jaw tumor syndrome and sporadic parathyroid carcinoma [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Later, its oncogenic properties were found in other cases [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, the clinicopathological and prognostic significances of CDC73 expression in breast cancer have been unveiled [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It is involved in tumorigenesis and progression of breast cancer via an uncertain mechanism.\u003c/p\u003e \u003cp\u003eIn this work, we identified CDC73\u0026rsquo;s promoting role in breast cancer. The expression of CDC73 increased in breast cancer tissues and cells, which was related to a poor prognosis in patients. Functional investigations showed that CDC73 downregulation suppressed breast cancer cell growth \u003cem\u003ein vitro\u003c/em\u003e. Subsequent studies demonstrated that CDC73 mediated breast cancer via targeting MAPK1 and activating mTOR pathway, and that CDC73 could regulate MAPK1 expression via affecting MAPK1 ubiquitination. Functionally, downregulating MAPK1 or mTOR inhibitor could reverse the effects of CDC73 overexpression on breast cancer development. Our results indicated that CDC73 might be a promising treatment target for breast cancer.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study obtained ethical support from Zhengzhou university life science ethics review committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissues collection, cell lines and mice culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA tissue microarray (TMA) including 40 breast cancer tissues and 42 normal tissues was employed. All the patients who provided the tissues signed informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMammary epithelial cell HBL-100 and three breast cancer cell lines MDA-MB-231, BT-549 and MCF-7 were purchased from American type culture collection (ATCC) (https://www.atcc.org/). HBL-100 and MDA-MB-231 cells were cultured in 1640+10%FBS and Leibovitz\u0026rsquo;s L-15 (PM151010)+10% FBS (164210-500)+1% P/S (PB180120), respectively. BT-549 and MCF-7 were grown in DMEM+10% FBS. The cells were incubated in a 37℃ incubator with 5% CO\u003csub\u003e2.\u0026nbsp;\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eThe four-week-old female BALB-c nude mice were purchased from Jiangsu Jicui Yaokang Biotechnology Co., Ltd, which were kept in cage (5 mice/cage); temperature: 22‑25˚C; humidity: 50-60%; 12 h light/dark cycle. Adequate water and food supplies ensured that mice could get them freely.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatics analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, CDC73 mRNA levels in breast cancer and normal tissues were investigated based on TCGA data through GEPIA 2 website (http://gepia2.cancer-pku.cn/#analysis). The ubiquitin E3 ligases of MAPK1 were predicted through the Ubibrowser website (http://ubibrowser.bio-it.cn/ubibrowser/home/index).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemical staining (IHC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIHC was used to observe the expression of antibodies in the lesion sites. The slides from TMA were deparaffinized with xylene for 3 times. Then, the slides were subjected to antigen repair using EDTA solution and block using 3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e. After that, primary and secondary antibodies were added, subsequent DAB and Hematoxylin were applied to visualize\u0026nbsp;the expression patterns of antibodies in the healthy and lesion sites.\u0026nbsp;IHC\u0026nbsp;scoring\u0026nbsp;included four\u0026nbsp;categories:\u0026nbsp;negative\u0026nbsp;(0), positive (1-4), ++ positive (5-8), or +++ positive (9-12), based on the staining intensity (varied from weak to strong) and staining extent scores. Finally, the high and moderate expression parameters were determined by the median of IHC experimental scores of all tissues. The details of\u0026nbsp;antibodies were listed as follows: CDC73 (1:500,\u0026nbsp;abcam, #ab223840), Ki67 (1:100,\u0026nbsp;abcam,\u0026nbsp;#ab16667),\u0026nbsp;Goat Anti-Rabbit\u0026nbsp;IgG H\u0026amp;L (HRP) (1:400,\u0026nbsp;abcam, #ab97080).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlasmid construction and lentivirus infection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing CDC73 and MAPK1 genes as template, the RNA interference target sequence of CDC73 (TAGGTCTTTGTCTGAAGCTAT) and MAPK1 (GTTCGAGTAGCTATCAAGAAA) were designed and corresponding shRNA lentiviral vector was constructed. CDC73 overexpression plasmids (LV-013) were generated through\u0026nbsp;pMD2.G and pSPAX2 vectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2\u0026times;10\u003csup\u003e5\u0026nbsp;\u003c/sup\u003eMDA-MB-231 and BT-549\u0026nbsp;cells\u0026nbsp;in logarithmic growth phase were infected under\u0026nbsp;ENI.S+Polybrene\u0026nbsp;using 40 \u0026mu;L 1\u0026times;108\u0026nbsp;TU/mL lentivirus, then were maintained in their corresponding medium. The infection efficiencies were evaluated by microscopic fluorescence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA extraction and Real-time quantitative PCR (qRT-PCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total RNA of cells\u0026nbsp;was extracted according to the manufacturer\u0026rsquo;s instruction of TRIzol reagent (Sigma, St. Louis, MO, USA), which was subsequently synthesis cDNA. 10 \u0026mu;L qRT-PCR system was conducted with SYBR Green Mastermixs Kit (Vazyme,\u0026nbsp;Nanjing, Jiangsu, China). The relative expression of mRNA was calculated based on 2-△△Ct\u0026nbsp;method. The primer sequences (5\u0026prime;-3\u0026prime;) were presented in \u003cstrong\u003eTable S1\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blot assay and co-immunoprecipitation (Co-IP)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;cells\u0026nbsp;were\u0026nbsp;lysed\u0026nbsp;in\u0026nbsp;1\u0026times;\u0026nbsp;Lysis Buffer lysis (Cell Signal Technology, Danvers, MA) and the total proteins were segregated by 10% SDS-PAGE. Then the proteins were transferred onto PVDF membranes, the membranes\u0026nbsp;were\u0026nbsp;blocked\u0026nbsp;with\u0026nbsp;TBST\u0026nbsp;solution\u0026nbsp;containing\u0026nbsp;5%\u0026nbsp;skim\u0026nbsp;milk at room temperature for 1 h and incubated with primary and secondary antibodies. After that, the membranes were washed with TBST for three times, 10 min each time. Finally,\u0026nbsp;the ECL+plusTM Western\u0026nbsp;blot system kit was used for color rendering and X-ray imaging was captured. In terms of Co-IP, the protein of MDA-MB-231 cells were collected and pulled down using IgG or Anti-CBL. Finally, western blot was performed using CBL and CDC73 antibodies. The details of\u0026nbsp;antibodies were listed as follows: CDC73 (1:1000,\u0026nbsp;Abcam, #ab223840),\u0026nbsp;MAPK1\u0026nbsp;(1:1000,\u0026nbsp;abcam,\u0026nbsp;#ab32537), mTOR (1:2000, Proteintech, #28273-1-AP), p-mTOR (1:1000, Proteintech, #67778-1-Ig), GAPDH\u0026nbsp;(1:3000, Bioworld, AP0063), Goat Anti-Rabbit (1:3000, Beyotime, #\u0026nbsp;A0208), Goat Anti-Mouse (1:3000, Beyotime, A0216).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCeligo cell counting assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter infection, the cells were\u0026nbsp;inoculated in a 96-well\u0026nbsp;plate\u0026nbsp;at the density of\u0026nbsp;2000\u0026nbsp;cells/well. The number of cell was counted for continuous 5 days using\u0026nbsp;Celigo. Finally, the data were statistically analyzed to plot the cell proliferation curve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCCK8 assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter infection, the\u0026nbsp;cells were inoculated in a 96-well\u0026nbsp;plate\u0026nbsp;with\u0026nbsp;5000\u0026nbsp;cells\u0026nbsp;per\u0026nbsp;well\u0026nbsp;for\u0026nbsp;culturing\u0026nbsp;and then were treated using mTOR inhibitor (AZD8055, 100nm, #SC0042-25mg) for 24 h.\u0026nbsp;Each\u0026nbsp;group contained five plates to determine continuous 5-day cell growth. Before termination of culture, 10 \u0026mu;L CCK-8 reagent was supplemented into the\u0026nbsp;96-well\u0026nbsp;plate.\u0026nbsp;After 4 h, the 96-well plate was placed on an oscillator and oscillated for 2-5 min. The OD value was detected by microplate reader at 450 nm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWound healing assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter infection, the\u0026nbsp;cells were cultured in a 96-well plate (7\u0026times;104\u0026nbsp;cells/well). Then, the cells were incubated in an incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e at\u0026nbsp;37\u0026deg;C. The images were graphed by a microscope at indicated time. The migration rate of cells was evaluated based on the scratch images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranswell assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter infection, the\u0026nbsp;cells were prepared at the density of 4\u0026times;105\u0026nbsp;cells/mL and loaded into the upper chamber incubated in serum-free medium. Then, the upper chamber was transferred to the lower chamber containing medium with 30% FBS and incubated for 72 h. After that, 400 \u0026micro;L Giemsa was added for cell staining and the cell migration ability was quantified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell apoptosis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLentivirus-infected cells were cultured in 6-well plates (2 mL/well) for 5 days. 10 \u0026mu;L Annexin V-APC was added for staining 10-15 min at room temperature in the dark. The cell apoptosis level was measured by using FACSCalibur (BD Biosciences, San\u0026nbsp;Jose,\u0026nbsp;CA,\u0026nbsp;USA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimeView human gene expression array\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted as described previously. The quality and integrity of RNA were determined by a Nanodrop 2000 (Thremo Fisher Scientific, Waltham, MA, USA) and Agilent 2100 and Agilent RNA 6000 Nano Kits (Agilent, Santa Clara, CA, USA). Referring to the manufacturer\u0026rsquo;s instructions, RNA sequencing was performed using Affymetrix human GeneChip PrimeView and the data were scanned by an Affymetrix Scanner 3000 (Affymetrix, Santa Clara, CA, USA). The statistical significance of the raw data was completed using a Welch t-test with Benjamini-Hochberg FDR (|fold change| \u0026ge; 1.3 and \u003cem\u003eFDR\u003c/em\u003e \u0026lt; 0.05 as significant). A significant difference analysis and functional analysis based on Ingenuity Pathway Analysis (IPA) (Qiagen, Hilden, Germany) were executed, and a |Z - score| \u0026gt; 0 is considered valuable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of protein degradation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze protein degradation, CDC73-depleted/CBL-overexpressed MDA-MB-231 and BT-549 cells were treated with 50 \u0026mu;g /mL cycloheximide (CHX) and harvested at indicated time points. The cell lysate was subjected to immunoblotting. To analyze the protein ubiquitination of MAPK1, cells were co-transfected with shCDC73/CBL and ubiquitin. At 24 h after infection, the proteasome inhibitor MG132 (10 \u0026mu;M) was added and the cells were incubated for 6 h. The MAPK1 or IgG antibodies were added to the cell lysate before incubation overnight at 4\u0026deg;C. The ubiquitin was detected using an ubiquitin antibody (1:2000, CST, #3936S).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe construction of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003etumor xenograft model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1\u0026times;107\u0026nbsp;MDA-MB-231 cells infected indicated lentiviruses were subcutaneously injected into the axilla of the animal\u0026apos;s right forelimb, each group containing 4 mice. The tumor volume was calculated at indicated time according to the following formula: tumor volume=\u0026pi;/6\u0026times;L\u0026times;W\u0026times;W, where L is tumor length and W is tumor width. After\u0026nbsp;32\u0026nbsp;days,\u0026nbsp;the\u0026nbsp;mice\u0026nbsp;were\u0026nbsp;sacrificed\u0026nbsp;and\u0026nbsp;the\u0026nbsp;tumors\u0026nbsp;were\u0026nbsp;removed\u0026nbsp;for\u0026nbsp;weighing\u0026nbsp;and photographing and finally frozen in liquid nitrogen and stored at \u0026minus;80\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the experiments were in triplicate. Data in this study were analyzed by GraphPad Prism 8 (San Diego, CA, USA) and SPSS 19.0 (IBM, SPSS, Chicago, IL, USA), and presented as the mean \u0026plusmn; SD. Student\u0026rsquo;s t-test and one-way ANOVA were used to analyze the statistical significance. The Spearman correlation analysis and Mann-Whitney U analysis were used to assess the relationship between the expression of CDC73 and clinicopathological characteristics of breast cancer patients. Kaplan-Meier survival analysis was performed to reveal the relationship between CDC73 expression and breast cancer patients\u0026rsquo; overall survival.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCDC73 expression is increased in human breast cancer tissues and is associated with poor prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the carcinogenic roles of CDC73 in breast cancer, we performed IHC analysis on a breast cancer TMA using CDC73 antibody. The results of staining visualized that\u0026nbsp;tumor tissues harbored elevated CDC73 especially in patient tissues with higher pathological stage (\u003cstrong\u003eFigure 1A\u003c/strong\u003e). Compared with normal ones, CDC73 was highly expressed in breast cancer tissues with a statistical significance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, \u003cstrong\u003eTable 1\u003c/strong\u003e). In addition, downregulation of CDC73 in breast cancer patients is closely linked to a favorable prognosis (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05, \u003cstrong\u003eFigure 1B\u003c/strong\u003e). Moreover, the link between CDC73 expression and patients\u0026rsquo; clinicopathological parameters was established. As summarized in \u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eTable 3\u003c/strong\u003e, CDC73 expression positively correlated with tumor infiltrate (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), lymphatic metastasis (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05), AJCC stage (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01) as well as tumor size (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). On the other hand, we applied\u0026nbsp;publicly available human patient-derived data from TCGA database to assess the expression level of CDC73 in human breast cancer. The result shows that the mRNA level of CDC73 is significantly higher in breast cancer than that in normal breast tissues (\u003cstrong\u003eFigure 1C\u003c/strong\u003e). However, we found that there were no close link between CDC73 levels and the prognosis of breast cancer patients according to the Kaplan-Meier plotter, which might be due to the small sample size (\u003cstrong\u003eFigure 1D\u003c/strong\u003e). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn vitro\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;functional analysis of the knockdown of CDC73\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this section, we aimed to investigate \u003cem\u003ein vitro\u003c/em\u003e functional roles of the knockdown of CDC73 in breast cancer cells. First, overexpression of CDC73 mRNA in breast cancer cell lines relative to human mammary epithelial cells, HBL-100 was verified by qRT-PCR (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). Then, MDA-MB-231 and BT-549 cells, with high and low endogenous CDC73 expression, respectively, were infected with shRNA targeting CDC73. qRT-PCR and western blot assays verified the status of infection (\u003cstrong\u003eFigure 2B and 2C\u003c/strong\u003e). Subsequently, Celigo cell counting assay was employed to assess the changes in MDA-MB-231 and BT-549 cell growth after knocking down CDC73. The data showed that the proliferation of CDC73-depleted cells was significantly suppressed (\u003cstrong\u003eFigure 2D\u003c/strong\u003e), which was highlighted via colony formation experiment (\u003cstrong\u003eFigure 2E\u003c/strong\u003e). In addition to cell viability, we also detected cell motility via wound-healing and transwell assays. As presented by the layout of \u003cstrong\u003eF and G in Figure 2\u003c/strong\u003e, the motility of CDC73-depleted cells was weakened, implying an arrest of cell migration caused by CDC73 downregulation. As we all know, cell apoptosis is involved in tumor development. Thus, flow cytometry was conducted, which revealed markedly enhanced levels of apoptosis in cells infected with shCDC73 as compared with those infected with shCtrl (\u003cstrong\u003eFigure 2H\u003c/strong\u003e). Taken together, silencing CDC73 attenuating breast cancer cell proliferation and migration while ameliorating cell apoptosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAPK1 is a downstream target of CDC73\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo uncover how CDC73 regulates breast cancer development, a genechip primeview human patharray\u003csup\u003eTM\u003c/sup\u003e was performed in\u0026nbsp;MDA-MB-231\u0026nbsp;cells with shCDC73 and shCtrl.\u0026nbsp;A total of 3760 differentially expressed genes (DEGs) were present in shCDC73 group compared with shCtrl group. Among them,\u0026nbsp;1662\u0026nbsp;were upregulated and\u0026nbsp;2098\u0026nbsp;were downregulated (\u003cstrong\u003eFigure 3A\u003c/strong\u003e). We also constructed a network between CDC73 and several classic signaling pathway such as\u0026nbsp;PI3K/AKT signaling, p53 signaling, ATM signaling, mTOR signaling and NF-kB signaling (\u003cstrong\u003eFigure 3B\u003c/strong\u003e).\u0026nbsp;Then we picked up three downregulated genes ATG13, INSR and MAPK1, with relatively higher fold changes, for further verification. Only MAPK1 mRNA levels were decreased in CDC73-depleted\u0026nbsp;MDA-MB-231 and BT-549 cells (\u003cstrong\u003eFigure S1A\u003c/strong\u003e). Immediately, we examined the response of MAPK1 protein levels to CDC73 depletion, followed by an obvious reduction of MAPK1 (\u003cstrong\u003eFigure S1B\u003c/strong\u003e). Besides,\u0026nbsp;MAPK1 was also found to be abundant in breast cancer cell lines (\u003cstrong\u003eFigure S1C\u003c/strong\u003e). These results together suggested that MAPK1 might be a downstream target of CDC73 regulating breast cancer. Here, a question raised as to how CDC73 affects MAPK1. By chance, we found that CBL was an interacting protein of CDC73, which could endogenously bind to CDC73 (\u003cstrong\u003eFigure 3C\u003c/strong\u003e). More interestingly, CBL is an E3 ligase of MAPK1 using ubibrowser website (http://ubibrowser.bio-it.cn/ubibrowser/strict/networkview/networkview/name/P28482/jobId/ubibrowse-I2022-11-04-97604-1667525967) (\u003cstrong\u003eFigure 3D\u003c/strong\u003e).\u0026nbsp;We thus inferred that CDC73\u0026nbsp;may regulate the expression of MAPK1\u0026nbsp;through CBL. To verify this hypothesis, the half-life of MAPK1 protein was investigated in CDC73-depleted and CBL-overexpressed\u0026nbsp;MDA-MB-231 cells\u0026nbsp;following CHX treatment. The findings indicated that both silencing CDC73 and overexpressing CBL shortened the half-life of MAPK1 protein (\u003cstrong\u003eFigure 3E\u003c/strong\u003e), which were reversed after MG132 addition, indicating that MAPK1 was degraded through the ubiquitin proteasome system (UPS) (\u003cstrong\u003eFigure 3F\u003c/strong\u003e). Additional ubiquitin assay demonstrated that silencing CDC73 and elevating CBL increased the level of MAPK1 ubiquitination (\u003cstrong\u003eFigure 3G\u003c/strong\u003e). Collectively, CDC73 stabilized MAPK1 level through blocking its ubiquitination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn vitro\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and \u003cem\u003ein vivo\u003c/em\u003e functional analysis of CDC73 and MAPK1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaving identified MAPK1 as downstream target of CDC73,\u0026nbsp;this section aimed to examine the effects of CDC73 and MAPK1 on breast cancer development \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;in vivo\u003c/em\u003e, we therewithal constructed\u0026nbsp;MDA-MB-231 and BT-549\u0026nbsp;cell models with merely overexpressing CDC73, merely silencing MAPK1, and simultaneously silencing MAPK1 and overexpressing CDC73. In contrast to the above data, forced CDC73 expression enhanced proliferation and migration of\u0026nbsp;MDA-MB-231 and BT-549\u0026nbsp;cells, while arresting cell apoptosis. Furthermore, silencing MAPK1 achieved the same effects on breast cancer cells as knocking down CDC73. More notably, MAPK1 knockdown inhibited the proliferation and migration of CDC73-overexpressing cells. Then, flow cytometry experiments showed that MAPK1 inhibition accelerated cell apoptosis of CDC73 overexpressing cells (\u003cstrong\u003eFigure 4A-4D\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, the above cell models were subcutaneously injected into nude mice to generate xenograft tumor models. We measured L and W of tumors at indicated time to calculate tumor volume. As expected, CDC73-forced expression increased tumor volume. The same observations were made for tumor weight. Besides, the increased expression of Ki67 in the tumors was verified by western blot. On the other hand, depleting MAPK1 impaired the growth of xenografts and reversed the malignant phenotypes of xenografts with CDC73-overexpressing\u0026nbsp;MDA-MB-231\u0026nbsp;cells (\u003cstrong\u003eFigure 4E-4H\u003c/strong\u003e). These data indicated that CDC73 and MAPK1 favor the development of breast cancer, both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCDC73 regulates breast cancer through mTOR pathway\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, we sought to gain insights on the downstream pathway involved in CDC73-induced breast cancer. As described previously (\u003cstrong\u003eFigure 3B\u003c/strong\u003e), a network visualized the link between CDC73 and mTOR pathway. We thus speculated that CDC73 might regulate breast cancer cell events through mTOR pathway. Subsequently, western blot analysis in CDC73-overexpressed\u0026nbsp;MDA-MB-231 and BT-549\u0026nbsp;cells showed an increase in CDC73 and p-mTOR protein levels, which was reversed after mTOR inhibitor treatment (\u003cstrong\u003eFigure 5A\u003c/strong\u003e). However, total levels of mTOR was not always obviously changed. Then, CCK8 assay showed that mTOR inhibitor disrupted the potential of cells to proliferate (\u003cstrong\u003eFigure 5B\u003c/strong\u003e), while expediting cell apoptosis (\u003cstrong\u003eFigure 5C\u003c/strong\u003e). These data demonstrated that CDC73 influences breast cancer through mTOR pathway.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, overexpressed CDC73 was found in breast cancer tissues and cells. Besides, elevated CDC73\u0026nbsp;positively correlated with multiple pathological parameters \u003cem\u003eeg.\u003c/em\u003e tumor infiltrate, lymphatic metastasis, AJCC stage as well as tumor size. Knocking down CDC73 in breast cancer cells suppressed cell events related to tumor development. Thus, CDC73 was identified as a tumor promoter in breast cancer development. Furthermore, we explored the downstream mechanism of CDC73 action and focused on MAPK1.\u003c/p\u003e\n\u003cp\u003eMitogen activating protein (MAP) kinase signaling cascade Ras-Raf-MEK-MAPK signaling pathway, an evolutionarily conserved signaling pathway\u0026nbsp;[18], has been reported to be involved in a variety of cellular and physiological processes related to life activities\u0026nbsp;[19, 20]. Once extracellular stimulators including cytokines, neurotransmitters and hormones bind to transmembrane receptors, Ras-GDP in the plasma membrane is activated and converted into Ras-GTP. Then, a homodimer or heterodimer consisting of Rafs were generated by Ras-GTP\u0026nbsp;[21]. The Raf enzymes subsequently catalyze the phosphorylation and activation of MEK, where MEK activates MAPK\u0026nbsp;[22, 23]. Finally, MAPK is activated to catalyze many cytoplasmic and nuclear substrates, such as transcription factors and regulatory molecules\u0026nbsp;[18, 24]. In summary, this signaling pathway controls cell growth, cell proliferation, cell survival, differentiation, immune response, metabolism, nervous system function, and transcription through a series of phosphorylation reactions. Thus, it is responsible for tumor development\u0026nbsp;[25, 26]. As one of the key components of this pathway, MAPK has been intensively studied and identified as oncogenes\u0026nbsp;[27, 28]. In this study, by combining \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments, we analyzed the carcinogenic roles of CDC73 and MAPK1 in breast cancer. We found high expression of MAPK1 in breast cancer cells, and that its knockdown inhibited breast cancer cell viability and migration but promoted apoptosis, indicating the carcinogenic role of MAPK1 in breast cancer. Moreover, by infection of MAPK1 overexpression vector in breast cancer cells with the presence of CDC73, the results revealed that MAPK1 was responsible for the function of CDC73 in breast cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe mechanism of CDC73 regulating MAPK1 was also uncovered. The process of ubiquitination involves a cascade of three enzymes, the third of which is an E3 ligase that transfers the ubiquitin from an E2 ubiquitin conjugating enzyme to specific substrates\u0026nbsp;[29]. Here, we found CBL, an interacting protein of CDC73, is an E3 ligase enzyme of MAPK1. Thus, we hypothesized that the UPS might play a role in MAPK1 protein degradation. Using protein synthesis inhibitor CHX, we found the half-life of MAPK1 was shortened after knocking down CDC73. Additionally, the protein level of MAPK1 was elevated in CDC73-depleted MDA-MB-231 cells following MG132 treatment, indicating that BECN1 is degraded through the UPS. Further study demonstrated that silencing CDC73 enhanced MPAK1 ubiquitination, thereby downregulating MAPK1 protein patterns.\u003c/p\u003e\n\u003cp\u003eOn the other hand, mTOR signaling pathway has been reported to be involved in regulating cell proliferation and apoptosis, thereby mediating cancer initiation\u0026nbsp;[30]\u0026nbsp;and progression as well as frequently being regarding as a therapeutic target\u0026nbsp;[31-34]. More notably, mTOR signaling pathway has been widely discussed in breast cancer. For instance, Lu \u003cem\u003eet al.\u0026nbsp;\u003c/em\u003ereported that UBE2C affects breast cancer proliferation through AKT/mTOR signaling pathway\u0026nbsp;[35]. Homoharringtonine (HHT), a natural alkaloid derived from the cephalotaxus, suppressed breast cancer cell growth and promoted apoptosis by mTOR signaling pathway\u0026nbsp;[36]. Consistent with their findings, this current study showed that the phosphorylation levels of mTOR were increased following CDC73 upregulation, while no influences on its total protein levels. Sequent cell functional experiments evidenced that mTOR inhibitor could disrupted the potential of cells to proliferate, while expediting cell apoptosis. These data demonstrated that CDC73 influences breast cancer through mTOR pathway.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our research provided evidence of CDC73 tumor promotor in breast cancer, indicating that CDC73 is a potential therapeutic target in breast cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCDC73: Human cell division cycle 73; Paf1C: polymerase-associated factor 1 complex; ATCC: American type culture collection; qRT-PCR: Real-time quantitative PCR; Co-IP: co-immunoprecipitation; IPA: Ingenuity Pathway Analysis; CHX: cycloheximide; DEGs: differentially expressed genes; UPS: ubiquitin proteasome system; HHT: Homoharringtonine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study obtained ethical support from Zhengzhou university life science ethics review committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the patients who provided the tissues signed informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated in this study are available within the article and its supplementary data files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted with support from\u0026nbsp;National Natural Science Foundation of China\u0026nbsp;(No. 82002806) and Natural Science Foundation of Anhui Province (No. 2008085MH295).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaige Zhang, Yu Tang, Xiaozhi Zhang and Jing Pei designed this research. Ya Gao, Mingming Du, Erhu Pan, Guopeng Sang and Chang Liu operated experiments. Fangfang Pei, Mingliang Sun, Zhifan Ruan and Yubo Pan participated in the processing and analysis of the data. Haige Zhang completed the manuscript which was reviewed by Yu Tang, Xiaozhi Zhang and Jing Pei. All authors have confirmed the submission of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A: Cancer statistics, 2022. CA Cancer J Clin 2022, 72(1):7\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFahad Ullah M: Breast Cancer: Current Perspectives on the Disease Status. Adv Exp Med Biol 2019, 1152:51\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatsura C, Ogunmwonyi I, Kankam HK, Saha S: Breast cancer: presentation, investigation and management. Br J Hosp Med (Lond) 2022, 83(2):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun W, Kuang XL, Liu YP, Tian LF, Yan XX, Xu W: Crystal structure of the N-terminal domain of human CDC73 and its implications for the hyperparathyroidism-jaw tumor (HPT-JT) syndrome. Sci Rep 2017, 7(1):15638.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarpten JD, Robbins CM, Villablanca A, Forsberg L, Presciuttini S, Bailey-Wilson J, Simonds WF, Gillanders EM, Kennedy AM, Chen JD \u003cem\u003eet al\u003c/em\u003e: HRPT2, encoding parafibromin, is mutated in hyperparathyroidism-jaw tumor syndrome. 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Mol Cell 2017, 65(4):685\u0026ndash;698 e688.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Bernecky C, Lee CT, Maier KC, Schwalb B, Tegunov D, Plitzko JM, Urlaub H, Cramer P: Architecture of the RNA polymerase II-Paf1C-TFIIS transcription elongation complex. Nat Commun 2017, 8:15741.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowell VM, Haven CJ, Kahnoski K, Khoo SK, Petillo D, Chen J, Fleuren GJ, Robinson BG, Delbridge LW, Philips J \u003cem\u003eet al\u003c/em\u003e: HRPT2 mutations are associated with malignancy in sporadic parathyroid tumours. J Med Genet 2003, 40(9):657\u0026ndash;663.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShattuck TM, Valimaki S, Obara T, Gaz RD, Clark OH, Shoback D, Wierman ME, Tojo K, Robbins CM, Carpten JD \u003cem\u003eet al\u003c/em\u003e: Somatic and germ-line mutations of the HRPT2 gene in sporadic parathyroid carcinoma. 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Br J Pharmacol 2000, 129(7):1481\u0026ndash;1489.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng R, Zhang HL, Huang JH, Cai RZ, Wang Y, Chen YH, Hu BX, Ye ZP, Li ZL, Mai J \u003cem\u003eet al\u003c/em\u003e: MAPK1/3 kinase-dependent ULK1 degradation attenuates mitophagy and promotes breast cancer bone metastasis. Autophagy 2021, 17(10):3011\u0026ndash;3029.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang ZY, Gao XH, Ma MY, Zhao CL, Zhang YL, Guo SS: CircRNA_101237 promotes NSCLC progression via the miRNA-490-3p/MAPK1 axis. Sci Rep 2020, 10(1):9024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHershko A, Ciechanover A: The ubiquitin system. Annu Rev Biochem 1998, 67:425\u0026ndash;479.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang S: mTOR Signaling in Metabolism and Cancer. \u003cem\u003eCells\u003c/em\u003e 2020, 9(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopova NV, Jucker M: The Role of mTOR Signaling as a Therapeutic Target in Cancer. Int J Mol Sci 2021, 22(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNunnery SE, Mayer IA: Targeting the PI3K/AKT/mTOR Pathway in Hormone-Positive Breast Cancer. Drugs 2020, 80(16):1685\u0026ndash;1697.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsati V, Mahapatra DK, Bharti SK: PI3K/Akt/mTOR and Ras/Raf/MEK/ERK signaling pathways inhibitors as anticancer agents: Structural and pharmacological perspectives. Eur J Med Chem 2016, 109:314\u0026ndash;341.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagaway C, Kim E, Jacinto E: Targeting mTOR and Metabolism in Cancer: Lessons and Innovations. \u003cem\u003eCells\u003c/em\u003e 2019, 8(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu ZN, Song J, Sun TH, Sun G: UBE2C affects breast cancer proliferation through the AKT/mTOR signaling pathway. Chin Med J (Engl) 2021, 134(20):2465\u0026ndash;2474.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang LB, Wang DN, Wu LG, Cao J, Tian JH, Liu R, Ma R, Yu JJ, Wang J, Huang Q \u003cem\u003eet al\u003c/em\u003e: Homoharringtonine inhibited breast cancer cells growth via miR-18a-3p/AKT/mTOR signaling pathway. Int J Biol Sci 2021, 17(4):995\u0026ndash;1009.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Expression patterns of CDC73 in breast cancer tissues and para-carcinoma tissues revealed in immunohistochemistry analysis.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\" rowspan=\"2\"\u003e\n \u003cp\u003eCDC73\u003c/p\u003e\n \u003cp\u003eexpression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003eTumor tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" colspan=\"2\"\u003e\n \u003cp\u003ePara-carcinoma tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.924050632911392%\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.050632911392405%\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.848101265822784%\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.72151898734177%\" rowspan=\"3\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.686746987951807%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.25301204819277%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.89156626506024%\"\u003e\n \u003cp\u003e57.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.50602409638554%\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.686746987951807%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.25301204819277%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.89156626506024%\"\u003e\n \u003cp\u003e42.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.50602409638554%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 2. Relationship between CDC73 expression and tumor characteristics in patients with breast cancer.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" rowspan=\"2\"\u003e\n \u003cp\u003eFeatures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\" rowspan=\"2\"\u003e\n \u003cp\u003eNo. of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\" colspan=\"2\"\u003e\n \u003cp\u003eCDC73 expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.84615384615385%\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e\u0026le;51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e\u0026gt;51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003eAJCC stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003eTumor infiltrate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003elymphatic metastasis (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e\u0026le;2cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.541666666666664%\"\u003e\n \u003cp\u003e\u0026gt;2cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 3. Relationship between CDC73 expression and tumor characteristics in patients with breast cancer.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eCDC73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eTumor infiltrate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eSpearman\u0026nbsp;correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eSignification (double-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003elymphatic metastasis (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eSpearman\u0026nbsp;correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eSignification (double-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eAJCC stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eSpearman\u0026nbsp;correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eSignification (double-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eSpearman\u0026nbsp;correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eSignification (double-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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":"Breast cancer, CDC73, MAPK1, ubiquitination, mTOR signaling pathway","lastPublishedDoi":"10.21203/rs.3.rs-3141760/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3141760/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast cancer is the most common global malignancy and the leading cause of cancer deaths. CDC73 (Human cell division cycle 73), a nuclear protein, participates transcription regulation and its functions are controversial in malignancies. CDC73 has been reported to be upregulated in breast cancer. The underlying mechanism, however, has not been fully illuminated. In breast cancer, CDC73 could promote the proliferation of tumor cells, and the expression of CDC73 was related to poor prognosis in patients.\u003c/p\u003e \u003cp\u003eHere, we found that CBL, an E3 ubiquitin ligase, could interact with CDC73 and promote MAPK1 ubiquitination and degradation of this protein. In addition, silencing MAPK1 led to a suppression of breast cancer cell growth \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e, and even abolished the promoting effects of CDC73 overexpression. We also found that mTOR pathway played a role in CDC73-mediated breast cancer. mTOR pathway inhibitor reversed cell phenotypes induced by CDC73 overexpression. Our study revealed the underlying mechanism of CDC73 in breast cancer: it promoted MAPK1 ubiquitination and degradation so that affected MAPK1 level and subsequently led to tumor progression, providing a novel therapeutic strategy to combat cancer.\u003c/p\u003e","manuscriptTitle":"CDC73 promotes breast cancer through impairing MAPK1 ubiquitination and activating mTOR signaling pathway ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-11 14:23:23","doi":"10.21203/rs.3.rs-3141760/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":"c97bfed1-f03e-4bda-ba22-920e68844546","owner":[],"postedDate":"July 11th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-07-14T13:44:31+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-11 14:23:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3141760","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3141760","identity":"rs-3141760","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}