NDC80 kinetochore complex serve as a potential prognostic predictor and correlate with immune infiltrates in Epithelial Ovarian Cancer patients

NDC80 kinetochore complex serve as a potential prognostic predictor and correlate with immune infiltrates in Epithelial Ovarian Cancer patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article NDC80 kinetochore complex serve as a potential prognostic predictor and correlate with immune infiltrates in Epithelial Ovarian Cancer patients Xiaodan Yu, Chen Shi, Meizhu Pan, Lili Jiang, Kuiran Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3460715/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2024 Read the published version in International Journal of General Medicine → Version 1 posted You are reading this latest preprint version Abstract Purpose : This study focus on evaluating the prognostic value of NDC80 Kinetochore Complex (NDC80 complex) in OC underlying the Gene Expression Omnibus database and the Cancer Genome Atlas database and reveal the relationship between NDC80 complex and immune infiltrates in OC. Methods : We collected data on NDC80 complex expression levels in both OC tissues and normal ovarian tissues from University Of Cingifornia Sisha Cruz Xena and the Gene Expression Omnibus databases. The clinicopathological characteristics correlated with overall survival were analysed using Cox regression and the Kaplan–Meier method. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, Gene set enrichment analysis and cibersort were performed using data from the Cancer Genome Atlas database. Immumohistochemical staining was used to verify higher expression level of NUF2 protein in OC in vitro. Meanwhile, we utilized the Tumor Immune Estimation Resource to analyze the correlation between NDC80 complex and immunocyte infiltration. All methods were performed in accordance with the relevant guidelines and regulations. Results : The NDC80 complex expression level was prominently higher in OC tissues than in normal ovarian tissues and correlated with advanced histologic grade characteristics. Gene Expression Profiling Interactive Analysis and the Kaplan–Meier survival curve and uncovered a close relationship between high expression of NDC80 complex with poor overall survival in OC patients. The unitivariate Cox regression hazard model proved that age, pathologic stage, tumor status, primary therapy outcome, SPC24 expression level and Karnofsky performance score as prognostic factors for OC patients. NDC80 complex expression levels were highly associated with immune cell infiltration, showing NK CD56bright cells and NK cells with negative correlation and Th2 cells with positive correlation( p <0.05). Conclusion : The findings gave the evidence that increased expression level of NDC80 complex was closely associated with the progression of OC and could also serve as a novel target of immunotherapy in OC. Bioinformatics Analysis Gene Expression Omnibus NDC80 kinetochore complex Epithelial Ovarian Cancer Immunoinfiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Background Ovarian cancer (OC), which is the most deadly and invasive malignancy in the female reproductive system, has been on the rise in recent years[Sigel RL, 2019]. Due to the insidious nature of OC's early stages, the majority of patients (60%) are diagnosed with advanced disease[Jessmon P, 2017], which is linked with a high fatality rate. The 5-year overall survival (OS) was as high as 45% owing to complicated symptoms and a lack of early diagnosis measures[Webb PM, 2017]. Given the fact that surgical resection performed on advanced OC needs sophisticated surgical techniques and usually goes with severe complications, immunotherapy has been utilized to treat OC. However, there are currently a limited selection of immune checkpoint inhibitors accessible[Yang C, 2020]. Thus, it is critical to understand the specific molecular pathways behind OC carcinogenesis, proliferation, and invasion and to figure out further effective diagnostic and therapeutic strategies for the management of OC[Jiang X, 2019]. Researchers have demonstrated that increased NDC80 kinetochore complex (NDC80 complex) expression is an excellent prognostic indicator in hepatocellular carcinoma[Shen S, 2019], pancreatic cancer[Meng QC, 2015], gastric cancer[Qu Y, 2014] and non-small cell lung cancer[Wei R, 2020]. However, the NDC80 complex's role in OC is uncertain. As a result, we use several databases to investigate the expression, prognosis, and tumor infiltrating lymphocytes of NDC80 complex in OC. NDC80 complex is consist of four major components, known as NDC80, NUF2, SPC24 and SPC25. NDC80 is required for proper chromosome segregation and is involved in the organization and stabilization of microtubule-kinetochore interactions. NUF2, SPC24 and SPC25 play an important role in kinetochore integrity and the organization of stable microtubule binding sites in the outer plate of the kinetochore[Tooley J, 2011]. In the present study, 4 OC datasets were retrieved from the Gene Expression Omnibus (GEO) database to verify NUF2 as a hub gene in OC. Immunohistochemical (IHC) staining was performed to demonstrate different expression level of NUF2 protein between OC and normal ovarian tissues. Considering NUF2 as an important component of NDC80 complex, 3 other major components were taken into account as well. To understand the biological functions and relative molecular mechanisms underlying carcinogenesis, a variety of bioinformatics methods have been used. To our knowledge, this study first investigated the relationship between the NDC80 complex and gene mutations and the tumour microenvironment (TME) in OC, then established a SPC24-related nomogram to study patient survival and demonstrated that the NDC80 kinetochore complex may accelerate the progression of OC by spindle and kinetochore-associated (SKA1) complex pathway. The workflow of the whole study was listed (Fig. 1 ). Materials and Methods RNA-Sequencing Data We downloaded the microarray data of four gene expression profile datasets (GSE14407, GSE38666, GSE40595 and GSE54388) from the GEO database. All datasets were collected from the GPL570[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array. GSE14407 includes data from 12 serous ovarian cancer epithelia (CEPI) samples and 12 healthy ovarian surface epithelia (OSE) samples. GSE38666 includes data from 7 CEPI samples and 8 OSE samples. GSE40595 includes data from 32 high grade serous CEPI samples and 6 OSE samples. GSE54388 includes data from 16 high grade serous CEPI samples and 6 OSE samples. We used data from these 4 datasets as training set. RNA-sequencing gene expression data of 427 OC samples from the Cancer Genome Atlas (TCGA) database[Goldman MJ, 2020] and 88 healthy ovarian surface epithelia samples from Genotype-Tissue Expression (GTEx) were retrieved and unified by Toil process[Vivian J, 2017]. We used the merged dataset as validation set. It is worth mentioning that pre- and postmenopausal ovarian tissues are taking into account in both training and validation sets in this study. Identification of differentially expressed genes (DEGs) GEO2R were applied to perform comparisons on original submitter-supplied processed data tables and the GEOquery and limma R packages[Smyth G, 2005] from the Bioconductor project were used to filtrate the DEGs between the OC patient group and the normal group(Fig. 2 A-D). Background correction, normalization, removal of batch effect and calculation of expression were performed during the process. The cut-off threshold in GEO was |log fold change| ≥ 2 and adjusted P value < 0.05. Integration of protein–protein interaction (PPI) network First, 4 GEO datasets were utilized to build a veen diagram of 104 overlapping DEGs(Fig. 2 E) and to determine the DEGs that encode proteins in OC. 104 DEGs were input in Cytoscape to generate PPI network and 64 DEGs with significant correlation were output(Fig. 2 F). Second, the NetworkAnalyst[Xia J, 2015] was used to construct a co-expression network with a confidence score of 900(Fig. 3 A). Third, the PPI network was imported in Cytoscape[Shannon P, 2003] and CytoHubba was used to compute the node degree and select a cluster of 36 DEGs for visualization(Fig. 3 B). Gene function analysis Enrichment studies of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed using the clusterProfiler package[Ashburner, 2000]. Afterwards, the bubble plot was created using the R GOplot. Three categories were defined in the ontology: biological process (BP), molecular function (MF), and cellular component (CC). P < 0.05 was used as the cut-off value in this study. IHC Staining This study had the approval of the ethics committee of Shengjing Hospital. (Reference number: KYCS2023063). Informed consent was waived under intense scrutiny by the ethics committee in this condition. Tissue samples were obtained from the specimen repository of Shengjing Hospital and there were no human participants involved in this study. For IHC staining, five formalin-fixed, paraffin-embedded ovarian cancer tissues and five normal ovarian tissues were prepared. The samples were cut into 4 um thick slices, mounted on glass slides, deparaffinised in xylene and rehydrated in graded series of alcohol. Antigen retrieval was performed at a high temperature through water bath. Endogenous peroxidases were quenched with 3% hydrogen peroxide after the sections cooled and rinsed. The sections were then washed three times with PBS, incubated with calf serum for 10 min to block non-specific antigens, incubated with polyclonal anti-NUF2 antibody (1:200, bs-7714R, Bioss, Beijing, China) at 4°C overnight, washed three times with PBS and incubated with secondary antibody at room temperature(RT) for 30–40 min. An optical microscope was used to observe dried sections. Two pathologists, blinded to the source of the clinical cases, observed and analysed the IHC staining results. Multiple Methods on Prognosis Analysis and Nomogram Construction First, the diagnostic and prognostic values of NDC80 kinetochore complex in OC patients were evaluated using receiver operating characteristic (ROC) and Kaplan-Meier survival curves. Second, univariate and multivariate regression analyses were conducted to identify the relationship between NDC80, NUF2, SPC24 and SPC25 expression and the OS rates of OC patients. For Cox regression analysis, P < 0.05 was considered statistically significant. SPC24-related nomogram is a simplifified model for predicting OC prognosis as a single numerical value. The probabilities of 3-year and 5-year OS rates are represented by the total points projected on the bottom scales. The R package “rms”[Frank E Harrell Jr, 2021] and “survival” was used to draw the nomogram. Gene set enrichment analysis (GSEA) The ggplot2 package was used to conduct GSEA, which serve as a statistical tool to detect whether selected gene set exhibits statistically significant and concordant differences between OC group and normal people group[Damian D, 2004]. In this study, after batch effect removal of raw data from TCGA, an ordered list of all genes and a single gene, NUF2 in OC were generated by GSEA, which aims to figure out statistically significant differences in survival rate for all genes between OC group and normal people group and between high and low NUF2 expression groups in order to identify signaling pathways regulated by OC-related genes and NUF2. To calculate and sort the enriched pathways in each phenotype, the normalised enrichment score and the nominal P value were used. C2.all.v6.2.symbols.gmt was selected as the reference gene set. The gene sets were permutated 1000 times. Correlation Analysis of Immune Cell Infiltration Single sample Gene Set Enrichment Analysis (ssGSEA) and Tumor Immune Estimation Resource (TIMER) were applied to study the correlations between NDC80 kinetochore complex and tumor purity, including 23 immunocytes. The correlation between NDC80 kinetochore complex and immune cell infiltration was examined via Spearman correlation analysis. We used consensus clustering analysis with the R package “ConsensusClusterPlus”[Wilkerson MD, 2010] to perform an overview of different immune cell infiltration in OC patients. Statistical Analyses P < 0.05 was selected as the significance level. We computed the data by R programme(v.3.6.3). The Chi-square and Fisher's tests were applied to analyze the clinical data. Additionally, the Wilcoxon rank sum test was performed. Results Clinical Characteristics The clinical information of 379 patients were collected from TCGA, including age, FIGO stage, race, primary therapy outcome, histologic grade, lymphatic invasion and tumor status. Data filtering was used to calculate clinical information. The details are provided in Table 1 . Table 1 Clinical Characteristics of the OV Patients Based on TCGA Characteristic Low expression of NDC80 High expression of NDC80 p n 189 190 FIGO stage, n (%) 0.247 Stage I 0 (0%) 1 (0.3%) Stage II 12 (3.2%) 11 (2.9%) Stage III 141 (37.5%) 154 (41%) Stage IV 34 (9%) 23 (6.1%) Primary therapy outcome, n (%) 0.247 PD 15 (4.9%) 12 (3.9%) SD 12 (3.9%) 10 (3.2%) PR 26 (8.4%) 17 (5.5%) CR 98 (31.8%) 118 (38.3%) Race, n (%) 0.232 Asian 6 (1.6%) 6 (1.6%) Black or African American 8 (2.2%) 17 (4.7%) White 163 (44.7%) 165 (45.2%) Age, n (%) 1.000 60 85 (22.4%) 86 (22.7%) Histologic grade, n (%) 0.028 G1 1 (0.3%) 0 (0%) G2 29 (7.9%) 16 (4.3%) G3 152 (41.2%) 170 (46.1%) G4 1 (0.3%) 0 (0%) Lymphatic invasion, n (%) 0.295 No 21 (14.1%) 27 (18.1%) Yes 55 (36.9%) 46 (30.9%) Tumor status, n (%) 0.089 Tumor free 29 (8.6%) 43 (12.8%) With tumor 139 (41.2%) 126 (37.4%) Age, meidan (IQR) 58 (50, 68) 59 (51, 67) 0.724 NDC80 kinetochore complex Expression in OC Was Elevated Differential expression of NDC80 kinetochore complex was analyzed in OC. Compared to normal tissue, NDC80, NUF2, SPC24 and SPC25 expression levels were significantly higher in OC tissues (Fig. 4 A-D) ( p < 0.05) and the expression level of NUF2 was found to increase in 32 different tumors except for kidney chromophobe (Fig. 4 E). A statistical difference between NDC80 and histologic grade was found by correlation analysis( p < 0.05) (Table 1 ). There was no remarkable difference in age, race, FIGO stage, primary therapy outcome, lymphatic invasion and tumor status. NUF2 protein expression in OC was further investigated by IHC staining and showed that the protein level was increased in OC compared to normal ovarian tissue (Fig. 5 ). GSEA and GO/KEGG analysis of OC-related genes GSEA identified a number of molecular pathways that were significantly altered in OC patient tissue compared to normal tissue. To filter out differentially enriched pathways between the OC group and the normal group, GSEA of the gene expression profiles was used. These results show that OC is predominantly correlated with reactome cell cycle, M phase, mitotic prometaphase, mitotic metaphase and anaphase, rho GTPase effectors and signaling by rho GTPase(Fig. 6 A-F). Taking enrichment analysis via GO/KEGG (Table 2 ) in account, we figured that OC-related genes are mainly enriched in nuclear division, mitotic nuclear division and organelle fission terms for BP. For CC, the DEGs were mainly enriched in spindle, midbody and chromosomal region terms, and for MF, they were mainly enriched in microtubule binding, histone kinase activity and tubulin binding terms(Fig. 6 G). For KEGG analysis, cell cycle, oocyte meiosis and p53 signaling pathway terms were enriched(Fig. 6 H). Table 2 Results of GO and KEGG analysis of OV-related genes Ontology ID Description GeneRatio BgRatio pvalue p.adjust qvalue BP GO:0140014 nuclear division 20/91 264/18670 1.32e-18 2.21e-15 1.85e-15 BP GO:0000070 mitotic nuclear division 16/91 151/18670 2.31e-17 1.17e-14 9.84e-15 BP GO:0098813 organelle fission 19/91 262/18670 2.39e-17 1.17e-14 9.84e-15 CC GO:0005819 spindle 15/95 347/19717 1.10e-10 2.10e-08 1.59e-08 CC GO:0000779 midbody 10/95 118/19717 2.64e-10 2.52e-08 1.90e-08 CC GO:0030496 chromosomal region 11/95 173/19717 7.12e-10 3.72e-08 2.81e-08 MF GO:0035173 microtubule binding 3/92 17/17697 8.77e-05 0.021 0.020 MF GO:0003777 histone kinase activity 4/92 84/17697 9.61e-04 0.099 0.094 MF GO:0019894 tubulin binding 3/92 42/17697 0.001 0.099 0.094 KEGG hsa04110 Cell cycle 7/35 124/8076 7.99e-07 6.07e-05 5.72e-05 KEGG hsa04114 Oocyte meiosis 5/35 129/8076 2.13e-04 0.006 0.006 KEGG hsa04115 p53 signaling pathway 4/35 73/8076 2.60e-04 0.006 0.006 Correlation Analysis of Diagnosis and Prognosis Area under curve (AUC) was 0.978 for NUF2, 0.975 for NDC80, 0.996 for SPC24 and 0.986 for SPC25(Fig. 7 A). This result indicated that NDC80 complex could differenciate between normal and tumor tissues. The Kaplan-Meier survival curve reveals that high NDC80、NUF2 and SPC25 levels are correlated with poor prognosis(Fig. 7 B-D). The survival curve run by GEPIA database shows that high SPC24 level is associated with poor prognosis(Fig. 7 E). Figure 8 A shows that only high SPC24 expression level has statistical significance in poor prognosis of OC in TCGA database (p < 0.05) along with age, tumor status, primary therapy outcome, tumor size and Karnofsky score(KFS). In the multivariate Cox model, only age, tumor status, primary therapy outcome and KFS were independent prognostic factors in OC patients(Fig. 8 B). Identifification of the Nomogram A prognostic nomogram was constructed on the basis of clinicopathological factors in order to have a quantitative method for the prediction of the prognosis of OC patients (Fig. 8 C). The nomogram integrated age, histologic grade, tumor status, primary therapy outcome and SPC24 expression, and the results indicated that histologic grade had the greatest influence on the model while patients with higher SPC24 expression had a greater risk of a non-ideal prognosis. Correlation Analysis of Immune Cell Infiltration OC is closely correlated with molecular genetic and the inflammatory environment. The relative percentage of 22 immunocyte in OC patients via cibersort was demonstrated to overview the immune microenvironment in OC patients(Fig. 9 A). TIMER data implies statistical significance not only between NDC80 and macrophages, neutrophils, dendritic cells but also between SPC25 and CD8 + T cell, macrophages, whereas no statistically significant difference exists between NUF2, SPC24 and other immunocytes(Fig. 9 B-E). Using the ssGSEA package, the correlation between NDC80, NUF2, SPC24, SPC25, and other immunocytes was determined(Fig. 10 A-D). There was a significant correlation between NDC80, NUF2, SPC24, SPC25 levels and immune cell infiltration, including NK CD56bright cells and NK cells with negative correlation and Th2 cells with positive correlation(p < 0.05). Eosinophils, iDC and Mast cells had a positive correlation with NDC80, NUF2 and SPC24. Th17 cells had a positive correlation with NDC80, NUF2 and SPC25 while T helper cells had a negative correlation with them. Discussion Despite extensive studies on OC biomarkers, research on prognostic markers for OC remains limited. High levels of NDC80 kinetochore complex, which is consist of NDC80, NUF2, SPC24 and SPC25 are required for tumor cells to retain malignant properties such as uncontrolled invasion, resistance to chemotherapy and metastasis. Overexpression of NDC80 genes has been identified in a variety of malignant tumors and is often associated with a poor clinical outcome[ 7 – 10 ]. The NDC80 complex has a central role in the formation and regulation of microtubule attachments, which is supported by additional factors such as the spindle and kinetochore-associated complex[Wimbish RT, 2020]. A present study demonstrated that inhibition of NDC80 expression enhances the sensitivity of human ovarian cancer cells to paclitaxel[Mo QQ, 2013]. NUF2 can serve as an stabilizer during microtubule-kinetochore interactions, which is related to proper chromosome segregation. Knockdown of NUF2 induced chromosomal misalignment during metaphase and a significant reduction in cancer cell proliferation[Sugimasa H, 2015]. However, few studies have been carried out on the expression of NDC80 genes in OC. As a result, it is crucial to recognize the expression of NDC80 genes in OC and its therapeutic and prognostic significance. First, this study examined the differential expression of the NDC80 kinetochore complex in OC and normal tissues. NDC80, NUF2, SPC24, and SPC25 levels were found to be significantly higher in OC tissues than in normal ovarian epithelial tissues. The mRNA levels of NDC80 complex were markedly distinct according to histologic grade. Next, the co-regulatory proteins of NUF2 were identified using the PPI network. We found that NUF2-related genes were mainly enriched in the Cell cycle, Oocyte meiosis and p53 signaling pathway. Then the protein expression level of NUF2 was verified by IHC staining under precise experiments. We also verified the correlation between NDC80 kinetochore complex and survival rate in OC patients. ROC and Kaplan–Meier survival analyses were performed to confirm that high expression of NDC80, NUF2, and SPC25 is closely linked to a worse prognosis and NUF2-related nomogram could be used as a reliable prognostic indicator in OC. Furthermore, studies have shown that NUF2 is closely linked with cancer progression through the meditator LncRNA AF339813. LncRNA AF339813 was positively regulated by NUF2. Researchers further demonstrated that knockdown of AF339813 by siRNA in caner cells significantly reduced cell proliferation and promoted apoptosis[Hu P, 2015]. On the contrary, NDC80 overexpression induced asymmetrical chromosome alignments, abnormal cell division, and thus rendered chromosomal instability[Qu Y, 2014]. In addition, OC is closely associated with immune microenvironment[Jiang Y, 2020]. TME refers to the niche, either primary or metastatic, in which tumor cells interact with the host stroma, including various immune cells, endothelial cells, fibroblasts and metabolic products. Recently, the critical role of TME in OC initiation, progression and resistance to anti-tumour therapy has been increasingly recognised. However, there are few studies investigating the connection between the NDC80 complex and immunocytes in OC. Using TIMER and ssGSEA, the correlation between NDC80 complex and immunocyte infiltration was evaluated In this study. Our results indicate that NDC80 complex significantly correlated with NK CD56bright cells, NK cells and Th2 cells. However, this research has some drawbacks, including a limited number of normal samples in TCGA database and no validation of the hypothesis using animal models. Clearly, these aspects must be verified through more research. Conclusion In conclusion, our study revealed the prognostic functions of NDC80 kinetochore complex in OC patients and a substantial association between NDC80 kinetochore complex and immune cell infiltration. Therefore, our results indicate that NDC80 kinetochore complex have a significant prognostic value in OC and it could serve as a potential immunotherapeutic target for OC patients. Declarations Statements and Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions Yu Xiaodan and Shi Chen conceived and designed the study, Yu Xiaodan analyzed the patient data, and was a major contributor in writing the manuscript, Jjiang Lili helped in writing and revising the manuscript. Pan Meizhu helped in IHC staining experiment. Liu Kuiran took participation in guidance whole course. All authors read and approved the final manuscript. Data Availability The datasets supporting the conclusions of this article are available in TCGA, GEO, GTEx and UCSC Xena repository, https://portal.gdc.cancer.gov/projects/TCGA-OV https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14407 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38666 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40595 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54388 https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Ovarian%20Cancer%20(OC)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 The datasets analysed during the current study are not publicly available due but are available from the corresponding author on reasonable request. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Shengjing Hospital of China Medical University. 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Int J Clin Exp Pathol. 2015;8(3):2638–2648. Published 2015 Mar 1. Qu Y, Li J, Cai Q, Liu B. Hec1/Ndc80 is overexpressed in human gastric cancer and regulates cell growth. J Gastroenterol. 2014;49(3):408–418. doi: 10.1007/s00535-013-0809-y Jiang Y, Wang C, Zhou S. Targeting tumor microenvironment in ovarian cancer: Premise and promise. Biochim Biophys Acta Rev Cancer. 2020;1873(2):188361. doi: 10.1016/j.bbcan.2020.188361 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Apr, 2024 Read the published version in International Journal of General Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-3460715","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":240996170,"identity":"d8d0cf84-d40d-48dc-8476-7cd30a12ab86","order_by":0,"name":"Xiaodan Yu","email":"","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaodan","middleName":"","lastName":"Yu","suffix":""},{"id":240996172,"identity":"2e1e6caa-eb37-4054-979e-5428b59fd595","order_by":1,"name":"Chen Shi","email":"","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Shi","suffix":""},{"id":240996174,"identity":"3eb64623-128a-49f3-8f02-91c11b6fa90f","order_by":2,"name":"Meizhu Pan","email":"","orcid":"","institution":"the First Affiliated Hospital of China Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meizhu","middleName":"","lastName":"Pan","suffix":""},{"id":240996175,"identity":"9e005d31-c468-42db-a848-c65b21d5e4c1","order_by":3,"name":"Lili Jiang","email":"","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Jiang","suffix":""},{"id":240996176,"identity":"60917628-e5aa-4df8-a666-b5321af82ec8","order_by":4,"name":"Kuiran Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIie3QOwrCQBCA4QmBTTMklhuU3EBYWUglyVkWQVvBJnYJwtp4A48hWCcETBN77dwb5Aa+SkEydhb71/PBzADYbH+Y74ILIBgGXlGYjkLYm2T+KNzVG8lJBF6kjabistADJBEPpVzrGOFqNHBIonHeuxhKddZzdPZK35Ywk3FJIFWhT+gO1VZwKNWRSO7IwkpzpBKVtwyRO2TCVpM8Y8hRPZ8sCLcEQX0Ic8HStGmM6bIk6iUfid/GbTabzfalB+gaNqkEk9kHAAAAAElFTkSuQmCC","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kuiran","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2023-10-18 06:14:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3460715/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3460715/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.2147/IJGM.S450976","type":"published","date":"2024-05-01T00:47:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":44951053,"identity":"4dc58397-1315-46c9-b6f7-f1ac25b7a02c","added_by":"auto","created_at":"2023-10-19 21:35:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131305,"visible":true,"origin":"","legend":"\u003cp\u003eThe framework of the whole study by workflow.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3460715/v1/41916f82cacd97ee671d9ff6.jpeg"},{"id":44952108,"identity":"19d19683-cf68-4120-8e5d-155be0cf249a","added_by":"auto","created_at":"2023-10-19 21:43:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":746599,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of differentially expressed genes (DEGs) in ovarian serous cystadenocarcinoma(OC). A. Up-regulated and down-regulated DEGs in GSE14407. B. Up-regulated and down-regulated DEGs in GSE38666. C. Up-regulated and down-regulated DEGs in GSE40595. D. Up-regulated and down-regulated DEGs in GSE54388. E. Venn diagram of up-regulated DEGs between 4 GEO datasets. F.The PPI network of 64 DEGs is shown.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3460715/v1/d7b2e572cc01fd0a06a90fab.png"},{"id":44951056,"identity":"a7c9a96f-df8b-4a5f-ba28-fa7f147e33e0","added_by":"auto","created_at":"2023-10-19 21:35:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":510337,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation and interaction between selected DEGs. A. Co-expression analysis was performed using NetworkAnalyst. B. A cluster of 36 most correlative DEGs was performed by CytoHubba.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3460715/v1/bbf769d7fb9ac9b18e504985.png"},{"id":44951059,"identity":"b4ee1fff-a806-4a1e-889c-d9b3e67bb7af","added_by":"auto","created_at":"2023-10-19 21:35:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":426257,"visible":true,"origin":"","legend":"\u003cp\u003eThe comparison of mRNA expression levels of NDC80 complex. A. NDC80, B. NUF2, C. SPC24 and D. SPC25 expression levels were confirmed significantly higher in OC samples than that in normal samples. E. NUF2 expression level was up-regulated in 32 different tumors except for KICH.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3460715/v1/d850cad76a7f641488aa3c1d.png"},{"id":44951060,"identity":"56ece3fe-1091-42c6-9bad-c34e65acd382","added_by":"auto","created_at":"2023-10-19 21:35:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2396353,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemical staining of NUF2 was performed in Ovarian cancer and normal ovarian tissues. Representative images are presented. Scare bars, 50 μm.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3460715/v1/e423dc191ecc39def46e71e3.png"},{"id":44951054,"identity":"62bb9fe2-e2ee-4a92-87d7-602678eccba1","added_by":"auto","created_at":"2023-10-19 21:35:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":712409,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA and GO/KEGG enrichment analysis results. OC-related genes were mainly enriched in A.cell cycle, B. M phase, C. mitotic prometaphase, D. mitotic metaphase and anaphase, E. rho GTPase effectors and F. signaling by rho GTPase. G. Bar plot of significantly enriched GO terms for the DEGs. H. Bar plot of significantly enriched KEGG terms for the DEGs.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3460715/v1/7aa513e5b66e72466cffe5e7.png"},{"id":44951051,"identity":"21498663-7b26-4855-a473-73f638d5bac0","added_by":"auto","created_at":"2023-10-19 21:35:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":555152,"visible":true,"origin":"","legend":"\u003cp\u003eROC and Kaplan-Meier survival curve of NDC80, NUF2, SPC24 and SPC25. A. ROC analysis showed 4 components of NDC80 complex were capable of distinguishing tumor from normal tissue with high accuracy. The AUC was 0.978 for NUF2, 0.975 for NDC80, 0.996 for SPC24 and 0.986 for SPC25. B-D. The Kaplan-Meier survival curve showed high level of NDC80, NUF2 and SPC25 with a poor prognosis of OC patients. E. Survival curve run by GEPIA displayed SPC24 as a negative predictor of prognosis.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3460715/v1/1aa2a0b0a331db620f4f59a3.png"},{"id":44952110,"identity":"cf574e90-2de1-42e2-9620-8a8fee0953a5","added_by":"auto","created_at":"2023-10-19 21:43:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":567437,"visible":true,"origin":"","legend":"\u003cp\u003eThe association of NDC80 complex expression and other clinicopathologic features with OS in OC was studied using univariate and multivariate cox analysis. A. The forest plot of univariate cox analysis. B. The forest plot of multivariate regression analysis. C. SPC24-related nomogram was used to assess prognosis of OC patients.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3460715/v1/73f64296fe885e7119dccb68.png"},{"id":44951058,"identity":"d99a8ca9-4603-4326-b56c-53bba5221a2d","added_by":"auto","created_at":"2023-10-19 21:35:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1466568,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of NDC80, NUF2, SPC24 and SPC25 expression with immunocyte infiltration in OC. A. The relative percentage of 22 immunocyte in OC patients via cibersort. B-E. The correlation of NDC80, NUF2, SPC24 and SPC25 expression with tumor purity and six types of immunocytes was evaluated by TIMER.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-3460715/v1/8bf2bce10cf14ac5500e2858.png"},{"id":44952109,"identity":"b2d6458d-3a58-4514-b317-c1f76336a307","added_by":"auto","created_at":"2023-10-19 21:43:51","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1583757,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of NDC80, NUF2, SPC24 and SPC25 expression with other immunocyte infiltration in OC. A-D. The correlation between NDC80, NUF2, SPC24, SPC25 and other immunocytes was studied using ssGSEA.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-3460715/v1/874c56dd818bbe988d41a964.png"},{"id":55697534,"identity":"9f2f6c78-14ae-49f7-940b-54e59490d9f9","added_by":"auto","created_at":"2024-05-02 02:13:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8402039,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3460715/v1/636985b2-6b36-4af3-b963-a6ef754ca32c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"NDC80 kinetochore complex serve as a potential prognostic predictor and correlate with immune infiltrates in Epithelial Ovarian Cancer patients","fulltext":[{"header":"Background","content":"\u003cp\u003eOvarian cancer (OC), which is the most deadly and invasive malignancy in the female reproductive system, has been on the rise in recent years[Sigel RL, 2019]. Due to the insidious nature of OC's early stages, the majority of patients (60%) are diagnosed with advanced disease[Jessmon P, 2017], which is linked with a high fatality rate. The 5-year overall survival (OS) was as high as 45% owing to complicated symptoms and a lack of early diagnosis measures[Webb PM, 2017]. Given the fact that surgical resection performed on advanced OC needs sophisticated surgical techniques and usually goes with severe complications, immunotherapy has been utilized to treat OC. However, there are currently a limited selection of immune checkpoint inhibitors accessible[Yang C, 2020]. Thus, it is critical to understand the specific molecular pathways behind OC carcinogenesis, proliferation, and invasion and to figure out further effective diagnostic and therapeutic strategies for the management of OC[Jiang X, 2019].\u003c/p\u003e \u003cp\u003eResearchers have demonstrated that increased NDC80 kinetochore complex (NDC80 complex) expression is an excellent prognostic indicator in hepatocellular carcinoma[Shen S, 2019], pancreatic cancer[Meng QC, 2015], gastric cancer[Qu Y, 2014] and non-small cell lung cancer[Wei R, 2020]. However, the NDC80 complex's role in OC is uncertain. As a result, we use several databases to investigate the expression, prognosis, and tumor infiltrating lymphocytes of NDC80 complex in OC. NDC80 complex is consist of four major components, known as NDC80, NUF2, SPC24 and SPC25. NDC80 is required for proper chromosome segregation and is involved in the organization and stabilization of microtubule-kinetochore interactions. NUF2, SPC24 and SPC25 play an important role in kinetochore integrity and the organization of stable microtubule binding sites in the outer plate of the kinetochore[Tooley J, 2011].\u003c/p\u003e \u003cp\u003eIn the present study, 4 OC datasets were retrieved from the Gene Expression Omnibus (GEO) database to verify NUF2 as a hub gene in OC. Immunohistochemical (IHC) staining was performed to demonstrate different expression level of NUF2 protein between OC and normal ovarian tissues. Considering NUF2 as an important component of NDC80 complex, 3 other major components were taken into account as well. To understand the biological functions and relative molecular mechanisms underlying carcinogenesis, a variety of bioinformatics methods have been used. To our knowledge, this study first investigated the relationship between the NDC80 complex and gene mutations and the tumour microenvironment (TME) in OC, then established a SPC24-related nomogram to study patient survival and demonstrated that the NDC80 kinetochore complex may accelerate the progression of OC by spindle and kinetochore-associated (SKA1) complex pathway. The workflow of the whole study was listed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRNA-Sequencing Data\u003c/h2\u003e \u003cp\u003eWe downloaded the microarray data of four gene expression profile datasets (GSE14407, GSE38666, GSE40595 and GSE54388) from the GEO database. All datasets were collected from the GPL570[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array. GSE14407 includes data from 12 serous ovarian cancer epithelia (CEPI) samples and 12 healthy ovarian surface epithelia (OSE) samples. GSE38666 includes data from 7 CEPI samples and 8 OSE samples. GSE40595 includes data from 32 high grade serous CEPI samples and 6 OSE samples. GSE54388 includes data from 16 high grade serous CEPI samples and 6 OSE samples. We used data from these 4 datasets as training set. RNA-sequencing gene expression data of 427 OC samples from the Cancer Genome Atlas (TCGA) database[Goldman MJ, 2020] and 88 healthy ovarian surface epithelia samples from Genotype-Tissue Expression (GTEx) were retrieved and unified by Toil process[Vivian J, 2017]. We used the merged dataset as validation set. It is worth mentioning that pre- and postmenopausal ovarian tissues are taking into account in both training and validation sets in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of differentially expressed genes (DEGs)\u003c/h2\u003e \u003cp\u003eGEO2R were applied to perform comparisons on original submitter-supplied processed data tables and the GEOquery and limma R packages[Smyth G, 2005] from the Bioconductor project were used to filtrate the DEGs between the OC patient group and the normal group(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D). Background correction, normalization, removal of batch effect and calculation of expression were performed during the process. The cut-off threshold in GEO was |log fold change| \u0026ge; 2 and adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIntegration of protein\u0026ndash;protein interaction (PPI) network\u003c/h2\u003e \u003cp\u003eFirst, 4 GEO datasets were utilized to build a veen diagram of 104 overlapping DEGs(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) and to determine the DEGs that encode proteins in OC. 104 DEGs were input in Cytoscape to generate PPI network and 64 DEGs with significant correlation were output(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Second, the NetworkAnalyst[Xia J, 2015] was used to construct a co-expression network with a confidence score of 900(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Third, the PPI network was imported in Cytoscape[Shannon P, 2003] and CytoHubba was used to compute the node degree and select a cluster of 36 DEGs for visualization(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGene function analysis\u003c/h2\u003e \u003cp\u003eEnrichment studies of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed using the clusterProfiler package[Ashburner, 2000]. Afterwards, the bubble plot was created using the R GOplot. Three categories were defined in the ontology: biological process (BP), molecular function (MF), and cellular component (CC). P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used as the cut-off value in this study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIHC Staining\u003c/b\u003e \u003c/p\u003e \u003cp\u003e This study had the approval of the ethics committee of Shengjing Hospital. (Reference number: KYCS2023063). Informed consent was waived under intense scrutiny by the ethics committee in this condition. Tissue samples were obtained from the specimen repository of Shengjing Hospital and there were no human participants involved in this study. For IHC staining, five formalin-fixed, paraffin-embedded ovarian cancer tissues and five normal ovarian tissues were prepared. The samples were cut into 4 um thick slices, mounted on glass slides, deparaffinised in xylene and rehydrated in graded series of alcohol. Antigen retrieval was performed at a high temperature through water bath. Endogenous peroxidases were quenched with 3% hydrogen peroxide after the sections cooled and rinsed. The sections were then washed three times with PBS, incubated with calf serum for 10 min to block non-specific antigens, incubated with polyclonal anti-NUF2 antibody (1:200, bs-7714R, Bioss, Beijing, China) at 4\u0026deg;C overnight, washed three times with PBS and incubated with secondary antibody at room temperature(RT) for 30\u0026ndash;40 min. An optical microscope was used to observe dried sections. Two pathologists, blinded to the source of the clinical cases, observed and analysed the IHC staining results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMultiple Methods on Prognosis Analysis and Nomogram Construction\u003c/h2\u003e \u003cp\u003eFirst, the diagnostic and prognostic values of NDC80 kinetochore complex in OC patients were evaluated using receiver operating characteristic (ROC) and Kaplan-Meier survival curves. Second, univariate and multivariate regression analyses were conducted to identify the relationship between NDC80, NUF2, SPC24 and SPC25 expression and the OS rates of OC patients. For Cox regression analysis, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. SPC24-related nomogram is a simplifified model for predicting OC prognosis as a single numerical value. The probabilities of 3-year and 5-year OS rates are represented by the total points projected on the bottom scales. The R package \u0026ldquo;rms\u0026rdquo;[Frank E Harrell Jr, 2021] and \u0026ldquo;survival\u0026rdquo; was used to draw the nomogram.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene set enrichment analysis (GSEA)\u003c/h2\u003e \u003cp\u003eThe ggplot2 package was used to conduct GSEA, which serve as a statistical tool to detect whether selected gene set exhibits statistically significant and concordant differences between OC group and normal people group[Damian D, 2004]. In this study, after batch effect removal of raw data from TCGA, an ordered list of all genes and a single gene, NUF2 in OC were generated by GSEA, which aims to figure out statistically significant differences in survival rate for all genes between OC group and normal people group and between high and low NUF2 expression groups in order to identify signaling pathways regulated by OC-related genes and NUF2. To calculate and sort the enriched pathways in each phenotype, the normalised enrichment score and the nominal P value were used. C2.all.v6.2.symbols.gmt was selected as the reference gene set. The gene sets were permutated 1000 times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis of Immune Cell Infiltration\u003c/h2\u003e \u003cp\u003eSingle sample Gene Set Enrichment Analysis (ssGSEA) and Tumor Immune Estimation Resource (TIMER) were applied to study the correlations between NDC80 kinetochore complex and tumor purity, including 23 immunocytes. The correlation between NDC80 kinetochore complex and immune cell infiltration was examined via Spearman correlation analysis. We used consensus clustering analysis with the R package \u0026ldquo;ConsensusClusterPlus\u0026rdquo;[Wilkerson MD, 2010] to perform an overview of different immune cell infiltration in OC patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was selected as the significance level. We computed the data by R programme(v.3.6.3). The Chi-square and Fisher's tests were applied to analyze the clinical data. Additionally, the Wilcoxon rank sum test was performed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics\u003c/h2\u003e \u003cp\u003eThe clinical information of 379 patients were collected from TCGA, including age, FIGO stage, race, primary therapy outcome, histologic grade, lymphatic invasion and tumor status. Data filtering was used to calculate clinical information. The details are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Characteristics of the OV Patients Based on TCGA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow expression of NDC80\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh expression of NDC80\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIGO stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary therapy outcome, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic grade, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (46.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphatic invasion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (36.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor free\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (37.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, meidan (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (50, 68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (51, 67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNDC80 kinetochore complex Expression in OC Was Elevated\u003c/h2\u003e \u003cp\u003eDifferential expression of NDC80 kinetochore complex was analyzed in OC. Compared to normal tissue, NDC80, NUF2, SPC24 and SPC25 expression levels were significantly higher in OC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-D) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the expression level of NUF2 was found to increase in 32 different tumors except for kidney chromophobe (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). A statistical difference between NDC80 and histologic grade was found by correlation analysis(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There was no remarkable difference in age, race, FIGO stage, primary therapy outcome, lymphatic invasion and tumor status. NUF2 protein expression in OC was further investigated by IHC staining and showed that the protein level was increased in OC compared to normal ovarian tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGSEA and GO/KEGG analysis of OC-related genes\u003c/h2\u003e \u003cp\u003eGSEA identified a number of molecular pathways that were significantly altered in OC patient tissue compared to normal tissue. To filter out differentially enriched pathways between the OC group and the normal group, GSEA of the gene expression profiles was used. These results show that OC is predominantly correlated with reactome cell cycle, M phase, mitotic prometaphase, mitotic metaphase and anaphase, rho GTPase effectors and signaling by rho GTPase(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-F). Taking enrichment analysis via GO/KEGG (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in account, we figured that OC-related genes are mainly enriched in nuclear division, mitotic nuclear division and organelle fission terms for BP. For CC, the DEGs were mainly enriched in spindle, midbody and chromosomal region terms, and for MF, they were mainly enriched in microtubule binding, histone kinase activity and tubulin binding terms(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). For KEGG analysis, cell cycle, oocyte meiosis and p53 signaling pathway terms were enriched(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of GO and KEGG analysis of OV-related genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOntology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneRatio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBgRatio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep.adjust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eqvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0140014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enuclear division\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20/91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e264/18670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32e-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.21e-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.85e-15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0000070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emitotic nuclear division\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16/91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e151/18670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.31e-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.84e-15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0098813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eorganelle fission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19/91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e262/18670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.39e-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.84e-15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003espindle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15/95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e347/19717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.10e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.59e-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0000779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emidbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118/19717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.64e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.52e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.90e-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echromosomal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11/95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e173/19717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.12e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.72e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.81e-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0035173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emicrotubule binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17/17697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.77e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0003777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehistone kinase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84/17697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.61e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0019894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etubulin binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42/17697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCell cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7/35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124/8076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.99e-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.07e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.72e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOocyte meiosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129/8076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.13e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep53 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73/8076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.60e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis of Diagnosis and Prognosis\u003c/h2\u003e \u003cp\u003eArea under curve (AUC) was 0.978 for NUF2, 0.975 for NDC80, 0.996 for SPC24 and 0.986 for SPC25(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). This result indicated that NDC80 complex could differenciate between normal and tumor tissues. The Kaplan-Meier survival curve reveals that high NDC80、NUF2 and SPC25 levels are correlated with poor prognosis(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-D). The survival curve run by GEPIA database shows that high SPC24 level is associated with poor prognosis(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA shows that only high SPC24 expression level has statistical significance in poor prognosis of OC in TCGA database (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) along with age, tumor status, primary therapy outcome, tumor size and Karnofsky score(KFS). In the multivariate Cox model, only age, tumor status, primary therapy outcome and KFS were independent prognostic factors in OC patients(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIdentifification of the Nomogram\u003c/h2\u003e \u003cp\u003eA prognostic nomogram was constructed on the basis of clinicopathological factors in order to have a quantitative method for the prediction of the prognosis of OC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). The nomogram integrated age, histologic grade, tumor status, primary therapy outcome and SPC24 expression, and the results indicated that histologic grade had the greatest influence on the model while patients with higher SPC24 expression had a greater risk of a non-ideal prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis of Immune Cell Infiltration\u003c/h2\u003e \u003cp\u003eOC is closely correlated with molecular genetic and the inflammatory environment. The relative percentage of 22 immunocyte in OC patients via cibersort was demonstrated to overview the immune microenvironment in OC patients(Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). TIMER data implies statistical significance not only between NDC80 and macrophages, neutrophils, dendritic cells but also between SPC25 and CD8\u0026thinsp;+\u0026thinsp;T cell, macrophages, whereas no statistically significant difference exists between NUF2, SPC24 and other immunocytes(Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB-E). Using the ssGSEA package, the correlation between NDC80, NUF2, SPC24, SPC25, and other immunocytes was determined(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-D). There was a significant correlation between NDC80, NUF2, SPC24, SPC25 levels and immune cell infiltration, including NK CD56bright cells and NK cells with negative correlation and Th2 cells with positive correlation(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Eosinophils, iDC and Mast cells had a positive correlation with NDC80, NUF2 and SPC24. Th17 cells had a positive correlation with NDC80, NUF2 and SPC25 while T helper cells had a negative correlation with them.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite extensive studies on OC biomarkers, research on prognostic markers for OC remains limited. High levels of NDC80 kinetochore complex, which is consist of NDC80, NUF2, SPC24 and SPC25 are required for tumor cells to retain malignant properties such as uncontrolled invasion, resistance to chemotherapy and metastasis. Overexpression of NDC80 genes has been identified in a variety of malignant tumors and is often associated with a poor clinical outcome[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe NDC80 complex has a central role in the formation and regulation of microtubule attachments, which is supported by additional factors such as the spindle and kinetochore-associated complex[Wimbish RT, 2020]. A present study demonstrated that inhibition of NDC80 expression enhances the sensitivity of human ovarian cancer cells to paclitaxel[Mo QQ, 2013]. NUF2 can serve as an stabilizer during microtubule-kinetochore interactions, which is related to proper chromosome segregation. Knockdown of NUF2 induced chromosomal misalignment during metaphase and a significant reduction in cancer cell proliferation[Sugimasa H, 2015]. However, few studies have been carried out on the expression of NDC80 genes in OC. As a result, it is crucial to recognize the expression of NDC80 genes in OC and its therapeutic and prognostic significance.\u003c/p\u003e \u003cp\u003eFirst, this study examined the differential expression of the NDC80 kinetochore complex in OC and normal tissues. NDC80, NUF2, SPC24, and SPC25 levels were found to be significantly higher in OC tissues than in normal ovarian epithelial tissues. The mRNA levels of NDC80 complex were markedly distinct according to histologic grade. Next, the co-regulatory proteins of NUF2 were identified using the PPI network. We found that NUF2-related genes were mainly enriched in the Cell cycle, Oocyte meiosis and p53 signaling pathway. Then the protein expression level of NUF2 was verified by IHC staining under precise experiments. We also verified the correlation between NDC80 kinetochore complex and survival rate in OC patients. ROC and Kaplan\u0026ndash;Meier survival analyses were performed to confirm that high expression of NDC80, NUF2, and SPC25 is closely linked to a worse prognosis and NUF2-related nomogram could be used as a reliable prognostic indicator in OC.\u003c/p\u003e \u003cp\u003eFurthermore, studies have shown that NUF2 is closely linked with cancer progression through the meditator LncRNA AF339813. LncRNA AF339813 was positively regulated by NUF2. Researchers further demonstrated that knockdown of AF339813 by siRNA in caner cells significantly reduced cell proliferation and promoted apoptosis[Hu P, 2015]. On the contrary, NDC80 overexpression induced asymmetrical chromosome alignments, abnormal cell division, and thus rendered chromosomal instability[Qu Y, 2014].\u003c/p\u003e \u003cp\u003eIn addition, OC is closely associated with immune microenvironment[Jiang Y, 2020]. TME refers to the niche, either primary or metastatic, in which tumor cells interact with the host stroma, including various immune cells, endothelial cells, fibroblasts and metabolic products. Recently, the critical role of TME in OC initiation, progression and resistance to anti-tumour therapy has been increasingly recognised. However, there are few studies investigating the connection between the NDC80 complex and immunocytes in OC. Using TIMER and ssGSEA, the correlation between NDC80 complex and immunocyte infiltration was evaluated In this study. Our results indicate that NDC80 complex significantly correlated with NK CD56bright cells, NK cells and Th2 cells.\u003c/p\u003e \u003cp\u003eHowever, this research has some drawbacks, including a limited number of normal samples in TCGA database and no validation of the hypothesis using animal models. Clearly, these aspects must be verified through more research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study revealed the prognostic functions of NDC80 kinetochore complex in OC patients and a substantial association between NDC80 kinetochore complex and immune cell infiltration. Therefore, our results indicate that NDC80 kinetochore complex have a significant prognostic value in OC and it could serve as a potential immunotherapeutic target for OC patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatements and Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYu Xiaodan and Shi Chen conceived and designed the study, Yu Xiaodan analyzed the patient data, and was a major contributor in writing the manuscript, Jjiang Lili helped in writing and revising the manuscript. Pan Meizhu helped in IHC staining experiment. Liu Kuiran took participation in guidance whole course. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in TCGA, GEO, \u0026nbsp;GTEx and UCSC Xena repository,\u003c/p\u003e\n\u003cp\u003ehttps://portal.gdc.cancer.gov/projects/TCGA-OV\u003c/p\u003e\n\u003cp\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14407\u003c/p\u003e\n\u003cp\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38666\u003c/p\u003e\n\u003cp\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40595\u003c/p\u003e\n\u003cp\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54388\u003c/p\u003e\n\u003cp\u003ehttps://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Ovarian%20Cancer%20(OC)\u0026amp;removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are not publicly available due but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Shengjing Hospital of China Medical University.\u0026nbsp;(Reference number: KYCS2023063)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is exempted from informed consent by the Ethics Committee of Shengjing Hospital of China Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is exempted from informed consent by the Ethics Committee of Shengjing Hospital of China Medical University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2019. 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Biochim Biophys Acta Rev Cancer. 2020;1873(2):188361. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbcan.2020.188361\u003c/span\u003e\u003cspan address=\"10.1016/j.bbcan.2020.188361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Bioinformatics Analysis, Gene Expression Omnibus, NDC80 kinetochore complex, Epithelial Ovarian Cancer, Immunoinfiltration","lastPublishedDoi":"10.21203/rs.3.rs-3460715/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3460715/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: This study focus on evaluating the prognostic value of NDC80 Kinetochore Complex (NDC80 complex) in OC underlying the Gene Expression Omnibus database and the Cancer Genome Atlas database and reveal the relationship between NDC80 complex and immune infiltrates in OC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We collected data on NDC80 complex expression levels in both OC tissues and normal ovarian tissues from University Of Cingifornia Sisha Cruz Xena and the Gene Expression Omnibus databases. The clinicopathological characteristics correlated with overall survival were analysed using Cox regression and the Kaplan–Meier method. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, Gene set enrichment analysis and cibersort were performed using data from the Cancer Genome Atlas database. Immumohistochemical staining was used to verify higher expression level of NUF2 protein in OC in vitro. Meanwhile, we utilized the Tumor Immune Estimation Resource to analyze the correlation between NDC80 complex and immunocyte infiltration. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The NDC80 complex expression level was prominently higher in OC tissues than in normal ovarian tissues and correlated with advanced histologic grade characteristics. Gene Expression Profiling Interactive Analysis and the Kaplan–Meier survival curve and uncovered a close relationship between high expression of NDC80 complex with poor overall survival in OC patients. The unitivariate Cox regression hazard model proved that age, pathologic stage, tumor status, primary therapy outcome, SPC24 expression level and Karnofsky performance score as prognostic factors for OC patients. NDC80 complex expression levels were highly associated with immune cell infiltration, showing NK CD56bright cells and NK cells with negative correlation and Th2 cells with positive correlation( \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The findings gave the evidence that increased expression level of NDC80 complex was closely associated with the progression of OC and could also serve as a novel target of immunotherapy in OC.\u003c/p\u003e","manuscriptTitle":"NDC80 kinetochore complex serve as a potential prognostic predictor and correlate with immune infiltrates in Epithelial Ovarian Cancer patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-19 21:35:45","doi":"10.21203/rs.3.rs-3460715/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":"e7054d99-a124-461e-a35e-2979ea94aef4","owner":[],"postedDate":"October 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-02T00:47:31+00:00","versionOfRecord":{"articleIdentity":"rs-3460715","link":"https://doi.org/10.2147/IJGM.S450976","journal":{"identity":"international-journal-of-general-medicine","isVorOnly":true,"title":"International Journal of General Medicine"},"publishedOn":"2024-05-01 00:47:31","publishedOnDateReadable":"May 1st, 2024"},"versionCreatedAt":"2023-10-19 21:35:45","video":"","vorDoi":"10.2147/IJGM.S450976","vorDoiUrl":"https://doi.org/10.2147/IJGM.S450976","workflowStages":[]},"version":"v1","identity":"rs-3460715","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3460715","identity":"rs-3460715","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}