Screening and identification of key genes between high-grade and low-grade serous ovarian carcinomas using integrated bioinformatics

Screening and identification of key genes between high-grade and low-grade serous ovarian carcinomas using integrated bioinformatics | 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 Screening and identification of key genes between high-grade and low-grade serous ovarian carcinomas using integrated bioinformatics Liwei Zhang, Zhenglan Pan, Weiguo Song, Wenyan Wang, Liutao Fu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4717976/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Epithelial ovarian cancer (EOC) is one of the most aggressive tumors in women. The most common pathological type of EOC is high-grade serous carcinoma (HGSC), which is often diagnosed at an advanced stage. Low-grade serous carcinoma (LGSC) is estimated to account for 10% of all serous carcinomas. Previous studies have demonstrated that molecular and clinical characteristics differences are apparent between these two subtypes of EOC. The objective of this study was to screen and identify key genes between HGSC and LGSC, and to explore potential molecular mechanisms in the pathogenesis of EOC. The microarray datasets GSE27651 and GSE14001, with a total of 23 LGSC tissue samples and 32 HGSC tissue samples, were obtained from the Gene Expression Omnibus (GEO). The differentially-expressed genes (DEGs) were selected out through the “affy” and “limma” package in R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed through the Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) analysis of DEGs was carried out through the Cytoscape software. Finally, survival analysis of some key geneswas conducted using the Kaplan Meier Plotter Online Tool. A total of 357 DEGs were found in HGSC, of which 181 were up regulated and 176 were down regulated. GO functional enrichment analysis showed that the DEGs were mainly associated with nucleus, cell proliferation and protein binding. KEGG pathway analysis showed that these genes were enriched in the PI3K-Akt signaling pathway, pathways in cancer, the p53 signaling pathway, cell cycle, microRNAs in cancer. Twelve hub genes (TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1, BUB1, RRM2 and PBK) were screened out from PPI network. The mRNA expression of TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1, BUB1, RRM2, and PBK were significantly increased in tumor tissues. The protein expression of TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1 and PBK were distinctly higher in serous ovarian cancer tissues than non-serous ovarian cancer tissues detected by immunohistochemical staining. Survival analysis showed that TOP2A, CCNB1, KIF11, AURKA, and BUB1 were significantly associated with clinical survival outcome. In addition, there is a significant correlation between the expression levels of twelve hub-genes and immune cell infiltration in serous ovarian cancer. In summary, the present study identified DEGs and hub genes by two GEO datasets mining, which might offer new insights into the molecular mechanisms of these two subtypes of EOC and provide some prognostic biomarkers for the treatment of EOC. high-grade serous ovarian cancer genes expression profiles microarray pathways Protein-protein interaction (PPI) survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 INTRODUCTION Epithelial ovarian cancer (EOC) is the most aggressive gynecologic malignancy, with 22440 estimated new cancer cases and 14080 estimated new cancer deaths in 2017 in the United States[ 1 ]. Serous, mucinous, endometrioid and clear cell were the main histologic subtypes of ovarian cancer, of which serous epithelial carcinomas dominate the largest proportion[ 2 ]. The high-grade serous cancer (HGSC) is the single largest group of EOC, which is responsible for almost two-thirds of ovarian cancer deaths[ 3 ]. However, low-grade serous cancer (LGSC) takes only 5–10% proportion of EOC[ 4 ]. Patients with LGSC have a longer overall five-year survival, and tend to be chemo-resistant compared to HGSC[ 5 ]. It is accepted that these two types of EOC are two distinct tumor types with different clinical and genetic characteristics. Although many efforts have been made to explore new therapeutic approaches, most women with HGSC develop recurrence and chemoresistance[ 6 ]. At present, the underlying molecular mechanisms are still poorly understood, which greatly limits the treatment of HGSC. Like many solid malignances, HGSC frequently have a high proportion of chromosomal instability, such as gene copy number amplifications and deletions, which are associated with tumor grade and patient outcomes[ 7 ]. In addition, the alterations of oncogenes and tumor suppressor genes are also involved in the tumorigenesis of HGSC, such as HER-2/neu, c-myc, BRCA1 and BRCA2[ 8 ]. Identification of these key genes could provide advances in diagnosis and in therapeutic strategies. Thus, investigating key genes and understanding the molecular mechanism are urgent and necessary. The pathogenesis of serous ovarian cancer at a molecular level has been explored for years. For example, Helland et al defined a pathway that may drive biological and clinical behavior of a distinct molecular subtype of HGSC[ 9 ]. Stronach et al declared that HDAC4 abrogated sensitivity to cisplatin through modulating STAT1 acetylation, phosphorylation, and nuclear translocation in ovarian cancer[ 10 ]. Zaid et al found that the FGFR4 is overexpression in HGSC and might be an indicator of poor prognosis[ 11 ]. However, most studies ignored to reveal key genes which regulate the occurrence and progress of serous ovarian cancer instead of concentrating on one certain molecular target. Nowadays, the microarray, a high throughput technique for large-scale gene expression analysis, makes it possible to investigate the molecular basis of human cancer on a genomic scale[ 12 , 13 ]. In the past decades, the gene expression profiles of ovarian cancer using microarray technology have been investigated to better understand the ovarian tumorigenesis[ 14 – 16 ]. Given the limited samples and conflicting outcomes of individual microarray, we performed an integrated bioinformatics analysis to evaluate key genes between HGSC and LGSC. Two gene expression profiles microarrays (GSE27651 and GSE14001) were obtained from Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo/ ) to screen out the DEGs between LGSC and HGSC. Then, gene ontology (GO) annotation and protein–protein interaction network (PPI) construction were applied to explore the function of DEGs. Finally, the hub genes were identified by the PPI network using the Cytoscape software. Its role in epithelial ovarian cancer were analyzed by studying its mRNA expression level, protein expression level, impact on survival, and immune cell infiltration. The present study aimed to compare the gene expression profiles between LGSC and HGSC at the molecular level and identify potential biomarkers of HGSC, providing a new approach for the clinical diagnosis and treatment of ovarian cancer patients. Materials and methods Microarray data Two microarrays of GSE14001 and GSE27651 were retrieved from GEO database. These two gene expression datasets were both based on Affymetrix GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array). The microarray data for GSE14001 consisted of 10 HGSC tissue samples and 10 LGSC tissue samples. The microarray data for GSE27651 consisted of 22 HGSC tissue samples and 13 LGSC tissue samples. Data processing and identification of DEGs The raw data files of two datasets were download from GEO included CEL files. The probes annotation was obtained from GPL570 platform. Statistical software R (version 3.4.0, http://www.r-project.org/ ) and packages of Bioconductor ( http://www.bioconductor.org/ ) were applied to analyze the DEGs between HGSC and LGSC samples. The method of Robust Multi-array Average was performed to normalize and to logarithmically convert raw expression profiles data[ 17 ]. We used the limma package (Linear Models for Microarray Analysis ) to select significant DEGs[ 18 ]. We used the empirical Bayes methods in the R package “sva” to adjust batch effects[ 19 ]. An adjusted P < 0.01 and a |logFC| ≥ 3 were defined as the threshold criteria. The clustering analysis of DEGs was performed by the ‘‘RColorBrewer’’, “gplots” packages in R. Functional and pathway enrichment analysis of DEGs The DAVID database ( https://david.ncifcrf.gov/ ) is a web bioinformatics resource that could extract biological features of large gene lists[ 20 ]. The Gene Ontology (GO) project is a web tool for functional interpretation of genes, genes products, and sequences[ 21 ]. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a comprehensive knowledge repository to provide higher order functional information of genes[ 22 ]. We used the DAVID database to perform the GO and KEGG pathways enrichment analysis of DEGs. The terms with P < 0.05 were considered as the significant enrichment. Protein–protein interaction (PPI) network construction The Search Tool for the Retrieval of Interacting Genes (STRING) database is a website tool that give evidence to the protein–protein biological interactions[ 23 ]. To better understand the direct and indirect interactions of key genes, the PPI network was constructed by the STRING database. A confidence score ≥ 0.7(high confidence score)was considered as significant. The PPI network of DEGs was reconstructed by the Cytoscape software. Cytoscape is an open source software that provides powerful function in integrating large data of genetic interactions, protein–DNA and protein–protein[ 24 ]. Finally, the CytoHubba46 plug-in in Cytoscape 3.5.1 was used to identify the hub genes of PPI, and the Molecular Complex Detection (MCODE) plug-in was used to select significant modules with degree cut-off = 10[ 25 ]. The protein expression of hub genes in cancer and normal tissues was also verified in the Human Protein Atlas (HPA) database ( http://www.proteinatlas.org/ )[ 26 ]. Survival analysis of twelve hub-genes Kaplan–Meier plotter ( www.kmplot.com ) is an online tool that could provide patient survival analysis of 54675 genes in four types of cancer, including breast cancer, ovarian cancer, lung cancer and gastric cancer[ 27 ]. By using the median expression value as a cut-off, the patients were divided into two groups. Then we plotted the Kaplan-Meir overall survival (OS) curves in serous ovarian cancer patients. The hazard ratio (HR) with 95% confidence intervals and log rank test P value were also calculated. TIMER Database Analysis The Tumor IMmune Estimation Resource (TIMER) database ( https://cistrome.shinyapps.io/timer/ ) is a reliable and comprehensive resource that allows the evaluation of the abundance of immune cell infiltration across diverse cancer types. In this study, the “Gene module” was used to evaluate the correlation between the expression level of immune-related genes, tumor purity, and the infiltration of immune cells including B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells (DCs) in serous ovarian cancer. Meanwhile, the “Correlation module” was used to investigate the correlation between the expression level of immune-related genes and several gene markers of tumor-infiltrating immune cells. Results Identification of DEGs The raw CEL files were downloaded from the GEO, and then normalized with the “affy” package. After eliminating batch effects in the expression data, we used the limma package to select DEGs. Using P < 0.01 and |logFC| ≥ 3 criteria, 357 DEGs were screened out from two GEO datasets, including 181 up-regulation DEGs and 176 down-regulation DEGs in HGSC compared to LGSC. The top 10 up-regulated and top 10 down-regulated DEGs were listed in Table 1 . In addition, the heat map of DEGs was shown as the Fig. 1. The hierarchical clustering analysis revealed a distinct separation in these two different types of ovarian cancer. Table 1 Top ten up-regulation and top ten down-regulation DEGs (high-grade serous ovarian cancer versus low-grade serous ovarian cancer) Probe set Gene symbol Log fold change P value Up-regulated 1565483_at EGFR 3.29 1.78E-12 214677_x_at IGLC1 3.19 1.91E-07 218542_at CEP55 3.10 1.61E-17 203560_at GGH 2.83 5.39E-13 201292_at TOP2A 2.77 2.86E-12 203764_at DLGAP5 2.77 5.43E-17 219787_s_at ECT2 2.74 9.80E-15 204533_at CXCL10 2.71 1.48E-09 219148_at PBK 2.66 2.25E-13 242546_at LINC01296 2.64 4.91E-11 Down-regulated 214218_s_at XIST -3.41 5.93E-11 229782_at RMST -3.39 3.47E-13 233249_at LOC100507073 -2.85 6.36E-13 204014_at DUSP4 -2.68 3.70E-08 229331_at SPATA18 -2.61 2.69E-07 205765_at CYP3A5 -2.58 3.42E-08 229245_at PLEKHA6 -2.58 4.60E-14 240065_at FAM81B -2.50 7.43E-06 225996_at 227188_at LONRF2 EVA1C -2.50 -2.40 1.14E-08 9.98E-09 Functional determination by GO terms and KEGG pathways To interpret the function of the DEGs, the gene lists were uploaded to the online software DAVID. The GO terms including cell component (CC), biological processes (BP) and molecular function (MF) ontologies were shown in Fig. 2. For the CC ontology, we found that most DEGs significantly enriched in nucleus and cytoplasm items, such as nucleus, nucleoplasm and cytosol. Some genes were associated with organelles in cytoplasm, such as centrosome, spindle and midbody (Fig. 2A). For the BP ontology, the majority GO terms were about cell proliferation items, such as cell division, G1/S transition of mitotic cell cycle. In addition, the other GO terms is significantly related to regulation activities of organism, including positive regulation of cell proliferation, positive regulation of GTPase activity (Fig. 2B). In the MF ontology, the binding function consisted a large proportion of GO categories, which involved protein binding, protein kinase binding, ATP binding and microtubule binding (Fig. 2C). As shown in the Table 2 , the KEGG pathway enrichment analysis found five significantly pathways, including the PI3K-Akt signaling pathway, pathways in cancer, p53 signaling pathway, cell cycle, microRNAs in cancer. Table 2 KEGG pathway enrichment of DEGs Term Count P value hsa04110: Cell cycle 12 1.97E-06 hsa04115: p53 signaling pathway 9 5.85E-06 hsa05200: Pathways in cancer 14 0.0053 hsa04151: PI3K-Akt signaling pathway 12 0.0133 hsa05206: MicroRNAs in cancer 10 0.0257 Count refers to the number of genes significantly enriched in this term PPI network and hub genes The protein-protein interaction network of DEGs was constructed through the STRING database. The 357 DEGs were uploaded to the STRING website to get PPI data. Then, the PPI data were analyzed by the Cytoscape software. The PPI which consisted of 167 nodes and 1794 edges shown in Fig. 3A . After the PPI network construction, the CytoHubba plug-in in Cytoscape was used to indentify hub genes. In PPI networks, 12 node protein, including TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1, BUB1, RRM2, PBK had a strong association with other nodes (Degree ≥ 60). From the PPI network, a significant module including 51 nodes and 1206 edges was selected by the MCODE (Fig. 3B). The GO and KEGG enrichment analysis show these genes in the module were enriched in the mitotic cytokinesis, ATP binding and p53 signaling pathway (Table 3 ). Table 3 GO and KEGG pathway enrichment analysis of the genes in module Term Description Count P value GO:0000281 mitotic cytokinesis 5 7.37E-07 GO:0032467 positive regulation of cytokinesis 4 5.82E-05 GO:0007018 microtubule-based movement 5 6.19E-05 GO:0035556 intracellular signal transduction 4 0.044449 GO:0030496 midbody 6 5.04E-06 GO:0005654 nucleoplasm 14 9.49E-05 GO:0005737 cytoplasm 19 4.15E-04 GO:0005634 nucleus 16 0.004541 GO:0016020 membrane 9 0.004243 GO:0005524 ATP binding 19 8.45E-11 GO:0016887 ATPase activity 5 1.27E-04 GO:0003682 chromatin binding 5 0.008074 KEGG:hsa04110 Cell cycle 10 6.71E-12 KEGG:hsa04115 p53 signaling pathway 5 2.13E-05 Count refers to the number of genes significantly enriched in this term Genetic Alterations of Twelve Hub-genes in Serious Ovarian Cancer Patients The cBioPortal tool was used for the analysis of genetic alterations of twelve hub-genes from the TCGA PanCancer Atlas dataset. As a result, 5% TOP2A, 9% CDK1, 11% CCNB1, 6% MAD2L1, 5% KIF11, 5% CCNb2, 6% TTK, 14% AURKA, 5%ACGAP1, 4%BUB1, 10%RRM2 and 10% PBK were altered in ten types of genetic alterations, including infame mutation (unknown significance), missense mutation (unknown significance), splice mutation (unknown significance), truncating mutation (unknown significance), amplification, deep deletion, mRNA high, mRNA low, protein high and protein low in the queried TCGA serious ovarian cancer samples (Fig. 4A). The alteration frequency derived from mutations, copy-number alterations, mRNA expression data and protein expression data were shown in serous ovarian cancer (Fig. 4B). To explore whether these hub genes amplification had an influence on its mRNA and protein level, the results indicated that TTK and AURKA were amplified along with the significantly high mRNA and protein level from TCGA-OV cohort (Fig. 4C). The mutation types, number, and sites of TOP2A, CDK1, TTK and BUB1 genetic alterations were displayed in Fig. 4D. The Expression Level of the Twelve Hub-genes in Ovarian Cancer Tissues with P53 Mutation GO functional enrichment analysis showed that the DEGs was associated with the p53 signaling pathway. We conducted research and analysis on 199 ovarian cancer samples with P53 mutations and 19 ovarian cancer samples without P53 mutations. The results showed that the mRNA expression levels of AURKA (Fig. 5A), BUB1 (Fig. 5B), CCNB1 (Fig. 5C), CDK1 (Fig. 5D), MAD2L1 (Fig. 5E), PBK (Fig. 5F), TOP2A (Fig. 5G), and TTK (Fig. 5H) in P53 mutated ovarian cancer samples were higher than those in P53 non mutated ovarian cancer samples. The Expression Level of the Twelve Hub-genes in Tumor Tissues To better understand the differential expression, the CPTAC dataset was used to assess the twelve hub-genes mRNA expression level in large-scale mRNA data from the National Cancer Institute. As shown in Fig. 6A , the mRNA expression of TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1, BUB1, RRM2, and PBK were significantly increased in tumor tissues. The GEPIA2 tool was also used to analyze the relationship between the twelve hub-genes expression and tumor pathological stage. Figure 6B showed stage-specific change of MAD2L1, RACGAP1, RRM2 and TTK in tumor tissues. On the other hand, the remaining hub genes were no clear association between the gene expression and patients’ stage. The Protein Expression of Twelve Hub-genes in Serous Ovarian Cancer tissues vs. Non-serous Ovarian Cancer tissues To assess the protein expression of twelve hub-genes in serous ovarian cancer tissues vs. non-serous ovarian cancer tissues, we performed immunohistochemical analysis. The expression levels of ten proteins, including TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1 and PBK were distinctly higher in serous ovarian cancer tissues than non-serous ovarian cancer tissues (Fig. 7). The Prognostic Value of Twelve Hub-genes in Patients with Serous Ovarian Cancer The prognostic value of twelve hub-genes were assessed using an online tool of KM plotter ( http://www.kmplot.com ). The survival curves were calculated according to the gene expression levels. Among those hub-genes, our results showed that high expression of TOP2A (HR 1.32 95%CIs [1.13–1.55], P = 0.00063), CCNB1 (HR 1.24 95%CIs [1.05–1.47], P = 0.01), KIF11 (HR 1.23 95%CIs [1.04–1.45], P = 0.017), AURKA (HR 1.47 95%CIs [1.23–1.75], P = 2.2e-05), and BUB1 (HR 1.18 95%CIs [1.02–1.38], P = 0.03) were associated with worse overall survival (Fig. 8A-E). We also found increased expression of MAD2L1 (HR 0.79 95%CIs [0.62–1.01], P = 0.055) was not significantly associated with worse overall survival (Fig. 8F). Correlation of Twelve Hub-Genes with Tumor Purity and Immune Cell Infiltration in Patients with Serous Ovarian Cancer Since the functional annotation analysis revealed that the twelve hub-genes participated in the process of the immune response, next, the correlation between the expression of twelve hub-genes and immune cell infiltration in the TIMER database was further analyzed. Interestingly, high expression levels of twelve hub-genes were found to be associated with high immune cell infiltration in serous ovarian cancer. A positive correlation between TOP2A expression and the infiltration of macrophage (Cor = 0.12, p = 8.67e − 03) and purity (Cor = 0.123, p = 6.83e − 03) were observed, while the TOP2A expression was negatively associated with the infiltration of CD8 + T cells (Cor = -0.121, p = 7.90e − 03). There is no significant correlation between the expression level of TOP2A and the infiltration level of B cells, CD4 + T cells, neutrophil and dendritic cells (Fig. 9A ) . The change of KIF11 expression level is the same as that of TOP2A (Fig. 9E ) . As shown in Fig. 9B , the expression level of CDK1 is positively correlated with tumor purity (Cor = 0.152, p = 1.63e − 02), but not significantly correlated with the infiltration level of B cells, CD4 + T cells, CD8 + T cells, macrophage, neutrophil and dendritic cells. Figure 9C showed that the expression level of CCNB1 is positively correlated with the infiltration level of macrophage (Cor = 0.119, p = 8.80e − 03) and neutrophil (Cor = 0.125, p = 6.01e − 03), but not significantly correlated with the infiltration level of B cells, CD4 + T cells, CD8 + T cells, dendritic cells and tumor purity. The expression level of MAD2L1 is positively correlated with the infiltration level of macrophage (Cor = 0.18, p = 7.00e − 05), neutrophil (Cor = 0.238, p = 1.38e − 07) and dendritic cells (Cor = 0.17, p = 1.75e − 04), but not significantly correlated with the infiltration level of B cells, CD4 + T cells, CD8 + T cells and tumor purity (Fig. 9D ) .The change of CCNB2 and TTK expression level is the same as that of MAD2L1 (Fig. 9F, G) . Figure 9H showed that the expression level of AURKA is positively correlated with the infiltration level of CD4 + T cells (Cor = 0.118, p = 9.90e − 03), macrophage (Cor = 0.225, p = 6.06e − 07) and neutrophil (Cor = 0.12, p = 8.49e − 03), but not significantly correlated with the infiltration level of B cells, CD8 + T cells, dendritic cells and tumor purity. The expression level of RACGAP1 and RRM2 are positively correlated with the infiltration level of CD4 + T cells, macrophage, neutrophil and dendritic cells, but not significantly correlated with the infiltration level of B cells and CD8 + T cells. In addition, the expression level of RACGAP is also positively correlated with tumor purity, while the expression level of RRM2 is not significantly correlated with tumor purity (Fig. 9I, K) . Figure 9J showed that the expression level of BUB1 is positively correlated with the infiltration level of macrophage (Cor = 0.128, p = 4.96e − 03) and tumor purity (Cor = 0.09 p = 4.78e − 02), while the BUB1 expression level was negatively associated with the infiltration of CD8 + T cells (C or = -0.121, p = 7.90e − 03). but not significantly correlated with the infiltration level of B cells, CD4 + T cells, neutrophil and dendritic cells. The expression level of PBK is positively correlated with the infiltration level of macrophage (Cor = 0.131, p = 4.14e − 03), but not significantly correlated with the infiltration level of B cells, CD4 + T cells, CD8 + T cells, macrophage, dendritic cells and tumor purity (Fig. 9L ) . Discussion In recent years, there has been a growing clinical and genetic evidence to support a two-pathway model of ovarian cancer[ 28 , 29 ]. From the evidence, the LGSC derived from a stepwise fashion from benign serous cystadenoma to LGSC. However, HGSC seemed to be in the absence of recognizable precursor lesion. From the molecular genetics perspective, an increasing number of studies have found that LGSC have a high frequency mutation of KRAS, BRAF, PTEN, CTNNB1, whereas the HGSC is associated strongly with p53 mutations[ 30 , 31 ]. In view of clinical features, women with HGSC usually present at advanced stages, have a sensitive responsive to chemotherapy, have a high incidence of recurrence and decreased survival[ 32 , 33 ]. However, many younger women with LGSC tend to be resistant to chemotherapy, but have an improved survival[ 34 ]. Thus, it is worth emphasizing that HGSC and LGSC are two distinctly different tumor types rather than different grades of the same neoplasm. Based on clinical and molecular features of ovarian cancer, it is generally accepted that ovarian carcinomas could be divided into Type I tumor which are slowly developing in a stepwise fashion; and Type II tumor which are rapidly growing, high-grade tumors[ 31 , 35 , 36 ]. Thus, it is necessary to compare the different molecular mechanisms about these two different tumor types and find potential candidate markers in a prognostic, predictive, or therapeutic sense. In this study, gene expression data of 23 LGSC tissue samples and 32 HGSC tissue samples were retrieved from two GEO datasets. We identified 357 DEGs between LGSC tissue samples and HGSC tissue samples and GO and pathway enrichment analysis were further performed. First, in CC ontology, most of the DEGs were significantly enriched in nuclear-related and cell mitosis items including nuclear, centrosome, spindle and microtubule cytoskeleton[ 37 ]. These cell biological processes were mainly associated with genomic stability and cancer etiology[ 38 ]. Many studies suggested that the spindle assembly are playing a role in the multi-step process of acquired paclitaxel resistance in ovarian cancer[ 39 , 40 ]. Then, for the GO category of BP, our results showed that the most significant items were the cell division items including mitotic nuclear division, G1/S transition of mitotic cell cycle. Admittedly, the majority of patients with HGSC will develop chemoresistance through multiple complex mechanisms. Some study found defects in mitotic checkpoints were associated with paclitaxel resistance in ovarian cancer cell lines[ 40 , 41 ]. In MF ontology, the binding-related items were the most significant items such as protein binding, protein kinase binding and ubiquitin protein ligase binding. These data suggested that these DEGs might participate in the binding functions among nucleus, protein, histone and extracellular matrix, which influence tumor microenvironment and signal transduction in cancer cells[ 42 ]. Obviously, protein binding and kinase binding are involved in the multiple signaling pathways in cancer. A large number of kinase and phosphatases were found in ovarian cancer tissue, which were considered to have taken part in biological process of ovarian cancer[ 43 ]. In the present study, pathway analysis found five significantly enriched pathways including p53 signaling pathway, PI3K-Akt signaling pathway[ 44 ]. Hayano et al reported a novel intact p53 pathway subtype of HGSC, which provided a new insight into the pathogenesis of HGSC with an intact p53 pathway[ 45 ]. In addition, the deregulation of the PI3K/Akt pathway is an important genomic change in ovarian cancer[ 46 ]. Tanaka et al reported that high levels of pAkt were correlated with decreased progression free survival and overall survival in ovarian cancer, thus Akt might be a potential molecular target[ 47 ]. This study also constructed the protein-protein interaction network of DEGs and screened out top 12 hub genes. Among these hub genes, TOP2A was the highest degree of connectivity. The TOP2A gene encode the enzyme topoisomerase IIa (topo IIa), which is responsible for resolving topological problems during the DNA metabolism[ 48 ]. TOP2A is located close to HER2 on chromosome 17q21 and the clinical value of TOP2A in breast cancer have been studied for years[ 49 , 50 ]. It reported that TOP2A gene copy number or protein overexpression could predict the treatment response of pegylated liposomal doxorubicin in platinum resistant in EOC[ 51 ]. So far, the carcinogenic effect of TOP2A for ovarian cancer have not explored fully. The second hub gene CDK1 is a mitotic cyclin-dependent kinases (CDKs), which is a key factor for G2/M phase transition[ 52 ]. CDK1 might have the potential to regulate resistance and suppression of CDK1 could reverse paclitaxel resistance in ovarian cancer cells[ 53 ]. Taken together, these results showed that these hub genes might be associated with the clinicopathological features of these two types of ovarian cancer. In our research, three hub genes were found to have significantly increased mRNA expression levels in ovarian cancer tissues with P53 mutation and tumor tissues, significantly increased protein expression levels in serous ovarian cancer tissues, and its high expression levels were significantly correlated with worse survival outcome, including TOP2A, CCNB1 and AURKA. Cyclins refer to proteins that differ in their levels to activate specific cyclin-dependent kinases (CDKs) required for progression in the cell cycle[ 54 ]. Cyclin B1 (CCNB1) has a pivotal role in regulating and forming a complex with CDK1 to promote the transition from the G2 phase of cell cycle to mitosis[ 55 ]. Increasing evidence demonstrates that the over-expression of CCNB1 is observed in certain number of human cancers including colorectal cancer[ 55 ], breast cancer[ 56 ], and ovarian cancer[ 57 ]. Yiping Zou et al reported that the mRNA expression level of CCNB1 is upregulated in various tumor tissues, including hepatocellular carcinoma (HCC). The high expression of CCNB1 gene is associated with poor prognosis in HCC patients[ 58 ]. Hui Zhang et al demonstrated that CCNB1 silencing activates the p53 signaling pathway and consequently inhibits cell proliferation and promotes cell senescence in pancreatic cancer[ 59 ]. As showed in our study, we found that the mRNA expression level and protein expression level of CCNB1 were significantly increased in tumor tissues, and the high expression of CCNB1 was positively correlated with adverse survival outcomes in ovarian cancer patients. Aurora kinases belong to serine/threonine kinases which share a highly conserved catalytic domain containing auto-phosphorylating sites[ 60 ]. This family contains three members: Aurora A (AURKA), Aurora B (AURKB), and Aurora C (AURKC). Both AURKA and AURKB play essential roles in regulating cell division during mitosis while AURKC has a unique physiological role in spermatogenesis[ 61 ]. Relatively less information is available for the roles of AURKC in cancer. AURKA and AURKB have been found to function as oncogenes to promote tumorigenesis in multiple types of cancer including solid tumors and hematological malignancies[ 62 ]. Apart from playing a role in mitosis, an increasing number of studies have suggested that AURKA, when abnormally expressed, could be an oncogene involved in tumorigenesis[ 63 ]. Gene amplification, transcriptional activation and inhibition of protein degradation could contribute to the elevated levels of AURKA expression in cancer tissues. AURKA promotes tumorigenesis by participating in the cancer cell proliferation, epithelial-mesenchymal transition (EMT), metastasis, apoptosis, and self-renewal of cancer stem cells[ 64 ]. Our research results were consistent with these findings. The mRNA and protein expression levels of AURKA were significantly increased in tumor tissue and ovarian cancer tissue, and the high expression of AURKA could predict adverse prognostic outcomes for ovarian cancer patients. In conclusion, this study aimed to identify DEGs between two different pathology types of serous ovarian cancer and find the potential predictor. In the present study, a total of 357 DEGs were selected and TOP2A, CCNB1 and AURKA might be prognostic biomarkers of HGSC. However, the absence of experimental verification is the major drawback of our study. Thus, further experiments are required to confirm our results obtained from bioinformatics analysis. Declarations Authors’ contributions Conceived and conducted the study: LWZ and JJF; analyzed the data: ZLP, WGS,WYW,LTF,JL,RHH,QYW,YW,SHZ,WJS,BW ; wrote the manuscript: LWZ and ZLP, all authors read and approved the final version. Funding This work was supported by The Second Hospital of Anhui Medical University, National Natural Science Foundation of China(grant numbers 2022GQFY09). Availability of data and materials The dataset used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Patient data were obtained from Gene Expression Omnibus (GEO) and Database for Annotation, Visualization and Integrated Discovery (DAVID),two publicly open database resource ,Informed patient consent was not required Competing interests The authors declare that there is no confict of interest regarding the publication of this paper. Conflicts of interest: The authors declare no competing financial interests. References R.L. Siegel, K.D. Miller, and A. Jemal, Cancer Statistics, 2017. CA Cancer J Clin, 2017. 67(1): p. 7-30. T. Bonome, J.Y. Lee, D.C. Park, M. Radonovich, C. Pise-Masison, J. Brady, et al., Expression profiling of serous low malignant potential, low-grade, and high-grade tumors of the ovary. Cancer Res, 2005. 65(22): p. 10602-12. D.D.L. Bowtell, The genesis and evolution of high-grade serous ovarian cancer. Nat Rev Cancer, 2010. 10(11): p. 803-8. R.J. Kurman, and M. Shih Ie, The Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded. Am J Pathol, 2016. 186(4): p. 733-47. R.N. Grisham, G. Iyer, K. Garg, D. Delair, D.M. Hyman, Q. Zhou, et al., BRAF mutation is associated with early stage disease and improved outcome in patients with low-grade serous ovarian cancer. Cancer, 2013. 119(3): p. 548-554. G.C. Jayson, E.C. Kohn, H.C. Kitchener, and J.A. Ledermann, Ovarian cancer. Lancet, 2014. 384(9951): p. 1376-88. B.T. Hennessy, R.L. Coleman, and M. Markman, Ovarian cancer. Lancet, 2009. 374(9698): p. 1371-82. B. Aunoble, R. Sanches, E. Didier, and Y.J. Bignon, Major oncogenes and tumor suppressor genes involved in epithelial ovarian cancer (review). Int J Oncol, 2000. 16(3): p. 567-76. A. Helland, M.S. Anglesio, J. George, P.A. Cowin, C.N. Johnstone, C.M. House, et al., Deregulation of MYCN, LIN28B and LET7 in a molecular subtype of aggressive high-grade serous ovarian cancers. PLoS One, 2011. 6(4): p. e18064. E.A. Stronach, A. Alfraidi, N. Rama, C. Datler, J.B. Studd, R. Agarwal, et al., HDAC4-regulated STAT1 activation mediates platinum resistance in ovarian cancer. Cancer Res, 2011. 71(13): p. 4412-22. T.M. Zaid, T.L. Yeung, M.S. Thompson, C.S. Leung, T. Harding, N.N. Co, et al., Identification of FGFR4 as a potential therapeutic target for advanced-stage, high-grade serous ovarian cancer. Clin Cancer Res, 2013. 19(4): p. 809-20. A. Sturn, J. Quackenbush, and Z. Trajanoski, Genesis: cluster analysis of microarray data. Bioinformatics, 2002. 18(1): p. 207-8. O.P. Kallioniemi, U. Wagner, J. Kononen, and G. Sauter, Tissue microarray technology for high-throughput molecular profiling of cancer. Hum Mol Genet, 2001. 10(7): p. 657-62. A.D. Santin, F.H. Zhan, S. Bellone, M. Palmieri, S. Cane, E. Bignotti, et al., Gene expression profiles in primary ovarian serous papillary tumors and normal ovarian epithelium: identification of candidate molecular markers for ovarian cancer diagnosis and therapy. Int J Cancer, 2004. 112(1): p. 14-25. C.D. Hough, C.A. Sherman-Baust, E.S. Pizer, F.J. Montz, D.D. Im, N.B. Rosenshein, et al., Large-scale serial analysis of gene expression reveals genes differentially expressed in ovarian cancer. Cancer Res, 2000. 60(22): p. 6281-7. J.B. Welsh, P.P. Zarrinkar, L.M. Sapinoso, S.G. Kern, C.A. Behling, B.J. Monk, et al., Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. Proc Natl Acad Sci U S A, 2001. 98(3): p. 1176-81. S. Hochreiter, D.A. Clevert, and K. Obermayer, A new summarization method for Affymetrix probe level data. Bioinformatics, 2006. 22(8): p. 943-9. G.K. Smyth, Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol, 2004. 3: p. Article3. W.E. Johnson, C. Li, and A. Rabinovic, Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 2007. 8(1): p. 118-27. W. Huang da, B.T. Sherman, and R.A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 2009. 4(1): p. 44-57. M.A. Harris, J. Clark, A. Ireland, J. Lomax, M. Ashburner, R. Foulger, et al., The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res, 2004. 32(Database issue): p. D258-61. M. Kanehisa, and S. Goto, KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 2000. 28(1): p. 27-30. A. Franceschini, D. Szklarczyk, S. Frankild, M. Kuhn, M. Simonovic, A. Roth, et al., STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res, 2013. 41(Database issue): p. D808-15. P. Shannon, A. Markiel, O. Ozier, N.S. Baliga, J.T. Wang, D. Ramage, et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 2003. 13(11): p. 2498-504. G.D. Bader, and C.W. Hogue, An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 2003. 4: p. 2. M. Uhlén, L. Fagerberg, B.M. Hallström, C. Lindskog, P. Oksvold, A. Mardinoglu, et al., Proteomics. Tissue-based map of the human proteome. Science, 2015. 347(6220): p. 1260419. B. Gyorffy, A. Lánczky, and Z. Szállási, Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients. Endocr Relat Cancer, 2012. 19(2): p. 197-208. C.J. O'Neill, M.T. Deavers, A. Malpica, H. Foster, and W.G. McCluggage, An immunohistochemical comparison between low-grade and high-grade ovarian serous carcinomas: significantly higher expression of p53, MIB1, BCL2, HER-2/neu, and C-KIT in high-grade neoplasms. Am J Surg Pathol, 2005. 29(8): p. 1034-41. C.N. Landen, M.J. Birrer, and A.K. Sood, Early events in the pathogenesis of epithelial ovarian cancer. J Clin Oncol, 2008. 26(6): p. 995-1005. K. Levanon, C. Crum, and R. Drapkin, New insights into the pathogenesis of serous ovarian cancer and its clinical impact. J Clin Oncol, 2008. 26(32): p. 5284-93. R. Dehari, R.J. Kurman, S. Logani, and I.M. Shih, The development of high-grade serous carcinoma from atypical proliferative (borderline) serous tumors and low-grade micropapillary serous carcinoma: a morphologic and molecular genetic analysis. Am J Surg Pathol, 2007. 31(7): p. 1007-12. S.C. Plaxe, Epidemiology of low-grade serous ovarian cancer. Am J Obstet Gynecol, 2008. 198(4): p. 459.e1-8; discussion 459.e8-9. A. Gockley, A. Melamed, A.J. Bregar, J.T. Clemmer, M. Birrer, J.O. Schorge, et al., Outcomes of Women With High-Grade and Low-Grade Advanced-Stage Serous Epithelial Ovarian Cancer. Obstet Gynecol, 2017. 129(3): p. 439-447. K.M. Schmeler, and D.M. Gershenson, Low-grade serous ovarian cancer: a unique disease. Curr Oncol Rep, 2008. 10(6): p. 519-23. K.R. Cho, and I.M. Shih, Ovarian cancer. Annu Rev Pathol, 2009. 4: p. 287-313. R.J. Kurman, Origin and molecular pathogenesis of ovarian high-grade serous carcinoma. Ann Oncol, 2013. 24 Suppl 10: p. x16-21. J. Wu, and A. Akhmanova, Microtubule-Organizing Centers. Annu Rev Cell Dev Biol, 2017. 33: p. 51-75. M. Chen, R. Linstra, and M. van Vugt, Genomic instability, inflammatory signaling and response to cancer immunotherapy. Biochim Biophys Acta Rev Cancer, 2022. 1877(1): p. 188661. A.A. Ahmed, A.D. Mills, A.E.K. Ibrahim, J. Temple, C. Blenkiron, M. Vias, et al., The extracellular matrix protein TGFBI induces microtubule stabilization and sensitizes ovarian cancers to paclitaxel. Cancer Cell, 2007. 12(6): p. 514-27. B. McGrogan, S. Phelan, P. Fitzpatrick, A. Maguire, M. Prencipe, D. Brennan, et al., Spindle assembly checkpoint protein expression correlates with cellular proliferation and shorter time to recurrence in ovarian cancer. Hum Pathol, 2014. 45(7): p. 1509-19. X. Hao, Z.G. Zhou, S.M. Ye, T. Zhou, Y.P. Lu, D. Ma, et al., Effect of Mad2 on paclitaxel-induced cell death in ovarian cancer cells. J Huazhong Univ Sci Technolog Med Sci, 2010. 30(5): p. 620-5. S. AlMusawi, M. Ahmed, and A.S. Nateri, Understanding cell-cell communication and signaling in the colorectal cancer microenvironment. Clin Transl Med, 2021. 11(2): p. e308. J.D. Wulfkuhle, J.A. Aquino, V.S. Calvert, D.A. Fishman, G. Coukos, L.A. Liotta, et al., Signal pathway profiling of ovarian cancer from human tissue specimens using reverse-phase protein microarrays. Proteomics, 2003. 3(11): p. 2085-90. M. Kessler, C. Fotopoulou, and T. Meyer, The molecular fingerprint of high grade serous ovarian cancer reflects its fallopian tube origin. Int J Mol Sci, 2013. 14(4): p. 6571-96. T. Hayano, Y. Yokota, K. Hosomichi, H. Nakaoka, K. Yoshihara, S. Adachi, et al., Molecular characterization of an intact p53 pathway subtype in high-grade serous ovarian cancer. PLoS One, 2014. 9(12): p. e114491. A. Astanehe, D. Arenillas, W.W. Wasserman, P.C. Leung, S.E. Dunn, B.R. Davies, et al., Mechanisms underlying p53 regulation of PIK3CA transcription in ovarian surface epithelium and in ovarian cancer. J Cell Sci, 2008. 121(Pt 5): p. 664-74. Y. Tanaka, Y. Terai, A. Tanabe, H. Sasaki, T. Sekijima, S. Fujiwara, et al., Prognostic effect of epidermal growth factor receptor gene mutations and the aberrant phosphorylation of Akt and ERK in ovarian cancer. Cancer Biol Ther, 2011. 11(1): p. 50-7. E. Arriola, C. Marchio, D.S. Tan, S.C. Drury, M.B. Lambros, R. Natrajan, et al., Genomic analysis of the HER2/TOP2A amplicon in breast cancer and breast cancer cell lines. Lab Invest, 2008. 88(5): p. 491-503. K.V. Nielsen, S. Müller, S. Møller, A. Schønau, E. Balslev, A.S. Knoop, et al., Aberrations of ERBB2 and TOP2A genes in breast cancer. Mol Oncol, 2010. 4(2): p. 161-8. K. Vang Nielsen, B. Ejlertsen, S. Møller, J. Trøst Jørgensen, A. Knoop, H. Knudsen, et al., The value of TOP2A gene copy number variation as a biomarker in breast cancer: Update of DBCG trial 89D. Acta Oncol, 2008. 47(4): p. 725-34. J. Erriquez, P. Becco, M. Olivero, R. Ponzone, F. Maggiorotto, A. Ferrero, et al., TOP2A gene copy gain predicts response of epithelial ovarian cancers to pegylated liposomal doxorubicin: TOP2A as marker of response to PLD in ovarian cancer. Gynecol Oncol, 2015. 138(3): p. 627-33. Q.H. Xi, M.H. Huang, Y.Y. Wang, J.X. Zhong, R. Liu, G.Q. Xu, et al., The expression of CDK1 is associated with proliferation and can be a prognostic factor in epithelial ovarian cancer. Tumour Biol, 2015. 36(7): p. 4939-48. T. Bae, K.Y. Weon, J.W. Lee, K.H. Eum, S. Kim, and J.W. Choi, Restoration of paclitaxel resistance by CDK1 intervention in drug-resistant ovarian cancer. Carcinogenesis, 2015. 36(12): p. 1561-71. J.U. Shin, C.H. Lee, K.T. Lee, J.K. Lee, K.H. Lee, K.M. Kim, et al., Prognostic significance of ATM and cyclin B1 in pancreatic neuroendocrine tumor. Tumour Biol, 2012. 33(5): p. 1645-51. Y. Fang, H. Yu, X. Liang, J. Xu, and X. Cai, Chk1-induced CCNB1 overexpression promotes cell proliferation and tumor growth in human colorectal cancer. Cancer Biol Ther, 2014. 15(9): p. 1268-79. S.P. Wang, S.Q. Wu, S.H. Huang, Y.X. Tang, L.Q. Meng, F. Liu, et al., FDI-6 inhibits the expression and function of FOXM1 to sensitize BRCA-proficient triple-negative breast cancer cells to Olaparib by regulating cell cycle progression and DNA damage repair. Cell Death Dis, 2021. 12(12): p. 1138. X. Yang, S. Zhou, C. Yang, C. Cao, M. He, and S. Zi, CCNB1, Negatively Regulated by miR-559, Promotes the Proliferation, Migration, and Invasion of Ovarian Carcinoma Cells. Mol Biotechnol, 2022. 64(9): p. 958-969. Y. Zou, S. Ruan, L. Jin, Z. Chen, H. Han, Y. Zhang, et al., CDK1, CCNB1, and CCNB2 are Prognostic Biomarkers and Correlated with Immune Infiltration in Hepatocellular Carcinoma. Med Sci Monit, 2020. 26: p. e925289. H. Zhang, X. Zhang, X. Li, W.B. Meng, Z.T. Bai, S.Z. Rui, et al., Effect of CCNB1 silencing on cell cycle, senescence, and apoptosis through the p53 signaling pathway in pancreatic cancer. J Cell Physiol, 2018. 234(1): p. 619-631. R. Du, C. Huang, K. Liu, X. Li and Z. Dong, Targeting AURKA in Cancer: molecular mechanisms and opportunities for Cancer therapy. Mol Cancer, 2021. 20(1): p. 15. S.K. Heo, E.K. Noh, Y.K. Jeong, L.J. Ju, J.Y. Sung, H.M. Yu, et al., Radotinib inhibits mitosis entry in acute myeloid leukemia cells via suppression of Aurora kinase A expression. Tumour Biol, 2019. 41(5): p. 1010428319848612. E.O. Dos Santos, T.C. Carneiro-Lobo, M.N. Aoki, E. Levantini, and D.S. Bassères, Aurora kinase targeting in lung cancer reduces KRAS-induced transformation. Mol Cancer, 2016. 15: p. 12. K. Sasai, W. Treekitkarnmongkol, K. Kai, H. Katayama, and S. Sen, Functional Significance of Aurora Kinases-p53 Protein Family Interactions in Cancer. Front Oncol, 2016. 6: p. 247. L. Yang, Q. Zhou, X. Chen, L. Su, B. Liu, and H. Zhang, Activation of the FAK/PI3K pathway is crucial for AURKA-induced epithelial-mesenchymal transition in laryngeal cancer. Oncol Rep, 2016. 36(2): p. 819-26. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4717976","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325480602,"identity":"0b2d79d1-52d3-456a-bc9d-4307f6995288","order_by":0,"name":"Liwei Zhang","email":"","orcid":"","institution":"The Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liwei","middleName":"","lastName":"Zhang","suffix":""},{"id":325480603,"identity":"3c205cb8-f84d-4ecc-9540-c12dc4bb3ec2","order_by":1,"name":"Zhenglan Pan","email":"","orcid":"","institution":"Hefei Maternal and Child Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhenglan","middleName":"","lastName":"Pan","suffix":""},{"id":325480604,"identity":"ae5b60be-23c1-4c59-8ec7-8570b103fc5e","order_by":2,"name":"Weiguo Song","email":"","orcid":"","institution":"The Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weiguo","middleName":"","lastName":"Song","suffix":""},{"id":325480605,"identity":"2d624cfa-dfba-4488-a72d-2864eba92a50","order_by":3,"name":"Wenyan Wang","email":"","orcid":"","institution":"The Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenyan","middleName":"","lastName":"Wang","suffix":""},{"id":325480606,"identity":"42e0c485-6656-408e-bb4f-c8e1c2754dcb","order_by":4,"name":"Liutao Fu","email":"","orcid":"","institution":"The Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liutao","middleName":"","lastName":"Fu","suffix":""},{"id":325480607,"identity":"4820a5f7-672f-4e13-887e-5d776d29a8f8","order_by":5,"name":"Jun Li","email":"","orcid":"","institution":"The Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Li","suffix":""},{"id":325480608,"identity":"6634357c-0c40-4fb3-a76f-d5b8259b1fea","order_by":6,"name":"Runhua He","email":"","orcid":"","institution":"The Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Runhua","middleName":"","lastName":"He","suffix":""},{"id":325480609,"identity":"3ac1e4f4-a485-43e1-9eb0-6e8b691c8af1","order_by":7,"name":"Qingyuan Wang","email":"","orcid":"","institution":"The Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qingyuan","middleName":"","lastName":"Wang","suffix":""},{"id":325480610,"identity":"7f020d2b-cfcf-436e-92da-2f77c14765ef","order_by":8,"name":"Yue Wang","email":"","orcid":"","institution":"The Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Wang","suffix":""},{"id":325480613,"identity":"7955103b-b497-4455-8338-31520d1b9a49","order_by":9,"name":"Shenghua Zhang","email":"","orcid":"","institution":"The Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shenghua","middleName":"","lastName":"Zhang","suffix":""},{"id":325480614,"identity":"7ca850e9-c75c-470e-a38f-d0a41c07ae60","order_by":10,"name":"Wenjun Shan","email":"","orcid":"","institution":"The Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenjun","middleName":"","lastName":"Shan","suffix":""},{"id":325480617,"identity":"ad241b01-54db-4dd3-bbe2-debe6bb3786a","order_by":11,"name":"Bing Wei","email":"","orcid":"","institution":"The Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Wei","suffix":""},{"id":325480620,"identity":"667b4be8-97d5-4fb9-804c-b5aab5ca678d","order_by":12,"name":"Juanjuan Fu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACNvnjBx//MLDhYWNvSHyQUFFDWAufBE+yMUNFmgw/z4HHBg/OHCOsRU6CwUya4cwhG8kZic8kH7YwE+Ew6YY06cK2AzwGZw6nVSQ2sDHwt3cn4Ncic/Cw9cy2OzwGx9vSbiTukGGQOHN2A34tDAmJN3jbngFtOQPUcoaNwUAil6AWAwnetsM8BjfyvxUktjEToUUiwUia58xhHskZCWkMxGnhOZNsOKMijQcYyMkSCWeO8RD0i3x7+8EHHwxs7EFR+fFHRY0cf3svfi0YgIc05aNgFIyCUTAKsAIAiXJOQjrD0fcAAAAASUVORK5CYII=","orcid":"","institution":"The Second Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Juanjuan","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2024-07-10 12:21:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4717976/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4717976/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61740018,"identity":"422eef72-f6db-4dcd-8796-3d0289ba3819","added_by":"auto","created_at":"2024-08-05 04:49:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":591713,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of 357 differentially expressed genes (181 up-regulated genes and 176 down-regulation genes). Red: up-regulation; Green: down-regulation.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4717976/v1/fdc4fade428359d0000a1a31.png"},{"id":61740011,"identity":"17258829-bb04-4bf2-8110-ce44c5156cd9","added_by":"auto","created_at":"2024-08-05 04:49:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88947,"visible":true,"origin":"","legend":"\u003cp\u003eA) GO enrichment analysis results of DEGs in cellular component ontology (If there were more than ten terms enriched in this category, top ten terms were selected according to \u003cem\u003eP\u003c/em\u003e value.) B) GO enrichment analysis results of DEGs in biological process ontology (If there were more than ten terms enriched in this category, top ten terms were selected according to \u003cem\u003eP\u003c/em\u003e value.) C): GO enrichment analysis results of DEGs in molecular function ontology (If there were more than ten terms enriched in this category, top ten terms were selected according to \u003cem\u003eP\u003c/em\u003e value.)\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4717976/v1/7a60fa28ec60ce5ad8dbb8b4.png"},{"id":61740013,"identity":"a12d134f-b117-433c-b2a9-23b4f21b3994","added_by":"auto","created_at":"2024-08-05 04:49:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":564141,"visible":true,"origin":"","legend":"\u003cp\u003eA) Protein–protein interaction network of DEGs. The green nodes represent the down-regulation genes. The red nodes stand for the up-regulation gene. The size of nodes stands for the level of degree. The lines represent interactions between genes.\u003c/p\u003e\n\u003cp\u003eB) A significant module screened from protein–protein interaction network with a molecular complex detection score=48.24. The red node represents the up-regulation genes. The lines represent interactions between genes.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4717976/v1/60fe028b3f68ac970bee1586.png"},{"id":61740012,"identity":"1e4cfe50-93cb-4665-a5ea-bec765f318cd","added_by":"auto","created_at":"2024-08-05 04:49:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":530689,"visible":true,"origin":"","legend":"\u003cp\u003eThe genetic alterations of twelve hub-genes in serous ovarian cancer patients. (A) OncoPrint summary of alterations on a query of twelve hub-genes. Ten types of genetic alterations were defined: Infame Mutation (unknown significance),\u003cstrong\u003e \u003c/strong\u003eMissense Mutation (unknown significance), Splice Mutation (unknown significance), Truncating Mutation (unknown significance), Amplification, Deep Deletion, mRNA high, mRNA low, Protein High and Protein Low. (B) Summary of the alteration frequency derived from mutations, copy-number alterations, mRNA expression data and protein expression data were shown in serous ovarian cancer in the TCGA cohort.\u003c/p\u003e\n\u003cp\u003e(C) Association of the TTK and AURKA copy number alterations with its mRNA expression in the TCGA ovarian cancer cohort. (D) Summary of the alteration frequency of TOP2A, CDK1, TTK and BUB1 derived from structural variant, mutations, and copy-number alterations data in TCGA ovarian cancer datasets.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4717976/v1/bece2879926cf085af6073a9.png"},{"id":61740014,"identity":"95441027-63c9-4521-b839-a990afe14110","added_by":"auto","created_at":"2024-08-05 04:49:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56329,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression level of twelve hub-genes in ovarian cancer tissues with P53 mutation. The mRNA expression levels of AURKA (\u003cstrong\u003eFigure 5A\u003c/strong\u003e), BUB1 (\u003cstrong\u003eFigure 5B\u003c/strong\u003e), CCNB1 (\u003cstrong\u003eFigure 5C\u003c/strong\u003e), CDK1 (\u003cstrong\u003eFigure 5D\u003c/strong\u003e), MAD2L1 (\u003cstrong\u003eFigure 5E\u003c/strong\u003e), PBK (\u003cstrong\u003eFigure 5F\u003c/strong\u003e), TOP2A (\u003cstrong\u003eFigure 5G\u003c/strong\u003e), and TTK (\u003cstrong\u003eFigure 5H\u003c/strong\u003e) in P53 mutated ovarian cancer samples and in P53 non mutated ovarian cancer samples. *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4717976/v1/ee277f24f7a1a78cfc037ecd.png"},{"id":61740019,"identity":"fcfedb2f-e15a-4834-bf6d-d7963663213d","added_by":"auto","created_at":"2024-08-05 04:49:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":147677,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression level of the twelve hub-genes in tumor tissues.\u003cstrong\u003eFigure 6A\u003c/strong\u003e: The mRNA expression of TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1, BUB1, RRM2, and PBK in tumor tissues. \u003cstrong\u003eFigure 6B\u003c/strong\u003e: Relationship between MAD2L1, RACGAP1, RRM2 and TTK expression and tumor pathological stage performed in GEPIA2. *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4717976/v1/573bf0ebce1213c04f9114c5.png"},{"id":61740017,"identity":"fa455fe7-f3ab-41d9-8135-2d53b89437ad","added_by":"auto","created_at":"2024-08-05 04:49:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1475399,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative immunohistochemistry staining for twelve hub-genes protein expression in serous ovarian cancer tissues vs. non-serous ovarian cancer tissues. Magnification, ×200.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4717976/v1/96998ff883c1a8f8a9e37e33.png"},{"id":61740015,"identity":"0796c3ef-8f2a-4c1f-84e2-859273cbcdf8","added_by":"auto","created_at":"2024-08-05 04:49:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":124179,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves comparing the high and low expressions of twelve hub-genes in patients with serous ovarian cancer. (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 has statistical significance) \u003cstrong\u003eFigure8A-E\u003c/strong\u003e: The high expression of TOP2A, CCNB1, KIF11, AURKA, and BUB1 were associated with worse overall survival. \u003cstrong\u003eFigure8F: \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003eincreased expression of MAD2L1 was not significantly associated with worse overall survival.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4717976/v1/1c0fe725328ef243e7541918.png"},{"id":61740016,"identity":"08efdc15-8ba2-4cfa-93ed-3fa663968843","added_by":"auto","created_at":"2024-08-05 04:49:12","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2419069,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between the expression of twelve hub-genes and immune cell infiltration (TIMER). The correlation between the abundance of immune cells and the expression of TOP2A(A), CDK1(B), CCNB1(C), MAD2L1(D), KIF11(E), CCNB2(F), TTK(G), AURKA(H), RACGAP1(I), BUB1(J), RRM2(K) and PBK(L) in TCGA-basal.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4717976/v1/68c312483268d6212b7f7f26.png"},{"id":61969456,"identity":"92ab5ac6-01ad-472d-99ec-9011d2b9dcda","added_by":"auto","created_at":"2024-08-07 16:32:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6595043,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4717976/v1/a629f9dd-c620-4802-98ee-31a1ee189653.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Screening and identification of key genes between high-grade and low-grade serous ovarian carcinomas using integrated bioinformatics","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eEpithelial ovarian cancer (EOC) is the most aggressive gynecologic malignancy, with 22440 estimated new cancer cases and 14080 estimated new cancer deaths in 2017 in the United States[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Serous, mucinous, endometrioid and clear cell were the main histologic subtypes of ovarian cancer, of which serous epithelial carcinomas dominate the largest proportion[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The high-grade serous cancer (HGSC) is the single largest group of EOC, which is responsible for almost two-thirds of ovarian cancer deaths[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, low-grade serous cancer (LGSC) takes only 5\u0026ndash;10% proportion of EOC[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Patients with LGSC have a longer overall five-year survival, and tend to be chemo-resistant compared to HGSC[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is accepted that these two types of EOC are two distinct tumor types with different clinical and genetic characteristics. Although many efforts have been made to explore new therapeutic approaches, most women with HGSC develop recurrence and chemoresistance[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. At present, the underlying molecular mechanisms are still poorly understood, which greatly limits the treatment of HGSC. Like many solid malignances, HGSC frequently have a high proportion of chromosomal instability, such as gene copy number amplifications and deletions, which are associated with tumor grade and patient outcomes[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition, the alterations of oncogenes and tumor suppressor genes are also involved in the tumorigenesis of HGSC, such as HER-2/neu, c-myc, BRCA1 and BRCA2[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Identification of these key genes could provide advances in diagnosis and in therapeutic strategies. Thus, investigating key genes and understanding the molecular mechanism are urgent and necessary.\u003c/p\u003e \u003cp\u003eThe pathogenesis of serous ovarian cancer at a molecular level has been explored for years. For example, Helland et al defined a pathway that may drive biological and clinical behavior of a distinct molecular subtype of HGSC[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Stronach et al declared that HDAC4 abrogated sensitivity to cisplatin through modulating STAT1 acetylation, phosphorylation, and nuclear translocation in ovarian cancer[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Zaid et al found that the FGFR4 is overexpression in HGSC and might be an indicator of poor prognosis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, most studies ignored to reveal key genes which regulate the occurrence and progress of serous ovarian cancer instead of concentrating on one certain molecular target. Nowadays, the microarray, a high throughput technique for large-scale gene expression analysis, makes it possible to investigate the molecular basis of human cancer on a genomic scale[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In the past decades, the gene expression profiles of ovarian cancer using microarray technology have been investigated to better understand the ovarian tumorigenesis[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the limited samples and conflicting outcomes of individual microarray, we performed an integrated bioinformatics analysis to evaluate key genes between HGSC and LGSC. Two gene expression profiles microarrays (GSE27651 and GSE14001) were obtained from Gene Expression Omnibus database (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to screen out the DEGs between LGSC and HGSC. Then, gene ontology (GO) annotation and protein\u0026ndash;protein interaction network (PPI) construction were applied to explore the function of DEGs. Finally, the hub genes were identified by the PPI network using the Cytoscape software. Its role in epithelial ovarian cancer were analyzed by studying its mRNA expression level, protein expression level, impact on survival, and immune cell infiltration. The present study aimed to compare the gene expression profiles between LGSC and HGSC at the molecular level and identify potential biomarkers of HGSC, providing a new approach for the clinical diagnosis and treatment of ovarian cancer patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMicroarray data\u003c/h2\u003e \u003cp\u003eTwo microarrays of GSE14001 and GSE27651 were retrieved from GEO database. These two gene expression datasets were both based on Affymetrix GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array). The microarray data for GSE14001 consisted of 10 HGSC tissue samples and 10 LGSC tissue samples. The microarray data for GSE27651 consisted of 22 HGSC tissue samples and 13 LGSC tissue samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData processing and identification of DEGs\u003c/h2\u003e \u003cp\u003eThe raw data files of two datasets were download from GEO included CEL files. The probes annotation was obtained from GPL570 platform. Statistical software R (version 3.4.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org/\u003c/span\u003e\u003cspan address=\"http://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and packages of Bioconductor (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioconductor.org/\u003c/span\u003e\u003cspan address=\"http://www.bioconductor.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were applied to analyze the DEGs between HGSC and LGSC samples. The method of Robust Multi-array Average was performed to normalize and to logarithmically convert raw expression profiles data[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. We used the limma package (Linear Models for Microarray Analysis ) to select significant DEGs[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We used the empirical Bayes methods in the R package \u0026ldquo;sva\u0026rdquo; to adjust batch effects[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. An adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and a |logFC| \u0026ge; 3 were defined as the threshold criteria. The clustering analysis of DEGs was performed by the \u0026lsquo;\u0026lsquo;RColorBrewer\u0026rsquo;\u0026rsquo;, \u0026ldquo;gplots\u0026rdquo; packages in R.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional and pathway enrichment analysis of DEGs\u003c/h2\u003e \u003cp\u003eThe DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a web bioinformatics resource that could extract biological features of large gene lists[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The Gene Ontology (GO) project is a web tool for functional interpretation of genes, genes products, and sequences[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a comprehensive knowledge repository to provide higher order functional information of genes[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We used the DAVID database to perform the GO and KEGG pathways enrichment analysis of DEGs. The terms with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered as the significant enrichment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eProtein\u0026ndash;protein interaction (PPI) network construction\u003c/h2\u003e \u003cp\u003eThe Search Tool for the Retrieval of Interacting Genes (STRING) database is a website tool that give evidence to the protein\u0026ndash;protein biological interactions[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. To better understand the direct and indirect interactions of key genes, the PPI network was constructed by the STRING database. A confidence score\u0026thinsp;\u0026ge;\u0026thinsp;0.7(high confidence score)was considered as significant. The PPI network of DEGs was reconstructed by the Cytoscape software. Cytoscape is an open source software that provides powerful function in integrating large data of genetic interactions, protein\u0026ndash;DNA and protein\u0026ndash;protein[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Finally, the CytoHubba46 plug-in in Cytoscape 3.5.1 was used to identify the hub genes of PPI, and the Molecular Complex Detection (MCODE) plug-in was used to select significant modules with degree cut-off =\u0026thinsp;10[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The protein expression of hub genes in cancer and normal tissues was also verified in the Human Protein Atlas (HPA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"http://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis of twelve hub-genes\u003c/h2\u003e \u003cp\u003eKaplan\u0026ndash;Meier plotter (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.ncbi.nlm.nih.gov/geo/\" target=\"_blank\"\u003ewww.kmplot.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.kmplot.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is an online tool that could provide patient survival analysis of 54675 genes in four types of cancer, including breast cancer, ovarian cancer, lung cancer and gastric cancer[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. By using the median expression value as a cut-off, the patients were divided into two groups. Then we plotted the Kaplan-Meir overall survival (OS) curves in serous ovarian cancer patients. The hazard ratio (HR) with 95% confidence intervals and log rank test \u003cem\u003eP\u003c/em\u003e value were also calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTIMER Database Analysis\u003c/h2\u003e \u003cp\u003eThe Tumor IMmune Estimation Resource (TIMER) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a reliable and comprehensive resource that allows the evaluation of the abundance of immune cell infiltration across diverse cancer types. In this study, the \u0026ldquo;Gene module\u0026rdquo; was used to evaluate the correlation between the expression level of immune-related genes, tumor purity, and the infiltration of immune cells including B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, neutrophils, macrophages, and dendritic cells (DCs) in serous ovarian cancer. Meanwhile, the \u0026ldquo;Correlation module\u0026rdquo; was used to investigate the correlation between the expression level of immune-related genes and several gene markers of tumor-infiltrating immune cells.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs\u003c/h2\u003e \u003cp\u003eThe raw CEL files were downloaded from the GEO, and then normalized with the \u0026ldquo;affy\u0026rdquo; package. After eliminating batch effects in the expression data, we used the limma package to select DEGs. Using \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and |logFC| \u0026ge; 3 criteria, 357 DEGs were screened out from two GEO datasets, including 181 up-regulation DEGs and 176 down-regulation DEGs in HGSC compared to LGSC. The top 10 up-regulated and top 10 down-regulated DEGs were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In addition, the heat map of DEGs was shown as the Fig.\u0026nbsp;1. The hierarchical clustering analysis revealed a distinct separation in these two different types of ovarian cancer.\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\u003eTop ten up-regulation and top ten down-regulation DEGs (high-grade serous ovarian cancer versus low-grade serous ovarian cancer)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbe set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog fold change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp-regulated\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1565483_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e214677_x_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIGLC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.91E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e218542_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCEP55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61E-17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e203560_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.39E-13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e201292_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOP2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.86E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e203764_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDLGAP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.43E-17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e219787_s_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eECT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.80E-15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e204533_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCXCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e219148_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePBK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.25E-13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e242546_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLINC01296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.91E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDown-regulated\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e214218_s_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.93E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e229782_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.47E-13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e233249_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLOC100507073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.36E-13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e204014_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDUSP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.70E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e229331_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPATA18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.69E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e205765_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP3A5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.42E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e229245_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLEKHA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.60E-14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e240065_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAM81B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.43E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e225996_at\u003c/p\u003e \u003cp\u003e227188_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLONRF2\u003c/p\u003e \u003cp\u003eEVA1C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.50\u003c/p\u003e \u003cp\u003e-2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14E-08\u003c/p\u003e \u003cp\u003e9.98E-09\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunctional determination by GO terms and KEGG pathways\u003c/h2\u003e \u003cp\u003eTo interpret the function of the DEGs, the gene lists were uploaded to the online software DAVID. The GO terms including cell component (CC), biological processes (BP) and molecular function (MF) ontologies were shown in Fig.\u0026nbsp;2. For the CC ontology, we found that most DEGs significantly enriched in nucleus and cytoplasm items, such as nucleus, nucleoplasm and cytosol. Some genes were associated with organelles in cytoplasm, such as centrosome, spindle and midbody (Fig.\u0026nbsp;2A). For the BP ontology, the majority GO terms were about cell proliferation items, such as cell division, G1/S transition of mitotic cell cycle. In addition, the other GO terms is significantly related to regulation activities of organism, including positive regulation of cell proliferation, positive regulation of GTPase activity (Fig.\u0026nbsp;2B). In the MF ontology, the binding function consisted a large proportion of GO categories, which involved protein binding, protein kinase binding, ATP binding and microtubule binding (Fig.\u0026nbsp;2C). As shown in the Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the KEGG pathway enrichment analysis found five significantly pathways, including the PI3K-Akt signaling pathway, pathways in cancer, p53 signaling pathway, cell cycle, microRNAs in cancer.\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\u003eKEGG pathway enrichment of DEGs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04110: Cell cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04115: p53 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.85E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05200: Pathways in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04151: PI3K-Akt signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05206: MicroRNAs in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCount refers to the number of genes significantly enriched in this term\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePPI network and hub genes\u003c/h2\u003e \u003cp\u003eThe protein-protein interaction network of DEGs was constructed through the STRING database. The 357 DEGs were uploaded to the STRING website to get PPI data. Then, the PPI data were analyzed by the Cytoscape software. The PPI which consisted of 167 nodes and 1794 edges shown in \u003cb\u003eFig.\u0026nbsp;3A\u003c/b\u003e. After the PPI network construction, the CytoHubba plug-in in Cytoscape was used to indentify hub genes. In PPI networks, 12 node protein, including TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1, BUB1, RRM2, PBK had a strong association with other nodes (Degree\u0026thinsp;\u0026ge;\u0026thinsp;60). From the PPI network, a significant module including 51 nodes and 1206 edges was selected by the MCODE (Fig.\u0026nbsp;3B). The GO and KEGG enrichment analysis show these genes in the module were enriched in the mitotic cytokinesis, ATP binding and p53 signaling pathway (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGO and KEGG pathway enrichment analysis of the genes in module\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0000281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emitotic cytokinesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.37E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0032467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive regulation of cytokinesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.82E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0007018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emicrotubule-based movement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.19E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0035556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eintracellular signal transduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0030496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emidbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.04E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0005654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enucleoplasm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.49E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0005737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecytoplasm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.15E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0005634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enucleus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0016020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emembrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0005524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATP binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.45E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0016887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATPase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0003682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echromatin binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG:hsa04110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.71E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG:hsa04115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep53 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.13E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCount refers to the number of genes significantly enriched in this term\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenetic Alterations of Twelve Hub-genes in Serious Ovarian Cancer Patients\u003c/h2\u003e \u003cp\u003eThe cBioPortal tool was used for the analysis of genetic alterations of twelve hub-genes from the TCGA PanCancer Atlas dataset. As a result, 5% TOP2A, 9% CDK1, 11% CCNB1, 6% MAD2L1, 5% KIF11, 5% CCNb2, 6% TTK, 14% AURKA, 5%ACGAP1, 4%BUB1, 10%RRM2 and 10% PBK were altered in ten types of genetic alterations, including infame mutation (unknown significance), missense mutation (unknown significance), splice mutation (unknown significance), truncating mutation (unknown significance), amplification, deep deletion, mRNA high, mRNA low, protein high and protein low in the queried TCGA serious ovarian cancer samples (Fig.\u0026nbsp;4A). The alteration frequency derived from mutations, copy-number alterations, mRNA expression data and protein expression data were shown in serous ovarian cancer (Fig.\u0026nbsp;4B). To explore whether these hub genes amplification had an influence on its mRNA and protein level, the results indicated that TTK and AURKA were amplified along with the significantly high mRNA and protein level from TCGA-OV cohort (Fig.\u0026nbsp;4C). The mutation types, number, and sites of TOP2A, CDK1, TTK and BUB1 genetic alterations were displayed in Fig.\u0026nbsp;4D.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe Expression Level of the Twelve Hub-genes in Ovarian Cancer Tissues with P53 Mutation\u003c/h2\u003e \u003cp\u003eGO functional enrichment analysis showed that the DEGs was associated with the p53 signaling pathway. We conducted research and analysis on 199 ovarian cancer samples with P53 mutations and 19 ovarian cancer samples without P53 mutations. The results showed that the mRNA expression levels of AURKA (Fig.\u0026nbsp;5A), BUB1 (Fig.\u0026nbsp;5B), CCNB1 (Fig.\u0026nbsp;5C), CDK1 (Fig.\u0026nbsp;5D), MAD2L1 (Fig.\u0026nbsp;5E), PBK (Fig.\u0026nbsp;5F), TOP2A (Fig.\u0026nbsp;5G), and TTK (Fig.\u0026nbsp;5H) in P53 mutated ovarian cancer samples were higher than those in P53 non mutated ovarian cancer samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe Expression Level of the Twelve Hub-genes in Tumor Tissues\u003c/h2\u003e \u003cp\u003eTo better understand the differential expression, the CPTAC dataset was used to assess the twelve hub-genes mRNA expression level in large-scale mRNA data from the National Cancer Institute. As shown in \u003cb\u003eFig.\u0026nbsp;6A\u003c/b\u003e, the mRNA expression of TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1, BUB1, RRM2, and PBK were significantly increased in tumor tissues. The GEPIA2 tool was also used to analyze the relationship between the twelve hub-genes expression and tumor pathological stage. Figure\u0026nbsp;6B showed stage-specific change of MAD2L1, RACGAP1, RRM2 and TTK in tumor tissues. On the other hand, the remaining hub genes were no clear association between the gene expression and patients\u0026rsquo; stage.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe Protein Expression of Twelve Hub-genes in Serous Ovarian Cancer tissues vs. Non-serous Ovarian Cancer tissues\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo assess the protein expression of twelve hub-genes in serous ovarian cancer tissues vs. non-serous ovarian cancer tissues, we performed immunohistochemical analysis. The expression levels of ten proteins, including TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1 and PBK were distinctly higher in serous ovarian cancer tissues than non-serous ovarian cancer tissues (Fig.\u0026nbsp;7).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eThe Prognostic Value of Twelve Hub-genes in Patients with Serous Ovarian Cancer\u003c/h2\u003e \u003cp\u003eThe prognostic value of twelve hub-genes were assessed using an online tool of KM plotter (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kmplot.com\u003c/span\u003e\u003cspan address=\"http://www.kmplot.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The survival curves were calculated according to the gene expression levels. Among those hub-genes, our results showed that high expression of TOP2A (HR 1.32 95%CIs [1.13\u0026ndash;1.55], P\u0026thinsp;=\u0026thinsp;0.00063), CCNB1 (HR 1.24 95%CIs [1.05\u0026ndash;1.47], P\u0026thinsp;=\u0026thinsp;0.01), KIF11 (HR 1.23 95%CIs [1.04\u0026ndash;1.45], P\u0026thinsp;=\u0026thinsp;0.017), AURKA (HR 1.47 95%CIs [1.23\u0026ndash;1.75], P\u0026thinsp;=\u0026thinsp;2.2e-05), and BUB1 (HR 1.18 95%CIs [1.02\u0026ndash;1.38], P\u0026thinsp;=\u0026thinsp;0.03) were associated with worse overall survival (Fig.\u0026nbsp;8A-E). We also found increased expression of MAD2L1 (HR 0.79 95%CIs [0.62\u0026ndash;1.01], P\u0026thinsp;=\u0026thinsp;0.055) was not significantly associated with worse overall survival (Fig.\u0026nbsp;8F).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation of Twelve Hub-Genes with Tumor Purity and Immune Cell Infiltration in Patients with Serous Ovarian Cancer\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSince the functional annotation analysis revealed that the twelve hub-genes participated in the process of the immune response, next, the correlation between the expression of twelve hub-genes and immune cell infiltration in the TIMER database was further analyzed. Interestingly, high expression levels of twelve hub-genes were found to be associated with high immune cell infiltration in serous ovarian cancer. A positive correlation between TOP2A expression and the infiltration of macrophage (Cor\u0026thinsp;=\u0026thinsp;0.12, p\u0026thinsp;=\u0026thinsp;8.67e\u0026thinsp;\u0026minus;\u0026thinsp;03) and purity (Cor\u0026thinsp;=\u0026thinsp;0.123, p\u0026thinsp;=\u0026thinsp;6.83e\u0026thinsp;\u0026minus;\u0026thinsp;03) were observed, while the TOP2A expression was negatively associated with the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells (Cor = -0.121, p\u0026thinsp;=\u0026thinsp;7.90e\u0026thinsp;\u0026minus;\u0026thinsp;03). There is no significant correlation between the expression level of TOP2A and the infiltration level of B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, neutrophil and dendritic cells (Fig.\u0026nbsp;9A\u003cb\u003e)\u003c/b\u003e. The change of KIF11 expression level is the same as that of TOP2A (Fig.\u0026nbsp;9E\u003cb\u003e)\u003c/b\u003e. As shown in \u003cb\u003eFig.\u0026nbsp;9B\u003c/b\u003e, the expression level of CDK1 is positively correlated with tumor purity (Cor\u0026thinsp;=\u0026thinsp;0.152, p\u0026thinsp;=\u0026thinsp;1.63e\u0026thinsp;\u0026minus;\u0026thinsp;02), but not significantly correlated with the infiltration level of B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, macrophage, neutrophil and dendritic cells. Figure\u0026nbsp;9C showed that the expression level of CCNB1 is positively correlated with the infiltration level of macrophage (Cor\u0026thinsp;=\u0026thinsp;0.119, p\u0026thinsp;=\u0026thinsp;8.80e\u0026thinsp;\u0026minus;\u0026thinsp;03) and neutrophil (Cor\u0026thinsp;=\u0026thinsp;0.125, p\u0026thinsp;=\u0026thinsp;6.01e\u0026thinsp;\u0026minus;\u0026thinsp;03), but not significantly correlated with the infiltration level of B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, dendritic cells and tumor purity. The expression level of MAD2L1 is positively correlated with the infiltration level of macrophage (Cor\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;7.00e\u0026thinsp;\u0026minus;\u0026thinsp;05), neutrophil (Cor\u0026thinsp;=\u0026thinsp;0.238, p\u0026thinsp;=\u0026thinsp;1.38e\u0026thinsp;\u0026minus;\u0026thinsp;07) and dendritic cells (Cor\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;=\u0026thinsp;1.75e\u0026thinsp;\u0026minus;\u0026thinsp;04), but not significantly correlated with the infiltration level of B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells and tumor purity (Fig.\u0026nbsp;9D\u003cb\u003e)\u003c/b\u003e.The change of CCNB2 and TTK expression level is the same as that of MAD2L1 (Fig.\u0026nbsp;9F, \u003cb\u003eG)\u003c/b\u003e. Figure\u0026nbsp;9H showed that the expression level of AURKA is positively correlated with the infiltration level of CD4\u0026thinsp;+\u0026thinsp;T cells (Cor\u0026thinsp;=\u0026thinsp;0.118, p\u0026thinsp;=\u0026thinsp;9.90e\u0026thinsp;\u0026minus;\u0026thinsp;03), macrophage (Cor\u0026thinsp;=\u0026thinsp;0.225, p\u0026thinsp;=\u0026thinsp;6.06e\u0026thinsp;\u0026minus;\u0026thinsp;07) and neutrophil (Cor\u0026thinsp;=\u0026thinsp;0.12, p\u0026thinsp;=\u0026thinsp;8.49e\u0026thinsp;\u0026minus;\u0026thinsp;03), but not significantly correlated with the infiltration level of B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, dendritic cells and tumor purity. The expression level of RACGAP1 and RRM2 are positively correlated with the infiltration level of CD4\u0026thinsp;+\u0026thinsp;T cells, macrophage, neutrophil and dendritic cells, but not significantly correlated with the infiltration level of B cells and CD8\u0026thinsp;+\u0026thinsp;T cells. In addition, the expression level of RACGAP is also positively correlated with tumor purity, while the expression level of RRM2 is not significantly correlated with tumor purity (Fig.\u0026nbsp;9I, \u003cb\u003eK)\u003c/b\u003e. Figure\u0026nbsp;9J showed that the expression level of BUB1 is positively correlated with the infiltration level of macrophage (Cor\u0026thinsp;=\u0026thinsp;0.128, p\u0026thinsp;=\u0026thinsp;4.96e\u0026thinsp;\u0026minus;\u0026thinsp;03) and tumor purity (Cor\u0026thinsp;=\u0026thinsp;0.09 p\u0026thinsp;=\u0026thinsp;4.78e\u0026thinsp;\u0026minus;\u0026thinsp;02), while the BUB1 expression level was negatively associated with the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells (C or = -0.121, p\u0026thinsp;=\u0026thinsp;7.90e\u0026thinsp;\u0026minus;\u0026thinsp;03). but not significantly correlated with the infiltration level of B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, neutrophil and dendritic cells. The expression level of PBK is positively correlated with the infiltration level of macrophage (Cor\u0026thinsp;=\u0026thinsp;0.131, p\u0026thinsp;=\u0026thinsp;4.14e\u0026thinsp;\u0026minus;\u0026thinsp;03), but not significantly correlated with the infiltration level of B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, macrophage, dendritic cells and tumor purity (Fig.\u0026nbsp;9L\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, there has been a growing clinical and genetic evidence to support a two-pathway model of ovarian cancer[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. From the evidence, the LGSC derived from a stepwise fashion from benign serous cystadenoma to LGSC. However, HGSC seemed to be in the absence of recognizable precursor lesion. From the molecular genetics perspective, an increasing number of studies have found that LGSC have a high frequency mutation of KRAS, BRAF, PTEN, CTNNB1, whereas the HGSC is associated strongly with p53 mutations[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In view of clinical features, women with HGSC usually present at advanced stages, have a sensitive responsive to chemotherapy, have a high incidence of recurrence and decreased survival[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, many younger women with LGSC tend to be resistant to chemotherapy, but have an improved survival[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Thus, it is worth emphasizing that HGSC and LGSC are two distinctly different tumor types rather than different grades of the same neoplasm. Based on clinical and molecular features of ovarian cancer, it is generally accepted that ovarian carcinomas could be divided into Type I tumor which are slowly developing in a stepwise fashion; and Type II tumor which are rapidly growing, high-grade tumors[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Thus, it is necessary to compare the different molecular mechanisms about these two different tumor types and find potential candidate markers in a prognostic, predictive, or therapeutic sense.\u003c/p\u003e \u003cp\u003eIn this study, gene expression data of 23 LGSC tissue samples and 32 HGSC tissue samples were retrieved from two GEO datasets. We identified 357 DEGs between LGSC tissue samples and HGSC tissue samples and GO and pathway enrichment analysis were further performed. First, in CC ontology, most of the DEGs were significantly enriched in nuclear-related and cell mitosis items including nuclear, centrosome, spindle and microtubule cytoskeleton[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These cell biological processes were mainly associated with genomic stability and cancer etiology[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Many studies suggested that the spindle assembly are playing a role in the multi-step process of acquired paclitaxel resistance in ovarian cancer[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Then, for the GO category of BP, our results showed that the most significant items were the cell division items including mitotic nuclear division, G1/S transition of mitotic cell cycle. Admittedly, the majority of patients with HGSC will develop chemoresistance through multiple complex mechanisms. Some study found defects in mitotic checkpoints were associated with paclitaxel resistance in ovarian cancer cell lines[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In MF ontology, the binding-related items were the most significant items such as protein binding, protein kinase binding and ubiquitin protein ligase binding. These data suggested that these DEGs might participate in the binding functions among nucleus, protein, histone and extracellular matrix, which influence tumor microenvironment and signal transduction in cancer cells[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Obviously, protein binding and kinase binding are involved in the multiple signaling pathways in cancer. A large number of kinase and phosphatases were found in ovarian cancer tissue, which were considered to have taken part in biological process of ovarian cancer[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In the present study, pathway analysis found five significantly enriched pathways including p53 signaling pathway, PI3K-Akt signaling pathway[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Hayano et al reported a novel intact p53 pathway subtype of HGSC, which provided a new insight into the pathogenesis of HGSC with an intact p53 pathway[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In addition, the deregulation of the PI3K/Akt pathway is an important genomic change in ovarian cancer[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Tanaka et al reported that high levels of pAkt were correlated with decreased progression free survival and overall survival in ovarian cancer, thus Akt might be a potential molecular target[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study also constructed the protein-protein interaction network of DEGs and screened out top 12 hub genes. Among these hub genes, TOP2A was the highest degree of connectivity. The TOP2A gene encode the enzyme topoisomerase IIa (topo IIa), which is responsible for resolving topological problems during the DNA metabolism[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. TOP2A is located close to HER2 on chromosome 17q21 and the clinical value of TOP2A in breast cancer have been studied for years[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. It reported that TOP2A gene copy number or protein overexpression could predict the treatment response of pegylated liposomal doxorubicin in platinum resistant in EOC[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. So far, the carcinogenic effect of TOP2A for ovarian cancer have not explored fully. The second hub gene CDK1 is a mitotic cyclin-dependent kinases (CDKs), which is a key factor for G2/M phase transition[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. CDK1 might have the potential to regulate resistance and suppression of CDK1 could reverse paclitaxel resistance in ovarian cancer cells[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Taken together, these results showed that these hub genes might be associated with the clinicopathological features of these two types of ovarian cancer.\u003c/p\u003e \u003cp\u003eIn our research, three hub genes were found to have significantly increased mRNA expression levels in ovarian cancer tissues with P53 mutation and tumor tissues, significantly increased protein expression levels in serous ovarian cancer tissues, and its high expression levels were significantly correlated with worse survival outcome, including TOP2A, CCNB1 and AURKA. Cyclins refer to proteins that differ in their levels to activate specific cyclin-dependent kinases (CDKs) required for progression in the cell cycle[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Cyclin B1 (CCNB1) has a pivotal role in regulating and forming a complex with CDK1 to promote the transition from the G2 phase of cell cycle to mitosis[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Increasing evidence demonstrates that the over-expression of CCNB1 is observed in certain number of human cancers including colorectal cancer[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], breast cancer[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], and ovarian cancer[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Yiping Zou et al reported that the mRNA expression level of CCNB1 is upregulated in various tumor tissues, including hepatocellular carcinoma (HCC). The high expression of CCNB1 gene is associated with poor prognosis in HCC patients[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Hui Zhang et al demonstrated that CCNB1 silencing activates the p53 signaling pathway and consequently inhibits cell proliferation and promotes cell senescence in pancreatic cancer[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. As showed in our study, we found that the mRNA expression level and protein expression level of CCNB1 were significantly increased in tumor tissues, and the high expression of CCNB1 was positively correlated with adverse survival outcomes in ovarian cancer patients. Aurora kinases belong to serine/threonine kinases which share a highly conserved catalytic domain containing auto-phosphorylating sites[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This family contains three members: Aurora A (AURKA), Aurora B (AURKB), and Aurora C (AURKC). Both AURKA and AURKB play essential roles in regulating cell division during mitosis while AURKC has a unique physiological role in spermatogenesis[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Relatively less information is available for the roles of AURKC in cancer. AURKA and AURKB have been found to function as oncogenes to promote tumorigenesis in multiple types of cancer including solid tumors and hematological malignancies[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Apart from playing a role in mitosis, an increasing number of studies have suggested that AURKA, when abnormally expressed, could be an oncogene involved in tumorigenesis[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Gene amplification, transcriptional activation and inhibition of protein degradation could contribute to the elevated levels of AURKA expression in cancer tissues. AURKA promotes tumorigenesis by participating in the cancer cell proliferation, epithelial-mesenchymal transition (EMT), metastasis, apoptosis, and self-renewal of cancer stem cells[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Our research results were consistent with these findings. The mRNA and protein expression levels of AURKA were significantly increased in tumor tissue and ovarian cancer tissue, and the high expression of AURKA could predict adverse prognostic outcomes for ovarian cancer patients.\u003c/p\u003e \u003cp\u003eIn conclusion, this study aimed to identify DEGs between two different pathology types of serous ovarian cancer and find the potential predictor. In the present study, a total of 357 DEGs were selected and TOP2A, CCNB1 and AURKA might be prognostic biomarkers of HGSC. However, the absence of experimental verification is the major drawback of our study. Thus, further experiments are required to confirm our results obtained from bioinformatics analysis.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceived and conducted the study: LWZ and JJF; analyzed the data: ZLP, WGS,WYW,LTF,JL,RHH,QYW,YW,SHZ,WJS,BW ; wrote the manuscript: LWZ and ZLP, all authors read and approved the final version. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by The Second Hospital of Anhui Medical University, National Natural Science Foundation of China(grant numbers 2022GQFY09). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient data were obtained from Gene Expression Omnibus (GEO) and Database for Annotation, Visualization and Integrated Discovery (DAVID),two publicly open database resource ,Informed patient consent was not required\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no confict of interest regarding the publication of this paper.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing financial interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eR.L. Siegel, K.D. Miller, and A. Jemal, Cancer Statistics, 2017. CA Cancer J Clin, 2017. 67(1): p. 7-30.\u003c/li\u003e\n\u003cli\u003eT. Bonome, J.Y. Lee, D.C. Park, M. Radonovich, C. Pise-Masison, J. Brady, et al., Expression profiling of serous low malignant potential, low-grade, and high-grade tumors of the ovary. Cancer Res, 2005. 65(22): p. 10602-12.\u003c/li\u003e\n\u003cli\u003eD.D.L. Bowtell, The genesis and evolution of high-grade serous ovarian cancer. Nat Rev Cancer, 2010. 10(11): p. 803-8.\u003c/li\u003e\n\u003cli\u003eR.J. Kurman, and M. Shih Ie, The Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded. Am J Pathol, 2016. 186(4): p. 733-47.\u003c/li\u003e\n\u003cli\u003eR.N. Grisham, G. Iyer, K. Garg, D. Delair, D.M. Hyman, Q. Zhou, et al., BRAF mutation is associated with early stage disease and improved outcome in patients with low-grade serous ovarian cancer. Cancer, 2013. 119(3): p. 548-554.\u003c/li\u003e\n\u003cli\u003eG.C. Jayson, E.C. Kohn, H.C. Kitchener, and J.A. Ledermann, Ovarian cancer. Lancet, 2014. 384(9951): p. 1376-88.\u003c/li\u003e\n\u003cli\u003eB.T. Hennessy, R.L. Coleman, and M. Markman, Ovarian cancer. Lancet, 2009. 374(9698): p. 1371-82.\u003c/li\u003e\n\u003cli\u003eB. Aunoble, R. Sanches, E. Didier, and Y.J. Bignon, Major oncogenes and tumor suppressor genes involved in epithelial ovarian cancer (review). Int J Oncol, 2000. 16(3): p. 567-76.\u003c/li\u003e\n\u003cli\u003eA. Helland, M.S. Anglesio, J. George, P.A. Cowin, C.N. Johnstone, C.M. House, et al., Deregulation of MYCN, LIN28B and LET7 in a molecular subtype of aggressive high-grade serous ovarian cancers. PLoS One, 2011. 6(4): p. e18064.\u003c/li\u003e\n\u003cli\u003eE.A. Stronach, A. Alfraidi, N. Rama, C. Datler, J.B. Studd, R. Agarwal, et al., HDAC4-regulated STAT1 activation mediates platinum resistance in ovarian cancer. Cancer Res, 2011. 71(13): p. 4412-22.\u003c/li\u003e\n\u003cli\u003eT.M. Zaid, T.L. Yeung, M.S. Thompson, C.S. Leung, T. Harding, N.N. Co, et al., Identification of FGFR4 as a potential therapeutic target for advanced-stage, high-grade serous ovarian cancer. Clin Cancer Res, 2013. 19(4): p. 809-20.\u003c/li\u003e\n\u003cli\u003eA. Sturn, J. Quackenbush, and Z. Trajanoski, Genesis: cluster analysis of microarray data. Bioinformatics, 2002. 18(1): p. 207-8.\u003c/li\u003e\n\u003cli\u003eO.P. Kallioniemi, U. Wagner, J. Kononen, and G. Sauter, Tissue microarray technology for high-throughput molecular profiling of cancer. Hum Mol Genet, 2001. 10(7): p. 657-62.\u003c/li\u003e\n\u003cli\u003eA.D. Santin, F.H. Zhan, S. Bellone, M. Palmieri, S. Cane, E. Bignotti, et al., Gene expression profiles in primary ovarian serous papillary tumors and normal ovarian epithelium: identification of candidate molecular markers for ovarian cancer diagnosis and therapy. Int J Cancer, 2004. 112(1): p. 14-25.\u003c/li\u003e\n\u003cli\u003eC.D. Hough, C.A. Sherman-Baust, E.S. Pizer, F.J. Montz, D.D. Im, N.B. Rosenshein, et al., Large-scale serial analysis of gene expression reveals genes differentially expressed in ovarian cancer. Cancer Res, 2000. 60(22): p. 6281-7.\u003c/li\u003e\n\u003cli\u003eJ.B. Welsh, P.P. Zarrinkar, L.M. Sapinoso, S.G. Kern, C.A. Behling, B.J. Monk, et al., Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. Proc Natl Acad Sci U S A, 2001. 98(3): p. 1176-81.\u003c/li\u003e\n\u003cli\u003eS. Hochreiter, D.A. Clevert, and K. Obermayer, A new summarization method for Affymetrix probe level data. Bioinformatics, 2006. 22(8): p. 943-9.\u003c/li\u003e\n\u003cli\u003eG.K. Smyth, Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol, 2004. 3: p. Article3.\u003c/li\u003e\n\u003cli\u003eW.E. Johnson, C. Li, and A. Rabinovic, Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 2007. 8(1): p. 118-27.\u003c/li\u003e\n\u003cli\u003eW. Huang da, B.T. Sherman, and R.A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 2009. 4(1): p. 44-57.\u003c/li\u003e\n\u003cli\u003eM.A. Harris, J. Clark, A. Ireland, J. Lomax, M. Ashburner, R. Foulger, et al., The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res, 2004. 32(Database issue): p. D258-61.\u003c/li\u003e\n\u003cli\u003eM. Kanehisa, and S. Goto, KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 2000. 28(1): p. 27-30.\u003c/li\u003e\n\u003cli\u003eA. Franceschini, D. Szklarczyk, S. Frankild, M. Kuhn, M. Simonovic, A. Roth, et al., STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res, 2013. 41(Database issue): p. D808-15.\u003c/li\u003e\n\u003cli\u003eP. Shannon, A. Markiel, O. Ozier, N.S. Baliga, J.T. Wang, D. Ramage, et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 2003. 13(11): p. 2498-504.\u003c/li\u003e\n\u003cli\u003eG.D. Bader, and C.W. Hogue, An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 2003. 4: p. 2.\u003c/li\u003e\n\u003cli\u003eM. Uhl\u0026eacute;n, L. Fagerberg, B.M. Hallstr\u0026ouml;m, C. Lindskog, P. Oksvold, A. Mardinoglu, et al., Proteomics. Tissue-based map of the human proteome. Science, 2015. 347(6220): p. 1260419.\u003c/li\u003e\n\u003cli\u003eB. Gyorffy, A. L\u0026aacute;nczky, and Z. Sz\u0026aacute;ll\u0026aacute;si, Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients. Endocr Relat Cancer, 2012. 19(2): p. 197-208.\u003c/li\u003e\n\u003cli\u003eC.J. O\u0026apos;Neill, M.T. Deavers, A. Malpica, H. Foster, and W.G. McCluggage, An immunohistochemical comparison between low-grade and high-grade ovarian serous carcinomas: significantly higher expression of p53, MIB1, BCL2, HER-2/neu, and C-KIT in high-grade neoplasms. Am J Surg Pathol, 2005. 29(8): p. 1034-41.\u003c/li\u003e\n\u003cli\u003eC.N. Landen, M.J. Birrer, and A.K. Sood, Early events in the pathogenesis of epithelial ovarian cancer. J Clin Oncol, 2008. 26(6): p. 995-1005.\u003c/li\u003e\n\u003cli\u003eK. Levanon, C. Crum, and R. Drapkin, New insights into the pathogenesis of serous ovarian cancer and its clinical impact. J Clin Oncol, 2008. 26(32): p. 5284-93.\u003c/li\u003e\n\u003cli\u003eR. Dehari, R.J. Kurman, S. Logani, and I.M. Shih, The development of high-grade serous carcinoma from atypical proliferative (borderline) serous tumors and low-grade micropapillary serous carcinoma: a morphologic and molecular genetic analysis. Am J Surg Pathol, 2007. 31(7): p. 1007-12.\u003c/li\u003e\n\u003cli\u003eS.C. Plaxe, Epidemiology of low-grade serous ovarian cancer. Am J Obstet Gynecol, 2008. 198(4): p. 459.e1-8; discussion 459.e8-9.\u003c/li\u003e\n\u003cli\u003eA. Gockley, A. Melamed, A.J. Bregar, J.T. Clemmer, M. Birrer, J.O. Schorge, et al., Outcomes of Women With High-Grade and Low-Grade Advanced-Stage Serous Epithelial Ovarian Cancer. Obstet Gynecol, 2017. 129(3): p. 439-447.\u003c/li\u003e\n\u003cli\u003eK.M. Schmeler, and D.M. Gershenson, Low-grade serous ovarian cancer: a unique disease. Curr Oncol Rep, 2008. 10(6): p. 519-23.\u003c/li\u003e\n\u003cli\u003eK.R. Cho, and I.M. Shih, Ovarian cancer. Annu Rev Pathol, 2009. 4: p. 287-313.\u003c/li\u003e\n\u003cli\u003eR.J. Kurman, Origin and molecular pathogenesis of ovarian high-grade serous carcinoma. Ann Oncol, 2013. 24 Suppl 10: p. x16-21.\u003c/li\u003e\n\u003cli\u003eJ. Wu, and A. Akhmanova, Microtubule-Organizing Centers. Annu Rev Cell Dev Biol, 2017. 33: p. 51-75.\u003c/li\u003e\n\u003cli\u003eM. Chen, R. Linstra, and M. van Vugt, Genomic instability, inflammatory signaling and response to cancer immunotherapy. Biochim Biophys Acta Rev Cancer, 2022. 1877(1): p. 188661.\u003c/li\u003e\n\u003cli\u003eA.A. Ahmed, A.D. Mills, A.E.K. Ibrahim, J. Temple, C. Blenkiron, M. Vias, et al., The extracellular matrix protein TGFBI induces microtubule stabilization and sensitizes ovarian cancers to paclitaxel. Cancer Cell, 2007. 12(6): p. 514-27.\u003c/li\u003e\n\u003cli\u003eB. McGrogan, S. Phelan, P. Fitzpatrick, A. Maguire, M. Prencipe, D. Brennan, et al., Spindle assembly checkpoint protein expression correlates with cellular proliferation and shorter time to recurrence in ovarian cancer. Hum Pathol, 2014. 45(7): p. 1509-19.\u003c/li\u003e\n\u003cli\u003eX. Hao, Z.G. Zhou, S.M. Ye, T. Zhou, Y.P. Lu, D. Ma, et al., Effect of Mad2 on paclitaxel-induced cell death in ovarian cancer cells. J Huazhong Univ Sci Technolog Med Sci, 2010. 30(5): p. 620-5.\u003c/li\u003e\n\u003cli\u003eS. AlMusawi, M. Ahmed, and A.S. Nateri, Understanding cell-cell communication and signaling in the colorectal cancer microenvironment. Clin Transl Med, 2021. 11(2): p. e308.\u003c/li\u003e\n\u003cli\u003eJ.D. Wulfkuhle, J.A. Aquino, V.S. Calvert, D.A. Fishman, G. Coukos, L.A. Liotta, et al., Signal pathway profiling of ovarian cancer from human tissue specimens using reverse-phase protein microarrays. Proteomics, 2003. 3(11): p. 2085-90.\u003c/li\u003e\n\u003cli\u003eM. Kessler, C. Fotopoulou, and T. Meyer, The molecular fingerprint of high grade serous ovarian cancer reflects its fallopian tube origin. Int J Mol Sci, 2013. 14(4): p. 6571-96.\u003c/li\u003e\n\u003cli\u003eT. Hayano, Y. Yokota, K. Hosomichi, H. Nakaoka, K. Yoshihara, S. Adachi, et al., Molecular characterization of an intact p53 pathway subtype in high-grade serous ovarian cancer. PLoS One, 2014. 9(12): p. e114491.\u003c/li\u003e\n\u003cli\u003eA. Astanehe, D. Arenillas, W.W. Wasserman, P.C. Leung, S.E. Dunn, B.R. Davies, et al., Mechanisms underlying p53 regulation of PIK3CA transcription in ovarian surface epithelium and in ovarian cancer. J Cell Sci, 2008. 121(Pt 5): p. 664-74.\u003c/li\u003e\n\u003cli\u003eY. Tanaka, Y. Terai, A. Tanabe, H. Sasaki, T. Sekijima, S. Fujiwara, et al., Prognostic effect of epidermal growth factor receptor gene mutations and the aberrant phosphorylation of Akt and ERK in ovarian cancer. Cancer Biol Ther, 2011. 11(1): p. 50-7.\u003c/li\u003e\n\u003cli\u003eE. Arriola, C. Marchio, D.S. Tan, S.C. Drury, M.B. Lambros, R. Natrajan, et al., Genomic analysis of the HER2/TOP2A amplicon in breast cancer and breast cancer cell lines. Lab Invest, 2008. 88(5): p. 491-503.\u003c/li\u003e\n\u003cli\u003eK.V. Nielsen, S. M\u0026uuml;ller, S. M\u0026oslash;ller, A. Sch\u0026oslash;nau, E. Balslev, A.S. Knoop, et al., Aberrations of ERBB2 and TOP2A genes in breast cancer. Mol Oncol, 2010. 4(2): p. 161-8.\u003c/li\u003e\n\u003cli\u003eK. Vang Nielsen, B. Ejlertsen, S. M\u0026oslash;ller, J. Tr\u0026oslash;st J\u0026oslash;rgensen, A. Knoop, H. Knudsen, et al., The value of TOP2A gene copy number variation as a biomarker in breast cancer: Update of DBCG trial 89D. Acta Oncol, 2008. 47(4): p. 725-34.\u003c/li\u003e\n\u003cli\u003eJ. Erriquez, P. Becco, M. Olivero, R. Ponzone, F. Maggiorotto, A. Ferrero, et al., TOP2A gene copy gain predicts response of epithelial ovarian cancers to pegylated liposomal doxorubicin: TOP2A as marker of response to PLD in ovarian cancer. Gynecol Oncol, 2015. 138(3): p. 627-33.\u003c/li\u003e\n\u003cli\u003eQ.H. Xi, M.H. Huang, Y.Y. Wang, J.X. Zhong, R. Liu, G.Q. Xu, et al., The expression of CDK1 is associated with proliferation and can be a prognostic factor in epithelial ovarian cancer. Tumour Biol, 2015. 36(7): p. 4939-48.\u003c/li\u003e\n\u003cli\u003eT. Bae, K.Y. Weon, J.W. Lee, K.H. Eum, S. Kim, and J.W. Choi, Restoration of paclitaxel resistance by CDK1 intervention in drug-resistant ovarian cancer. Carcinogenesis, 2015. 36(12): p. 1561-71.\u003c/li\u003e\n\u003cli\u003eJ.U. Shin, C.H. Lee, K.T. Lee, J.K. Lee, K.H. Lee, K.M. Kim, et al., Prognostic significance of ATM and cyclin B1 in pancreatic neuroendocrine tumor. Tumour Biol, 2012. 33(5): p. 1645-51.\u003c/li\u003e\n\u003cli\u003eY. Fang, H. Yu, X. Liang, J. Xu, and X. Cai, Chk1-induced CCNB1 overexpression promotes cell proliferation and tumor growth in human colorectal cancer. Cancer Biol Ther, 2014. 15(9): p. 1268-79.\u003c/li\u003e\n\u003cli\u003eS.P. Wang, S.Q. Wu, S.H. Huang, Y.X. Tang, L.Q. Meng, F. Liu, et al., FDI-6 inhibits the expression and function of FOXM1 to sensitize BRCA-proficient triple-negative breast cancer cells to Olaparib by regulating cell cycle progression and DNA damage repair. Cell Death Dis, 2021. 12(12): p. 1138.\u003c/li\u003e\n\u003cli\u003eX. Yang, S. Zhou, C. Yang, C. Cao, M. He, and S. Zi, CCNB1, Negatively Regulated by miR-559, Promotes the Proliferation, Migration, and Invasion of Ovarian Carcinoma Cells. Mol Biotechnol, 2022. 64(9): p. 958-969.\u003c/li\u003e\n\u003cli\u003eY. Zou, S. Ruan, L. Jin, Z. Chen, H. Han, Y. Zhang, et al., CDK1, CCNB1, and CCNB2 are Prognostic Biomarkers and Correlated with Immune Infiltration in Hepatocellular Carcinoma. Med Sci Monit, 2020. 26: p. e925289.\u003c/li\u003e\n\u003cli\u003eH. Zhang, X. Zhang, X. Li, W.B. Meng, Z.T. Bai, S.Z. Rui, et al., Effect of CCNB1 silencing on cell cycle, senescence, and apoptosis through the p53 signaling pathway in pancreatic cancer. J Cell Physiol, 2018. 234(1): p. 619-631.\u003c/li\u003e\n\u003cli\u003eR. Du, C. Huang, K. Liu, X. Li and Z. Dong, Targeting AURKA in Cancer: molecular mechanisms and opportunities for Cancer therapy. Mol Cancer, 2021. 20(1): p. 15.\u003c/li\u003e\n\u003cli\u003eS.K. Heo, E.K. Noh, Y.K. Jeong, L.J. Ju, J.Y. Sung, H.M. Yu, et al., Radotinib inhibits mitosis entry in acute myeloid leukemia cells via suppression of Aurora kinase A expression. Tumour Biol, 2019. 41(5): p. 1010428319848612.\u003c/li\u003e\n\u003cli\u003eE.O. Dos Santos, T.C. Carneiro-Lobo, M.N. Aoki, E. Levantini, and D.S. Bass\u0026egrave;res, Aurora kinase targeting in lung cancer reduces KRAS-induced transformation. Mol Cancer, 2016. 15: p. 12.\u003c/li\u003e\n\u003cli\u003eK. Sasai, W. Treekitkarnmongkol, K. Kai, H. Katayama, and S. Sen, Functional Significance of Aurora Kinases-p53 Protein Family Interactions in Cancer. Front Oncol, 2016. 6: p. 247.\u003c/li\u003e\n\u003cli\u003eL. Yang, Q. Zhou, X. Chen, L. Su, B. Liu, and H. Zhang, Activation of the FAK/PI3K pathway is crucial for AURKA-induced epithelial-mesenchymal transition in laryngeal cancer. Oncol Rep, 2016. 36(2): p. 819-26.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"high-grade serous ovarian cancer, genes expression profiles, microarray, pathways, Protein-protein interaction (PPI), survival","lastPublishedDoi":"10.21203/rs.3.rs-4717976/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4717976/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEpithelial ovarian cancer (EOC) is one of the most aggressive tumors in women. The most common pathological type of EOC is high-grade serous carcinoma (HGSC), which is often diagnosed at an advanced stage. Low-grade serous carcinoma (LGSC) is estimated to account for 10% of all serous carcinomas. Previous studies have demonstrated that molecular and clinical characteristics differences are apparent between these two subtypes of EOC. The objective of this study was to screen and identify key genes between HGSC and LGSC, and to explore potential molecular mechanisms in the pathogenesis of EOC. The microarray datasets GSE27651 and GSE14001, with a total of 23 LGSC tissue samples and 32 HGSC tissue samples, were obtained from the Gene Expression Omnibus (GEO). The differentially-expressed genes (DEGs) were selected out through the “affy” and “limma” package in R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed through the Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) analysis of DEGs was carried out through the Cytoscape software. Finally, survival analysis of some key geneswas conducted using the Kaplan Meier Plotter Online Tool. A total of 357 DEGs were found in HGSC, of which 181 were up regulated and 176 were down regulated. GO functional enrichment analysis showed that the DEGs were mainly associated with nucleus, cell proliferation and protein binding. KEGG pathway analysis showed that these genes were enriched in the PI3K-Akt signaling pathway, pathways in cancer, the p53 signaling pathway, cell cycle, microRNAs in cancer. Twelve hub genes (TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1, BUB1, RRM2 and PBK) were screened out from PPI network. The mRNA expression of TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1, BUB1, RRM2, and PBK were significantly increased in tumor tissues. The protein expression of TOP2A, CDK1, CCNB1, MAD2L1, KIF11, CCNB2, TTK, AURKA, RACGAP1 and PBK were distinctly higher in serous ovarian cancer tissues than non-serous ovarian cancer tissues detected by immunohistochemical staining. Survival analysis showed that TOP2A, CCNB1, KIF11, AURKA, and BUB1 were significantly associated with clinical survival outcome. In addition, there is a significant correlation between the expression levels of twelve hub-genes and immune cell infiltration in serous ovarian cancer. In summary, the present study identified DEGs and hub genes by two GEO datasets mining, which might offer new insights into the molecular mechanisms of these two subtypes of EOC and provide some prognostic biomarkers for the treatment of EOC.\u003c/p\u003e","manuscriptTitle":"Screening and identification of key genes between high-grade and low-grade serous ovarian carcinomas using integrated bioinformatics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 04:49:07","doi":"10.21203/rs.3.rs-4717976/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":"39d147f4-717b-4a7b-9ceb-f3fe104456bc","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-07T16:24:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-05 04:49:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4717976","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4717976","identity":"rs-4717976","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}