Recognition of multiple potential diagnostic biomarkers and therapeutic targets for ovarian serous cystadenocarcinoma (OSOC)

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Therefore, novel molecular pathways that contribute to OSOC therapy and diagnostics are urgently needed. Methods: Based on the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases, we acquired differentially expressed genes (DEGs) from GEO and clinical information from TCGA, and then we performed functional analysis of the 2DEGs. Protein-protein interaction (PPI) network and Robust Rank Aggregation (RRA) analysis were used to screen the hub genes. Next, diagnostic, prognostic, immune infiltration and pancancer analysis of hub genes were performed. In addition, the drug sensitivity and drug interaction networks of the hub genes were assessed. Finally, we performed transcription factors (TFs) and competing endogenous RNA(ceRNA) regulatory analyses of the hub genes to analyze the underlying molecular mechanisms associated with the hub genes. Results: Ultimately, we screened 6 hub genes, namely GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7. According to the ROC analysis, all 6 hub genes demonstrated excellent efficacy. Correlation analysis revealed that the expression of the hub genes was significantly correlated with the deterioration of OSOC. Conclusion: By combining the PPI network, this research identified 6 hub genes that could serve as novel targets for the diagnosis and treatment of OSOC. bioinformatic analysis biomarker ovarian serous cystadenocarcinoma drugs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Ovarian cancer (OC) is the deadliest cancer in women, and rank fourth among all female fatal diseases( 1 ). OC can be subdivided into different histological subtypes, including epithelial cancer, which accounts for approximately 90% of all OCs, as well as mucinous, endometrioid, clear cell, and serous cancers, which have different morphologies and tissue structures under the microscope. Among these types, high-grade serous carcinoma (HGSOC) is the most common. There are many risk factors for developing OC,including delayed childbearing, early menarche, endometriosis, and long-term estrogen replacement therapy of more than 5 years, and recent studies have also revealed that BRCA gene mutations are important risk factors. OC usually presents vague gastrointestinal, urinary, or nonacute pelvic or abdominal discomfort, as the ovary is anatomically located deep in the pelvis. Because early-stage OC is difficult to detect, when patients present with clinical symptoms and are confirmed to have an ovarian mass through pelvic examination or imaging studies, approximately 75–80% of patients have advanced peritoneal metastasis at the time of diagnosis( 2 , 3 ). Blood CA125 (a tumor marker for OC) is a common screening method for OC, and in patients with ovarian cancer, serum CA125 levels may be elevated, but its sensitivity for early diagnosis is low.Moreover CA125 levels may also increase in other physiological or pathological conditions such as menstruation, pregnancy, endometriosis, and inflammatory diseases of the peritoneum, so its specificity is low( 4 , 5 ). Therefore, there is currently no effective early ovarian cancer screening method. Studies have shown that combined transvaginal ultrasound (TVUS) with high CA125 levels may be a relatively good screening method for OC( 6 ). In addition, the treatment for OC includes surgery, and achieving complete tumor resection under a microscope at the time of initial debulking surgery is the most important prognostic factor for advanced OC( 7 ). After surgery, patients receive platinum-based chemotherapy, which is the most commonly used platinum-paclitaxel treatment regimen. However, this chemotherapy regimen is often accompanied by many complications, including leukopenia,and thrombocytopenia( 8 ). These complications significantly reduce the quality of life of patients and may even threaten their lives. According to the latest guidelines, even with the addition of bevacizumab, the five-year survival rate is only approximately 47% due to relapse and chemotherapy resistance( 9 ). Materials and methods Data acquisition and preprocessing Ovarian cancer datasets for the TCGA (https://portal.gdc.cancer.gov/). One of the gene expression profiling datasets (GSE14001) included 3 normal human ovarian surface epithelial (HOSE) cells, 10 low-grade samples and 10 high-grade serous ovarian cancer samples. Another gene expression profiling dataset (GSE14407) included 12 samples of healthy ovarian surface epithelium (OSE) and 12 samples of serous ovarian cancer epithelium (CEPI) obtained via laser capture microdissection.The Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/database) was used to download and measure in the array. Screening of DEGs The original microarray data of the GSE14001 and GSE14407 datasets were analyzed to screen for differentially expressed genes (DEGs). Adjective p value < 0.05, logFC, with 3％ E2 as the critical value. The DrawVenn diagram tools (http://bioinformatics.psb.ugent.be/webtools/Venn/) was used to calculate the DEGs derived from two different datasets at the intersection between two differenct DEGs,which represented a common differentially expressed gene (common DEG). GSEA biological function and pathway enrichment analysis Gene set enrichment analysis (GSEA) is a computational method used to assess whether previously defined gene sets exhibit statistical significance and consistent differences between two biological states. Single-gene GSEA was performed via the Xiantao Academic (XT). A P value < 0.05 and a p value < 0.05 are regarded as the critical value standards. Identification of upregulated and downregulated cDEGs and KEGG and GO functional and pathway enrichment analyses Differentially expressed genes (DEGs) between groups were identified via DESeq2. Genes with |log2FC| > 1 and FDR-adjusted p-value < 0.05 were classified as upregulated or downregulated cDEGs.Functional enrichment analysis of cdeg was conducted via clusterProfiler. The KEGG pathway and GO terms were tested with an FDR < 0.05 as the critical value. PPI network construction as well as hub gene recognition and expression value evaluation The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) (https://string-db.org/) was used to insert cDEGs into STRING software to construct and visualize protein-protein interaction (PPI) networks. In addition, cytoHubba in Cytoscape software was used to screen for hub genes. The first six genes with a connectivity greater than 5 were selected as hub genes, namely, GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7. Clinical values of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 Receiver operating characteristic (ROC) curve analysis was performed on six Hub genes, GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7, via XT. An AUC > 90% was set as the cutoff value to determine the clinical diagnostic value of the hub gene. For survival analysis, the gene expression values were divided into low-expression groups and high-expression groups. The hazard ratio (HR) was determined through the Cox regression model, and the survival curve was plotted via Kaplan-Meierthe estimation. P% 3C 0.05 was considered to indicate a statistically significant difference. Analysis of the relationships between GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 and immune cells Spearman rank correlation was used to analyze the expression levels of the hub genes and the immune cell infiltration scores. The correlation coefficient (ρ) was < 0.05, and the adjusted p value (FDR) was < 0.05, which was considered statistically significant. ceRNA regulatory and TF regulatory,network construction and drug sensitivity analysis of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 The regulation of the central gene network, including the interactions between ceRNAs (miRNA-circRNA) and transcription factors (TFs), was analyzed via XT.The miRNA targets of the hub genes were predicted by TargetScan (context++ score cutoff > 0.9), and the interactions with prediction scores exceeding the threshold were retained for network visualization.The correlations between the hub genes and immune cell infiltration levels were analyzed via the Immune Correlation module of XT, with Spearman’s method. Significant correlations (FDR 0.7, and in the pharmacogenomic analysis, Spearman correlation was applied to evaluate the relationship between the expression level of the hub genes and the drug IC50 value between the tumor cell lines (FDR < 0.05). Expression values of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in crosscancers The pancancer expression profile of the hub genes was analyzed via the 'TCGA pancancer' module of XT, and the differential expression was evaluated by the Wilcoxon rank sum test (tumor versus normal). Diagnostic and prognostic values of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 across cancers. The ROC curves of the hub genes for the 33 TCGA cancer types were constructed via the "diagnostic analysis" module of XT, and the area under the curve (AUC) value and 95% confidence interval (CI) for each cancer type (tumor versus normal samples) were calculated.Moreover, among 33 types of TCGA cancers, the prognostic association between central gene expression and overall survival (OS) was evaluated by univariate Cox regression. Immune infiltration analysis of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 across cancers. The immune infiltration level of cancer is estimated through the "immune infiltration" module of XT. Spearman correlation analysis was conducted between hub gene expression and the abundance of immune cells (ρ > 0.3, FDR < 0.05). Results 1.Screening of DEGs in GSE14001 and GSE14407 We conducted DEG analysis on the GEO datasets GSE14407 and GSE14001,and identified 1664 upregulated DEGs, 1726 downregulated DEGs,1687 upregulated DEGs and 1265 downregulated DEGs on the basis of the criterion of P< 0.05. The volcano piots of the DEGs are shown in (Figure 1A,B).Meanwhile, the expression of different genes in the reference group and the test group varied from high to low. A heatmap of the DEGs is shown in (Figure 1C,D). GSEA of DEGs in GSE14001 and GSE14407 To evaluate the functions associated with the DEGs, we performed GSEA.The results revealed that the DEGs in GSE14001 are involved in mediating cellular responses to stimuli,and that the DEGs in GSE14407 are involved in mediating the resolution of sister chromatid cohesion. 3.cDEG identification from GSE14001 and GSE14407 A Venn diagram revealed that 360 upregulated cDEGs were cross-screened in the datasets GSE14407 and GSE14001(Figure 3A).A Venn diagram revealed that 564 downregulated cDEGs were cross-screened in the GSE14407 and GSE14001datasets(Figure 3B).We performed GO and KEGG functional enrichment analyses. The upregulated cDEGs are involved mainly in the regulation of cell growth during blood pressure assessment,and are localized to the ruffle membrane in CC upregulated cDEGs associated with MFs, including serine hydrolase activity,extracellular matrix structural constituents and other functions(Figure 3C).The downregulated cDEGs are involved mainly in the regulation of DNA-binding transcription factor activity in blood pressure assessment,and are localized to cell−substrate junctions and focal adhesion proteins in CCs.Through KEGG enrichment analysis, the downregulated genes were showen to play important roles in regulating proteoglycan expression in cancer_(Figure 3D). 4.PPI network construction and identification of hub genes. From the PPI network, we obtained 6 genes, namely GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7, through the cross-screened upregulated cDEGs(Figure 4A).We detected significantly greater expression of these hub genes in HGSOC samples than in normal control samples.(Figure 4 B-G). 5. Diagnostic, prognostic,and clinical value evaluation of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in OSOC We performed Kaplan-Meier(KM) survival analysis on the 6 hub genes to determine their potential as prognostic biomarkers for HGSOC. As shown in Figure 5,lower expression of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 was correlated with a worse patient survival rate(Figure 5A-F).To explore the diagnostic efficacy of the 6 hub genes, we implemented a receiver operating characteristic (ROC) curve analysis in which hub genes with an area under the curve (AUC) value > 0.7 were used as diagnostic markers. The AUC values were 0.988 for E2F7,0.986 for ORC6, 0.996 for PKMYT1, 0.989 for RAD54L, 0.997 for UBE2C, and 0.987 or GTSE1(Figure 5 G-L).Finally, we analyzed the correlation between disease progression and genes, and the results indicated that the expression levels of the six genes were significantly correlated with different stages of OSOC.(Figure 5 M). 6. Immune infiltration analysis of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in OSOC As shown in figure 6,it displys the distribution of 22 different immune cell types in each sample within the high-expression and low-expression groups of the 6 hub genes. 7. ceRNA and TF regulatory network construction as well as prediction of targeted drugs and drug sensitivity In figure 7,we predicted the genes targeted by the hub genes and constructed a network graph. The figure indicates that the hub genes are potential targets for transcriptional regulation, and that their expression levels may be finely regulated by multiple transcription factors(Fiure 7 A).To predict the targeted circRNAs, we constructed the a Sanjit diagram of ceRNAs (Fiure 7 B).Through GSCA analysis, we predicted targeted drugs for GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 and constructed a network (Fiure 7 C). 8.Expression values of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in different cancers As shown in figure 8,the expression of the 6 hub genes in different cancers was analyzed, and the results revealed that, compared with that in normal tissues, their expression in different cancers was increased to different degrees.The results revealed that in UCEC(uterine corpus endometrial carcinoma), the expression levels of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in normal tissues were significantly greater than those in cancer tissues.In UCS(uterine carcinosarcoma) tissue and OV(ovarian cancer) tissue, the expression level was also relatively high.Meanwhile, the figure also shows that the expression of the six genes in cancer tissues is increased compared with that in normal tissues. 9. Diagnostic and prognostic values as well as immune cell associations of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in different cancers We constructed a pancancer prognosis heatmap to analyze the prognostic significance of six genes in the TCGA database in various cancer types. The figure shows that RAD545L and UBE2C are positively correlated with poor prognosis of UCEC patients(Figure 9A-F). To further analyze the correlation between genes and the clinical outcome of cancer patients, we constructed a forest plot. The results revealed that high expression of RAD545L and UBE2C increased the risk of death in patients with UCEC and was a risk factor leading to poor prognosis (Figure 9G-L). Discussion HGOSCs are the most common type of OC, accounting for approximately 70% of all ovarian tumors. It is highly invasive and is mostly diagnosed at an advanced stage, with a relatively low 5-year overall survival rate. The treatment for this disease includes cytoreductive surgery and combination chemotherapy with platinum agents and taxanes. GTSE1, with a predicted molecular weight of approximately 77 kDa, is expressed only in the S and G2 phases of the cell cycle and is involved mainly in the regulation of the cell cycle. Overexpressed GTSE1 can promote the proliferation, migration and invasion of cancer cells by affecting the growth cycle of cancer cells and DNA damage repair, and can affect chemotherapy resistance.(10, 11) ORC6, a subunit of the initiation complex, has a molecular weight of approximately 32.1 kDa and plays an important role in cell cycle regulation by coordinating the assembly of proteins required for precise DNA replication during cell division. During the development and progression of cancer, it promotes cell proliferation, affects cell cycle progression, and enhances cell migration and invasion.(12, 13) The molecular weight of PKMYT1 is approximately 54.521 kDa. It negatively regulates cell division by disrupting the distribution of the cell cycle, and plays an important role in regulating the cell cycle and maintaining normal DNA replication by interfering with the distribution of the cell cycle. Moreover, it promotes the proliferation,and migration of cancer cells and inhibits their apoptosis by regulating the cell cycle.(14, 15). RAD54L2, with a predicted molecular weight of approximately 23.28 kDa,is involved mainly in DNA repair and cell cycle regulation. By increasing the sensitivity of cells to DNA damage and weakening their ability to repair damage, it promotes the abnormal proliferation and migration of cancer cells. (16) UBE2C has a total molecular weight of 19.65 kDa and is responsible for transferring ubiquitin to targeted proteins for proteasome-mediated degradation. It participates in cell cycle progression and checkpoint control by targeting the degradation of short-lived proteins. UBE2C overexpression has been found only in cancer cells and is absent in normal tissues, and it has been described as a promoter of tumor cell migration and invasion in several types of cancer.(17, 18)The molecular weight of E2F7 is approximately 99.535 kDa. As a transcription factor, E2F7 can bind to DNA independently,and recognize and bind to specific E2 recognition sites to regulate gene transcription. It also mediates the inhibition of G1/S phase regulatory genes, thereby regulating cell cycle progression. The unrestricted activation of E2F7-dependent transcription is regarded as an important driving factor for tumorigenesis. Recent studies have reported that E2F7 is significantly upregulated in various cancer types and promotes tumorigenesis.(19) Study Limitations This study has several limitations. The findings are derived from a retrospective bioinformatic analysis of public datasets (TCGA, GEO), and the sample size of normal tissues is relatively small. Consequently, the proposed roles of the six hub genes require further validation through in vitro and in vivo experiments. Additionally, the identified diagnostic and prognostic values lack confirmation from independent clinical cohorts. Conclusion After obtaining gene and clinical information from different databases and using various bioinformatic analysis methods, we ultimately identified 6 key genes,GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7,which can serve as diagnostic biomarkers and potential theorapeutic drugs for HGSOC.In addition, we also performed a pancancer analysis to determine the diagonstic,prognostic and immune infiltration roles of these 6 hub genes in different cancer types. Abbreviations Abbreviation Full name Description OSOC Ovarian Serous Cystadenocarcinoma Ovarian serous cystadenocarcinoma GEO Gene Expression Omnibus Gene expression database TCGA The Cancer Genome Atlas Cancer genomics database DEGs Differentially Expressed Genes Genes with significant expression changes PPI Protein-Protein Interaction Protein interaction network RRA Robust Rank Aggregation Statistical analysis method ROC Receiver Operating Characteristic Diagnostic performance metric AUC Area Under the Curve ROC curve performance measure HGSOC High-Grade Serous Ovarian Cancer Aggressive ovarian cancer subtype GO Gene Ontology Gene functional annotation system KEGG Kyoto Encyclopedia of Genes and Genomes Pathway analysis database HR Hazard Ratio Survival analysis metric TF Transcription Factor Gene expression regulator CeRNA Competing Endogenous RNA RNA regulatory mechanism Declarations Funding: Not applicable. The consent to publish statement: Not applicable. Clinical trial number: not applicable. Consent to Publish declarations: All authors have reviewed and approved the final version of the manuscript. They have no objections to the research content, data analysis, or conclusions, and consent to its submission for publication. Ethics, Consent to Participate: not applicable. Author Contribution ZYW analyzed the data,ZYW and JLP wrote the main manuscript text and .All authors reviewed the manuscript. Data Availability The datasets analyzed during this study are publicly available from the following sources:TCGA ovarian cancer data,available through the NCI Genomic Data Commons Portal (https://portal.gdc.cancer.gov/projects/TCGA-OV), referenced under study ID TCGA-OV.GEO datasets:GSE14001: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14001 (Platform GPL96)GSE14407: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14407 (Platform GPL570) References Chandra A, Pius C, Nabeel M, Nair M, Vishwanatha JK, Ahmad S, et al. 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Chen KQ, Lei HB, Liu X, Wang SZ. The roles of E2F7 in cancer: Current knowledge and future prospects. HELIYON. [Journal Article; Review]. 2024 2024/7/30;10(14):e34362. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Dec, 2025 Reviews received at journal 20 Nov, 2025 Reviews received at journal 20 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers invited by journal 31 Oct, 2025 Editor invited by journal 22 Oct, 2025 Editor assigned by journal 10 Oct, 2025 Submission checks completed at journal 22 Sep, 2025 First submitted to journal 22 Sep, 2025 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. 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15:21:02","extension":"html","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73118,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7523248/v1/ee42b42be0c17ccad1984b59.html"},{"id":95748601,"identity":"bf56990c-68c8-4c71-a289-4792c07702ed","added_by":"auto","created_at":"2025-11-12 15:21:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":602944,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot and heatmap of (DEGs. (A-B):Volcano plot of DEGs in GSE14407 and GSE14001.Red: upregulated genes; blue: downregulated genes; (C-D)Heatmap of the expression levels of different genes in the reference group test group and in GSE14407 and GSE14001.Red:high expression; blue: low expression.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7523248/v1/59a1258312b8bf271ead352a.png"},{"id":95801165,"identity":"d6b33da5-1d71-4f06-adcb-f72eaa13f09e","added_by":"auto","created_at":"2025-11-13 08:24:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":500745,"visible":true,"origin":"","legend":"\u003cp\u003e(A):GSEA functional enrichment analysis of DEGs in GSE14001; (B):GSEA functional enrichment analysis of DEGs in GSE114407.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7523248/v1/fced7697903fa7f232dbb046.png"},{"id":95748600,"identity":"94a96179-572e-47b0-a655-dee5d06ddb59","added_by":"auto","created_at":"2025-11-12 15:21:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":608914,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of the intersection of genes related to HGSOC.(A-B)Venn diagram of cDEGs in the GSE14407 and GSE14001 datasets.(C)The GO results are displayed with a bubble plot.(D) A bubble plot was constructed to illustrate the KEGG outcomes.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7523248/v1/dd8ca6283e8c6434d417f9ea.png"},{"id":95748603,"identity":"969a906e-572f-4511-afdb-550cddba8065","added_by":"auto","created_at":"2025-11-12 15:21:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":844430,"visible":true,"origin":"","legend":"\u003cp\u003e(A):Screening of the hub genes via the PPI network. (B-G)Expression of the hub genes and validation.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7523248/v1/ac17594f3886c21eef630168.png"},{"id":95801810,"identity":"4f2ae875-7c26-471f-b78c-014faaaab4e2","added_by":"auto","created_at":"2025-11-13 08:26:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":774586,"visible":true,"origin":"","legend":"\u003cp\u003e(A-F):Kaplan-Meier survival analysis of genes associated with HGSOC patient survival. The x-axis represents time in days, and the y-axis represents the percentage of surviving patients. The red line represents patients with high gene expression, whereas the blue line represents patients with low gene expression.(G-L):Hub genes were analyzed via receiver operating characteristic (ROC) curves.(M):Heatmap displaying the correlation between six genes and disease progression.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7523248/v1/93a8b8ce4675164b51a75814.png"},{"id":95748606,"identity":"1f1fda60-e4aa-42a5-b726-27356ae007cb","added_by":"auto","created_at":"2025-11-12 15:21:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1363365,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of immune cell infiltration in individuals in the high-expression and low-expression groups. (A-F):Bar plot indicating the distribution of 22 types of immune cells in the high-expression and low-expression groups of the 6 hub genes.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7523248/v1/c4dc9bc633e1b74810d14054.png"},{"id":95800751,"identity":"8aa401e7-c5bd-48ad-824f-330855b0678a","added_by":"auto","created_at":"2025-11-13 08:23:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":820295,"visible":true,"origin":"","legend":"\u003cp\u003e(A):Nnetwork diagram for predicting the targeted miRNAs of the hub genes.(B):Sangji diagram of the prediction of targeted circRNAs.(C):Network diagram for predicting the targeted drugs.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7523248/v1/4aab42ceef45ed82a07dc114.png"},{"id":95748611,"identity":"e5951f86-9d1b-476e-8f8c-f26f47a8b5e9","added_by":"auto","created_at":"2025-11-12 15:21:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1145923,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots showing the expression of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in different cancer tissues and normal tissues.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7523248/v1/1b3ddc860cd8f5e79b82ec77.png"},{"id":95748620,"identity":"1e18941c-8b91-4222-b894-00a5ef379dbf","added_by":"auto","created_at":"2025-11-12 15:21:01","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1271783,"visible":true,"origin":"","legend":"\u003cp\u003e(A-F):Prognostic heatmaps of six genes in different cancers.(G-L):Forest plot for analyzing the correlation between genes and the clinical outcomes of patients with cancer.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7523248/v1/da17f756325396449de2bd39.png"},{"id":95818735,"identity":"2c86b399-1c76-41c2-97e0-5746ed3010b8","added_by":"auto","created_at":"2025-11-13 10:32:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8324945,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7523248/v1/5ce84b0b-87e7-42ca-b85b-df52c716baf4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Recognition of multiple potential diagnostic biomarkers and therapeutic targets for ovarian serous cystadenocarcinoma (OSOC)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer (OC) is the deadliest cancer in women, and rank fourth among all female fatal diseases(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). OC can be subdivided into different histological subtypes, including epithelial cancer, which accounts for approximately 90% of all OCs, as well as mucinous, endometrioid, clear cell, and serous cancers, which have different morphologies and tissue structures under the microscope. Among these types, high-grade serous carcinoma (HGSOC) is the most common. There are many risk factors for developing OC,including delayed childbearing, early menarche, endometriosis, and long-term estrogen replacement therapy of more than 5 years, and recent studies have also revealed that BRCA gene mutations are important risk factors. OC usually presents vague gastrointestinal, urinary, or nonacute pelvic or abdominal discomfort, as the ovary is anatomically located deep in the pelvis. Because early-stage OC is difficult to detect, when patients present with clinical symptoms and are confirmed to have an ovarian mass through pelvic examination or imaging studies, approximately 75\u0026ndash;80% of patients have advanced peritoneal metastasis at the time of diagnosis(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Blood CA125 (a tumor marker for OC) is a common screening method for OC, and in patients with ovarian cancer, serum CA125 levels may be elevated, but its sensitivity for early diagnosis is low.Moreover CA125 levels may also increase in other physiological or pathological conditions such as menstruation, pregnancy, endometriosis, and inflammatory diseases of the peritoneum, so its specificity is low(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Therefore, there is currently no effective early ovarian cancer screening method. Studies have shown that combined transvaginal ultrasound (TVUS) with high CA125 levels may be a relatively good screening method for OC(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In addition, the treatment for OC includes surgery, and achieving complete tumor resection under a microscope at the time of initial debulking surgery is the most important prognostic factor for advanced OC(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). After surgery, patients receive platinum-based chemotherapy, which is the most commonly used platinum-paclitaxel treatment regimen. However, this chemotherapy regimen is often accompanied by many complications, including leukopenia,and thrombocytopenia(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These complications significantly reduce the quality of life of patients and may even threaten their lives. According to the latest guidelines, even with the addition of bevacizumab, the five-year survival rate is only approximately 47% due to relapse and chemotherapy resistance(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003col\u003e\n \u003cli\u003eData acquisition and preprocessing\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eOvarian cancer datasets for the TCGA (https://portal.gdc.cancer.gov/). One of the gene expression profiling datasets (GSE14001) included 3 normal human ovarian surface epithelial (HOSE) cells, 10 low-grade samples and 10 high-grade serous ovarian cancer samples. Another gene expression profiling dataset (GSE14407) included 12 samples of healthy ovarian surface epithelium (OSE) and 12 samples of serous ovarian cancer epithelium (CEPI) obtained via laser capture microdissection.The Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/database) was used to download and measure in the array.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003eScreening of DEGs\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe original microarray data of the GSE14001 and GSE14407 datasets were analyzed to screen for differentially expressed genes (DEGs). Adjective p value \u0026lt; 0.05, logFC, with 3％\u0026nbsp;E2 as the critical value. The DrawVenn diagram tools (http://bioinformatics.psb.ugent.be/webtools/Venn/) was used to calculate the DEGs derived from two different datasets at the intersection between two differenct DEGs,which represented a common differentially expressed gene (common DEG).\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003eGSEA biological function and pathway enrichment analysis\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eGene set enrichment analysis (GSEA) is a computational method used to assess whether previously defined gene sets exhibit statistical significance and consistent differences between two biological states. Single-gene GSEA was performed via the Xiantao Academic (XT). A P value \u0026lt; 0.05 and a p value \u0026lt; 0.05 are regarded as the critical value standards.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n \u003cli\u003eIdentification of upregulated and downregulated cDEGs and KEGG and GO functional and pathway enrichment analyses\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) between groups were identified via DESeq2. \u0026nbsp; Genes with |log2FC| \u0026gt; 1 and FDR-adjusted p-value \u0026lt; 0.05 were classified as upregulated or downregulated cDEGs.Functional enrichment analysis of cdeg was conducted via clusterProfiler. The KEGG pathway and GO terms were tested with an FDR \u0026lt; 0.05 as the critical value.\u003c/p\u003e\n\u003col start=\"5\"\u003e\n \u003cli\u003ePPI network construction as well as hub gene recognition and expression value evaluation\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) (https://string-db.org/) was used to insert cDEGs into STRING software to construct and visualize protein-protein interaction (PPI) networks. In addition, cytoHubba in Cytoscape software was used to screen for hub genes. The first six genes with a connectivity greater than 5 were selected as hub genes, namely, GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n \u003cli\u003eClinical values of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curve analysis was performed on six Hub genes, GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7, via XT. An AUC \u0026gt; 90% was set as the cutoff value to determine the clinical diagnostic value of the hub gene. For survival analysis, the gene expression values were divided into low-expression groups and high-expression groups. The hazard ratio (HR) was determined through the Cox regression model, and the survival curve was plotted via Kaplan-Meierthe estimation. P% 3C 0.05 was considered to indicate a statistically significant difference.\u003c/p\u003e\n\u003col start=\"7\"\u003e\n \u003cli\u003eAnalysis of the relationships between GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 and immune cells\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eSpearman rank correlation was used to analyze the expression levels of the hub genes and the immune cell infiltration scores. The correlation coefficient (\u0026rho;) was \u0026lt; 0.05, and the adjusted p value (FDR) was \u0026lt; 0.05, which was considered statistically significant.\u003c/p\u003e\n\u003col start=\"8\"\u003e\n \u003cli\u003eceRNA regulatory and TF regulatory,network construction and drug sensitivity analysis of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe regulation of the central gene network, including the interactions between ceRNAs (miRNA-circRNA) and transcription factors (TFs), was analyzed via XT.The miRNA targets of the hub genes were predicted by TargetScan (context++ score cutoff \u0026gt; 0.9), and the interactions with prediction scores exceeding the threshold were retained for network visualization.The correlations between the hub genes and immune cell infiltration levels were analyzed via the Immune Correlation module of XT, with Spearman\u0026rsquo;s method. Significant correlations (FDR \u0026lt; 0.05) were visualized as a heatmap.The \u0026quot;drug-gene network\u0026quot; module in XT was utilized to construct the drug-gene interaction network of the hub genes. The interaction confidence score was set at \u0026gt; 0.7, and in the pharmacogenomic analysis, Spearman correlation was applied to evaluate the relationship between the expression level of the hub genes and the drug IC50 value between the tumor cell lines (FDR \u0026lt; 0.05).\u003c/p\u003e\n\u003col start=\"9\"\u003e\n \u003cli\u003eExpression values of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in crosscancers\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe pancancer expression profile of the hub genes was analyzed via the \u0026apos;TCGA pancancer\u0026apos; module of XT, and the differential expression was evaluated by the Wilcoxon rank sum test (tumor versus normal).\u003c/p\u003e\n\u003col start=\"10\"\u003e\n \u003cli\u003eDiagnostic and prognostic values of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 across cancers.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe ROC curves of the hub genes for the 33 TCGA cancer types were constructed via the \u0026quot;diagnostic analysis\u0026quot; module of XT, and the area under the curve (AUC) value and 95% confidence interval (CI) for each cancer type (tumor versus normal samples) were calculated.Moreover, among 33 types of TCGA cancers, the prognostic association between central gene expression and overall survival (OS) was evaluated by univariate Cox regression.\u003c/p\u003e\n\u003col start=\"11\"\u003e\n \u003cli\u003eImmune infiltration analysis of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 across cancers.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe immune infiltration level of cancer is estimated through the \u0026quot;immune infiltration\u0026quot; module of XT. Spearman correlation analysis was conducted between hub gene expression and the abundance of immune cells (\u0026rho; \u0026gt; 0.3, FDR \u0026lt; 0.05).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1.Screening of DEGs in GSE14001 and GSE14407\u003c/p\u003e\n\u003cp\u003eWe conducted DEG analysis on the GEO datasets GSE14407 and GSE14001,and identified 1664 upregulated DEGs, 1726 downregulated DEGs,1687 upregulated DEGs and 1265 downregulated DEGs on the basis of the criterion of P\u0026lt; 0.05. The volcano piots of the DEGs are shown in (Figure 1A,B).Meanwhile, the expression of different genes in the reference group and the test group varied from high to low. A heatmap of the DEGs is shown in (Figure 1C,D).\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003eGSEA of DEGs in GSE14001 and GSE14407\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo evaluate the functions associated with the DEGs, we performed GSEA.The results revealed that the DEGs in GSE14001 are involved in mediating cellular responses to stimuli,and that the DEGs in GSE14407 are involved in mediating the resolution of sister chromatid cohesion.\u003c/p\u003e\n\u003cp\u003e3.cDEG identification from GSE14001 and GSE14407\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA Venn diagram revealed that 360 upregulated cDEGs were cross-screened in the datasets GSE14407 and GSE14001(Figure 3A).A Venn diagram revealed that 564 downregulated cDEGs were cross-screened in the GSE14407 and GSE14001datasets(Figure 3B).We performed GO and KEGG functional enrichment analyses. The upregulated cDEGs are involved mainly in the regulation of cell growth during blood pressure assessment,and are localized to the ruffle membrane in CC upregulated cDEGs associated with MFs, including serine hydrolase activity,extracellular matrix structural constituents and other functions(Figure 3C).The downregulated cDEGs are involved mainly in the regulation of DNA-binding transcription factor activity in blood pressure assessment,and are localized to cell\u0026minus;substrate junctions and focal adhesion proteins in CCs.Through KEGG enrichment analysis, the downregulated genes were showen to play important roles in regulating proteoglycan expression in cancer_(Figure 3D).\u003c/p\u003e\n\u003cp\u003e4.PPI network construction and identification of hub genes.\u003c/p\u003e\n\u003cp\u003eFrom the PPI network, we obtained 6 genes, namely GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7, through the cross-screened upregulated cDEGs(Figure 4A).We detected significantly greater expression of these hub genes in HGSOC samples than in normal control samples.(Figure 4 B-G).\u003c/p\u003e\n\u003cp\u003e5. Diagnostic, prognostic,and clinical value evaluation of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in OSOC\u003c/p\u003e\n\u003cp\u003eWe performed Kaplan-Meier(KM) survival analysis on the 6 hub genes to determine their potential as prognostic biomarkers for HGSOC. As shown in Figure 5,lower expression of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 was correlated with a worse patient survival rate(Figure 5A-F).To explore the diagnostic efficacy of the 6 hub genes, we implemented a receiver operating characteristic (ROC) curve analysis in which hub genes with an area under the curve (AUC) value \u0026gt; 0.7 were used as diagnostic markers. The AUC values were 0.988 for E2F7,0.986 for ORC6, 0.996 for PKMYT1, 0.989 for RAD54L, 0.997 for UBE2C, and 0.987 or GTSE1(Figure 5 G-L).Finally, we analyzed the correlation between disease progression and genes, and the results indicated that the expression levels of the six genes were significantly correlated with different stages of OSOC.(Figure 5 M).\u003c/p\u003e\n\u003cp\u003e6. Immune infiltration analysis of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in OSOC\u003c/p\u003e\n\u003cp\u003eAs shown in figure 6,it displys the distribution of 22 different immune cell types in each sample within the high-expression and low-expression groups of the 6 hub genes.\u003c/p\u003e\n\u003cp\u003e7. ceRNA and TF regulatory network construction as well as prediction of targeted drugs and drug sensitivity\u003c/p\u003e\n\u003cp\u003eIn figure 7,we predicted the genes targeted by the hub genes and constructed a network graph. The figure indicates that the hub genes are potential targets for transcriptional regulation, and that their expression levels may be finely regulated by multiple transcription factors(Fiure 7 A).To predict the targeted circRNAs, we constructed the a Sanjit diagram of ceRNAs (Fiure 7 B).Through GSCA analysis, we predicted targeted drugs for GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 and constructed a network (Fiure 7 C).\u003c/p\u003e\n\u003cp\u003e8.Expression values of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in different cancers\u003c/p\u003e\n\u003cp\u003eAs shown in figure 8,the expression of the 6 hub genes in different cancers was analyzed, and the results revealed \u0026nbsp;that, compared with that in normal tissues, their expression in different cancers was increased to different degrees.The results revealed that in UCEC(uterine corpus endometrial carcinoma), the expression levels of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in normal tissues were significantly greater than those in cancer tissues.In UCS(uterine carcinosarcoma) tissue and OV(ovarian cancer) tissue, the expression level was also relatively high.Meanwhile, the figure also shows that the expression of the six genes in cancer tissues is increased compared with that in normal tissues.\u003c/p\u003e\n\u003cp\u003e9. Diagnostic and prognostic values as well as immune cell associations of GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7 in different cancers\u003c/p\u003e\n\u003cp\u003eWe constructed a pancancer prognosis heatmap to analyze the prognostic significance of six genes in the TCGA database in various cancer types. The figure shows that RAD545L and UBE2C are positively correlated with poor prognosis of UCEC patients(Figure 9A-F). To further analyze the correlation between genes and the clinical outcome of cancer patients, we constructed a forest plot. The results revealed that high expression of RAD545L and UBE2C increased the risk of death in patients with UCEC and was a risk factor leading to poor prognosis (Figure 9G-L).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHGOSCs are the most common type of OC, accounting for approximately 70% of all ovarian tumors. It is highly invasive and is mostly diagnosed at an advanced stage, with a relatively low 5-year overall survival rate. The treatment for this disease includes cytoreductive surgery and combination chemotherapy with platinum agents and taxanes.\u003c/p\u003e\n\u003cp\u003eGTSE1, with a predicted molecular weight of approximately 77 kDa, is expressed only in the S and G2 phases of the cell cycle and is involved mainly in the regulation of the cell cycle. Overexpressed GTSE1 can promote the proliferation, migration and invasion of cancer cells by affecting the growth cycle of cancer cells and DNA damage repair, and can affect chemotherapy resistance.(10, 11)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eORC6, a subunit of the initiation complex, has a molecular weight of approximately 32.1 kDa and plays an important role in cell cycle regulation by coordinating the assembly of proteins required for precise DNA replication during cell division. During the development and progression of cancer, it promotes cell proliferation, affects cell cycle progression, and enhances cell migration and invasion.(12, 13)\u003c/p\u003e\n\u003cp\u003eThe molecular weight of PKMYT1 is approximately 54.521 kDa. It negatively regulates cell division by disrupting the distribution of the cell cycle, and plays an important role in regulating the cell cycle and maintaining normal DNA replication by interfering with the distribution of the cell cycle. Moreover, it promotes the proliferation,and migration of cancer cells and inhibits their apoptosis by regulating the cell cycle.(14, 15).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRAD54L2, with a predicted molecular weight of approximately 23.28 kDa,is involved mainly in DNA repair and cell cycle regulation. By increasing the sensitivity of cells to DNA damage and weakening their ability to repair damage, it promotes the abnormal proliferation and migration of cancer cells.\u0026nbsp;(16)\u0026nbsp;UBE2C has a total molecular weight of 19.65 kDa and is responsible for transferring ubiquitin to targeted proteins for proteasome-mediated degradation. It participates in cell cycle progression and checkpoint control by targeting the degradation of short-lived proteins. UBE2C overexpression has been found only in cancer cells and is absent in normal tissues, and it has been described as a promoter of tumor cell migration and invasion in several types of cancer.(17, 18)The molecular weight of E2F7 is approximately 99.535 kDa. As a transcription factor, E2F7 can bind to DNA independently,and recognize and bind to specific E2 recognition sites to regulate gene transcription. It also mediates the inhibition of G1/S phase regulatory genes, thereby regulating cell cycle progression. The unrestricted activation of E2F7-dependent transcription is regarded as an important driving factor for tumorigenesis. Recent studies have \u0026nbsp;reported that E2F7 is significantly upregulated in various cancer types and promotes tumorigenesis.(19)\u003c/p\u003e\n\u003cp\u003eStudy Limitations\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. The findings are derived from a retrospective bioinformatic analysis of public datasets (TCGA, GEO), and the sample size of normal tissues is relatively small. Consequently, the proposed roles of the six hub genes require further validation through in vitro and in vivo experiments. Additionally, the identified diagnostic and prognostic values lack confirmation from independent clinical cohorts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAfter obtaining gene and clinical information from different databases and using various bioinformatic analysis methods, we ultimately identified 6 key genes,GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7,which can serve as diagnostic biomarkers and potential theorapeutic drugs for HGSOC.In addition, we also performed a pancancer analysis to determine the diagonstic,prognostic and immune infiltration roles of these 6 hub genes in different cancer types.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFull name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOSOC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOvarian Serous Cystadenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOvarian serous cystadenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene expression database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCancer genomics database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGenes with significant expression changes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProtein-Protein Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProtein interaction network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRobust Rank Aggregation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStatistical analysis method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDiagnostic performance metric\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eROC curve performance measure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHGSOC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-Grade Serous Ovarian Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAggressive ovarian cancer subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene functional annotation system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePathway analysis database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSurvival analysis metric\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTranscription Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene expression regulator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCeRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCompeting Endogenous RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRNA regulatory mechanism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding:\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eThe consent to publish statement:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eClinical trial number:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003eConsent to Publish declarations:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors have reviewed and approved the final version of the manuscript. They have no objections to the research content, data analysis, or conclusions, and consent to its submission for publication.\u003c/p\u003e\n\u003cp\u003eEthics, Consent to Participate:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003eAuthor Contribution\u003c/p\u003e\n\u003cp\u003eZYW analyzed the data,ZYW and JLP wrote the main manuscript text and .All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during this study are publicly available from the following sources:TCGA ovarian cancer data,available through the NCI Genomic Data Commons Portal (https://portal.gdc.cancer.gov/projects/TCGA-OV), referenced under study ID TCGA-OV.GEO datasets:GSE14001: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14001 (Platform GPL96)GSE14407: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14407 (Platform GPL570)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChandra A, Pius C, Nabeel M, Nair M, Vishwanatha JK, Ahmad S, et al. Ovarian cancer: Current status and strategies for improving therapeutic outcomes. CANCER MED-US. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov\u0026apos;t; Review]. 2019 2019/11/1;8(16):7018-31.\u003c/li\u003e\n\u003cli\u003eLu Z, Chen J. [Introduction of WHO classification of tumours of female reproductive organs, fourth edition]. Zhonghua Bing Li Xue Za Zhi. [Journal Article]. 2014 2014/10/1;43(10):649-50.\u003c/li\u003e\n\u003cli\u003eOrr B, Edwards RP. Diagnosis and Treatment of Ovarian Cancer. HEMATOL ONCOL CLIN N. [Journal Article; Review]. 2018 2018/12/1;32(6):943-64.\u003c/li\u003e\n\u003cli\u003eOlivier RI, Lubsen-Brandsma MA, Verhoef S, van Beurden M. CA125 and transvaginal ultrasound monitoring in high-risk women cannot prevent the diagnosis of advanced ovarian cancer. GYNECOL ONCOL. [Journal Article]. 2006 2006/1/1;100(1):20-6.\u003c/li\u003e\n\u003cli\u003eBuamah P. Benign conditions associated with raised serum CA-125 concentration. J SURG ONCOL. [Journal Article]. 2000 2000/12/1;75(4):264-5.\u003c/li\u003e\n\u003cli\u003eBosse K, Rhiem K, Wappenschmidt B, Hellmich M, Madeja M, Ortmann M, et al. Screening for ovarian cancer by transvaginal ultrasound and serum CA125 measurement in women with a familial predisposition: a prospective cohort study. GYNECOL ONCOL. [Evaluation Study; Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 2006 2006/12/1;103(3):1077-82.\u003c/li\u003e\n\u003cli\u003eSchmalfeldt B. [Cytoreductive surgery and hyperthermic intraperitoneal chemotherapy in ovarian cancer]. Chirurgie (Heidelb). [English Abstract; Journal Article; Review]. 2022 2022/12/1;93(12):1144-51.\u003c/li\u003e\n\u003cli\u003eMikuła-Pietrasik J, Witucka A, Pakuła M, Uruski P, Begier-Krasińska B, Niklas A, et al. Comprehensive review on how platinum- and taxane-based chemotherapy of ovarian cancer affects biology of normal cells. CELL MOL LIFE SCI. [Journal Article; Review]. 2019 2019/2/1;76(4):681-97.\u003c/li\u003e\n\u003cli\u003eMatulonis UA, Sood AK, Fallowfield L, Howitt BE, Sehouli J, Karlan BY. Ovarian cancer. NAT REV DIS PRIMERS. [Journal Article; Review]. 2016 2016/8/25;2:16061.\u003c/li\u003e\n\u003cli\u003eWang C, Xu J, Liu M, Liu J, Huang Y, Zhou L. [Relationship between GTSE1 and Cell Cycle and Potential Regulatory Mechanisms \u2029in Lung Cancer Cells]. Zhongguo Fei Ai Za Zhi. [English Abstract; Journal Article; Review]. 2024 2024/6/20;27(6):451-8.\u003c/li\u003e\n\u003cli\u003eXie C, Xiang W, Shen H, Shen J. GTSE1 is possibly involved in the DNA damage repair and cisplatin resistance in osteosarcoma. J ORTHOP SURG RES. [Journal Article]. 2021 2021/12/7;16(1):713.\u003c/li\u003e\n\u003cli\u003eChen L, Zhang D, Chen Y, Zhu H, Liu Z, Yu Z, et al. ORC6 acts as an effective prognostic predictor for non‑small cell lung cancer and is closely associated with tumor progression. ONCOL LETT. 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Pan-Cancer Analysis of Oncogenic Role of RAD54L and Experimental Validation in Hepatocellular Carcinoma. J INFLAMM RES. [Journal Article]. 2023 2023/1/20;16:3997-4017.\u003c/li\u003e\n\u003cli\u003eHao Z, Zhang H, Cowell J. Ubiquitin-conjugating enzyme UBE2C: molecular biology, role in tumorigenesis, and potential as a biomarker. Tumour Biol. [Journal Article; Review]. 2012 2012/6/1;33(3):723-30.\u003c/li\u003e\n\u003cli\u003eDomentean S, Paisana E, Casc\u0026atilde;o R, Faria CC. Role of UBE2C in Brain Cancer Invasion and Dissemination. INT J MOL SCI. [Journal Article; Review]. 2023 2023/10/31;24(21).\u003c/li\u003e\n\u003cli\u003eChen KQ, Lei HB, Liu X, Wang SZ. The roles of E2F7 in cancer: Current knowledge and future prospects. HELIYON. [Journal Article; Review]. 2024 2024/7/30;10(14):e34362.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"bioinformatic analysis, biomarker, ovarian serous cystadenocarcinoma, drugs","lastPublishedDoi":"10.21203/rs.3.rs-7523248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7523248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eOvarian serous cystadenocarcinoma (OSOC) is the most common gynecologic cancer (GC), but existing therapeutics are limited. Therefore, novel molecular pathways that contribute to OSOC therapy and diagnostics are urgently needed.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eBased on the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases, we acquired differentially expressed genes (DEGs) from GEO and clinical information from TCGA, and then we performed functional analysis of the 2DEGs. Protein-protein interaction (PPI) network and Robust Rank Aggregation (RRA) analysis were used to screen the hub genes. Next, diagnostic, prognostic, immune infiltration and pancancer analysis of hub genes were performed. In addition, the drug sensitivity and drug interaction networks of the hub genes were assessed. Finally, we performed transcription factors (TFs) and competing endogenous RNA(ceRNA) regulatory analyses of the hub genes to analyze the underlying molecular mechanisms associated with the hub genes.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eUltimately, we screened 6 hub genes, namely GTSE1, ORC6, PKMYT1, RAD54L, UBE2C and E2F7. According to the ROC analysis, all 6 hub genes demonstrated excellent efficacy. Correlation analysis revealed that the expression of the hub genes was significantly correlated with the deterioration of OSOC.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eBy combining the PPI network, this research identified 6 hub genes that could serve as novel targets for the diagnosis and treatment of OSOC.\u003c/p\u003e","manuscriptTitle":"Recognition of multiple potential diagnostic biomarkers and therapeutic targets for ovarian serous cystadenocarcinoma (OSOC)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 15:20:56","doi":"10.21203/rs.3.rs-7523248/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-10T07:26:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T07:03:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T06:59:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330817421536202777027059047218731203111","date":"2025-11-19T13:16:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191368924516961287146790807485456808290","date":"2025-11-13T09:31:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25706097789096296187340823285932330584","date":"2025-11-13T08:15:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-31T11:32:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-22T18:26:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-10T10:45:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-23T01:16:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-09-22T13:06:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11e013aa-ebf1-4d49-85b2-edacbd90c4bb","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T21:38:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-12 15:20:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7523248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7523248","identity":"rs-7523248","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}