Deubiquitinases as Prognostic Biomarker and Potential Drug Target for Gynecological Cancers

Deubiquitinases as Prognostic Biomarker and Potential Drug Target for Gynecological Cancers | 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 Deubiquitinases as Prognostic Biomarker and Potential Drug Target for Gynecological Cancers Mavika Kondapally, Anubha Dey, Harshitha Velangani Golagana, Shashi Kiran, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6544915/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Nov, 2025 Read the published version in International Journal of Clinical Oncology → Version 1 posted 5 You are reading this latest preprint version Abstract Background To develop Deubiquitinase-Associated Signatures (DAS) to predict the prognosis of gynecological cancer patients. Methods Using a cox-lasso regression model, we have developed Deubiquitinase-associated signatures for Cervical, Ovarian, and Uterine cancers. Developed DAS were validated in TCGA and GEO datasets. Survival analysis was carried out to know the effect of factors like menopausal stage and grade on DAS. The survival prediction accuracy of DAS was analyzed using ROC curves. Immune infiltration scores of 22 immune subtypes were explored using the CIBERSORT package in risk groups classified by DAS. Further, to target the unfavorable deubiquitinases (DUBs), compounds were identified using CMap database. Results Three DAS were developed for Cervical, Ovarian, and Uterine cancer types. DAS was able to predict survival and classify patients into two groups in TCGA and GEO datasets. DAS is an independent predictor of survival irrespective of tumor grade and menopausal stage. DAS, along with the clinical features, improves the accuracy of predictions. CIBERSORT analysis has shown that Immune cell infiltration is associated with risk groups divided by DAS. Using CMap, 52 compounds were identified to target unfavorable DUBs. Conclusion DAS is a good predictor of survival, and targeting unfavorable DUBs can decrease tumor progression in gynecological cancers. Deubiquitinases Cervical Ovarian and Uterine Cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Gynecological cancers (Cervical, ovarian, and uterine) constitute 14.4% of all new cancers affecting women, with significant mortality rates 1 . Identifying outcome predictors in such cancers has been a major effort of researchers around the world to better manage such patients 2 . Gene expression signatures of the tumors that can distinguish long-surviving patients from short-surviving ones can be used for differentiating high and low-risk patients and their prospective management. Prognostic gene signatures based on analysis of all genes have been identified for cervical 3 – 5 , ovarian 6 , 7 , and uterine cancers 8 , 9 . In cancers, including gynecological cancers, ubiquitination is the most commonly altered pathway in cancers 10 . Ubiquitination is carried out by a large group of genes many of which are involved in cancers 11 . Accordingly, ubiquitination based prognostic genes were identified in cervical 12 – 14 , ovarian 15 – 17 and uterine cancers 18 . While ubiquitination is a very large family of proteins with ~ 600 genes, deubiquitination is carried out by a small family of ~ 100 deubiquitinases or DUBs. Unlike ubiquitination protein complexes, which are multiprotein complexes, DUBs are single or few-unit enzyme proteins with a distinct enzymatic pocket that can be targeted for therapy 11 . Therefore, DUBs can be targeted for manipulation of the ubiquitination process in cancers. A DUB-based signature for Hepato-cellular carcinoma (HCC) was recently reported, which also identified possible inhibitors that can be used for the treatment of such cancers 19 . However, no attempt has been reported to identify DUBs of prognostic significance in gynecological cancers. Therefore, we used the TCGA expression data to identify prognostic DUB signatures in cervical, ovarian, and uterine cancers that effectively distinguish patients with better survival from poor ones. We also characterized the immune infiltration of high-risk patients identified by our DUB signature and identified inhibitors that can target DUBs with positive/ negative survival coefficients. MATERIALS AND METHODS 1. Data Collection Gene expression data (HTseq-FPKM) and clinical information of the patients were downloaded from the UCSC XENA ( https://xenabrowser.net/ ) for three types of Gynecological cancers (Cervical, Ovarian, and Uterine). Independent test datasets were downloaded from GEO (GSE44001, GSE63885, GSE119041). We downloaded the list of human deubiquitinases from iUUCD( https://iuucd.biocuckoo.org/ ). The tumor samples with incomplete clinical data were removed from the TCGA RNASeq data. The GEO dataset was processed using the following steps: 1) Samples with incomplete clinical data were removed; 2) the most sensitive probe for each gene was considered. The sample information of 3 TCGA datasets and 3 GEO datasets for Cervical, Ovarian, and Uterine types of cancers are shown in Table S1 . 2. Construction of Prognostic signature based on Deubiquitinases Firstly, TCGA gene expression data was z-transformed. Then we divided the TCGA patients into 80% training set and the remaining 20% into unseen-testing set using “caret” package. As various clinical variables can have an effect on the survival of patients, we considered confounding factors for each cancer type. The 80% training set of data was again divided into 70% training and 30% test. We performed multivariate cox regression analysis using the “survival” package on the training set of data, which was used to analyze the prognostic potential of each DUB by controlling the effect of the confounding factors 20 . The genes with significant association (p < 0.05) with prognosis were considered for further steps. LASSO regression analysis was performed using the “glmnet” package. The risk score was calculated for the patients in both the test and train dataset using the signature obtained after cox-lasso analysis. Survcutpoint function in R was used to stratify patients into high-risk and low-risk in both test and training sets. Survival differences between high-risk and low-risk groups were compared using the likelihood ratio test. All the steps mentioned above were repeated 1000 times. We selected the models that showed significant differences in test and training sets. We considered the median risk score of the DUBs from the significant models as the final risk coefficient. Based on the number of times the significant models picked up a particular DUB in 1000 bootstraps, the top 10 DUBs were selected. Among the top 10 DUBs, we selected the DUBs with median cox coefficient > 0.15 and variance < 0.15 as the final prognostic DUBs. 3. Validation of DAS in Training and Independent Datasets We validated DAS developed for Cervical, Ovarian, and Uterine cancers in unseen 20% testing (TCGA) and independent (GEO) datasets. The risk score was calculated, and the optimal cutoff point was decided using survcutpoint function. The cutoff was used to stratify patients into high-risk and low-risk groups. We performed a Kaplan-Meier analysis to estimate the survival rate of patients using the “survminer” package. Log-rank test was performed to compare the survival distributions of the two groups. 4. Effect of other factors on DAS To check if the risk score generated from DAS has an effect by menopause stage and tumor grade, complete TCGA data was divided based on menopausal status and tumor grade, respectively, and the risk score was calculated. As the information on menopausal status was not available for ovarian cancer, we considered the average age of menopause, i.e., (50) as the cutpoint. As mentioned, survcutpoint was used to decide the cutoff for stratifying patients. KM plots for Pre and Post menopause-stage patients were plotted, and log-rank test was performed. A similar analysis was performed on patients with different tumor grades for cervical, ovarian, and uterine cancer types. 5. Prediction of survival by DAS and other clinical factors The prediction accuracy of survival by DAS and other clinical characteristics was assessed by comparing the AUC values generated using the pROC package. 6. Prediction of Substrate Proteins After developing the DAS and validating it in independent datasets, we further wanted to target the unfavorable DUBs because the higher the expression of these DUBs, the poorer the prognosis of patients. Firstly, the substrates of DUBs with positive risk coefficient (unfavorable DUBs) for the prognosis of patients in cervical, ovarian, and uterine cancers were identified using UbiBrowser ( http://ubibrowser.ncpsb.org ). We considered all the experimentally validated known substrates and predicted substrates with a confidence score > 0.85 for these DUBs. The network of the DUBs and substrates was visualized using Cytoscape (version 3.10.1). 7. Functional Enrichment Analysis Functional enrichment analysis was performed on the substrates of unfavorable DUBs using “clusterProfiler” package. 8. Tumor Immune Microenvironment Analysis After stratifying the TCGA patients into high-risk and low-risk groups based on DAS, the “ESTIMATE” package was used to calculate the immune, stromal, and estimate scores. To analyze the relation between the immune infiltration of 22 immune cells and two risk groups, “CIBERSOFT” package was used. 9. Prediction of Drug sensitivity and drug screening In-vivo drug responses of each sample in two risk groups divided using DAS were predicted using the “OncoPredict” package based on GDSC data( https://www.cancerrxgene.org/ ) for the commonly used chemotherapy agents for cervical, ovarian, and uterine cancer types. A connectivity map ( https://clue.io/ ) database was used to identify small-molecule drugs that target unfavorable DUBs. RESULTS 1. Deubiquitinase-associated signatures (DAS) for cervical, ovarian, and uterine cancers The RNASeq data for all three gynecological cancers was downloaded from UCSC Xena, which was then split into 80:20 train and test datasets. Cox-lasso regression was performed on the training set of data to identify DUBs associated with survival [Figure S1 ]. After training the cox-lasso model on 80% of the TCGA dataset, the top 10 DUBs were selected. Among the top 10 DUBs, the DUBs with a median cox coefficient greater than 0.15 were considered [Figure S2]. Finally, three deubiquitinase-associated signatures (DAS) were developed for Cervical, Ovarian, and Uterine cancer types. The signatures developed for three cancer types are as follows: Uterine Cancer (0.260*OTUD7A) + (0.174*USP26) + (-0.162*OTUD7B) + (0.155*STAMBP) + (0.164*COPS5) Ovarian Cancer (-0.1513*USP18) + (-0.1587*USP28) + (-0.1759*USP51) + (0.1511*OTUD7A) + (0.1521*USP43) + (-0.1714*USP6) Cervical Cancer (0.2147*USP12) + (-0.1878*OTUD7A) + (-0.2453*MPND) + (0.1796*UFSP1) + (-0.1571*UFD1L) + (0.1561*OTUB2) Risk scores calculated for each DAS are in the following format. $$\:\sum\:_{i=1}^{n}{C}_{i}*{x}_{i}$$ Where, \(\:{C}_{i}\) is the risk coefficient, which was calculated for each DUB by the cox-lasso model, and \(\:{x}_{i}\) is the expression value of each DUB. In the signature, the DUBs with positive risk coefficients act as unfavorable DUBs for prognosis, whereas the DUBs with negative risk coefficients act as favorable DUBs for prognosis. OTUD7A gene was a common DUB identified as prognostic and part of the signature in all three cancers. However, the role of OTUD7A was different; it acts as an unfavorable DUB in ovarian and uterine cancers and as a favorable DUB in cervical cancer. 2. DAS is a predictor of survival in the training and independent dataset To evaluate the performance of DAS in survival prediction, we calculated the risk score using DAS for each patient in unseen TCGA (20%), TCGA complete, and GEO datasets. The patients were stratified into high-risk and low-risk based on the optimal cutoff generated using survcutpoin t. Survival analysis has shown that the high-risk group has poorer survival rates than the low-risk group, except in the uterine external validation set [Figure S3]. The patients can be significantly divided into high-risk and low-risk, indicating that the DAS can predict the survival of patients with cervical, ovarian, and uterine cancers [Figure 1 ]. The expression of prognostic DUBs in both risk groups is shown in Figure S4. 3. DAS is an independent predictor of survival To check if DAS can predict high-risk and low-risk groups in different grades and menopausal stages, the dataset was divided. Patients in the postmenopausal stage were higher in ovarian and uterine cancer and less in cervical cancer [Figure 2 a-c]. In the same way, patients with high-grade cancer were more in ovarian and uterine cancer dataset and less in cervical cancer [Figure 2 j-l]. KM curves have shown that DAS significantly stratified the patients into two groups. KM plots of patients with differences in menopause status and grades are shown in Fig. 2 . This analysis shows that DAS can categorize patients into high-risk and low-risk patients irrespective of their menopausal stage [Figure 2 d-i] and tumor grade [Figure 2 m-t]. This indicates that DAS is an independent predictor of survival, and there is no effect of menopausal stage and tumor grade on DAS. 4. DAS with clinical variables improves the survival prediction Clinical factors influence the prognosis of gynecological cancers, and studies have shown that clinical factors act as prognostic factors 21 . However, clinical factors alone cannot be good prognostic markers 22 . Integrating clinical factors with biomarkers can increase the accuracy of survival prediction. To further confer this, we assessed the accuracy of survival prediction by individual clinical variables, a combination of clinical variables, DAS alone, and DAS along with clinical variables (combination), and compared the AUC values. We have observed that DAS, if considered along with clinical variables(combination) improves the survival prediction. It showed the highest accuracy (0.95,0.76,1 in cervical, ovarian, and uterine cancers, respectively) in predicting the patient's survival compared to individual clinical variable or considering all the clinical variables together [Figure 3 ]. DeLong test was performed to assess whether the combination model significantly outperformed other models. The combination model has shown significantly higher AUC than other models. This analysis indicates that integrating DAS and clinical variables improves prediction accuracy in all three gynecological cancers. 5. Substrates of Unfavorable DUBs As the expression of unfavorable DUBs leads to poor prognosis, we next identified the drugs that can reduce the expression of target genes. We identified seven DUBs with positive coefficients and, therefore, are poor prognostic DUBs in DAS of cervical, ovarian, and uterine cancer types. UFSP1, an unfavorable DUB in cervical cancer, has no predicted and known substrates, and for the remaining DUBs, 145 substrates were found using UbiBrowser. The network of DUBs and their substrates identifies DUBs with common targets and DUBs targeting multiple proteins [Figure 4 a]. Interestingly, OTUD7A, belonging to the OTU family of DUBs, was a common DUB present in the signatures of all three gynecological cancers considered in the present study. It acts as a favorable marker in cervical cancer and unfavorable in the case of ovarian and uterine cancer. Studies suggest that OTUD7A exhibits a dual role in tumor suppression 23 and tumor progression 24 , but the role of OTUD7A in gynecological cancers has never been studied. 6. Substrates of unfavorable DUBs associated with gynecological cancer-related pathways We next sought to find functional enrichment of targets of unfavorable DUBs. Gene set enrichment analysis of substrates of unfavorable DUBs suggests that genes in DAS are involved in various viral infection pathways like Human Papillomavirus Infection, Epstein-Barr Virus Infection, Hepatitis C, Human Immunodeficiency Virus 1 Infection, Kaposi Sarcoma-Associated Herpesvirus Infection and various signaling pathways like MAPK Signaling Pathway, Wnt Signaling Pathway, Calcium Signaling Pathway, NF-kappaB Signaling Pathway, and Toll-Like Receptor Signaling Pathway [Figure 4 b]. GO enrichment analysis shows a strong connection between ubiquitin signaling, NF-kappaB activation, and transcription regulation, all influenced by deubiquitinases (DUBs). Given their role in tumor progression, DUBs may be potential regulators and therapeutic targets in gynecological cancers [Figure 4 c]. 7. Immune infiltration We performed ESTIMATE analysis to explore the differences in the immune response between high-risk and low-risk groups and compared immune cell infiltration. ESTIMATE analysis showed that the low-risk group had elevated immune scores compared to the high-risk in all three gynecological cancers. The stromal and estimate scores were higher in the low-risk group in uterine and cervical cancers [Figure S4]. As the immune scores were higher in the low-risk group, we further evaluated the infiltration of 22 immune cells to identify cell types contributing to effective immune response in low-risk groups. We have observed that the infiltration of immune cells was higher in the low-risk group than in the high-risk group for cervical and uterine cancer, suggesting a stronger anti-tumor immune response and better prognosis 25 , 26 . This reflects active immune surveillance, a favorable tumor microenvironment that can effectively detect and combat tumor cells [Figure 5 a and 5 b]. Whereas in the case of ovarian cancer, infiltration of CD4 T cells, CD4 memory resting cells, M0 macrophages, and resting dendritic cells are higher in the high-risk group indicating a weaker, less effective immune response 27 and the higher levels of dendritic cells and activated NK cells in low-risk suggest a stronger immune response 28 , 29 [Figure 5 c]. 8. High-risk group corresponds with low drug sensitivity To analyze the relationship between DAS and drug sensitivity, we calculated the half-maximal inhibitory concentration (IC50) values of drugs used in chemotherapy of cervical 30 , ovarian 31 , 32 , and uterine cancers. We observed that the high-risk group showed significantly low drug sensitivity (high IC50) [Figure 6 ]. These results indicated that the high-risk group patients classified using DAS are associated with resistance to drugs used. 9. Small molecular compounds for unfavorable prognostic DUBs Using the CMap database we identified small molecular compounds to target unfavorable DUBs. As the expression of unfavorable DUBs affects patient’s survival, targeting them can be effective in controlling the tumor progression. We considered the compounds with connectivity score > 90 for each DUB [Figure S7]. Few of the compounds identified have potential roles in the treatment of gynecological cancers, such as doxorubicin, capecitabine, pembrolizumab, and metformin. DISCUSSION Gynecological cancers are the most common cancer type in women. Detection of the cancer at the late stage is one of the causes of the high mortality rate worldwide. So, there is a need for biomarkers that can detect the cancer, predict the survival, and can be targeted for efficient treatment. DUBs play a role in tumorigenesis and are emerging drug targets. In this study, we wanted to investigate the role of DUBs as prognostic marker and built three deubiquitinase-associated signatures for the three top most occurring gynecological cancer types (cervical, ovarian and uterine cancers) using the Cox-Lasso model. The DAS developed effectively predicted the survival of patients in the unseen internal TCGA dataset for the three gynecological cancers and the external GEO dataset for ovarian and cervical cancers. In the case of uterine cancer, the DAS could not predict the survival of risk groups, which was due to the expression difference of three prognostic DUBs, OTUD7A, OTUD7B, and USP26 [Figure S6]. DAS is an independent predictor of survival, irrespective of factors that are associated with the risk of gynecological cancers such as menopausal stage 33 .DAS significantly increases the accuracy of prediction when integrated with clinical factors. Pathway analysis has shown that the substrates of unfavorable DUBs are associated with various infection pathways 34 and signalling pathways 35 , 36 associated with gynecological cancers. Through CIBERSOFT analysis we observed that Immune cell infiltration is associated with the risk score calculated using DAS. Studies have shown the unfavorable DUBs we obtained in our DAS have a role in the tumor progression of that particular cancer. For example, USP12 and OTUB2 are two unfavorable DUBs, in the DAS we constructed for cervical cancer, USP12 regulates the growth of cervical cancer by increasing BMI-1, c-Myc, and cyclin D2 levels 37 . OTUB2 promotes cervical cancer by stabilizing FOXM1 38 , RBM15-mediated m6A modification, and AKT/mTOR signaling 39 . USP43, an unfavorable DUB for ovarian cancer, promotes the growth of ovarian cancer through stabilization of HDAC2 and activation of the Wnt/β-catenin signaling pathway 40 . The studies above support that the expression of unfavorable DUBs in DAS promotes tumor growth. These DUBs can act as new targets in Cervical, Ovarian, and Uterine cancer treatment. Declarations Authors declare no conflicts of interest Acknowledgments We thank the members of SKlab and MKlab for the helpful discussions. KM is a registered Integrated Systems Biology master’s student, AD and GVH are registered PhD students at the University of Hyderabad. AD and MK also acknowledge funding support from UoH-IoE Grant (UoH-IoE-RC2–21–012) and DBT-BUILDER. GVH and SK acknowledge funding support from ANRF (ANRF-SRG/2020/001099) and UoH-IoE Grant (UoH-IoE-RC2-21-020). Data Availability All codes used are available on https://github.com/Mavikakondapally/codes References Siegel Mph RL, Miller KD, Sandeep N, et al. Cancer statistics, 2023. Wiley Online Library . 2023;73(1):17-48. doi:10.3322/caac.21763 Zhu W, Xie L, Han J, Cancers XG, 2020 undefined. 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Gene . 2016;578(1). doi:10.1016/j.gene.2015.12.006 Xiao J, Wang L, Zhuang Y, Zhu Q, … WLAJ of, 2024 undefined. The deubiquitinase OTUB2 promotes cervical cancer growth through stabilizing FOXM1. ncbi.nlm.nih.gov . Accessed July 23, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10839374/ Song Y, Wu Q. RBM15 m6A modification-mediated OTUB2 upregulation promotes cervical cancer progression via the AKT/mTOR signaling. Environ Toxicol . 2023;38(9). doi:10.1002/tox.23852 Pei L, Zhao F, Zhang Y. USP43 impairs cisplatin sensitivity in epithelial ovarian cancer through HDAC2-dependent regulation of Wnt/β-catenin signaling pathway. Apoptosis . 2024;29(1-2). doi:10.1007/s10495-023-01873-x Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 07 Nov, 2025 Read the published version in International Journal of Clinical Oncology → Version 1 posted Editorial decision: Major revisions 04 Jun, 2025 Reviewers agreed at journal 04 May, 2025 Reviewers invited by journal 01 May, 2025 Editor assigned by journal 29 Apr, 2025 First submitted to journal 27 Apr, 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. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6544915","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450785898,"identity":"68762ac0-c198-4130-b24c-56bf5c307235","order_by":0,"name":"Mavika Kondapally","email":"","orcid":"","institution":"University of Hyderabad School of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mavika","middleName":"","lastName":"Kondapally","suffix":""},{"id":450785899,"identity":"f33fe054-996a-492d-9d80-6bd62525b8d1","order_by":1,"name":"Anubha Dey","email":"","orcid":"","institution":"University of Hyderabad School of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Anubha","middleName":"","lastName":"Dey","suffix":""},{"id":450785900,"identity":"fd9e3406-c40d-40a7-9b32-fe24e29459bf","order_by":2,"name":"Harshitha Velangani Golagana","email":"","orcid":"","institution":"University of Hyderabad School of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Harshitha","middleName":"Velangani","lastName":"Golagana","suffix":""},{"id":450785901,"identity":"abbbd0e1-97f0-4446-93b8-3df433859cb9","order_by":3,"name":"Shashi Kiran","email":"","orcid":"","institution":"University of Hyderabad School of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shashi","middleName":"","lastName":"Kiran","suffix":""},{"id":450785902,"identity":"12c87e5c-4cb4-4659-bf56-6b86a284fa62","order_by":4,"name":"Manjari Kiran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYJACAwYGCRDB+ICB4QAYgcWI0cJsQLQWmD42CYQWPEC3/eyBgp97LBjM2c+YVfNU3EnsO8D88ANDwR2cWszO5CUY9jyTYLDsyTG7zXPmWeLMA2zGQHc+w63lQI6BAc8BoJobPGa3edsOJ244wGAGdOdh3FrOvzEw/APVUgzRwv4Nv5YbOQbGMFuYIVp4CNhy442BscwBCR6DM2nFknPOHDaeeZinWCIBr8NyzAzfHKiTMzh+eOOHNxWHZfuOt2/88OEPbi1AwAaKNh4Yz7GBGUgm4NMAjPQHyDx7/IpHwSgYBaNgJAIAXq5YSNJDu3cAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-0153-7072","institution":"University of Hyderabad School of Life Sciences","correspondingAuthor":true,"prefix":"","firstName":"Manjari","middleName":"","lastName":"Kiran","suffix":""}],"badges":[],"createdAt":"2025-04-28 07:21:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6544915/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6544915/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10147-025-02880-5","type":"published","date":"2025-11-07T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82177960,"identity":"a6c23233-856c-45bf-8798-9eac31520eba","added_by":"auto","created_at":"2025-05-07 11:23:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":278328,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of DAS in TCGA complete, TCGA test, and GEO datasets in Cervical (a,b,c), Ovarian(d,e,f) cancers and validation in TCGA complete, TCGA test in Uterine (g,h) cancer respectively\u003c/p\u003e\n\u003cp\u003eDAS, Deubiquitinase associated signatures; TCGA, The Cancer Genome Atlas; GEO, Gene Expression Omnibus\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6544915/v1/3912d7d3ffc1db1a0476d8bf.jpg"},{"id":82177970,"identity":"7ebe9909-bdcd-4d52-908a-c618276fca09","added_by":"auto","created_at":"2025-05-07 11:23:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2775865,"visible":true,"origin":"","legend":"\u003cp\u003eBarplots showing the count based on DAS across the menopausal stage(a,b,c), tumor grade(j,k,l), and Survival analysis of two risk groups of patients in post-menopause(d,e,f),pre-menopause (g,h,i), G2 (o,p,q), G3 (r,s,t) in Cervical, Ovarian and Uterine cancers respectively and G1 (m,n) in Cervical and Uterine cancers\u003c/p\u003e\n\u003cp\u003eDAS, Deubiquitinase associated signatures\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6544915/v1/13f19389b103a52471e74a6b.jpg"},{"id":82176835,"identity":"9b879b13-0db0-4158-a4ce-f98fff1be5f5","added_by":"auto","created_at":"2025-05-07 11:15:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":224987,"visible":true,"origin":"","legend":"\u003cp\u003eCombined model showing higher AUC compared to clinical factors, DAS, stage, race, and age across Cervical, Ovarian, and Uterine cancers (p \u0026lt; 0.05, DeLong test)\u003c/p\u003e\n\u003cp\u003eDAS, Deubiquitinase associated signatures; AUC, Area under the curve\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6544915/v1/3b3f4c74775813e63b8fd33f.jpg"},{"id":82176857,"identity":"ef1e569d-885f-4cca-9a58-22bf132f39a5","added_by":"auto","created_at":"2025-05-07 11:15:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2792258,"visible":true,"origin":"","legend":"\u003cp\u003e(a)Network of unfavorable DUBs and their substrates. Colored rings around the node.Green: cervical, yellow: ovarian, and dark green: uterine cancer. Dashed lines: predicted substrates, solid lines: known substrates. Size of the node represents degree. (b) KEGG pathway analysis and (c) GO enrichment performed on unfavorable DUB substrates\u003c/p\u003e\n\u003cp\u003eDUB, Deubiquitinase; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6544915/v1/9efdf1949e1bf099acc5b93f.jpg"},{"id":82177965,"identity":"d57d1fcd-e018-406f-a298-bedc98547f87","added_by":"auto","created_at":"2025-05-07 11:23:22","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2944652,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots showing the difference in the infiltration of immune cells in two risk groups divided using DAS in Cervical(a), Uterine(b), and Ovarian cancers(c) \u003csup\u003ens\u003c/sup\u003e p \u0026gt;0.05, \u003csup\u003e*\u003c/sup\u003e p\u0026lt;0.05, \u003csup\u003e**\u003c/sup\u003e p\u0026lt;0.01,\u003csup\u003e *** \u003c/sup\u003ep\u0026lt;0.001, \u003csup\u003e****\u003c/sup\u003e p\u0026lt;0.0001, wilcox test\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6544915/v1/b3718391e3c9ff3ab18947e5.jpg"},{"id":82179036,"identity":"7a896d80-2f6d-49ce-82a8-9b0135ccca69","added_by":"auto","created_at":"2025-05-07 11:31:22","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1873567,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots showing high drug IC50 in high-risk group in cervical(a), uterine (b), and ovarian (c) cancers\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ens\u003c/sup\u003e p \u0026gt;0.05, \u003csup\u003e*\u003c/sup\u003e p\u0026lt;0.05, \u003csup\u003e**\u003c/sup\u003e p\u0026lt;0.01,\u003csup\u003e *** \u003c/sup\u003ep\u0026lt;0.001, \u003csup\u003e****\u003c/sup\u003e p\u0026lt;0.0001, wilcox test\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6544915/v1/ddf0fbb1be569e0f5a6fe3a6.jpg"},{"id":95564023,"identity":"c056faa2-e469-4a1d-b924-8c413e82e9cb","added_by":"auto","created_at":"2025-11-10 16:06:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12050317,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6544915/v1/7afa2cd5-046c-4a6a-afd4-27c5a25910fc.pdf"},{"id":82176845,"identity":"dcddf7b4-db08-4c76-add6-9f4c01577bc3","added_by":"auto","created_at":"2025-05-07 11:15:22","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1940236,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6544915/v1/95e2998eee084fc59a407164.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eDeubiquitinases as Prognostic Biomarker and Potential Drug Target for Gynecological Cancers\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGynecological cancers (Cervical, ovarian, and uterine) constitute 14.4% of all new cancers affecting women, with significant mortality rates\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Identifying outcome predictors in such cancers has been a major effort of researchers around the world to better manage such patients \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Gene expression signatures of the tumors that can distinguish long-surviving patients from short-surviving ones can be used for differentiating high and low-risk patients and their prospective management. Prognostic gene signatures based on analysis of all genes have been identified for cervical\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, ovarian\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and uterine cancers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In cancers, including gynecological cancers, ubiquitination is the most commonly altered pathway in cancers\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Ubiquitination is carried out by a large group of genes many of which are involved in cancers\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Accordingly, ubiquitination based prognostic genes were identified in cervical \u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, ovarian\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and uterine cancers\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. While ubiquitination is a very large family of proteins with ~\u0026thinsp;600 genes, deubiquitination is carried out by a small family of ~\u0026thinsp;100 deubiquitinases or DUBs. Unlike ubiquitination protein complexes, which are multiprotein complexes, DUBs are single or few-unit enzyme proteins with a distinct enzymatic pocket that can be targeted for therapy\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Therefore, DUBs can be targeted for manipulation of the ubiquitination process in cancers. A DUB-based signature for Hepato-cellular carcinoma (HCC) was recently reported, which also identified possible inhibitors that can be used for the treatment of such cancers\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, no attempt has been reported to identify DUBs of prognostic significance in gynecological cancers. Therefore, we used the TCGA expression data to identify prognostic DUB signatures in cervical, ovarian, and uterine cancers that effectively distinguish patients with better survival from poor ones. We also characterized the immune infiltration of high-risk patients identified by our DUB signature and identified inhibitors that can target DUBs with positive/ negative survival coefficients.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1. Data Collection\u003c/h2\u003e \u003cp\u003eGene expression data (HTseq-FPKM) and clinical information of the patients were downloaded from the UCSC XENA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for three types of Gynecological cancers (Cervical, Ovarian, and Uterine). Independent test datasets were downloaded from GEO (GSE44001, GSE63885, GSE119041). We downloaded the list of human deubiquitinases from iUUCD(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iuucd.biocuckoo.org/\u003c/span\u003e\u003cspan address=\"https://iuucd.biocuckoo.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The tumor samples with incomplete clinical data were removed from the TCGA RNASeq data. The GEO dataset was processed using the following steps: 1) Samples with incomplete clinical data were removed; 2) the most sensitive probe for each gene was considered. The sample information of 3 TCGA datasets and 3 GEO datasets for Cervical, Ovarian, and Uterine types of cancers are shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2. Construction of Prognostic signature based on Deubiquitinases\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFirstly, TCGA gene expression data was z-transformed. Then we divided the TCGA patients into 80% training set and the remaining 20% into unseen-testing set using \u0026ldquo;caret\u0026rdquo; package. As various clinical variables can have an effect on the survival of patients, we considered confounding factors for each cancer type. The 80% training set of data was again divided into 70% training and 30% test. We performed multivariate cox regression analysis using the \u0026ldquo;survival\u0026rdquo; package on the training set of data, which was used to analyze the prognostic potential of each DUB by controlling the effect of the confounding factors \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The genes with significant association (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with prognosis were considered for further steps. LASSO regression analysis was performed using the \u0026ldquo;glmnet\u0026rdquo; package. The risk score was calculated for the patients in both the test and train dataset using the signature obtained after cox-lasso analysis. Survcutpoint function in R was used to stratify patients into high-risk and low-risk in both test and training sets. Survival differences between high-risk and low-risk groups were compared using the likelihood ratio test. All the steps mentioned above were repeated 1000 times. We selected the models that showed significant differences in test and training sets. We considered the median risk score of the DUBs from the significant models as the final risk coefficient. Based on the number of times the significant models picked up a particular DUB in 1000 bootstraps, the top 10 DUBs were selected. Among the top 10 DUBs, we selected the DUBs with median cox coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.15 and variance\u0026thinsp;\u0026lt;\u0026thinsp;0.15 as the final prognostic DUBs.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e3. Validation of DAS in Training and Independent Datasets\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe validated DAS developed for Cervical, Ovarian, and Uterine cancers in unseen 20% testing (TCGA) and independent (GEO) datasets. The risk score was calculated, and the optimal cutoff point was decided using \u003cem\u003esurvcutpoint\u003c/em\u003e function. The cutoff was used to stratify patients into high-risk and low-risk groups. We performed a Kaplan-Meier analysis to estimate the survival rate of patients using the \u0026ldquo;survminer\u0026rdquo; package. Log-rank test was performed to compare the survival distributions of the two groups.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003e4. Effect of other factors on DAS\u003c/b\u003e\u003c/div\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo check if the risk score generated from DAS has an effect by menopause stage and tumor grade, complete TCGA data was divided based on menopausal status and tumor grade, respectively, and the risk score was calculated. As the information on menopausal status was not available for ovarian cancer, we considered the average age of menopause, i.e., (50) as the cutpoint. As mentioned, \u003cem\u003esurvcutpoint\u003c/em\u003e was used to decide the cutoff for stratifying patients. KM plots for Pre and Post menopause-stage patients were plotted, and log-rank test was performed. A similar analysis was performed on patients with different tumor grades for cervical, ovarian, and uterine cancer types.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e5. Prediction of survival by DAS and other clinical factors\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe prediction accuracy of survival by DAS and other clinical characteristics was assessed by comparing the AUC values generated using the pROC package.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e6. Prediction of Substrate Proteins\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAfter developing the DAS and validating it in independent datasets, we further wanted to target the unfavorable DUBs because the higher the expression of these DUBs, the poorer the prognosis of patients. Firstly, the substrates of DUBs with positive risk coefficient (unfavorable DUBs) for the prognosis of patients in cervical, ovarian, and uterine cancers were identified using UbiBrowser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ubibrowser.ncpsb.org\u003c/span\u003e\u003cspan address=\"http://ubibrowser.ncpsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We considered all the experimentally validated known substrates and predicted substrates with a confidence score\u0026thinsp;\u0026gt;\u0026thinsp;0.85 for these DUBs. The network of the DUBs and substrates was visualized using Cytoscape (version 3.10.1).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e7. Functional Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFunctional enrichment analysis was performed on the substrates of unfavorable DUBs using \u0026ldquo;clusterProfiler\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e8. Tumor Immune Microenvironment Analysis\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAfter stratifying the TCGA patients into high-risk and low-risk groups based on DAS, the \u0026ldquo;ESTIMATE\u0026rdquo; package was used to calculate the immune, stromal, and estimate scores. To analyze the relation between the immune infiltration of 22 immune cells and two risk groups, \u0026ldquo;CIBERSOFT\u0026rdquo; package was used.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e9. Prediction of Drug sensitivity and drug screening\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn-vivo drug responses of each sample in two risk groups divided using DAS were predicted using the \u0026ldquo;OncoPredict\u0026rdquo; package based on GDSC data(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the commonly used chemotherapy agents for cervical, ovarian, and uterine cancer types.\u003c/p\u003e \u003cp\u003eA connectivity map (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clue.io/\u003c/span\u003e\u003cspan address=\"https://clue.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database was used to identify small-molecule drugs that target unfavorable DUBs.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e1. Deubiquitinase-associated signatures (DAS) for cervical, ovarian, and uterine cancers\u003c/h2\u003e \u003cp\u003eThe RNASeq data for all three gynecological cancers was downloaded from UCSC Xena, which was then split into 80:20 train and test datasets. Cox-lasso regression was performed on the training set of data to identify DUBs associated with survival [Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter training the cox-lasso model on 80% of the TCGA dataset, the top 10 DUBs were selected. Among the top 10 DUBs, the DUBs with a median cox coefficient greater than 0.15 were considered [Figure S2]. Finally, three deubiquitinase-associated signatures (DAS) were developed for Cervical, Ovarian, and Uterine cancer types. The signatures developed for three cancer types are as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eUterine Cancer\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003cp\u003e(0.260*OTUD7A) + (0.174*USP26) + (-0.162*OTUD7B) + (0.155*STAMBP) + (0.164*COPS5)\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section4\"\u003e \u003ch2\u003eOvarian Cancer\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e(-0.1513*USP18) + (-0.1587*USP28) + (-0.1759*USP51) + (0.1511*OTUD7A) + (0.1521*USP43)\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003cp\u003e+ (-0.1714*USP6)\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eCervical Cancer\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section4\"\u003e \u003cp\u003e(0.2147*USP12) + (-0.1878*OTUD7A) + (-0.2453*MPND) + (0.1796*UFSP1) + (-0.1571*UFD1L) + (0.1561*OTUB2)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRisk scores calculated for each DAS are in the following format.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\sum\\:_{i=1}^{n}{C}_{i}*{x}_{i}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the risk coefficient, which was calculated for each DUB by the cox-lasso model, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the expression value of each DUB.\u003c/p\u003e \u003cp\u003eIn the signature, the DUBs with positive risk coefficients act as unfavorable DUBs for prognosis, whereas the DUBs with negative risk coefficients act as favorable DUBs for prognosis. OTUD7A gene was a common DUB identified as prognostic and part of the signature in all three cancers. However, the role of OTUD7A was different; it acts as an unfavorable DUB in ovarian and uterine cancers and as a favorable DUB in cervical cancer.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2. DAS is a predictor of survival in the training and independent dataset\u003c/h2\u003e \u003cp\u003eTo evaluate the performance of DAS in survival prediction, we calculated the risk score using DAS for each patient in unseen TCGA (20%), TCGA complete, and GEO datasets. The patients were stratified into high-risk and low-risk based on the optimal cutoff generated using \u003cem\u003esurvcutpoin\u003c/em\u003et. Survival analysis has shown that the high-risk group has poorer survival rates than the low-risk group, except in the uterine external validation set [Figure S3]. The patients can be significantly divided into high-risk and low-risk, indicating that the DAS can predict the survival of patients with cervical, ovarian, and uterine cancers [Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]. The expression of prognostic DUBs in both risk groups is shown in Figure S4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3. DAS is an independent predictor of survival\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo check if DAS can predict high-risk and low-risk groups in different grades and menopausal stages, the dataset was divided. Patients in the postmenopausal stage were higher in ovarian and uterine cancer and less in cervical cancer [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-c]. In the same way, patients with high-grade cancer were more in ovarian and uterine cancer dataset and less in cervical cancer [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ej-l]. KM curves have shown that DAS significantly stratified the patients into two groups. KM plots of patients with differences in menopause status and grades are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This analysis shows that DAS can categorize patients into high-risk and low-risk patients irrespective of their menopausal stage [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed-i] and tumor grade [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003em-t]. This indicates that DAS is an independent predictor of survival, and there is no effect of menopausal stage and tumor grade on DAS.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4. DAS with clinical variables improves the survival prediction\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eClinical factors influence the prognosis of gynecological cancers, and studies have shown that clinical factors act as prognostic factors\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, clinical factors alone cannot be good prognostic markers\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Integrating clinical factors with biomarkers can increase the accuracy of survival prediction. To further confer this, we assessed the accuracy of survival prediction by individual clinical variables, a combination of clinical variables, DAS alone, and DAS along with clinical variables (combination), and compared the AUC values. We have observed that DAS, if considered along with clinical variables(combination) improves the survival prediction. It showed the highest accuracy (0.95,0.76,1 in cervical, ovarian, and uterine cancers, respectively) in predicting the patient's survival compared to individual clinical variable or considering all the clinical variables together [Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e]. DeLong test was performed to assess whether the combination model significantly outperformed other models. The combination model has shown significantly higher AUC than other models. This analysis indicates that integrating DAS and clinical variables improves prediction accuracy in all three gynecological cancers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5. Substrates of Unfavorable DUBs\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs the expression of unfavorable DUBs leads to poor prognosis, we next identified the drugs that can reduce the expression of target genes. We identified seven DUBs with positive coefficients and, therefore, are poor prognostic DUBs in DAS of cervical, ovarian, and uterine cancer types. UFSP1, an unfavorable DUB in cervical cancer, has no predicted and known substrates, and for the remaining DUBs, 145 substrates were found using UbiBrowser. The network of DUBs and their substrates identifies DUBs with common targets and DUBs targeting multiple proteins [Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\u003cp\u003eInterestingly, OTUD7A, belonging to the OTU family of DUBs, was a common DUB present in the signatures of all three gynecological cancers considered in the present study. It acts as a favorable marker in cervical cancer and unfavorable in the case of ovarian and uterine cancer. Studies suggest that OTUD7A exhibits a dual role in tumor suppression\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and tumor progression\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, but the role of OTUD7A in gynecological cancers has never been studied.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e6. Substrates of unfavorable DUBs associated with gynecological cancer-related pathways\u003c/h2\u003e \u003cp\u003eWe next sought to find functional enrichment of targets of unfavorable DUBs. Gene set enrichment analysis of substrates of unfavorable DUBs suggests that genes in DAS are involved in various viral infection pathways like Human Papillomavirus Infection, Epstein-Barr Virus Infection, Hepatitis C, Human Immunodeficiency Virus 1 Infection, Kaposi Sarcoma-Associated Herpesvirus Infection and various signaling pathways like MAPK Signaling Pathway, Wnt Signaling Pathway, Calcium Signaling Pathway, NF-kappaB Signaling Pathway, and Toll-Like Receptor Signaling Pathway [Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb]. GO enrichment analysis shows a strong connection between ubiquitin signaling, NF-kappaB activation, and transcription regulation, all influenced by deubiquitinases (DUBs). Given their role in tumor progression, DUBs may be potential regulators and therapeutic targets in gynecological cancers [Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e7. Immune infiltration\u003c/h2\u003e \u003cp\u003eWe performed ESTIMATE analysis to explore the differences in the immune response between high-risk and low-risk groups and compared immune cell infiltration. ESTIMATE analysis showed that the low-risk group had elevated immune scores compared to the high-risk in all three gynecological cancers. The stromal and estimate scores were higher in the low-risk group in uterine and cervical cancers [Figure S4]. As the immune scores were higher in the low-risk group, we further evaluated the infiltration of 22 immune cells to identify cell types contributing to effective immune response in low-risk groups. We have observed that the infiltration of immune cells was higher in the low-risk group than in the high-risk group for cervical and uterine cancer, suggesting a stronger anti-tumor immune response and better prognosis\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This reflects active immune surveillance, a favorable tumor microenvironment that can effectively detect and combat tumor cells [Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb]. Whereas in the case of ovarian cancer, infiltration of CD4 T cells, CD4 memory resting cells, M0 macrophages, and resting dendritic cells are higher in the high-risk group indicating a weaker, less effective immune response\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and the higher levels of dendritic cells and activated NK cells in low-risk suggest a stronger immune response\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e [Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e8. High-risk group corresponds with low drug sensitivity\u003c/h2\u003e \u003cp\u003eTo analyze the relationship between DAS and drug sensitivity, we calculated the half-maximal inhibitory concentration (IC50) values of drugs used in chemotherapy of cervical\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, ovarian\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and uterine cancers. We observed that the high-risk group showed significantly low drug sensitivity (high IC50) [Figure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e]. These results indicated that the high-risk group patients classified using DAS are associated with resistance to drugs used.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e9. Small molecular compounds for unfavorable prognostic DUBs\u003c/h2\u003e \u003cp\u003eUsing the CMap database we identified small molecular compounds to target unfavorable DUBs. As the expression of unfavorable DUBs affects patient\u0026rsquo;s survival, targeting them can be effective in controlling the tumor progression. We considered the compounds with connectivity score\u0026thinsp;\u0026gt;\u0026thinsp;90 for each DUB [Figure S7]. Few of the compounds identified have potential roles in the treatment of gynecological cancers, such as doxorubicin, capecitabine, pembrolizumab, and metformin.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eGynecological cancers are the most common cancer type in women. Detection of the cancer at the late stage is one of the causes of the high mortality rate worldwide. So, there is a need for biomarkers that can detect the cancer, predict the survival, and can be targeted for efficient treatment. DUBs play a role in tumorigenesis and are emerging drug targets.\u003c/p\u003e \u003cp\u003eIn this study, we wanted to investigate the role of DUBs as prognostic marker and built three deubiquitinase-associated signatures for the three top most occurring gynecological cancer types (cervical, ovarian and uterine cancers) using the Cox-Lasso model. The DAS developed effectively predicted the survival of patients in the unseen internal TCGA dataset for the three gynecological cancers and the external GEO dataset for ovarian and cervical cancers. In the case of uterine cancer, the DAS could not predict the survival of risk groups, which was due to the expression difference of three prognostic DUBs, OTUD7A, OTUD7B, and USP26 [Figure S6].\u003c/p\u003e \u003cp\u003eDAS is an independent predictor of survival, irrespective of factors that are associated with the risk of gynecological cancers such as menopausal stage\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.DAS significantly increases the accuracy of prediction when integrated with clinical factors. Pathway analysis has shown that the substrates of unfavorable DUBs are associated with various infection pathways\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and signalling pathways\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e associated with gynecological cancers. Through CIBERSOFT analysis we observed that Immune cell infiltration is associated with the risk score calculated using DAS. Studies have shown the unfavorable DUBs we obtained in our DAS have a role in the tumor progression of that particular cancer. For example, USP12 and OTUB2 are two unfavorable DUBs, in the DAS we constructed for cervical cancer, USP12 regulates the growth of cervical cancer by increasing BMI-1, c-Myc, and cyclin D2 levels\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. OTUB2 promotes cervical cancer by stabilizing FOXM1\u003csup\u003e38\u003c/sup\u003e, RBM15-mediated m6A modification, and AKT/mTOR signaling\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. USP43, an unfavorable DUB for ovarian cancer, promotes the growth of ovarian cancer through stabilization of HDAC2 and activation of the Wnt/β-catenin signaling pathway\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The studies above support that the expression of unfavorable DUBs in DAS promotes tumor growth. These DUBs can act as new targets in Cervical, Ovarian, and Uterine cancer treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthors declare no conflicts of interest\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe thank the members of SKlab and MKlab for the helpful discussions. KM is a registered Integrated Systems Biology master\u0026rsquo;s student, AD and GVH are registered PhD students at the University of Hyderabad. AD and MK also acknowledge funding support from UoH-IoE Grant (UoH-IoE-RC2\u0026ndash;21\u0026ndash;012) and DBT-BUILDER. GVH and SK acknowledge funding support from ANRF (ANRF-SRG/2020/001099) and UoH-IoE Grant (UoH-IoE-RC2-21-020).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eAll codes used are available on \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Mavikakondapally/codes\u003c/span\u003e\u003cspan address=\"https://github.com/Mavikakondapally/codes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel Mph RL, Miller KD, Sandeep N, et al. Cancer statistics, 2023. \u003cem\u003eWiley Online Library\u003c/em\u003e. 2023;73(1):17-48. doi:10.3322/caac.21763\u003c/li\u003e\n\u003cli\u003eZhu W, Xie L, Han J, Cancers XG, 2020 undefined. The application of deep learning in cancer prognosis prediction. \u003cem\u003emdpi.comW Zhu, L Xie, J Han, X GuoCancers, 2020\u0026bull;mdpi.com\u003c/em\u003e. Accessed March 5, 2025. https://www.mdpi.com/2072-6694/12/3/603\u003c/li\u003e\n\u003cli\u003eLiu W, Jiang Q, Sun C, et al. Developing a 5-gene prognostic signature for cervical cancer by integrating mRNA and copy number variations. \u003cem\u003eSpringerW Liu, Q Jiang, C Sun, SH Liu, Z Zhao, D WuBMC cancer, 2022\u0026bull;Springer\u003c/em\u003e. 2021;22(1). doi:10.1186/s12885-022-09291-z\u003c/li\u003e\n\u003cli\u003eQu X, Shi Z, Guo J, et al. Identification of a novel six‐gene signature with potential prognostic and therapeutic value in cervical cancer. \u003cem\u003eWiley Online Library\u003c/em\u003e. 2021;10(19):6881-6896. doi:10.1002/cam4.4054\u003c/li\u003e\n\u003cli\u003eYuan L, Lu Z, Sun G, Medicine DC, 2022 undefined. Identification and verification of a 4-gene signature predicting the overall survival of cervical cancer. \u003cem\u003ejournals.lww.com\u003c/em\u003e. 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USP43 impairs cisplatin sensitivity in epithelial ovarian cancer through HDAC2-dependent regulation of Wnt/\u0026beta;-catenin signaling pathway. \u003cem\u003eApoptosis\u003c/em\u003e. 2024;29(1-2). doi:10.1007/s10495-023-01873-x\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijco","sideBox":"Learn more about [International Journal of Clinical Oncology](http://link.springer.com/journal/10147)","snPcode":"10147","submissionUrl":"https://www.editorialmanager.com/ijco/default2.aspx","title":"International Journal of Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Deubiquitinases, Cervical, Ovarian and Uterine Cancer","lastPublishedDoi":"10.21203/rs.3.rs-6544915/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6544915/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo develop Deubiquitinase-Associated Signatures (DAS) to predict the prognosis of gynecological cancer patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing a cox-lasso regression model, we have developed Deubiquitinase-associated signatures for Cervical, Ovarian, and Uterine cancers. Developed DAS were validated in TCGA and GEO datasets. Survival analysis was carried out to know the effect of factors like menopausal stage and grade on DAS. The survival prediction accuracy of DAS was analyzed using ROC curves. Immune infiltration scores of 22 immune subtypes were explored using the CIBERSORT package in risk groups classified by DAS. Further, to target the unfavorable deubiquitinases (DUBs), compounds were identified using CMap database.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThree DAS were developed for Cervical, Ovarian, and Uterine cancer types. DAS was able to predict survival and classify patients into two groups in TCGA and GEO datasets. DAS is an independent predictor of survival irrespective of tumor grade and menopausal stage. DAS, along with the clinical features, improves the accuracy of predictions. CIBERSORT analysis has shown that Immune cell infiltration is associated with risk groups divided by DAS. Using CMap, 52 compounds were identified to target unfavorable DUBs.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDAS is a good predictor of survival, and targeting unfavorable DUBs can decrease tumor progression in gynecological cancers.\u003c/p\u003e","manuscriptTitle":"Deubiquitinases as Prognostic Biomarker and Potential Drug Target for Gynecological Cancers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 11:15:17","doi":"10.21203/rs.3.rs-6544915/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-06-04T19:18:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-05-04T07:14:09+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-01T21:30:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-29T14:31:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Clinical Oncology","date":"2025-04-28T03:20:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijco","sideBox":"Learn more about [International Journal of Clinical Oncology](http://link.springer.com/journal/10147)","snPcode":"10147","submissionUrl":"https://www.editorialmanager.com/ijco/default2.aspx","title":"International Journal of Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e2d5eeaa-b220-4f2b-9764-800fa5635676","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:00:37+00:00","versionOfRecord":{"articleIdentity":"rs-6544915","link":"https://doi.org/10.1007/s10147-025-02880-5","journal":{"identity":"international-journal-of-clinical-oncology","isVorOnly":false,"title":"International Journal of Clinical Oncology"},"publishedOn":"2025-11-07 15:56:53","publishedOnDateReadable":"November 7th, 2025"},"versionCreatedAt":"2025-05-07 11:15:17","video":"","vorDoi":"10.1007/s10147-025-02880-5","vorDoiUrl":"https://doi.org/10.1007/s10147-025-02880-5","workflowStages":[]},"version":"v1","identity":"rs-6544915","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6544915","identity":"rs-6544915","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}