Construction and validation of an IHC-based risk model predicting postoperative disease- free survival and overall survival in Chinese endometrial cancer patients

Construction and validation of an IHC-based risk model predicting postoperative disease- free survival and overall survival in Chinese endometrial cancer patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Construction and validation of an IHC-based risk model predicting postoperative disease- free survival and overall survival in Chinese endometrial cancer patients Xingchen Li, Fengbo Yang, Lijun Zhao, Jianliu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2643530/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : The objective of this study was to characterize the expression features base on three genes, including DUSP1, BUB1 and MCM7, and investigate their clinical significance in Chinese cohort with endometrial carcinoma. Materials and methods . Immunohistochemistry (IHC) was performed for assessing the expression of DUSP1, BUB1, and MCM7 in these patients. The associations between the expressions of three genes and the clinicopathological features were analyzed. The risk model was finally validated in TCGA cohort. Results : The expressions of DUSP1, BUB1, and MCM7 were associated with clinicopathological features ( P ＜0.05 for all). Patients with high expression level of DUSP1 and low expression level of BUB1 and MCM7 had better prognosis. The construction combining these three genes is an independent risk factor and had a high accuracy in predicting overall survival (OS, AUC=0.923) and disease-free survival (DFS, AUC=0.963). The three-gene risk signature was validated in TCGA-EC cohort, and the results showed a great precision for predicting OS and DFS (both AUC>0.7) in an external group. Conclusions : Our study revealed a three-gene prognostic signature, including DUSP1, BUB1, and MCM7, and its related nomogram could guide personalized strategy, providing a basis for the molecular analysis in EC, thereby improving their OS and DFS. Biological sciences/Cancer/Cancer genetics Biological sciences/Cancer/Cancer stem cells Biological sciences/Cancer/Cancer therapy Biological sciences/Cancer/Tumour immunology Biological sciences/Cancer Biological sciences/Cell biology Biological sciences/Immunology endometrial carcinoma prognostic biomarker risk model Chinese cohort DUSP1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Endometrial carcinoma (EC) is one of the most common gynecological malignancies. Among all pathological types of EC, endometrioid endometrial adenocarcinoma (EEA) is the most common, accounting for 70%~80% of the total cases 1 .Compared with serous endometrial adenocarcinoma (SEA), clear cell carcinoma and other special pathological types, EEA has a better prognosis 1 . In studies on the molecular characteristics of endometrial carcinoma, researchers found that EEA has a high degree of genetic heterogeneity and inter-individual differences 2 . For example, some patients with FIGO stage I-II endometrial carcinoma suffered from early recurrence, while a considerable number of patients with FIGO stage III-IV could survive for a long time. At present, it is difficult to accurately assess the prognosis of endometrial carcinoma patients based on pathological diagnosis alone, which is difficult to guide individual adjuvant treatment and prognosis monitoring. Therefore, it is necessary to detect the gene expression that may have prognostic significance, and to evaluate the risk of recurrence and death based on the clinicopathological characteristics and molecular characteristics of endometrial carcinoma patients. Based on the previous research of our team over the past ten years, three characteristic genes that may show significance in the prognosis of EC were screened out, including DUSP1, BUB1 and MCM7 3 . Thus, these three genes may be related to the recurrence and survival of endometrial carcinoma patients. During the past years, prognostic biomarkers were discovered in ovarian cancer. The function of the mentioned three genes are well known in some kind of tumors. For example, over expression of DUSP1 can inhibit the MAPK signaling pathway in tumor cells, thus inhibiting the proliferation of endometrial carcinoma 4 . Silencing DUSP1 can activate the MAPK signaling pathway and promote the proliferation and metastasis of tumor cells. BUB1 regulates the mitotic cycle by phosphorylating the mitotic checkpoint complex and activating the spindles. BUB1 can also inhibit the activity of the anaphase promoting complex and play a crucial role in the DNA damage response. BUB1 gene is abnormally expressed in a variety of malignancies, including endometrial carcinoma, colorectal cancer, gastric cancer, esophageal cancer, prostate cancer, and ovarian cancer 5-8 . MCM7 plays an important role in the occurrence, development and treatment of various malignant tumors, such as endometrial carcinoma, hepatocellular carcinoma, non-small cell lung cancer, and colorectal cancer 9-11 . Genome-sequencing technology has revealed the prognostic power of gene signatures for EC. Key molecular parameters for EC prognosis include mRNA, lncRNA and microRNAs 3, 12 . Analysis of publicly available genomic data has revealed multiple and efficient prognosis gene signatures. However, gene signatures alone while disregarding clinical parameters for predicting overall survival has been counterproductive. As such, it is important to combine novel gene signatures and clinical parameters for more efficient prognosis prediction. In this study, we analyzed the expression and clinical significance of DUSP1, BUB1 and MCM7 in endometrial carcinoma. Furthermore, we also the constructed a prognostic multigene signature with the 3 genes. A predictive nomogram was built, both internally and externally validated in the TCGA cohort. As a whole, this prognostic model and nomogram might provide some new insights for targeted therapy and prognosis assessment of endometrial carcinoma. 2. Materials and methods Patients and samples In this study, 152 endometrial carcinoma cases treated in the Department of Gynecology at Peking University People’s Hospital between January 2006 and December 2011 were included, and all the cases were endometrioid adenocarcinoma. The 152 patients were followed up for more than 5 years after the primary surgery by telephone or outpatient review. None of these patients underwent preoperative chemoradiotherapy or endocrine therapy. The clinicopathological data of these cases were collected through the comprehensive inquiry system of medical records. This study was approved by the medical ethics committee of Peking University People’s Hospital, and we confirm that all methods were performed in accordance with the relevant guidelines and regulations. Informed consents were obtained from all participants by their permission. Immunohistochemical assays In this study, immunohistochemistry was used to detect the expression of DUSP1, BUB1 and MCM7 in endometrial carcinoma tissues. The Envision method of immunohistochemistry was used to detect and analyze. Immunohistochemical assays are as follows. (1) Conventional paraffin sections were baked for 2h using an oven at 65℃. (2) Xylene I, II and III solutions were used for dewaxing for 10min each. (3) Anhydrous ethanol I and II solutions were used for dehydration for 5min each. (4) 95% ethanol was used for dehydration for 5min. (5) 80% ethanol was used for dehydration for 5min. (6) The sections were rinsed with the running water carefully. (7) PBS buffer was used to wash sections for 3x3min. (8) Wait for the EDTA PH 9.0 or citrate repair solution to boil and then put into the tissue sections, water bath 2min30s and cool naturally. (9) The sections were rinsed with PBS buffer solution for 3 times, 3min each. (10) Rinse with 3% H2O2 solution for 10min to remove endogenous peroxidase. (11) Wash these sections with PBS buffer solution for 3 times, 3min each. (12) Add the primary antibody and incubate these sections overnight in a refrigerator at 4℃. (13) The primary antibody was removed and washed these sections with PBS buffer solution for 3 times, 3min each. (14) Add the universal secondary antibody by drop and incubate at room temperature for 30min. (15) Wash away the secondary antibody and rinse with PBS buffer solution for 3 times, 3min each. (16) DAB chromogenic solution was used for controlled chromogenic development under a microscope. (17) The nucleus was re-stained with hematoxylin solution. (18) Alcohol hydrochloride was used for differentiation and ammonia water was used for regurgitation. (19) Dehydrate with alcohol and clear with xylene. (20) Use neutral gum for sealing the sections. (21) Read the sections. Data explanation For each tissue section, ten high power microscopic fields were randomly selected. The results were observed and determined by two experienced physicians in the Department of Pathology at Peking University People’s Hospital through double-blind method, and a sample with a difference of over 10% was subjected to re-assessment 13, 14 . DUSP1 was mainly cytoplasmic staining, which was determined by Immunoreactive score (IRS). First, the cells were scored according to the staining intensity. 0: the cells were colorless; 1 point: the staining of the cells was weak and light yellow; 2 points: the cells were moderately stained and yellowish brown; 3 points: the cells are strongly stained and tan. Then scored according to the proportion of positive cells. 0: no positive cells; 1 point: positive cells≤10%; 2 points: positive cells 11%~50%; 3 points: positive cells 51%~75%; 4 points: positive cells > 75%. IRS = staining intensity × percentage of positive cells. IRS > 3 is defined as DUSP1 highly expressed, and IRS≤3 is defined as DUSP1 poorly expressed 15 . MCM7 was mainly nuclear staining, so according to the percentage of expressed positive cells, it can be divided into two groups: the percentage of positive cells≥25% means high expression of MCM7, and the percentage of positive cells < 25% means low expression of MCM7 16 . BUB1 was also mainly stained by the nucleus, so it can be divided into two groups according to the percentage of positive cells: the percentage of positive cells≥10% means high expression of BUB1, and the percentage of positive cells < 10% means low expression of BUB1 6 . Nomogram development and validation for prognostic risk model Univariate and multivariate Cox regression analyses were used to determine whether the risk signature was independent risk factors for CC patients. A nomogram with these independent prognostic factors was constructed using “rms” package. Risk score of each patient was calculated by Coefi, which was referred as the multivariate regression coefficient of 3 genes in COX analysis The prognostic evaluation of nomogram was then performed with Kaplan–Meier survival analysis and the area under the tdROC curve (AUC). Construction and validation of the 3-gene signature Patients were then divided into low- and high-risk groups according to the median value of risk score. The Kaplan–Meier survival analysis of the low- and high-risk groups was performed using the “survival” package of R software. The “timeROC” package of R software was used for drawing the ROC curve of the model to evaluate the sensitivity and specificity of the risk signature. Statistical analysis The categorical variables were described by composition ratio, and the chi-square test or Fisher's exact test method was used to compare the differences between groups. Kaplan-Meier survival curve was used to describe the prognosis of endometrial carcinoma patients with different expression levels of DUSP1, BUB1 and MCM7, and log-rank test was used to compare the influence of various clinicopathological features and different expressions of DUSP1, BUB1 and MCM7 on the prognosis of endometrial carcinoma patients. Multivariate Cox regression was used to establish the prognostic prediction model. Time-dependent ROC curves and nomograms were established. SPSS 23.0 (IBM SPSS Statistics, NW, USA) and R 3.4.3 (R core team, 2017) were used for all statistical analysis. In the statistical analysis, P < 0.05 is considered statistically significant. 3. Results The expression of DUSP1, BUB1 and MCM7 in endometrial carcinoma The flow chart of this study is shown as Figure 1 . In our previous study, we developed a biomarker panel utilizing 21 EC prognosis-related genes, which played a role in predicting outcomes for Chinese EC patients. We also revealed 2110 DEGs with samples from the original sites of patients with or without recurrence. Finally, there are 4 common genes by venn diagram ( Figure 2A ). In this study, we conduct in-depth study on BUB1, DUSP1, and MCM7. In our preliminary experiment, the differential expression of NCD80 between cancer and normal tissues was not obvious. Furthermore, in the 152 cases of endometrial carcinoma, DUSP1, BUB1 and MCM7 expressed in varying degrees. DUSP1 expression level was low in 34 cases and high in 118 cases. There were 104 cases with low BUB1 expression level and 48 cases with high BUB1 expression level. There were 73 cases with low MCM7 expression level and 79 cases with high MCM7 expression level. The immunohistochemical staining results of the three proteins in endometrial carcinoma tissues are shown in Figure 2B . The associations between the expressions of DUSP1, BUB1, MCM7 and clinicopathological characteristics The association between expression of these three genes by IHC and clinicopathological characteristics of 152 cases were analyzed. There was significant difference in the expression level of BUB1 in endometrial carcinoma tissues of stage I, stage II, and stage III-IV patients ( Figure 3A ). The distribution of tumor grade is also significant between low- and high-expression BUB1 groups ( Figure 3B ). Up-regulated BUB1 also causes myometrial invasive ( Figure 3C ). The expression of DUSP1 in different clinicopathological characteristics is just opposite to that of BUB1. Patients with high stage and deep myometrial invasion generally expressed low degree of DUSP1. What’s more, the proportion of patients with low DUSP1 expression level in patients with FIGO stage I-II and stage III-IV was 27/130 and 7/22, respectively. There was significant difference in the expression of DUSP1 in patients with different FIGO stages ( Figure 3D-F ). MCM7 has a similar expression pattern with BUB1. For example, the proportion of cases with low MCM7 expression was relatively lower in high stage, poorly differentiated, and deep myometrial invasive EC tissues ( Figure 3G-I ). These results indicates that BUB1, DUSP1, and MCM7 plays an essential role in the progression of EC. The association between the combination of DUSP1, BUB1, MCM7 and prognosis Kaplan-Meier survival curve showed that patients with high expression level of BUB1 had a relatively shorter disease free survival (DFS), but the difference was not statistically significant ( Figure 4A ). Survival analysis showed that patients with low DUSP1 and high MCM7 expression level had significantly shorter disease-free survival ( Figure 4B-C ). By analyzing the overall survival, the difference is not statistically significant between the high- and low-expression of BUB1 ( Figure 4D ). Higher DUSP1 has a better overall survival ( P <0.05), while lower expression also has a better prognosis, but the difference is not obvious enough ( Figure 4E-F ). Considering the limited significance in predicting DFS and OS for EC patients, we combined the expression of these three together to predict the survival. The results showed that the survival rate is the worst in patients with high DUSP1, low BUB1, and low MCM7, for both OS and DFS ( Figure S1 ). Independent risk factors of DFS and OS in EC patients To further illustrate the significance of the three genes, we constructed a risk model by multivariate analysis. The OS risk model is listed as following: risk model= BUB1*0.681-DUSP1*1.503+MCM7*1.049. The DFS risk model is: BUB1*0.117-DUSP1*2.135+MCM7*1.163. The total score of each patient is calculated and analyzed to predict the progression and survival of EC patients. Univariate Cox regression analysis showed that the 3-gene model was an important risk factor for both disease free survival and overall survival ( Figure 5A-B ). For disease free survival, multivariate Cox analysis revealed that FIGO stage and 3-gene signature are two independent risk factors ( Figure 5C ). Meanwhile, stage, grade, and our gene signature are independent for overall survival ( Figure 5D ). Establishment and validation of the nomogram We designed a nomogram to predict the survival probability of each patient, including DFS and OS. In the nomogram, each predictor was assigned a score. Based on the Cox analysis results, two features were integrated in the nomogram to predict the DFS probability of EC patients ( Figure 6A ). Stage, grade, and the 3-gene signature are included in the nomogram predicting the OS of EC patients ( Figure 6B) . We then calculate the total score of the patients according to the nomogram and divided the patients into low score or high score group based on the median value of the risk score. The survival analysis indicated that patients in high score group had a worse prognosis for both DFS and OS ( Figure 6C-D ). ROC curve analysis revealed that the area under the ROC curve (AUC) of the prognostic nomogram was 0.923 and 0.963 for DFS and OS, respectively ( Figure 6E-F ). Validation of the risk model in TCGA cohort We then verify the expression pattern in different clinicopathological features in TCGA cohorts. As shown in Figure 7A-I , the results indicated that the level of BUB1 and MCM7 are relatively elevated in patients with high stage, high grade, and more invasive capability. Meanwhile, the expression pattern of BUB1 are opposite from the other two genes. What’s more, the expression of BUB1 and MCM7 are also higher in EC tissues than that in normal tissues in the samples of our center ( Figure 7J ). In agreement with the results from the our own cohort, the KM curve analysis of the subgroup with low DUSP1 and high BUB1/MCM7 presented worse outcomes than other subgroup in the TCGA cohort with a shorter DFS and OS time ( Figure 7K-L ). The risk model was then introduced into the TCGA cohort, and each individual’s risk score was calculated. Based on the training cohort cut-off risk score, the patients in the testing cohort were classified as high-risk and low-risk individuals. The AUC of the risk model in the TCGA cohort was 0.723 and 0.752 for predicting DFS and OS, respectively ( Figure 7M-N ). These results indicated that the expression model of the three genes outperformed in an external validation group. Discussion Endometrial cancer is a worldwide spread malignant tumor that threatens women’s lives, which usually occurs in post- menopausal women and is difficult to diagnose at the early stage 17 . Recently, many risk factors have been linked to the occurrence of EC, such as obesity, diabetes and hyperinsulinemia 18 . Most previous risk models for prognostic prediction of EC have been constructed by foreign cohort, especially with TCGA cohort 19 . Few studies concentrated on Chinese people and established a native cohort risk model. What’s more, the molecular mechanisms underlying the development of EC have not yet been fully characterized and consequently are poorly understood 20 . The clinical heterogeneity of patients with EC probably reflects variation at the molecular level 21 ; thus, the prognostic mechanisms of EC likely vary as well. One study constructed a nine DNA repair-related gene prognostic classifier to predict prognosis in patients with EC, and patients with low risk were more likely to have sensitivity to paclitaxel, vinblastine, rapamycin, metformin, imatinib, Akt inhibitor and lapatinib 22 . In this study, we aim to investigate the key genes expression profiles and their values in the prognosis in EC through the bioinformatics analysis from our own center. The results were verified in clinical specimens through polymerase chain reaction (PCR) and immunohistochemistry (IHC). We first narrowed the scope of our research, identified three key genes on the basis of our previous research, explored the relationship between their expression and clinicopathological characteristics, and used these three key genes to construct and verify the prognosis prediction model of endometrial cancer. Afterwards, TCGA database is used to verify the accuracy of the model, and the results confirm that the model has high accuracy in predicting endometrial cancer. In addition, the three genes we revealed in this study have also been reported to play an essential role in the prognosis of some kind of tumor. Previously in vitro studies have shown that the expression level of DUSP1 might be related to the therapeutic effect of medroxyprogesterone acetate 23 . As for the correlation between DUSP1 and the clinicopathological features of other malignancies, various studies have obtained different results. For example, in the mouse model of hepatocellular carcinoma, low DUSP1 expression level is closely related to poor tissue differentiation ( P < 0.001) and advanced TNM stage ( P =0.023) 24 . According to these results, the decreased expression level of DUSP1 was associated with the high risk clinicopathologic features of endometrial carcinoma and other malignancies. The molecular mechanism regulating DUSP1 is still unclear. There are two PR reaction elements downstream of the DUSP1 transcription promoter. Combined with the reaction elements, PR can enhance the activity of DUSP1 promoter and up-regulate the expression of DUSP1 25 . It suggested that DUSP1 expression level was decreased in endometrial carcinoma, which may be related to abnormal function of PR and PR response elements. High expression of BUB1 may indicate a higher malignant potential for endometrial cancer. As for the association between BUB1 and other malignant tumors, various studies have found that the expression of BUB1 has organ heterogeneity. For example, in colorectal cancer tissues, the low expression levels of BUB1 and BUBR1 are significantly correlated with the presence of lymph node metastasis ( P < 0.01) 5 . BUB1 plays different roles in endometrial carcinoma and colorectal cancer, thus individualized treatment related to BUB1 needs to be formulated in combination with the specific type of malignancy. In our study, patients with high BUB1 expression level have relatively poor prognosis. Therefore, BUB1 may be a new target for therapy in endometrial carcinoma. The detection of BUB1 expression can stratify the prognostic risks of endometrial carcinoma patients, which is expected to play an important role in the accurate treatment and prognostic assessment of endometrial carcinoma patients. We also revealed that high expression level of MCM7 is associated with some adverse clinicopathological features. The increased expression of MCM7 is also associated with shorter disease-free survival in patients with colorectal cancer 26 , gastric adenocarcinoma 27 , and meningioma 28 . At present, there are many researches on the prognosis prediction model of endometrial cancer. Some are simple clinical prediction models, which establish prognosis prediction models according to the clinicopathological information characteristics of endometrial cancer patients. In our previous, we developed an available nomogram with effective external validation and relatively appreciable discrimination and conformity for the accurate assessment of 3- and 5-year DFS in Chinese women with EC 29 . In another study, nomograms were designed to accurately predict OS and cancer specific survival (CSS) in EC patients. The nomograms can be used for estimating OS and CSS of individual patients and establishing their risk stratification 30 . However, these studies ignored the genetic and molecular characteristics of EC. There are also studies that combine the genetic characteristics and clinical pathological characteristics of patients. For example, an EMT-related signature model was constructed to predict the prognosis of EC patients, and it might offer a reference for predicting individualized response to immune checkpoint inhibitors and chemotherapeutic drugs 31 . Study of our center also reveals a link between glycolysis-related gene signature and immunity, and provides personalized therapeutic targets for EC. The accuracy of this risk model for predicting the overall survival of AUC reached to 0.822 32 . In addition, there are many other similar studies. However, most of these studies are based on TCGA database, which main source of samples are Americans. There is a lack of research on Chinese people. This study identified three key genes through a large-scale sequencing results of Chinese EC patients, and used these three genes to construct a prognosis predictive model combining gene characteristics with clinicopathological characteristics in the EC population in our center. This conclusion was verified by TCGA database and the accuracy of OS and DFS is more than 0.7. Therefore, the model has high prediction accuracy for endometrial cancer patients in Chinese population. Our research also has some shortcomings. First of all, our research is conducted in a single center, and the results have not been verified among Chinese people in other centers. In addition, the functions and the specific mechanisms of the three genes that affect the occurrence and development of endometrial cancer have not been explored. Finally, a larger sample of EC patients should be sequenced to verify the accuracy of the model. Conclusion We identified three genes that have significant correlation with the prognosis of endometrial cancer through our previous studies. Furthermore, a prognosis related predictive model of endometrial cancer combining genetic features and clinicopathological characteristics is constructed and verified. This model not only has high accuracy, but also is more suitable for the characteristics of Chinese population. Declarations Funding This study is supported by the Research and Development Fund of Peking University People’s Hospital (Grant No. RS2021-05 and RDY2021-13), National Natural Science Foundation of China (Grant Nos. 82103419 and 81874108). Author Contributions Xingchen Li is responsible for conceptualization; Fengbo Yang are responsible for methodology. Lijun Zhao is responsible for software; Fengbo Yang is responsible for formal analysis; Lijun Zhao is responsible for investigation; Fengbo Yang and Xingchen Li are responsible for data curation; Xingchen Li is responsible for writing the original draft; Jianliu Wang and Xingchen Li are responsible for funding acquisition. Jianliu Wang and Xingchen Li is responsible for writing-review & editing the article. All authors read and approved the final manuscript. Acknowledgement We especially appreciate Jiayang Jin, PhD of Peking University People’s Hospital for statistics, study deign and editing the manuscript. Supplementary data Supplementary data are available online in Briefings in Bioinformatics. Data availability statement The data underlying this article are available in the article and in its online supplementary material. Conflicts of interest The authors declare that they have no competing interests. References Amalinei C, Aignatoaei AM, Balan RA, et al. Clinicopathological significance and prognostic value of myoinvasive patterns in endometrial endometrioid carcinoma. Rom J Morphol Embryol . 2018;59:13-22. Reiter JG, Makohon-Moore AP, Gerold JM, et al. Minimal functional driver gene heterogeneity among untreated metastases. Science (New York, NY) . 2018;361:1033-1037. Xu X, Li X, Zhou J and Wang J. Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer. 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J Neurooncol . 2016;131:1-9. Cheng Y, Dong Y, Tian W, et al. Nomogram for Predicting Recurrence-Free Survival in Chinese Women with Endometrial Cancer after Initial Therapy: External Validation. J Oncol . 2020;2020:2363545. Li X, Fan Y, Dong Y, et al. Development and Validation of Nomograms Predicting the Overall and the Cancer-Specific Survival in Endometrial Cancer Patients. Front Med (Lausanne) . 2020;7:614629. Liu J, Cui G, Shen S, et al. Establishing a Prognostic Signature Based on Epithelial-Mesenchymal Transition-Related Genes for Endometrial Cancer Patients. Front Immunol . 2021;12:805883. Yang X, Li X, Cheng Y, et al. Comprehensive Analysis of the Glycolysis-Related Gene Prognostic Signature and Immune Infiltration in Endometrial Cancer. Front Cell Dev Biol . 2021;9:797826. Additional Declarations No competing interests reported. Supplementary Files FigureS1.jpg Figure S1 A-H. Survival curve of different combination from 3 genes for DFS and OS. 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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-2643530","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":182686652,"identity":"9f4f1f68-8764-4d5f-8a87-17f51a2b73a1","order_by":0,"name":"Xingchen Li","email":"","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xingchen","middleName":"","lastName":"Li","suffix":""},{"id":182686653,"identity":"cbab96fa-bf86-42d5-b609-1ef18c8c6b5a","order_by":1,"name":"Fengbo Yang","email":"","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fengbo","middleName":"","lastName":"Yang","suffix":""},{"id":182686654,"identity":"84e199f7-25d7-4fe6-9815-6bc50da2b008","order_by":2,"name":"Lijun Zhao","email":"","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Zhao","suffix":""},{"id":182686655,"identity":"e19ef757-686d-4ef3-be33-28f861ba0a37","order_by":3,"name":"Jianliu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3PMQrCMBTG8VcK6RJ0jQQ9w5OCFCx6FUXI1KGjk6QUOnmAbr2CR2gN1sUDCDoogrPgIuhg1c2h7SiY/xi+H48A6HQ/WSYBfAbECsLDBd1BTYIMGlTl3dgXk5qXEKDDxIjTy9KQleNMBVcfnXEEHtoupiZYarUoJWkW8hhZQTZ48nDfACrEtoz00kxy+iLGHG0PzyYw2qsi4f1NTIrcQWXIGiT6XCFkxKEOGRakXxA7ombenaOYkKq/tOL1aUens3aSHMPD7eEOmpbKSwmw9OuBlM5fNWXlRKfT6f69J9u7S+xHNrfXAAAAAElFTkSuQmCC","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jianliu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-03-01 14:14:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2643530/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2643530/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34238307,"identity":"da6647d3-0006-4929-9684-341d5a9a8cab","added_by":"auto","created_at":"2023-03-14 14:51:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":205825,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design and flow chart.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2643530/v1/42e643e43b2939aa364f4900.jpg"},{"id":34240969,"identity":"b6ae1c02-6128-4fde-8d77-9a27d5b31a66","added_by":"auto","created_at":"2023-03-14 15:07:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1334716,"visible":true,"origin":"","legend":"\u003cp\u003eselection and validation of 4 hub genes. A. Venn diagram to identify common genes between microarray and DEGs that were correlated with recurrence. B. representatives immunohistochemistry photos of DUSP1, BUB1, and MCM7.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2643530/v1/ee235edd1804d07eeaf3c818.jpg"},{"id":34239775,"identity":"1040c054-91c5-4a35-84ab-2433747c8437","added_by":"auto","created_at":"2023-03-14 14:59:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":909780,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of different clinicopathological factors in low- and high-expression of 3 hub genes. A. Different stage in BUB1. B. Different grade in BUB1. C. Different myometrial invasion in BUB1. D. Different stage in DUSP1. E. Different grade in DUSP1. F. Different myometrial invasion in DUSP1. G. Different stage in MCM7. H. Different grade in MCM7. I. Different myometrial invasion in MCM7.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2643530/v1/61c56452b076930478922107.jpg"},{"id":34238310,"identity":"8697506b-a8e6-48f1-afda-1e5b63c881f6","added_by":"auto","created_at":"2023-03-14 14:51:10","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":910627,"visible":true,"origin":"","legend":"\u003cp\u003eDisease free survival (DFS) and overall survival (OS) analysis of 3 hub genes in EC patients in our center. A. DFS of BUB1. B. DFS of DUSP1. C. DFS of MCM7. D. OS of BUB1. E. OS of DUSP1. F. OS of MCM7.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2643530/v1/561f6378233b40fdd4920105.jpg"},{"id":34238305,"identity":"9578a83f-33ee-4cf1-b9ff-b412d941d8cf","added_by":"auto","created_at":"2023-03-14 14:51:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1470540,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of risk factors including 3 hub genes and 3-gene signature for DFS and OS in EC patients. A. Univariate analysis for DFS in EC patients. B. Univariate analysis for OS in EC patients. C. Multivariate analysis for DFS. D. Multivariate analysis for OS.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2643530/v1/db10a1c23f2a9792ff95202c.jpg"},{"id":34239776,"identity":"e7621032-f7bc-46d9-a049-0e2266eabd5a","added_by":"auto","created_at":"2023-03-14 14:59:10","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1549001,"visible":true,"origin":"","legend":"\u003cp\u003eEstablishment and validation of nomogram for predicting DFS and OS in EC patients. A. Nomogram for DFS. B. Nomogram for OS. C. Survival curve for patients in low and high score from the DFS nomogram. D. Survival curve for patients in low and high score from the OS nomogram. E. ROC curve of different risk factors for predicting DFS. F. ROC curve of different risk factors for predicting OS.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2643530/v1/f094e09c73108ed4d48f8d26.jpg"},{"id":34238312,"identity":"1ad0059a-cd7d-4ae8-826c-88634df628fb","added_by":"auto","created_at":"2023-03-14 14:51:11","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2169507,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of expression and risk score with patients in TCGA-UCEC dataset. A. Expression of BUB1 in different stages. B. Expression of BUB1 in different grades. C. Expression of BUB1 in different LNM status. D. Expression of DUSP1 in different stages. E. Expression of DUSP1 in different grades. F. Expression of DUSP1 in different LNM status. G. Expression of MCM7 in different stages. H. Expression of MCM7 in different grades. I. Expression of MCM7 in different LNM status. J. Expression of BUB1, DUSP1, and MCM7 in normal and EC tissues. K. Kaplan-Meier survival curve of different combination from 3 genes for DFS. L. Kaplan-Meier survival curve of different combination from 3 genes for OS. M. ROC curve of the nomogram for predicting DFS in TCGA patients. N. ROC curve of the nomogram for predicting OS in TCGA patients.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2643530/v1/6340a842365479ad2f9bc1c9.jpg"},{"id":39219019,"identity":"2d1b533a-8053-4f6c-bec8-87017d5866f8","added_by":"auto","created_at":"2023-06-28 08:44:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1381616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2643530/v1/40a4b5ad-b63c-468a-926b-54bd1200d8bd.pdf"},{"id":34239774,"identity":"5c567403-b2b2-4f77-986a-c27a850eb9c5","added_by":"auto","created_at":"2023-03-14 14:59:10","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1583249,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1 A-H. Survival curve of different combination from 3 genes for DFS and OS.\u003c/p\u003e","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2643530/v1/3053376da3925f280254c8c3.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction and validation of an IHC-based risk model predicting postoperative disease- free survival and overall survival in Chinese endometrial cancer patients","fulltext":[{"header":"1. Introduction ","content":"\u003cp\u003eEndometrial carcinoma (EC) is one of the most common gynecological malignancies. Among all pathological types of EC, endometrioid endometrial adenocarcinoma (EEA) is the most common, accounting for 70%~80% of the total cases\u003csup\u003e1\u003c/sup\u003e.Compared with serous endometrial adenocarcinoma (SEA), clear cell carcinoma and other special pathological types, EEA has a better prognosis\u003csup\u003e1\u003c/sup\u003e. In studies on the molecular characteristics of endometrial carcinoma, researchers found that EEA has a high degree of genetic heterogeneity and inter-individual differences\u003csup\u003e2\u003c/sup\u003e. For example, some patients with FIGO stage I-II endometrial carcinoma suffered from early recurrence, while a considerable number of patients with FIGO stage III-IV could survive for a long time. At present, it is difficult to accurately assess the prognosis of endometrial carcinoma patients based on pathological diagnosis alone, which is difficult to guide individual adjuvant treatment and prognosis monitoring. Therefore, it is necessary to detect the gene expression that may have prognostic significance, and to evaluate the risk of recurrence and death based on the clinicopathological characteristics and molecular characteristics of endometrial carcinoma patients. Based on the previous research of our team over the past ten years, three characteristic genes that may show significance in the prognosis of EC were screened out, including DUSP1, BUB1 and MCM7\u0026nbsp;\u003csup\u003e3\u003c/sup\u003e. Thus, these three genes may be related to the recurrence and survival of endometrial carcinoma patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring the past years, prognostic biomarkers were discovered in ovarian cancer. The function of the mentioned three genes are well known in some kind of tumors. For example, over expression of DUSP1 can inhibit the MAPK signaling pathway in tumor cells, thus inhibiting the proliferation of endometrial carcinoma\u0026nbsp;\u003csup\u003e4\u003c/sup\u003e. Silencing DUSP1 can activate the MAPK signaling pathway and promote the proliferation and metastasis of tumor cells. BUB1 regulates the mitotic cycle by phosphorylating the mitotic checkpoint complex and activating the spindles. BUB1 can also inhibit the activity of the anaphase promoting complex and play a crucial role in the DNA damage response. \u003cem\u003eBUB1\u003c/em\u003e gene is abnormally expressed in a variety of malignancies, including endometrial carcinoma, colorectal cancer, gastric cancer, esophageal cancer, prostate cancer, and ovarian cancer\u003csup\u003e5-8\u003c/sup\u003e. MCM7 plays an important role in the occurrence, development and treatment of various malignant tumors, such as endometrial carcinoma, hepatocellular carcinoma, non-small cell lung cancer, and colorectal cancer\u003csup\u003e9-11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eGenome-sequencing technology has revealed the prognostic power of gene signatures for EC. Key molecular parameters for EC prognosis include mRNA, lncRNA and microRNAs\u003csup\u003e3, 12\u003c/sup\u003e. Analysis of publicly available genomic data has revealed multiple and efficient prognosis gene signatures. However, gene signatures alone while disregarding clinical parameters for predicting overall survival has been counterproductive. As such, it is important to combine novel gene signatures and clinical parameters for more efficient prognosis prediction.\u003c/p\u003e\n\u003cp\u003eIn this study, we analyzed the expression and clinical significance of DUSP1, BUB1 and MCM7 in endometrial carcinoma. Furthermore, we also the constructed a prognostic multigene signature with the 3 genes. A predictive nomogram was built, both internally and externally validated in the TCGA cohort. As a whole, this prognostic model and nomogram might provide some new insights for targeted therapy and prognosis assessment of endometrial carcinoma.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003ePatients and samples\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, 152 endometrial carcinoma cases treated in the Department of Gynecology at Peking University People\u0026rsquo;s Hospital between January 2006 and December 2011 were included, and all the cases were endometrioid adenocarcinoma. The 152 patients were followed up for more than 5 years after the primary surgery by telephone or outpatient review. None of these patients underwent preoperative chemoradiotherapy or endocrine therapy. The clinicopathological data of these cases were collected through the comprehensive inquiry system of medical records. This study was approved by the medical ethics committee of Peking University People\u0026rsquo;s Hospital, and we confirm that all methods were performed in accordance with the relevant guidelines and regulations. Informed consents were obtained from all participants by their permission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemical assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, immunohistochemistry was used to detect the expression of DUSP1, BUB1 and MCM7 in endometrial carcinoma tissues. The Envision method of immunohistochemistry was used to detect and analyze. Immunohistochemical assays \u0026nbsp;are as follows.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(1) Conventional paraffin sections were baked for 2h using an oven at 65℃. (2) Xylene I, II and III solutions were used for dewaxing for 10min each. (3) Anhydrous ethanol I and II solutions were used for dehydration for 5min each. (4) 95% ethanol was used for dehydration for 5min. (5) 80% ethanol was used for dehydration for 5min. (6) The sections were rinsed with the running water carefully. (7) PBS buffer was used to wash sections for 3x3min. (8) Wait for the EDTA PH 9.0 or citrate repair solution to boil and then put into the tissue sections, water bath 2min30s and cool naturally. (9) The sections were rinsed with PBS buffer solution for 3 times, 3min each. (10) Rinse with 3% H2O2 solution for 10min to remove endogenous peroxidase. (11) Wash these sections with PBS buffer solution for 3 times, 3min each. (12) Add the primary antibody and incubate these sections overnight in a refrigerator at 4℃. (13) The primary antibody was removed and washed these sections with PBS buffer solution for 3 times, 3min each. (14) Add the universal secondary antibody by drop and incubate at room temperature for 30min. (15) Wash away the secondary antibody and rinse with PBS buffer solution for 3 times, 3min each. (16) DAB chromogenic solution was used for controlled chromogenic development under a microscope. (17) The nucleus was re-stained with hematoxylin solution. (18) Alcohol hydrochloride was used for differentiation and ammonia water was used for regurgitation. (19) Dehydrate with alcohol and clear with xylene. (20) Use neutral gum for sealing the sections. (21) Read the sections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData explanation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each tissue section, ten high power microscopic fields were randomly selected. The results were observed and determined by two experienced physicians in the Department of Pathology at Peking University People\u0026rsquo;s Hospital through double-blind method, and a sample with a difference of over 10% was subjected to re-assessment\u003csup\u003e13, 14\u003c/sup\u003e. DUSP1 was mainly cytoplasmic staining, which was determined by Immunoreactive score (IRS). First, the cells were scored according to the staining intensity. 0: the cells were colorless; 1 point: the staining of the cells was weak and light yellow; 2 points: the cells were moderately stained and yellowish brown; 3 points: the cells are strongly stained and tan. Then scored according to the proportion of positive cells. 0: no positive cells; 1 point: positive cells\u0026le;10%; 2 points: positive cells 11%~50%; 3 points: positive cells 51%~75%; 4 points: positive cells \u0026gt; 75%. IRS = staining intensity \u0026times; percentage of positive cells. IRS \u0026gt; 3 is defined as DUSP1 highly expressed, and IRS\u0026le;3 is defined as DUSP1 poorly expressed\u0026nbsp;\u003csup\u003e15\u003c/sup\u003e. MCM7 was mainly nuclear staining, so according to the percentage of expressed positive cells, it can be divided into two groups: the percentage of positive cells\u0026ge;25% means high expression of MCM7, and the percentage of positive cells \u0026lt; 25% means low expression of MCM7\u003csup\u003e16\u003c/sup\u003e. BUB1 was also mainly stained by the nucleus, so it can be divided into two groups according to the percentage of positive cells: the percentage of positive cells\u0026ge;10% means high expression of BUB1, and the percentage of positive cells \u0026lt; 10% means low expression of BUB1\u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNomogram development and validation for prognostic risk model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate and multivariate Cox regression analyses were used to determine whether the risk signature was independent risk factors for CC patients. A nomogram with these independent prognostic factors was constructed using \u0026ldquo;rms\u0026rdquo; package. Risk score of each patient was calculated by Coefi, which was referred as the multivariate regression coefficient of 3 genes in COX analysis The prognostic evaluation of nomogram was then performed with Kaplan\u0026ndash;Meier survival analysis and the area under the tdROC curve (AUC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and validation of the 3-gene signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were then divided into low- and high-risk groups according to the median value of risk score. The Kaplan\u0026ndash;Meier survival analysis of the low- and high-risk groups was performed using the \u0026ldquo;survival\u0026rdquo; package of R software. The \u0026ldquo;timeROC\u0026rdquo; package of R software was used for drawing the ROC curve of the model to evaluate the sensitivity and specificity of the risk signature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe categorical variables were described by composition ratio, and the chi-square test or Fisher\u0026apos;s exact test method was used to compare the differences between groups. Kaplan-Meier survival curve was used to describe the prognosis of endometrial carcinoma patients with different expression levels of DUSP1, BUB1 and MCM7, and log-rank test was used to compare the influence of various clinicopathological features and different expressions of DUSP1, BUB1 and MCM7 on the prognosis of endometrial carcinoma patients. Multivariate Cox regression was used to establish the prognostic prediction model. Time-dependent ROC curves and nomograms were established. SPSS 23.0 (IBM SPSS Statistics, NW, USA) and R 3.4.3 (R core team, 2017) were used for all statistical analysis. In the statistical analysis, P \u0026lt; 0.05 is considered statistically significant.\u003c/p\u003e"},{"header":"3. Results ","content":"\u003cp\u003e\u003cstrong\u003eThe expression of DUSP1, BUB1 and MCM7 in endometrial carcinoma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe flow chart of this study is shown as \u003cstrong\u003eFigure 1\u003c/strong\u003e. In our previous study, we developed a biomarker panel utilizing 21 EC prognosis-related genes, which played a role in predicting outcomes for Chinese EC patients. We also revealed 2110 DEGs with samples from the original sites of patients with or without recurrence. Finally, there are 4 common genes by venn diagram (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). In this study, we conduct in-depth study on BUB1, DUSP1, and MCM7. In our preliminary experiment, the differential expression of NCD80 between cancer and normal tissues was not obvious. Furthermore, in the 152 cases of endometrial carcinoma, DUSP1, BUB1 and MCM7 expressed in varying degrees. DUSP1 expression level was low in 34 cases and high in 118 cases. There were 104 cases with low BUB1 expression level and 48 cases with high BUB1 expression level. There were 73 cases with low MCM7 expression level and 79 cases with high MCM7 expression level. The immunohistochemical staining results of the three proteins in endometrial carcinoma tissues are shown in \u003cstrong\u003eFigure 2B\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe associations between the expressions of DUSP1, BUB1, MCM7 and clinicopathological characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between expression of these three genes by IHC and clinicopathological characteristics of 152 cases were analyzed. There was significant difference in the expression level of BUB1 in endometrial carcinoma tissues of stage I, stage II, and stage III-IV patients (\u003cstrong\u003eFigure 3A\u003c/strong\u003e). The distribution of tumor grade is also significant between low- and high-expression BUB1 groups (\u003cstrong\u003eFigure 3B\u003c/strong\u003e). Up-regulated BUB1 also causes myometrial invasive (\u003cstrong\u003eFigure 3C\u003c/strong\u003e). The expression of DUSP1 in different clinicopathological characteristics is just opposite to that of BUB1. Patients with high stage and deep myometrial invasion generally expressed low degree of DUSP1. What\u0026rsquo;s more, the proportion of patients with low DUSP1 expression level in patients with FIGO stage I-II and stage III-IV was 27/130 and 7/22, respectively. There was significant difference in the expression of DUSP1 in patients with different FIGO stages (\u003cstrong\u003eFigure 3D-F\u003c/strong\u003e). MCM7 has a similar expression pattern with BUB1. For example, the proportion of cases with low MCM7 expression was relatively lower in high stage, poorly differentiated, and deep myometrial invasive EC tissues (\u003cstrong\u003eFigure 3G-I\u003c/strong\u003e). These results indicates that BUB1, DUSP1, and MCM7 plays an essential role in the progression of EC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe association between the combination of DUSP1, BUB1, MCM7 and prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier survival curve showed that patients with high expression level of BUB1 had a relatively shorter disease free survival (DFS), but the difference was not statistically significant (\u003cstrong\u003eFigure 4A\u003c/strong\u003e). Survival analysis showed that patients with low DUSP1 and high MCM7 expression level had significantly shorter disease-free survival (\u003cstrong\u003eFigure 4B-C\u003c/strong\u003e). By analyzing the overall survival, the difference is not statistically significant between the high- and low-expression of BUB1 (\u003cstrong\u003eFigure 4D\u003c/strong\u003e). Higher DUSP1 has a better overall survival (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), while lower expression also has a better prognosis, but the difference is not obvious enough (\u003cstrong\u003eFigure 4E-F\u003c/strong\u003e). Considering the limited significance in predicting DFS and OS for EC patients, we combined the expression of these three together to predict the survival. The results showed that the survival rate is the worst in patients with high DUSP1, low BUB1, and low MCM7, for both OS and DFS (\u003cstrong\u003eFigure S1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent risk factors of DFS and OS in EC patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further illustrate the significance of the three genes, we constructed a risk model by multivariate analysis. The OS risk model is listed as following: risk model= BUB1*0.681-DUSP1*1.503+MCM7*1.049. The DFS risk model is: BUB1*0.117-DUSP1*2.135+MCM7*1.163. The total score of each patient is calculated and analyzed to predict the progression and survival of EC patients. Univariate Cox regression analysis showed that the 3-gene model was an important risk factor for both disease free survival and overall survival (\u003cstrong\u003eFigure 5A-B\u003c/strong\u003e). For disease free survival, multivariate Cox analysis revealed that FIGO stage and 3-gene signature are two independent risk factors (\u003cstrong\u003eFigure 5C\u003c/strong\u003e). Meanwhile, stage, grade, and our gene signature are independent for overall survival (\u003cstrong\u003eFigure 5D\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstablishment and validation of the nomogram\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe designed a nomogram to predict the survival probability of each patient, including DFS and OS. In the nomogram, each predictor was assigned a score. Based on the Cox analysis results, two features were integrated in the nomogram to predict the DFS probability of EC patients (\u003cstrong\u003eFigure 6A\u003c/strong\u003e). Stage, grade, and the 3-gene signature are included in the nomogram predicting the OS of EC patients (\u003cstrong\u003eFigure 6B)\u003c/strong\u003e. We then calculate the total score of the patients according to the nomogram and divided the patients into low score or high score group based on the median value of the risk score. The survival analysis indicated that patients in high score group had a worse prognosis for both DFS and OS (\u003cstrong\u003eFigure 6C-D\u003c/strong\u003e). ROC curve analysis revealed that the area under the ROC curve (AUC) of the prognostic nomogram was 0.923 and 0.963 for DFS and OS, respectively (\u003cstrong\u003eFigure 6E-F\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of the risk model in TCGA cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe then verify the expression pattern in different clinicopathological features in TCGA cohorts. As shown in \u003cstrong\u003eFigure 7A-I\u003c/strong\u003e, the results indicated that the level of BUB1 and MCM7 are relatively elevated in patients with high stage, high grade, and more invasive capability. Meanwhile, the expression pattern of BUB1 are opposite from the other two genes. What\u0026rsquo;s more, the expression of BUB1 and MCM7 are also higher in EC tissues than that in normal tissues in the samples of our center (\u003cstrong\u003eFigure 7J\u003c/strong\u003e). In agreement with the results from the our own cohort, the KM curve analysis of the subgroup with low DUSP1 and high BUB1/MCM7 presented worse outcomes than other subgroup in the TCGA cohort with a shorter DFS and OS time (\u003cstrong\u003eFigure 7K-L\u003c/strong\u003e). The risk model was then introduced into the TCGA cohort, and each individual\u0026rsquo;s risk score was calculated. Based on the training cohort cut-off risk score, the patients in the testing cohort were classified as high-risk and low-risk individuals. The AUC of the risk model in the TCGA cohort was 0.723 and 0.752 for predicting DFS and OS, respectively (\u003cstrong\u003eFigure 7M-N\u003c/strong\u003e). These results indicated that the expression model of the three genes outperformed in an external validation group.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eEndometrial cancer is a worldwide spread malignant tumor that threatens women\u0026rsquo;s lives, which usually occurs in post- menopausal women and is difficult to diagnose at the early stage\u0026nbsp;\u003csup\u003e17\u003c/sup\u003e. Recently, many risk factors have been linked to the occurrence of EC, such as obesity, diabetes and hyperinsulinemia\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e. Most previous risk models for prognostic prediction of EC have been constructed by foreign cohort, especially with TCGA cohort\u003csup\u003e19\u003c/sup\u003e. Few studies concentrated on Chinese people and established a native cohort risk model. What\u0026rsquo;s more, the molecular mechanisms underlying the development of EC have not yet been fully characterized and consequently are poorly understood\u0026nbsp;\u003csup\u003e20\u003c/sup\u003e. The clinical heterogeneity of patients with EC probably reflects variation at the molecular level\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e; thus, the prognostic mechanisms of EC likely vary as well. One study constructed a nine DNA repair-related gene prognostic classifier to predict prognosis in patients with EC, and patients with low risk were more likely to have sensitivity to paclitaxel, vinblastine, rapamycin, metformin, imatinib, Akt inhibitor and lapatinib\u003csup\u003e22\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we aim to investigate the key genes expression profiles and their values in the prognosis in EC through the bioinformatics analysis from our own center. The results were verified in clinical specimens through polymerase chain reaction (PCR) and immunohistochemistry (IHC). We first narrowed the scope of our research, identified three key genes on the basis of our previous research, explored the relationship between their expression and clinicopathological characteristics, and used these three key genes to construct and verify the prognosis prediction model of endometrial cancer. Afterwards, TCGA database is used to verify the accuracy of the model, and the results confirm that the model has high accuracy in predicting endometrial cancer. In addition, the three genes we revealed in this study have also been reported to play an essential role in the prognosis of some kind of tumor. Previously in vitro studies have shown that the expression level of DUSP1 might be related to the therapeutic effect of medroxyprogesterone acetate\u003csup\u003e23\u003c/sup\u003e. As for the correlation between DUSP1 and the clinicopathological features of other malignancies, various studies have obtained different results. For example, in the mouse model of hepatocellular carcinoma, low DUSP1 expression level is closely related to poor tissue differentiation (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and advanced TNM stage (\u003cem\u003eP\u003c/em\u003e=0.023)\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e. According to these results, the decreased expression level of DUSP1 was associated with the high risk clinicopathologic features of endometrial carcinoma and other malignancies. The molecular mechanism regulating DUSP1 is still unclear. There are two PR reaction elements downstream of the DUSP1 transcription promoter. Combined with the reaction elements, PR can enhance the activity of DUSP1 promoter and up-regulate the expression of DUSP1\u003csup\u003e25\u003c/sup\u003e. It suggested that DUSP1 expression level was decreased in endometrial carcinoma, which may be related to abnormal function of PR and PR response elements. High expression of BUB1 may indicate a higher malignant potential for endometrial cancer. As for the association between BUB1 and other malignant tumors, various studies have found that the expression of BUB1 has organ heterogeneity. For example, in colorectal cancer tissues, the low expression levels of BUB1 and BUBR1 are significantly correlated with the presence of lymph node metastasis (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01)\u003csup\u003e5\u003c/sup\u003e. BUB1 plays different roles in endometrial carcinoma and colorectal cancer, thus individualized treatment related to BUB1 needs to be formulated in combination with the specific type of malignancy. In our study, patients with high BUB1 expression level have relatively poor prognosis. Therefore, BUB1 may be a new target for therapy in endometrial carcinoma. The detection of BUB1 expression can stratify the prognostic risks of endometrial carcinoma patients, which is expected to play an important role in the accurate treatment and prognostic assessment of endometrial carcinoma patients. We also revealed that high expression level of MCM7 is associated with some adverse clinicopathological features. The increased expression of MCM7 is also associated with shorter disease-free survival in patients with colorectal cancer\u003csup\u003e26\u003c/sup\u003e, gastric adenocarcinoma\u003csup\u003e27\u003c/sup\u003e, and meningioma\u003csup\u003e28\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt present, there are many researches on the prognosis prediction model of endometrial cancer. Some are simple clinical prediction models, which establish prognosis prediction models according to the clinicopathological information characteristics of endometrial cancer patients. In our previous, we developed an available nomogram with effective external validation and relatively appreciable discrimination and conformity for the accurate assessment of 3- and 5-year DFS in Chinese women with EC\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e. In another study, nomograms were designed to accurately predict OS and cancer specific survival (CSS) in EC patients. The nomograms can be used for estimating OS and CSS of individual patients and establishing their risk stratification\u0026nbsp;\u003csup\u003e30\u003c/sup\u003e. However, these studies ignored the genetic and molecular characteristics of EC. There are also studies that combine the genetic characteristics and clinical pathological characteristics of patients. For example, an EMT-related signature model was constructed to predict the prognosis of EC patients, and it might offer a reference for predicting individualized response to immune checkpoint inhibitors and chemotherapeutic drugs\u0026nbsp;\u003csup\u003e31\u003c/sup\u003e. Study of our center also reveals a link between glycolysis-related gene signature and immunity, and provides personalized therapeutic targets for EC. The accuracy of this risk model for predicting the overall survival of AUC reached to 0.822\u0026nbsp;\u003csup\u003e32\u003c/sup\u003e. In addition, there are many other similar studies. However, most of these studies are based on TCGA database, which main source of samples are Americans. There is a lack of research on Chinese people. This study identified three key genes through a large-scale sequencing results of Chinese EC patients, and used these three genes to construct a prognosis predictive model combining gene characteristics with clinicopathological characteristics in the EC population in our center. This conclusion was verified by TCGA database and the accuracy of OS and DFS is more than 0.7. Therefore, the model has high prediction accuracy for endometrial cancer patients in Chinese population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur research also has some shortcomings. First of all, our research is conducted in a single center, and the results have not been verified among Chinese people in other centers. In addition, the functions and the specific mechanisms of the three genes that affect the occurrence and development of endometrial cancer have not been explored. Finally, a larger sample of EC patients should be sequenced to verify the accuracy of the model.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion ","content":"\u003cp\u003eWe identified three genes that have significant correlation with the prognosis of endometrial cancer through our previous studies. Furthermore, a prognosis related predictive model of endometrial cancer combining genetic features and clinicopathological characteristics is constructed and verified. This model not only has high accuracy, but also is more suitable for the characteristics of Chinese population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported by the Research and Development Fund of Peking University People\u0026rsquo;s Hospital (Grant No. RS2021-05 and RDY2021-13), National Natural Science Foundation of China (Grant Nos. 82103419 and 81874108).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXingchen Li is responsible for conceptualization; Fengbo Yang are responsible for methodology. Lijun Zhao is responsible for software; Fengbo Yang is responsible for formal analysis; Lijun Zhao is responsible for investigation; Fengbo Yang and Xingchen Li are responsible for data curation; Xingchen Li is responsible for writing the original draft; Jianliu Wang and Xingchen Li are responsible for funding acquisition. Jianliu Wang and Xingchen Li is responsible for writing-review \u0026amp; editing the article. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe especially appreciate Jiayang Jin, PhD of Peking University People\u0026rsquo;s Hospital for statistics, study deign and editing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data are available online in Briefings in Bioinformatics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article are available in the article and in its online supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmalinei C, Aignatoaei AM, Balan RA, et al. 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Establishing a Prognostic Signature Based on Epithelial-Mesenchymal Transition-Related Genes for Endometrial Cancer Patients. \u003cem\u003eFront Immunol\u003c/em\u003e. 2021;12:805883.\u003c/li\u003e\n\u003cli\u003eYang X, Li X, Cheng Y, et al. Comprehensive Analysis of the Glycolysis-Related Gene Prognostic Signature and Immune Infiltration in Endometrial Cancer. \u003cem\u003eFront Cell Dev Biol\u003c/em\u003e. 2021;9:797826.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"endometrial carcinoma, prognostic biomarker, risk model, Chinese cohort, DUSP1","lastPublishedDoi":"10.21203/rs.3.rs-2643530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2643530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e:\u003cem\u003e \u003c/em\u003eThe objective of this study was to characterize the expression features base on three genes, including DUSP1, BUB1 and MCM7, and investigate their clinical significance in Chinese cohort with endometrial carcinoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods\u003c/strong\u003e.\u003cem\u003e \u003c/em\u003eImmunohistochemistry (IHC) was performed for assessing the expression of DUSP1, BUB1, and MCM7 in these patients. The associations between the expressions of three genes and the clinicopathological features were analyzed. The risk model was finally validated in TCGA cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The expressions of DUSP1, BUB1, and MCM7 were associated with clinicopathological features (\u003cem\u003eP\u003c/em\u003e＜0.05 for all). Patients with high expression level of DUSP1 and low expression level of BUB1 and MCM7 had better prognosis. The construction combining these three genes is an independent risk factor and had a high accuracy in predicting overall survival (OS, AUC=0.923) and disease-free survival (DFS, AUC=0.963). The three-gene risk signature was validated in TCGA-EC cohort, and the results showed a great precision for predicting OS and DFS (both AUC\u0026gt;0.7) in an external group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Our study revealed a three-gene prognostic signature, including DUSP1, BUB1, and MCM7, and its related nomogram could guide personalized strategy, providing a basis for the molecular analysis in EC, thereby improving their OS and DFS.\u003c/p\u003e","manuscriptTitle":"Construction and validation of an IHC-based risk model predicting postoperative disease- free survival and overall survival in Chinese endometrial cancer patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-14 14:51:05","doi":"10.21203/rs.3.rs-2643530/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22764c9b-32ce-4009-9e8c-a413567b39eb","owner":[],"postedDate":"March 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":19844064,"name":"Biological sciences/Cancer/Cancer genetics"},{"id":19844065,"name":"Biological sciences/Cancer/Cancer stem cells"},{"id":19844066,"name":"Biological sciences/Cancer/Cancer therapy"},{"id":19844067,"name":"Biological sciences/Cancer/Tumour immunology"},{"id":19844068,"name":"Biological sciences/Cancer"},{"id":19844069,"name":"Biological sciences/Cell biology"},{"id":19844070,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2023-06-28T08:44:41+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-14 14:51:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2643530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2643530","identity":"rs-2643530","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}