Differential Expression of Estrogen and Progesterone Receptors Before and After Surgery in Endometrial Cancer and Analysis of Recurrence Prediction Model

Differential Expression of Estrogen and Progesterone Receptors Before and After Surgery in Endometrial Cancer and Analysis of Recurrence Prediction Model | 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 Differential Expression of Estrogen and Progesterone Receptors Before and After Surgery in Endometrial Cancer and Analysis of Recurrence Prediction Model Ruixue Fan, Peng Jiang, Zhuoying Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7441185/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2026 Read the published version in World Journal of Surgical Oncology → Version 1 posted 11 You are reading this latest preprint version Abstract Background Endometrial cancer has rising incidence and mortality, with high recurrence rates (10–70%) post-surgery. Discrepancies between preoperative biopsy and postoperative pathology, particularly in estrogen receptor (ER) and progesterone receptor (PR) expression, may misguide treatment. This study explored ER/PR expression dynamics pre- and post-surgery and developed a recurrence prediction model. Methods A retrospective cohort of 600 stage I–III endometrial cancer patients (2017–2021) from a single center was analyzed. Preoperative biopsies (blind vs. hysteroscopy-guided) and postoperative specimens underwent ER/PR immunohistochemical testing. Concordance was assessed via Cohen’s kappa. Survival analysis (Kaplan-Meier), ROC curves, and Cox regression identified prognostic factors. A nomogram integrating PR expression dynamics and clinicopathological parameters was developed and validated. Results ER and PR expression showed moderate-to-substantial overall concordance (86.8%, κ = 0.481; 87.1%, κ = 0.676), with hysteroscopy-guided biopsies demonstrating superior agreement (ER: 94.0%, κ = 0.733; PR: 91.4%, κ = 0.742) versus blind biopsies. Combined pre-/postoperative PR expression improved recurrence prediction (AUC = 0.680). The integrated nomogram (AUC = 0.864) effectively stratified high-risk patients (3-year recurrence-free survival: 53.40% vs. 86.05% in non-high-risk), who benefited from adjuvant therapy. Conclusions Hysteroscopy-guided biopsy enhances ER/PR assessment accuracy. The nomogram integrating PR dynamics and clinical parameters enables precise recurrence risk stratification, aiding personalized adjuvant therapy decisions. Trial registration: Not applicable. endometrial cancer ER PR immunohistochemistry preoperative biopsy final pathology inconsistency Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 BACKGROUND Endometrial cancer is one of the most common malignant tumors in women [ 1 ] , ranking as the second most prevalent gynecological malignancy in China. In recent years, both the incidence and mortality rates of endometrial cancer have been on the rise [ 2 ] . According to data from the National Cancer Center, approximately 77,000 new cases of endometrial cancer were reported in China in 2022, with an incidence rate of 7.03 per 100,000. During the same period, 13,500 deaths were attributed to endometrial cancer, with a mortality rate of 1.06 per 100,000 [ 3 ] .Currently, the primary treatment for endometrial cancer is surgical intervention. Clinical management mainly relies on surgical pathological staging, which is supplemented by individualized treatment strategies based on the tumor's histological type, grade, and molecular characteristics. This approach is considered key to improving therapeutic outcomes.Based on the biological characteristics of the tumor, clinicians select appropriate surgical approaches and combine them with adjuvant therapies such as chemotherapy, radiotherapy, or immunotherapy. Treatment strategies are dynamically adjusted at different stages to address disease progression and resistance, thereby improving patient prognosis and survival rates [ 4 ] . However, even with standardized surgical treatment, approximately 87% of patients experience recurrence within three years postoperatively [ 5 ] . The recurrence risk for early-stage patients is about 10–15% [ 6 , 7 ] , while for advanced-stage patients, the recurrence risk can reach as high as 40–70% [ 8 , 9 ] . For patients with recurrent or metastatic endometrial cancer, the median survival time is typically less than 12 months [ 10 ] .In recent years, studies have shown inconsistencies between preoperative biopsy and postoperative pathology reports in pathological parameters such as histological grade and type [ 11 ] . These discrepancies may affect treatment decisions, leading to overtreatment (e.g., unnecessary radiotherapy or chemotherapy) or undertreatment (e.g., failure to apply timely adjuvant therapies), ultimately impacting treatment outcomes. Therefore, in-depth research on the relationship between these discrepancies and patient prognosis is crucial for optimizing treatment strategies for endometrial cancer.Studies have shown that hormone receptor status, particularly estrogen receptor (ER) and progesterone receptor (PR), plays an important role in predicting the prognosis of endometrial cancer [ 12 ] . Positive expression of ER or PR is generally considered a marker of favorable prognosis, while negative expression of ER or PR is more commonly observed in advanced-stage, poorly differentiated tumors, indicating a poor prognosis [ 13 ] .Compared to gene sequencing technology, immunohistochemistry (IHC) detection offers significant advantages in terms of ease of operation and low cost, making it more suitable for routine clinical screening. However, IHC detection is influenced by factors such as the sampling site, interobserver variability in subjective interpretation, and tumor heterogeneity.As a result, discrepancies in the expression levels of estrogen receptor (ER) and progesterone receptor (PR) may exist between preoperative biopsy and postoperative pathology results. METHODS Study Population The study selected patients with stage I-III endometrial cancer who underwent preoperative biopsy and initial surgical treatment at the First Affiliated Hospital of Chongqing Medical University between February 2017 and August 2021. Clinical information of these patients was retrospectively collected during this period, including: General clinical characteristics : Age, height, weight, preoperative biopsy method, and surgical approach. Pathological information : Tumor location, cervical stromal invasion, depth of myometrial invasion, lymph node metastasis, tumor histological type, and grading. Hormone receptor status : Immunohistochemical expression of ER/PR in preoperative biopsy and postoperative pathology. Treatment details : Postoperative adjuvant treatment status and the specific adjuvant therapy methods (e.g.radiotherapy, chemotherapy, or chemoradiotherapy) determined based on domestic and international guidelines and multidisciplinary discussions [ 14 ] . Inclusion criteria : Patients underwent endometrial biopsy before surgery and were initially diagnosed with endometrial cancer. Patients received surgical treatment for the first time, with postoperative pathological confirmation of endometrial cancer and staging according to FIGO (2009) as stage I-III (stage IV patients were excluded because they typically do not undergo surgery and have distant metastases). Exclusion criteria : Patients who did not undergo standard surgery (total hysterectomy + bilateral salpingo-oophorectomy ± pelvic/para-aortic lymphadenectomy). Patients who received preoperative adjuvant therapy. Patients with other malignant tumors. Patients with incomplete medical records. Research Methods Preoperative Biopsy Methods All patients included in this study underwent preoperative biopsy. The biopsy methods were mainly categorized into two types: 1. Blind biopsy methods: Blind fractional curettage;Aspiration biopsy. 2. Hysteroscopy-guided endometrial biopsy (Figure 2): Diagnostic curettage under hysteroscopic guidance;Targeted forceps biopsy under hysteroscopic guidance. Follow-up Protocol Patients underwent quarterly follow-ups in the first two postoperative years, semiannual evaluations during years 3-5, and annual assessments thereafter [14] . Surveillance included scheduled physical examinations and indicated diagnostic tests (e.g., transvaginal/pelvic ultrasound, MRI, serum CA125 measurements). The follow-up period extended through October 2024, with all enrolled subjects achieving a minimum follow-up of 3 years. Recurrence Assessment Recurrence was confirmed by at least two gynecologic oncologists through integrated evaluation comprising physical examination, serum biomarker assays , radiographic imaging, and histopathological verification [15] . Recurrence patterns were categorized as: vaginal cuff recurrence, pelvic central recurrence, para-aortic lymph node metastasis, peritoneal metastasis, or distant organ metastasis [16] . Recurrence-free survival (RFS) was defined as the time from the date of surgery to the date of confirmed recurrence. Overall survival (OS) was defined as the time from the date of surgery to death [9] . Pathological Analysis Protocol All preoperative biopsy specimens and surgical tissues were immediately fixed in 10% neutral-buffered formalin after resection. Specimens were fully submerged in fixative solution at a 10:1 to 20:1 volume ratio (fixative-to-tissue) and processed for 24-48 hours at room temperature to ensure optimal fixation. Within 24 hours post-fixation, specimens were transferred to the Pathology Laboratory Center of Chongqing Medical University for standardized processing, including dehydration, paraffin embedding, sectioning , H&E staining, and immunohistochemical (IHC) analysis. Pathological findings, such as tumor size, histological subtype and grade , myometrial invasion depth, cervical stromal involvement, lymphovascular space invasion (LVSI), and lymph node involvement status, were assessed by professional pathologists. All specimens underwent automated IHC staining (Leica Bond Max; Milton Keynes, UK). In the most active tumor regions, five random high-power fields (HPFs) were observed. For each field, 100 tumor cells were evaluated, and the average percentage of ER and PR positivity (0–100%) across the five fields was calculated. The evaluation process was independently carried out by two experienced pathologists, and their results were recorded separately. Interobserver concordance was defined as ≤10% discrepancy in positive cell counts; cases with >10% variation underwent joint re-evaluation to reach consensus. Finally, the average of the two observers' results was calculated to represent the final interpretation of the IHC analysis [15, 17] . ER and PR Immunohistochemical Evaluation The evaluation criteria for ER and PR IHC results are as follows: Positive: Defined as ≥1% of tumor cell nuclei showing clear staining. Negative: Defined as either <1% nuclear staining or absence of significant specific staining [18] . (Figure 3, Figure 4) Definition of ER and PR Expression Status Based on differential immunohistochemical expression observed in preoperative biopsies and postoperative specimens, four combined ER expression patterns were defined: ER(+)/ER(+): Positive expression in both biopsy and surgical specimens. ER(-)/ER(-): Negative expression in both biopsy and surgical specimens. ER(+)/ER(-): Positive in biopsy but negative in surgical specimen. ER(-)/ER(+): Negative in biopsy but positive in surgical specimen. The same classification was applied analogously to define PR expression patterns:PR(+)/PR(+);PR(-)/PR(-);PR(+)/PR(-);PR(-)/PR(+). Statistical Analysis Cohen’s kappa coefficient was used to evaluate the concordance between preoperative biopsy and postoperative pathology results for ER and PR expression, and a Sankey diagram was employed to visually display the changes in expression [19] . The interpretation of the kappa coefficient (κ) is as follows: κ<0.01 (poor), 0.01-0.20 (slight), 0.21-0.40 (fair), 0.41-0.60 (moderate), 0.61-0.80 (substantial), and 0.81-1.00 (almost perfect) agreement [20] . Receiver operating characteristic (ROC) curve analysis quantified the predictive value of combined pre-/postoperative ER/PR expression patterns versus isolated pre- or postoperative status for recurrence risk. Kaplan-Meier survival curves were utilized to evaluate the differences in survival prognosis among subgroups with varying combined preoperative and postoperative ER/PR expression statuses. Univariate Cox regression identified recurrence-associated predictors, followed by multivariate Cox regression determining the independent prognostic value of combined ER/PR expression patterns and other clinicopathological parameters for endometrial carcinoma recurrence. A nomogram predicting recurrence risk was then developed based on multivariate results, with calibration curves evaluating concordance between predicted probabilities and observed outcomes. ROC analysis compared predictive performance (quantified by AUC) among combined PR preoperative and postoperative combined expression, conventional clinical parameters, and integrated models.The optimal risk threshold for the model was determined using the Youden index, allowing for risk stratification of patients [21] . Finally, Kaplan-Meier survival curves were plotted for patients in different risk groups, and the log-rank test was used to compare differences between groups. Survival prognosis in different risk stratifications was evaluated, and subgroup survival analyses for adjuvant therapy were further performed. Categorical variables were reported as frequencies (%). Normally distributed continuous variables were expressed as mean ± standard deviation (SD), while non-normally distributed variables were summarized as median (P25, P75). All analyses were performed using SPSS Statistics (version 27.0) and R software (version 4.0.3; https://www.R-project.org). RESULTS Patient Baseline Characteristics The study enrolled 600 endometrial carcinoma patients with a median follow-up of 39 months.Baseline characteristics are detailed in Table 1. Key demographic and clinicopathological features were: mean age 53.97±9.36 years, mean body mass index (BMI) 24.53±3.90 kg/m², and predominant use of blind biopsies (61.0%) versus hysteroscopy-guided biopsies (39.0%) for preoperative assessment. Postoperative pathology revealed high-risk features including lymph node metastasis (30.5%), deep myometrial invasion (≥50% in 30.5%), and lymphovascular space invasion (31.2%). Adjuvant therapy was administered to 67.8% of patients (radiotherapy 36.2%, chemoradiotherapy 29.6%). During follow-up, the recurrence rate was 15.0% (predominantly distant metastases) with an 11.2% mortality rate. Discrepancy Analysis of ER and PR Immunohistochemical Expression Between Preoperative Biopsy and Postoperative Pathology The expression status of estrogen receptor (ER) and progesterone receptor (PR) showed some discrepancies between preoperative biopsies and postoperative pathology. As shown in Tables 2 and 3, and Figures 5 and 6, 10.0% of patients initially diagnosed as ER-positive by biopsy were reclassified as ER-negative in postoperative pathology, while 3.2% showed the reverse conversion from ER-negative to ER-positive status. Similar discrepancies were observed for PR expression, with 7.8% of cases transitioning from PR-positive to PR-negative and 2.8% converting from PR-negative to PR-positive upon surgical specimen evaluation. Overall analysis revealed moderate concordance (86.8%; κ=0.481) between preoperative and postoperative ER expression status, while PR expression demonstrated substantial agreement (87.1%; κ=0.676). Stratified analysis revealed the impact of different biopsy methods on the concordance of ER and PR expression. Hysteroscopy-guided biopsies showed superior agreement with final pathology for both ER (94.0% concordance rate; κ=0.733) and PR (91.4%; κ=0.742) status. In contrast, blind biopsies (including fractional curettage and aspiration biopsy) exhibited significantly lower concordance rates (ER: 82.2%, κ=0.347; PR: 84.4%, κ=0.637). Predictive Value of Combined Preoperative-Postoperative ER/PR Expression for Endometrial Carcinoma Recurrence Combining preoperative biopsy and postoperative pathology results for ER and PR expression demonstrates superior predictive performance for postoperative recurrence compared to using either alone. ROC curve analysis shows that the combined approach yields higher AUC values: 0.642 for ER and 0.680 for PR. These values are superior to those obtained using only preoperative biopsy data (AUC 0.590 for ER and 0.622 for PR) or solely postoperative pathology (AUC 0.638 for ER and 0.671 for PR). (Figure 7, Table 4, Table 5). Kaplan-Meier survival analysis demonstrated significant prognostic disparities among ER/PR expression subgroups (Figure 8). The ER(+)/ER(+) cohort exhibited the most favorable outcomes, while ER(-)/ER(-) patients showed the poorest prognosis. Intermediate survival rates were observed in both ER(+)/ER(-) and ER(-)/ER(+) groups , falling between the two extremes. Similarly, PR(+)/PR(+) patients achieved optimal survival, contrasting with the worst outcomes in PR(-)/PR(-) cases, while PR(+)/PR(-) and PR(-)/PR(+) subgroups displayed intermediate prognosis (detailed in Tables 6-7). Prognostic Value of Conventional Clinical Parameters and Combined ER/PR Expression Patterns in Endometrial Carcinoma Recurrence Recurrence-free survival (RFS) is a crucial prognostic indicator. Univariate Cox regression analysis (Table 8) identified multiple conventional clinicopathological parameters significantly associated with RFS, including age, FIGO stage, final pathological type, myometrial invasion, cervical stromal invasion, lymphovascular space invasion (LVSI), CA125 levels, and combined preoperative-postoperative ER/PR expression patterns (all P<0.05). Factors with a p-value less than 0.05 in the univariate analysis were then included in a multivariate Cox regression analysis.Subsequent multivariate Cox analysis revealed seven independent prognostic factors: age (95% CI: 1.037–2.607, P=0.035), FIGO stage ( 95% CI: 2.481–7.171, P<0.001), histologic type ( 95% CI: 1.183–3.284, P=0.009), myometrial invasion ( 95% CI:1.189–2.831, P=0.006), LVSI ( 95% CI: 1.027–2.702, P=0.039), CA125 levels (95%CI: 1.092–2.721, P=0.019), and combined PR expression status (95% CI: 1.192–5.663, P=0.016). Integrated Model Combining PR Expression Dynamics and Conventional Clinical Parameters for Endometrial Carcinoma Recurrence Prediction Multivariate Cox regression confirmed the independent prognostic value of combined preoperative-postoperative PR expression patterns alongside six conventional clinical parameters (age, FIGO stage, histologic type, myometrial invasion depth, LVSI, and CA125 levels) for RFS. Recognizing the limitations of isolated PR status evaluation, we systematically compared three prediction approaches: PR expression dynamics (preoperative-postoperative concordance patterns) alone (AUC=0.680, 95% CI: 0.614-0.746), conventional clinical parameters alone (AUC=0.817, 95% CI: 0.766-0.868), and their combined integration. The composite model demonstrated superior predictive accuracy (AUC=0.864, 95% CI: 0.827-0.900), representing a statistically significant improvement over either individual approach (Figure 9, Table 9). Based on the multivariate Cox analysis results, we constructed a comprehensive nomogram model (Figure 10) that synergistically combines PR expression dynamics with established clinical parameters (age, FIGO stage, histologic type, myometrial invasion, LVSI, and CA125 levels) to provide individualized predictions of 1-year, 3-year, and 5-year recurrence-free survival (RFS) probabilities. This model employs an intuitive linear scoring system where clinicians assign points for each parameter (e.g.35 points for age ≥60 years, 100 points for FIGO Stage III, 90 points for PR[-]/PR[-] status), sum the total score, and directly read the corresponding predicted RFS rates from the bottom probability axis (illustratively, a 225-point total translates to 93.5% 1-year RFS, 67% 3-year RFS, and 63% 5-year RFS). To evaluate the predictive performance of this model, we further generated calibration curves to assess the agreement between the predicted probabilities and the actual observed outcomes. The calibration curves demonstrate only minor deviations between the model prediction line and the ideal reference line, indicating good overall model fit. (Figure 11A, B, C) Given that most recurrences occur within 3 years postoperatively [5], we focused our validation on the model's 3-year recurrence-free survival (RFS) predictive performance. ROC analysis identified 0.14 as the optimal risk threshold (Youden index=0.569), achieving a balanced sensitivity of 0.77 and specificity of 0.79 for 3-year RFS prediction (Figure 12). Based on the risk threshold determined from the ROC curve analysis, this study stratified patients into a high-risk group (3-year RFS < 0.14, n=170) and a non-high-risk group (3-year RFS ≥ 0.14, n=430). Kaplan-Meier analysis with log-rank testing revealed significantly inferior 3-year RFS in the high-risk cohort (53.40% vs 86.05%), with distinct curve separation emerging as early as 6 months postoperatively (Figure 13, Table 10). Stratified analysis based on post-operative adjuvant therapy revealed no significant difference in survival outcomes between patients who received adjuvant therapy and those who did not within the non-high-risk group (Figure 14A, B). However, within the high-risk group, patients who received adjuvant therapy demonstrated significantly better overall prognosis compared to those who did not receive adjuvant therapy (Figure 14C, D). DISCUSSION Existing studies primarily focus on traditional pathological parameters like histological type and FIGO grade [4] . However, research exploring the discrepancies in molecular marker expression (such as ER and PR) and their clinical significance remains limited.The emergence of molecular subtyping in endometrial cancer has provided valuable insights into its biological characteristics, predicting clinical behavior and treatment response [22] . This has led to the integration of gene sequencing technologies for prognostic assessment. However, widespread adoption of gene sequencing is hampered by several factors: high costs, demanding technical expertise, longer turnaround times due to reliance on external testing facilities, and the need for specialized bioinformatics support. These limitations restrict the accessibility of this technology in many healthcare settings. In contrast, immunohistochemistry (IHC) has emerged as a clinically viable alternative, offering distinct advantages like lower cost for routine practice. Most hospitals in China have standardized IHC protocols with extensive clinical validation data. Furthermore, IHC can reveal intratumoral heterogeneity to a certain extent. Spatial heterogeneity in molecular marker expression within the tumor can be visually demonstrated by IHC, providing valuable information for tumor characterization and clinical decision-making [23, 24] . This single-center cohort study systematically evaluated ER/PR immunohistochemical expression discordance between preoperative biopsies and definitive surgical specimens. Our data demonstrate substantial receptor concordance rates (ER: 86.8%, κ=0.481; PR: 87.1%, κ=0.676), with hysteroscopy-guided biopsies achieving superior agreement (ER: 94.0%, κ=0.733; PR: 91.4%, κ=0.742) compared to blind sampling techniques (ER: 82.2%, κ=0.347; PR: 84.4%, κ=0.637). This improvement in diagnostic accuracy likely reflects the visual targeting advantage of hysteroscopy, which enables precise sampling of the most representative tumor areas [20,23] .In contrast, blind biopsies carry inherent limitations due to their inability to provide direct visual localization of lesions, potentially resulting in inadequate sampling or deviation from target areas, thereby compromising the reliability of test results. Therefore, in clinical practice, hysteroscopy-guided biopsy may represent a superior approach for patients with suspected intrauterine lesions, as it not only enhances the detection accuracy of molecular markers but also reduces diagnostic errors and missed diagnoses. The findings of this study further substantiate the critical diagnostic value of hysteroscopic biopsy in endometrial carcinoma. The results of this study demonstrate that the prognosis of endometrial carcinoma patients is closely associated with ER and PR expression status in both preoperative biopsies and postoperative pathological examinations. The ER/PR double-positive group exhibited the most favorable prognosis, while the double-negative group showed the poorest outcomes, with other expression patterns demonstrating intermediate prognostic characteristics. These prognostic disparities suggest that relying solely on either preoperative biopsy or postoperative pathology for prognosis assessment may have limitations—particularly for patients undergoing blind biopsies, where potential sampling inadequacies could lead to lower concordance between preoperative ER and PR results and postoperative pathology, ultimately compromising prognostic prediction accuracy. Therefore, comprehensive evaluation of both preoperative biopsy and postoperative pathology results is essential. This is further substantiated by ROC curve analysis, which demonstrates that the combined analysis of pre- and postoperative ER and PR expression significantly improves predictive efficacy compared to using either preoperative biopsy or postoperative pathology results alone. Furthermore, by integrating the combined pre- and postoperative expression of PR with conventional clinicopathological clinical parameters, we have developed a nomogram to predict the risk of endometrial cancer recurrence. ROC curve analysis confirmed that the predictive efficacy of this integrated model is significantly superior to models using traditional clinical parameters alone or those analyzing combined pre- and postoperative PR expression in isolation. Kaplan-Meier survival analysis validated the model's robust risk stratification capability, revealing significantly worse prognosis in high-risk group patients compared to non-high-risk counterparts. Furthermore, Kaplan-Meier survival analysis demonstrated improved prognoses for high-risk patients who received adjuvant therapy, suggesting that this model can effectively identify the patient population that would benefit from such treatment. This study employed both univariate and multivariate Cox regression analyses to evaluate the associations between conventional clinical parameters, combined preoperative-postoperative ER/PR expression status, and postoperative recurrence in endometrial carcinoma patients. The multivariate analysis results revealed no significant correlation between combined preoperative-postoperative ER expression patterns and postoperative recurrence. We postulate that this finding may primarily stem from the high collinearity between ER and PR expression. In endometrial carcinoma, ER and PR expression patterns are typically associated with tumor hormone dependence [13] , potentially explaining their high correlation in expression profiles. In multivariate Cox regression models, such collinearity may lead to unstable parameter estimates and inflated standard errors, consequently reducing the statistical significance of certain variables. In our study, the model appeared to preferentially emphasize the prognostic contribution of PR while potentially masking the independent predictive value of ER, which may lead to an underestimation of the clinical significance of ER in the clinical application of this model. Although statistical analysis failed to demonstrate a significant independent correlation between ER status and prognosis, the well-established pathophysiological role of ER in endometrial carcinoma warrants careful consideration of its potential biological influence in clinical decision-making. Future studies should employ more robust statistical approaches to address the collinearity between ER and PR [25] . Alternatively, stratified analysis during study design may help better elucidate the independent prognostic contributions of each receptor.In summary, this study's findings do not negate the significant role of ER in endometrial cancer. The relatively weak association observed for ER in our current model likely stems from methodological limitations in statistical analysis rather than diminished biological relevance. Future investigations should adopt more comprehensive approaches to evaluate ER's contribution to both the biological behavior of endometrial cancer and patient prognosis. Furthermore, as this study employed a retrospective design, its findings require validation in larger prospective cohort studies. The established prediction model also necessitates external validation to ensure its reliability and generalizability in clinical practice. CONCLUSION This study confirms a high degree of concordance between pre- and postoperative ER/PR expression in endometrial cancer. Preoperative hysteroscopy-guided biopsy demonstrates significantly better agreement with postoperative pathology compared to blind biopsy. Therefore, for medical institutions with the requisite capabilities, hysteroscopy-guided biopsy is recommended for diagnosis to improve the accuracy of preoperative assessment.This study developed a clinically practical nomogram integrating combined preoperative-postoperative PR expression patterns with conventional clinicopathological parameters, which demonstrated excellent predictive performance for endometrial carcinoma recurrence risk and effectively identified high-risk populations. Although combined ER expression dynamics did not reach statistical significance, its potential biological impact should be carefully considered in clinical decision-making.Future multicenter validation is required to confirm the model's generalizability, and incorporation of molecular classification systems may further refine prognostic accuracy by elucidating ER/PR interaction networks. Such advancements could ultimately optimize therapeutic strategies and improve survival outcomes in endometrial carcinoma patients. Abbreviations EC endometrial cancer ER estrogen receptor PR progesterone receptor BMI body mass index FIGO International Federation of Gynecology and Obstetrics LVSI lymphatic vessel space invasion CI confidence interval RFS recurrence-free survival OS overall survival AUC area under the curve ROC receiver operating characteristic HR Hazard ratio Declarations Ethics approval and consent to participate : This study was approved by the Institutional Review Board (IRB) of the First Affiliated Hospital of Chongqing Medical University (IRB Nos. 2021-676 and 2023-002). All data were collected from pre-existing clinical records. The IRB waived the requirement for informed consent. Consent for publication : Not applicable Availability of data and materials : The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests : The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding : Not applicable Authors' contributions : Zhuoying Hu: Conceptualization, Methodology, Writing - Review & Editing Ruixue Fan: Methodology, Data curation, Investigation, Software, Formal analysis, Writing- Original draft preparation, Writing - Review & Editing Peng Jiang: Data curation, Investigation, Writing- Original draft preparation, Writing - Review & Editing All authors critically reviewed the paper and approved the final version. Acknowledgements : Not applicable References Morice P, Leary A, Creutzberg C, et al. Endometrial cancer [J]. Lancet, 2016, 387(10023): 1094-1108. Siegel R L, Miller K D, Fuchs H E, et al. Cancer statistics, 2022 [J]. CA Cancer J Clin, 2022, 72(1): 7-33. Wang Shaoming, et al. Analysis of age characteristics of malignant tumor incidence and mortality in the Chinese population in 2022. [J].Chinese Oncology 33. 03 (2024): 165-174. Lago V, Martin B, Ballesteros E, et al. Tumor Grade Correlation Between Preoperative Biopsy and Final Surgical Specimen in Endometrial Cancer: The Use of Different Diagnostic Methods and Analysis of Associated Factors [J]. Int J Gynecol Cancer, 2018, 28(7): 1258-1263. Sohaib S A, Houghton S L, Meroni R, et al. Recurrent endometrial cancer: patterns of recurrent disease and assessment of prognosis [J]. Clin Radiol, 2007, 62(1): 28-34; discussion 35-26. Creutzberg C L, van Putten W L, Koper P C, et al. Surgery and postoperative radiotherapy versus surgery alone for patients with stage-1 endometrial carcinoma: multicentre randomised trial. PORTEC Study Group. Post Operative Radiation Therapy in Endometrial Carcinoma [J]. Lancet, 2000, 355(9213): 1404-1411. Keys H M, Roberts J A, Brunetto V L, et al. A phase III trial of surgery with or without adjunctive external pelvic radiation therapy in intermediate risk endometrial adenocarcinoma: a Gynecologic Oncology Group study [J]. Gynecol Oncol, 2004, 92(3): 744-751. Huijgens A N, Mertens H J. Factors predicting recurrent endometrial cancer [J]. Facts Views Vis Obgyn, 2013, 5(3): 179-186. Kurra V, Krajewski K M, Jagannathan J, et al. Typical and atypical metastatic sites of recurrent endometrial carcinoma [J]. Cancer Imaging, 2013, 13(1): 113-122. Odagiri T, Watari H, Hosaka M, et al. Multivariate survival analysis of the patientswith recurrent endometrial cancer [J]. J Gynecol Oncol, 2011, 22(1): 3-8. Vrede S W, Hulsman A M C, Reijnen C, et al. The amount of preoperative endometrial tissue surface in relation to final endometrial cancer classification [J]. Gynecol Oncol, 2022, 167(2): 196-204. Smith D, Stewart C J R, Clarke E M, et al. ER and PR expression and survival after endometrial cancer [J]. Gynecol Oncol, 2018, 148(2): 258-266. Wang C, Tran D A, Fu M Z, et al. Estrogen Receptor, Progesterone Receptor, and HER2 Receptor Markers in Endometrial Cancer [J]. J Cancer, 2020, 11(7): 1693-1701. Colombo N, Creutzberg C, Amant F, et al. ESMO-ESGO-ESTRO Consensus Conference on Endometrial Cancer: diagnosis, treatment and follow-up [J]. Ann Oncol, 2016, 27(1): 16-41. Jiang P, Wang J, Gong C, et al. A Nomogram Model for Predicting Recurrence of Stage I-III Endometrial Cancer Based on Inflammation-Immunity-Nutrition Score (IINS) and Traditional Classical Predictors [J]. J Inflamm Res, 2022, 15: 3021-3037. Jiang P, Jia M, Hu J, et al. A Nomogram Model Involving Immunohistochemical Markers for Predicting the Recurrence of Stage I-II Endometrial Cancer [J]. Front Oncol, 2020, 10: 586081. Jiang P, Yuan R. Analysis of Factors Related to Lymph Node Metastasis in Early-Stage Type 1 Endometrial Cancer: Verifying the Clinical Value of Positive Threshold of the Immunohistochemical Parameter Ki67 [J]. Cancer Manag Res, 2021, 13: 6319-6328. Tangen I L, Onyango T B, Kopperud R, et al. Androgen receptor as potential therapeutic target in metastatic endometrial cancer [J]. Oncotarget, 2016, 7(31): 49289-49298. Daix M, Angeles M A, Migliorelli F, et al. Concordance between preoperative ESMO-ESGO-ESTRO risk classification and final histology in early-stage endometrial cancer [J]. J Gynecol Oncol, 2021, 32(4): e48. Lukanovic D, Matjasic M, Kobal B. Accuracy of preoperative sampling diagnosis for predicting final pathology in patients with endometrial carcinoma: a review [J]. Transl Cancer Res, 2020, 9(12): 7785-7796. Schisterman E F, Perkins N J, Liu A, et al. Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples [J]. Epidemiology, 2005, 16(1): 73-81. Raffone A, Travaglino A, Mascolo M, et al. TCGA molecular groups of endometrial cancer: Pooled data about prognosis [J]. Gynecol Oncol, 2019, 155(2): 374-383. Di Spiezio Sardo A, De Angelis M C, Della Corte L, et al. Should endometrial biopsy under direct hysteroscopic visualization using the grasp technique become the new gold standard for the preoperative evaluation of the patient with endometrial cancer? [J]. Gynecol Oncol, 2020, 158(2): 347-353. Dagogo-Jack I, Shaw A T. Tumour heterogeneity and resistance to cancer therapies [J]. Nat Rev Clin Oncol, 2018, 15(2): 81-94. Lin F J. Solving Multicollinearity in the Process of Fitting Regression Model Using the Nested Estimate Procedure [J]. Quality & Quantity, 2008, 42(3): 417-426. Tables TABLE 1 Baseline characteristics of patients. Variable Age [yrs, mean (± SD)] BMI [kg/m2, mean (± SD)] FIGO staging I 409(68.2%) II 62(10.3%) III 129(21.5%) Myometrial invasion <1/2 417(69.5%) ≥1/2 183(30.5%) Cervical stromal invasion No 490（81.7%） Yes 110（18.3%） LVSI Negative 413（68.8%） Positive 187（31.2%） Lymph node metastasis Yes 183（30.5%） No 417（69.5%） Preoperative sampling method D&C or Pipelle biopsy 366（61.0%） Hysteroscopic biopsy 234（39.0%） Adjuvant treatment Follow-up 193（32.2%） Only radiotherapy 217（36.2%） Only chemotherapy 12（2.0%） Chemoradiotherapy 178（29.6%） Pathological type Preoperative biopsy I 409（68. 1%） II 191（31.8%） Final pathology I 395（65.8%） II 205（34.2%） Recurrence No 510（85.0%） Yes 90（15.0%） Sites of relapsed (n=90) Vaginal stump 4（4.4%） Central pelvic region 27（30.0%） Lymph nodes (upper para-aortic) 11（12.2%） Peritoneal metastases 20（22.2%） Metastasis to other organs 28（31.2 %） Death Dead 67（11.2%） Alive 533（88.8%） Follow-up time [months, median (P25, P75)] 39.00（30.00,48.00） Abbreviations:BMI, body mass index; FIGO, Int ernational Federation of Gynecology and Obstetrics; LVSI,lymphatic vessel space invasion;D&C,dilation and curettage. TABLE 2 Consistency of ER expression between preoperative biopsy and final pathology. TABLE 3 Consistency of PR expression between preoperative biopsy and final pathology. TABLE 4 Predictive Value of Combined Preoperative-Postoperative ER Expression for Endometrial Carcinoma Recurrence Group AUC(95%CI) Preoperative ER expression 0.590(0.521-0.659) Postoperative ER expression 0.628(0.561-0.696) 4-tier ER expression 0.642(0.574-0.710) TABLE 5 Predictive Value of Combined Preoperative-Postoperative PR Expression for Endometrial Carcinoma Recurrence Group AUC(95%CI) Preoperative PR expression 0.622(0.554-0.690) Postoperative PR expression 0.628(0.605-0.736) 4-tier PR expression 0.642(0.614-0.746) Table 6: Analysis of survival differences among 4 subgroups of 4-tier ER expression 4-tier ER expression Number of recurrences (n=90) 3-year RFS rate (95%CI) P-value Number of deaths (n=67) ER（+）/ER（+） (n=472, 78.7%) 50 82.08% (79.99%-84.17%) <0.001 40 ER（+）/ER（-）(n=60, 10.0%) 16 69.06% (61.09%-77.03%) 10 ER（-）/ER（+）(n=19,3.2 %) 4 67.81% (57.21%-78.42%) 3 ER（-）/ER（-） (n=49, 8.1%) 20 46.56% (39.52%-53.60%) 14 Table 5: Analysis of survival differences among 4 subgroups of 4-tier PR expression 4-tier PR expression Number of recurrences (n=90) 3-year RFS rate (95%CI) P-value Number of deaths (n=67) PR（+）/PR（+） (n=444, 74%) 41 83.16% (81.15%-85.17%) <0.001 35 PR（+）/PR（-）(n=47,7.8 %) 14 72.00% (64.64%-79.36%) 8 PR（-）/PR（+）(n=17,2.8 %) 2 62.12% (53.15%-71.09%) 2 PR（-）/PR（-） (n=92,15.4 %) 33 43.05% (38.04%-48.07%) 22 TABLE 8 Univariate and multivariate Cox regression analysis of RFS of EC. Variables Univariate analysis Multivariate analysis Hazard ratio 95% CI P-value Hazard ratio 95% CI P-value (≥60 vs 1.771 1.150-2.728 0.009 1.644 1.037-2.607 0.035 BMI 1.034 0.982-1.089 0.198 FIGO stage I ref ＜0.001 ref ＜0.001 II 2.161 1.023-4.565 0.044 3.319 1.224-8.998 0.018 III 4.547 4.547-11.315 ＜0.001 4.218 2.481-7.171 ＜0.001 Myometrial invasion (≥1/2 vs <1/2) 2.895 1.911-4.384 ＜0.001 1.835 1.189-2.831 0.006 Cervical stromal invasion (Yes vs No) 2.100 2.100-3.296 0.001 0.813 0.437-1.512 0.513 LVSI (Positive vs Negative) 3.912 2.559-5.980 ＜0.001 1.666 1.027-2.702 0.039 CA125 2.331 1.531-3.547 ＜0.001 1.724 1.092-2.721 0.019 Pathological type in final pathology(Type II vs Type I) 4.064 2.678-6.168 ＜0.001 1.971 1.183-3.284 0.009 Adjuvant treatment (Yes vs No) 1.079 0.910-1.279 0.380 PR expression PR(+)/PR(+) ref ＜0.001 0.050 PR(+)/PR(-) 2.671 1.456-4.901 0.002 0.989 0.359-2.727 0.983 PR(-)/PR(+) 1.447 0.350-5.984 0.610 1.035 0.194-5.525 0.968 PR(-)/PR(-) 5.594 3.535-8.853 ＜0.001 2.591 1.192-5.633 0.016 ER expression ER(+)/ER(+) ref ＜0.001 0.911 ER(+)/ER(-) 2.752 1.567-4.833 ＜0.001 1.131 0.456-2.805 0.790 ER(-)/ER(+) 2.097 0.757-5.809 0.154 0.677 0.196-2.343 0.538 ER(-)/ER(-) 4.677 2.784-7.858 ＜0.001 1.001 0.468-2. 141 0.997 Table 9 AUC of 4-tier PR expression, classic clinicopathological parameters, and their combination for predicting the recurrence of EC Group AUC (95%CI) 4-tier PR expression 0.680(0.614-0.746) Classic clinicopathological parameters 0.817(0.766-0.868) Combination 0.864(0.614-0.746) Table 10 Analysis of survival differences between high-risk and non-high-risk groups Group Number of recurrences (n=90) 3-year RFS rate (95%CI) P-value Non-high-risk group (n=430, 71.7%) High-risk group (n=170, 28.3%) 21 69 86.05% (61.60%-78.50%) 53.40% (61.60%-78.50%) <0.001 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2026 Read the published version in World Journal of Surgical Oncology → Version 1 posted Editorial decision: Revision requested 13 Dec, 2025 Reviews received at journal 12 Dec, 2025 Reviews received at journal 07 Dec, 2025 Reviewers agreed at journal 29 Nov, 2025 Reviewers agreed at journal 22 Nov, 2025 Reviews received at journal 05 Oct, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 27 Aug, 2025 Editor assigned by journal 27 Aug, 2025 Submission checks completed at journal 25 Aug, 2025 First submitted to journal 23 Aug, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7441185","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508885218,"identity":"9d3c6f8a-ceab-42a5-aab9-38a8b2b78c99","order_by":0,"name":"Ruixue Fan","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruixue","middleName":"","lastName":"Fan","suffix":""},{"id":508885219,"identity":"1e21433a-03ff-469e-8601-4d445a2d4f24","order_by":1,"name":"Peng Jiang","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Jiang","suffix":""},{"id":508885220,"identity":"bcad1364-9177-42ba-aa8c-baceafa933f1","order_by":2,"name":"Zhuoying Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACAwYGNhDNw8DAfIAhgUQtbAmkaQHpMiDOYeYS6c8e81QcljHnX/P5w8Mddgz87d34LbOckZBuzHPmMI/ljLfbJBLPJDNInDm7Ab/DbiQck+ZtO8xjcOPsNobENmYGA4lcQloS26Bazjz+kNhWT4yWZDaIlvM9DBKJbYeJ0HLmGZvknDPpQFvYzIBajvMQ9svx9GcSbyqs7Q3OH3788WdbtRx/ey9+LVDQzMAgkQBm8RCjHATqGBj4DxCreBSMglEwCkYaAAAzM0hDJHT7cgAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhuoying","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-08-23 12:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7441185/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7441185/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12957-026-04214-9","type":"published","date":"2026-04-13T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90543090,"identity":"abbb4f48-faf9-4d8a-b5b3-ba0b30c506c2","added_by":"auto","created_at":"2025-09-04 00:09:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50490,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/dae6644b2e223451eb6b7750.jpg"},{"id":90544288,"identity":"4824b317-ae93-4fa1-8e88-894711b5c3a3","added_by":"auto","created_at":"2025-09-04 00:17:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1434758,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/386d829e39ed7f6feb995c63.jpg"},{"id":90543127,"identity":"ea9a929d-d34d-48a9-b46a-66f8a6c8fd58","added_by":"auto","created_at":"2025-09-04 00:09:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1434758,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/2550967969e797e9a79dc467.jpg"},{"id":90543155,"identity":"40b1c892-0298-47d7-88de-6631c79cec75","added_by":"auto","created_at":"2025-09-04 00:09:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":134606,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/454623694963907faacaa823.jpg"},{"id":90544303,"identity":"0a3f91eb-d3cc-4b2d-b448-cc3567dcfecd","added_by":"auto","created_at":"2025-09-04 00:17:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":131797,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/7935102b4656dbc5d602f9e4.jpg"},{"id":90543131,"identity":"bae5a129-573b-4703-a7ce-d4ca76b455ce","added_by":"auto","created_at":"2025-09-04 00:09:45","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":90761,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/ce1eb8fc9d5ec520006bcb0b.jpg"},{"id":90543130,"identity":"9c49a451-152c-4520-85de-0589f78327f1","added_by":"auto","created_at":"2025-09-04 00:09:45","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":54647,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/94195c7833c5274d6a70d3f1.jpg"},{"id":90543133,"identity":"ef4cf9bd-abf9-4884-b566-3b4294dc105c","added_by":"auto","created_at":"2025-09-04 00:09:45","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":110371,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/b8c6077870ea7c46632aa0d8.jpg"},{"id":90543162,"identity":"32647751-329a-4da7-b949-890af7c42eaa","added_by":"auto","created_at":"2025-09-04 00:09:47","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":94111,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/fa55b2b63afb0bfd6a806449.jpg"},{"id":90543104,"identity":"4d53052a-90a8-4a3f-bcd7-dfb4c2267b58","added_by":"auto","created_at":"2025-09-04 00:09:44","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":49066,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/17dec7c09520bfccd63534ff.jpg"},{"id":90545196,"identity":"f40e6c1f-5f51-4cdc-a43f-6d2e77400f4a","added_by":"auto","created_at":"2025-09-04 00:33:45","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":86247,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/c6452c47b2d576ce0a36c68c.jpg"},{"id":90543101,"identity":"76182fc4-dff9-4d96-9186-a2721a6f5687","added_by":"auto","created_at":"2025-09-04 00:09:43","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":45248,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/0f889c7a3c6c1cfb0cfaab02.jpg"},{"id":90543123,"identity":"d91ad2c6-40e1-4871-b2b2-2c0538020626","added_by":"auto","created_at":"2025-09-04 00:09:45","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":29349,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/b49ea72df210d5494abc70bb.jpg"},{"id":90543109,"identity":"012cd7e7-8930-48b0-987b-aca7684a9d4e","added_by":"auto","created_at":"2025-09-04 00:09:44","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":94754,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/d4c1ab42b2f84f8a8def77fb.jpg"},{"id":90543134,"identity":"555d41f7-1286-49d2-a001-742e74c76ec7","added_by":"auto","created_at":"2025-09-04 00:09:45","extension":"jpg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":202573,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"FIGURE14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/77df89508c5d98e50ee7b7a2.jpg"},{"id":107351071,"identity":"7dd2a36c-d833-4d1d-89a5-f7ed11de91ab","added_by":"auto","created_at":"2026-04-20 16:08:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5312837,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7441185/v1/6f93725d-5172-49e7-a2d9-c8cc9bab4f0b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differential Expression of Estrogen and Progesterone Receptors Before and After Surgery in Endometrial Cancer and Analysis of Recurrence Prediction Model","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eEndometrial cancer is one of the most common malignant tumors in women\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, ranking as the second most prevalent gynecological malignancy in China. In recent years, both the incidence and mortality rates of endometrial cancer have been on the rise\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. According to data from the National Cancer Center, approximately 77,000 new cases of endometrial cancer were reported in China in 2022, with an incidence rate of 7.03 per 100,000. During the same period, 13,500 deaths were attributed to endometrial cancer, with a mortality rate of 1.06 per 100,000\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.Currently, the primary treatment for endometrial cancer is surgical intervention. Clinical management mainly relies on surgical pathological staging, which is supplemented by individualized treatment strategies based on the tumor's histological type, grade, and molecular characteristics. This approach is considered key to improving therapeutic outcomes.Based on the biological characteristics of the tumor, clinicians select appropriate surgical approaches and combine them with adjuvant therapies such as chemotherapy, radiotherapy, or immunotherapy. Treatment strategies are dynamically adjusted at different stages to address disease progression and resistance, thereby improving patient prognosis and survival rates\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. However, even with standardized surgical treatment, approximately 87% of patients experience recurrence within three years postoperatively\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The recurrence risk for early-stage patients is about 10–15% \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, while for advanced-stage patients, the recurrence risk can reach as high as 40–70%\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. For patients with recurrent or metastatic endometrial cancer, the median survival time is typically less than 12 months\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.In recent years, studies have shown inconsistencies between preoperative biopsy and postoperative pathology reports in pathological parameters such as histological grade and type \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. These discrepancies may affect treatment decisions, leading to overtreatment (e.g., unnecessary radiotherapy or chemotherapy) or undertreatment (e.g., failure to apply timely adjuvant therapies), ultimately impacting treatment outcomes. Therefore, in-depth research on the relationship between these discrepancies and patient prognosis is crucial for optimizing treatment strategies for endometrial cancer.Studies have shown that hormone receptor status, particularly estrogen receptor (ER) and progesterone receptor (PR), plays an important role in predicting the prognosis of endometrial cancer \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Positive expression of ER or PR is generally considered a marker of favorable prognosis, while negative expression of ER or PR is more commonly observed in advanced-stage, poorly differentiated tumors, indicating a poor prognosis \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.Compared to gene sequencing technology, immunohistochemistry (IHC) detection offers significant advantages in terms of ease of operation and low cost, making it more suitable for routine clinical screening. However, IHC detection is influenced by factors such as the sampling site, interobserver variability in subjective interpretation, and tumor heterogeneity.As a result, discrepancies in the expression levels of estrogen receptor (ER) and progesterone receptor (PR) may exist between preoperative biopsy and postoperative pathology results.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eStudy Population\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe study selected patients with stage I-III endometrial cancer who underwent preoperative biopsy and initial surgical treatment at the First Affiliated Hospital of Chongqing Medical University between February 2017 and August 2021. Clinical information of these patients was retrospectively collected during this period, including:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eGeneral clinical characteristics\u003c/b\u003e: Age, height, weight, preoperative biopsy method, and surgical approach.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePathological information\u003c/b\u003e: Tumor location, cervical stromal invasion, depth of myometrial invasion, lymph node metastasis, tumor histological type, and grading.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHormone receptor status\u003c/b\u003e: Immunohistochemical expression of ER/PR in preoperative biopsy and postoperative pathology.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTreatment details\u003c/b\u003e: Postoperative adjuvant treatment status and the specific adjuvant therapy methods (e.g.radiotherapy, chemotherapy, or chemoradiotherapy) determined based on domestic and international guidelines and multidisciplinary discussions \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cb\u003eInclusion criteria\u003c/b\u003e:\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePatients underwent endometrial biopsy before surgery and were initially diagnosed with endometrial cancer.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePatients received surgical treatment for the first time, with postoperative pathological confirmation of endometrial cancer and staging according to FIGO (2009) as stage I-III (stage IV patients were excluded because they typically do not undergo surgery and have distant metastases).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003cb\u003eExclusion criteria\u003c/b\u003e:\u003c/p\u003e\u003col\u003e\u003cli\u003e\u003cp\u003ePatients who did not undergo standard surgery (total hysterectomy + bilateral salpingo-oophorectomy ± pelvic/para-aortic lymphadenectomy).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePatients who received preoperative adjuvant therapy.\u003c/p\u003e\u003c/li\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePatients with other malignant tumors.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePatients with incomplete medical records.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003cstrong\u003eResearch Methods\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreoperative Biopsy Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients included in this study underwent preoperative biopsy. The biopsy methods were mainly categorized into two types: 1. Blind biopsy methods: Blind fractional curettage;Aspiration biopsy. 2. Hysteroscopy-guided endometrial biopsy (Figure 2): Diagnostic curettage under hysteroscopic guidance;Targeted forceps biopsy under hysteroscopic guidance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFollow-up Protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients underwent quarterly follow-ups in the first two postoperative years, semiannual evaluations during years 3-5, and annual assessments thereafter \u003csup\u003e[14]\u003c/sup\u003e. Surveillance included scheduled physical examinations and indicated diagnostic tests (e.g., transvaginal/pelvic ultrasound, MRI, serum CA125 measurements). The follow-up period extended through October 2024, with all enrolled subjects achieving a minimum follow-up of 3 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecurrence Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecurrence was confirmed by at least two gynecologic oncologists through integrated evaluation comprising physical examination, serum biomarker assays , radiographic imaging, and histopathological verification\u003csup\u003e\u0026nbsp;[15]\u003c/sup\u003e. Recurrence patterns were categorized as: vaginal cuff recurrence, pelvic central recurrence, para-aortic lymph node metastasis, peritoneal metastasis, or distant organ metastasis \u003csup\u003e[16]\u003c/sup\u003e. Recurrence-free survival (RFS) was defined as the time from the date of surgery to the date of confirmed recurrence. Overall survival (OS) was defined as the time from the date of surgery to death\u003csup\u003e\u0026nbsp;[9]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Pathological Analysis Protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll preoperative biopsy specimens and surgical tissues were immediately fixed in 10% neutral-buffered formalin after resection. Specimens were fully submerged in fixative solution at a 10:1 to 20:1 volume ratio (fixative-to-tissue) and processed for 24-48 hours at room temperature to ensure optimal fixation. Within 24 hours post-fixation, specimens were transferred to the Pathology Laboratory Center of Chongqing Medical University for standardized processing, including dehydration, paraffin embedding, sectioning , H\u0026amp;E staining, and immunohistochemical (IHC) analysis. \u0026nbsp;Pathological findings, such as tumor size, histological subtype and grade , myometrial invasion depth, cervical stromal involvement, lymphovascular space invasion (LVSI), and lymph node involvement status, were assessed by professional pathologists.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All specimens underwent automated IHC staining (Leica Bond Max; Milton Keynes, UK). In the most active tumor regions, five random high-power fields (HPFs) were observed. For each field, 100 tumor cells were evaluated, and the average percentage of ER and PR positivity (0–100%) across the five fields was calculated. The evaluation process was independently carried out by two experienced pathologists, and their results were recorded separately. Interobserver concordance was defined as ≤10% discrepancy in positive cell counts; cases with \u0026gt;10% variation underwent joint re-evaluation to reach consensus. Finally, the average of the two observers' results was calculated to represent the final interpretation of the IHC analysis \u003csup\u003e[15, 17]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eER and PR Immunohistochemical Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;The evaluation criteria for ER and PR IHC results are as follows: Positive: Defined as ≥1% of tumor cell nuclei showing clear staining. Negative: Defined as either \u0026lt;1% nuclear staining or absence of significant specific staining \u003csup\u003e[18]\u003c/sup\u003e. (Figure 3, Figure 4)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of ER and PR Expression Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on differential immunohistochemical expression observed in preoperative biopsies and postoperative specimens, four combined ER expression patterns were defined:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eER(+)/ER(+): Positive expression in both biopsy and surgical specimens.\u003c/li\u003e\n \u003cli\u003eER(-)/ER(-): Negative expression in both biopsy and surgical specimens.\u003c/li\u003e\n \u003cli\u003eER(+)/ER(-): Positive in biopsy but negative in surgical specimen.\u003c/li\u003e\n \u003cli\u003eER(-)/ER(+): Negative in biopsy but positive in surgical specimen.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe same classification was applied analogously to define PR expression patterns:PR(+)/PR(+);PR(-)/PR(-);PR(+)/PR(-);PR(-)/PR(+).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCohen’s kappa coefficient was used to evaluate the concordance between preoperative biopsy and postoperative pathology results for ER and PR expression, and a Sankey diagram was employed to visually display the changes in expression \u003csup\u003e[19]\u003c/sup\u003e. The interpretation of the kappa coefficient (κ) is as follows: κ\u0026lt;0.01 (poor), 0.01-0.20 (slight), 0.21-0.40 (fair), 0.41-0.60 (moderate), 0.61-0.80 (substantial), and 0.81-1.00 (almost perfect) agreement\u003csup\u003e\u0026nbsp;[20]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curve analysis quantified the predictive value of combined pre-/postoperative ER/PR expression patterns versus isolated pre- or postoperative status for recurrence risk. Kaplan-Meier survival curves were utilized to evaluate the differences in survival prognosis among subgroups with varying combined preoperative and postoperative ER/PR expression statuses.\u003c/p\u003e\n\u003cp\u003eUnivariate Cox regression identified recurrence-associated predictors, followed by multivariate Cox regression determining the independent prognostic value of combined ER/PR expression patterns and other clinicopathological parameters for endometrial carcinoma recurrence. A nomogram predicting recurrence risk was then developed based on multivariate results, with calibration curves evaluating concordance between predicted probabilities and observed outcomes. ROC analysis compared predictive performance (quantified by AUC) among combined PR preoperative and postoperative combined expression, conventional clinical parameters, and integrated models.The optimal risk threshold for the model was determined using the Youden index, allowing for risk stratification of patients\u003csup\u003e\u0026nbsp;[21]\u003c/sup\u003e. Finally, Kaplan-Meier survival curves were plotted for patients in different risk groups, and the log-rank test was used to compare differences between groups. Survival prognosis in different risk stratifications was evaluated, and subgroup survival analyses for adjuvant therapy were further performed.\u003c/p\u003e\n\u003cp\u003eCategorical variables were reported as frequencies (%). Normally distributed continuous variables were expressed as mean ± standard deviation (SD), while non-normally distributed variables were summarized as median (P25, P75). All analyses were performed using SPSS Statistics (version 27.0) and R software (version 4.0.3; https://www.R-project.org).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003ePatient Baseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study enrolled 600 endometrial carcinoma patients with a median follow-up of 39 months.Baseline characteristics are detailed in Table 1. Key demographic and clinicopathological features were: mean age 53.97\u0026plusmn;9.36 years, mean body mass index (BMI) 24.53\u0026plusmn;3.90 kg/m\u0026sup2;, and predominant use of blind biopsies (61.0%) versus hysteroscopy-guided biopsies (39.0%) for preoperative assessment. Postoperative pathology revealed high-risk features including lymph node metastasis (30.5%), deep myometrial invasion (\u0026ge;50% in 30.5%), and lymphovascular space invasion (31.2%). Adjuvant therapy was administered to 67.8% of patients (radiotherapy 36.2%, chemoradiotherapy 29.6%). During follow-up, the recurrence rate was 15.0% (predominantly distant metastases) with an 11.2% mortality rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscrepancy Analysis of ER and PR Immunohistochemical Expression Between Preoperative Biopsy and Postoperative Pathology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression status of estrogen receptor (ER) and progesterone receptor (PR) showed some discrepancies between preoperative biopsies and postoperative pathology. As shown in Tables 2 and 3, and Figures 5 and 6,\u0026nbsp;10.0% of patients initially diagnosed as ER-positive by biopsy were reclassified as ER-negative in postoperative pathology, while 3.2% showed the reverse conversion from ER-negative to ER-positive status. Similar discrepancies were observed for PR expression, with 7.8% of cases transitioning from PR-positive to PR-negative and 2.8% converting from PR-negative to PR-positive upon surgical specimen evaluation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Overall analysis revealed moderate concordance (86.8%; \u0026kappa;=0.481) between preoperative and postoperative ER expression status, while PR expression demonstrated substantial agreement (87.1%; \u0026kappa;=0.676).\u003c/p\u003e\n\u003cp\u003eStratified analysis revealed the impact of different biopsy methods on the concordance of ER and PR expression. Hysteroscopy-guided biopsies showed superior agreement with final pathology for both ER (94.0% concordance rate; \u0026kappa;=0.733) and PR (91.4%; \u0026kappa;=0.742) status. In contrast, blind biopsies (including fractional curettage and aspiration biopsy) exhibited significantly lower concordance rates (ER: 82.2%, \u0026kappa;=0.347; PR: 84.4%, \u0026kappa;=0.637).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive Value of Combined Preoperative-Postoperative ER/PR Expression for Endometrial Carcinoma Recurrence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCombining preoperative biopsy and postoperative pathology results for ER and PR expression\u0026nbsp;demonstrates superior predictive performance for postoperative recurrence\u0026nbsp;compared to using either alone. ROC curve analysis shows that the combined approach yields higher AUC values: 0.642 for ER and 0.680 for PR. These values are superior to those obtained using only preoperative biopsy data (AUC 0.590 for ER and 0.622 for PR) or solely postoperative pathology (AUC 0.638 for ER and 0.671 for PR). (Figure 7, Table 4, Table 5).\u003c/p\u003e\n\u003cp\u003eKaplan-Meier survival analysis demonstrated significant prognostic disparities among ER/PR expression subgroups (Figure 8). The ER(+)/ER(+) cohort exhibited the most favorable outcomes, while ER(-)/ER(-) patients showed the poorest prognosis. Intermediate survival rates were observed in both ER(+)/ER(-) and ER(-)/ER(+) groups , falling between the two extremes. Similarly, PR(+)/PR(+) patients achieved optimal survival, contrasting with the worst outcomes in PR(-)/PR(-) cases, while PR(+)/PR(-) and PR(-)/PR(+) subgroups displayed intermediate prognosis (detailed in Tables 6-7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic Value of Conventional Clinical Parameters and Combined ER/PR Expression Patterns in Endometrial Carcinoma Recurrence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecurrence-free survival (RFS) is a crucial prognostic indicator.\u0026nbsp;Univariate Cox regression analysis (Table 8) identified\u0026nbsp;multiple conventional clinicopathological parameters\u0026nbsp;significantly associated with RFS, including age, FIGO stage, final pathological type, myometrial invasion, cervical stromal invasion, lymphovascular space invasion (LVSI), CA125 levels, and combined\u0026nbsp;preoperative-postoperative ER/PR expression patterns (all P\u0026lt;0.05).\u0026nbsp;Factors with a p-value less than 0.05 in the univariate analysis were then included in a multivariate Cox regression\u0026nbsp;analysis.Subsequent multivariate Cox analysis revealed seven independent prognostic factors: age (95% CI: 1.037\u0026ndash;2.607, P=0.035), FIGO stage ( 95% CI: 2.481\u0026ndash;7.171, P\u0026lt;0.001), histologic type ( 95% CI: 1.183\u0026ndash;3.284, P=0.009), myometrial invasion ( 95% CI:1.189\u0026ndash;2.831, P=0.006), LVSI ( 95% CI: 1.027\u0026ndash;2.702, P=0.039), CA125 levels (95%CI: 1.092\u0026ndash;2.721, P=0.019), and combined PR expression status (95% CI: 1.192\u0026ndash;5.663, P=0.016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated Model Combining PR Expression Dynamics and Conventional Clinical Parameters for Endometrial Carcinoma Recurrence Prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate Cox regression confirmed the independent prognostic value of combined preoperative-postoperative PR expression patterns alongside six conventional clinical parameters (age, FIGO stage, histologic type, myometrial invasion depth, LVSI, and CA125 levels) for RFS. Recognizing the limitations of isolated PR status evaluation, we systematically compared three prediction approaches: PR expression dynamics (preoperative-postoperative concordance patterns) alone (AUC=0.680, 95% CI: 0.614-0.746), conventional clinical parameters alone (AUC=0.817, 95% CI: 0.766-0.868), and their combined integration. The composite model demonstrated superior predictive accuracy (AUC=0.864, 95% CI: 0.827-0.900), representing a statistically significant improvement over either individual approach (Figure 9, Table 9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Based on the multivariate Cox analysis results, we constructed a comprehensive nomogram model (Figure 10) that synergistically combines PR expression dynamics with established clinical parameters (age, FIGO stage, histologic type, myometrial invasion, LVSI, and CA125 levels) to provide individualized predictions of 1-year, 3-year, and 5-year recurrence-free survival (RFS) probabilities. This model employs an intuitive linear scoring system where clinicians assign points for each parameter (e.g.35 points for age \u0026ge;60 years, 100 points for FIGO Stage III, 90 points for PR[-]/PR[-] status), sum the total score, and directly read the corresponding predicted RFS rates from the bottom probability axis (illustratively, a 225-point total translates to 93.5% 1-year RFS, 67% 3-year RFS, and 63% 5-year RFS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate the predictive performance of this model, we further generated calibration curves to assess the agreement between the predicted probabilities and the actual observed outcomes. The calibration curves demonstrate only minor deviations between the model prediction line and the ideal reference line, indicating good overall model fit. (Figure 11A, B, C)\u003c/p\u003e\n\u003cp\u003eGiven that most recurrences occur within 3 years postoperatively [5], we focused our validation on the model\u0026apos;s 3-year recurrence-free survival (RFS) predictive performance. ROC analysis identified 0.14 as the optimal risk threshold (Youden index=0.569), achieving a balanced sensitivity of 0.77 and specificity of 0.79 for 3-year RFS prediction (Figure 12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the risk threshold determined from the ROC curve analysis, this study stratified patients into a high-risk group (3-year RFS \u0026lt; 0.14, n=170) and a non-high-risk group (3-year RFS \u0026ge; 0.14, n=430). Kaplan-Meier analysis with log-rank testing revealed significantly inferior 3-year RFS in the high-risk cohort (53.40% vs 86.05%), with distinct curve separation emerging as early as 6 months postoperatively (Figure 13, Table 10).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStratified analysis based on post-operative adjuvant therapy revealed no significant difference in survival outcomes between patients who received adjuvant therapy and those who did not within the non-high-risk group (Figure 14A, B). However, within the high-risk group, patients who received adjuvant therapy demonstrated significantly better overall prognosis compared to those who did not receive adjuvant therapy (Figure 14C, D).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eExisting studies primarily focus on traditional pathological parameters like histological type and FIGO grade\u003csup\u003e\u0026nbsp;[4]\u003c/sup\u003e. However, research exploring the discrepancies in molecular marker expression (such as ER and PR) and their clinical significance remains limited.The emergence of molecular subtyping in endometrial cancer has provided valuable insights into its biological characteristics, predicting clinical behavior and treatment response \u003csup\u003e[22]\u003c/sup\u003e. This has led to the integration of gene sequencing technologies for prognostic assessment. However, widespread adoption of gene sequencing is hampered by several factors: high costs, demanding technical expertise, longer turnaround times due to reliance on external testing facilities, and the need for specialized bioinformatics support. These limitations restrict the accessibility of this technology in many healthcare settings.\u003c/p\u003e\n\u003cp\u003eIn contrast, immunohistochemistry (IHC) has emerged as a clinically viable alternative, offering distinct advantages like lower cost for routine practice. Most hospitals in China have standardized IHC protocols with extensive clinical validation data. Furthermore, IHC can reveal intratumoral heterogeneity to a certain extent. Spatial heterogeneity in molecular marker expression within the tumor can be visually demonstrated by IHC, providing valuable information for tumor characterization and clinical decision-making \u003csup\u003e[23, 24]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis single-center cohort study systematically evaluated ER/PR immunohistochemical expression discordance between preoperative biopsies and definitive surgical specimens. Our data demonstrate substantial receptor concordance rates (ER: 86.8%, \u0026kappa;=0.481; PR: 87.1%, \u0026kappa;=0.676), with hysteroscopy-guided biopsies achieving superior agreement (ER: 94.0%, \u0026kappa;=0.733; PR: 91.4%, \u0026kappa;=0.742) compared to blind sampling techniques (ER: 82.2%, \u0026kappa;=0.347; PR: 84.4%, \u0026kappa;=0.637). This improvement in diagnostic accuracy likely reflects the visual targeting advantage of hysteroscopy, which enables precise sampling of the most representative tumor areas \u003csup\u003e[20,23]\u003c/sup\u003e.In contrast, blind biopsies carry inherent limitations due to their inability to provide direct visual localization of lesions, potentially resulting in inadequate sampling or deviation from target areas, thereby compromising the reliability of test results.\u0026nbsp;Therefore, in clinical practice, hysteroscopy-guided biopsy may represent a superior approach for patients with suspected intrauterine lesions, as it not only enhances the detection accuracy of molecular markers but also reduces diagnostic errors and missed diagnoses. The findings of this study further substantiate the critical diagnostic value of hysteroscopic biopsy in endometrial carcinoma.\u003c/p\u003e\n\u003cp\u003eThe results of this study demonstrate that the prognosis of endometrial carcinoma patients is closely associated with ER and PR expression status in both preoperative biopsies and postoperative pathological examinations. The ER/PR double-positive group exhibited the most favorable prognosis, while the double-negative group showed the poorest outcomes, with other expression patterns demonstrating intermediate prognostic characteristics. These prognostic disparities suggest that relying solely on either preoperative biopsy or postoperative pathology for prognosis assessment may have limitations\u0026mdash;particularly for patients undergoing blind biopsies, where potential sampling inadequacies could lead to\u0026nbsp;lower\u0026nbsp;concordance between preoperative ER\u0026nbsp;and\u0026nbsp;PR results and postoperative pathology, ultimately compromising prognostic prediction accuracy.\u003c/p\u003e\n\u003cp\u003eTherefore, comprehensive evaluation of both preoperative biopsy and postoperative pathology results is essential. This is further substantiated by ROC curve analysis, which demonstrates that the combined analysis of pre- and postoperative ER and PR expression significantly improves predictive efficacy compared to using either preoperative biopsy or postoperative pathology results alone. \u0026nbsp;Furthermore, by integrating the combined pre- and postoperative expression of PR with conventional clinicopathological clinical parameters, we have developed a nomogram to predict the risk of endometrial cancer recurrence.\u003c/p\u003e\n\u003cp\u003eROC curve analysis confirmed that the predictive efficacy of this integrated model is significantly superior to models using traditional clinical parameters alone or those analyzing combined pre- and postoperative PR expression in isolation. Kaplan-Meier survival analysis validated the model\u0026apos;s robust risk stratification capability, revealing significantly worse prognosis in high-risk group patients compared to non-high-risk counterparts. Furthermore, Kaplan-Meier survival analysis demonstrated improved prognoses for high-risk patients who received adjuvant therapy, suggesting that this model can effectively identify the patient population that would benefit from such treatment.\u003c/p\u003e\n\u003cp\u003eThis study employed both univariate and multivariate Cox regression analyses to evaluate the associations between conventional clinical parameters, combined preoperative-postoperative ER/PR expression status, and postoperative recurrence in endometrial carcinoma patients. The multivariate analysis results revealed no significant correlation between combined preoperative-postoperative ER expression patterns and postoperative recurrence. We postulate that this finding may primarily stem from the high collinearity between ER and PR expression. In endometrial carcinoma, ER and PR expression patterns are typically associated with tumor hormone dependence \u003csup\u003e[13]\u003c/sup\u003e, potentially explaining their high correlation in expression profiles. In multivariate Cox regression models, such collinearity may lead to unstable parameter estimates and inflated standard errors, consequently reducing the statistical significance of certain variables. In our study, the model appeared to preferentially emphasize the prognostic contribution of PR while potentially masking the independent predictive value of ER, which may lead to an underestimation of the clinical significance of ER in the clinical application of this model.\u003c/p\u003e\n\u003cp\u003eAlthough statistical analysis failed to demonstrate a significant independent correlation between ER status and prognosis, the well-established pathophysiological role of ER in endometrial carcinoma warrants careful consideration of its potential biological influence in clinical decision-making. Future studies should employ more robust statistical approaches\u0026nbsp;to address the collinearity between ER and PR \u003csup\u003e[25]\u003c/sup\u003e. Alternatively, stratified analysis during study design may help better elucidate the independent prognostic contributions of each receptor.In summary, this study\u0026apos;s findings do not negate the\u0026nbsp;significant role of ER in endometrial cancer.\u0026nbsp;The relatively weak association observed for ER in our current model likely stems from\u0026nbsp;methodological limitations in statistical analysis rather than diminished biological relevance. Future investigations should adopt more comprehensive approaches to evaluate ER\u0026apos;s contribution to both the biological behavior of endometrial cancer and patient prognosis.\u003c/p\u003e\n\u003cp\u003eFurthermore, as this study employed a retrospective design, its findings require validation in larger prospective cohort studies. The established prediction model also necessitates external validation to ensure its reliability and generalizability in clinical practice.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study confirms a high degree of concordance between pre- and postoperative ER/PR expression in endometrial cancer. Preoperative hysteroscopy-guided biopsy demonstrates significantly better agreement with postoperative pathology compared to blind biopsy. Therefore, for medical institutions with the requisite capabilities, hysteroscopy-guided biopsy is recommended for diagnosis to improve the accuracy of preoperative assessment.This study developed a clinically practical nomogram integrating combined preoperative-postoperative PR expression patterns with conventional clinicopathological parameters, which demonstrated excellent predictive performance for endometrial carcinoma recurrence risk and effectively identified high-risk populations. Although combined ER expression dynamics did not reach statistical significance, its potential biological impact should be carefully considered in clinical decision-making.Future multicenter validation is required to confirm the model\u0026apos;s generalizability, and incorporation of molecular classification systems may further refine prognostic accuracy by elucidating ER/PR interaction networks. Such advancements could ultimately optimize therapeutic strategies and improve survival outcomes in endometrial carcinoma patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eendometrial cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eER\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eestrogen receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprogesterone receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFIGO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Federation of Gynecology and Obstetrics\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLVSI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elymphatic vessel space invasion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRFS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003erecurrence-free survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eoverall survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003earea under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eThis study was approved by the Institutional Review Board (IRB) of the First Affiliated Hospital of Chongqing Medical University (IRB Nos. 2021-676 and 2023-002). All data were collected from pre-existing clinical records. The IRB waived the requirement for informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eZhuoying Hu: Conceptualization, Methodology, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eRuixue Fan: Methodology, Data curation, Investigation, Software, Formal analysis, Writing- Original draft preparation, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003ePeng Jiang: Data curation, Investigation, Writing- Original draft preparation, Writing - Review \u0026amp; Editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors critically reviewed the paper and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMorice P, Leary A, Creutzberg C, et al. Endometrial cancer [J]. Lancet, 2016, 387(10023): 1094-1108. \u003c/li\u003e\n\u003cli\u003eSiegel R L, Miller K D, Fuchs H E, et al. Cancer statistics, 2022 [J]. CA Cancer J Clin, 2022, 72(1): 7-33. \u003c/li\u003e\n\u003cli\u003eWang Shaoming, et al. Analysis of age characteristics of malignant tumor incidence and mortality in the Chinese population in 2022. [J].Chinese Oncology 33. 03 (2024): 165-174.\u003c/li\u003e\n\u003cli\u003eLago V, Martin B, Ballesteros E, et al. Tumor Grade Correlation Between Preoperative Biopsy and Final Surgical Specimen in Endometrial Cancer: The Use of Different Diagnostic Methods and Analysis of Associated Factors [J]. Int J Gynecol Cancer, 2018, 28(7): 1258-1263. \u003c/li\u003e\n\u003cli\u003eSohaib S A, Houghton S L, Meroni R, et al. Recurrent endometrial cancer: patterns of recurrent disease and assessment of prognosis [J]. Clin Radiol, 2007, 62(1): 28-34; discussion 35-26. \u003c/li\u003e\n\u003cli\u003eCreutzberg C L, van Putten W L, Koper P C, et al. Surgery and postoperative radiotherapy versus surgery alone for patients with stage-1 endometrial carcinoma: multicentre randomised trial. PORTEC Study Group. Post Operative Radiation Therapy in Endometrial Carcinoma [J]. Lancet, 2000, 355(9213): 1404-1411. \u003c/li\u003e\n\u003cli\u003eKeys H M, Roberts J A, Brunetto V L, et al. A phase III trial of surgery with or without adjunctive external pelvic radiation therapy in intermediate risk endometrial adenocarcinoma: a Gynecologic Oncology Group study [J]. Gynecol Oncol, 2004, 92(3): 744-751. \u003c/li\u003e\n\u003cli\u003eHuijgens A N, Mertens H J. Factors predicting recurrent endometrial cancer [J]. Facts Views Vis Obgyn, 2013, 5(3): 179-186. \u003c/li\u003e\n\u003cli\u003eKurra V, Krajewski K M, Jagannathan J, et al. Typical and atypical metastatic sites of recurrent endometrial carcinoma [J]. Cancer Imaging, 2013, 13(1): 113-122. \u003c/li\u003e\n\u003cli\u003eOdagiri T, Watari H, Hosaka M, et al. Multivariate survival analysis of the patientswith recurrent endometrial cancer [J]. J Gynecol Oncol, 2011, 22(1): 3-8. \u003c/li\u003e\n\u003cli\u003eVrede S W, Hulsman A M C, Reijnen C, et al. The amount of preoperative endometrial tissue surface in relation to final endometrial cancer classification [J]. Gynecol Oncol, 2022, 167(2): 196-204. \u003c/li\u003e\n\u003cli\u003eSmith D, Stewart C J R, Clarke E M, et al. ER and PR expression and survival after endometrial cancer [J]. Gynecol Oncol, 2018, 148(2): 258-266. \u003c/li\u003e\n\u003cli\u003eWang C, Tran D A, Fu M Z, et al. Estrogen Receptor, Progesterone Receptor, and HER2 Receptor Markers in Endometrial Cancer [J]. J Cancer, 2020, 11(7): 1693-1701. \u003c/li\u003e\n\u003cli\u003eColombo N, Creutzberg C, Amant F, et al. ESMO-ESGO-ESTRO Consensus Conference on Endometrial Cancer: diagnosis, treatment and follow-up [J]. Ann Oncol, 2016, 27(1): 16-41. \u003c/li\u003e\n\u003cli\u003eJiang P, Wang J, Gong C, et al. A Nomogram Model for Predicting Recurrence of Stage I-III Endometrial Cancer Based on Inflammation-Immunity-Nutrition Score (IINS) and Traditional Classical Predictors [J]. J Inflamm Res, 2022, 15: 3021-3037. \u003c/li\u003e\n\u003cli\u003eJiang P, Jia M, Hu J, et al. A Nomogram Model Involving Immunohistochemical Markers for Predicting the Recurrence of Stage I-II Endometrial Cancer [J]. Front Oncol, 2020, 10: 586081. \u003c/li\u003e\n\u003cli\u003eJiang P, Yuan R. Analysis of Factors Related to Lymph Node Metastasis in Early-Stage Type 1 Endometrial Cancer: Verifying the Clinical Value of Positive Threshold of the Immunohistochemical Parameter Ki67 [J]. Cancer Manag Res, 2021, 13: 6319-6328. \u003c/li\u003e\n\u003cli\u003eTangen I L, Onyango T B, Kopperud R, et al. Androgen receptor as potential therapeutic target in metastatic endometrial cancer [J]. Oncotarget, 2016, 7(31): 49289-49298. \u003c/li\u003e\n\u003cli\u003eDaix M, Angeles M A, Migliorelli F, et al. Concordance between preoperative ESMO-ESGO-ESTRO risk classification and final histology in early-stage endometrial cancer [J]. J Gynecol Oncol, 2021, 32(4): e48. \u003c/li\u003e\n\u003cli\u003eLukanovic D, Matjasic M, Kobal B. Accuracy of preoperative sampling diagnosis for predicting final pathology in patients with endometrial carcinoma: a review [J]. Transl Cancer Res, 2020, 9(12): 7785-7796. \u003c/li\u003e\n\u003cli\u003eSchisterman E F, Perkins N J, Liu A, et al. Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples [J]. Epidemiology, 2005, 16(1): 73-81. \u003c/li\u003e\n\u003cli\u003eRaffone A, Travaglino A, Mascolo M, et al. TCGA molecular groups of endometrial cancer: Pooled data about prognosis [J]. Gynecol Oncol, 2019, 155(2): 374-383. \u003c/li\u003e\n\u003cli\u003eDi Spiezio Sardo A, De Angelis M C, Della Corte L, et al. Should endometrial biopsy under direct hysteroscopic visualization using the grasp technique become the new gold standard for the preoperative evaluation of the patient with endometrial cancer? [J]. Gynecol Oncol, 2020, 158(2): 347-353. \u003c/li\u003e\n\u003cli\u003eDagogo-Jack I, Shaw A T. Tumour heterogeneity and resistance to cancer therapies [J]. Nat Rev Clin Oncol, 2018, 15(2): 81-94. \u003c/li\u003e\n\u003cli\u003eLin F J. Solving Multicollinearity in the Process of Fitting Regression Model Using the Nested Estimate Procedure [J]. Quality \u0026amp; Quantity, 2008, 42(3): 417-426.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTABLE\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1 Baseline characteristics of\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003epatients.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eAge [yrs,\u0026nbsp;mean (\u0026plusmn; SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eBMI [kg/m2,\u0026nbsp;mean (\u0026plusmn; SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eFIGO\u0026nbsp;staging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e409(68.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e62(10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e129(21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eMyometrial invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e\u0026lt;1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e417(69.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e\u0026ge;1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e183(30.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eCervical stromal\u0026nbsp;invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e490（81.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e110（18.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eLVSI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e413（68.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e187（31.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e183（30.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e417（69.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003ePreoperative sampling method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eD\u0026amp;C or Pipelle biopsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e366（61.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eHysteroscopic biopsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e234（39.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eAdjuvant treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e193（32.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eOnly radiotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e217（36.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eOnly chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e12（2.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eChemoradiotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e178（29.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003ePathological type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003ePreoperative biopsy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e409（68.\u0026nbsp;1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e191（31.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eFinal pathology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e395（65.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e205（34.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e510（85.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e90（15.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eSites\u0026nbsp;of\u0026nbsp;relapsed\u0026nbsp;(n=90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eVaginal\u0026nbsp;stump\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e4（4.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eCentral pelvic region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e27（30.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eLymph nodes (upper para-aortic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e11（12.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003ePeritoneal metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e20（22.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eMetastasis to other\u0026nbsp;organs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e28（31.2 %）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e67（11.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e533（88.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eFollow-up\u0026nbsp;time [months,\u0026nbsp;median\u0026nbsp;(P25, P75)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e39.00（30.00,48.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:BMI, body mass index; FIGO, Int\u003c/strong\u003e\u003cstrong\u003eernational Federation of Gynecology and\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Obstetrics; LVSI,lymphatic vessel space invasion;D\u0026amp;C,dilation and curettage.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2 Consistency of ER expression between preoperative biopsy and final pathology.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"643\" height=\"406\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3 Consistency of PR expression between preoperative biopsy and final pathology.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"643\" height=\"411\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4 \u0026nbsp;Predictive Value of Combined Preoperative-Postoperative ER Expression for Endometrial Carcinoma Recurrence\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAUC(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePreoperative ER expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e0.590(0.521-0.659)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePostoperative ER expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e0.628(0.561-0.696)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e4-tier ER expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e0.642(0.574-0.710)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 5 \u0026nbsp;Predictive Value of Combined Preoperative-Postoperative PR Expression for Endometrial Carcinoma Recurrence\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAUC(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePreoperative PR expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e0.622(0.554-0.690)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePostoperative PR expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e0.628(0.605-0.736)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e4-tier PR expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e0.642(0.614-0.746)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6:\u003c/strong\u003e\u003cstrong\u003eAnalysis of survival differences among 4 subgroups of 4-tier\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eER\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;expression\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"88%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e4-tier\u0026nbsp;ER expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eNumber of recurrences\u003c/p\u003e\n \u003cp\u003e(n=90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3-year RFS rate (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eNumber of deaths\u003c/p\u003e\n \u003cp\u003e(n=67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eER（+）/ER（+）\u003c/p\u003e\n \u003cp\u003e(n=472, 78.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e82.08%\u003c/p\u003e\n \u003cp\u003e(79.99%-84.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eER（+）/ER（-）(n=60,\u0026nbsp;10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e69.06%\u003c/p\u003e\n \u003cp\u003e(61.09%-77.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eER（-）/ER（+）(n=19,3.2\u0026nbsp;%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e67.81%\u003c/p\u003e\n \u003cp\u003e(57.21%-78.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eER（-）/ER（-）\u003c/p\u003e\n \u003cp\u003e(n=49,\u0026nbsp;8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e46.56%\u003c/p\u003e\n \u003cp\u003e(39.52%-53.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAnalysis of survival differences among 4 subgroups of 4-tier\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePR\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;expression\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"94%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e4-tier\u0026nbsp;PR\u0026nbsp;expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNumber of recurrences\u003c/p\u003e\n \u003cp\u003e(n=90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e3-year RFS rate (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eNumber of deaths\u003c/p\u003e\n \u003cp\u003e(n=67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003ePR（+）/PR（+）\u003c/p\u003e\n \u003cp\u003e(n=444, 74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e83.16%\u003c/p\u003e\n \u003cp\u003e(81.15%-85.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003ePR（+）/PR（-）(n=47,7.8\u0026nbsp;%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e72.00%\u003c/p\u003e\n \u003cp\u003e(64.64%-79.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003ePR（-）/PR（+）(n=17,2.8\u0026nbsp;%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e62.12%\u003c/p\u003e\n \u003cp\u003e(53.15%-71.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003ePR（-）/PR（-）\u003c/p\u003e\n \u003cp\u003e(n=92,15.4\u0026nbsp;%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e43.05%\u003c/p\u003e\n \u003cp\u003e(38.04%-48.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e8\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Univariate and multivariate Cox regression\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eof\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eRFS\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eof\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEC.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"656\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eHazard\u0026nbsp;ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e95%\u0026nbsp;CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eHazard\u0026nbsp;ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e95%\u0026nbsp;CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cimg width=\"27\" height=\"28\" src=\"data:image/png;base64,R0lGODlhKAAqAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAoACoAgAAAAAAAAAKwhI+py23holQBzuss3qlqvkFeI3bAF1kVo47aKr3Lu5ZPfIwI/KjnyZv9RMHfDlhCUWy64+15URaTuSJpZsU6KcYOj0qzaZdfj7mXBXVvSrWJ6ybVhvGYuV0X4vP8vv/P17antieWV5jzhgMFl7il54glg4LXlAbktUO5lmjIydh1qRM0RekjOBX5+Cno1ZKpyRWm2BrLMmcLKjXIpghVO/FFO4bh2QsMKJTMstycVwAAOw==\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp;(\u0026ge;60 \u0026nbsp; \u0026nbsp;vs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.150-2.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.037-2.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.982-1.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 656px;\"\u003e\n \u003cp\u003eFIGO\u0026nbsp;stage\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.023-4.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e3.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.224-8.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e4.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e4.547-11.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e4.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2.481-7.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eMyometrial\u003c/p\u003e\n \u003cp\u003einvasion (\u0026ge;1/2 vs \u0026lt;1/2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.911-4.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.189-2.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eCervical\u0026nbsp;stromal\u003c/p\u003e\n \u003cp\u003einvasion \u0026nbsp; \u0026nbsp; (Yes\u003c/p\u003e\n \u003cp\u003evs\u0026nbsp;No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2.100-3.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.437-1.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eLVSI \u0026nbsp; (Positive vs Negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e3.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2.559-5.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.027-2.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eCA125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.531-3.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.092-2.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePathological\u003c/p\u003e\n \u003cp\u003etype \u0026nbsp; \u0026nbsp;in \u0026nbsp; \u0026nbsp;final\u003cimg width=\"1\" height=\"16\" src=\"data:image/png;base64,R0lGODlhAQAYAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAQABABcAgQAAAPj6+/j5+////wIFxCapy1wAOw==\" alt=\"image\"\u003e\u0026nbsp;pathology(Type\u003cimg width=\"1\" height=\"16\" src=\"data:image/png;base64,R0lGODlhAQAYAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAQABABcAgQAAAPn5+/f5+wECAwIFhIynyVcAOw==\" alt=\"image\"\u003e\u0026nbsp;II vs Type I)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e4.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2.678-6.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.183-3.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eAdjuvant\u003c/p\u003e\n \u003cp\u003etreatment \u0026nbsp; (Yes vs No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.910-1.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 656px;\"\u003e\n \u003cp\u003ePR expression\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePR(+)/PR(+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePR(+)/PR(-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.456-4.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.359-2.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePR(-)/PR(+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.350-5.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.194-5.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePR(-)/PR(-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3.535-8.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.192-5.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 656px;\"\u003e\n \u003cp\u003eER expression\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eER(+)/ER(+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eER(+)/ER(-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.567-4.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.456-2.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eER(-)/ER(+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.757-5.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.196-2.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eER(-)/ER(-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e4.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2.784-7.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.468-2.\u0026nbsp;141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;AUC of 4-tier PR expression, classic clinicopathological parameters, and their combination for predicting the recurrence of EC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 323px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003e4-tier PR expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 323px;\"\u003e\n \u003cp\u003e0.680(0.614-0.746)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003eClassic clinicopathological parameters\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 323px;\"\u003e\n \u003cp\u003e0.817(0.766-0.868)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003eCombination\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 323px;\"\u003e\n \u003cp\u003e0.864(0.614-0.746)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Analysis of survival differences between high-risk\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand non-high-risk groups\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eNumber of\u0026nbsp;recurrences\u003c/p\u003e\n \u003cp\u003e(n=90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e3-year RFS rate\u003c/p\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eNon-high-risk group\u0026nbsp;(n=430, 71.7%)\u003c/p\u003e\n \u003cp\u003eHigh-risk group \u0026nbsp; \u0026nbsp; (n=170, 28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e86.05%\u003c/p\u003e\n \u003cp\u003e(61.60%-78.50%)\u003c/p\u003e\n \u003cp\u003e53.40%\u003c/p\u003e\n \u003cp\u003e(61.60%-78.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"endometrial cancer, ER PR immunohistochemistry, preoperative biopsy, final pathology, inconsistency","lastPublishedDoi":"10.21203/rs.3.rs-7441185/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7441185/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eEndometrial cancer has rising incidence and mortality, with high recurrence rates (10\u0026ndash;70%) post-surgery. Discrepancies between preoperative biopsy and postoperative pathology, particularly in estrogen receptor (ER) and progesterone receptor (PR) expression, may misguide treatment. This study explored ER/PR expression dynamics pre- and post-surgery and developed a recurrence prediction model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective cohort of 600 stage I\u0026ndash;III endometrial cancer patients (2017\u0026ndash;2021) from a single center was analyzed. Preoperative biopsies (blind vs. hysteroscopy-guided) and postoperative specimens underwent ER/PR immunohistochemical testing. Concordance was assessed via Cohen\u0026rsquo;s kappa. Survival analysis (Kaplan-Meier), ROC curves, and Cox regression identified prognostic factors. A nomogram integrating PR expression dynamics and clinicopathological parameters was developed and validated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eER and PR expression showed moderate-to-substantial overall concordance (86.8%, κ\u0026thinsp;=\u0026thinsp;0.481; 87.1%, κ\u0026thinsp;=\u0026thinsp;0.676), with hysteroscopy-guided biopsies demonstrating superior agreement (ER: 94.0%, κ\u0026thinsp;=\u0026thinsp;0.733; PR: 91.4%, κ\u0026thinsp;=\u0026thinsp;0.742) versus blind biopsies. Combined pre-/postoperative PR expression improved recurrence prediction (AUC\u0026thinsp;=\u0026thinsp;0.680). The integrated nomogram (AUC\u0026thinsp;=\u0026thinsp;0.864) effectively stratified high-risk patients (3-year recurrence-free survival: 53.40% vs. 86.05% in non-high-risk), who benefited from adjuvant therapy.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eHysteroscopy-guided biopsy enhances ER/PR assessment accuracy. The nomogram integrating PR dynamics and clinical parameters enables precise recurrence risk stratification, aiding personalized adjuvant therapy decisions.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Differential Expression of Estrogen and Progesterone Receptors Before and After Surgery in Endometrial Cancer and Analysis of Recurrence Prediction Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 00:09:37","doi":"10.21203/rs.3.rs-7441185/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-14T03:42:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-12T12:54:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T03:13:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221123807710001571692866529118400436288","date":"2025-11-29T16:28:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298477334754614256110844608845300683120","date":"2025-11-22T13:11:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T16:08:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256692292461203840331887030152718172582","date":"2025-09-29T19:30:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-27T09:23:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-27T07:56:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-25T07:06:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Surgical Oncology","date":"2025-08-23T11:56:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7664d728-847a-40d1-8f91-9aeb4567ed3d","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:06:42+00:00","versionOfRecord":{"articleIdentity":"rs-7441185","link":"https://doi.org/10.1186/s12957-026-04214-9","journal":{"identity":"world-journal-of-surgical-oncology","isVorOnly":false,"title":"World Journal of Surgical Oncology"},"publishedOn":"2026-04-13 15:57:00","publishedOnDateReadable":"April 13th, 2026"},"versionCreatedAt":"2025-09-04 00:09:37","video":"","vorDoi":"10.1186/s12957-026-04214-9","vorDoiUrl":"https://doi.org/10.1186/s12957-026-04214-9","workflowStages":[]},"version":"v1","identity":"rs-7441185","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7441185","identity":"rs-7441185","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}