A clinicopathological nomogram integrating hormone receptor status to predict overall survival in high-grade serous ovarian carcinoma

A clinicopathological nomogram integrating hormone receptor status to predict overall survival in high-grade serous ovarian carcinoma | 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 A clinicopathological nomogram integrating hormone receptor status to predict overall survival in high-grade serous ovarian carcinoma chengcheng zhu, Hengliang Sun, Yonghong Luo, Dandan Ge, Shun Yao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8816231/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background High-grade serous ovarian cancer (HGSC) causes high mortality rates worldwide due to its insidious onset and poor prognosis. The statuses of hormone receptors (ER/PR) have been shown to have significant prognostic value in hormone-related tumors such as breast cancer, but their roles in HGSC remain unclear. Methods This study retrospectively analyzed the relationship between estrogen receptor (ER) and progesterone receptor (PR) expression status, clinical features, and survival outcomes in 176 patients with HGSC. Kaplan-Meier survival analysis and univariate and multivariate Cox regression models were used to evaluate how various factors relate to overall survival (OS). Propensity score matching (PSM) was employed to control for confounding factors, ultimately constructing a nomogram model incorporating ER/PR status and other independent prognostic factors. Results The expression status of ER/PR was significantly associated with patient survival. The ER(+)/PR(+) group showed the best prognosis, while the double-negative group had the worst. Moreover, the combined ER/PR expression remained an independent prognostic factor even in multivariate analysis (HR = 11.610, 95% CI: 6.225–21.653). Additionally, the nomogram based on ER/PR and other clinicopathological characteristics accurately predicted the 1-year, 3-year, and 5-year survival rates of patients, demonstrating good discriminative performance. Conclusion The independent prognostic value of hormone receptors in HGSC serves as a structured risk assessment tool for personalized management. This tool could help guide clinicians in selecting patients who may benefit and facilitate the precise application of endocrine-targeted therapy in ovarian cancer. High-grade serous ovarian carcinoma Hormone receptors Estrogen receptor Progesterone receptor Prognosis Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1.Introduction High-grade serous ovarian cancer (HGSOC) is the most aggressive form of ovarian cancer. It accounts for approximately 70% of all ovarian cancer cases. This type of cancer is often diagnosed at an advanced stage, resulting in a poor prognosis with a five-year survival rate below 45%[ 1 ]. Notably, the development of HGSOC is complex and associated with multiple factors, including genetic variations, alterations in the tumor microenvironment, and hormone receptor status. Currently, the main treatments for HGSOC include surgical resection and chemotherapy. However, these methods often have limited effectiveness due to patient heterogeneity and the tumor's biological characteristics[ 2 ]. Therefore, it is imperative to develop more effective prognostic assessment tools and personalized treatment strategies to enhance patient survival rates and quality of life. Ovarian cancer is considered a hormone-driven malignant tumor, especially because the expression of estrogen (ER) and progesterone (PR) receptors is closely linked to patient prognosis. A systematic assessment showed that ER positivity is significantly higher in low-grade serous ovarian cancer (LGSOC) compared to HGSOC[ 3 ]. This suggested that differences in hormone receptor expression among ovarian cancer subtypes may influence clinical presentation and prognosis.Among the various subtypes, endometrioid carcinoma and low-grade serous ovarian cancer have been shown to be associated with ER and PR expression, and the presence of these receptors may influence patient prognosis[ 4 , 5 ]. However, the relationship between HGSOC and ER/PR expression remains unclear, complicating the analysis of prognostic factors for this subtype. PR expression is recognized as a favorable prognostic biomarker in HGSOC[ 6 ]. Although ER positivity in HGSOC can reach 86%-92%, its prognostic value varies widely, and not all such tumors respond to anti-estrogen therapy[ 7 ]. Notably, patients with recurrent HGSOC and peritoneal metastasis who exhibit better clinical outcomes showed an ER positivity rate as high as 86%, suggesting ER may influence disease progression, though the mechanisms remain unclear[ 8 ]. In hormone-related tumors, breast cancer studies showed that patients with the ER+/PR+ phenotype had the greatest survival benefits[ 9 ], providing a theoretical basis for studying combined biomarkers in HGSOC. Several exploratory studies on hormones have indicated that AR (androgen receptor), ER, and PR, both individually and in combination, have predictive and prognostic value[ 10 ]. Nonetheless, no systematic evaluation has been conducted on the link between ER/PR combined expression subtypes and clinical features. This study addresses the clinical significance of ER and PR expression profiles in HGSC patients based on identified research gaps. It also explores their relationships with prognosis. We include combined ER and PR expression status in a multifactor analysis to assess its association with clinical, pathological factors, and survival outcomes. Predictive tools such as nomograms will be introduced to integrate molecular profiles with clinical variables. These tools aim to improve risk assessment and clinical usability, providing both a theoretical basis and practical support for individualized prognosis assessment and management of HGSC patients. 2.Methods 2.1 Study Population and Data Collection This study included patients with epithelial ovarian cancer treated at the Obstetrics and Gynecology Department of the First Affiliated Hospital of Wannan Medical College from August 2012 to July 2020. All patients underwent surgery and received systemic chemotherapy. Exclusion criteria included low-grade serous, mucinous, endometrioid, and clear cell ovarian cancers, as well as insufficient follow-up data. A total of 176 cases of HGSC were selected, as shown in the flowchart in Fig. 1 . Clinical and pathological data were extracted from EMRs and pathology reports, including age, FIGO stage, tumor size, ascites, vascular cancer thrombus, and the status of ER, PR, P53, and Ki67. 2.2 Immunohistochemistry (IHC) Testing Standardized IHC protocols stained formalin-fixed paraffin-embedded tissue sections. ER, PR, P53, and Ki67 status was classified as positive or negative. Positive staining for ER and PR was defined as ≥ 1% of tumor cell nuclei being immunoreactive, according to the Ovarian Tumor Tissue Analysis (OTTA) consortium's research protocol[ 11 ]. To be consistent, nuclear staining for all P53 and Ki67 antibodies exceeding 10% was considered positive[ 12 , 13 ]. All sections were independently evaluated by two pathologists who were blinded to the clinical outcomes. 2.3 Statistical Analysis Overall survival (OS) is calculated as the time from diagnosis until death or last follow-up. Survival differences between ER/PR subgroups were assessed using Kaplan-Meier curves and log-rank tests. Independent prognostic factors were identified by univariate and multivariate Cox proportional hazards regression models. Variables with a univariate P-value less than 0.05 were included in the multivariate model. Propensity score matching (PSM) was performed in a 1:1 ratio, based on covariates using logistic regression. The balance between matched groups was assessed by standardized mean differences below 0.1. A predictive nomogram integrating ER/PR status and clinicopathological variables was constructed using the rms package in R software. 3.Results Clinical and pathological features This study included 176 patients with HGSC. They were divided into four groups based on estrogen receptor (ER) and progesterone receptor (PR) expression status: ER(+)/PR(+) with 61 cases (34.7%), ER(+)/PR(−) with 71 cases (40.3%), ER(−)/PR(+) with 13 cases (7.4%), and ER(−)/PR(−) with 31 cases (17.6%). No significant differences were observed between the groups regarding clinical features including age, ascites, tumor size, and pathological staging. Similarly, there were no significant differences in pathological features such as P53 distribution. However, the groups showed statistically significant differences in vascular cancer emboli and Ki67 (P = 0.028 and P = 0.024) (Table 1 ). Immunohistochemical analysis showed positive ER expression in 132 patients (75.0%) and positive PR expression in 74 patients (42.0%). ER was predominantly located in the nucleus, exhibiting a brownish granular distribution with moderate to high intensity (Fig. 2 A); PR was also located in the nucleus but showed weaker or patchy distribution in some cases, indicating greater expression heterogeneity (Fig. 2 B). Table 1 Clinicopathological characteristics of the study cohort. Characteristics ER(+)/PR(+) ER(+)/PR(-) ER(-)/PR(+) ER(-)/PR(-) P value n 61 71 13 31 Age(years), n (%) 0.124 <60 44 (25%) 43 (24.4%) 5 (2.8%) 19 (10.8%) ≥ 60 17 (9.7%) 28 (15.9%) 8 (4.5%) 12 (6.8%) Pathologic.stage, n (%) 0.232 Stage I 11 (6.2%) 8 (4.5%) 4 (2.3%) 2 (1.1%) Stage II 9 (5.1%) 5 (2.8%) 3 (1.7%) 4 (2.3%) Stage III 39 (22.2%) 55 (31.2%) 5 (2.8%) 23 (13.1%) Stage IV 2 (1.1%) 3 (1.7%) 1 (0.6%) 2 (1.1%) tumor size(cm), n (%) 0.784 ≤ 5 13 (7.4%) 20 (11.4%) 3 (1.7%) 9 (5.1%) >5 48 (27.3%) 51 (29%) 10 (5.7%) 22 (12.5%) cancer emboli, n (%) 0.028 Negative 37 (21%) 39 (22.2%) 8 (4.5%) 9 (5.1%) Positive 24 (13.6%) 32 (18.2%) 5 (2.8%) 22 (12.5%) Ascites, n (%) 0.993 Negative 33 (18.8%) 37 (21%) 7 (4%) 17 (9.7%) Positive 28 (15.9%) 34 (19.3%) 6 (3.4%) 14 (8%) P53, n (%) 0.806 Negative 21 (11.9%) 23 (13.1%) 6 (3.4%) 10 (5.7%) Positive 40 (22.7%) 48 (27.3%) 7 (4%) 21 (11.9%) Ki67, n (%) 0.024 Negative 7 (4%) 18 (10.2%) 6 (3.4%) 9 (5.1%) Positive 54 (30.7%) 53 (30.1%) 7 (4%) 22 (12.5%) Prognosis and survival analysis of hormone receptor expression After 5 years of follow-up, we analyzed the impact of ER and PR expression status on patient prognosis. Kaplan-Meier survival curves were used for this analysis. The median survival time was 55 months for ER-positive patients, significantly longer than the 47 months for ER-negative patients (Fig. 3 A). For PR status, the median survival time was 58 months in positive patients, significantly longer than 48 months in negative patients (Fig. 3 B). The results show that positive expression of both ER and PR is significantly associated with better prognosis. To investigate how combined receptor expression affects prognosis in HGSC, patients were divided into three groups based on ER and PR status: ER(+)/PR(+), ER(+)/PR(−) or ER(−)/PR(+), and ER(−)/PR(−). The ER(+)/PR(+) group had the longest median survival time of 58 months, while the ER(−)/PR(−) group had the shortest at 38 months, with a statistically significant difference between these groups (P < 0.001)(Fig. 3 C). These results indicate that patients with both ER and PR positive expression have the best prognosis, while those with double negative expression have the worst. Further analysis of the intermediate group showed no statistically significant difference in prognosis between ER(+)/PR(−) and ER(−)/PR(+), although the latter had a longer median survival time (55 months vs. 50 months)(Fig. 3 D). Subgroup analysis by pathological stage showed that survival differences among the three groups were statistically significant in early-stage patients (stage I–II) (P = 0.036)(Fig. 3 E). In advanced-stage patients (stage III–IV), the median survival time for the ER(+)/PR(+) group was 54 months, significantly better than the 36 months for the ER(−)/PR(−) group (P < 0.001)(Fig. 3 F), which indicated that hormone receptors were more clinically valuable for predicting outcomes in advanced tumors. Independent prognostic analysis and PMS for risk models To identify independent prognostic factors in patients with high-grade serous ovarian cancer, this study first performed univariate Cox regression on clinical pathological variables. The univariate analysis showed that advanced pathological stage (stage III–IV, HR = 6.001, P 5 cm (HR = 2.011, P = 0.004), positive cancer emboli (HR = 2.193, P < 0.001), positive P53 (HR = 4.298, P < 0.001), positive Ki67 (HR = 3.111, P < 0.001), and ER/PR status (compared to the ER+/PR+ group, mixed group HR = 1.571, P = 0.043; double-negative status HR = 3.532, P < 0.001) were significant adverse prognostic factors. Next, we included the statistically significant variables (P < 0.05) from the univariate analysis in a multivariate Cox proportional hazards mode (Table 2 ). All variables met the proportional hazards assumption (Supplementary Fig. 1). The results of the multivariate analysis indicated that, after adjusting for other confounding factors, advanced pathological stage (HR = 9.146, P 5 cm (HR = 3.307, P < 0.001), cancer emboli positivity (HR = 1.919, P = 0.002), positive P53 (HR = 7.353, P < 0.001), positive Ki67 (HR = 3.217, P < 0.001), and ER/PR expression status remained independent prognostic factors affecting overall survival. Of these, double-negative ER/PR expression was one of the strongest independent risk factors, with a mortality risk 11.610 times higher than that of patients with double-positive ER/PR. The mortality risk for patients with mixed ER/PR expression was also significantly higher than that for the double-positive group. Table 2 Univariate and multivariate Cox regression analysis of clinicopathological characteristics and OS. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Age(years) 176 <60 111 Reference ≥ 60 65 1.027 (0.703–1.500) 0.892 Pathologic.stage 176 Stage I or Stage II 46 Reference Reference Stage III or Stage IV 130 6.001 (3.209–11.221) < 0.001 9.146 (4.756–17.585) 5 131 2.011 (1.252–3.231) 0.004 3.307 (1.922–5.690) < 0.001 cancer emboli 176 Negative 93 Reference Reference Positive 83 2.193 (1.511–3.183) < 0.001 1.919 (1.282–2.873) 0.002 Ascites 176 Negative 94 Reference Positive 82 1.133 (0.784–1.637) 0.506 P53 176 Negative 60 Reference Reference Positive 116 4.298 (2.663–6.938) < 0.001 7.353 (4.374–12.363) < 0.001 Ki67 176 Negative 40 Reference Reference Positive 136 3.111 (1.776–5.449) < 0.001 3.217 (1.790–5.784) < 0.001 group 176 ER(+)/PR(+) 61 Reference Reference ER(+)/PR(-) or ER(-)/PR(+) 84 1.571 (1.015–2.430) 0.043 3.674 (2.252–5.992) < 0.001 ER(-)/PR(-) 31 3.532 (2.106–5.924) < 0.001 11.610 (6.225–21.653) < 0.001 HR, hazard ratio; CI, confidence interval. To control for the impact of confounding factors in baseline data on prognostic analysis and reduce selection bias, this study employed propensity score matching (PSM). A 1:1 match was performed between the ER/PR double-positive group and the double-negative group (Supplementary Table 1). This approach aimed to make the comparison more valid and verify the reliability of the results. The matching variables included pathological stage, tumor size, cancer emboli, P53, and Ki67 status. Before matching, there were significant differences between the double-positive group (n = 61) and the double-negative group (n = 31), notably in cancer emboli (P = 0.004) and Ki67 expression (P = 0.036). After PSM matching, each group included 31 patients. All clinical pathological characteristics—such as pathological stage, tumor size, vascular cancer emboli, P53, and Ki67—were well balanced. No statistical differences were found between the groups, indicating successful matching and comparability of baseline characteristics. Subsequently, we conducted multivariate Cox regression and prognostic analyses on patients after PSM (Supplementary Table 2). The results showed that ER/PR double-negative expression remained the strongest independent risk factor associated with overall survival (HR = 11.122, P < 0.001), and the survival rate was significantly lower than that of the double-positive group (Supplementary Fig. 2). Development of a Predictive Nomogram Incorporating Clinical Risk Factors We constructed a nomogram to predict the 1-, 3-, and 5-year survival probabilities of HGSC patients (Fig. 4 ). The nomogram includes clinicopathological factors related to hormone receptors (ER/PR) identified through multifactor analysis. The prognostic model generated risk scores that classified patients into low-risk and high-risk groups. Kaplan-Meier analysis revealed a significant difference in survival time between high-risk and low-risk patients (Fig. 5 B). Additionally, a risk factor heatmap showed that high-risk patients were more likely to be at advanced stages (III/IV), have larger tumors, positive vascular cancer emboli, positive P53 and Ki67, and negative ER/PR status (Fig. 5 A). These factors correspond to the higher scores in the nomogram. Calibration curves demonstrated good agreement between the nomogram’s predicted survival probabilities and the actual observed outcomes (Fig. 5 C). We used time-dependent ROC curves to evaluate the prognostic model’s ability to distinguish between 1-year and 3-year survival (Fig. 5 D). The prognostic model maintained high AUC and C-index values throughout the 1- to 5-year period, reflecting its robust performance in predicting HGSC prognosis over 5 years (Fig. 5 E-F). Decision Curve Analysis (DCA) showed that the model provided better net benefits for clinical decision-making at various thresholds compared to individual predictive factors. In patients with advanced HGSC and poor prognosis, the model demonstrated consistent net benefits across a broad range of decision thresholds, regardless of clinical decision preferences (Fig. 5 G-I). 4.Discussion HGSC has a low overall survival rate because it is often diagnosed at an advanced stage and exhibits strong resistance to conventional chemotherapy[ 14 ]. Unfortunately, endocrine therapy is generally ineffective for gynecological tumors other than breast cancer. This ineffectiveness stems from a lack of prognostic studies on combined hormone receptor markers in the histological subtypes of gynecological tumors. As a result, precise targeted therapies are scarce. Surprisingly, breast tumors and high-grade serous ovarian tumors share significant molecular similarities, including BRCA gene mutations, which indicate common causes and potential treatment targets[ 15 ]. Studies show a negative correlation between ER/PR-positive breast cancer and the risk of ovarian cancer. This means that ER/PR-positive patients have a lower likelihood of developing ovarian cancer[ 16 ]. Therefore, analysis of ER and PR expression patterns and their associations with patients' clinical characteristics and survival outcomes, along with the construction of a survival prediction model integrating molecular markers and clinical parameters, could help develop personalized endocrine therapies[ 17 ]. ER/PR function as nuclear receptors that regulate the transcription of target genes at the molecular level. This regulation modulates cell proliferation, differentiation, and apoptosis. Ki67 is a cell proliferation marker that typically indicates a more active cell cycle[ 18 ]. The ER/PR signaling pathway, when activated, regulates downstream genes related to proliferation. This regulation influences the growth rate of tumor cells. Transcriptomic studies of breast cancer have shown that ER/PR-positive status is often associated with higher differentiation and lower proliferation. Conversely, absence of ER/PR increases tumor invasiveness by strongly suppressing antigen presentation via MHC-II[ 19 ]. Notably, heterogeneity in PR expression is linked to multiple mechanisms. These include gene methylation, splice variants, and microenvironmental signals. The variation in expression among tumor subtypes may reflect their complex adaptations to hormone dependence and resistance[ 20 ]. Additionally, the formation of vascular cancer thrombi indicates that tumors have an enhanced ability to invade blood vessels. ER/PR signaling may promote local tumor infiltration and metastasis by regulating molecules such as matrix metalloproteinases (MMPs)[ 21 ]. ER/PR expression is closely linked to the mechanisms underlying proliferation, differentiation, and vascular invasion, which highlights the molecular basis of tumor heterogeneity. Interestingly, no significant difference exists in the age at diagnosis between early- and late-stage patients, suggesting that biological behavior rather than screening delays is the primary cause of late diagnosis[ 22 ]. This study shows that patients positive for ER or PR have a longer median survival time. Among them, those positive for both receptors have the best prognosis, while patients negative for both have the worst. Mechanistically, ER/PR-mediated pathways not only directly regulate the tumor cell cycle, apoptosis, and DNA repair but also modulate key pathways such as PI3K/AKT and MAPK to influence sensitivity to chemotherapy and endocrine therapy[ 20 ]. The absence of hormone receptors often causes insensitivity to the apoptosis signals, resulting in continuous tumor proliferation and drug resistance. ER/PR-negative tumors exhibit greater genomic instability and aggressiveness, an effect that is especially pronounced in tumors with P53 mutations[ 23 ]. Consistent with previous large multicenter studies, ER/PR positivity generally predicts better survival, although the protective effect varies across histological subtypes[ 24 ]. We analyzed patients with HGSC in detail and found that ER/PR expression status more accurately predicts prognosis in advanced-stage tumors. Multivariate Cox regression analysis identified several independent prognostic factors. These include advanced stage, tumor volume, tumor thrombus, P53, Ki67, and ER/PR status. Among these, ER/PR double-negative status was the strongest risk factor. Clinical staging reflects tumor burden and metastatic potential. Tumor volume is linked to local stress and hypoxia, factors that contribute to tumor progression and chemotherapy resistance[ 25 ]. P53 mutations lead to impaired apoptosis and genomic instability, while high Ki67 expression indicates active cell cycle activity. Both factors are closely related to tumor progression and chemotherapy resistance[ 26 ]. The absence of ER/PR may interact with the high-risk factors mentioned above, jointly enhancing tumor aggressiveness. This interaction leads to increased proliferation, higher metastatic potential, and primary resistance to endocrine therapy. Advanced stage, tumor size, and molecular markers are consistently included in risk scoring across different cohorts and international prediction models. However, the weighting of these factors varies across ethnic groups and geographic regions[ 27 , 28 ]. In retrospective cohort studies, PSM is widely used to address confounding factors such as clinical characteristics and treatment methods. When the sample size is sufficient, PSM can reduce bias more effectively than other methods such as inverse probability weighting and stratified analysis[ 29 , 30 ]. However, PSM has drawbacks, including sample size loss and sensitivity to variable selection. Some literature suggests combining multiple statistical methods in multi-center, large-sample studies to improve robustness[ 31 , 32 ]. Therefore, we applied PSM solely to eliminate confounding from baseline characteristic imbalance, while the prognostic model was developed using the original sample to verify the adverse effect of ER/PR double negativity. Endocrine therapy has gradually been introduced for ovarian cancer in recent years. However, its effectiveness is significantly reduced due to complex resistance mechanisms[ 33 , 34 ]. Research shows that there are significant differences in drug sensitivity among individuals. Prognostic models that combine hormone receptors with clinical and pathological features help identify patients who may benefit from endocrine therapy and those at high risk of primary or acquired resistance. In developing prognostic assessment tools, we integrated ER/PR and several clinical and pathological parameters to establish a visual nomogram model, which shows great predictive accuracy and is clinically useful. Multi-factor risk integration, based on the concept that a single biomarker cannot fully capture tumor diversity, improves the performance of predictive models by combining multiple parameters[ 35 ]. Compared to previous nomogram models for ovarian cancer and other solid tumors, multi-factor models incorporating molecular and clinical factors generally outperform traditional staging systems in metrics such as AUC (area under the curve), C-index, and DCA (decision curve analysis) clinical net benefit[ 28 , 36 , 37 ]. This model is innovative not only in integrating biomarkers for outcome prediction, but also in providing a framework for personalized prognostic assessment. This study controlled for confounding factors to a significant extent through rigorous propensity score matching and multivariate regression. However, it was a single-center retrospective study. As a result, selection and information biases during patient screening and follow-up might still exist, potentially affecting the generalizability of the results. Additionally, the small number of PR-positive cases has reduced statistical power in some subgroups. Moreover, the subjective nature of immunohistochemical scoring might affect the reliability of conclusions regarding PR expression variability. Therefore, some findings require further validation in larger, multicenter, prospective studies. 5.Conclusion In this cohort of HGSC patients, the combined expression status of ER and PR served as a robust and independent prognostic indicator. The integration of this biomarker into a clinically applicable nomogram provides a valuable tool for individualized risk assessment. This model shows great potential to improve patient stratification and guide future clinical trials of endocrine-targeted therapies, especially for patients with distinct hormone receptor profiles. Abbreviations AR androgen receptor AUC area under the curve DCA Decision Curve Analysis ER estrogen receptor HGSC High-grade serous ovarian cancer IHC Immunohistochemistry LGSOC low-grade serous ovarian cancer MMPs matrix metalloproteinases OS overall survival OTTA Ovarian Tumor Tissue Analysis PR progesterone receptor PSM Propensity score matching ROC Receiver Operating Characteristic Declarations Ethics approval and consent to participate All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The study was approved by the ethical committee of Wannan Medical College (approval number: 2024207), in which informed consent was waived for retrospective observational study. Consent for publication Not applicable. Competing Interests The authors declare no competing interests. Funding Author Contribution Chengcheng Zhu and Hengliang Sun developed the project, designed the methodology, curated and analyzed data, and drafted the original manuscript; Yonghong Luo, Dandan Ge, Shun Yao, Yi Wang and Meng Yan contributed to data curation, validation, investigation and manuscript revision; Yuanyuan Lyu and Jiangli Liu supervised the project, revised and edited the manuscript, and provided conceptual guidance. All the authors have read and approved the final manuscript. Acknowledgement The authors would like to express their sincere gratitude to Xiantao Academic (website: www.xiantao.love) for the valuable support provided in the figure preparation of this study. Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. References Smolarz B, Biernacka K, łukasiewicz H, Samulak D, Piekarska E, Romanowicz H, et al. 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Comparison of the clinical characteristics and prognosis between clear cell carcinomas and high-grade serous ovarian carcinomas. Ginekol Pol. 2023;94(10):792–8. 10.5603/GP.a2022.0123 . Ali AT, Al-Ani O, Al-Ani F. Epidemiology and risk factors for ovarian cancer. Prz Menopauzalny. 2023;22(2):93–104. 10.5114/pm.2023.128661 . Linh NT, Hang NT, Cuong BK, Linh DT, Phuong LN, Nguyen-Van D, et al. Establishment of cancer cell line originating from a patient with high-grade serous ovarian carcinoma. Future Sci OA. 2023;9(8):FSO875. 10.2144/fsoa-2023-0025 . Lee MW, Anderson ZS, Girma AM, Klar M, Roman LD, Carlson JW, et al. Diagnosis shift in site of origin of tubo-ovarian carcinoma. Obstet Gynecol. 2024;143(5):660–9. 10.1097/AOG.0000000000005562 . Wang T, Fu X, Zhang L, Liu S, Tao Z, Wang F. Prognostic factors and a predictive nomogram of cancer-specific survival of epithelial ovarian cancer patients with pelvic exenteration treatment. Int J Clin Pract. 2023;2023:9219067. 10.1155/2023/9219067 . Avci BS, Yesiloglu O, Yildirim A, Genc O, Simsek Y, Urfalioglu AB, et al. Association between a high modified nutrition risk in critically ill score and 30-day mortality in critically ill adults: a retrospective cohort study. Ir J Med Sci. 2025. 10.1007/s11845-025-04066-4 . Kishore TA, Kaddu DJ, Sodhi BS, Srinivasan SP, Unni NV. Robotic kidney transplant beyond the learning curve: 8-year single-center experience and matched comparison with open kidney transplant. Urology. 2024;183:100–5. 10.1016/j.urology.2023.10.031 . Soliman M, Khan A, Pollina J. Comparison of prone transpsoas and standard lateral lumbar interbody fusion surgery for degenerative lumbar spine disease: a retrospective radiographic propensity score-matched analysis. World Neurosurg. 2022;157:e11–21. 10.1016/j.wneu.2021.08.097 . Yanagawa Y, Jitsuiki K, Muramatsu KI, Kushida Y, Ikegami S, Nagasawa H, et al. Clinical investigation of burn patients transported by helicopter based on the japan trauma data bank. Air Med J. 2020;39(6):464–7. 10.1016/j.amj.2020.08.007 . Ottenbourgs T, Van Nieuwenhuysen E. Novel endocrine therapeutic opportunities for estrogen receptor-positive ovarian cancer-what can we learn from breast cancer? Cancers (Basel). 2024;16(10):1862. 10.3390/cancers16101862 . Ribeiro JR, Freiman RN. Estrogen signaling crosstalk: implications for endocrine resistance in ovarian cancer. J Steroid Biochem Mol Biol. 2014;143:160–73. 10.1016/j.jsbmb.2014.02.010 . Jelicic J, Larsen TS, Andjelic B, Juul-Jensen K, Bukumiric Z. Should we use nomograms for risk predictions in diffuse large b cell lymphoma patients? A systematic review. Crit Rev Oncol Hematol. 2024;196:104293. 10.1016/j.critrevonc.2024.104293 . Zhang J, Guo G, Li T, Guo C, Han Y, Zhou X. Development and validation of a prognostic nomogram for early hepatocellular carcinoma treated with microwave ablation. Front Oncol. 2025;15:1486149. 10.3389/fonc.2025.1486149 . Li J, Ma C, Yuan X, Li N, Xu Y, Guo J, et al. Competing risk nomogram for predicting prognosis of patients with spinal and pelvic chordoma: a seer-based retrospective study. Eur Spine J. 2023;32(4):1334–44. 10.1007/s00586-023-07590-y . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8816231","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594819117,"identity":"19164fd0-e111-4dbd-aed2-75afa4d6f3da","order_by":0,"name":"chengcheng zhu","email":"","orcid":"","institution":"The First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"chengcheng","middleName":"","lastName":"zhu","suffix":""},{"id":594819119,"identity":"3d6e55c7-d2c4-4c3a-80a7-cb9954fa0230","order_by":1,"name":"Hengliang Sun","email":"","orcid":"","institution":"Second People’s Hospital of Wuhu City","correspondingAuthor":false,"prefix":"","firstName":"Hengliang","middleName":"","lastName":"Sun","suffix":""},{"id":594819122,"identity":"0a62b691-2557-4852-a315-e0bf9ca1ca44","order_by":2,"name":"Yonghong Luo","email":"","orcid":"","institution":"The First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yonghong","middleName":"","lastName":"Luo","suffix":""},{"id":594819123,"identity":"176337fa-db26-47e0-8344-28c69340a905","order_by":3,"name":"Dandan Ge","email":"","orcid":"","institution":"The First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Ge","suffix":""},{"id":594819124,"identity":"00f6a513-eff6-44c6-aaf1-a8b1de04d498","order_by":4,"name":"Shun Yao","email":"","orcid":"","institution":"The First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Shun","middleName":"","lastName":"Yao","suffix":""},{"id":594819125,"identity":"9a979a1d-306b-4a58-9bf0-c5d509ab56b6","order_by":5,"name":"Yi Wang","email":"","orcid":"","institution":"Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Wang","suffix":""},{"id":594819126,"identity":"80af1417-7801-40df-9dce-4e1db04c5d3c","order_by":6,"name":"Meng Yan","email":"","orcid":"","institution":"Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Yan","suffix":""},{"id":594819127,"identity":"5a4b1f91-ba6c-4ca9-92de-e58ac15b00db","order_by":7,"name":"Yuanyuan Lyu","email":"","orcid":"","institution":"The First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Lyu","suffix":""},{"id":594819128,"identity":"c24a81bf-5fcb-43de-a4d8-99038d57d429","order_by":8,"name":"Jiangli Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3QvWrDMBDAcQmBsgi8Xil5BwWBupj0Vc4ENLWds9VQcBaTrAl9CT2Cimi7+AEcPNRdPHXI6C79Cp1KkZMtg/7jcb/hjpBY7ARLGHMO53C7+h3QfIicLYqsbauUbvJDiawqNXktDLXuUEJq1IDcM1VfP9qepGPrWNeGBF2jARSe6/rGbEtilHX8QoYIA3wCBC90c6UbQnxmneAQIhyyAlB6UPd78jlMhPBMIhopz/fEDRMYFbRFlyK8vOltKWdq831XkFz6ZPfw/gGYlJWu+/l0vHy+64LkTz+vYkfsx2KxWOz/vgDiN07MsGoBbgAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Wannan Medical College","correspondingAuthor":true,"prefix":"","firstName":"Jiangli","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-02-07 14:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8816231/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8816231/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103345815,"identity":"9c8abfee-875a-4f98-9646-fb9c534a382a","added_by":"auto","created_at":"2026-02-24 16:15:43","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114251,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the patient selection.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8816231/v1/8c586e6986a15ef6cdc892de.jpeg"},{"id":103505969,"identity":"b80aced0-ed39-4106-97c3-4ab36c3e0715","added_by":"auto","created_at":"2026-02-26 13:33:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":246673,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression status of ER and PR in immunohistochemistry. (A) The expression of ER in each group. (B) The expression of PR in each group.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8816231/v1/d956c88bca541b58e320b548.jpeg"},{"id":103345816,"identity":"2a4c7582-8590-469e-8d97-37818baa37f1","added_by":"auto","created_at":"2026-02-24 16:15:43","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129288,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier analysis of OS in HGSC patients with different hormone receptors. (A) ER-positive and ER-negative. (B) PR-positive and PR-negative. (C) ER(+)/PR(+), ER(+)/PR(−) or ER(−)/PR(+), and ER(−)/PR(−). (D) ER(+)/PR(+), ER(+)/PR(−), ER(−)/PR(+), and ER(−)/PR(−). The I–II stage (E) and III–IV stage (F) in the pathological staging subgroup.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8816231/v1/db036d922278d94eaa88a4c6.jpeg"},{"id":103345817,"identity":"dbfdd651-ae68-421e-b8c1-2aadae7d05aa","added_by":"auto","created_at":"2026-02-24 16:15:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":117516,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram of the prognostic model based on hormone receptors for individual survival prediction.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8816231/v1/6ba41102edc96a2fa91a0ade.png"},{"id":103507156,"identity":"96239bc2-3df7-443b-bfbd-6bd4e8d5d2ca","added_by":"auto","created_at":"2026-02-26 13:40:37","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":150526,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the performance and clinical benefits of prognostic models. (A) Heatmap of risk factors in prognostic models. (B) Survival differences between the high-risk group and low-risk group in prognostic scoring. (C) Calibration curve of the predictive model. (D) Time-dependent ROC curve of high-risk group and low-risk group. (E) Time-dependent AUC. (F) C-index of the nomogram from 1 to 5 years. DCA of the prognostic model at 1 (G), 3 (H), and 5 (I) years.\u003c/p\u003e\n\u003cp\u003eROC, Receiver Operating Characteristic; AUC, area under curve; DCA, Decision Curve Analysis.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8816231/v1/553e1104c4a03b5147e54ac8.jpeg"},{"id":103904391,"identity":"442750ef-dee3-4093-ad65-b9bed70cea13","added_by":"auto","created_at":"2026-03-04 10:28:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1863665,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8816231/v1/eada8898-095d-48a3-88e2-4df798a11505.pdf"},{"id":103345820,"identity":"4752d1c8-e737-4d90-bbf3-53f3b8c5d60d","added_by":"auto","created_at":"2026-02-24 16:15:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":318190,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8816231/v1/ad6a210cec2bfc6e26dd4223.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A clinicopathological nomogram integrating hormone receptor status to predict overall survival in high-grade serous ovarian carcinoma","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eHigh-grade serous ovarian cancer (HGSOC) is the most aggressive form of ovarian cancer. It accounts for approximately 70% of all ovarian cancer cases. This type of cancer is often diagnosed at an advanced stage, resulting in a poor prognosis with a five-year survival rate below 45%[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Notably, the development of HGSOC is complex and associated with multiple factors, including genetic variations, alterations in the tumor microenvironment, and hormone receptor status. Currently, the main treatments for HGSOC include surgical resection and chemotherapy. However, these methods often have limited effectiveness due to patient heterogeneity and the tumor's biological characteristics[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, it is imperative to develop more effective prognostic assessment tools and personalized treatment strategies to enhance patient survival rates and quality of life.\u003c/p\u003e \u003cp\u003eOvarian cancer is considered a hormone-driven malignant tumor, especially because the expression of estrogen (ER) and progesterone (PR) receptors is closely linked to patient prognosis. A systematic assessment showed that ER positivity is significantly higher in low-grade serous ovarian cancer (LGSOC) compared to HGSOC[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This suggested that differences in hormone receptor expression among ovarian cancer subtypes may influence clinical presentation and prognosis.Among the various subtypes, endometrioid carcinoma and low-grade serous ovarian cancer have been shown to be associated with ER and PR expression, and the presence of these receptors may influence patient prognosis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the relationship between HGSOC and ER/PR expression remains unclear, complicating the analysis of prognostic factors for this subtype.\u003c/p\u003e \u003cp\u003ePR expression is recognized as a favorable prognostic biomarker in HGSOC[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although ER positivity in HGSOC can reach 86%-92%, its prognostic value varies widely, and not all such tumors respond to anti-estrogen therapy[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Notably, patients with recurrent HGSOC and peritoneal metastasis who exhibit better clinical outcomes showed an ER positivity rate as high as 86%, suggesting ER may influence disease progression, though the mechanisms remain unclear[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In hormone-related tumors, breast cancer studies showed that patients with the ER+/PR+ phenotype had the greatest survival benefits[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], providing a theoretical basis for studying combined biomarkers in HGSOC. Several exploratory studies on hormones have indicated that AR (androgen receptor), ER, and PR, both individually and in combination, have predictive and prognostic value[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nonetheless, no systematic evaluation has been conducted on the link between ER/PR combined expression subtypes and clinical features.\u003c/p\u003e \u003cp\u003eThis study addresses the clinical significance of ER and PR expression profiles in HGSC patients based on identified research gaps. It also explores their relationships with prognosis. We include combined ER and PR expression status in a multifactor analysis to assess its association with clinical, pathological factors, and survival outcomes. Predictive tools such as nomograms will be introduced to integrate molecular profiles with clinical variables. These tools aim to improve risk assessment and clinical usability, providing both a theoretical basis and practical support for individualized prognosis assessment and management of HGSC patients.\u003c/p\u003e"},{"header":"2.Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population and Data Collection\u003c/h2\u003e \u003cp\u003eThis study included patients with epithelial ovarian cancer treated at the Obstetrics and Gynecology Department of the First Affiliated Hospital of Wannan Medical College from August 2012 to July 2020. All patients underwent surgery and received systemic chemotherapy. Exclusion criteria included low-grade serous, mucinous, endometrioid, and clear cell ovarian cancers, as well as insufficient follow-up data. A total of 176 cases of HGSC were selected, as shown in the flowchart in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Clinical and pathological data were extracted from EMRs and pathology reports, including age, FIGO stage, tumor size, ascites, vascular cancer thrombus, and the status of ER, PR, P53, and Ki67.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Immunohistochemistry (IHC) Testing\u003c/h2\u003e \u003cp\u003eStandardized IHC protocols stained formalin-fixed paraffin-embedded tissue sections. ER, PR, P53, and Ki67 status was classified as positive or negative. Positive staining for ER and PR was defined as \u0026ge;\u0026thinsp;1% of tumor cell nuclei being immunoreactive, according to the Ovarian Tumor Tissue Analysis (OTTA) consortium's research protocol[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To be consistent, nuclear staining for all P53 and Ki67 antibodies exceeding 10% was considered positive[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. All sections were independently evaluated by two pathologists who were blinded to the clinical outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003eOverall survival (OS) is calculated as the time from diagnosis until death or last follow-up. Survival differences between ER/PR subgroups were assessed using Kaplan-Meier curves and log-rank tests. Independent prognostic factors were identified by univariate and multivariate Cox proportional hazards regression models. Variables with a univariate P-value less than 0.05 were included in the multivariate model. Propensity score matching (PSM) was performed in a 1:1 ratio, based on covariates using logistic regression. The balance between matched groups was assessed by standardized mean differences below 0.1. A predictive nomogram integrating ER/PR status and clinicopathological variables was constructed using the rms package in R software.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.Results","content":"\u003cp\u003e \u003cb\u003eClinical and pathological features\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study included 176 patients with HGSC. They were divided into four groups based on estrogen receptor (ER) and progesterone receptor (PR) expression status: ER(+)/PR(+) with 61 cases (34.7%), ER(+)/PR(\u0026minus;) with 71 cases (40.3%), ER(\u0026minus;)/PR(+) with 13 cases (7.4%), and ER(\u0026minus;)/PR(\u0026minus;) with 31 cases (17.6%). No significant differences were observed between the groups regarding clinical features including age, ascites, tumor size, and pathological staging. Similarly, there were no significant differences in pathological features such as P53 distribution. However, the groups showed statistically significant differences in vascular cancer emboli and Ki67 (P\u0026thinsp;=\u0026thinsp;0.028 and P\u0026thinsp;=\u0026thinsp;0.024) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Immunohistochemical analysis showed positive ER expression in 132 patients (75.0%) and positive PR expression in 74 patients (42.0%). ER was predominantly located in the nucleus, exhibiting a brownish granular distribution with moderate to high intensity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA); PR was also located in the nucleus but showed weaker or patchy distribution in some cases, indicating greater expression heterogeneity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological characteristics of the study cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER(+)/PR(+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eER(+)/PR(-)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eER(-)/PR(+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eER(-)/PR(-)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic.stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (31.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etumor size(cm), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecancer emboli, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP53, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi67, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePrognosis and survival analysis of hormone receptor expression\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter 5 years of follow-up, we analyzed the impact of ER and PR expression status on patient prognosis. Kaplan-Meier survival curves were used for this analysis. The median survival time was 55 months for ER-positive patients, significantly longer than the 47 months for ER-negative patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). For PR status, the median survival time was 58 months in positive patients, significantly longer than 48 months in negative patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The results show that positive expression of both ER and PR is significantly associated with better prognosis.\u003c/p\u003e \u003cp\u003eTo investigate how combined receptor expression affects prognosis in HGSC, patients were divided into three groups based on ER and PR status: ER(+)/PR(+), ER(+)/PR(\u0026minus;) or ER(\u0026minus;)/PR(+), and ER(\u0026minus;)/PR(\u0026minus;). The ER(+)/PR(+) group had the longest median survival time of 58 months, while the ER(\u0026minus;)/PR(\u0026minus;) group had the shortest at 38 months, with a statistically significant difference between these groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). These results indicate that patients with both ER and PR positive expression have the best prognosis, while those with double negative expression have the worst. Further analysis of the intermediate group showed no statistically significant difference in prognosis between ER(+)/PR(\u0026minus;) and ER(\u0026minus;)/PR(+), although the latter had a longer median survival time (55 months vs. 50 months)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eSubgroup analysis by pathological stage showed that survival differences among the three groups were statistically significant in early-stage patients (stage I\u0026ndash;II) (P\u0026thinsp;=\u0026thinsp;0.036)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). In advanced-stage patients (stage III\u0026ndash;IV), the median survival time for the ER(+)/PR(+) group was 54 months, significantly better than the 36 months for the ER(\u0026minus;)/PR(\u0026minus;) group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), which indicated that hormone receptors were more clinically valuable for predicting outcomes in advanced tumors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIndependent prognostic analysis and PMS for risk models\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo identify independent prognostic factors in patients with high-grade serous ovarian cancer, this study first performed univariate Cox regression on clinical pathological variables. The univariate analysis showed that advanced pathological stage (stage III\u0026ndash;IV, HR\u0026thinsp;=\u0026thinsp;6.001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tumor size\u0026thinsp;\u0026gt;\u0026thinsp;5 cm (HR\u0026thinsp;=\u0026thinsp;2.011, P\u0026thinsp;=\u0026thinsp;0.004), positive cancer emboli (HR\u0026thinsp;=\u0026thinsp;2.193, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), positive P53 (HR\u0026thinsp;=\u0026thinsp;4.298, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), positive Ki67 (HR\u0026thinsp;=\u0026thinsp;3.111, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and ER/PR status (compared to the ER+/PR+ group, mixed group HR\u0026thinsp;=\u0026thinsp;1.571, P\u0026thinsp;=\u0026thinsp;0.043; double-negative status HR\u0026thinsp;=\u0026thinsp;3.532, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant adverse prognostic factors.\u003c/p\u003e \u003cp\u003eNext, we included the statistically significant variables (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from the univariate analysis in a multivariate Cox proportional hazards mode (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All variables met the proportional hazards assumption (Supplementary Fig.\u0026nbsp;1). The results of the multivariate analysis indicated that, after adjusting for other confounding factors, advanced pathological stage (HR\u0026thinsp;=\u0026thinsp;9.146, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tumor size\u0026thinsp;\u0026gt;\u0026thinsp;5 cm (HR\u0026thinsp;=\u0026thinsp;3.307, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cancer emboli positivity (HR\u0026thinsp;=\u0026thinsp;1.919, P\u0026thinsp;=\u0026thinsp;0.002), positive P53 (HR\u0026thinsp;=\u0026thinsp;7.353, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), positive Ki67 (HR\u0026thinsp;=\u0026thinsp;3.217, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and ER/PR expression status remained independent prognostic factors affecting overall survival. Of these, double-negative ER/PR expression was one of the strongest independent risk factors, with a mortality risk 11.610 times higher than that of patients with double-positive ER/PR. The mortality risk for patients with mixed ER/PR expression was also significantly higher than that for the double-positive group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate Cox regression analysis of clinicopathological characteristics and OS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.027 (0.703\u0026ndash;1.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic.stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I or Stage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III or Stage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.001 (3.209\u0026ndash;11.221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.146 (4.756\u0026ndash;17.585)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etumor size(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.011 (1.252\u0026ndash;3.231)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.307 (1.922\u0026ndash;5.690)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecancer emboli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.193 (1.511\u0026ndash;3.183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.919 (1.282\u0026ndash;2.873)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.133 (0.784\u0026ndash;1.637)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.298 (2.663\u0026ndash;6.938)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.353 (4.374\u0026ndash;12.363)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.111 (1.776\u0026ndash;5.449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.217 (1.790\u0026ndash;5.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER(+)/PR(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER(+)/PR(-) or ER(-)/PR(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.571 (1.015\u0026ndash;2.430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.674 (2.252\u0026ndash;5.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER(-)/PR(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.532 (2.106\u0026ndash;5.924)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.610 (6.225\u0026ndash;21.653)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHR, hazard ratio; CI, confidence interval.\u003c/p\u003e \u003cp\u003eTo control for the impact of confounding factors in baseline data on prognostic analysis and reduce selection bias, this study employed propensity score matching (PSM). A 1:1 match was performed between the ER/PR double-positive group and the double-negative group (Supplementary Table\u0026nbsp;1). This approach aimed to make the comparison more valid and verify the reliability of the results. The matching variables included pathological stage, tumor size, cancer emboli, P53, and Ki67 status. Before matching, there were significant differences between the double-positive group (n\u0026thinsp;=\u0026thinsp;61) and the double-negative group (n\u0026thinsp;=\u0026thinsp;31), notably in cancer emboli (P\u0026thinsp;=\u0026thinsp;0.004) and Ki67 expression (P\u0026thinsp;=\u0026thinsp;0.036). After PSM matching, each group included 31 patients. All clinical pathological characteristics\u0026mdash;such as pathological stage, tumor size, vascular cancer emboli, P53, and Ki67\u0026mdash;were well balanced. No statistical differences were found between the groups, indicating successful matching and comparability of baseline characteristics. Subsequently, we conducted multivariate Cox regression and prognostic analyses on patients after PSM (Supplementary Table\u0026nbsp;2). The results showed that ER/PR double-negative expression remained the strongest independent risk factor associated with overall survival (HR\u0026thinsp;=\u0026thinsp;11.122, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the survival rate was significantly lower than that of the double-positive group (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDevelopment of a Predictive Nomogram Incorporating Clinical Risk Factors\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe constructed a nomogram to predict the 1-, 3-, and 5-year survival probabilities of HGSC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The nomogram includes clinicopathological factors related to hormone receptors (ER/PR) identified through multifactor analysis. The prognostic model generated risk scores that classified patients into low-risk and high-risk groups. Kaplan-Meier analysis revealed a significant difference in survival time between high-risk and low-risk patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Additionally, a risk factor heatmap showed that high-risk patients were more likely to be at advanced stages (III/IV), have larger tumors, positive vascular cancer emboli, positive P53 and Ki67, and negative ER/PR status (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). These factors correspond to the higher scores in the nomogram. Calibration curves demonstrated good agreement between the nomogram\u0026rsquo;s predicted survival probabilities and the actual observed outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eWe used time-dependent ROC curves to evaluate the prognostic model\u0026rsquo;s ability to distinguish between 1-year and 3-year survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The prognostic model maintained high AUC and C-index values throughout the 1- to 5-year period, reflecting its robust performance in predicting HGSC prognosis over 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-F). Decision Curve Analysis (DCA) showed that the model provided better net benefits for clinical decision-making at various thresholds compared to individual predictive factors. In patients with advanced HGSC and poor prognosis, the model demonstrated consistent net benefits across a broad range of decision thresholds, regardless of clinical decision preferences (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG-I).\u003c/p\u003e "},{"header":"4.Discussion","content":"\u003cp\u003eHGSC has a low overall survival rate because it is often diagnosed at an advanced stage and exhibits strong resistance to conventional chemotherapy[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Unfortunately, endocrine therapy is generally ineffective for gynecological tumors other than breast cancer. This ineffectiveness stems from a lack of prognostic studies on combined hormone receptor markers in the histological subtypes of gynecological tumors. As a result, precise targeted therapies are scarce. Surprisingly, breast tumors and high-grade serous ovarian tumors share significant molecular similarities, including BRCA gene mutations, which indicate common causes and potential treatment targets[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies show a negative correlation between ER/PR-positive breast cancer and the risk of ovarian cancer. This means that ER/PR-positive patients have a lower likelihood of developing ovarian cancer[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, analysis of ER and PR expression patterns and their associations with patients' clinical characteristics and survival outcomes, along with the construction of a survival prediction model integrating molecular markers and clinical parameters, could help develop personalized endocrine therapies[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eER/PR function as nuclear receptors that regulate the transcription of target genes at the molecular level. This regulation modulates cell proliferation, differentiation, and apoptosis. Ki67 is a cell proliferation marker that typically indicates a more active cell cycle[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The ER/PR signaling pathway, when activated, regulates downstream genes related to proliferation. This regulation influences the growth rate of tumor cells. Transcriptomic studies of breast cancer have shown that ER/PR-positive status is often associated with higher differentiation and lower proliferation. Conversely, absence of ER/PR increases tumor invasiveness by strongly suppressing antigen presentation via MHC-II[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Notably, heterogeneity in PR expression is linked to multiple mechanisms. These include gene methylation, splice variants, and microenvironmental signals. The variation in expression among tumor subtypes may reflect their complex adaptations to hormone dependence and resistance[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, the formation of vascular cancer thrombi indicates that tumors have an enhanced ability to invade blood vessels. ER/PR signaling may promote local tumor infiltration and metastasis by regulating molecules such as matrix metalloproteinases (MMPs)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. ER/PR expression is closely linked to the mechanisms underlying proliferation, differentiation, and vascular invasion, which highlights the molecular basis of tumor heterogeneity. Interestingly, no significant difference exists in the age at diagnosis between early- and late-stage patients, suggesting that biological behavior rather than screening delays is the primary cause of late diagnosis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study shows that patients positive for ER or PR have a longer median survival time. Among them, those positive for both receptors have the best prognosis, while patients negative for both have the worst. Mechanistically, ER/PR-mediated pathways not only directly regulate the tumor cell cycle, apoptosis, and DNA repair but also modulate key pathways such as PI3K/AKT and MAPK to influence sensitivity to chemotherapy and endocrine therapy[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The absence of hormone receptors often causes insensitivity to the apoptosis signals, resulting in continuous tumor proliferation and drug resistance. ER/PR-negative tumors exhibit greater genomic instability and aggressiveness, an effect that is especially pronounced in tumors with P53 mutations[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Consistent with previous large multicenter studies, ER/PR positivity generally predicts better survival, although the protective effect varies across histological subtypes[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We analyzed patients with HGSC in detail and found that ER/PR expression status more accurately predicts prognosis in advanced-stage tumors.\u003c/p\u003e \u003cp\u003eMultivariate Cox regression analysis identified several independent prognostic factors. These include advanced stage, tumor volume, tumor thrombus, P53, Ki67, and ER/PR status. Among these, ER/PR double-negative status was the strongest risk factor. Clinical staging reflects tumor burden and metastatic potential. Tumor volume is linked to local stress and hypoxia, factors that contribute to tumor progression and chemotherapy resistance[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. P53 mutations lead to impaired apoptosis and genomic instability, while high Ki67 expression indicates active cell cycle activity. Both factors are closely related to tumor progression and chemotherapy resistance[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The absence of ER/PR may interact with the high-risk factors mentioned above, jointly enhancing tumor aggressiveness. This interaction leads to increased proliferation, higher metastatic potential, and primary resistance to endocrine therapy. Advanced stage, tumor size, and molecular markers are consistently included in risk scoring across different cohorts and international prediction models. However, the weighting of these factors varies across ethnic groups and geographic regions[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In retrospective cohort studies, PSM is widely used to address confounding factors such as clinical characteristics and treatment methods. When the sample size is sufficient, PSM can reduce bias more effectively than other methods such as inverse probability weighting and stratified analysis[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, PSM has drawbacks, including sample size loss and sensitivity to variable selection. Some literature suggests combining multiple statistical methods in multi-center, large-sample studies to improve robustness[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, we applied PSM solely to eliminate confounding from baseline characteristic imbalance, while the prognostic model was developed using the original sample to verify the adverse effect of ER/PR double negativity.\u003c/p\u003e \u003cp\u003eEndocrine therapy has gradually been introduced for ovarian cancer in recent years. However, its effectiveness is significantly reduced due to complex resistance mechanisms[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Research shows that there are significant differences in drug sensitivity among individuals. Prognostic models that combine hormone receptors with clinical and pathological features help identify patients who may benefit from endocrine therapy and those at high risk of primary or acquired resistance. In developing prognostic assessment tools, we integrated ER/PR and several clinical and pathological parameters to establish a visual nomogram model, which shows great predictive accuracy and is clinically useful. Multi-factor risk integration, based on the concept that a single biomarker cannot fully capture tumor diversity, improves the performance of predictive models by combining multiple parameters[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Compared to previous nomogram models for ovarian cancer and other solid tumors, multi-factor models incorporating molecular and clinical factors generally outperform traditional staging systems in metrics such as AUC (area under the curve), C-index, and DCA (decision curve analysis) clinical net benefit[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This model is innovative not only in integrating biomarkers for outcome prediction, but also in providing a framework for personalized prognostic assessment.\u003c/p\u003e \u003cp\u003eThis study controlled for confounding factors to a significant extent through rigorous propensity score matching and multivariate regression. However, it was a single-center retrospective study. As a result, selection and information biases during patient screening and follow-up might still exist, potentially affecting the generalizability of the results. Additionally, the small number of PR-positive cases has reduced statistical power in some subgroups. Moreover, the subjective nature of immunohistochemical scoring might affect the reliability of conclusions regarding PR expression variability. Therefore, some findings require further validation in larger, multicenter, prospective studies.\u003c/p\u003e"},{"header":"5.Conclusion","content":"\u003cp\u003eIn this cohort of HGSC patients, the combined expression status of ER and PR served as a robust and independent prognostic indicator. The integration of this biomarker into a clinically applicable nomogram provides a valuable tool for individualized risk assessment. This model shows great potential to improve patient stratification and guide future clinical trials of endocrine-targeted therapies, especially for patients with distinct hormone receptor profiles.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eandrogen receptor\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\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision Curve Analysis\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\"\u003eHGSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-grade serous ovarian cancer IHC Immunohistochemistry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGSOC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-grade serous ovarian cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ematrix metalloproteinases\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\"\u003eOTTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOvarian Tumor Tissue Analysis\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\"\u003ePSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePropensity score matching\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 \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The study was approved by the ethical committee of Wannan Medical College (approval number: 2024207), in which informed consent was waived for retrospective observational study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eChengcheng Zhu and Hengliang Sun developed the project, designed the methodology, curated and analyzed data, and drafted the original manuscript; Yonghong Luo, Dandan Ge, Shun Yao, Yi Wang and Meng Yan contributed to data curation, validation, investigation and manuscript revision; Yuanyuan Lyu and Jiangli Liu supervised the project, revised and edited the manuscript, and provided conceptual guidance. All the authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to express their sincere gratitude to Xiantao Academic (website: www.xiantao.love) for the valuable support provided in the figure preparation of this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSmolarz B, Biernacka K, łukasiewicz H, Samulak D, Piekarska E, Romanowicz H, et al. Ovarian cancer-epidemiology, classification, pathogenesis, treatment, and estrogen receptors' molecular backgrounds. 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Eur Spine J. 2023;32(4):1334\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00586-023-07590-y\u003c/span\u003e\u003cspan address=\"10.1007/s00586-023-07590-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"High-grade serous ovarian carcinoma, Hormone receptors, Estrogen receptor, Progesterone receptor, Prognosis, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-8816231/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8816231/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHigh-grade serous ovarian cancer (HGSC) causes high mortality rates worldwide due to its insidious onset and poor prognosis. The statuses of hormone receptors (ER/PR) have been shown to have significant prognostic value in hormone-related tumors such as breast cancer, but their roles in HGSC remain unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study retrospectively analyzed the relationship between estrogen receptor (ER) and progesterone receptor (PR) expression status, clinical features, and survival outcomes in 176 patients with HGSC. Kaplan-Meier survival analysis and univariate and multivariate Cox regression models were used to evaluate how various factors relate to overall survival (OS). Propensity score matching (PSM) was employed to control for confounding factors, ultimately constructing a nomogram model incorporating ER/PR status and other independent prognostic factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe expression status of ER/PR was significantly associated with patient survival. The ER(+)/PR(+) group showed the best prognosis, while the double-negative group had the worst. Moreover, the combined ER/PR expression remained an independent prognostic factor even in multivariate analysis (HR\u0026thinsp;=\u0026thinsp;11.610, 95% CI: 6.225\u0026ndash;21.653). Additionally, the nomogram based on ER/PR and other clinicopathological characteristics accurately predicted the 1-year, 3-year, and 5-year survival rates of patients, demonstrating good discriminative performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe independent prognostic value of hormone receptors in HGSC serves as a structured risk assessment tool for personalized management. This tool could help guide clinicians in selecting patients who may benefit and facilitate the precise application of endocrine-targeted therapy in ovarian cancer.\u003c/p\u003e","manuscriptTitle":"A clinicopathological nomogram integrating hormone receptor status to predict overall survival in high-grade serous ovarian carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 16:15:38","doi":"10.21203/rs.3.rs-8816231/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3263db7e-cc1d-4323-9011-426d2a25f11b","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T10:27:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 16:15:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8816231","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8816231","identity":"rs-8816231","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}