Prognostic role of age, hormonal status, and tumour subtype on estrogen receptor expression in breast cancer: a retrospective cohort study

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Estrogen receptor alpha (ERα) and beta (ERβ) show distinct expression patterns that may influence survival, but their independent contribution is debated. Methods A retrospective cohort of 116 women with invasive breast cancer treated in Surakarta was analysed. Immunohistochemistry determined ERα/ERβ expression (Allred score) and molecular subtype (Luminal A, Luminal B, HER2-enriched, triple-negative). Associations with age, menopausal status, and 5-year overall survival were tested using non-parametric statistics, multivariate logistic regression, and Kaplan–Meier analysis (SPSS v29). Results Younger patients (≤ 40 years) showed higher ERβ expression than older patients (median 54.7% vs 37.4%, p = 0.095), but age was not an independent survival predictor (p = 0.235). Premenopausal women demonstrated a near-significant survival advantage (OR = 2.78, p = 0.055). Triple-negative breast cancers (TNBC) exhibited the lowest ERα (12.8%) and the highest ERβ (70.3%) compared with other subtypes (all p < 0.01). Multivariate and stepwise regression confirmed tumour subtype as the sole independent determinant of 5-year survival (p = 0.019), with TNBC conferring 42% poorer outcomes (OR = 0.58). Conclusions Molecular subtype is the dominant prognostic factor in breast cancer and should guide therapeutic decision-making. The distinctive ERβ enrichment in TNBC highlights a potential biomarker and therapeutic target, warranting future studies of ERβ isoforms and subtype-specific interventions. Trial registration Not applicable (retrospective observational study). Breast cancer estrogen receptor alpha estrogen receptor beta hormonal status molecular subtype survival outcomes Figures Figure 1 BACKGROUND Breast cancer remains a leading cause of cancer-related mortality worldwide, with survival outcomes strongly influenced by tumor biology and patient-specific factors. 1 Among these variables, age, hormonal status, and molecular subtype have emerged as critical determinants of prognosis, particularly for 5-year survival rates. 2 Nevertheless, their combined influence is not fully understood, warranting systematic evaluation. Age is an established prognostic factor: younger patients (< 50 years) often present with more aggressive disease than older counterparts (≥ 50 years). 3 This difference may reflect age-related hormone-receptor changes. Estrogen receptor alpha (ERα) expression decreases with advancing age, whereas estrogen receptor beta (ERβ) shows variable patterns that may affect therapeutic response. 4,5 Triple-negative breast cancer (TNBC), more common in younger women, exhibits distinct ERβ expression profiles, although its prognostic relevance remains debated. 4 , 5 Hormonal status further modifies disease behavior. Postmenopausal women frequently present with ERα-low/ERβ-high tumors, which may respond differently to endocrine therapy. 6 Meta-analyses indicate that ERβ expression correlates with improved survival in ERα-negative cases, yet isoform-specific roles—such as ERβ1 acting as a tumor suppressor and ERβ2 promoting tamoxifen resistance—complicate clinical interpretation. 7 – 9 Molecular classification into Luminal A, Luminal B, HER2-enriched, and TNBC subtypes refines prognostic stratification. 10 Luminal A tumors, typically ERα-positive and low-grade, achieve 5-year survival rates near 90%, whereas TNBC shows the poorest outcomes, around 60%. 11,12 Emerging evidence suggests that age and hormonal status interact with subtype: younger women with TNBC experience worse survival than older TNBC patients, possibly reflecting unique ERβ dynamics. 13 , 14 This study investigates how age, hormonal status, and molecular subtype collectively influence 5-year survival in breast cancer, with emphasis on ERα/ERβ expression patterns. By clarifying these interactions and addressing controversies surrounding ERβ’s dual roles, our findings aim to inform personalized therapeutic strategies and guide future research. METHODS Study design and setting This retrospective cohort study investigated the association between estrogen receptor expression (ERα and ERβ), tumor subtypes, hormonal status, and 5-year survival outcomes in breast cancer patients treated at Dr. Moewardi General Hospital, Surakarta, Indonesia. Data were obtained from anonymized medical records of consecutive cases diagnosed. Participants and eligibility criteria Inclusion criteria: Complete immunohistochemistry (IHC) results for ERα, ERβ, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67. Documented 5-year survival status. Exclusion criteria: Male breast cancer cases. Missing or incomplete IHC or survival data. Non-primary or metastatic tumors at diagnosis. The final cohort (N = 116) reflected typical breast cancer demographics, with age ranging from 29–85 years (median = 53). Data collection and variables Clinical variables in this study comprised patient age in years, hormonal status categorized as premenopausal or menopausal, and five-year survival status identified as alive or deceased. Estrogen receptor alpha (ERα) and estrogen receptor beta (ERβ) expression were assessed using the Allred scoring system, which provides an ordinal scale from 0 to 8. Tumor subtypes were classified as Luminal A, Luminal B, HER2-enriched, or triple-negative breast cancer (TNBC) based on immunohistochemical profiles of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and the proliferation marker Ki-67. Archival paraffin-embedded tissue blocks were processed using standardized IHC protocols at the hospital pathology laboratory. Statistical analysis All statistical analyses were conducted using IBM SPSS Statistics version 29. The distribution of ERα and ERβ expression was evaluated with Shapiro–Wilk and Kolmogorov–Smirnov tests, which confirmed non-normality and warranted the use of non-parametric methods. Bivariate analysis employed Mann–Whitney U tests to compare age and hormonal status, while Kruskal–Wallis tests followed by Dunn’s post-hoc comparisons were used to examine differences among tumor subtypes. For multivariate analysis, logistic regression with both full and stepwise models was applied to identify independent predictors of five-year survival. Time-to-event outcomes were assessed through Kaplan–Meier survival curves and Cox proportional hazards models, with careful verification of proportional-hazards assumptions. All findings were expressed as p-values, effect sizes such as Cohen’s d, and 95% confidence intervals, with statistical significance set at an alpha level of 0.05. Ethics approval The study protocol was approved by the Health Research Ethics Committee of Dr. Moewardi General Hospital (approval no. 1901/VIII/HREC/2025). All patient data were anonymized prior to analysis to maintain confidentiality. Trial registration was not applicable for this retrospective design. RESULTS Age-Based Differences in Erα and ERβ Expression and Survival Outcomes Comparison of patients aged ≤ 40 years with those > 40 years showed no significant difference in ERα expression (see Table 1 ). ERβ expression was higher in the younger group. Multivariate logistic regression confirmed that age was not an independent predictor of 5-year survival (see Table 4 ). Table 1 ERα and ERβ expression by age group and association with 5-year survival in breast cancer patients ER Expression Age ≤ 40 (n = 15) Age 41+ (n = 101) p-value ERα (%) 27.67 ± 32.23 34.01 ± 34.50 0.504 ERβ (%) 54.67 (IQR 36.81) 37.38 (IQR 37.19) 0.095 Hormonal Status and Erα and ERβ Expression with 5-Year Survival Comparison of premenopausal and postmenopausal patients showed no significant difference in ERα expression (35.3 ± 33.8% vs 31.8 ± 34.6%; p = 0.587). ERβ expression was slightly higher in the premenopausal group (median 43.0%) than in the postmenopausal group (median 37.4%), but this difference was not significant (see Table 2 ). Logistic regression revealed a near-significant association between hormonal status and 5-year survival ( p = 0.055), with premenopausal patients demonstrating 2.78-fold higher odds of survival (OR = 2.78; 95% CI 0.98–7.85; see Table 4 ). However, hormonal status did not remain an independent predictor in the multivariate model. Table 2 ERα and ERβ expression by hormonal status and association with 5-year survival in breast cancer patients ER Expression Premenopausal (n = 46) Menopause (n = 70) p-value ERα (%) 35.3 ± 33.8 31.8 ± 34.6 0.587 ERβ (%) 43.0 (IQR 39.8) 37.4 (IQR 35.9) 0.426 Tumor Subtypes (Luminal A, Luminal B, HER2-Enriched, Triple-Negative) Analysis of estrogen receptor expression by tumor subtype demonstrated significant variation for both ERα and ERβ. ERα expression differed markedly across groups ( p < 0.001), with triple-negative tumors showing the lowest mean level (12.82 ± 17.80%) compared with Luminal A (47.18 ± 33.71%), Luminal B (38.24 ± 41.08%), and HER2-enriched (40.95 ± 36.32%). Post-hoc confirmed significantly lower ERα in triple-negative tumors versus each of the other subtypes ( p < 0.05; see Table 3 ). In contrast, ERβ expression displayed an opposite pattern. Triple-negative tumors exhibited the highest median ERβ level (70.26 ± 21.06%), significantly exceeding Luminal A (24.36 ± 35.08%), Luminal B (21.47 ± 35.78%), and HER2-enriched (25.71 ± 32.95%) groups ( p < 0.001; see Table 3 ). Multivariate logistic regression identified tumor subtype as the only independent predictor of 5-year survival ( p = 0.019; OR = 0.58; 95% CI 0.36–0.91; see Table 4 ). Table 3 ERα and ERβ expression by tumor subtype and association with 5-year survival in breast cancer patients Subtype ERα ERβ p-value Post-Hoc Luminal A 47.18 ± 33.71 24.36 ± 35.08 < 0.001 TN vs Luminal A: p < 0.001 (ERα↓, ERβ↑) Luminal B 38.24 ± 41.08 21.47 ± 35.78 < 0.001 TN vs Luminal B: p = 0.030 (ERα↓), p < 0.001 (ERβ↑) HER2 40.95 ± 36.32 25.71 ± 32.95 < 0.001 TN vs HER2: p = 0.006 (ERα↓), p < 0.001 (ERβ↑) Triple Negative 12.82 ± 17.80 70.26 ± 21.06 0.05) Multivariate Logistic Regression of 5-Year Survival A multivariate logistic regression model was constructed to evaluate the combined effects of age, hormonal status, tumor subtype, and ERα/ERβ expression on five-year survival. In the full model, tumor subtype emerged as the only independent predictor of survival ( p = 0.019; OR = 0.578; 95% CI: −1.01 to − 0.09), indicating poorer outcomes for more aggressive subtypes such as triple-negative breast cancer. Hormonal status (premenopausal vs. menopause) showed a near-significant association with improved survival ( p = 0.055; OR = 2.775; 95% CI: −0.02 to 2.06). Age and ERα/ERβ expression were not significant predictors. Stepwise selection confirmed tumor subtype as the sole retained variable ( p = 0.009; OR = 0.650; 95% CI: −0.75 to − 0.11), reinforcing the primacy of molecular classification in forecasting patient outcomes. Table 4 Multivariate logistic regression analysis of tumor subtype, hormonal factors, and ER expression on 5-year survival in breast cancer patients Variable Coefficient p-value OR 95% CI Full Model Age (≤ 40 vs. >40) −0.856 0.235 0.425 −2.25 to 0.54 Hormonal status (premenopausal) 1.021 0.055 2.775 −0.02 to 2.06 Tumor subtype −0.548 0.019 0.578 −1.01 to − 0.09 ERα (%) −0.004 0.554 0.996 −0.02 to 0.01 ERβ (%) 0.005 0.501 1.005 −0.01 to 0.02 Stepwise Model (Final) Tumor subtype −0.431 0.009 0.650 −0.75 to − 0.11 Post-hoc Comparisons of Tumor Subtypes Post-hoc pairwise testing demonstrated marked contrasts in ERα and ERβ expression across molecular subtypes, with triple-negative breast cancer (TNBC) showing the most divergent profile. TNBC displayed significantly lower ERα levels and markedly higher ERβ expression compared with Luminal A, Luminal B, and HER2-enriched tumors (p < 0.05 for all relevant comparisons; see Table 5 ). This paradoxical ERβ elevation (mean 70.26 ± 21.06%) despite characteristic ERα negativity (mean 12.82 ± 17.80%) suggests a distinctive receptor pattern unique to TNBC. By contrast, Luminal A and Luminal B subtypes showed no significant differences in either ERα or ERβ expression, indicating that their prognostic differences are likely related to other factors such as proliferative index. HER2-enriched tumors retained moderate ERα expression and intermediate ERβ levels, with no significant differences relative to Luminal groups. Table 5 Post-hoc pairwise comparisons of ERα and ERβ expression across breast-cancer molecular subtypes Comparison ERα Difference (%)* p-value ERβ Difference (%)* p-value TNBC vs Luminal A −34.36 0.000 + 45.90 0.000 TNBC vs Luminal B −25.42 0.030 + 48.79 0.000 TNBC vs HER2 −28.13 0.006 + 44.54 0.000 Luminal A vs Luminal B Not significant > 0.05 Not significant > 0.05 Luminal A vs HER2 Not significant > 0.05 Not significant > 0.05 *Difference calculated as mean % (Group1 – Group2). Stepwise Regression Model Sequential stepwise logistic regression was performed to identify the most parsimonious model predicting 5-year survival. Starting with all covariates (age, hormonal status, tumor subtype, ERα, and ERβ), only tumor subtype remained significant throughout the elimination process. Hormonal status showed a borderline effect in the early steps but was excluded as non-significant. ERα, ERβ, and age were progressively removed for lack of independent association (see Table 6 ). Table 6 Stepwise elimination model for predictors of 5-year survival Step Variables Retained Statistical Outcome 1 Age, Hormonal Status, Subtype, ERα, ERβ Only subtype significant ( p = 0.019) 2 Age, Hormonal Status, Subtype, ERβ Subtype significant ( p = 0.018); ERα removed 3 Age, Hormonal Status, Subtype Subtype significant ( p = 0.011); ERβ removed 4 Hormonal Status, Subtype Subtype significant ( p = 0.007); Age removed 5 (Final) Subtype only p = 0.009; OR = 0.650 (95% CI: 0.43–0.88) The analysis confirmed molecular subtype as the sole independent predictor of 5-year survival (final model: p = 0.009; OR = 0.650, 95% CI 0.48–0.88), indicating that aggressive phenotypes such as triple-negative breast cancer carry the greatest mortality risk. Hormonal status showed a borderline association in the full model (p = 0.055; OR = 2.78) but was eliminated after adjustment for subtype. Age, ERα, and ERβ expression were not significant predictors at any step (see Fig. 1 ). DISCUSSION Breast cancer prognosis reflects a complex interaction between tumor biology and host factors, with growing evidence that molecular classification provides the most robust prognostic information compared with traditional clinicopathological parameters. 1 , 2 In this retrospective cohort of Indonesian women, we investigated how molecular subtype, hormone receptor expression, and clinical variables jointly influence 5-year survival. Our data show that molecular subtype is the dominant predictor, while patient age, menopausal status, and individual estrogen receptor (ER) profiles offer only limited additional prognostic value. Age-Related ER Expression Age at diagnosis remains a critical modifier of breast cancer biology and outcome. In our cohort, younger patients (≤ 40 years) showed higher median ERβ expression (54.67% vs. 37.38% in older women), a difference that approached but did not reach statistical significance, consistent with evidence that early age at onset is linked to more aggressive disease features. Large real-world data from the French ESME metastatic breast cancer cohort demonstrated that women under 40% more often with HER2-positive and triple-negative subtypes and greater visceral involvement yet achieved slightly longer median overall survival than those over 60, with multivariate analysis confirming age < 40 as an independent favorable prognostic factor (HR 0.75, 95% CI 0.69–0.82). 15 In contrast, a prospective 10-year study of non-metastatic cases in Lebanon reported significantly lower overall survival (77.6 %vs. 98.6 % and disease-free survival (70.4 %vs. 90 % for patients under 40, with young age remaining an independent predictor of recurrence and mortality after adjusting for subtype and other variables. 16 Together, these data highlight that while elevated ERβ in younger women reflects distinct tumor biology, its prognostic significance varies by disease stage, portending poorer outcomes in early disease but not necessarily in the metastatic setting. Despite the receptor difference, 5-year survival did not vary significantly by age ( p = 0.235), indicating that population-specific genetic backgrounds or the overriding influence of molecular subtype may account for any apparent discrepancy. ERα levels were stable across age groups (27.67% vs. 34.01%, p = 0.504), consistent with evidence that ERα expression is largely age independent. 17 Multivariate analysis confirmed that once subtype was included, neither ERα nor patient age provided incremental prognostic information. Hormonal Status and ER Expression Menopausal status is frequently associated with variability in estrogen receptor expression in breast cancer. Our finding that ERα levels did not differ between premenopausal and postmenopausal women, and that ERβ was only modestly higher in the premenopausal group without statistical significance, aligns with the report by Choi et al. (2022) In their study, elevated ERβ2 isoform expression in postmenopausal patients with ERα-negative breast cancer correlated with early relapse and reduced disease-free survival, indicating that isoform-specific regulation of Erβ may influence disease progression in the postmenopausal population. 18 This supports the view that even minor differences in overall ERβ expression by menopausal status can mask the prognostic impact of distinct ERβ isoforms in breast cancer. Interestingly, premenopausal patients showed a near-significant survival advantage (OR = 2.775; p = 0.055). This trend may reflect protective effects of endogenous estrogen, earlier detection, or subtle differences in tumor biology across menopausal status. While our sample size of 46 premenopausal and 70 postmenopausal cases provided adequate power to detect large effects, it may have been insufficient to uncover smaller, clinically relevant differences—particularly given the heterogeneity of ERβ isoform expression. Molecular Subtype as the Key Prognostic Factor he most important finding of this study is the primacy of molecular subtype as a prognostic determinant. Multivariate logistic regression identified subtype as the only significant independent predictor of 5-year survival ( p = 0.019; OR = 0.578). Patients with aggressive subtypes, notably TNBC, experienced the poorest outcomes, a result entirely consistent with large multicenter analyses demonstrating the prognostic superiority of intrinsic molecular classification over traditional clinicopathological features. Indeed, Peres et al. (2023) showed in a Brazilian cohort that molecular subtype remained a robust independent predictor of overall survival, outperforming factors such as grade and nodal status, and Hung et al. (2023) found that subtype retained its predictive strength for disease-free and overall survival even after adjusting for age, stage, and treatment. 19 , 20 Stepwise Regression Confirmation The stepwise logistic regression reinforced these findings. Beginning with all covariates (age, hormonal status, ERα, ERβ, and subtype), sequential elimination based on p > 0.10 criteria consistently identified molecular subtype as the sole independent predictor. The final model demonstrated an odds ratio of 0.650 ( p = 0.009), indicating that aggressive subtypes were associated with a 35% lower likelihood of 5-year survival even after adjusting for other variables. Hormonal status showed borderline significance in the early stages ( p = 0.055) but was removed in later steps, likely reflecting its collinearity with the higher prevalence of TNBC among younger, premenopausal women. 14 ERβ, despite its high expression in TNBC, was ultimately eliminated, a paradox that underscores the necessity of isoform-specific measurements and may point to context-dependent effects. Integration with Existing Literature Our results integrate smoothly with global evidence while offering new regional insights. Numerous large-scale studies, including gene-expression based classifications such as PAM50, have established molecular subtyping as the single most important prognostic factor across diverse populations. 21 , 22 The elevated ERβ expression in TNBC (mean 70.26 ± 21.06 %) arees with emerging evidence that ERβ may participate in compensatory or resistance pathways in ERα-negative disease. Yet, the absence of an independent survival association in our cohort suggests that ERβ’s effect is context-specific and likely isoform-dependent, aligning with studies distinguishing ERβ1 from ERβ2 roles. Clinical Implications These data have direct clinical ramifications and underscore the central role of molecular subtyping in breast cancer management and treatment planning. Universal molecular subtyping should become a standard practice, as once the subtype is established, supplementary details such as patient age or aggregate estrogen receptor (ER) expression contribute little additional prognostic value. In clinical trials, participant stratification should prioritize molecular subtype rather than isolated receptor status to ensure that therapeutic comparisons are both meaningful and precise. The hormonal milieu remains an area for continued investigation, since menopausal status, while not independently prognostic, demonstrated borderline significance and may reveal subtle biological effects when examined in studies with sufficient statistical power. Study Limitations Several limitations of this study merit consideration. The retrospective, single-center design introduces potential selection bias and restricts the generalizability of the findings. Molecular subtyping was based on immunohistochemistry, which, while routine in clinical practice, is less precise than gene-expression profiling. The sample size, with a total cohort of 116 participants and only 15 patients aged 40 years or younger, provides adequate power to detect large effects but limits the ability to identify smaller associations, particularly in subgroup analyses of ERβ expression. Detailed information on treatment adherence, precise menopausal staging, and ERβ isoform expression was unavailable, and these factors could influence receptor profiles and survival outcomes. Addressing these gaps in future research will be essential to validate and extend the current findings. Future Directions Future investigations should prioritize isoform-specific estrogen receptor beta (ERβ) assays to delineate the distinct prognostic roles of ERβ1 and ERβ2. Building on this, large prospective multicenter cohorts employing RNA sequencing and thorough covariate adjustment are needed to clarify how age, hormonal status, and receptor expression interact to influence outcomes. Incorporating advanced diagnostics such as AI-assisted histopathology and digital image analysis will improve the precision of molecular classification, an especially valuable approach in resource-limited settings. Finally, therapeutic development targeting ERβ modulation warrants exploration, particularly for patients with ERα-negative or triple-negative breast cancer, where conventional hormone therapies provide limited clinical benefit. CONCLUSIONS In conclusion, our study confirms that molecular subtype is the most reliable and clinically relevant predictor of five-year survival in breast cancer, surpassing age, menopausal status, and ERα/ERβ expression. Tumor subtype retained significance in every multivariate and stepwise model (p = 0.009; OR = 0.650). Although younger age and premenopausal status were associated with higher ERβ expression, these factors did not translate into independent survival differences. The paradox of high ERβ levels in TNBC without prognostic impact underscores the need for isoform-specific analyses. These findings support the universal adoption of molecular subtyping in treatment planning and caution against overinterpretation of isolated receptor expression, particularly in triple-negative disease where ERβ’s role remains unresolved. Future large, prospective, and isoform-focused studies are essential to confirm these observations and to guide the development of personalized therapeutic approaches, ultimately improving outcomes across diverse breast cancer populations. Abbreviations AI Artificial Intelligence CI Confidence Interval ER Estrogen Receptor ERα Estrogen Receptor alpha ERβ Estrogen Receptor beta HER2 Human Epidermal Growth Factor Receptor 2 IHC Immunohistochemistry OR Odds Ratio PR Progesterone Receptor SPSS Statistical Package for the Social Sciences TNBC Triple–Negative Breast Cancer Declarations Ethics approval and consent to participate The study protocol was approved by the Health Research Ethics Committee of Dr. Moewardi General Hospital, Surakarta, Indonesia (approval no. 1901/VIII/HREC/2025). All patient data were fully anonymized prior to analysis; therefore, the requirement for informed consent was waived by the committee. Consent for publication Not applicable. This study does not contain any individual person’s data in any form (including images or videos). Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to privacy restrictions but are available from the corresponding author, Widyanti Soewoto ( [email protected] ), on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Faculty of Medicine Research Group, Sebelas Maret University, through the Sebelas Maret University research grant (No. 228/UN27.22/PT.01.03/2023). Authors’ contributions WS (Widyanti Soewoto): Conceptualization, methodology, formal analysis, and writing—original draft. JP (Joko Purnomo): Histopathological evaluation and supervision. DAS (Dea Alberta Setiawati): Data curation, statistical analysis, and critical review. All authors read and approved the final manuscript. 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Genomic Characteristics Related to Histology-Based Immune Features in Breast Cancer. Mod Pathol. 2025;38(5):100736. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor invited by journal 11 Nov, 2025 Editor assigned by journal 03 Oct, 2025 Submission checks completed at journal 03 Oct, 2025 First submitted to journal 30 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Soewoto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie2SMUsDMRTH/3KSLqlZ36Fev0Lg4Bah/SonhU5XF0EcSnGKk/WzuByOkUBdzg/gJnRVCBQKQi0mpwjC3bk65DfkJTx+/N+DAIHAfySC9IW7m3Y1ET8dhrxLkU5hOTTS+OpPBV+KD5K/FbQo8jEq6f1+ewRRrdd2Jgk0NRazIQ56LYphF/FN5QajaUl66ZWzCWE5BuMvjUpmeEZ95ZV+Cc22c1CRuUU0GDWneCX+8IqoVlbvfEqRWuy6lcM6BQXoQdWKpD3VrowMOz85VqkbbJLR00IS468ZnS7GvG2X+NbcPb+pZARhVvZyI0n03GB2M0wG180pjn2q4/T3k/kjr/9DK5HtaAYCgUAA+ARjtk+NYFHNhQAAAABJRU5ErkJggg==","orcid":"","institution":"Sebelas Maret University","correspondingAuthor":true,"prefix":"","firstName":"Widyanti","middleName":"","lastName":"Soewoto","suffix":""},{"id":556968357,"identity":"5202249d-0bf3-43ce-98fc-9133f4b47700","order_by":1,"name":"Joko Purnomo","email":"","orcid":"","institution":"Sebelas Maret University","correspondingAuthor":false,"prefix":"","firstName":"Joko","middleName":"","lastName":"Purnomo","suffix":""},{"id":556968358,"identity":"96c0d068-8f00-4b41-af4b-68fc9ce9e723","order_by":2,"name":"Dea Alberta Setiawati","email":"","orcid":"","institution":"Dr. Arif Zainudin Hospital Surakarta, Central Java","correspondingAuthor":false,"prefix":"","firstName":"Dea","middleName":"Alberta","lastName":"Setiawati","suffix":""}],"badges":[],"createdAt":"2025-09-30 10:53:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7750700/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7750700/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97932948,"identity":"d40fff4b-9f98-40d8-af89-0bc29d611334","added_by":"auto","created_at":"2025-12-11 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00:47:51","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":96509,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7750700/v1/709d13c7056cdf6c7217c94b.html"},{"id":98421608,"identity":"a450d5fb-f848-4875-9618-48796d76b320","added_by":"auto","created_at":"2025-12-17 16:28:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":286573,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7750700/v1/1ebbfaebb281afcb724693c1.png"},{"id":98774561,"identity":"ea0def42-cd0a-4636-a64e-ce2de142c008","added_by":"auto","created_at":"2025-12-22 12:01:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1245218,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7750700/v1/a7b88647-a4d4-4e48-baeb-189ffaeac68c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic role of age, hormonal status, and tumour subtype on estrogen receptor expression in breast cancer: a retrospective cohort study","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eBreast cancer remains a leading cause of cancer-related mortality worldwide, with survival outcomes strongly influenced by tumor biology and patient-specific factors.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Among these variables, age, hormonal status, and molecular subtype have emerged as critical determinants of prognosis, particularly for 5-year survival rates.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Nevertheless, their combined influence is not fully understood, warranting systematic evaluation.\u003c/p\u003e\u003cp\u003eAge is an established prognostic factor: younger patients (\u0026lt;\u0026thinsp;50 years) often present with more aggressive disease than older counterparts (\u0026ge;\u0026thinsp;50 years).\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e This difference may reflect age-related hormone-receptor changes. Estrogen receptor alpha (ERα) expression decreases with advancing age, whereas estrogen receptor beta (ERβ) shows variable patterns that may affect therapeutic response. \u003csup\u003e4,5\u003c/sup\u003e Triple-negative breast cancer (TNBC), more common in younger women, exhibits distinct ERβ expression profiles, although its prognostic relevance remains debated.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eHormonal status further modifies disease behavior. Postmenopausal women frequently present with ERα-low/ERβ-high tumors, which may respond differently to endocrine therapy.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Meta-analyses indicate that ERβ expression correlates with improved survival in ERα-negative cases, yet isoform-specific roles\u0026mdash;such as ERβ1 acting as a tumor suppressor and ERβ2 promoting tamoxifen resistance\u0026mdash;complicate clinical interpretation.\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eMolecular classification into Luminal A, Luminal B, HER2-enriched, and TNBC subtypes refines prognostic stratification.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Luminal A tumors, typically ERα-positive and low-grade, achieve 5-year survival rates near 90%, whereas TNBC shows the poorest outcomes, around 60%.\u003csup\u003e11,12\u003c/sup\u003e Emerging evidence suggests that age and hormonal status interact with subtype: younger women with TNBC experience worse survival than older TNBC patients, possibly reflecting unique ERβ dynamics.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThis study investigates how age, hormonal status, and molecular subtype collectively influence 5-year survival in breast cancer, with emphasis on ERα/ERβ expression patterns. By clarifying these interactions and addressing controversies surrounding ERβ\u0026rsquo;s dual roles, our findings aim to inform personalized therapeutic strategies and guide future research.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and setting\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study investigated the association between estrogen receptor expression (ERα and ERβ), tumor subtypes, hormonal status, and 5-year survival outcomes in breast cancer patients treated at Dr. Moewardi General Hospital, Surakarta, Indonesia. Data were obtained from anonymized medical records of consecutive cases diagnosed.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParticipants and eligibility criteria\u003c/h3\u003e\n\u003cp\u003eInclusion criteria:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eComplete immunohistochemistry (IHC) results for ERα, ERβ, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDocumented 5-year survival status.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eExclusion criteria:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMale breast cancer cases.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMissing or incomplete IHC or survival data.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eNon-primary or metastatic tumors at diagnosis.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe final cohort (N\u0026thinsp;=\u0026thinsp;116) reflected typical breast cancer demographics, with age ranging from 29\u0026ndash;85 years (median\u0026thinsp;=\u0026thinsp;53).\u003c/p\u003e\n\u003ch3\u003eData collection and variables\u003c/h3\u003e\n\u003cp\u003eClinical variables in this study comprised patient age in years, hormonal status categorized as premenopausal or menopausal, and five-year survival status identified as alive or deceased. Estrogen receptor alpha (ERα) and estrogen receptor beta (ERβ) expression were assessed using the Allred scoring system, which provides an ordinal scale from 0 to 8. Tumor subtypes were classified as Luminal A, Luminal B, HER2-enriched, or triple-negative breast cancer (TNBC) based on immunohistochemical profiles of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and the proliferation marker Ki-67. Archival paraffin-embedded tissue blocks were processed using standardized IHC protocols at the hospital pathology laboratory.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics version 29. The distribution of ERα and ERβ expression was evaluated with Shapiro\u0026ndash;Wilk and Kolmogorov\u0026ndash;Smirnov tests, which confirmed non-normality and warranted the use of non-parametric methods. Bivariate analysis employed Mann\u0026ndash;Whitney U tests to compare age and hormonal status, while Kruskal\u0026ndash;Wallis tests followed by Dunn\u0026rsquo;s post-hoc comparisons were used to examine differences among tumor subtypes.\u003c/p\u003e\u003cp\u003eFor multivariate analysis, logistic regression with both full and stepwise models was applied to identify independent predictors of five-year survival. Time-to-event outcomes were assessed through Kaplan\u0026ndash;Meier survival curves and Cox proportional hazards models, with careful verification of proportional-hazards assumptions. All findings were expressed as p-values, effect sizes such as Cohen\u0026rsquo;s d, and 95% confidence intervals, with statistical significance set at an alpha level of 0.05.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthics approval\u003c/h3\u003e\n\u003cp\u003eThe study protocol was approved by the Health Research Ethics Committee of Dr. Moewardi General Hospital (approval no. 1901/VIII/HREC/2025). All patient data were anonymized prior to analysis to maintain confidentiality. Trial registration was not applicable for this retrospective design.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eAge-Based Differences in Erα and ERβ Expression and Survival Outcomes\u003c/h2\u003e\u003cp\u003eComparison of patients aged\u0026thinsp;\u0026le;\u0026thinsp;40 years with those\u0026thinsp;\u0026gt;\u0026thinsp;40 years showed no significant difference in ERα expression (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). ERβ expression was higher in the younger group. Multivariate logistic regression confirmed that age was not an independent predictor of 5-year survival (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eERα and ERβ expression by age group and association with 5-year survival in breast cancer patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER Expression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026le;\u0026thinsp;40\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAge 41+\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;101)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eERα (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e27.67\u0026thinsp;\u0026plusmn;\u0026thinsp;32.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.01\u0026thinsp;\u0026plusmn;\u0026thinsp;34.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eERβ (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e54.67 (IQR 36.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.38 (IQR 37.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHormonal Status and Erα and ERβ Expression with 5-Year Survival\u003c/h3\u003e\n\u003cp\u003eComparison of premenopausal and postmenopausal patients showed no significant difference in ERα expression (35.3\u0026thinsp;\u0026plusmn;\u0026thinsp;33.8% vs 31.8\u0026thinsp;\u0026plusmn;\u0026thinsp;34.6%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.587). ERβ expression was slightly higher in the premenopausal group (median 43.0%) than in the postmenopausal group (median 37.4%), but this difference was not significant (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Logistic regression revealed a near-significant association between hormonal status and 5-year survival (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055), with premenopausal patients demonstrating 2.78-fold higher odds of survival (OR\u0026thinsp;=\u0026thinsp;2.78; 95% CI 0.98\u0026ndash;7.85; see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, hormonal status did not remain an independent predictor in the multivariate model.\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\u003eERα and ERβ expression by hormonal status and association with 5-year survival in breast cancer patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER Expression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePremenopausal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMenopause\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eERα (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.3\u0026thinsp;\u0026plusmn;\u0026thinsp;33.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.8\u0026thinsp;\u0026plusmn;\u0026thinsp;34.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eERβ (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.0 (IQR 39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.4 (IQR 35.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eTumor Subtypes (Luminal A, Luminal B, HER2-Enriched, Triple-Negative)\u003c/h2\u003e\u003cp\u003eAnalysis of estrogen receptor expression by tumor subtype demonstrated significant variation for both ERα and ERβ. ERα expression differed markedly across groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with triple-negative tumors showing the lowest mean level (12.82\u0026thinsp;\u0026plusmn;\u0026thinsp;17.80%) compared with Luminal A (47.18\u0026thinsp;\u0026plusmn;\u0026thinsp;33.71%), Luminal B (38.24\u0026thinsp;\u0026plusmn;\u0026thinsp;41.08%), and HER2-enriched (40.95\u0026thinsp;\u0026plusmn;\u0026thinsp;36.32%). Post-hoc confirmed significantly lower ERα in triple-negative tumors versus each of the other subtypes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, ERβ expression displayed an opposite pattern. Triple-negative tumors exhibited the highest median ERβ level (70.26\u0026thinsp;\u0026plusmn;\u0026thinsp;21.06%), significantly exceeding Luminal A (24.36\u0026thinsp;\u0026plusmn;\u0026thinsp;35.08%), Luminal B (21.47\u0026thinsp;\u0026plusmn;\u0026thinsp;35.78%), and HER2-enriched (25.71\u0026thinsp;\u0026plusmn;\u0026thinsp;32.95%) groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Multivariate logistic regression identified tumor subtype as the only independent predictor of 5-year survival (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019; OR\u0026thinsp;=\u0026thinsp;0.58; 95% CI 0.36\u0026ndash;0.91; see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eERα and ERβ expression by tumor subtype and association with 5-year survival in breast cancer patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubtype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eERα\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eERβ\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=\"c5\"\u003e\u003cp\u003ePost-Hoc\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLuminal A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.18\u0026thinsp;\u0026plusmn;\u0026thinsp;33.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.36\u0026thinsp;\u0026plusmn;\u0026thinsp;35.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTN vs Luminal A: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (ERα\u0026darr;, ERβ\u0026uarr;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLuminal B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.24\u0026thinsp;\u0026plusmn;\u0026thinsp;41.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.47\u0026thinsp;\u0026plusmn;\u0026thinsp;35.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTN vs Luminal B: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030 (ERα\u0026darr;), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (ERβ\u0026uarr;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHER2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.95\u0026thinsp;\u0026plusmn;\u0026thinsp;36.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.71\u0026thinsp;\u0026plusmn;\u0026thinsp;32.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTN vs HER2: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006 (ERα\u0026darr;), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (ERβ\u0026uarr;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriple Negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.82\u0026thinsp;\u0026plusmn;\u0026thinsp;17.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.26\u0026thinsp;\u0026plusmn;\u0026thinsp;21.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo significant differences among Luminal A/B/HER2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMultivariate Logistic Regression of 5-Year Survival\u003c/h2\u003e\u003cp\u003eA multivariate logistic regression model was constructed to evaluate the combined effects of age, hormonal status, tumor subtype, and ERα/ERβ expression on five-year survival. In the full model, tumor subtype emerged as the only independent predictor of survival (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019; OR\u0026thinsp;=\u0026thinsp;0.578; 95% CI: \u0026minus;1.01 to \u0026minus;\u0026thinsp;0.09), indicating poorer outcomes for more aggressive subtypes such as triple-negative breast cancer. Hormonal status (premenopausal vs. menopause) showed a near-significant association with improved survival (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055; OR\u0026thinsp;=\u0026thinsp;2.775; 95% CI: \u0026minus;0.02 to 2.06). Age and ERα/ERβ expression were not significant predictors. Stepwise selection confirmed tumor subtype as the sole retained variable (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009; OR\u0026thinsp;=\u0026thinsp;0.650; 95% CI: \u0026minus;0.75 to \u0026minus;\u0026thinsp;0.11), reinforcing the primacy of molecular classification in forecasting patient outcomes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic regression analysis of tumor subtype, hormonal factors, and ER expression on 5-year survival in breast cancer patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFull Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (\u0026le;\u0026thinsp;40 vs. \u0026gt;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;2.25 to 0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHormonal status (premenopausal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.02 to 2.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor subtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;1.01 to \u0026minus;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eERα (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.02 to 0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eERβ (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.01 to 0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStepwise Model (Final)\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor subtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.75 to \u0026minus;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePost-hoc Comparisons of Tumor Subtypes\u003c/h2\u003e\u003cp\u003ePost-hoc pairwise testing demonstrated marked contrasts in ERα and ERβ expression across molecular subtypes, with triple-negative breast cancer (TNBC) showing the most divergent profile. TNBC displayed significantly lower ERα levels and markedly higher ERβ expression compared with Luminal A, Luminal B, and HER2-enriched tumors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all relevant comparisons; see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis paradoxical ERβ elevation (mean 70.26\u0026thinsp;\u0026plusmn;\u0026thinsp;21.06%) despite characteristic ERα negativity (mean 12.82\u0026thinsp;\u0026plusmn;\u0026thinsp;17.80%) suggests a distinctive receptor pattern unique to TNBC. By contrast, Luminal A and Luminal B subtypes showed no significant differences in either ERα or ERβ expression, indicating that their prognostic differences are likely related to other factors such as proliferative index. HER2-enriched tumors retained moderate ERα expression and intermediate ERβ levels, with no significant differences relative to Luminal groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePost-hoc pairwise comparisons of ERα and ERβ expression across breast-cancer molecular subtypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComparison\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eERα Difference (%)*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eERβ Difference (%)*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNBC vs Luminal A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;34.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;45.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNBC vs Luminal B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;25.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;48.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNBC vs HER2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;28.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;44.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLuminal A vs Luminal B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLuminal A vs HER2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.05\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\u003e*Difference calculated as mean % (Group1 \u0026ndash; Group2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStepwise Regression Model\u003c/h2\u003e\u003cp\u003eSequential stepwise logistic regression was performed to identify the most parsimonious model predicting 5-year survival. Starting with all covariates (age, hormonal status, tumor subtype, ERα, and ERβ), only tumor subtype remained significant throughout the elimination process. Hormonal status showed a borderline effect in the early steps but was excluded as non-significant. ERα, ERβ, and age were progressively removed for lack of independent association (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStepwise elimination model for predictors of 5-year survival\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables Retained\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStatistical Outcome\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge, Hormonal Status, Subtype, ERα, ERβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOnly subtype significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge, Hormonal Status, Subtype, ERβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSubtype significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018); ERα removed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge, Hormonal Status, Subtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSubtype significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011); ERβ removed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHormonal Status, Subtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSubtype significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007); Age removed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 (Final)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubtype only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009; OR\u0026thinsp;=\u0026thinsp;0.650 (95% CI: 0.43\u0026ndash;0.88)\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\u003eThe analysis confirmed molecular subtype as the sole independent predictor of 5-year survival (final model: p\u0026thinsp;=\u0026thinsp;0.009; OR\u0026thinsp;=\u0026thinsp;0.650, 95% CI 0.48\u0026ndash;0.88), indicating that aggressive phenotypes such as triple-negative breast cancer carry the greatest mortality risk. Hormonal status showed a borderline association in the full model (p\u0026thinsp;=\u0026thinsp;0.055; OR\u0026thinsp;=\u0026thinsp;2.78) but was eliminated after adjustment for subtype. Age, ERα, and ERβ expression were not significant predictors at any step (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eBreast cancer prognosis reflects a complex interaction between tumor biology and host factors, with growing evidence that molecular classification provides the most robust prognostic information compared with traditional clinicopathological parameters.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e In this retrospective cohort of Indonesian women, we investigated how molecular subtype, hormone receptor expression, and clinical variables jointly influence 5-year survival. Our data show that molecular subtype is the dominant predictor, while patient age, menopausal status, and individual estrogen receptor (ER) profiles offer only limited additional prognostic value.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eAge-Related ER Expression\u003c/h2\u003e\u003cp\u003eAge at diagnosis remains a critical modifier of breast cancer biology and outcome. In our cohort, younger patients (\u0026le;\u0026thinsp;40 years) showed higher median ERβ expression (54.67% vs. 37.38% in older women), a difference that approached but did not reach statistical significance, consistent with evidence that early age at onset is linked to more aggressive disease features. Large real-world data from the French ESME metastatic breast cancer cohort demonstrated that women under 40% more often with HER2-positive and triple-negative subtypes and greater visceral involvement yet achieved slightly longer median overall survival than those over 60, with multivariate analysis confirming age\u0026thinsp;\u0026lt;\u0026thinsp;40 as an independent favorable prognostic factor (HR 0.75, 95% CI 0.69\u0026ndash;0.82).\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e In contrast, a prospective 10-year study of non-metastatic cases in Lebanon reported significantly lower overall survival (77.6 %vs. 98.6 % and disease-free survival (70.4 %vs. 90 % for patients under 40, with young age remaining an independent predictor of recurrence and mortality after adjusting for subtype and other variables.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Together, these data highlight that while elevated ERβ in younger women reflects distinct tumor biology, its prognostic significance varies by disease stage, portending poorer outcomes in early disease but not necessarily in the metastatic setting.\u003c/p\u003e\u003cp\u003eDespite the receptor difference, 5-year survival did not vary significantly by age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.235), indicating that population-specific genetic backgrounds or the overriding influence of molecular subtype may account for any apparent discrepancy. ERα levels were stable across age groups (27.67% vs. 34.01%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.504), consistent with evidence that ERα expression is largely age independent.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Multivariate analysis confirmed that once subtype was included, neither ERα nor patient age provided incremental prognostic information.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eHormonal Status and ER Expression\u003c/h2\u003e\u003cp\u003eMenopausal status is frequently associated with variability in estrogen receptor expression in breast cancer. Our finding that ERα levels did not differ between premenopausal and postmenopausal women, and that ERβ was only modestly higher in the premenopausal group without statistical significance, aligns with the report by Choi et al. (2022) In their study, elevated ERβ2 isoform expression in postmenopausal patients with ERα-negative breast cancer correlated with early relapse and reduced disease-free survival, indicating that isoform-specific regulation of Erβ may influence disease progression in the postmenopausal population.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e This supports the view that even minor differences in overall ERβ expression by menopausal status can mask the prognostic impact of distinct ERβ isoforms in breast cancer.\u003c/p\u003e\u003cp\u003eInterestingly, premenopausal patients showed a near-significant survival advantage (OR\u0026thinsp;=\u0026thinsp;2.775; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055). This trend may reflect protective effects of endogenous estrogen, earlier detection, or subtle differences in tumor biology across menopausal status. While our sample size of 46 premenopausal and 70 postmenopausal cases provided adequate power to detect large effects, it may have been insufficient to uncover smaller, clinically relevant differences\u0026mdash;particularly given the heterogeneity of ERβ isoform expression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eMolecular Subtype as the Key Prognostic Factor\u003c/h2\u003e\u003cp\u003ehe most important finding of this study is the primacy of molecular subtype as a prognostic determinant. Multivariate logistic regression identified subtype as the only significant independent predictor of 5-year survival (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019; OR\u0026thinsp;=\u0026thinsp;0.578). Patients with aggressive subtypes, notably TNBC, experienced the poorest outcomes, a result entirely consistent with large multicenter analyses demonstrating the prognostic superiority of intrinsic molecular classification over traditional clinicopathological features. Indeed, Peres et al. (2023) showed in a Brazilian cohort that molecular subtype remained a robust independent predictor of overall survival, outperforming factors such as grade and nodal status, and Hung et al. (2023) found that subtype retained its predictive strength for disease-free and overall survival even after adjusting for age, stage, and treatment.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eStepwise Regression Confirmation\u003c/h2\u003e\u003cp\u003eThe stepwise logistic regression reinforced these findings. Beginning with all covariates (age, hormonal status, ERα, ERβ, and subtype), sequential elimination based on \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.10 criteria consistently identified molecular subtype as the sole independent predictor. The final model demonstrated an odds ratio of 0.650 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), indicating that aggressive subtypes were associated with a 35% lower likelihood of 5-year survival even after adjusting for other variables.\u003c/p\u003e\u003cp\u003eHormonal status showed borderline significance in the early stages (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055) but was removed in later steps, likely reflecting its collinearity with the higher prevalence of TNBC among younger, premenopausal women.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e ERβ, despite its high expression in TNBC, was ultimately eliminated, a paradox that underscores the necessity of isoform-specific measurements and may point to context-dependent effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eIntegration with Existing Literature\u003c/h2\u003e\u003cp\u003eOur results integrate smoothly with global evidence while offering new regional insights. Numerous large-scale studies, including gene-expression based classifications such as PAM50, have established molecular subtyping as the single most important prognostic factor across diverse populations.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e The elevated ERβ expression in TNBC (mean 70.26\u0026thinsp;\u0026plusmn;\u0026thinsp;21.06 %) arees with emerging evidence that ERβ may participate in compensatory or resistance pathways in ERα-negative disease. Yet, the absence of an independent survival association in our cohort suggests that ERβ\u0026rsquo;s effect is context-specific and likely isoform-dependent, aligning with studies distinguishing ERβ1 from ERβ2 roles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implications\u003c/h2\u003e\u003cp\u003eThese data have direct clinical ramifications and underscore the central role of molecular subtyping in breast cancer management and treatment planning. Universal molecular subtyping should become a standard practice, as once the subtype is established, supplementary details such as patient age or aggregate estrogen receptor (ER) expression contribute little additional prognostic value. In clinical trials, participant stratification should prioritize molecular subtype rather than isolated receptor status to ensure that therapeutic comparisons are both meaningful and precise. The hormonal milieu remains an area for continued investigation, since menopausal status, while not independently prognostic, demonstrated borderline significance and may reveal subtle biological effects when examined in studies with sufficient statistical power.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eStudy Limitations\u003c/h2\u003e\u003cp\u003eSeveral limitations of this study merit consideration. The retrospective, single-center design introduces potential selection bias and restricts the generalizability of the findings. Molecular subtyping was based on immunohistochemistry, which, while routine in clinical practice, is less precise than gene-expression profiling. The sample size, with a total cohort of 116 participants and only 15 patients aged 40 years or younger, provides adequate power to detect large effects but limits the ability to identify smaller associations, particularly in subgroup analyses of ERβ expression. Detailed information on treatment adherence, precise menopausal staging, and ERβ isoform expression was unavailable, and these factors could influence receptor profiles and survival outcomes. Addressing these gaps in future research will be essential to validate and extend the current findings.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eFuture Directions\u003c/h2\u003e\u003cp\u003eFuture investigations should prioritize isoform-specific estrogen receptor beta (ERβ) assays to delineate the distinct prognostic roles of ERβ1 and ERβ2. Building on this, large prospective multicenter cohorts employing RNA sequencing and thorough covariate adjustment are needed to clarify how age, hormonal status, and receptor expression interact to influence outcomes. Incorporating advanced diagnostics such as AI-assisted histopathology and digital image analysis will improve the precision of molecular classification, an especially valuable approach in resource-limited settings. Finally, therapeutic development targeting ERβ modulation warrants exploration, particularly for patients with ERα-negative or triple-negative breast cancer, where conventional hormone therapies provide limited clinical benefit.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, our study confirms that molecular subtype is the most reliable and clinically relevant predictor of five-year survival in breast cancer, surpassing age, menopausal status, and ERα/ERβ expression. Tumor subtype retained significance in every multivariate and stepwise model (p\u0026thinsp;=\u0026thinsp;0.009; OR\u0026thinsp;=\u0026thinsp;0.650). Although younger age and premenopausal status were associated with higher ERβ expression, these factors did not translate into independent survival differences. The paradox of high ERβ levels in TNBC without prognostic impact underscores the need for isoform-specific analyses.\u003c/p\u003e\u003cp\u003eThese findings support the universal adoption of molecular subtyping in treatment planning and caution against overinterpretation of isolated receptor expression, particularly in triple-negative disease where ERβ\u0026rsquo;s role remains unresolved. Future large, prospective, and isoform-focused studies are essential to confirm these observations and to guide the development of personalized therapeutic approaches, ultimately improving outcomes across diverse breast cancer populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArtificial Intelligence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\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\"\u003eERα\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEstrogen Receptor alpha\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eERβ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEstrogen Receptor beta\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHER2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman Epidermal Growth Factor Receptor 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImmunohistochemistry\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\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\"\u003eSPSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTriple\u0026ndash;Negative Breast Cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Health Research Ethics Committee of Dr. Moewardi General Hospital, Surakarta, Indonesia (approval no. 1901/VIII/HREC/2025). All patient data were fully anonymized prior to analysis; therefore, the requirement for informed consent was waived by the committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not contain any individual person\u0026rsquo;s data in any form (including images or videos).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to privacy restrictions but are available from the corresponding author, Widyanti Soewoto ([email protected]), on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Faculty of Medicine Research Group, Sebelas Maret University, through the Sebelas Maret University research grant (No. 228/UN27.22/PT.01.03/2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eWS (Widyanti Soewoto): Conceptualization, methodology, formal analysis, and writing\u0026mdash;original draft.\u003c/li\u003e\n \u003cli\u003eJP (Joko Purnomo): Histopathological evaluation and supervision.\u003c/li\u003e\n \u003cli\u003eDAS (Dea Alberta Setiawati): Data curation, statistical analysis, and critical review.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to the Faculty of Medicine, Sebelas Maret University, Surakarta, Central Java, Indonesia, for the continuous support and valuable contribution to this research\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational Agency for Research on Cancer. Breast cancer fact sheet [Internet]. World Health Organization. 2023 [cited 2025 Sep 27]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gco.iarc.fr/\u003c/span\u003e\u003cspan address=\"https://gco.iarc.fr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHowlader N, Cronin KA, Kurian AW, Andridge R. Differences in Breast Cancer Survival by Molecular Subtypes in the United States. Cancer Epidemiol Biomarkers Prev. 2018;27(6):619\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNishimura R, Osako T, Okumura Y, Nakano M, Otsuka H, Fujisue M, et al. Triple Negative Breast Cancer: An Analysis of the Subtypes and the Effects of Menopausal Status on Invasive Breast Cancer. J Clin Med. 2022;11(9):2331.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakano EA, Younes MM, Meehan K, Spalding L, Yan M, Allan P, et al. Estrogen receptor beta expression in triple negative breast cancers is not associated with recurrence or survival. BMC Cancer. 2023;23(1):459.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSellitto A, D\u0026rsquo;Agostino Y, Alexandrova E, Lamberti J, Pecoraro G, Memoli D, et al. Insights into the Role of Estrogen Receptor β in Triple-Negative Breast Cancer. Cancers (Basel). 2020;12(6):1477.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurillo-Ortiz B, P\u0026eacute;rez-Luque E, Malacara JM, Daza-Ben\u0026iacute;tez L, Hern\u0026aacute;ndez-Gonz\u0026aacute;lez M, Ben\u0026iacute;tez-Bribiesca L. Expression of Estrogen Receptor Alpha and Beta in Breast Cancers of Pre- and Post-menopausal Women. Pathol Oncol Res. 2008;14(4):435\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShalabi MG, Abbas AM, Mills J, Kheirelseid MA, Elderdery AY. The Prognostic Value of Estrogen Receptor β Isoform With Correlation of Estrogen Receptor α Among Sudanese Breast Cancer Patients. Breast Cancer (Auckl). 2021;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpeirs V, Viale G, Mousa K, Palmieri C, Reed SN, Nicholas H, et al. Prognostic and predictive value of ERβ1 and ERβ2 in the Intergroup Exemestane Study (IES)\u0026mdash;first results from PathIES. Ann Oncol. 2015;26(9):1890\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMal R, Magner A, David J, Datta J, Vallabhaneni M, Kassem M et al. Estrogen Receptor Beta (ERβ): A Ligand Activated Tumor Suppressor. Front Oncol. 2020;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParker JS, Mullins M, Cheang MCU, Leung S, Voduc D, Vickery T, et al. Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes. J Clin Oncol. 2009;27(8):1160\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAldaz-Rold\u0026aacute;n P, Pardo-V\u0026aacute;squez DF, Chamba-Morales GN, Aguirre-Reyes DF, Castillo-Calvas JM, Noblecilla-Ar\u0026eacute;valo G. Immunohistochemical subtype and its relationship with 5-year overall survival in breast cancer patients. Ecancermedicalscience. 2023;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCulha Y, Davarci SE, \u0026Uuml;nl\u0026uuml; B, \u0026Ouml;zaşkin D, Demir H, Baykara M. Comparison of clinicopathological and prognostic features of breast cancer patients younger than 40 years and older than 65 years. Discover Oncol. 2024;15(1):126.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCurtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486(7403):346\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKather JN, Pearson AT, Halama N, J\u0026auml;ger D, Krause J, Loosen SH, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25(7):1054\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrank S, Carton M, Dubot C, Campone M, Pistilli B, Dalenc F, et al. Impact of age at diagnosis of metastatic breast cancer on overall survival in the real-life ESME metastatic breast cancer cohort. Breast. 2020;52:50\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouferraa Y, Haibe Y, Chedid A, Jabra E, Charafeddine M, Temraz S et al. The impact of young age (\u0026lt;\u0026thinsp;40 years) on the outcome of a cohort of patients with primary non-metastatic breast cancer: analysis of 10-year survival of a prospective study. BMC Cancer. 2022 Dec 1;22(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantandrea G, Bellarosa C, Gibertoni D, Cucchi MC, Sanchez AM, Franceschini G, et al. Hormone Receptor Expression Variations in Normal Breast Tissue: Preliminary Results of a Prospective Observational Study. J Pers Med. 2021;11(5):387.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoi Y. Estrogen Receptor β Expression and Its Clinical Implication in Breast Cancers: Favorable or Unfavorable? J Breast Cancer. 2022;25(2):75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHung SK, Yang HJ, Lee MS, Liu DW, Chen LC, Chew CH, et al. Molecular subtypes of breast cancer predicting clinical benefits of radiotherapy after breast-conserving surgery: a propensity-score-matched cohort study. Breast Cancer Res. 2023;25(1):149.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeres SV, Arantes PE, Fagundes M, de Ab\u0026rsquo;Saber A, Gimenes AM, Curado DL. MP, et al. Molecular subtypes as a prognostic breast cancer factor in women users of the S\u0026atilde;o Paulo public health system, Brazil. Volume 26. Revista Brasileira de Epidemiologia; 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurova P, Kushnarev V, Baranov O, Butusova A, Menshikova S, Yong ST, et al. The Breast Cancer Classifier refines molecular breast cancer classification to delineate the HER2-low subtype. NPJ Breast Cancer. 2025;11(1):19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCha YJ, O\u0026rsquo;Connell CE, Calhoun BC, Felsheim BM, Fernandez-Martinez A, Fan C, et al. Genomic Characteristics Related to Histology-Based Immune Features in Breast Cancer. Mod Pathol. 2025;38(5):100736.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, estrogen receptor alpha, estrogen receptor beta, hormonal status, molecular subtype, survival outcomes","lastPublishedDoi":"10.21203/rs.3.rs-7750700/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7750700/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBreast cancer prognosis depends on patient and tumour factors, yet the prognostic interplay between age, hormonal status, and molecular subtype remains unclear. Estrogen receptor alpha (ERα) and beta (ERβ) show distinct expression patterns that may influence survival, but their independent contribution is debated.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective cohort of 116 women with invasive breast cancer treated in Surakarta was analysed. Immunohistochemistry determined ERα/ERβ expression (Allred score) and molecular subtype (Luminal A, Luminal B, HER2-enriched, triple-negative). Associations with age, menopausal status, and 5-year overall survival were tested using non-parametric statistics, multivariate logistic regression, and Kaplan\u0026ndash;Meier analysis (SPSS v29).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eYounger patients (\u0026le;\u0026thinsp;40 years) showed higher ERβ expression than older patients (median 54.7% vs 37.4%, p\u0026thinsp;=\u0026thinsp;0.095), but age was not an independent survival predictor (p\u0026thinsp;=\u0026thinsp;0.235). Premenopausal women demonstrated a near-significant survival advantage (OR\u0026thinsp;=\u0026thinsp;2.78, p\u0026thinsp;=\u0026thinsp;0.055). Triple-negative breast cancers (TNBC) exhibited the lowest ERα (12.8%) and the highest ERβ (70.3%) compared with other subtypes (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Multivariate and stepwise regression confirmed tumour subtype as the sole independent determinant of 5-year survival (p\u0026thinsp;=\u0026thinsp;0.019), with TNBC conferring 42% poorer outcomes (OR\u0026thinsp;=\u0026thinsp;0.58).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eMolecular subtype is the dominant prognostic factor in breast cancer and should guide therapeutic decision-making. The distinctive ERβ enrichment in TNBC highlights a potential biomarker and therapeutic target, warranting future studies of ERβ isoforms and subtype-specific interventions.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e\u003cp\u003eNot applicable (retrospective observational study).\u003c/p\u003e","manuscriptTitle":"Prognostic role of age, hormonal status, and tumour subtype on estrogen receptor expression in breast cancer: a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-11 00:47:47","doi":"10.21203/rs.3.rs-7750700/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-18T16:24:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122772519977545168174961118610551089926","date":"2025-12-18T12:37:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108303139985811678613753365346243408261","date":"2025-12-16T21:33:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68204078088193206562173911465251052644","date":"2025-12-08T12:14:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-08T12:07:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-11T06:30:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-03T09:13:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-03T09:13:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-09-30T10:41:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b1f8d0bb-8a9b-4a99-aa6d-b95fbca2e47f","owner":[],"postedDate":"December 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-11T00:47:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-11 00:47:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7750700","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7750700","identity":"rs-7750700","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}