Towards a Universal Ovarian Cancer Biomarker Model: Clinical Validation of Trx1 and CA125 Dual Strategy

Towards a Universal Ovarian Cancer Biomarker Model: Clinical Validation of Trx1 and CA125 Dual Strategy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Towards a Universal Ovarian Cancer Biomarker Model: Clinical Validation of Trx1 and CA125 Dual Strategy Mi A Park, Songhak Kim, Xiaoguang Yang, Jong Am Song, Geon Woo Lee, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7359728/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Ovarian cancer is the fifth leading cause of cancer-related mortality in women, with early detection being critical for improving patient outcomes. Current diagnostic biomarkers, including Cancer antigen 125 (CA125), human epididymis protein 4 (HE4), and their combined Risk of Ovarian Malignancy Algorithm (ROMA), exhibit limited sensitivity and specificity, particularly in early-stage disease and premenopausal women. Thioredoxin-1 (Trx1) has emerged as a potential complementary biomarker. This study evaluates the diagnostic utility of Trx1 in combination with CA125 and HE4. Methods Serum levels of CA125, HE4, and Trx1 were measured in 80 ovarian cancer patients and 200 controls. The influence of age, menopausal status, obstetric history, pathological characteristics, and cancer stage on biomarker performance was analyzed. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) curve analysis, with sensitivity, specificity, and area under the curve (AUC) values calculated for individual biomarkers and their combinations. Performance comparisons were conducted across premenopausal and postmenopausal subgroups. Results Among individual biomarkers, CA125 demonstrated the highest diagnostic accuracy (AUC = 0.872), followed by HE4 (AUC = 0.815) and Trx1 (AUC = 0.588). The combination of Trx1 and CA125, termed the Dual-marker Ovarian Cancer Risk Algorithm (DORA), improved diagnostic performance (AUC = 0.852), while the addition of HE4 further enhanced accuracy (AUC = 0.878). Trx1 exhibited stable expression across demographic subgroups. ROMA demonstrated low sensitivity in premenopausal women (42.86%) despite high specificity (92.86%), whereas postmenopausal women showed slightly improved sensitivity (66.10%) with perfect specificity (100.00%). DORA was non-inferior to ROMA and achieved superior sensitivity in both premenopausal (76.19%) and postmenopausal (91.53%) patients, maintaining consistent performance across cancer stages. Conclusion DORA represents a promising alternative to ROMA, offering enhanced sensitivity and reliable diagnostic performance across diverse patient populations. Trx1 has the potential to improve ovarian cancer detection, particularly in early-stage and premenopausal cases where current diagnostic approaches remain inadequate. Ovarian cancer early diagnosis liquid biopsy Thioredoxin 1 Cancer antigen 125 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Background Ovarian cancer is one of the most prevalent gynecological malignancies and ranks as the fifth leading cause of cancer-related mortality among women worldwide [ 1 ]. Unlike cervical cancer, whose incidence has declined due to vaccination programs, ovarian cancer remains a significant concern, with an increasing risk and a decreasing age of onset in some populations [ 1 ]. Due to the lack of early symptoms and effective screening methods, most ovarian cancer cases are diagnosed at an advanced stage, leading to poor prognoses. While early-stage ovarian cancer (Stages I and II) has a five-year survival rate of approximately 90%, this rate drops significantly to 20–40% in late-stage cases (Stages III and IV) [ 4 , 5 ]. The pathogenesis of ovarian cancer remains incompletely understood, though genetic and endocrine factors have been identified as key contributors [ 6 ]. Therefore, developing reliable early detection strategies is crucial for improving patient outcomes. Among the various diagnostic approaches, blood-based biomarkers are considered the most practical and minimally invasive method for ovarian cancer detection. Biomarkers can facilitate early diagnosis, monitor disease progression, and assess treatment response. CA125 was the first biomarker widely used for ovarian cancer detection and monitoring following the discovery of the OC125 antibody in 1981 [ 7 ]. While CA125 demonstrates substantial clinical utility, however, CA125 has notable limitations: while it is elevated in approximately 80% of ovarian cancer cases, its sensitivity for early-stage detection (Stages I and II) is limited to 50–60% [ 8 , 9 ]. Furthermore, CA125 levels can be elevated in benign gynecological conditions (5%) and non-gynecological malignancies (28%), Furthermore, CA125 levels can be elevated in benign gynecological conditions (5%) and non-gynecological malignancies (28%), reducing its specificity and leading to potential false-positive results [ 9 – 11 ]. To improve diagnostic accuracy, several scoring systems, such as the Risk of Malignancy Index (RMI), which integrates CA125 levels with transvaginal ultrasound findings, have been developed [ 12 ]. To enhance diagnostic accuracy, HE4 was introduced as an additional ovarian cancer biomarker [ 14 , 15 ]. First identified by Hellström et al. in 2003 [ 13 ], HE4 was approved by the FDA in 2009 for monitoring ovarian cancer progression [ 16 ]. Studies have demonstrated that combining HE4 with CA125 improves diagnostic performance compared to either biomarker alone [ 17 ]. The Risk of Ovarian Malignancy Algorithm (ROMA), which integrates HE4 and CA125 levels with menopausal status, was introduced to assess malignancy risk in women with pelvic masses [ 16 ]. FDA-approved in 2011, ROMA demonstrates high effectiveness in postmenopausal women but is less reliable in premenopausal populations [ 18 ]. Despite its clinical utility, HE4 levels can be affected by age, pregnancy, and non-gynecological conditions such as renal failure and lung cancer, increasing the false-positive rate in ROMA assessments [ 19 – 21 ]. Furthermore, recent studies suggest that neither HE4 alone nor ROMA provides a significant improvement in early-stage ovarian cancer detection compared to CA125 alone [ 22 ]. This underscores the urgent need for additional biomarkers that enhance sensitivity and specificity, particularly in differentiating malignant from benign pelvic masses. Recently, Thioredoxin-1 (Trx1) has emerged as a promising biomarker for cancer detection, particularly in female malignancies. Trx1 is a redox-regulating protein involved in oxidative stress response and cellular signaling [ 23 ]. Recent studies have indicated that serum Trx1 levels are elevated in ovarian cancer patients who are CA125-negative, suggesting its potential as a complementary biomarker [ 24 , 25 ]. Although Trx1 levels are also elevated in other malignancies, including breast [ 26 , 27 ], gastric [ 28 ], and colorectal cancers [ 29 ], its strong association with ovarian cancer highlights its diagnostic potential. However, to be clinically effective, an ideal biomarker should demonstrate high sensitivity and specificity, regardless of patient demographics such as age or menopausal status. Notably, Trx1 expression remains stable regardless of age or menopausal status, presenting a significant advantage over HE4 in the ROMA algorithm [ 26 ]. Given these findings, investigating the diagnostic potential of Trx1 in ovarian cancer is essential, both as a standalone biomarker and in combination with existing markers such as CA125 and HE4. This study aims to evaluate the clinical utility of Trx1 for ovarian cancer detection, determine whether Trx1 can overcome the limitations of current liquid biopsy methods, and assess whether a Trx1-CA125 combination could provide comparable or superior diagnostic efficacy to the ROMA algorithm. 2. Methods 2.1. Participant Selection and Serum Collection Informed consent for the use of serum samples in research was obtained from all participants. Clinical samples were collected from patients visiting Chungnam National University (CNU) Hospital (Daejeon, South Korea), in accordance with the guidelines of the Independent Ethics Committee of the CNU College of Medicine (IRB approval no. 2022-04-048; approval date: May 3, 2022). Preoperative serum samples were collected from 80 ovarian cancer patients and 200 patients with benign tumors or other conditions. The serum samples were provided by the biobank of CNU Hospital, a member of the Korean Biobank Network, and were retrospectively registered in the present study. Clinical data were retrospectively obtained from the Electronic Medical Records (EMR) system at CNU Hospital. To qualify for inclusion, participants had to meet the following criteria: (1) a confirmed pathological diagnosis of ovarian cancer; (2) no prior chemotherapy or radiotherapy before blood collection; (3) an assessment for hemolysis prior to serum collection to ensure sample quality; and (4) exclusion of patients with other existing malignant tumors. Blood samples were collected into Serum Separator Tubes (SST), allowed to clot, and then centrifuged at 1500 × g for 15 minutes. The resulting serum supernatant was stored at −80°C in the CNU Hospital biobank. For this analysis, serum samples were completely thawed prior to testing. 2.2. Quantification of Biomarker Levels in Serum Three biomarkers were analyzed in this study: CA125, HE4, and Trx1. Among these, Trx1 was the only biomarker detected using a non-commercial kit, requiring a more specific detection process. CA125 and HE4 levels were measured using commercially available kits, with CA125 tested manually and HE4 using a fully automated detector. The detection methods for CA125 and HE4 are well-established and can be verified through the product specifications. To detect Trx1, a pair of monoclonal antibodies was generated. Using these antibodies, a Trx1 ELISA kit (E&S Healthcare, South Korea) was developed to semi-quantify Trx1 levels in serum. The kit is based on sandwich enzyme-linked immunosorbent assay (ELISA) technology, and the test procedure adhered to the manufacturer’s protocol. Briefly, serum samples or purified Trx1 protein (used as a calibrator, positive control, and negative control) were pre-mixed with detection antibodies (anti-Trx1 monoclonal antibody conjugated with peroxidase) and adjusted to 100 μL using PBSA. In parallel, 100 μL of the prepared samples was added to pre-coated antibody wells on a 96-well plate. The reaction was allowed to proceed for one hour, resulting in the formation of antigen-antibody complexes. After three washes with a washing solution, TMB (3,3′,5,5′-tetramethylbenzidine) was used as the substrate. The enzymatic reaction was halted with the addition of 2N sulfuric acid, causing a color change from blue to yellow. The intensity of the color change was measured at 450 nm using a microplate reader. A standard curve was constructed based on absorbance values of various known concentrations of recombinant human Trx1 protein, which was used to determine the Trx1 concentration in the serum samples. CA125 levels in serum were measured using a sandwich ELISA with the Accubind kit (Monobind Inc., California, USA), following the manufacturer's protocol. Quality control parameters, as outlined in the manufacturer's protocol, ensured test validity. Each sample was tested in duplicate, and the mean absorbance value was used for analysis. HE4 levels in serum were measured using the Elecsys HE4 assay (Roche, Germany) on a Cobas 8000 e602 analyzer (Roche, Germany), in accordance with the manufacturer's instructions. 2.3. Algorithm and Calculation of Biomarker Combinations Four combinations of the three biomarkers were investigated in this study. The specific biomarker combinations and the associated calculation formulas used to assess diagnostic performance are detailed in Table 3S. Two combinations warrant particular mention: the ROMA index (CA125 + HE4) and the CA125 + Trx1 combination (Dual-marker Ovarian Cancer Risk Algorithm, DORA) index. The ROMA index was calculated using the following formulas, as referenced from its commercial diagnostic kit: Premenopausal ROMA index formula: 12 + (2.38 × ln(HE4)) + (0.062 × ln(CA125)) Postmenopausal ROMA index formula: 8.09 + (1.04 × ln(HE4)) + (0.732 × ln(CA125)) In this study, multivariate logistic regression analysis was applied to evaluate the association between CA125 and Trx1 concentrations and the likelihood of ovarian cancer. This method was selected to estimate the individual contributions of these biomarkers to the binary outcome. The dependent variable was the ovarian cancer diagnosis, where 0 represented cancer and 1 represented non-cancer (benign or normal). The independent variables included log-transformed serum levels of CA125 and Trx1. Data were randomly split into training and testing sets at a 7:3 ratios. Using the coefficients, variables, and model constant from the training dataset, a predicted probability (PP) was calculated for the test data. PP values ranged from 0% to 100% for each model. A test result was considered negative if the PP was below a selected threshold and positive if the PP met or exceeded the threshold. Sensitivity and specificity were calculated for the corresponding PP values. 2.4. ROC Analysis Receiver Operating Characteristic (ROC) and precision-recall curve analyses were conducted using MedCalc software (v20.014; MedCalc Software Ltd., Ostend, Belgium) to assess the diagnostic accuracy of each biomarker signature. Univariate ROC analysis was performed for each biomarker combination to determine the ROC curve, area under the curve (AUC), standard error (SE) of the AUC, and 95% confidence interval (CI). The biomarker combination with the highest AUC and the lowest SE was selected as the "outstanding" combination for further evaluation [30]. 2.5. Data Analysis Immunoassay experiments were independently performed in duplicate. Data are presented as the mean ± standard deviation (SD). All statistical analyses were performed using either an unpaired Student’s t-test or one-way analysis of variance (ANOVA) with multiple comparisons, as appropriate, using Prism 10 (v10.2.0; GraphPad Software, San Diego, CA, USA). A p-value of p < 0.05, p < 0.01, or *p < 0.001 was considered statistically significant, while "ns" denotes non-significant results. To determine the optimal clinical performance of each immunoassay, AUC values were compared using a bivariate binomial model. 3. Results 3.1. Distribution of Demographic Clinical Variables in Participants This study analyzed the clinical characteristics of 80 ovarian cancer patients and 200 non-cancer control subjects, as summarized in Table S1 . The collected demographic data included age, menopausal status, and obstetric history, while clinical information covered histopathological classification and FIGO staging. In terms of demographic data, the mean age of ovarian cancer patients was 58.7 ± 14.3 years, compared to 46.9 ± 15.7 years for the control group, indicating a statistically significant difference between the two groups. Among ovarian cancer patients, 8.75% were aged 30 or younger, and the remaining cases were distributed relatively evenly across age groups from their 40s to 70s. In contrast, younger individuals (≤ 30 years) constituted the largest subgroup within the control population (34.50%), followed by those in their 40s (25.50%), 50s (15.50%), 60s (13.00%), and 70s (11.50%). Menopausal status analysis revealed that the majority of ovarian cancer patients (73.75%) were postmenopausal, while the control group exhibited a higher proportion of premenopausal individuals (63.00%). Obstetric history assessment indicated that a larger proportion of ovarian cancer patients (86.25%) had a history of childbirth compared to the control group (69.50%), suggesting a potential correlation between reproductive factors and ovarian cancer risk. Histopathological classification identified serous carcinoma as the predominant subtype, accounting for 52.50% of ovarian cancer cases, followed by non-serous types in 41.25% of cases. The classification for 6.25% of patients remained undetermined. The observed distribution aligns with the latest WHO classification, where high-grade serous carcinoma constitutes the most prevalent subtype (70%), followed by endometrioid (10%), clear cell (6–10%), low-grade serous (5%), and mucinous carcinoma (3–4%) [ 2 , 3 ]. This suggests that the study cohort reflects a representative distribution of ovarian cancer subtypes. Among control subjects, histopathological findings revealed that 40.00% had cervical carcinoma in situ or neoplasia, 45.50% presented with benign uterine or cervical conditions, and 14.50% were diagnosed with benign ovarian disorders. FIGO staging analysis indicated that 43.75% of ovarian cancer patients were diagnosed at early stages (Stages I and II), whereas 42.50% had advanced-stage disease (Stages III and IV). Staging information was unavailable for 11 patients. 3.2. Comparative Clinical Performance of Individual Biomarkers To assess the diagnostic utility of CA125, HE4, and Trx1 for ovarian cancer, their serum levels were analyzed in 280 participants. The ovarian cancer group (n = 80) exhibited significantly elevated mean levels of CA125 (217.00 U/mL), HE4 (386.00 pmol/L), and Trx1 (39.20 U/mL) compared to the control group (n = 200), where the respective values were 6.43 U/mL, 58.00 pmol/L, and 28.80 U/mL ( Table S2 ). All biomarkers demonstrated statistically significant differences between cases and controls (P < 0.001 for CA125 and HE4, P < 0.05 for Trx1) (Fig. 1 A). ROC curve analysis showed AUC values of 0.872 (CA125), 0.815 (HE4), and 0.588 (Trx1) (Fig. 1 B), confirming that CA125 had the highest diagnostic performance, followed by HE4 and Trx1 (Fig. 1 C). Based on optimal cut-off values, CA125 had a sensitivity of 53.75% and specificity of 96.00%, while HE4 showed 73.75% sensitivity and 71.00% specificity. Trx1 demonstrated 47.50% sensitivity and 71.00% specificity ( Table S3 ). These findings underscore the need for biomarker combinations to enhance diagnostic accuracy for ovarian cancer. Biomarker levels were further analyzed based on age, menopausal status, and childbirth history. HE4 showed a significant positive correlation with age in both ovarian cancer (r = 0.367, P < 0.001) and control groups (r = 0.396, P < 0.001), while CA125 exhibited a weaker but significant positive correlation in ovarian cancer patients (r = 0.258, P < 0.05) and a negative correlation in controls (r = -0.194, P < 0.01). Trx1 levels remained stable across all age groups ( Fig. S1 A, Table S5 ). Menopausal status significantly affected CA125 and HE4 levels, but not Trx1, particularly in ovarian cancer patients ( Fig. S1 B ). Similarly, CA125 and HE4 levels varied significantly with childbirth history in controls, whereas Trx1 remained unaffected ( Fig. S1 C ). These findings suggest that Trx1 is a demographically stable biomarker, making it a potential complementary tool in ovarian cancer diagnosis. Expression patterns of histopathological and clinical stage associations were further analyzed between serous and non-serous ovarian cancer cases (n = 80). CA125 and HE4 exhibited significantly higher expression in serous tumors than in non-serous types (P < 0.001). However, Trx1 showed no significant variation based on histopathological classification (Fig. 2 ). Importantly, in relation to FIGO staging, CA125 and HE4 levels were significantly elevated in advanced-stage ovarian cancer (Stages III–IV) compared to early-stage cases (Stages I–II). In contrast, Trx1 expression did not differ significantly between early and advanced stages (Fig. 3 ). This suggests that Trx1 may complement CA125 and HE4 by being less influenced by tumor type and stage, potentially contributing to improved early detection of ovarian cancer. 3.3. Comparison of Clinical Performance of Biomarker Combinations The diagnostic performance of four biomarker combinations was assessed using logistic regression equations to evaluate their clinical utility ( Table S4 ). The evaluation included ROC curve analysis, AUC, clinical sensitivity, and specificity. The performance of these combinations was compared with the existing ROMA algorithm (CA125 + HE4), with a focus on incorporating Trx1 to enhance diagnostic accuracy while considering patient age, menopausal status, and overall clinical applicability. The ROC curve analysis (Fig. 4 A) revealed that the Trx1 + CA125 + HE4 combination demonstrated the highest diagnostic accuracy, achieving a sensitivity of 70.00% and a specificity of 95.50%. The Trx1 + CA125 combination followed, with a sensitivity of 63.75% and a specificity of 93.50%, surpassing the current ROMA algorithm (72.50% sensitivity, 93.50% specificity). Notably, the Trx1 + HE4 combination exhibited the weakest diagnostic performance, with sensitivity and specificity of 68.75% and 86.00%, respectively. Statistical analysis confirmed that the AUC for Trx1 + HE4 was significantly lower than for Trx1 + CA125 + HE4 and CA125 + HE4 (P < 0.01, P < 0.05) (Fig. 4 B). Given that Trx1 + CA125 significantly enhanced ROMA’s diagnostic power with only marginal improvement from adding HE4, the Trx1 + CA125 combination (DORA) was selected for further evaluation. Comparative analysis of ROMA and DORA based on patient age, menopausal status, and cancer stage revealed that ROMA’s diagnostic performance correlated significantly with age, whereas DORA remained stable across all age groups ( Fig. S2 A ). Additionally, ROMA’s sensitivity varied with menopausal status, whereas DORA demonstrated consistent performance, particularly in ovarian cancer patients ( Fig. S2 B ). This suggests that DORA may serve as a stable diagnostic tool unaffected by age or menopausal status, complementing ROMA. Further evaluation (Fig. 5 , Table 1 ) indicated that DORA maintained higher sensitivity and specificity across different cancer stages and menopausal statuses, outperforming ROMA in key clinical parameters. Among premenopausal women, ROMA demonstrated a sensitivity of 42.86%, a specificity of 92.86%, and an AUC of 0.731 at a cut-off of 11.40. It exhibited low sensitivity in both early-stage (Stage I-II) and late-stage (Stage III-IV) premenopausal ovarian cancer patients (Table 1 ). Among postmenopausal women, ROMA had a sensitivity of 66.10%, a specificity of 100.00%, and an AUC of 0.945. However, postmenopausal women with early-stage ovarian cancer showed low sensitivity (45.00%), while ROMA was relatively more sensitive in late-stage cancer patients (75.00%). ROMA demonstrated a sensitivity of 61.25%, a specificity of 95.00%, and an AUC of 0.879. In contrast, DORA maintained a consistent cut-off value of 46.86, regardless of menopausal status. In premenopausal women, DORA achieved a sensitivity of 76.19%, a specificity of 71.43%, and an AUC of 0.741. In postmenopausal women, it exhibited higher sensitivity (91.53%), specificity (97.30%), and AUC (0.952). DORA also demonstrated superior sensitivity and specificity for early-stage ovarian cancer (80.00% and 80.50%, respectively) and for late-stage ovarian cancer (93.33% and 80.50%) (Table 1 ). Table 1 Comparison of Clinical Performance between ROMA and DORA Variables Conditions Cut off Sensitivity (95% CI) Specificity (95% CI) AUC (95% CI) P value ROMA Premenopausal status 11.40 42.86 (21.8–66.0) 92.86 (86.9–96.7) 0.731 (0.652–0.801) 0.0018 Early stage (I-II) 42.86 (17.7–71.1) 95.00 (91.0–97.6) 0.806 (0.747–0.857) < 0.0001 Late stage (III-IV) 42.86 (9.9–81.6) 95.00 (91.0–97.6) 0.839 (0.782–0.887) 0.0001 Postmenopausal status 29.90 66.10 (52.6–77.9) 100.00 (95.1–100.0) 0.945 (0.897–0.977) < 0.0001 Early stage (I-II) 45.00 (23.1–68.5) 100.00 (98.2–100.0) 0.799 (0.740–0.850) 0.0001 Late stage (III-IV) 75.00 (56.6–88.5) 100.00 (98.2–100.0) 0.945 (0.907–0.970) < 0.0001 All - 61.25 (49.7–71.9) 95.00 (91.0–97.6) 0.879 (0.835–0.915) < 0.0001 DORA Premenopausal status 46.86 76.19 (52.8–91.8) 71.43 (62.7–79.1) 0.741 (0.662–0.810) 0.0005 Postmenopausal status 91.53 (81.3–97.2) 97.30 (90.6–99.7) 0.952 (0.900–0.981) < 0.0001 Early stage (I-II) 80.00 (63.1–91.6) 80.50 (74.3–85.8) 0.818 (0.763–0.865) < 0.0001 Late stage (III-IV) 93.33 (81.7–98.6) 80.50 (74.3–85.8) 0.921 (0.880–0.952) < 0.0001 All 87.50 (78.2–93.8) 80.50 (74.3–85.8) 0.876 (0.832–0.912) < 0.0001 Overall, DORA demonstrated a sensitivity of 87.50%, a specificity of 80.50%, and an AUC of 0.876, providing a uniform diagnostic approach applicable across all patient groups. These findings suggest that DORA could serve as a valuable complement to ROMA for ovarian cancer diagnosis, offering enhanced sensitivity, particularly in early-stage detection. 4. Discussion This study aimed to improve the sensitivity of ovarian cancer detection while reducing false negatives and enhancing early-stage cancer diagnosis through a simple and applicable approach compared to the ROMA algorithm. The ROMA algorithm, which combines CA125 and HE4, has been widely used in ovarian cancer diagnostics, particularly among postmenopausal women. However, HE4, a product of the WFDC2 gene, is not specific to ovarian cancer and can be elevated in other conditions, such as endometrial cancer, lung tumors, and inflammatory diseases, including COVID-19 and sepsis [ 31 – 39 ]. Additionally, HE4 levels fluctuate with age and menopausal status, leading to potential misclassification in diagnosis [ 18 , 19 ]. To address these limitations, we developed the DORA algorithm, incorporating Trx1, a redox-regulating protein involved in cancer progression [ 23 ]. Trx1 has been investigated as a biomarker in various cancers, including breast, colon, and gastric cancers [ 40 – 47 ], and its expression remains stable regardless of age, TNM stage, or menopausal status [ 24 , 26 , 27 ]. Our findings confirmed that Trx1 exhibits stable expression across these variables, in contrast to HE4. Consequently, we developed the DORA algorithm, which combines Trx1 and CA125 to enhance ovarian cancer detection reliability. The incorporation of Trx1 as a biomarker presents significant potential benefits, as it is not influenced by age, menopausal status, or childbirth history, and it compensates for the low sensitivity of current biomarkers in detecting early-stage ovarian cancer ( Fig. S1 and S2 ). ROMA score are variable and the clinically acceptable minimum value is generally specificity of 75%, the optimal ROMA cutoff score and clinical utility of the test are determined as sensitivity > 80.0% and specificity ≥ 75.0% [ 48 – 51 ]. In our study, ROMA demonstrated a sensitivity of 42.9% and specificity of 92.9% in premenopausal women, and 66.1% and 100.0% in postmenopausal women. In contrast, DORA significantly improved sensitivity, reaching 76.2% in premenopausal women and 91.5% in postmenopausal women, with specificity values of 71.4% and 97.3%, respectively (Fig. 5 , Table 1 ). Although DORA had slightly reduced specificity compared to ROMA, its higher sensitivity makes it more effective in detecting ovarian cancer, particularly in non-serous subtypes where CA125 and HE4 perform poorly (Fig. 2 ). This suggests that Trx1 plays a critical role in improving diagnostic accuracy for ovarian cancer subtypes that are typically difficult to detect using conventional biomarkers. Trx1’s overexpression in some early-stage ovarian cancer patients highlights its potential for early detection (Fig. 3 ). In contrast, HE4 is affected by age and conditions such as hypertension and diabetes [ 52 ], with false positives commonly arising from renal failure, liver, and lung diseases [ 53 – 55 ]. Trx1 overexpression, on the other hand, is linked to inflammatory and immune diseases and is closely associated with tumor development and cancer progression [ 41 , 56 – 58 ]. In this study, the non-OC (non-ovarian cancer) group was classified as a benign group rather than a normal group. As illustrated in Fig. 1 , patients in the non-OC group exhibited high Trx1 expression, contributing to its lower specificity compared to CA125 and HE4. The inclusion of a normal group could potentially increase the specificity of the algorithm. While most studies categorize groups as normal, benign, and cancer, the heterogeneity of clinical patients increases the risk of false positives and negatives. Despite its slightly lower specificity compared to ROMA, DORA minimizes the risk of false negatives, reducing the likelihood of undiagnosed cancer cases. This makes DORA more clinically useful, particularly since Trx1 demonstrated superior performance in detecting early-stage ovarian cancer (Fig. 3 ). Trx1’s ability to detect early-stage cancer may stem from its crucial role in tumor development [ 56 – 58 ], and its overexpression has also been associated with metastatic breast cancer [ 59 ]. Before clinical application, some limitations must be addressed. This study was conducted in a single center with Korean participants, limiting its generalizability. Future studies with diverse populations and more clinical samples are essential for validation. 5. Conclusions This study underscores the importance of integrating multiple biomarkers to improve the early detection of ovarian cancer. CA125 remains a key biomarker for ovarian cancer diagnosis, its diagnostic power is significantly enhanced when combined with additional markers such as HE4 (ROMA) and Trx1 (DORA). Among these, Trx1 demonstrated stable expression across diverse demographic factors, addressing some of the variability seen with HE4. Furthermore, the DORA algorithm, which incorporates Trx1, exhibited comparable or superior performance to ROMA, particularly in its stability across both pre- and post-menopausal women. Notably, DORA showed greater potential for detecting early-stage ovarian cancer compared to ROMA. This suggests that Trx1 could serve as a complementary biomarker to CA125, potentially improving diagnostic accuracy, especially in cases where current algorithms like ROMA show limitations. The feasibility of integrating Trx1 into a biomarker combination with CA125 to enhance the sensitivity and specificity of ovarian cancer diagnosis was demonstrated. However, further research and clinical validation are necessary to confirm these findings and to assess the potential incorporation of Trx1 into routine diagnostic algorithms for ovarian cancer. Abbreviations WHO - World Health Organization OC - Ovarian Cancer CA125 - Cancer Antigen 125 HE4 - Human Epididymis Protein 4 Trx1 -Thioredoxin 1 RMI - Risk of Malignancy Index ROMA - Risk of Ovarian Malignancy Algorithm ROC - Receiver Operating Characteristic AUC - Area Under the Curve EMR - Electronic Medical Records SST – Serum Separating Tube ANOVA - Analysis of Variance TNM - Tumor, Node, Metastasis Declarations Acknowledgements We would like to express our sincere gratitude to the Innovation Center for Industrial Mathematics, National Institute for Mathematical Sciences for their invaluable support and contributions to this study. Additionally, we extend our appreciation to the Chungnam National University Hospital Biobank for providing essential resources and assistance with sample collection and management. Their support has been instrumental in the successful completion of this research. Authors' contributions KHS and YBK conceptualized the study. YBK supervised the study. MAP curated the data. SHK analyzed the data. SHK and XY interpreted the results. SHK and MAP drafted the manuscript. All authors have reviewed and approved the submitted version of the manuscript. Ethics approval and consent to participate This study was approved by the Institutional Review Board of Chungnam National University Health System (IRB approval no. 2022-04-; approval date: May 3, 2022; Daejeon, South Korea). Written informed consent for the use of blood samples in research was obtained from all patients, and the study was conducted in accordance with the Declaration of Helsinki. Consent for publication: Not applicable Competing interests: The authors declare that they have no competing interests. Funding This study was supported by the Chungnam National University Hospital Research Fund for HIT discovery. Data Availability The raw datasets generated and/or analysed during the current study are provided in the supplementary file of this article. Additional data are available from the corresponding author on reasonable request. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48. Serov SF, Scully RE. HISTOLOGICAL TYPING OF OVARIAN TUMOURS. Zamwar UM, Anjankar AP, Aetiology. Epidemiology, Histopathology, Classification, Detailed Evaluation, and Treatment of Ovarian Cancer. Cureus. 2022;14(10):e30561. Zeppernick F, Meinhold-Heerlein I. The new FIGO staging system for ovarian, fallopian tube, and primary peritoneal cancer. Arch Gynecol Obstet. 2014;290(5):839–42. Schiavone MB, Herzog TJ, Lewin SN, Deutsch I, Sun X, Burke WM, et al. Natural history and outcome of mucinous carcinoma of the ovary. Am J Obstet Gynecol. 2011;205(5):e4801–8. Holschneider CH, Berek JS. Ovarian cancer: epidemiology, biology, and prognostic factors. Semin Surg Oncol. 2000;19(1):3–10. Bast RC, Feeney M, Lazarus H, Nadler LM, Colvin RB, Knapp RC. Reactivity of a monoclonal antibody with human ovarian carcinoma. J Clin Invest. 1981;68(5):1331–7. Munkarah A, Chatterjee M, Tainsky MA. Update on ovarian cancer screening. Curr Opin Obstet Gynecol. 2007;19(1):22–6. Jacobs I, Bast RC Jr. The CA 125 tumour-associated antigen: a review of the literature. Hum Reprod. 1989;4(1):1–12. Sell S. Tumor Markers: Physiology, Pathobiology, Technology and Clinical Applications. Eleftherios P. Diamandis, Hervert A. Fritche, Hans Lilja, Daniel W. Chan, and Morton K. Schwartz, eds. Washington, DC: AACC Press, 2002, 513 pp., $ 99.00 ( $ 79.00 AACC members), softcover. ISBN 1-890883-71-9. Clinical Chemistry. 2003;49(2):342. Bast RC, Xu FJ, Yu YH, Barnhill S, Zhang Z, Mills GB. CA 125: the past and the future. Int J Biol Markers. 1998;13(4):179–87. Dochez V, Caillon H, Vaucel E, Dimet J, Winer N, Ducarme G. Biomarkers and algorithms for diagnosis of ovarian cancer: CA125, HE4, RMI and ROMA, a review. J Ovarian Res. 2019;12(1):28. Hellström I, Raycraft J, Hayden-Ledbetter M, Ledbetter JA, Schummer M, McIntosh M, et al. The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma. Cancer Res. 2003;63(13):3695–700. Scholler N, Crawford M, Sato A, Drescher CW, O’Briant KC, Kiviat N, et al. Bead-based ELISA for validation of ovarian cancer early detection markers. Clin Cancer Res. 2006;12(7 Pt 1):2117–24. Moore RG, Brown AK, Miller MC, Badgwell D, Lu Z, Allard WJ, et al. Utility of a novel serum tumor biomarker HE4 in patients with endometrioid adenocarcinoma of the uterus. Gynecol Oncol. 2008;110(2):196–201. Cviič D, Jagarlamudi K, Meglič L, Škof E, Zore A, Lukanović D, et al. A Dual Biomarker TK1 Protein and CA125 or HE4-Based Algorithm as a Better Diagnostic Tool than ROMA Index in Early Detection of Ovarian Cancer. Cancers. 2023;15(5):1593. Samborski A, Miller MC, Blackman A, MacLaughlan-David S, Jackson A, Lambert-Messerlian G, et al. HE4 and CA125 serum biomarker monitoring in women with epithelial ovarian cancer. Tumour Biol. 2022;44(1):205–13. Moore RG, Miller MC, Disilvestro P, Landrum LM, Gajewski W, Ball JJ, et al. Evaluation of the diagnostic accuracy of the risk of ovarian malignancy algorithm in women with a pelvic mass. Obstet Gynecol. 2011;118(2 Pt 1):280–8. Montagnana M, Danese E, Ruzzenente O, Bresciani V, Nuzzo T, Gelati M, et al. The ROMA (Risk of Ovarian Malignancy Algorithm) for estimating the risk of epithelial ovarian cancer in women presenting with pelvic mass: is it really useful? Clin Chem Lab Med. 2011;49(3):521–5. Zhan Y, Chen J, Wu J, Gu Y, Huang Q, Deng Z, et al. Human epididymis protein 4 aggravates airway inflammation and remodeling in chronic obstructive pulmonary disease. Respir Res. 2022;23(1):120. Sütő R, Pócsi M, Szabó Z, Fejes Z, Ivády G, Kerekes G, et al. Elevated level of serum human epididymis protein 4 (HE4) predicts disease severity and mortality in COVID-19 pneumonia. BMC Pulm Med. 2023;23(1):512. Jacob F, Meier M, Caduff R, Goldstein D, Pochechueva T, Hacker N, et al. No benefit from combining HE4 and CA125 as ovarian tumor markers in a clinical setting. Gynecol Oncol. 2011;121(3):487–91. Kallis GB, Holmgren A. Differential reactivity of the functional sulfhydryl groups of cysteine-32 and cysteine-35 present in the reduced form of thioredoxin from Escherichia coli. J Biol Chem. 1980;255(21):10261–5. Park BJ, Cha MK, Kim IH. Thioredoxin 1 as a serum marker for ovarian cancer and its use in combination with CA125 for improving the sensitivity of ovarian cancer diagnoses. Biomarkers. 2014;19(7):604–10. Cha MK, Park BJ, Kim IH. Thioredoxin1 as a novel serum marker for human ovarian cancer. The FASEB Journal. 2012;26(S1):967.5-967.5. Park BJ, Cha MK, Kim IH. Thioredoxin 1 as a serum marker for breast cancer and its use in combination with CEA or CA15-3 for improving the sensitivity of breast cancer diagnoses. BMC Res Notes. 2014;7:7. Lee YJ, Kim Y, Choi BB, Kim JR, Ko HM, Suh KH, et al. The blood level of thioredoxin 1 as a supporting biomarker in the detection of breast cancer. BMC Cancer. 2022;22(1):12. Shang W, Xie Z, Lu F, Fang D, Tang T, Bi R, et al. Increased Thioredoxin-1 Expression Promotes Cancer Progression and Predicts Poor Prognosis in Patients with Gastric Cancer. Oxid Med Cell Longev. 2019;2019:9291683. Raffel J, Bhattacharyya AK, Gallegos A, Cui H, Einspahr JG, Alberts DS, et al. Increased expression of thioredoxin-1 in human colorectal cancer is associated with decreased patient survival. J Lab Clin Med. 2003;142(1):46–51. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315–6. Clauss A, Lilja H, Lundwall A. A locus on human chromosome 20 contains several genes expressing protease inhibitor domains with homology to whey acidic protein. Biochem J. 2002;368(Pt 1):233–42. Li J, Dowdy S, Tipton T, Podratz K, Lu WG, Xie X, et al. HE4 as a biomarker for ovarian and endometrial cancer management. Expert Rev Mol Diagn. 2009;9(6):555–66. Behrouzi R, Barr CE, Crosbie EJ. HE4 as a Biomarker for Endometrial Cancer. Cancers. 2021;13(19):4764. Nagy B, Bhattoa HP, Steiber Z, Csobán M, Szilasi M, Méhes G, et al. Serum human epididymis protein 4 (HE4) as a tumor marker in men with lung cancer. Clin Chem Lab Med (CCLM). 2014;52(11):1639–48. Small DM, Doherty DF, Dougan CM, Weldon S, Taggart CC. The role of whey acidic protein four-disulfide-core proteins in respiratory health and disease. Biol Chem. 2017;398(4):425–40. Lai X, Kang M, Chen Y, Xu F, Wang K, Cao J. Elevated serum level of human epididymal protein 4 (HE4) predicts poor prognosis in the critically ill with sepsis: A prospective observational cohort study. Clin Biochem. 2022;109–110:79–85. Luo J, Liang J, Wang S, Huang S, Zhou L, Shi Y, et al. Serum human epididymis secretory protein 4 correlates with sepsis-associated acute respiratory distress syndrome and 28-day mortality in critically ill patients. Ann Clin Biochem. 2022;59(5):338–46. Schirinzi A, Cazzolla AP, Lovero R, Lo Muzio L, Testa NF, Ciavarella D, et al. New Insights in Laboratory Testing for COVID-19 Patients: Looking for the Role and Predictive Value of Human epididymis secretory protein 4 (HE4) and the Innate Immunity of the Oral Cavity and Respiratory Tract. Microorganisms. 2020;8(11):1718. Wei X, Su J, Yang K, Wei J, Wan H, Cao X, et al. Elevations of serum cancer biomarkers correlate with severity of COVID-19. J Med Virol. 2020;92(10):2036–41. Awan MUN, Yan F, Mahmood F, Bai L, Liu J, Bai J. The Functions of Thioredoxin 1 in Neurodegeneration. Antioxid Redox Signal. 2022;36(13–15):1023–36. Griffiths HR, Bennett SJ, Olofsson P, Dunston CR. Thioredoxin as a putative biomarker and candidate target in age-related immune decline. Biochem Soc Trans. 2014;42(4):922–7. Whayne TF, Parinandi N, Maulik N. Thioredoxins in cardiovascular disease. Can J Physiol Pharmacol. 2015;93(11):903–11. Zeng XS, Geng WS, Chen L, Jia JJ. Thioredoxin as a Therapeutic Target in Cerebral Ischemia. Curr Pharm Des. 2018;24(25):2986–92. Noike T, Miwa S, Soeda J, Kobayashi A, Miyagawa S. ichi. Increased expression of thioredoxin-1, vascular endothelial growth factor, and redox factor-1 is associated with poor prognosis in patients with liver metastasis from colorectal cancer. Hum Pathol. 2008;39(2):201–8. Guan X, Ning J, Fu W, Wang Y, Zhang J, Ding S. Helicobacter pylori with trx1 high expression promotes gastric diseases via upregulating the IL23A/NF-κB/IL8 pathway. Helicobacter. 2024;29(2):e13072. Ribback S, Winter S, Klatte T, Schaeffeler E, Gellert M, Stühler V, et al. Thioredoxin 1 (Trx1) is associated with poor prognosis in clear cell renal cell carcinoma (ccRCC): an example for the crucial role of redox signaling in ccRCC. World J Urol. 2022;40(3):739–46. Kakolyris S, Giatromanolaki A, Koukourakis M, Powis G, Souglakos J, Sivridis E, et al. Thioredoxin expression is associated with lymph node status and prognosis in early operable non-small cell lung cancer. Clin Cancer Res. 2001;7(10):3087–91. Moore RG, McMeekin DS, Brown AK, DiSilvestro P, Miller MC, Allard WJ, et al. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol Oncol. 2009;112(1):40–6. Karlsen MA, Høgdall EVS, Christensen IJ, Borgfeldt C, Kalapotharakos G, Zdrazilova-Dubska L, et al. A novel diagnostic index combining HE4, CA125 and age may improve triage of women with suspected ovarian cancer - An international multicenter study in women with an ovarian mass. Gynecol Oncol. 2015;138(3):640–6. Karlsen MA, Sandhu N, Høgdall C, Christensen IJ, Nedergaard L, Lundvall L, et al. Evaluation of HE4, CA125, risk of ovarian malignancy algorithm (ROMA) and risk of malignancy index (RMI) as diagnostic tools of epithelial ovarian cancer in patients with a pelvic mass. Gynecol Oncol. 2012;127(2):379–83. Sandri MT, Bottari F, Franchi D, Boveri S, Candiani M, Ronzoni S, et al. Comparison of HE4, CA125 and ROMA algorithm in women with a pelvic mass: correlation with pathological outcome. Gynecol Oncol. 2013;128(2):233–8. Zhang M, Zhao B, Xie J, Liang Y, Yang Z. Serum Human Epididymis Protein 4 is Associated with Renal Function and Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus. Biomed Res Int. 2019;2019(1):4831459. Nagy B, Krasznai ZT, Balla H, Csobán M, Antal-Szalmás P, Hernádi Z, et al. Elevated human epididymis protein 4 concentrations in chronic kidney disease. Ann Clin Biochem. 2012;49(Pt 4):377–80. Yu Z, Nian C, Sun W, Liu X, Nian X. Elevated serum HE4 levels as a novel biomarker of disease severity and hepatic fibrosis in autoimmune hepatitis. Clin Chim Acta. 2024;559:119682. Meng K, Tian M, Gui X, Xie M, Gao Y, Shi S, et al. Human epididymis protein 4 is associated with severity and poor prognosis of connective tissue disease-associated interstitial lung disease with usual interstitial pneumonia pattern. Int Immunopharmacol. 2022;108:108704. Muri J, Thut H, Feng Q, Kopf M. Thioredoxin-1 distinctly promotes NF-κB target DNA binding and NLRP3 inflammasome activation independently of Txnip. Horng T, Rath S, editors. eLife. 2020;9:e53627. Grogan TM, Fenoglio-Prieser C, Zeheb R, Bellamy W, Frutiger Y, Vela E, et al. Thioredoxin, a putative oncogene product, is overexpressed in gastric carcinoma and associated with increased proliferation and increased cell survival. Hum Pathol. 2000;31(4):475–81. Ceccarelli J, Delfino L, Zappia E, Castellani P, Borghi M, Ferrini S, et al. The redox state of the lung cancer microenvironment depends on the levels of thioredoxin expressed by tumor cells and affects tumor progression and response to prooxidants. Int J Cancer. 2008;123(8):1770–8. Bhatia M, McGrath KL, Di Trapani G, Charoentong P, Shah F, King MM, et al. The thioredoxin system in breast cancer cell invasion and migration. Redox Biol. 2016;8:68–78. Additional Declarations No competing interests reported. Supplementary Files Supplementarydata.docx OCRawData.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 04 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviewers invited by journal 15 Sep, 2025 Editor assigned by journal 10 Sep, 2025 Editor invited by journal 20 Aug, 2025 Submission checks completed at journal 20 Aug, 2025 First submitted to journal 20 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7359728","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517911535,"identity":"597bf51e-e6dd-4fe6-a9c8-de0646c7fd82","order_by":0,"name":"Mi A Park","email":"","orcid":"","institution":"Chungnam National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mi","middleName":"A","lastName":"Park","suffix":""},{"id":517911536,"identity":"5238cf38-d566-4df9-8a44-a7c46f5dd90d","order_by":1,"name":"Songhak Kim","email":"","orcid":"","institution":"E\u0026S Healthcare","correspondingAuthor":false,"prefix":"","firstName":"Songhak","middleName":"","lastName":"Kim","suffix":""},{"id":517911537,"identity":"49cf4bae-9f72-4c2d-9687-afd245dc6254","order_by":2,"name":"Xiaoguang Yang","email":"","orcid":"","institution":"E\u0026S Healthcare","correspondingAuthor":false,"prefix":"","firstName":"Xiaoguang","middleName":"","lastName":"Yang","suffix":""},{"id":517911538,"identity":"e73745bf-5499-4c8b-ab14-4a2c3f804c4c","order_by":3,"name":"Jong Am Song","email":"","orcid":"","institution":"E\u0026S Healthcare","correspondingAuthor":false,"prefix":"","firstName":"Jong","middleName":"Am","lastName":"Song","suffix":""},{"id":517911539,"identity":"621c637d-8636-4547-9154-2c4f8680d27f","order_by":4,"name":"Geon Woo Lee","email":"","orcid":"","institution":"Chungnam National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Geon","middleName":"Woo","lastName":"Lee","suffix":""},{"id":517911540,"identity":"01fcf3f9-f43c-48f5-ab11-f6e975127a5e","order_by":5,"name":"Heon Jong Yoo","email":"","orcid":"","institution":"Chungnam National University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Heon","middleName":"Jong","lastName":"Yoo","suffix":""},{"id":517911541,"identity":"e4e7679b-e36a-4671-a28b-8b113d54ff01","order_by":6,"name":"Soyoung Kim","email":"","orcid":"","institution":"National Institute for Mathematical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Soyoung","middleName":"","lastName":"Kim","suffix":""},{"id":517911542,"identity":"c362e8ef-0042-45e5-a666-750157b832c9","order_by":7,"name":"Kyoung Hoon Suh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAk0lEQVRIiWNgGAWjYHACxgcg0oAULcwGJGthkyBNC79087GKjzvqGMwZmB8+ukGMFsk5x9JuzjxzmMGygc3YOIcYLQY3csxu87YdqN9wgIdNmkgt+d+K/7bVMRiQoCWHjZmxjZkELZIz0owle9sOMxgcJtYv/BLJDz/8BDnsePPDx0RpQQBm0pSPglEwCkbBKMAHAMOZLIC8z/8TAAAAAElFTkSuQmCC","orcid":"","institution":"E\u0026S Healthcare","correspondingAuthor":true,"prefix":"","firstName":"Kyoung","middleName":"Hoon","lastName":"Suh","suffix":""},{"id":517911543,"identity":"0e2a7489-d0fe-4399-ada5-5cc5a18c3965","order_by":8,"name":"Young Bok Ko","email":"","orcid":"","institution":"Chungnam National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Young","middleName":"Bok","lastName":"Ko","suffix":""}],"badges":[],"createdAt":"2025-08-13 01:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7359728/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7359728/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91897635,"identity":"b9c6ffae-2755-4184-8b67-79a2e198ac61","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":656367,"visible":true,"origin":"","legend":"","description":"","filename":"OCmanuscriptBMCCancer.docx","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/00d9482fdee2c0969475d268.docx"},{"id":91897633,"identity":"72eb1377-d869-45d3-9959-11bfe6af0232","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10986,"visible":true,"origin":"","legend":"","description":"","filename":"53c6ea1fe6374d939c998aef1108ecd8.json","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/08077ee5588c4bcefc850e00.json"},{"id":91898023,"identity":"7ae54b08-853d-4d01-be97-5793836db1cc","added_by":"auto","created_at":"2025-09-22 19:01:46","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49290,"visible":true,"origin":"","legend":"","description":"","filename":"OCRawData.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/dc462d5da29ddbdd6e3d2604.xlsx"},{"id":91897643,"identity":"7146fd77-5784-4512-ae85-9979a41ea6af","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":611874,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/42d59bff075321c7c16b37ea.docx"},{"id":91898024,"identity":"e2ca8bc2-d300-435c-9bf9-5c89c9bf97e5","added_by":"auto","created_at":"2025-09-22 19:01:46","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144797,"visible":true,"origin":"","legend":"","description":"","filename":"53c6ea1fe6374d939c998aef1108ecd81enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/85c541d507ac01c5a9c67ae0.xml"},{"id":91898030,"identity":"b6018cbc-0949-4462-b466-9a0c56c65000","added_by":"auto","created_at":"2025-09-22 19:01:46","extension":"emf","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":937332,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.emf","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/2f6011a918f84f50c98a2155.emf"},{"id":91898248,"identity":"4cb58d46-c8c0-4cd8-bc94-f490a470a012","added_by":"auto","created_at":"2025-09-22 19:09:46","extension":"emf","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":319324,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.emf","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/2bdea9ee6cf32c4ad870dbbb.emf"},{"id":91898026,"identity":"582c4c36-069c-4b6d-8a94-9ae70192011c","added_by":"auto","created_at":"2025-09-22 19:01:46","extension":"emf","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":262240,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.emf","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/72ea3d2e7716f5544e73fded.emf"},{"id":91898029,"identity":"fc8aa91a-7b4a-417b-9da8-4f3cfde2009b","added_by":"auto","created_at":"2025-09-22 19:01:46","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75822,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/10653d01011d244621dbce5f.jpeg"},{"id":91897649,"identity":"05f22b68-12a9-4389-95cf-fd7b993ca7e3","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"emf","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":269292,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.emf","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/994765f7031704b5d55bcdc8.emf"},{"id":91898247,"identity":"6bea42e2-96a3-4d1a-822a-0ed1b53c55a5","added_by":"auto","created_at":"2025-09-22 19:09:46","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24134,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/204caca27c2a1f3648f1c97f.png"},{"id":91898025,"identity":"3a5b5a0e-9669-40c7-a3c8-e16d7ab080e2","added_by":"auto","created_at":"2025-09-22 19:01:46","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18936,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/3988990f819777fea1c0b9d4.png"},{"id":91897644,"identity":"eeda8bab-f849-4449-b2d6-8d122e535b1e","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14072,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/a0aa38fbe2b43eb76443d0ef.png"},{"id":91897640,"identity":"46882433-d49f-462b-964a-feda5b7463d7","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25844,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/abec699a390341cbfe7bb497.png"},{"id":91898021,"identity":"dc130150-f477-45fd-8261-2ceb5c35fa1e","added_by":"auto","created_at":"2025-09-22 19:01:46","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21987,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/b2b92d0e45352fb2e65c9fff.png"},{"id":91897655,"identity":"49dd8aa4-656d-41cc-a116-0c091ca954e1","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141433,"visible":true,"origin":"","legend":"","description":"","filename":"53c6ea1fe6374d939c998aef1108ecd81structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/fc4d35f2957846a7c1dbb3aa.xml"},{"id":91897653,"identity":"c895ca11-3617-42db-94b9-cfc27cf837c2","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154568,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/92f614094fad8147d3488141.html"},{"id":91897631,"identity":"d5cf2234-2be0-489b-8724-2d43133feb54","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92431,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical Performance Analysis for Individual Biomarkers for Ovarian Cancer. T\u003c/strong\u003ehe clinical performance of CA125, HE4, and Trx1 was evaluated in 80 ovarian cancer patients and 200 non-cancer patients (including 196 with benign conditions and 4 with squamous cell carcinoma). (A) Serum levels of CA125, HE4, and Trx1 in study participants. Black horizontal lines indicate the mean values, while red dotted lines represent biomarker cut-off levels. (B) ROC curve analysis of CA125, HE4, and Trx1. (C) Comparison of AUC values for the three biomarkers. A single asterisk (*) indicates p \u0026lt; 0.05, three asterisks (**) indicate p \u0026lt; 0.001, and ‘ns’ denotes non-significant differences.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/502b09d7685d83cf31635a38.png"},{"id":91897632,"identity":"835dba6c-1cc0-4aef-bd15-14cb7c4cd65f","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50638,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Analysis between Pathological Results and Biomarker Levels in Ovarian Cancer Patients. \u003c/strong\u003eThis analysis examines biomarker expression according to the pathological subtypes of ovarian cancer. The \"Serous\" category includes high-grade serous ovarian carcinoma, low-grade serous ovarian carcinoma, and borderline serous ovarian carcinoma. The \"Non-serous\" category encompasses mucinous, clear cell, granulosa cell, and endometrioid carcinomas. \"NOS\" stands for \"Not Otherwise Specified.\" A single asterisk (*) indicates a p-value of less than 0.05, two asterisks (**) indicate a p-value of less than 0.01, and three asterisks (***) indicate a p-value of less than 0.001. \"ns\" denotes a statistically non-significant result.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/0e17c82c9ebcfefc88b9f794.png"},{"id":91897647,"identity":"a2ca47eb-6598-44d8-a009-6d4409317d14","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of Biomarker Expression Patterns according to Ovarian Cancer FIGO Stage Progression. \u003c/strong\u003eBox-and-whisker plots displaying the expression levels of CA125, HE4, and Trx1 in early-stage (I-II) and late-stage (III-IV) ovarian cancer. Three asterisks (***) indicate a p-value of less than 0.001. \"ns\" denotes a statistically non-significant result.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/965d802105e649a60d4d8b59.png"},{"id":91897634,"identity":"a624928f-c204-4254-9bd8-f7af0dbdd99b","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":212591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical Performance Analysis of Combined Biomarkers for Ovarian Cancer. \u003c/strong\u003e(A) AUC analysis from logistic regression using Trx1, CA125, and HE4 for multi-marker validation.(B) Comparison of AUC values for combined biomarkers. A single asterisk (*) indicates a p-value of less than 0.05, two asterisks (**) indicate a p-value of less than 0.01, and \"ns\" denotes a statistically non-significant result.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/af8027dcb86a9ce7881039a0.png"},{"id":91898022,"identity":"e8cb1bea-da01-4a60-b4e8-280ef5ad862b","added_by":"auto","created_at":"2025-09-22 19:01:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":74109,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of Clinical Performance between ROMA and DORA by Menopausal Status. \u003c/strong\u003e(A) ROC analysis results for 147 premenopausal subjects. (B) ROC analysis results for 133 postmenopausal subjects. (C) ROC analysis results for the combination of ROMA and DORA in all 280 subjects.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/c94caf7616e7539e56d7bb1b.png"},{"id":91898391,"identity":"948db83b-511d-4b69-958f-eac6545b19a4","added_by":"auto","created_at":"2025-09-22 19:17:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1422321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/685dc311-4b6f-4966-bfe3-a7354e9575a7.pdf"},{"id":91897639,"identity":"346f53d4-b9a8-4e27-831b-f46d9e3d3e2a","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":611874,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/5ba252ac50bdcca2e72e8faa.docx"},{"id":91897636,"identity":"93ccfd02-26ba-49a7-a93d-dac4152b492e","added_by":"auto","created_at":"2025-09-22 18:53:46","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49290,"visible":true,"origin":"","legend":"","description":"","filename":"OCRawData.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7359728/v1/2cc05e03af1c5bcb45957757.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Towards a Universal Ovarian Cancer Biomarker Model: Clinical Validation of Trx1 and CA125 Dual Strategy","fulltext":[{"header":"1. Background","content":"\u003cp\u003eOvarian cancer is one of the most prevalent gynecological malignancies and ranks as the fifth leading cause of cancer-related mortality among women worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Unlike cervical cancer, whose incidence has declined due to vaccination programs, ovarian cancer remains a significant concern, with an increasing risk and a decreasing age of onset in some populations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Due to the lack of early symptoms and effective screening methods, most ovarian cancer cases are diagnosed at an advanced stage, leading to poor prognoses. While early-stage ovarian cancer (Stages I and II) has a five-year survival rate of approximately 90%, this rate drops significantly to 20\u0026ndash;40% in late-stage cases (Stages III and IV) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The pathogenesis of ovarian cancer remains incompletely understood, though genetic and endocrine factors have been identified as key contributors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, developing reliable early detection strategies is crucial for improving patient outcomes.\u003c/p\u003e\u003cp\u003eAmong the various diagnostic approaches, blood-based biomarkers are considered the most practical and minimally invasive method for ovarian cancer detection. Biomarkers can facilitate early diagnosis, monitor disease progression, and assess treatment response. CA125 was the first biomarker widely used for ovarian cancer detection and monitoring following the discovery of the OC125 antibody in 1981 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. While CA125 demonstrates substantial clinical utility, however, CA125 has notable limitations: while it is elevated in approximately 80% of ovarian cancer cases, its sensitivity for early-stage detection (Stages I and II) is limited to 50\u0026ndash;60% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, CA125 levels can be elevated in benign gynecological conditions (5%) and non-gynecological malignancies (28%), Furthermore, CA125 levels can be elevated in benign gynecological conditions (5%) and non-gynecological malignancies (28%), reducing its specificity and leading to potential false-positive results [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To improve diagnostic accuracy, several scoring systems, such as the Risk of Malignancy Index (RMI), which integrates CA125 levels with transvaginal ultrasound findings, have been developed [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo enhance diagnostic accuracy, HE4 was introduced as an additional ovarian cancer biomarker [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. First identified by Hellstr\u0026ouml;m et al. in 2003 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], HE4 was approved by the FDA in 2009 for monitoring ovarian cancer progression [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies have demonstrated that combining HE4 with CA125 improves diagnostic performance compared to either biomarker alone [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The Risk of Ovarian Malignancy Algorithm (ROMA), which integrates HE4 and CA125 levels with menopausal status, was introduced to assess malignancy risk in women with pelvic masses [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. FDA-approved in 2011, ROMA demonstrates high effectiveness in postmenopausal women but is less reliable in premenopausal populations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Despite its clinical utility, HE4 levels can be affected by age, pregnancy, and non-gynecological conditions such as renal failure and lung cancer, increasing the false-positive rate in ROMA assessments [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, recent studies suggest that neither HE4 alone nor ROMA provides a significant improvement in early-stage ovarian cancer detection compared to CA125 alone [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This underscores the urgent need for additional biomarkers that enhance sensitivity and specificity, particularly in differentiating malignant from benign pelvic masses.\u003c/p\u003e\u003cp\u003eRecently, Thioredoxin-1 (Trx1) has emerged as a promising biomarker for cancer detection, particularly in female malignancies. Trx1 is a redox-regulating protein involved in oxidative stress response and cellular signaling [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Recent studies have indicated that serum Trx1 levels are elevated in ovarian cancer patients who are CA125-negative, suggesting its potential as a complementary biomarker [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Although Trx1 levels are also elevated in other malignancies, including breast [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], gastric [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and colorectal cancers [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], its strong association with ovarian cancer highlights its diagnostic potential. However, to be clinically effective, an ideal biomarker should demonstrate high sensitivity and specificity, regardless of patient demographics such as age or menopausal status. Notably, Trx1 expression remains stable regardless of age or menopausal status, presenting a significant advantage over HE4 in the ROMA algorithm [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven these findings, investigating the diagnostic potential of Trx1 in ovarian cancer is essential, both as a standalone biomarker and in combination with existing markers such as CA125 and HE4. This study aims to evaluate the clinical utility of Trx1 for ovarian cancer detection, determine whether Trx1 can overcome the limitations of current liquid biopsy methods, and assess whether a Trx1-CA125 combination could provide comparable or superior diagnostic efficacy to the ROMA algorithm.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.1. Participant Selection and Serum Collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent for the use of serum samples in research was obtained from all participants. Clinical samples were collected from patients visiting Chungnam National University (CNU) Hospital (Daejeon, South Korea), in accordance with the guidelines of the Independent Ethics Committee of the CNU College of Medicine (IRB approval no. 2022-04-048; approval date: May 3, 2022). Preoperative serum samples were collected from 80 ovarian cancer patients and 200 patients with benign tumors or other conditions. The serum samples were provided by the biobank of CNU Hospital, a member of the Korean Biobank Network, and were retrospectively registered in the present study. Clinical data were retrospectively obtained from the Electronic Medical Records (EMR) system at CNU Hospital. To qualify for inclusion, participants had to meet the following criteria: (1) a confirmed pathological diagnosis of ovarian cancer; (2) no prior chemotherapy or radiotherapy before blood collection; (3) an assessment for hemolysis prior to serum collection to ensure sample quality; and (4) exclusion of patients with other existing malignant tumors. Blood samples were collected into Serum Separator Tubes (SST), allowed to clot, and then centrifuged at 1500 × g for 15 minutes. The resulting serum supernatant was stored at −80°C in the CNU Hospital biobank. For this analysis, serum samples were completely thawed prior to testing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2. Quantification of Biomarker Levels in Serum\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree biomarkers were analyzed in this study: CA125, HE4, and Trx1. Among these, Trx1 was the only biomarker detected using a non-commercial kit, requiring a more specific detection process. CA125 and HE4 levels were measured using commercially available kits, with CA125 tested manually and HE4 using a fully automated detector. The detection methods for CA125 and HE4 are well-established and can be verified through the product specifications.\u003c/p\u003e\n\u003cp\u003eTo detect Trx1, a pair of monoclonal antibodies was generated. Using these antibodies, a Trx1 ELISA kit (E\u0026amp;S Healthcare, South Korea) was developed to semi-quantify Trx1 levels in serum. The kit is based on sandwich enzyme-linked immunosorbent assay (ELISA) technology, and the test procedure adhered to the manufacturer’s protocol. Briefly, serum samples or purified Trx1 protein (used as a calibrator, positive control, and negative control) were pre-mixed with detection antibodies (anti-Trx1 monoclonal antibody conjugated with peroxidase) and adjusted to 100 μL using PBSA. In parallel, 100 μL of the prepared samples was added to pre-coated antibody wells on a 96-well plate. The reaction was allowed to proceed for one hour, resulting in the formation of antigen-antibody complexes. After three washes with a washing solution, TMB (3,3′,5,5′-tetramethylbenzidine) was used as the substrate. The enzymatic reaction was halted with the addition of 2N sulfuric acid, causing a color change from blue to yellow. The intensity of the color change was measured at 450 nm using a microplate reader. A standard curve was constructed based on absorbance values of various known concentrations of recombinant human Trx1 protein, which was used to determine the Trx1 concentration in the serum samples.\u003c/p\u003e\n\u003cp\u003eCA125 levels in serum were measured using a sandwich ELISA with the Accubind kit (Monobind Inc., California, USA), following the manufacturer's protocol. Quality control parameters, as outlined in the manufacturer's protocol, ensured test validity. Each sample was tested in duplicate, and the mean absorbance value was used for analysis.\u003c/p\u003e\n\u003cp\u003eHE4 levels in serum were measured using the Elecsys HE4 assay (Roche, Germany) on a Cobas 8000 e602 analyzer (Roche, Germany), in accordance with the manufacturer's instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3. Algorithm and Calculation of Biomarker Combinations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour combinations of the three biomarkers were investigated in this study. The specific biomarker combinations and the associated calculation formulas used to assess diagnostic performance are detailed in Table 3S. Two combinations warrant particular mention: the ROMA index (CA125 + HE4) and the CA125 + Trx1 combination (Dual-marker Ovarian Cancer Risk Algorithm, DORA) index. \u003c/p\u003e\n\u003cp\u003eThe ROMA index was calculated using the following formulas, as referenced from its commercial diagnostic kit:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePremenopausal ROMA index formula: 12 + (2.38 × ln(HE4)) + (0.062 × ln(CA125))\u003c/li\u003e\n\u003cli\u003ePostmenopausal ROMA index formula: 8.09 + (1.04 × ln(HE4)) + (0.732 × ln(CA125))\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn this study, multivariate logistic regression analysis was applied to evaluate the association between CA125 and Trx1 concentrations and the likelihood of ovarian cancer. This method was selected to estimate the individual contributions of these biomarkers to the binary outcome. The dependent variable was the ovarian cancer diagnosis, where 0 represented cancer and 1 represented non-cancer (benign or normal). The independent variables included log-transformed serum levels of CA125 and Trx1. Data were randomly split into training and testing sets at a 7:3 ratios. Using the coefficients, variables, and model constant from the training dataset, a predicted probability (PP) was calculated for the test data. PP values ranged from 0% to 100% for each model. A test result was considered negative if the PP was below a selected threshold and positive if the PP met or exceeded the threshold. Sensitivity and specificity were calculated for the corresponding PP values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4. ROC Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceiver Operating Characteristic (ROC) and precision-recall curve analyses were conducted using MedCalc software (v20.014; MedCalc Software Ltd., Ostend, Belgium) to assess the diagnostic accuracy of each biomarker signature. Univariate ROC analysis was performed for each biomarker combination to determine the ROC curve, area under the curve (AUC), standard error (SE) of the AUC, and 95% confidence interval (CI). The biomarker combination with the highest AUC and the lowest SE was selected as the \"outstanding\" combination for further evaluation [30].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.5. Data Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmunoassay experiments were independently performed in duplicate. Data are presented as the mean ± standard deviation (SD). All statistical analyses were performed using either an unpaired Student’s t-test or one-way analysis of variance (ANOVA) with multiple comparisons, as appropriate, using Prism 10 (v10.2.0; GraphPad Software, San Diego, CA, USA). A p-value of p \u0026lt; 0.05, p \u0026lt; 0.01, or *p \u0026lt; 0.001 was considered statistically significant, while \"ns\" denotes non-significant results. To determine the optimal clinical performance of each immunoassay, AUC values were compared using a bivariate binomial model.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Distribution of Demographic Clinical Variables in Participants\u003c/h2\u003e\u003cp\u003eThis study analyzed the clinical characteristics of 80 ovarian cancer patients and 200 non-cancer control subjects, as summarized in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. The collected demographic data included age, menopausal status, and obstetric history, while clinical information covered histopathological classification and FIGO staging.\u003c/p\u003e\u003cp\u003eIn terms of demographic data, the mean age of ovarian cancer patients was 58.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3 years, compared to 46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7 years for the control group, indicating a statistically significant difference between the two groups. Among ovarian cancer patients, 8.75% were aged 30 or younger, and the remaining cases were distributed relatively evenly across age groups from their 40s to 70s. In contrast, younger individuals (\u0026le;\u0026thinsp;30 years) constituted the largest subgroup within the control population (34.50%), followed by those in their 40s (25.50%), 50s (15.50%), 60s (13.00%), and 70s (11.50%).\u003c/p\u003e\u003cp\u003eMenopausal status analysis revealed that the majority of ovarian cancer patients (73.75%) were postmenopausal, while the control group exhibited a higher proportion of premenopausal individuals (63.00%). Obstetric history assessment indicated that a larger proportion of ovarian cancer patients (86.25%) had a history of childbirth compared to the control group (69.50%), suggesting a potential correlation between reproductive factors and ovarian cancer risk.\u003c/p\u003e\u003cp\u003eHistopathological classification identified serous carcinoma as the predominant subtype, accounting for 52.50% of ovarian cancer cases, followed by non-serous types in 41.25% of cases. The classification for 6.25% of patients remained undetermined. The observed distribution aligns with the latest WHO classification, where high-grade serous carcinoma constitutes the most prevalent subtype (70%), followed by endometrioid (10%), clear cell (6\u0026ndash;10%), low-grade serous (5%), and mucinous carcinoma (3\u0026ndash;4%) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This suggests that the study cohort reflects a representative distribution of ovarian cancer subtypes. Among control subjects, histopathological findings revealed that 40.00% had cervical carcinoma in situ or neoplasia, 45.50% presented with benign uterine or cervical conditions, and 14.50% were diagnosed with benign ovarian disorders. FIGO staging analysis indicated that 43.75% of ovarian cancer patients were diagnosed at early stages (Stages I and II), whereas 42.50% had advanced-stage disease (Stages III and IV). Staging information was unavailable for 11 patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Comparative Clinical Performance of Individual Biomarkers\u003c/h2\u003e\u003cp\u003eTo assess the diagnostic utility of CA125, HE4, and Trx1 for ovarian cancer, their serum levels were analyzed in 280 participants. The ovarian cancer group (n\u0026thinsp;=\u0026thinsp;80) exhibited significantly elevated mean levels of CA125 (217.00 U/mL), HE4 (386.00 pmol/L), and Trx1 (39.20 U/mL) compared to the control group (n\u0026thinsp;=\u0026thinsp;200), where the respective values were 6.43 U/mL, 58.00 pmol/L, and 28.80 U/mL (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). All biomarkers demonstrated statistically significant differences between cases and controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for CA125 and HE4, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for Trx1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). ROC curve analysis showed AUC values of 0.872 (CA125), 0.815 (HE4), and 0.588 (Trx1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), confirming that CA125 had the highest diagnostic performance, followed by HE4 and Trx1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Based on optimal cut-off values, CA125 had a sensitivity of 53.75% and specificity of 96.00%, while HE4 showed 73.75% sensitivity and 71.00% specificity. Trx1 demonstrated 47.50% sensitivity and 71.00% specificity (\u003cb\u003eTable S3\u003c/b\u003e). These findings underscore the need for biomarker combinations to enhance diagnostic accuracy for ovarian cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBiomarker levels were further analyzed based on age, menopausal status, and childbirth history. HE4 showed a significant positive correlation with age in both ovarian cancer (r\u0026thinsp;=\u0026thinsp;0.367, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and control groups (r\u0026thinsp;=\u0026thinsp;0.396, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while CA125 exhibited a weaker but significant positive correlation in ovarian cancer patients (r\u0026thinsp;=\u0026thinsp;0.258, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a negative correlation in controls (r = -0.194, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Trx1 levels remained stable across all age groups (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA, Table S5\u003c/b\u003e). Menopausal status significantly affected CA125 and HE4 levels, but not Trx1, particularly in ovarian cancer patients (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB\u003c/b\u003e). Similarly, CA125 and HE4 levels varied significantly with childbirth history in controls, whereas Trx1 remained unaffected (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC\u003c/b\u003e). These findings suggest that Trx1 is a demographically stable biomarker, making it a potential complementary tool in ovarian cancer diagnosis.\u003c/p\u003e\u003cp\u003eExpression patterns of histopathological and clinical stage associations were further analyzed between serous and non-serous ovarian cancer cases (n\u0026thinsp;=\u0026thinsp;80). CA125 and HE4 exhibited significantly higher expression in serous tumors than in non-serous types (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, Trx1 showed no significant variation based on histopathological classification (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Importantly, in relation to FIGO staging, CA125 and HE4 levels were significantly elevated in advanced-stage ovarian cancer (Stages III\u0026ndash;IV) compared to early-stage cases (Stages I\u0026ndash;II). In contrast, Trx1 expression did not differ significantly between early and advanced stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests that Trx1 may complement CA125 and HE4 by being less influenced by tumor type and stage, potentially contributing to improved early detection of ovarian cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Comparison of Clinical Performance of Biomarker Combinations\u003c/h2\u003e\u003cp\u003eThe diagnostic performance of four biomarker combinations was assessed using logistic regression equations to evaluate their clinical utility (\u003cb\u003eTable S4\u003c/b\u003e). The evaluation included ROC curve analysis, AUC, clinical sensitivity, and specificity. The performance of these combinations was compared with the existing ROMA algorithm (CA125\u0026thinsp;+\u0026thinsp;HE4), with a focus on incorporating Trx1 to enhance diagnostic accuracy while considering patient age, menopausal status, and overall clinical applicability.\u003c/p\u003e\u003cp\u003eThe ROC curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) revealed that the Trx1\u0026thinsp;+\u0026thinsp;CA125\u0026thinsp;+\u0026thinsp;HE4 combination demonstrated the highest diagnostic accuracy, achieving a sensitivity of 70.00% and a specificity of 95.50%. The Trx1\u0026thinsp;+\u0026thinsp;CA125 combination followed, with a sensitivity of 63.75% and a specificity of 93.50%, surpassing the current ROMA algorithm (72.50% sensitivity, 93.50% specificity). Notably, the Trx1\u0026thinsp;+\u0026thinsp;HE4 combination exhibited the weakest diagnostic performance, with sensitivity and specificity of 68.75% and 86.00%, respectively. Statistical analysis confirmed that the AUC for Trx1\u0026thinsp;+\u0026thinsp;HE4 was significantly lower than for Trx1\u0026thinsp;+\u0026thinsp;CA125\u0026thinsp;+\u0026thinsp;HE4 and CA125\u0026thinsp;+\u0026thinsp;HE4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Given that Trx1\u0026thinsp;+\u0026thinsp;CA125 significantly enhanced ROMA\u0026rsquo;s diagnostic power with only marginal improvement from adding HE4, the Trx1\u0026thinsp;+\u0026thinsp;CA125 combination (DORA) was selected for further evaluation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eComparative analysis of ROMA and DORA based on patient age, menopausal status, and cancer stage revealed that ROMA\u0026rsquo;s diagnostic performance correlated significantly with age, whereas DORA remained stable across all age groups (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA\u003c/b\u003e). Additionally, ROMA\u0026rsquo;s sensitivity varied with menopausal status, whereas DORA demonstrated consistent performance, particularly in ovarian cancer patients (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB\u003c/b\u003e). This suggests that DORA may serve as a stable diagnostic tool unaffected by age or menopausal status, complementing ROMA. Further evaluation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) indicated that DORA maintained higher sensitivity and specificity across different cancer stages and menopausal statuses, outperforming ROMA in key clinical parameters. Among premenopausal women, ROMA demonstrated a sensitivity of 42.86%, a specificity of 92.86%, and an AUC of 0.731 at a cut-off of 11.40. It exhibited low sensitivity in both early-stage (Stage I-II) and late-stage (Stage III-IV) premenopausal ovarian cancer patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among postmenopausal women, ROMA had a sensitivity of 66.10%, a specificity of 100.00%, and an AUC of 0.945. However, postmenopausal women with early-stage ovarian cancer showed low sensitivity (45.00%), while ROMA was relatively more sensitive in late-stage cancer patients (75.00%). ROMA demonstrated a sensitivity of 61.25%, a specificity of 95.00%, and an AUC of 0.879. In contrast, DORA maintained a consistent cut-off value of 46.86, regardless of menopausal status. In premenopausal women, DORA achieved a sensitivity of 76.19%, a specificity of 71.43%, and an AUC of 0.741. In postmenopausal women, it exhibited higher sensitivity (91.53%), specificity (97.30%), and AUC (0.952). DORA also demonstrated superior sensitivity and specificity for early-stage ovarian cancer (80.00% and 80.50%, respectively) and for late-stage ovarian cancer (93.33% and 80.50%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\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\u003eComparison of Clinical Performance between ROMA and DORA\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConditions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCut off\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eROMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePremenopausal status\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e11.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.86\u003c/p\u003e\u003cp\u003e(21.8\u0026ndash;66.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92.86\u003c/p\u003e\u003cp\u003e(86.9\u0026ndash;96.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003cp\u003e(0.652\u0026ndash;0.801)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEarly stage\u003c/p\u003e\u003cp\u003e(I-II)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.86\u003c/p\u003e\u003cp\u003e(17.7\u0026ndash;71.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.00\u003c/p\u003e\u003cp\u003e(91.0\u0026ndash;97.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.806\u003c/p\u003e\u003cp\u003e(0.747\u0026ndash;0.857)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLate stage\u003c/p\u003e\u003cp\u003e(III-IV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.86\u003c/p\u003e\u003cp\u003e(9.9\u0026ndash;81.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.00\u003c/p\u003e\u003cp\u003e(91.0\u0026ndash;97.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.839\u003c/p\u003e\u003cp\u003e(0.782\u0026ndash;0.887)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePostmenopausal status\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e29.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.10\u003c/p\u003e\u003cp\u003e(52.6\u0026ndash;77.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003cp\u003e(95.1\u0026ndash;100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003cp\u003e(0.897\u0026ndash;0.977)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEarly stage\u003c/p\u003e\u003cp\u003e(I-II)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.00\u003c/p\u003e\u003cp\u003e(23.1\u0026ndash;68.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003cp\u003e(98.2\u0026ndash;100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003cp\u003e(0.740\u0026ndash;0.850)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLate stage\u003c/p\u003e\u003cp\u003e(III-IV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.00\u003c/p\u003e\u003cp\u003e(56.6\u0026ndash;88.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003cp\u003e(98.2\u0026ndash;100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003cp\u003e(0.907\u0026ndash;0.970)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61.25\u003c/p\u003e\u003cp\u003e(49.7\u0026ndash;71.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.00\u003c/p\u003e\u003cp\u003e(91.0\u0026ndash;97.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003cp\u003e(0.835\u0026ndash;0.915)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDORA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePremenopausal status\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e46.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.19\u003c/p\u003e\u003cp\u003e(52.8\u0026ndash;91.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.43\u003c/p\u003e\u003cp\u003e(62.7\u0026ndash;79.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.741\u003c/p\u003e\u003cp\u003e(0.662\u0026ndash;0.810)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePostmenopausal status\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.53\u003c/p\u003e\u003cp\u003e(81.3\u0026ndash;97.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.30\u003c/p\u003e\u003cp\u003e(90.6\u0026ndash;99.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003cp\u003e(0.900\u0026ndash;0.981)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEarly stage\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(I-II)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003cp\u003e(63.1\u0026ndash;91.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.50\u003c/p\u003e\u003cp\u003e(74.3\u0026ndash;85.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003cp\u003e(0.763\u0026ndash;0.865)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLate stage\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(III-IV)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.33 (81.7\u0026ndash;98.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.50 (74.3\u0026ndash;85.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.921 (0.880\u0026ndash;0.952)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.50 (78.2\u0026ndash;93.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.50 (74.3\u0026ndash;85.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.876 (0.832\u0026ndash;0.912)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eOverall, DORA demonstrated a sensitivity of 87.50%, a specificity of 80.50%, and an AUC of 0.876, providing a uniform diagnostic approach applicable across all patient groups. These findings suggest that DORA could serve as a valuable complement to ROMA for ovarian cancer diagnosis, offering enhanced sensitivity, particularly in early-stage detection.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study aimed to improve the sensitivity of ovarian cancer detection while reducing false negatives and enhancing early-stage cancer diagnosis through a simple and applicable approach compared to the ROMA algorithm. The ROMA algorithm, which combines CA125 and HE4, has been widely used in ovarian cancer diagnostics, particularly among postmenopausal women. However, HE4, a product of the WFDC2 gene, is not specific to ovarian cancer and can be elevated in other conditions, such as endometrial cancer, lung tumors, and inflammatory diseases, including COVID-19 and sepsis [\u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35 CR36 CR37 CR38\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Additionally, HE4 levels fluctuate with age and menopausal status, leading to potential misclassification in diagnosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To address these limitations, we developed the DORA algorithm, incorporating Trx1, a redox-regulating protein involved in cancer progression [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Trx1 has been investigated as a biomarker in various cancers, including breast, colon, and gastric cancers [\u003cspan additionalcitationids=\"CR41 CR42 CR43 CR44 CR45 CR46\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and its expression remains stable regardless of age, TNM stage, or menopausal status [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our findings confirmed that Trx1 exhibits stable expression across these variables, in contrast to HE4. Consequently, we developed the DORA algorithm, which combines Trx1 and CA125 to enhance ovarian cancer detection reliability. The incorporation of Trx1 as a biomarker presents significant potential benefits, as it is not influenced by age, menopausal status, or childbirth history, and it compensates for the low sensitivity of current biomarkers in detecting early-stage ovarian cancer (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eROMA score are variable and the clinically acceptable minimum value is generally specificity of 75%, the optimal ROMA cutoff score and clinical utility of the test are determined as sensitivity\u0026thinsp;\u0026gt;\u0026thinsp;80.0% and specificity\u0026thinsp;\u0026ge;\u0026thinsp;75.0% [\u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In our study, ROMA demonstrated a sensitivity of 42.9% and specificity of 92.9% in premenopausal women, and 66.1% and 100.0% in postmenopausal women. In contrast, DORA significantly improved sensitivity, reaching 76.2% in premenopausal women and 91.5% in postmenopausal women, with specificity values of 71.4% and 97.3%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although DORA had slightly reduced specificity compared to ROMA, its higher sensitivity makes it more effective in detecting ovarian cancer, particularly in non-serous subtypes where CA125 and HE4 perform poorly (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This suggests that Trx1 plays a critical role in improving diagnostic accuracy for ovarian cancer subtypes that are typically difficult to detect using conventional biomarkers.\u003c/p\u003e\u003cp\u003eTrx1\u0026rsquo;s overexpression in some early-stage ovarian cancer patients highlights its potential for early detection (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, HE4 is affected by age and conditions such as hypertension and diabetes [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], with false positives commonly arising from renal failure, liver, and lung diseases [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Trx1 overexpression, on the other hand, is linked to inflammatory and immune diseases and is closely associated with tumor development and cancer progression [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In this study, the non-OC (non-ovarian cancer) group was classified as a benign group rather than a normal group. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, patients in the non-OC group exhibited high Trx1 expression, contributing to its lower specificity compared to CA125 and HE4. The inclusion of a normal group could potentially increase the specificity of the algorithm. While most studies categorize groups as normal, benign, and cancer, the heterogeneity of clinical patients increases the risk of false positives and negatives. Despite its slightly lower specificity compared to ROMA, DORA minimizes the risk of false negatives, reducing the likelihood of undiagnosed cancer cases. This makes DORA more clinically useful, particularly since Trx1 demonstrated superior performance in detecting early-stage ovarian cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Trx1\u0026rsquo;s ability to detect early-stage cancer may stem from its crucial role in tumor development [\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and its overexpression has also been associated with metastatic breast cancer [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBefore clinical application, some limitations must be addressed. This study was conducted in a single center with Korean participants, limiting its generalizability. Future studies with diverse populations and more clinical samples are essential for validation.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study underscores the importance of integrating multiple biomarkers to improve the early detection of ovarian cancer. CA125 remains a key biomarker for ovarian cancer diagnosis, its diagnostic power is significantly enhanced when combined with additional markers such as HE4 (ROMA) and Trx1 (DORA). Among these, Trx1 demonstrated stable expression across diverse demographic factors, addressing some of the variability seen with HE4. Furthermore, the DORA algorithm, which incorporates Trx1, exhibited comparable or superior performance to ROMA, particularly in its stability across both pre- and post-menopausal women. Notably, DORA showed greater potential for detecting early-stage ovarian cancer compared to ROMA. This suggests that Trx1 could serve as a complementary biomarker to CA125, potentially improving diagnostic accuracy, especially in cases where current algorithms like ROMA show limitations. The feasibility of integrating Trx1 into a biomarker combination with CA125 to enhance the sensitivity and specificity of ovarian cancer diagnosis was demonstrated. However, further research and clinical validation are necessary to confirm these findings and to assess the potential incorporation of Trx1 into routine diagnostic algorithms for ovarian cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eWHO\u003c/strong\u003e - World Health Organization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOC\u003c/strong\u003e - Ovarian Cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCA125\u0026nbsp;\u003c/strong\u003e- Cancer Antigen 125\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHE4\u0026nbsp;\u003c/strong\u003e-\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHuman Epididymis Protein 4\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrx1\u0026nbsp;\u003c/strong\u003e-Thioredoxin 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRMI -\u0026nbsp;\u003c/strong\u003eRisk of Malignancy Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROMA\u0026nbsp;\u003c/strong\u003e- Risk of Ovarian Malignancy Algorithm\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC\u003c/strong\u003e - Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e - Area Under the Curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEMR\u003c/strong\u003e - Electronic Medical Records\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSST\u003c/strong\u003e \u0026ndash; Serum Separating Tube\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANOVA\u003c/strong\u003e - Analysis of Variance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTNM\u003c/strong\u003e - Tumor, Node, Metastasis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to the Innovation Center for Industrial Mathematics, National Institute for Mathematical Sciences for their invaluable support and contributions to this study. Additionally, we extend our appreciation to the Chungnam National University Hospital Biobank for providing essential resources and assistance with sample collection and management. Their support has been instrumental in the successful completion of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKHS and YBK conceptualized the study. YBK supervised the study. MAP curated the data. SHK analyzed the data. SHK and XY interpreted the results. SHK and MAP drafted the manuscript. All authors have reviewed and approved the submitted version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Chungnam National University Health System (IRB approval no. 2022-04-; approval date: May 3, 2022; Daejeon, South Korea). Written informed consent for the use of blood samples in research was obtained from all patients, and the study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\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 Chungnam National University Hospital Research Fund for HIT discovery.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw datasets generated and/or analysed during the current study are provided in the supplementary file of this article. Additional data are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSerov SF, Scully RE. HISTOLOGICAL TYPING OF OVARIAN TUMOURS.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZamwar UM, Anjankar AP, Aetiology. Epidemiology, Histopathology, Classification, Detailed Evaluation, and Treatment of Ovarian Cancer. Cureus. 2022;14(10):e30561.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeppernick F, Meinhold-Heerlein I. The new FIGO staging system for ovarian, fallopian tube, and primary peritoneal cancer. Arch Gynecol Obstet. 2014;290(5):839\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchiavone MB, Herzog TJ, Lewin SN, Deutsch I, Sun X, Burke WM, et al. Natural history and outcome of mucinous carcinoma of the ovary. Am J Obstet Gynecol. 2011;205(5):e4801\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolschneider CH, Berek JS. Ovarian cancer: epidemiology, biology, and prognostic factors. Semin Surg Oncol. 2000;19(1):3\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBast RC, Feeney M, Lazarus H, Nadler LM, Colvin RB, Knapp RC. Reactivity of a monoclonal antibody with human ovarian carcinoma. J Clin Invest. 1981;68(5):1331\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMunkarah A, Chatterjee M, Tainsky MA. Update on ovarian cancer screening. Curr Opin Obstet Gynecol. 2007;19(1):22\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJacobs I, Bast RC Jr. The CA 125 tumour-associated antigen: a review of the literature. Hum Reprod. 1989;4(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSell S. Tumor Markers: Physiology, Pathobiology, Technology and Clinical Applications. Eleftherios P. Diamandis, Hervert A. Fritche, Hans Lilja, Daniel W. Chan, and Morton K. Schwartz, eds. Washington, DC: AACC Press, 2002, 513 pp., \u003cspan\u003e$\u003c/span\u003e99.00 (\u003cspan\u003e$\u003c/span\u003e79.00 AACC members), softcover. ISBN 1-890883-71-9. Clinical Chemistry. 2003;49(2):342.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBast RC, Xu FJ, Yu YH, Barnhill S, Zhang Z, Mills GB. CA 125: the past and the future. Int J Biol Markers. 1998;13(4):179\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDochez V, Caillon H, Vaucel E, Dimet J, Winer N, Ducarme G. Biomarkers and algorithms for diagnosis of ovarian cancer: CA125, HE4, RMI and ROMA, a review. J Ovarian Res. 2019;12(1):28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHellstr\u0026ouml;m I, Raycraft J, Hayden-Ledbetter M, Ledbetter JA, Schummer M, McIntosh M, et al. The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma. Cancer Res. 2003;63(13):3695\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScholler N, Crawford M, Sato A, Drescher CW, O\u0026rsquo;Briant KC, Kiviat N, et al. Bead-based ELISA for validation of ovarian cancer early detection markers. Clin Cancer Res. 2006;12(7 Pt 1):2117\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoore RG, Brown AK, Miller MC, Badgwell D, Lu Z, Allard WJ, et al. Utility of a novel serum tumor biomarker HE4 in patients with endometrioid adenocarcinoma of the uterus. Gynecol Oncol. 2008;110(2):196\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCviič D, Jagarlamudi K, Meglič L, Škof E, Zore A, Lukanović D, et al. A Dual Biomarker TK1 Protein and CA125 or HE4-Based Algorithm as a Better Diagnostic Tool than ROMA Index in Early Detection of Ovarian Cancer. Cancers. 2023;15(5):1593.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSamborski A, Miller MC, Blackman A, MacLaughlan-David S, Jackson A, Lambert-Messerlian G, et al. HE4 and CA125 serum biomarker monitoring in women with epithelial ovarian cancer. Tumour Biol. 2022;44(1):205\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoore RG, Miller MC, Disilvestro P, Landrum LM, Gajewski W, Ball JJ, et al. Evaluation of the diagnostic accuracy of the risk of ovarian malignancy algorithm in women with a pelvic mass. Obstet Gynecol. 2011;118(2 Pt 1):280\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMontagnana M, Danese E, Ruzzenente O, Bresciani V, Nuzzo T, Gelati M, et al. The ROMA (Risk of Ovarian Malignancy Algorithm) for estimating the risk of epithelial ovarian cancer in women presenting with pelvic mass: is it really useful? Clin Chem Lab Med. 2011;49(3):521\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhan Y, Chen J, Wu J, Gu Y, Huang Q, Deng Z, et al. Human epididymis protein 4 aggravates airway inflammation and remodeling in chronic obstructive pulmonary disease. Respir Res. 2022;23(1):120.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eS\u0026uuml;tő R, P\u0026oacute;csi M, Szab\u0026oacute; Z, Fejes Z, Iv\u0026aacute;dy G, Kerekes G, et al. Elevated level of serum human epididymis protein 4 (HE4) predicts disease severity and mortality in COVID-19 pneumonia. BMC Pulm Med. 2023;23(1):512.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJacob F, Meier M, Caduff R, Goldstein D, Pochechueva T, Hacker N, et al. No benefit from combining HE4 and CA125 as ovarian tumor markers in a clinical setting. Gynecol Oncol. 2011;121(3):487\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKallis GB, Holmgren A. Differential reactivity of the functional sulfhydryl groups of cysteine-32 and cysteine-35 present in the reduced form of thioredoxin from Escherichia coli. J Biol Chem. 1980;255(21):10261\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark BJ, Cha MK, Kim IH. Thioredoxin 1 as a serum marker for ovarian cancer and its use in combination with CA125 for improving the sensitivity of ovarian cancer diagnoses. Biomarkers. 2014;19(7):604\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCha MK, Park BJ, Kim IH. Thioredoxin1 as a novel serum marker for human ovarian cancer. The FASEB Journal. 2012;26(S1):967.5-967.5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark BJ, Cha MK, Kim IH. Thioredoxin 1 as a serum marker for breast cancer and its use in combination with CEA or CA15-3 for improving the sensitivity of breast cancer diagnoses. BMC Res Notes. 2014;7:7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee YJ, Kim Y, Choi BB, Kim JR, Ko HM, Suh KH, et al. The blood level of thioredoxin 1 as a supporting biomarker in the detection of breast cancer. BMC Cancer. 2022;22(1):12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShang W, Xie Z, Lu F, Fang D, Tang T, Bi R, et al. Increased Thioredoxin-1 Expression Promotes Cancer Progression and Predicts Poor Prognosis in Patients with Gastric Cancer. Oxid Med Cell Longev. 2019;2019:9291683.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaffel J, Bhattacharyya AK, Gallegos A, Cui H, Einspahr JG, Alberts DS, et al. Increased expression of thioredoxin-1 in human colorectal cancer is associated with decreased patient survival. J Lab Clin Med. 2003;142(1):46\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClauss A, Lilja H, Lundwall A. A locus on human chromosome 20 contains several genes expressing protease inhibitor domains with homology to whey acidic protein. Biochem J. 2002;368(Pt 1):233\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Dowdy S, Tipton T, Podratz K, Lu WG, Xie X, et al. HE4 as a biomarker for ovarian and endometrial cancer management. Expert Rev Mol Diagn. 2009;9(6):555\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBehrouzi R, Barr CE, Crosbie EJ. HE4 as a Biomarker for Endometrial Cancer. Cancers. 2021;13(19):4764.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNagy B, Bhattoa HP, Steiber Z, Csob\u0026aacute;n M, Szilasi M, M\u0026eacute;hes G, et al. Serum human epididymis protein 4 (HE4) as a tumor marker in men with lung cancer. Clin Chem Lab Med (CCLM). 2014;52(11):1639\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmall DM, Doherty DF, Dougan CM, Weldon S, Taggart CC. The role of whey acidic protein four-disulfide-core proteins in respiratory health and disease. Biol Chem. 2017;398(4):425\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLai X, Kang M, Chen Y, Xu F, Wang K, Cao J. Elevated serum level of human epididymal protein 4 (HE4) predicts poor prognosis in the critically ill with sepsis: A prospective observational cohort study. Clin Biochem. 2022;109\u0026ndash;110:79\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo J, Liang J, Wang S, Huang S, Zhou L, Shi Y, et al. Serum human epididymis secretory protein 4 correlates with sepsis-associated acute respiratory distress syndrome and 28-day mortality in critically ill patients. Ann Clin Biochem. 2022;59(5):338\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchirinzi A, Cazzolla AP, Lovero R, Lo Muzio L, Testa NF, Ciavarella D, et al. New Insights in Laboratory Testing for COVID-19 Patients: Looking for the Role and Predictive Value of Human epididymis secretory protein 4 (HE4) and the Innate Immunity of the Oral Cavity and Respiratory Tract. Microorganisms. 2020;8(11):1718.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei X, Su J, Yang K, Wei J, Wan H, Cao X, et al. Elevations of serum cancer biomarkers correlate with severity of COVID-19. J Med Virol. 2020;92(10):2036\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAwan MUN, Yan F, Mahmood F, Bai L, Liu J, Bai J. The Functions of Thioredoxin 1 in Neurodegeneration. Antioxid Redox Signal. 2022;36(13\u0026ndash;15):1023\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGriffiths HR, Bennett SJ, Olofsson P, Dunston CR. Thioredoxin as a putative biomarker and candidate target in age-related immune decline. Biochem Soc Trans. 2014;42(4):922\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhayne TF, Parinandi N, Maulik N. Thioredoxins in cardiovascular disease. Can J Physiol Pharmacol. 2015;93(11):903\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeng XS, Geng WS, Chen L, Jia JJ. Thioredoxin as a Therapeutic Target in Cerebral Ischemia. Curr Pharm Des. 2018;24(25):2986\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNoike T, Miwa S, Soeda J, Kobayashi A, Miyagawa S. ichi. Increased expression of thioredoxin-1, vascular endothelial growth factor, and redox factor-1 is associated with poor prognosis in patients with liver metastasis from colorectal cancer. Hum Pathol. 2008;39(2):201\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuan X, Ning J, Fu W, Wang Y, Zhang J, Ding S. Helicobacter pylori with trx1 high expression promotes gastric diseases via upregulating the IL23A/NF-κB/IL8 pathway. Helicobacter. 2024;29(2):e13072.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRibback S, Winter S, Klatte T, Schaeffeler E, Gellert M, St\u0026uuml;hler V, et al. Thioredoxin 1 (Trx1) is associated with poor prognosis in clear cell renal cell carcinoma (ccRCC): an example for the crucial role of redox signaling in ccRCC. World J Urol. 2022;40(3):739\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKakolyris S, Giatromanolaki A, Koukourakis M, Powis G, Souglakos J, Sivridis E, et al. Thioredoxin expression is associated with lymph node status and prognosis in early operable non-small cell lung cancer. Clin Cancer Res. 2001;7(10):3087\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoore RG, McMeekin DS, Brown AK, DiSilvestro P, Miller MC, Allard WJ, et al. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol Oncol. 2009;112(1):40\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarlsen MA, H\u0026oslash;gdall EVS, Christensen IJ, Borgfeldt C, Kalapotharakos G, Zdrazilova-Dubska L, et al. A novel diagnostic index combining HE4, CA125 and age may improve triage of women with suspected ovarian cancer - An international multicenter study in women with an ovarian mass. Gynecol Oncol. 2015;138(3):640\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarlsen MA, Sandhu N, H\u0026oslash;gdall C, Christensen IJ, Nedergaard L, Lundvall L, et al. Evaluation of HE4, CA125, risk of ovarian malignancy algorithm (ROMA) and risk of malignancy index (RMI) as diagnostic tools of epithelial ovarian cancer in patients with a pelvic mass. Gynecol Oncol. 2012;127(2):379\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSandri MT, Bottari F, Franchi D, Boveri S, Candiani M, Ronzoni S, et al. Comparison of HE4, CA125 and ROMA algorithm in women with a pelvic mass: correlation with pathological outcome. Gynecol Oncol. 2013;128(2):233\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang M, Zhao B, Xie J, Liang Y, Yang Z. Serum Human Epididymis Protein 4 is Associated with Renal Function and Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus. Biomed Res Int. 2019;2019(1):4831459.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNagy B, Krasznai ZT, Balla H, Csob\u0026aacute;n M, Antal-Szalm\u0026aacute;s P, Hern\u0026aacute;di Z, et al. Elevated human epididymis protein 4 concentrations in chronic kidney disease. Ann Clin Biochem. 2012;49(Pt 4):377\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu Z, Nian C, Sun W, Liu X, Nian X. Elevated serum HE4 levels as a novel biomarker of disease severity and hepatic fibrosis in autoimmune hepatitis. Clin Chim Acta. 2024;559:119682.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeng K, Tian M, Gui X, Xie M, Gao Y, Shi S, et al. Human epididymis protein 4 is associated with severity and poor prognosis of connective tissue disease-associated interstitial lung disease with usual interstitial pneumonia pattern. Int Immunopharmacol. 2022;108:108704.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuri J, Thut H, Feng Q, Kopf M. Thioredoxin-1 distinctly promotes NF-κB target DNA binding and NLRP3 inflammasome activation independently of Txnip. Horng T, Rath S, editors. eLife. 2020;9:e53627.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrogan TM, Fenoglio-Prieser C, Zeheb R, Bellamy W, Frutiger Y, Vela E, et al. Thioredoxin, a putative oncogene product, is overexpressed in gastric carcinoma and associated with increased proliferation and increased cell survival. Hum Pathol. 2000;31(4):475\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCeccarelli J, Delfino L, Zappia E, Castellani P, Borghi M, Ferrini S, et al. The redox state of the lung cancer microenvironment depends on the levels of thioredoxin expressed by tumor cells and affects tumor progression and response to prooxidants. Int J Cancer. 2008;123(8):1770\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhatia M, McGrath KL, Di Trapani G, Charoentong P, Shah F, King MM, et al. The thioredoxin system in breast cancer cell invasion and migration. Redox Biol. 2016;8:68\u0026ndash;78.\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":"Ovarian cancer, early diagnosis, liquid biopsy, Thioredoxin 1, Cancer antigen 125","lastPublishedDoi":"10.21203/rs.3.rs-7359728/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7359728/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eOvarian cancer is the fifth leading cause of cancer-related mortality in women, with early detection being critical for improving patient outcomes. Current diagnostic biomarkers, including Cancer antigen 125 (CA125), human epididymis protein 4 (HE4), and their combined Risk of Ovarian Malignancy Algorithm (ROMA), exhibit limited sensitivity and specificity, particularly in early-stage disease and premenopausal women. Thioredoxin-1 (Trx1) has emerged as a potential complementary biomarker. This study evaluates the diagnostic utility of Trx1 in combination with CA125 and HE4.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eSerum levels of CA125, HE4, and Trx1 were measured in 80 ovarian cancer patients and 200 controls. The influence of age, menopausal status, obstetric history, pathological characteristics, and cancer stage on biomarker performance was analyzed. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) curve analysis, with sensitivity, specificity, and area under the curve (AUC) values calculated for individual biomarkers and their combinations. Performance comparisons were conducted across premenopausal and postmenopausal subgroups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong individual biomarkers, CA125 demonstrated the highest diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.872), followed by HE4 (AUC\u0026thinsp;=\u0026thinsp;0.815) and Trx1 (AUC\u0026thinsp;=\u0026thinsp;0.588). The combination of Trx1 and CA125, termed the Dual-marker Ovarian Cancer Risk Algorithm (DORA), improved diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.852), while the addition of HE4 further enhanced accuracy (AUC\u0026thinsp;=\u0026thinsp;0.878). Trx1 exhibited stable expression across demographic subgroups. ROMA demonstrated low sensitivity in premenopausal women (42.86%) despite high specificity (92.86%), whereas postmenopausal women showed slightly improved sensitivity (66.10%) with perfect specificity (100.00%). DORA was non-inferior to ROMA and achieved superior sensitivity in both premenopausal (76.19%) and postmenopausal (91.53%) patients, maintaining consistent performance across cancer stages.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eDORA represents a promising alternative to ROMA, offering enhanced sensitivity and reliable diagnostic performance across diverse patient populations. Trx1 has the potential to improve ovarian cancer detection, particularly in early-stage and premenopausal cases where current diagnostic approaches remain inadequate.\u003c/p\u003e","manuscriptTitle":"Towards a Universal Ovarian Cancer Biomarker Model: Clinical Validation of Trx1 and CA125 Dual Strategy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 18:53:41","doi":"10.21203/rs.3.rs-7359728/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-04T17:03:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121737439310707988016419893204578261679","date":"2026-04-30T16:03:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T16:57:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226933039787145047808304285080349497948","date":"2025-09-22T13:13:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-15T04:54:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-10T04:34:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-21T03:07:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-21T00:34:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-08-21T00:31:18+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":"efe8ac16-1ff8-440e-acf8-0f4e550a0e0d","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-04T17:03:49+00:00","index":110,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T18:53:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 18:53:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7359728","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7359728","identity":"rs-7359728","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}