A tumor-associated autoantibody panel for early detection of breast cancer in Sana’a, Yemen

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Al-Athwary, Khaled A. Al-Moyed, Ahmed Y. Al-Jaufy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8472357/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Background: Breast cancer remains a major global health challenge and is the leading cause of cancer-related mortality among women worldwide. Early and accurate diagnosis is crucial for effective management. Tumor-associated autoantibodies may arise early during tumorigenesis and represent potential biomarkers for early cancer detection. This study aimed to evaluate a panel of tumor-associated autoantibodies for the early detection of breast cancer in Sana'a, Yemen. Methods: This diagnostic case-control study included 45 patients with newly diagnosed early-stage breast cancer and 45 healthy subjects in Sana'a city. Serum IgG autoantibodies against p53, MUC1, HER2, Cyclin B1, and c-Myc were measured using enzyme-linked immunosorbent assay (ELISA). An optimal diagnostic panel was constructed using forward stepwise logistic regression. The diagnostic performance of individual autoantibodies and the autoantibody panel was assessed using receiver operating characteristic (ROC) curve analysis. Key diagnostic indices, including sensitivity, specificity, positive and negative predictive values (PPV and NPV), and positive and negative likelihood ratios (PLR and NLR), were calculated. Results: Patients with early-stage breast cancer exhibited significantly higher serum levels of tumor-associated autoantibodies than healthy subjects (p < 0.01). The positivity frequencies of individual tumor-associated autoantibodies in breast cancer patients ranged from 26.7% to 40.0%. An optimized panel composed of tumor-associated autoantibodies against p53, HER2, Cyclin B1, and c-Myc demonstrated a marked increase in diagnostic sensitivity to 68.9% at a specificity of 93.3%, with an area under the ROC curve (AUC) of 0.898 (95% CI, 0.832–0.963). The panel showed a PPV of 91.2% and an NPV of 75.0%, with PLR and NLR values of 10.3 and 0.33, respectively. No significant correlation was observed between the levels of tumor-associated autoantibodies and breast cancer size, grade, or stage. Conclusions: The panel of tumor-associated autoantibodies targeting p53, HER2, Cyclin B1, and c-Myc shows potential for detecting early-stage breast cancer and could serve as a complementary tool to mammography, particularly in younger women and those with dense breast tissue, where imaging sensitivity is reduced. Further large-scale, multicenter validation studies are needed before clinical implementation. Breast cancer Early diagnosis Biomarker Tumor-associated autoantibody Yemen Figures Figure 1 Figure 2 Background Breast cancer remains a major global health challenge due to its biological complexity and heterogeneous clinical presentation [ 1 , 2 ]. In 2022, it was the most commonly diagnosed cancer worldwide, with approximately 2.3 million new cases and 666,000 deaths among women, accounting for one in four cancer diagnoses and one in six cancer deaths [ 3 ]. As the global population grows, this annual incidence is projected to reach 3.2 million by 2050 [ 4 ]. In Yemen, breast cancer is the most prevalent malignancy and the leading cause of cancer-related mortality [ 5 ]. According to GLOBOCAN 2022 data, breast cancer ranked first among all cancers in Yemen, accounting for 2,872 new cases (30.8%) and 1,528 deaths (12.9%) in women [ 6 ]. The disease profile in Yemen is characterized by early onset and aggressive biology, with a majority of patients diagnosed under age 50 and presenting with advanced-stage tumors [ 7 ]. Accurate early detection is critical for improving survival and treatment outcomes [ 8 , 9 ]. Mammography, the current gold standard for population screening, has well- documented limitations including reduced sensitivity in young women and those with dense breast tissue, radiation exposure, and risks of false-positive results and overdiagnosis [ 9 , 10 ]. Similarly, established serum biomarkers like CA 15 − 3, Carcinoembryonic Antigen (CEA), and CA 27–29 lack the sensitivity and specificity required for early detection; guidelines from the American Society of Clinical Oncology restrict their use to monitoring metastatic disease [ 11 , 12 ]. Consequently, there is a clear need for novel, non-invasive biomarkers for early breast cancer detection. Tumor-associated autoantibodies (TAAbs) present a promising solution. They are generated by the humoral immune system in response to abnormal tumor-associated antigens (TAAs) that are no longer recognized as "self" [ 13 , 14 ]. TAAbs possess several advantageous characteristics for early detection: they are produced early in tumorigenesis, are highly stable in serum, undergo biological amplification making them easier to detect than their corresponding antigens, and their detection is unaffected by breast tissue density [ 11 , 15 , 16 , 17 ]. However, due to the heterogeneity of breast cancer, a single TAAb lacks sufficient diagnostic accuracy. Detecting a panel of multiple TAAbs is therefore essential to achieve the sensitivity and specificity required for clinical utility [ 11 , 18 ]. Based on prior meta-analyses indicating their frequent association with breast cancer, we selected a panel of five TAAb against p53, MUC1, HER2, c-Myc, and Cyclin B1 for the evaluation of TAAbs for the early detection of breast cancer [ 19 ]. Delayed diagnosis of breast cancer remains a significant challenge, particularly in developing countries such as Yemen, where most women are diagnosed at an advanced stage. To our knowledge, no previous study has evaluated the utility of TAAb panels for the early detection of breast cancer in Yemen. Therefore, this study aimed to assess the diagnostic value of a panel of five TAAbs for the early detection of breast cancer in Sana’a, Yemen. Method Study design, participants, and setting A diagnostic case-control study was conducted at the Life Center for Early Cancer Detection, National Cancer Control Foundation (NCCF), in Sana’a City, Yemen, from October 2021 to January 2023. The study included 90 female participants, who were split into two group: a patient group of 45 and a healthy subject group of 45 women. The patient group consisted of female patients with a recent diagnosis of early-stage breast cancer before any treatment was administered. Diagnosis was confirmed via mammography followed by histopathological examination (biopsy), which served as the reference standard. Tumor staging was performed according to the 8th edition of the American Joint Committee on Cancer (AJCC) staging system [ 20 ]. Early-stage breast cancer was defined as ductal carcinoma in situ (DCIS) or stages I, IIA, IIB, and IIIA, in accordance with the National Cancer Institute (NCI) Dictionary of Cancer Terms [ 21 ]. Patients with a history of any other cancer or benign breast tumors were excluded. The healthy subject group comprised women aged 40 years or older, which is the recommended threshold for routine mammography screening. These individuals had no personal history of malignancy and had received negative mammogram results. Data and Sample collection The data for each patient and healthy participant were recorded using a structured questionnaire developed specifically for the current study (Supplementary File 1), which included demographic characteristics and relevant clinical information. A venous blood sample of five mL was collected from each participant using standard venipuncture techniques and transferred into a plain blood collection tube. After clotting, samples were centrifuged at 1,000 rpm for 20 minutes, and the separated serum was promptly stored at − 20°C until further analysis. Measurement of tumor-associated autoantibodies Serum levels of IgG autoantibodies against five antigens p53, MUC1, HER2, Cyclin B1, and c-Myc were quantified using commercial indirect enzyme-linked immunosorbent assay (ELISA) kits (Cloud-Clone Corp., USA). All assays were performed in strict adherence to the manufacturer's protocols. Briefly, serum samples were diluted 1:100 in phosphate-buffered saline. Standards, blanks, and diluted samples (100 µL) were added in duplicate to antigen-pre-coated wells and incubated for 1 hour at 37°C. After removing liquid, 100 µl of detection reagent A was added and incubated again for 1 hour. The plate was washed five times with 350 µl of wash solution, making sure to aspirate thoroughly between washes, and then tapped to eliminate residual buffer. Subsequently, 90 µl of substrate solution was added and incubated at 37°C for 10–20 minutes away from light. The reaction was halted with 50 µl of stop solution, changing the liquid's color to yellow. The optical density (OD) of each well was measured using a microplate reader (HumaReader HS, Human, Germany) at 450 nm. Statistical analysis The data were entered and analyzed using IBM SPSS Statistics, Version 23.0 (IBM Corp., Armonk, NY, USA). Mann–Whitney’s U test was used to compare the significant differences in autoantibody levels between cancer and control subjects. Diagnostic performance for each individual TAAb was assessed via Receiver Operating Characteristic (ROC) curve analysis. The optimal cut-off value was defined as the point maximizing the Youden Index. The area under the ROC curve (AUC) with a 95% confidence interval (CI) was calculated as a measure of overall accuracy. To select an optimized panel of autoantibody biomarkers for diagnosis, we applied logistic regression analysis with a forward stepwise selection. The sensitivity, specificity, positive and negative (PLR and NLR) likelihood ratios, and positive (PPV) and negative predictive values (NPV) were calculated following the methodology outlined in the epidemiology textbook [ 22 ]. Pearson’s correlation coefficient (r) was used to assess associations between continuous autoantibody levels and clinical characteristics (tumor size, grade and stage) within the patient group. A p -value of < 0.05 was considered statistically significant. Result Participates characteristics This study included a total of 90 participants, comprising 45 patients with early-stage breast cancer and 45 healthy subjects. The demographic and clinicopathological characteristics of the study population are summarized in Table 1 . The mean age of patients with early-stage breast cancer was 42.8 ± 9.6 years, which was lower than that of the healthy control group (49.3 ± 6.6 years). The majority of patients (64%) were younger than 50 years. Most tumors measured between 2.0 and 5.0 cm in size (82.2%), were of histological grade II (73.3%), and were classified as stage II (71.1%). Table 1 Demographic and clinicopathologic characteristics of early-stage breast cancer patients and healthy subjects in Sana'a city, Yemen Characteristics Early-stage breast cancer n (%) Healthy subjects n (%) Mean Age ± SD (years) 42.84 ± 9.6 49.31 ± 6.6 Age range (years) 27–64 40–70 2.0 cm 4 (8.9) - 2.0–5 cm 37 (82.2) - > 5 cm 4 (8.9) - Histological grade 1 0 (0.0) - 2 33 (73.3) - 3 12 (26.7) - TNM stage DCIS 4 (8.9) - I 9 (20.0) - II 32 (71.1) - SD standard deviation, n numbers, % percentage, TNM tumor, nodes, metastasis, DCIS ductal carcinoma in situ . , Serum levels of tumor-associated autoantibodies Serum levels of five tumor-associated autoantibodies (p53, MUC1, HER2, Cyclin B1, and c-Myc) were measured using enzyme-linked immunosorbent assay (ELISA). Initial comparisons using the Mann–Whitney U test revealed significantly elevated levels of all five autoantibodies in patients with early-stage breast cancer compared to healthy subjects (p < 0.01), as illustrated in Fig. 1 . Optimization of an autoantibody panel by logistic regression ROC curve analysis was used to evaluate the diagnostic accuracy of the individual autoantibodies and the optimized autoantibody panel in early-stage breast cancer (Fig. 2). The cutoff optical density (OD) values were determined by maximizing the Youden index and were 0.108, 0.115, 0.120, 0.125, 0.129, and 0.590 for p53, MUC1, HER2, Cyclin B1, c-Myc, and the four-autoantibody panel, respectively. Based on these cutoffs, positivity rates for individual autoantibodies and the panel were significantly higher in early-stage breast cancer patients compared with healthy subjects (p < 0.01) (Table 2). The four-autoantibody panel achieved an AUC of 0.898 (95% CI: 0.832–0.963), with a sensitivity of 68.9% and a specificity of 93.3%. This performance exceeded that of any single autoantibody, which showed AUC values ranging from 0.735 to 0.797 and sensitivities between 26.7% and 40.0%. The panel had PPV and NPV of 91.2 and 75.0%, respectively, and PLR and NLR of 10.3 and 0.33%, respectively (Table 3). Table 2 Positivity frequencies of tumor-associated autoantibodies in patients with early-stage breast cancer and healthy subjects in Sana’a, Yemen Group p53 n (%) MUC1 n (%) HER2 n (%) Cyclin B1 n (%) c-Myc n (%) Panel n (%) Early-stage breast cancer (n=45) 18(40.0) *** 13(28.9) *** 17 (37.8) *** 16 (35.6) *** 12 (26.7) *** 31(68.9) * ** Healthy subjects (n=45) 3(6.7) 4(8.9) 2 (4.4) 3 (6.7) 4 (8.9) 3 (6.7) Panel positivity was defined as positivity for at least one of the four autoantibodies (p53, HER2, Cyclin B1, or c-Myc). *** p < 0.01 compared with healthy subjects. Correlation of autoantibody levels with clinicopathological parameters Pearson’s correlation analysis was conducted to assess the association between serum levels of individual autoantibodies, as well as the autoantibody panel, and clinicopathological parameters in patients with early-stage breast cancer. No statistically significant correlations were observed between the levels of individual autoantibodies or the autoantibody panel and tumor size, histological grade, or TNM stage (p > 0.05) (Table 4). Table 3 Diagnostic performance of tumor-associated autoantibodies in patients with early-stage breast cancer in Sana’a, Yemen Tumor-associated autoantibodies AUC (95%CI) Accuracy Sensitivity Specificity PPV NPV PLR NLR p53 0.797 (0.690-0.905) 66.6 40.0 93.3 85.7 60.9 6.00 0.64 MUC1 0.755 (0. 643-0.868) 60.0 28.9 91.1 76.5 56.2 3.25 0.78 HER2 0.761 (0. 651-0.871) 66.7 37.8 95.6 89.5 60.6 8.5 0.65 Cyclin B1 0.757 (0.650-0.864) 64.4 35.6 93.3 84.2 59.2 5.33 0.69 c-Myc 0.735 (0.621-0.848) 58.8 26.7 91.1 75.0 55.4 3.0 0.80 Four-autoantibody Panel 0.898 (0.832-0.963) 81.1 68.9 93.3 91.2 75.0 10.3 0.33 ACU, area under the curve; CI, confidence interval; NLR, negative likelihood ratio; NPV, negative predictive value; PLR, positive likelihood ratio; PPV, positive predictive value. Four- autoantibodies panel: autoantibodies against p53, HER2, Cyclin B1, and c-Myc. Table 4 Pearson's correlation coefficients (r) between the autoantibody levels with clinicopathologic parameters in patients with early-stage breast cancer in Sana'a, Yemen Parmeter p53 (r) p MUC1 ( r) p HER2 (r) p CyclinB1 (r) p c-Myc ( r) p Panel ( r) p Tumor size (.0 )1.0 (.23) .13 (.0) 1.0 (.0) 1.0 (-.11) .47 (.0) 1.0 Histological grade (.07) .66 (-.25) .10 (.08) .60 (.10).72 (-.08) .62 (.2) .18 TNM stage (.25) .10 (.20).18 (-.25) .10 (.04) .79 (.0) .98 (-.15) .33 r,Pearson's correlation coefficients; Correlation was significant at p value ≤ 0.05 level; panel includes autoantibodies against p53, HER2, Cyclin B1, and c-Myc. Discussion The early detection of breast cancer is crucial for reducing mortality rates and improving treatment outcomes [ 23 ]. While mammography is the standard method for early breast cancer detection, it has limitations, especially in identifying small tumors in women with dense breast tissue. This creates a need for additional diagnostic tools [ 11 , 16 ]. In recent years, great efforts have been made on the development of blood-based biomarkers which show the potential for earlier detection of breast cancer. Despite tremendous efforts in this research area, clinical applications are still in their infancy and are challenging to achieve [ 11 ]. CEA and CA15-3 are the most frequent blood markers used in breast cancer management but they lack the sensitivity and specificity required for early-stage detection [ 11 , 17 ]. Serum TAAbs from patients with cancer offer opportunities as biomarkers, particularly for early cancer detection, including breast cancer. The detection of TAAbs could potentially identify breast cancer before the appearance of symptoms, making them an important area of research for early cancer detection [ 11 ]. Consistent with the literature, the sensitivity of any single autoantibody was low, rendering them unsuitable for standalone early screening [ 24 ]. This limitation is largely attributed to the biological heterogeneity of breast cancer, where different patients exhibit aberrant expression of various proteins [ 24 ]. To overcome this limitation, researchers have focused on developing autoantibody panels to improve diagnostic sensitivity. For instance, Chapman et al. [ 25 ] measured multiple autoantibodies against tumor-associated antigens (BRCA1, BRCA2, c-myc, HER2, MUC1, NY-ESO-1, and p53) and demonstrated that, while individual TAAbs showed low sensitivity (8–34% in primary cancer, 3–23% in DCIS), a combined panel increased sensitivity to 64% and 45%, respectively. In our study, we applied logistic regression to develop an optimized panel. The model excluded anti-MUC1 ( p = 0.142), potentially reflecting differences in antigen immunogenicity or tumor biology within our specific cohort. The final model retained four TAAbs against p53, HER2, Cyclin B1, c-Myc, generating a composite panel score. Using this panel, sensitivity increased dramatically to 68.9%, while maintaining a high specificity of 93.3%. This finding strongly supports the principle that a multi-marker panel is superior to any single autoantibody for the early identification of breast cancer. The diagnostic performance of the four-autoantibody panel in the present study (sensitivity, 68.9%; specificity, 93.3%) is comparable to that reported for other autoantibody panels in the literature. These results are consistent with a previous study using a panel comprising Imp1, p16, Koc, survivin, Cyclin B1, and c-Myc, which reported a sensitivity of 67.3% and a specificity of 92.2% [ 26 ], and exceed the performance of another panel consisting of BMI-1, HSP70, NY-ESO-1, and p53, which achieved a sensitivity of 59.6% and a specificity of 90.2% [ 11 ]. However, the performance of the present panel was slightly lower than that of a previously reported panel including p53, Cyclin B1, p16, p62, 14-3-3ζ, and survivin, which demonstrated a sensitivity of 72.1% and a specificity of 96.7% [ 27 ]. These differences may be attributable to variations in sample size, autoantibody panel composition, and population characteristics. Overall, these findings support the accumulating evidence that the expansion and optimization of tumor-associated autoantibody panels can substantially improve diagnostic performance. The predictive performance of the panel in the present study demonstrated clinically relevant utility. The PPV and NPV of our panel (91.2% and 75.0%, respectively) were closely comparable to those reported for the panel of a previous study (91.7% and 68.6%) [ 26 ], exceeded the PPV (82.3%) of another study's panel, and showed a similar NPV (74.5%) [ 11 ]. A PLR above 10 or NLR below 0.1 reflects a test with high diagnostic utility for confirming or excluding a disease, respectively [ 28 ]. Our panel had a PLR of 10.3 and an NLR of 0.33, supporting its potential clinical value. Notably, the PLR of our panel was higher and its NLR was lower than those reported in comparable studies (PLR, 8.52; NLR, 0.36) [ 26 ] and (PLR, 6.08; NLR, 0.45) [ 11 ], suggesting improved discriminative performance. The diagnostic accuracy of the individual TAAbs and the autoantibody panel was assessed using ROC curve analysis. The AUC, which reflects the test's ability to discriminate between diseased and non-diseased individuals, ranges from 0.5 (no discrimination) to 1.0 (perfect discrimination). An AUC value > 0.80 is generally considered clinically useful [ 29 ]. The autoantibody panel in the present study achieved an AUC of 0.898 (95% CI: 0.832–0.963) with an overall accuracy of 81.1%, demonstrating a strong capacity to differentiate patients with early-stage breast cancer from healthy subjects. This performance exceeded that of the panel in a previous study, with an AUC of 0.81 and an accuracy of 77% [ 30 ]. However, the performance of our panel was slightly lower than that reported in another study, which demonstrated an AUC of 0.916 and an accuracy of 82.4% [ 27 ]. This difference may be attributable to the larger sample size in that study (184 breast cancer patients and 184 controls), suggesting that a cohort size is important factors in optimizing the diagnostic accuracy. This study revealed no significant correlation (p > 0.05) between the levels of the autoantibody panel or individual autoantibodies and the clinical characteristics of breast cancer. This finding is consistent with previous studies [ 11 , 25 ] that also reported no significant association between autoantibody levels and clinicopathological parameters. In contrast, another study reported that only the histological grade was associated with autoantibody seropositivity [ 31 ]. The difference is likely attributable to variations in autoantibody panel composition, study populations, or detection methodologies. This study has several strengths, including its exclusive focus on early-stage breast cancer and the use of an optimized statistical modeling approach. To our knowledge, this is the first study to evaluate a TAAb panel for the early detection of breast cancer in Yemen. Nevertheless, several limitations should be acknowledged. The relatively small sample size and single-center design may limit the generalizability of the findings, and the lack of an independent validation cohort precludes immediate clinical translation. Future multicenter studies with larger cohorts and external validation, potentially incorporating high-throughput autoantibody profiling platforms, are warranted to confirm and extend these findings. Conclusion The panel of tumor-associated autoantibodies targeting p53, HER2, Cyclin B1, and c-Myc has the potential to detect early-stage breast cancer and could serve as a complementary tool to mammography, particularly in younger women and those with dense breast tissue, where imaging sensitivity is reduced. Further large-scale, multicenter validation studies are needed before clinical implementation can be considered. Abbreviations AUC: Area under the curve; BRCA 1/2; BReast CAncer gene 1/2; CA 15-3; Cancer antigen 15-3; CEA: carcinoembryonic antigen; c-Myc; Cellular Myc protein; CI: Confidence interval; DCIS: Ductal carcinoma in situ ; ELISA: enzyme-linked immunosorbent assay; HER2; Human epidermal growth factor receptor 2; MUC1: Mucin-1 protein; PLR: Positive likelihood ratio; NCCF: National Cancer Control Foundation; NLR: Negative likelihood ratio; NPV: Negative predictive value; OD: optical density ;PPV: Positive predictive value; P53: Protein 53; ROC: Receiver operating characteristic; TAAbs: Tumor-associated autoantibodies; TAAs: Tumor-associated antigens; TNM: Tumor, nodes, metastasis Declarations Acknowledgements The authors gratefully acknowledge all patients and healthy volunteers who participated in this study. We also thank the Life Center for Early Cancer Detection at the National Cancer Control Foundation (NCCF) for facilitating sample and data collection, with special thanks Marwa A. Al-Maqrami. Author contributions AAA conceived and designed the study, performed the experimental work, collected and analyzed the data, and drafted the manuscript. KAA and AYA supervised the research and critically revised the manuscript. All authors read and approved the final manuscript. Funding Not applicable. Data availability The data supporting the findings of this study are available from the corresponding author upon reasonable request. Ethical approval and consent to participate This study was approved by the Institutional Ethics Committee of the Faculty of Medicine and Health Sciences, Sana’a University, Yemen. Written informed consent was obtained from all participants prior to enrollment after a clear explanation of the study objectives. Participants were informed that their personal information would remain confidential and that they could withdraw from the study at any time without penalty. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Department of Medical Microbiology and Immunology, Faculty of Medicine and Health Sciences, Sana’a University, Sana’a, Yemen. 2 Laboratory Department, Ibn Sina Specialist Hospital, Sana’a, Yemen. References 1 Xiong X, Zheng LW, Ding Y, Chen YF, Cai YW, Wang LP, et al. Breast cancer: pathogenesis and treatments. Signal Transduct Target Ther. 2025;10(1):49. Palma M. Advancing breast cancer treatment: the role of immunotherapy and cancer vaccines in overcoming therapeutic challenges. Vaccines (Basel). 2025;13(4):344. Zhang Y, Ji Y, Liu S, Li J, Wu J, Jin Q, et al. Global burden of female breast cancer: new estimates in 2022, temporal trends and future projections to 2050 based on GLOBOCAN. J Natl Cancer Cent. 2025;5(3):287. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8472357","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583756265,"identity":"e9fcf579-01ee-4026-b717-9e21d24fbc34","order_by":0,"name":"Abdulalim A. Al-Athwary","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACdgYGZgijIfHBByDNxk5ICzMDYzOYwXPgseEMkBZmorVIJD6T5oGI4Af8zczPHxe22eUbHEhOk7b5tU2eD2jIh485uLVIHGYzbJ7Zlmy54cCxZOvcvtuGbcwMzJIzt+Gx5jCDYTPvNmYDg4M9ibdze24zArWwMfPi0SJ/mP0jUEu9gcFh/g/Slj237QlqMTjMA7LlsIHBMYYkaYYftxMJajE8zFM4m/ffcQPJMwzJhr0Nt5PbmBmb8fpF7nj7hs88Z6oN+O4/SHzw489t2/ntzQc/fMTnfRhQOAAkGNtATMYGItQDgTxY3R/iFI+CUTAKRsHIAgD3xlOE82ugqQAAAABJRU5ErkJggg==","orcid":"","institution":"Sana’a University","correspondingAuthor":true,"prefix":"","firstName":"Abdulalim","middleName":"A.","lastName":"Al-Athwary","suffix":""},{"id":583756267,"identity":"d9504deb-2aa6-4972-a99b-a2e68034825e","order_by":1,"name":"Khaled A. Al-Moyed","email":"","orcid":"","institution":"Sana’a University","correspondingAuthor":false,"prefix":"","firstName":"Khaled","middleName":"A.","lastName":"Al-Moyed","suffix":""},{"id":583756269,"identity":"8d35596a-49f0-4742-85d5-59e0dcc9f537","order_by":2,"name":"Ahmed Y. Al-Jaufy","email":"","orcid":"","institution":"Sana’a University","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Y.","lastName":"Al-Jaufy","suffix":""}],"badges":[],"createdAt":"2025-12-29 11:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8472357/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8472357/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101881360,"identity":"f87f36ad-875f-482a-8cfb-aae615b044ee","added_by":"auto","created_at":"2026-02-04 15:11:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82176,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot for serum levels of individual tumor-associated autoantibodies in early-stage breast cancer patients and healthy subjects. ***P \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8472357/v1/adb8ba6d13813fb26f2af4c5.png"},{"id":101789288,"identity":"5a0513d7-0474-4640-9e5c-a7d8d014cd6c","added_by":"auto","created_at":"2026-02-03 15:57:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80526,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic performance of autoantibodies for early-stage breast cancer detection. ROC curve analysis of individual autoantibodies and the four-autoantibody panel comprising p53, HER2, Cyclin B1, and c-Myc.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8472357/v1/354c1c2e9844f75c5c37eeb5.png"},{"id":101882965,"identity":"b8f07283-d658-4625-b51b-8ba4ca3a6319","added_by":"auto","created_at":"2026-02-04 15:26:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":939091,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8472357/v1/4f677c5a-743d-4f61-9c24-3bb9446fa094.pdf"},{"id":101789290,"identity":"fb565ea8-8e28-4d20-8b0d-a5541db80666","added_by":"auto","created_at":"2026-02-03 15:57:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":122895,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8472357/v1/d11872571bf8017fdfa54c60.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A tumor-associated autoantibody panel for early detection of breast cancer in Sana’a, Yemen","fulltext":[{"header":"Background","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBreast cancer remains a major global health challenge due to its biological complexity and heterogeneous clinical presentation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2022, it was the most commonly diagnosed cancer worldwide, with approximately 2.3\u0026nbsp;million new cases and 666,000 deaths among women, accounting for one in four cancer diagnoses and one in six cancer deaths [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As the global population grows, this annual incidence is projected to reach 3.2\u0026nbsp;million by 2050 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In Yemen, breast cancer is the most prevalent malignancy and the leading cause of cancer-related mortality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to GLOBOCAN 2022 data, breast cancer ranked first among all cancers in Yemen, accounting for 2,872 new cases (30.8%) and 1,528 deaths (12.9%) in women [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The disease profile in Yemen is characterized by early onset and aggressive biology, with a majority of patients diagnosed under age 50 and presenting with advanced-stage tumors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccurate early detection is critical for improving survival and treatment outcomes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Mammography, the current gold standard for population screening, has well- documented limitations including reduced sensitivity in young women and those with dense breast tissue, radiation exposure, and risks of false-positive results and overdiagnosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, established serum biomarkers like CA 15\u0026thinsp;\u0026minus;\u0026thinsp;3, Carcinoembryonic Antigen (CEA), and CA 27\u0026ndash;29 lack the sensitivity and specificity required for early detection; guidelines from the American Society of Clinical Oncology restrict their use to monitoring metastatic disease [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Consequently, there is a clear need for novel, non-invasive biomarkers for early breast cancer detection.\u003c/p\u003e\u003cp\u003eTumor-associated autoantibodies (TAAbs) present a promising solution. They are generated by the humoral immune system in response to abnormal tumor-associated antigens (TAAs) that are no longer recognized as \"self\" [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. TAAbs possess several advantageous characteristics for early detection: they are produced early in tumorigenesis, are highly stable in serum, undergo biological amplification making them easier to detect than their corresponding antigens, and their detection is unaffected by breast tissue density [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, due to the heterogeneity of breast cancer, a single TAAb lacks sufficient diagnostic accuracy. Detecting a panel of multiple TAAbs is therefore essential to achieve the sensitivity and specificity required for clinical utility [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Based on prior meta-analyses indicating their frequent association with breast cancer, we selected a panel of five TAAb against p53, MUC1, HER2, c-Myc, and Cyclin B1 for the evaluation of TAAbs for the early detection of breast cancer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eDelayed diagnosis of breast cancer remains a significant challenge, particularly in developing countries such as Yemen, where most women are diagnosed at an advanced stage. To our knowledge, no previous study has evaluated the utility of TAAb panels for the early detection of breast cancer in Yemen. Therefore, this study aimed to assess the diagnostic value of a panel of five TAAbs for the early detection of breast cancer in Sana\u0026rsquo;a, Yemen.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, participants, and setting\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA diagnostic case-control study was conducted at the Life Center for Early Cancer Detection, National Cancer Control Foundation (NCCF), in Sana\u0026rsquo;a City, Yemen, from October 2021 to January 2023. The study included 90 female participants, who were split into two group: a patient group of 45 and a healthy subject group of 45 women. The patient group consisted of female patients with a recent diagnosis of early-stage breast cancer before any treatment was administered. Diagnosis was confirmed via mammography followed by histopathological examination (biopsy), which served as the reference standard. Tumor staging was performed according to the 8th edition of the American Joint Committee on Cancer (AJCC) staging system [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Early-stage breast cancer was defined as ductal carcinoma in situ (DCIS) or stages I, IIA, IIB, and IIIA, in accordance with the National Cancer Institute (NCI) Dictionary of Cancer Terms [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Patients with a history of any other cancer or benign breast tumors were excluded. The healthy subject group comprised women aged 40 years or older, which is the recommended threshold for routine mammography screening. These individuals had no personal history of malignancy and had received negative mammogram results.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData and Sample collection\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe data for each patient and healthy participant were recorded using a structured questionnaire developed specifically for the current study (Supplementary File 1), which included demographic characteristics and relevant clinical information. A venous blood sample of five mL was collected from each participant using standard venipuncture techniques and transferred into a plain blood collection tube. After clotting, samples were centrifuged at 1,000 rpm for 20 minutes, and the separated serum was promptly stored at \u0026minus;\u0026thinsp;20\u0026deg;C until further analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMeasurement of tumor-associated autoantibodies\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSerum levels of IgG autoantibodies against five antigens p53, MUC1, HER2, Cyclin B1, and c-Myc were quantified using commercial indirect enzyme-linked immunosorbent assay (ELISA) kits (Cloud-Clone Corp., USA). All assays were performed in strict adherence to the manufacturer's protocols. Briefly, serum samples were diluted 1:100 in phosphate-buffered saline. Standards, blanks, and diluted samples (100 \u0026micro;L) were added in duplicate to antigen-pre-coated wells and incubated for 1 hour at 37\u0026deg;C. After removing liquid, 100 \u0026micro;l of detection reagent A was added and incubated again for 1 hour. The plate was washed five times with 350 \u0026micro;l of wash solution, making sure to aspirate thoroughly between washes, and then tapped to eliminate residual buffer. Subsequently, 90 \u0026micro;l of substrate solution was added and incubated at 37\u0026deg;C for 10\u0026ndash;20 minutes away from light. The reaction was halted with 50 \u0026micro;l of stop solution, changing the liquid's color to yellow. The optical density (OD) of each well was measured using a microplate reader (HumaReader HS, Human, Germany) at 450 nm.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe data were entered and analyzed using IBM SPSS Statistics, Version 23.0 (IBM Corp., Armonk, NY, USA). Mann\u0026ndash;Whitney\u0026rsquo;s U test was used to compare the significant differences in autoantibody levels between cancer and control subjects. Diagnostic performance for each individual TAAb was assessed via Receiver Operating Characteristic (ROC) curve analysis. The optimal cut-off value was defined as the point maximizing the Youden Index. The area under the ROC curve (AUC) with a 95% confidence interval (CI) was calculated as a measure of overall accuracy. To select an optimized panel of autoantibody biomarkers for diagnosis, we applied logistic regression analysis with a forward stepwise selection. The sensitivity, specificity, positive and negative (PLR and NLR) likelihood ratios, and positive (PPV) and negative predictive values (NPV) were calculated following the methodology outlined in the epidemiology textbook [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Pearson\u0026rsquo;s correlation coefficient (r) was used to assess associations between continuous autoantibody levels and clinical characteristics (tumor size, grade and stage) within the patient group. A \u003cem\u003ep\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipates characteristics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study included a total of 90 participants, comprising 45 patients with early-stage breast cancer and 45 healthy subjects. The demographic and clinicopathological characteristics of the study population are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of patients with early-stage breast cancer was 42.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6 years, which was lower than that of the healthy control group (49.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6 years). The majority of patients (64%) were younger than 50 years. Most tumors measured between 2.0 and 5.0 cm in size (82.2%), were of histological grade II (73.3%), and were classified as stage II (71.1%).\u003c/p\u003e \u003c/div\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\u003eDemographic and clinicopathologic characteristics of early-stage breast cancer patients and healthy subjects in Sana'a city, Yemen\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly-stage breast cancer n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy subjects n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.84\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.31\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge range (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u003c/b\u003e\u0026thinsp;50 years\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u003c/b\u003e\u0026thinsp;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (64)\u003c/p\u003e \u003cp\u003e16 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (53)\u003c/p\u003e \u003cp\u003e21 (47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;2.0 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.0\u0026ndash;5 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 5 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSD standard deviation, n numbers, % percentage, TNM tumor, nodes, metastasis, DCIS ductal\u003c/p\u003e \u003cp\u003ecarcinoma \u003cem\u003ein situ\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e,\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSerum levels of tumor-associated autoantibodies\u003c/h3\u003e\n\u003cp\u003eSerum levels of five tumor-associated autoantibodies (p53, MUC1, HER2, Cyclin B1, and c-Myc) were measured using enzyme-linked immunosorbent assay (ELISA). Initial comparisons using the Mann\u0026ndash;Whitney U test revealed significantly elevated levels of all five autoantibodies in patients with early-stage breast cancer compared to healthy subjects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eOptimization of an autoantibody panel by logistic regression\u003c/h3\u003e\n\u003cp\u003eROC curve analysis was used to evaluate the diagnostic accuracy of the individual autoantibodies and the optimized autoantibody panel in early-stage breast\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ecancer (Fig. 2). The cutoff optical density (OD) values were determined by maximizing the Youden index and were 0.108, 0.115, 0.120, 0.125, 0.129, and 0.590 for p53, MUC1, HER2, Cyclin B1, c-Myc, and the four-autoantibody panel, respectively. Based on these cutoffs, positivity rates for individual autoantibodies and the panel were significantly higher in early-stage breast cancer patients compared with healthy subjects (p \u0026lt; 0.01) (Table 2). The four-autoantibody panel achieved an AUC of 0.898 (95% CI: 0.832\u0026ndash;0.963), with a sensitivity of 68.9% and a specificity of 93.3%. This performance exceeded that of any single autoantibody, which showed AUC values ranging from 0.735 to 0.797 and sensitivities between 26.7% and 40.0%. The panel had PPV and NPV of 91.2 and 75.0%, respectively, and PLR and NLR of 10.3 and 0.33%, respectively (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table 2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ePositivity frequencies of tumor-associated autoantibodies in patients with early-stage breast \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; cancer and healthy subjects in Sana\u0026rsquo;a, Yemen\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"630\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep53\u003c/strong\u003e n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMUC1\u003c/strong\u003e n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2\u003c/strong\u003e n (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Cyclin B1\u003c/strong\u003e n (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ec-Myc\u003c/strong\u003e n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePanel\u003c/strong\u003e n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eEarly-stage breast cancer (n=45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e18(40.0)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e13(28.9) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e17 (37.8)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 16 (35.6)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e12 (26.7)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e31(68.9)\u003csup\u003e\u0026nbsp;* **\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eHealthy subjects (n=45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3(6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e4(8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e2 (4.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e4 (8.9)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e3 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePanel positivity was defined as positivity for at least one of the four autoantibodies (p53, HER2, Cyclin B1, or c-Myc).\u003cbr\u003e***\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01 compared with healthy subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation of autoantibody levels with clinicopathological parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson\u0026rsquo;s correlation analysis was conducted to assess the association between serum levels of individual autoantibodies, as well as the autoantibody panel, and clinicopathological parameters in patients with early-stage breast cancer. No statistically significant correlations were observed between the levels of individual autoantibodies or the autoantibody panel and tumor size, histological grade, or TNM stage (p \u0026gt; 0.05) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eDiagnostic performance of tumor-associated autoantibodies in patients with early-stage breast cancer in Sana\u0026rsquo;a, Yemen\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor-associated autoantibodies\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC (95%CI)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003ep53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.797 (0.690-0.905)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e66.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e40.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e93.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e60.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eMUC1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.755 (0. 643-0.868)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e60.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e28.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e91.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e76.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e56.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eHER2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.761 (0. 651-0.871)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e66.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e37.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e95.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e89.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e60.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eCyclin B1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.757 (0.650-0.864)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e64.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e35.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e93.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e84.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e59.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003ec-Myc\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.735 (0.621-0.848)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e58.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e26.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e91.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e55.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eFour-autoantibody Panel\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.898 (0.832-0.963)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e81.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e68.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e93.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eACU, area under the curve; CI, confidence interval; NLR, negative likelihood ratio; NPV, negative predictive value; PLR, positive likelihood ratio; PPV, positive predictive value. Four- autoantibodies panel: autoantibodies \u0026nbsp;against p53, HER2, Cyclin B1, and c-Myc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003ePearson\u0026apos;s correlation coefficients (r) between the autoantibody levels with clinicopathologic parameters in patients with early-stage breast cancer in Sana\u0026apos;a, Yemen\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParmeter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep53\u003c/strong\u003e (r) \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMUC1 (\u003c/strong\u003er) \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2\u0026nbsp;\u003c/strong\u003e(r) \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCyclinB1\u003c/strong\u003e (r) \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ec-Myc (\u003c/strong\u003er) \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePanel (\u003c/strong\u003er) \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e(.0 )1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e(.23) .13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e(.0) 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;(.0) 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e(-.11) .47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e(.0) 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eHistological grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e(.07) .66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e(-.25) .10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e(.08) .60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp; (.10).72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e(-.08) .62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e(.2) .18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eTNM stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e(.25) .10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e(.20).18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e(-.25) .10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp; (.04) .79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e(.0) .98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e(-.15) .33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003er,Pearson\u0026apos;s correlation coefficients; Correlation was significant at \u003cem\u003ep\u003c/em\u003e value \u0026le; 0.05 level; panel includes autoantibodies against p53, HER2, Cyclin B1, and c-Myc.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe early detection of breast cancer is crucial for reducing mortality rates and improving treatment outcomes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. While mammography is the standard method for early breast cancer detection, it has limitations, especially in identifying small tumors in women with dense breast tissue. This creates a need for additional diagnostic tools [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In recent years, great efforts have been made on the development of blood-based biomarkers which show the potential for earlier detection of breast cancer. Despite tremendous efforts in this research area, clinical applications are still in their infancy and are challenging to achieve [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. CEA and CA15-3 are the most frequent blood markers used in breast cancer management but they lack the sensitivity and specificity required for early-stage detection [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Serum TAAbs from patients with cancer offer opportunities as biomarkers, particularly for early cancer detection, including breast cancer. The detection of TAAbs could potentially identify breast cancer before the appearance of symptoms, making them an important area of research for early cancer detection [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eConsistent with the literature, the sensitivity of any single autoantibody was low, rendering them unsuitable for standalone early screening [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This limitation is largely attributed to the biological heterogeneity of breast cancer, where different patients exhibit aberrant expression of various proteins [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To overcome this limitation, researchers have focused on developing autoantibody panels to improve diagnostic sensitivity. For instance, Chapman et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] measured multiple autoantibodies against tumor-associated antigens (BRCA1, BRCA2, c-myc, HER2, MUC1, NY-ESO-1, and p53) and demonstrated that, while individual TAAbs showed low sensitivity (8\u0026ndash;34% in primary cancer, 3\u0026ndash;23% in DCIS), a combined panel increased sensitivity to 64% and 45%, respectively. In our study, we applied logistic regression to develop an optimized panel. The model excluded anti-MUC1 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.142), potentially reflecting differences in antigen immunogenicity or tumor biology within our specific cohort. The final model retained four TAAbs against p53, HER2, Cyclin B1, c-Myc, generating a composite panel score. Using this panel, sensitivity increased dramatically to 68.9%, while maintaining a high specificity of 93.3%. This finding strongly supports the principle that a multi-marker panel is superior to any single autoantibody for the early identification of breast cancer.\u003c/p\u003e \u003cp\u003eThe diagnostic performance of the four-autoantibody panel in the present study (sensitivity, 68.9%; specificity, 93.3%) is comparable to that reported for other autoantibody panels in the literature. These results are consistent with a previous study using a panel comprising Imp1, p16, Koc, survivin, Cyclin B1, and c-Myc, which reported a sensitivity of 67.3% and a specificity of 92.2% [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and exceed the performance of another panel consisting of BMI-1, HSP70, NY-ESO-1, and p53, which achieved a sensitivity of 59.6% and a specificity of 90.2% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the performance of the present panel was slightly lower than that of a previously reported panel including p53, Cyclin B1, p16, p62, 14-3-3ζ, and survivin, which demonstrated a sensitivity of 72.1% and a specificity of 96.7% [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These differences may be attributable to variations in sample size, autoantibody panel composition, and population characteristics. Overall, these findings support the accumulating evidence that the expansion and optimization of tumor-associated autoantibody panels can substantially improve diagnostic performance.\u003c/p\u003e \u003cp\u003eThe predictive performance of the panel in the present study demonstrated clinically relevant utility. The PPV and NPV of our panel (91.2% and 75.0%, respectively) were closely comparable to those reported for the panel of a previous study (91.7% and 68.6%) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], exceeded the PPV (82.3%) of another study's panel, and showed a similar NPV (74.5%) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A PLR above 10 or NLR below 0.1 reflects a test with high diagnostic utility for confirming or excluding a disease, respectively [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our panel had a PLR of 10.3 and an NLR of 0.33, supporting its potential clinical value. Notably, the PLR of our panel was higher and its NLR was lower than those reported in comparable studies (PLR, 8.52; NLR, 0.36) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and (PLR, 6.08; NLR, 0.45) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], suggesting improved discriminative performance.\u003c/p\u003e \u003cp\u003eThe diagnostic accuracy of the individual TAAbs and the autoantibody panel was assessed using ROC curve analysis. The AUC, which reflects the test's ability to discriminate between diseased and non-diseased individuals, ranges from 0.5 (no discrimination) to 1.0 (perfect discrimination). An AUC value\u0026thinsp;\u0026gt;\u0026thinsp;0.80 is generally considered clinically useful [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The autoantibody panel in the present study achieved an AUC of 0.898 (95% CI: 0.832\u0026ndash;0.963) with an overall accuracy of 81.1%, demonstrating a strong capacity to differentiate patients with early-stage breast cancer from healthy subjects. This performance exceeded that of the panel in a previous study, with an AUC of 0.81 and an accuracy of 77% [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, the performance of our panel was slightly lower than that reported in another study, which demonstrated an AUC of 0.916 and an accuracy of 82.4% [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This difference may be attributable to the larger sample size in that study (184 breast cancer patients and 184 controls), suggesting that a cohort size is important factors in optimizing the diagnostic accuracy.\u003c/p\u003e \u003cp\u003eThis study revealed no significant correlation (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between the levels of the autoantibody panel or individual autoantibodies and the clinical characteristics of breast cancer. This finding is consistent with previous studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] that also reported no significant association between autoantibody levels and clinicopathological parameters. In contrast, another study reported that only the histological grade was associated with autoantibody seropositivity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The difference is likely attributable to variations in autoantibody panel composition, study populations, or detection methodologies.\u003c/p\u003e \u003cp\u003eThis study has several strengths, including its exclusive focus on early-stage breast cancer and the use of an optimized statistical modeling approach. To our knowledge, this is the first study to evaluate a TAAb panel for the early detection of breast cancer in Yemen. Nevertheless, several limitations should be acknowledged. The relatively small sample size and single-center design may limit the generalizability of the findings, and the lack of an independent validation cohort precludes immediate clinical translation. Future multicenter studies with larger cohorts and external validation, potentially incorporating high-throughput autoantibody profiling platforms, are warranted to confirm and extend these findings.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe panel of tumor-associated autoantibodies targeting p53, HER2, Cyclin B1, and c-Myc has the potential to detect early-stage breast cancer and could serve as a complementary tool to mammography, particularly in younger women and those with dense breast tissue, where imaging sensitivity is reduced. Further large-scale, multicenter validation studies are needed before clinical implementation can be considered.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC: Area under the curve;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBRCA 1/2; BReast CAncer gene 1/2;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCA 15-3; Cancer antigen 15-3; CEA: carcinoembryonic antigen;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ec-Myc; Cellular Myc protein;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCI: Confidence interval;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDCIS: Ductal carcinoma \u003cem\u003ein situ\u003c/em\u003e;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eELISA: enzyme-linked immunosorbent assay;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHER2; Human epidermal growth factor receptor 2;\u0026nbsp;MUC1: Mucin-1 protein; PLR: Positive likelihood ratio; NCCF: National Cancer Control Foundation; NLR: Negative likelihood ratio;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNPV: Negative predictive value;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eOD: optical density\u0026nbsp;;PPV: Positive predictive value; P53: Protein 53;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eROC: Receiver operating characteristic;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTAAbs: Tumor-associated autoantibodies;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTAAs: Tumor-associated antigens;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTNM: Tumor, nodes, metastasis\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors gratefully acknowledge all patients and healthy volunteers who participated in this study. We also thank the Life Center for Early Cancer Detection at the\u0026nbsp;National Cancer Control Foundation\u0026nbsp;(NCCF) for facilitating sample and data collection, with special thanks Marwa A. Al-Maqrami.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eAAA conceived and designed the study, performed the experimental work, collected and analyzed the data, and drafted the manuscript. KAA and AYA supervised the research and critically revised the manuscript. \u0026nbsp; All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Ethics Committee of the Faculty of Medicine and Health Sciences, Sana\u0026rsquo;a University, Yemen. Written informed consent was obtained from all participants prior to enrollment after a clear explanation of the study objectives. Participants were informed that their personal information would remain confidential and that they could withdraw from the study at any time without penalty. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor details\u003c/h2\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Medical Microbiology and Immunology, Faculty of Medicine and Health Sciences, Sana\u0026rsquo;a University, Sana\u0026rsquo;a, Yemen.\u003csup\u003e\u0026nbsp;2\u003c/sup\u003eLaboratory Department, Ibn Sina Specialist Hospital, Sana\u0026rsquo;a, Yemen. \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1 Xiong X, Zheng LW, Ding Y, Chen YF, Cai YW, Wang LP, et al. 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Ann Surg Oncol. 2018;25(7):1783\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Cancer Institute. Early-stage breast cancer. Bethesda (MD): NCI; 2024 [cited 2024 Nov 1]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCelentano DD, Szklo M, Farag Y. Gordis epidemiology. 7th ed. Philadelphia: Elsevier; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlsharawneh A, Othman EH, Albadawi RS. Breast cancer screening practices and determinants of participation. SAGE Open Nurs. 2025;11:23779608251343500.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLacombe J, Mang\u0026eacute; A, Solassol J. Use of autoantibodies to detect the onset of breast cancer. 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Perspect Clin Res. 2018;9(2):99\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ccedil;orbacıoğlu ŞK, Aksel G. ROC curve analysis in diagnostic accuracy studies. Turk J Emerg Med. 2023;23(4):195\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLacombe J, Mang\u0026eacute; A, Jarlier M, Bascoul-Mollevi C, Rouanet P, Lamy PJ, Maudelonde T, Solassol J. Identification and validation of new autoantibodies for the diagnosis of DCIS and node-negative early-stage breast cancers. Int J Cancer. 2013;132(5):1105\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSumazaki M, Ogata H, Nabeya Y, Kuwajima A, Hiwasa T, Shimada H. Multipanel assay of 17 tumor-associated antibodies for serological detection of stage 0/I breast cancer. Cancer Sci. 2021;112(5):1955\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, Early diagnosis, Biomarker, Tumor-associated autoantibody, Yemen","lastPublishedDoi":"10.21203/rs.3.rs-8472357/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8472357/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eBreast cancer remains a major global health challenge and is the leading cause of cancer-related mortality among women worldwide. Early and accurate diagnosis is crucial for effective management. Tumor-associated autoantibodies may arise early during tumorigenesis and represent potential biomarkers for early cancer detection. \u0026nbsp;This study aimed to evaluate a panel of tumor-associated autoantibodies for the early detection of breast cancer in Sana'a, Yemen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis diagnostic case-control study included 45 patients with newly diagnosed early-stage breast cancer and 45 healthy subjects in Sana'a city. Serum IgG autoantibodies against p53, MUC1, HER2, Cyclin B1, and c-Myc were measured using enzyme-linked immunosorbent assay (ELISA). An optimal diagnostic panel was constructed using forward stepwise logistic regression. The diagnostic performance of individual autoantibodies and the autoantibody panel was assessed using receiver operating characteristic (ROC) curve analysis. Key diagnostic indices, including sensitivity, specificity, positive and negative predictive values (PPV and NPV), and positive and negative likelihood ratios (PLR and NLR), were calculated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003ePatients with early-stage breast cancer exhibited significantly higher serum levels of tumor-associated autoantibodies than healthy subjects (p \u0026lt; 0.01). The positivity frequencies of individual tumor-associated autoantibodies in breast cancer patients ranged from 26.7% to 40.0%. An optimized panel composed of tumor-associated autoantibodies against p53, HER2, Cyclin B1, and c-Myc demonstrated a marked increase in diagnostic sensitivity to 68.9% at a specificity of 93.3%, with an area under the ROC curve (AUC) of 0.898 (95% CI, 0.832–0.963). The panel showed a PPV of 91.2% and an NPV of 75.0%, with PLR and NLR values of 10.3 and 0.33, respectively. No significant correlation was observed between the levels of tumor-associated autoantibodies and breast cancer size, grade, or stage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe panel of tumor-associated autoantibodies targeting p53, HER2, Cyclin B1, and c-Myc shows potential for detecting early-stage breast cancer and could serve as a complementary tool to mammography, particularly in younger women and those with dense breast tissue, where imaging sensitivity is reduced. Further large-scale, multicenter validation studies are needed before clinical implementation.\u003c/p\u003e","manuscriptTitle":"A tumor-associated autoantibody panel for early detection of breast cancer in Sana’a, Yemen","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:56:47","doi":"10.21203/rs.3.rs-8472357/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-13T21:17:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T21:34:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T00:33:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T12:38:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T21:07:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260405203840543148619831460892847491405","date":"2026-02-08T18:10:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145738149775961221792937811719068432236","date":"2026-02-08T16:13:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82590998592381687110285703523882591754","date":"2026-02-06T10:21:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34782788171596175714233301782008245027","date":"2026-02-06T08:52:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298697600237637919170356746504445250645","date":"2026-02-04T12:20:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87506158048205228242228817174526468870","date":"2026-02-01T14:09:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188457116526598057611050964294683781170","date":"2026-02-01T08:18:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T07:27:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-27T07:44:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-06T18:37:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-04T17:14:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-01-04T17:09:25+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":"d006795e-a3b7-40e9-96c1-aa60c8f579e7","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-13T21:17:56+00:00","index":88,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T15:56:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 15:56:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8472357","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8472357","identity":"rs-8472357","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}