DNA Methylation Levels in Cervical Scrapes for Predicting the Risk of Cervical Lesions and Cancer

DNA Methylation Levels in Cervical Scrapes for Predicting the Risk of Cervical Lesions and Cancer | 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 DNA Methylation Levels in Cervical Scrapes for Predicting the Risk of Cervical Lesions and Cancer Zhenping Liu, Lu Liu, Danhua Wang, Xiaoying Li, Hui Lian, Longying Zhu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6182081/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Accurate assessment of cervical cancer progression and severity is crucial for precise diagnosis and timely risk prediction. DNA methylation, which can be easily readily detected using molecular methods, has emerged as a promising approach for identifying novel biomarkers for cervical cancer diagnosis. However, routine methylation evaluation in cervical lesions primarily determines its presence or absence, failing to capture the nuances of disease progression and severity. Methods This study evaluated the DNA methylation level of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 for their potential in diagnosing cervical lesions and predicting cancer risk. The study cohort comprised 14 cases of cervical cancer (CC), 23 cases of CIN3, 53 cases of CIN2, 115 cases of CIN1, and 55 normal cases. All patients underwent high-risk HPV (hrHPV) testing, ThinPrep cytological testing (TCT), colposcopic biopsy, and DNA methylation analysis. Methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 progressively declined from normal tissue to CIN1, CIN2, CIN3, and cervical cancer. These markers exhibited superior performance in detecting CIN2 + and assessing the risk of cervical cancer. The receiver operating characteristic (ROC) curves for differentiating CIN1- from CIN2 + using these six markers ranged from 0.713 to 0.816. Outperforming common screening methods such as TCT (0.554), hrHPV testing (0.617), and HPV16/18 testing (0.653). Among the six markers, ZNF671 demonstrated the highest diagnostic accuracy, with an area under the curve (AUC) of 0.816 for detecting CIN2+. Furthermore, a △Cp level of 0.4 for ZNF671 showed high predictive value in identifying CIN2 + lesions at risk of progressing to cervical cancer, yielding an AUC of 0.854. Conclusions Our findings indicate that DNA methylation levels can serve as accurate diagnostic and screening tools for assessing the progression and severity of cervical disease. cervical lesions cervical cancer risk prediction methylation level Figures Figure 1 Figure 2 Figure 3 Background Cervical cancer remains a major public health concern worldwide, particularly in low and middle-income countries where screening programs are less established( 1 ). Primary HPV testing and cytology are widely used in cervical cancer screening to reduce the incidence and mortality ( 2 ). However, women with positive HPV test results or abnormal cytological findings require a thorough evaluation through colposcopy-directed biopsy to determine the need for further interventions( 3 ). The complexity of interpretation and management presents a significant challenge for clinicians. The relatively low specificity of HPV testing can result in false-positive outcome, leading to unnecessary colposcopy and follow-up diagnostic procedures, which consume medical resources and impose a financial burden for patients( 4 – 6 ). Additionally, the accuracy of cytology depends heavily on sample quality and the physician's clinical judgment, increasing the risk of diagnostic errors and false-negative results ( 6 – 8 ). Furthermore, a second or third colposcopic biopsy may be necessary to confirm a diagnosis, as a single biopsy can fail to detect high-grade squamous intraepithelial lesions (HSIL) in 30–50% of cases ( 9 – 11 ). Incorporating more detailed molecular information is crucial to accurately assess cervical cancer risk, enhance diagnostic precision and guide treatment decisions. In recent years, methylation of the promoter regions of tumor suppressor genes has been identified as a mechanism that silences gene expression, thereby contributing to the pathogenesis of cervical cancer( 12 , 13 ). Compared to conventional cervical cancer screening methods, DNA methylation testing offers superior accuracy, convenience, and automation( 14 ). Furthermore, PCDHGB7( 15 ), PAX1/SOX1( 16 ), and FAM19A4/miR124-2( 17 ) alterations have emerged as promising biomarkers for cervical cancer screening. However, most DNA methylation-based studies have primarily focused on evaluating sensitivity and specificity of these markers as diagnostic tools rather than exploring dynamic methylation changes that could provide crucial insights into lesion severity( 18 ). Numerous studies have highlighted the significance of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 methylation in diagnosing cervical cancer( 19 – 21 ). ZNF671 has demonstrated remarkable diagnostic efficacy in detecting high-grade lesions (CIN3+)( 22 ). Additionally, research has revealed the differential expression of SOX17 across normal cervical tissue, high-grade squamous intraepithelial lesions, and cervical cancer( 23 ). Previously, we conducted a comprehensive genome-wide methylation analysis of 100 cervical scrape samples with varying pathological outcomes. Our findings established a strong correlation between the methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 and cervical cancer progression, with methylation levels progressively increasing from CIN2 onwards. Given the robust diagnostic performance of these markers, further investigation into methylation changes across different cervical lesion types is warranted. The purpose of this study was to evaluate the methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 genes in cervical secretions to assess their potential for identifying cervical lesions and cancer. Additionally, the study compared these methylation markers with traditional screening methods, including HPV testing and TCT. This study aimed to evaluate the predictive value of these gene methylation patterns in cervical lesion progression, ultimately providing a more precise diagnostic framework, guiding treatment decisions, and supporting individualized patient management. Methods Study population This study recruited 283 patients with cervical-related diseases who received treatment at the First People's Hospital of Linping District, Hangzhou, between May 2023 and June 2024. Following histopathological confirmation of cervical disease, cervical brush samples from HPV testing were collected and cryopreserved at -40°C until analysis. Patients with a history of prior surgery for cervical disease, sexual exposure, or non-pregnant individuals (e.g., hysterectomy, laser therapy, chemotherapy/radiotherapy, pregnancy, or extracervical malignancy) were excluded based on the predefined criteria. A total of 260 samples retained for methylation testing, and the specific sample analysis process was illustrated in Fig. 1 . All included cases underwent HPV testing, ThinPrep cytological testing (TCT), cervicoscopic biopsy, and DNA methylation analysis. Based on pathological results, patients were categorized into cervical cancer (CC, n = 14), CIN3 (n = 23), CIN2 (n = 53), CIN1 (n = 115), and normal (n = 55). hrHPV status, TCT results, and pathology diagnoses were collected from the original institutions. This study was approved by the Ethics Committee of the First People's Hospital of Linping District, Hangzhou (ethics number 2020001). HPV testing We employed the Sansure high-risk HPV DNA Fluorescence Diagnostic Kit (Sansure, Hunan, China) for genotyping HPV DNA in cervical scrape samples, The targeted HPV types included: 6, 11, 16, 18, 26, 31, 33, 35, 39, 42, 43, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 81, and 82. HPV results were categorized into two groups: HPV16/18 -positive and hrHPV-positive. DNA methylation analysis by quantitative methylation-specific PCR Methylation analysis was performed using the remaining cervical brush samples from HPV testing. Genomic DNA was extracted from the samples using standard protocols and subsequently converted to bisulfite form utilizing the EpiTect® Fast Bisulfite Kit (Qiagen, Germany). Quantitative methylation-specific PCR (qMSP) was carried out on an ABI7500 Real-Time PCR system (Life Technologies; Thermo Scientific, USA) to detect the methylation of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671, in accordance with the manufacturer’s guidelines. The Acetylcholinesterase (ACHE) gene served as an internal control for normalization. The △Cp value was calculated as the difference between the Cp values of the target genes and ACHE gene, was calculated using the formula: △Cp = Cp target gene - Cp ACHE. A lower △Cp indicates a higher degree of methylation. Positive results for the six genes were determined based on the manufacturer-defined ΔCp cutoff values (cutoff of 9 for ASTN1, DLX1, ITGA4, RXFP3, SOX17, 10 for ZNF671). A result was classified as positive if the ΔCp value was below the respective cutoff, and the corresponding ΔCp value was recorded. For samples with undetectable methylation, the ΔCp values were substituted with the predefined cutoff values. The score for each marker was predefined: 3 for ZNF671; 1 for each of ASTN1, ITGA4, RXFP3, and SOX17. The methylation panel was considered positive if the total score of all six markers was ≥ 3. Statistical analysis Descriptive statistics were used to characterize the study population. Histology and HPV genotypes characteristics were presented as counts (%) and assessed using the chi-square test. DNA methylation levels in each group were represented as median and interquartile range (IQR), with the Mann-Whitney U test applied for comparative analysis. The discrimination performances of DNA Methylation and other screening methods were evaluated by the ROC curve and AUC value, reported with corresponding 95% confidence interval. An AUC of 0.50–0.69 was considered mild, 0.70–0.89 was moderate, and 0.90–1.00 was good. The accuracy, sensitivity, and specificity were calculated from the ROC curve according to the cut-off value that maximizes the Youden index, equal to (sensitivity + specificity)-1. ROC curves between different models were compared using the DeLong method. SPSS software version 22.0 (SPSS, Inc., Chicago, IL, USA), MedCalc statistical software, GraphPad Prism software (Ver 8.0.1, La Jolla, CA, USA) and R version 4.4.1 ( http://www.R-project.org ) were used for statistical analysis, with P < 0.05 being statistically significant. This analysis involved a suite of packages including ggplot2, tidyverse、dplyr、compareGroups、hrbrthemes、ggthemes、RColorBrewer、gridExtra、patchwork. Results Study characteristics Basic participant information and the enrollment flowchart are presented in Table 1 and Supplemental S1 . The study included 260 patients with a median age of 41.5 years (interquartile range [IQR], 31–51 years). Histopathological classification divided patients into the following groups: normal tissue (n = 55, 21.2%), CIN1 (n = 115, 44.2%), CIN2 (n = 53, 20.4%), CIN3 (n = 23, 8.85%), and cervical cancer (n = 14, 5.38%). HPV testing identified 8 patients (30%) as HPV16/18 positive, while 219 patients (84.2%) tested positive for other high-risk HPV types. The median DNA methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 ranged from 7.39 to 10.0, with DLX1 exhibiting the highest median methylation level (7.39), indicating the highest positivity rate among participants. Table 1 Sheet of basic information for participants. Clinical Character (n = 260) Number (%) or median [Q1; Q3] Age 41.5 [31.0;51.0] Histology Normal 55 (21.2%) CIN1 115 (44.2%) CIN2 53 (20.4%) CIN3 23 (8.85%) Cancer 14 (5.38%) DNA methylation ASTN1 9.00 [6.42;9.00] DLX1 7.39 [5.36;9.00] ITGA4 9.00 [9.00;9.00] RXFP3 9.00 [8.88;9.00] SOX17 9.00 [8.47;9.00] ZNF671 10.0 [8.30;10.0] HPV testing Negative 41 (15.8%) Positive 219 (84.2%) HPV16/18 Negative 182 (70.0%) Positive 78 (30.0%) Comparison of DNA methylation levels for Cervical Cancer and various CIN The methylation levels of the genes ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 progressively decreased with increasing severity of cervical disease, from normal tissue to Cervical Intraepithelial Neoplasia 1 (CIN1), CIN2, CIN3, and cervical cancer. ( Fig. 1 ) . The △Cp levels of these six genes across histological groups are summarized in Table 2 . Statistical analysis revealed significant differences in gene methylation levels among the groups (Mann–Whitney U test, P < 0.001). Notably, compared to normal tissue, the methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 were significantly lower in cervical cancer. Table 2 ΔCp values of genes by groups of Histology Genes Normal CIN1 CIN2 CIN3 Cancer p N = 55 N = 115 N = 53 N = 23 N = 14 ASTN1 9.00 [8.29;9.00] 9.00 [8.23;9.00] 9.00 [4.47;9.00] 3.59 [2.42;7.46] 0.87 [-0.20;3.82] < .001 DLX1 8.60 [7.08;9.00] 7.60 [6.27;9.00] 6.85 [4.75;9.00] 3.44 [2.66;6.31] 2.43 [0.90;4.24] < .001 ITGA4 9.00 [9.00;9.00] 9.00 [9.00;9.00] 9.00 [9.00;9.00] 8.35 [5.46;9.00] 2.48 [0.84;6.63] < .001 RXFP3 9.00 [9.00;9.00] 9.00 [9.00;9.00] 9.00 [7.66;9.00] 6.24 [5.17;8.42] 5.19 [2.15;7.13] < .001 SOX17 9.00 [9.00;9.00] 9.00 [9.00;9.00] 9.00 [7.05;9.00] 6.60 [5.06;8.31] 3.40 [2.06;5.08] < .001 ZNF671 10.0 [10.0;10.0] 10.0 [10.0;10.0] 9.12 [3.91;10.0] 2.58 [1.45;3.61] 0.42 [-0.41;1.55] < .001 Values are Median [Q1;Q3] Performance of DNA methylation and other screening methods for CIN1- and CIN2+ We analyzed and compared the areas under the curve (AUC) for HPV16/18, hrHPV, TCT, and the methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 in cervical scrapings using receiver operating characteristic (ROC) curves to differentiate between CIN1- and CIN2 + lesions. As shown in Table 3 , the methylation markers demonstrated relatively high diagnostic accuracy, with mean AUC values ranging from 0.713 to 0.816, surpassing common screening methods (TCT: 0.554; hrHPV: 0.617; HPV16/18: 0.653). Among these, the△Cp level of ZNF671 exhibited the highest diagnostic efficacy, achieving a Youden's index of 0.59 and an AUC of 0.816. It demonstrated a sensitivity of 68.89% and a specificity of 90.58% for CIN2 + detection. The combined use of six markers yielded an AUC of 0.767 (95% confidence interval [CI]: 0.711–0.817) for CIN2 + detection. However, increasing the sensitivity to 72.22% resulted in a reduction in specificity to 81.18%. Table 3 6 DNA Methylation and other screening methods between normal, CIN1 and CIN2, CIN3, Cancer (N = 260) Methods Sensitivity (%) Specificity (%) Youden Index AUC p [95% CI] [95% CI] [95% CI] TCT 77.78 32.94 - 0.554 0.06 67.8–85.9 25.9–40.6 [0.491–0.615] hr HPV 31.11 92.35 - 0.617 < .001 [21.8–41.7] [87.3–95.9] [0.555–0.677] HPV16/18 50.00 80.59 - 0.653 < .001 [39.3–60.7] [73.8–86.2] 0.592–0.711 ASTN1 51.11 88.24 0.3935 0.713 < .001 [40.3–61.8] [82.4–92.7] [0.654–0.767] DLX1 62.22 77.06 0.3928 0.719 < .001 [51.4–72.2] [70.0-83.1] [0.661–0.773] RXFP3 51.11 91.76 0.4288 0.725 < .001 [40.3–61.8] [85.2–94.5] [0.666–0.778] SOX17 54.44 93.53 0.4797 0.750 < .001 [43.6–65.0] [88.7–96.7] [0.692–0.801] ZNF671 68.89 90.59 0.5948 0.816 < .001 [58.3–78.2] [85.2–94.5] [0.764–0.861] ITGA4 43.33 97.65 0.4098 0.707 < .001 [32.9–54.2] [94.1–99.4] [0.648–0.762] GynTect® 72.22 81.18 - 0.767 < .001 [61.8–81.1] [74.5–86.8] [0.711–0.817] Performance of DNA Methylation and other screening methods between CIN3- and Cancer In the study cohort, 90 women were diagnosed with CIN2+. When cervical cancer was considered the outcome, risk stratification for the six methylation markers revealed similar trends, with the △Cp level of the cancer group significantly lower than that of CIN3- group (p < 0.001) ( Table 4 ) . In contrast, no significant differences were observed in HPV testing and TCT screening methods. Table 4 6 DNA Methylation and other screening methods between CIN3- and Cancer Screening methods [ALL] CIN3-† Cancer p N = 90 N = 76 N = 14 DNA methylation ASTN1 6.40 [2.17;9.00] 7.56 [3.02;9.00] 0.87 [-0.20;3.82] < .01 DLX1 5.74 [2.90;7.96] 6.31 [3.21;8.59] 2.43 [0.90;4.24] < .001 RXFP3 8.63 [5.56;9.00] 9.00 [5.94;9.00] 5.19 [2.15;7.13] < .001 SOX17 7.96 [4.66;9.00] 9.00 [5.86;9.00] 3.40 [2.06;5.08] < .001 ZNF671 4.13 [1.44;10.0] 6.81 [2.29;10.0] 0.42 [-0.41;1.55] < .001 ITGA4 9.00 [4.63;9.00] 9.00 [6.11;9.00] 2.48 [0.84;6.63] < .001 TCT 0.179 Negative 20 (22.2%) 19 (25.0%) 1 (7.14%) Positive 70 (77.8%) 57 (75.0%) 13 (92.9%) hr HPV 1.000 Negative 28 (31.1%) 24 (31.6%) 4 (28.6%) Positive 62 (68.9%) 52 (68.4%) 10 (71.4%) HPV16/18 0.383 Negative 45 (50.0%) 40 (52.6%) 5 (35.7%) Positive 45 (50.0%) 36 (47.4%) 9 (64.3%) GynTect® result <.01 Negative 25 (27.8%) 25 (32.9%) 0 (0.00%) Positive 65 (72.2%) 51 (67.1%) 14 (100%) Values are Median [Q1; Q3] or N (%) † CIN3-, CIN2 and CIN3 We evaluated the specificity and sensitivity of the six methylation markers. Among patients with CIN2 or CIN3 who progressed to cervical cancer, the ROC curves ranged from 0.804 to 0.854 ( Supplemental Table S2 ). ZNF671 demonstrated the highest classification performance, with an AUC of 0.854, a sensitivity of 78.57%, and a specificity of 81.58% (Fig. 2 ). Levels of ZNF671 methylation inform the extent of cervical lesions The performance of ZNF671 (ZNF671 m ) methylation outperformed other screening methods in our study. We then evaluated whether ZNF671 m levels correlated with the severity of cervical lesions. Our analysis revealed that ZNF671 m levels were significantly lower in patients with CIN1- than those with CIN2+. To assess the prognostic significance of ZNF671m levels in distinguishing CIN1 from CIN2 + and predicting the progression of CIN3- to cervical cancer, we determined the optimal cutoff value using the ROC curve based on the Youden index, calculated as (sensitivity + specificity) -1. The analysis identified that when △Cp (ZNF671) was less than 0.6, the risk of CIN2 + increased significantly. Furthermore, the optimal △Cp (ZNF671) cutoff for predicting the progression of high-grade lesions to cervical cancer was determined to be 0.4 ( Fig. 3 ). Discussion In this study, we analyzed the methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 during the progression of cervical lesions. We observed a decreasing trend in methylation levels as disease severity advanced from normal tissue to cervical cancer. All six genes exhibited statistically significant differences across various CIN and invasive cervical cancer stages, allowing for a clear distinction between disease stages. ZNF671 methylation demonstrated the highest diagnostic performance, effectively differentiating CIN1- from CIN2 + lesions and stratifying CIN3- and cervical cancer within high-grade squamous intraepithelial lesions. Our findings provide detailed insights into the extent of methylation and its clinical implications, offering a more comprehensive comparison to previous studies on these six methylation markers. The incidence of cervical cancer has significantly declined due to hrHPV testing and cytology-based screening, both of which exhibit high sensitivity for detecting high-grade lesions ( 2 ). However, our study revealed that these conventional strategies showed significantly lower diagnostic performance (AUC: hrHPV = 0.617; TCT = 0.554) compared to methylation analysis of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671. The AUC for hrHPV testing alone was 0.617. Affected by low specificity (Sensitivity (%) :31.11, Specificity (%):92.35). Leading to potential overtreatment, TCT also demonstrated deficiencies in diagnostic accuracy (AUC: 0.554), primarily due to low specificity (32.94%) despite relatively higher sensitivity (77.78), leading to potential overtreatment. Compared with TCT and hrHPV testing, HPV16/18 testing demonstrated moderate performance with AUC 0.653 in risk stratification. The diagnostic efficacy of above is consistent with previous literature reports( 24 , 25 ). In contrast, all six methylation markers achieved superior AUC values ranging from 0.713 to 0.816. underscoring the enhanced accuracy of methylation-based approaches. Among these, ZNF671 exhibited the highest AUC (0.816) and specificity (90.58%) while maintaining robust sensitivity (68.89%). This enhanced performance addresses a critical clinical need, particularly in reducing the high false-positive rates and potential overtreatment associated with current screening methods. Previous literatures report that the methylation panel (ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671) exhibits high sensitivity and specificity for detecting CIN3 and cervical cancer. For instance, one study reported a sensitivity of 78.0% and specificity of 86.0% for CIN3 + detection( 26 ). Another study found that the GynTect® assay, which includes these markers, had a specificity of 94.6% for CIN3+( 27 ). Moreover, Shi et al, observed that methylation levels of these genes increase with the severity, as reflected in the positive rates of methylation markers in the GynTect® assay( 28 ). However, their study did not investigate the intrinsic changes of cervical lesions. In this study, we analyzed the changes in △Cp values of the studied markers in cervical lesions. Our findings identified ZNF671 as the most effective marker, demonstrating the highest diagnostic accuracy (AUC: 0.816) for CIN2 + detection with an optimal sensitivity of 68.89% and specificity of 90.58%. This exceptional performance was further validated in cancer prediction, where ZNF671 achieved an AUC of 0.854, with a sensitivity of 78.57% and a specificity of 81.58%. The robust performance of ZNF671 highlights its potential as an optimized biomarker for cervical cancer screening. These findings align with those of previous studies. Zhu et al. reported a sensitivity of 93% for ZNF671 in detecting cervical cancer, making it a highly reliable marker.( 22 ) while another study found that ZNF671 had significantly higher odds ratios for detecting CIN2 + and CIN3 + compared to other markers in the GynTect® assay( 29 ). However, the combination of all yielded an AUC of 0.767 for CIN2 + detection, which was slightly lower than that of ZNF671 alone. This contrasts with previously reported findings ( 29 ). Possibly due to sample size limitations. In our study, CIN1 cases constituted 44.2% of the cohort, and there may be some patients have been misclassified as not having CIN2+. Since the patient involved only one colposcopic biopsy for pathological examination. Notably, most patients with colposcopy findings indicative of CIN1 underwent only a single colposcopic biopsy, which is known to have a limited ability to detect HSIL in a substantial proportion of cases( 30 ). Despite these limitations, ZNF671 testing remains a cost-effective strategy for optimizing laboratory workflows, ensuring quality control, and standardizing screening protocols. Furthermore, the identification of specific △Cp thresholds of ZNF671 methylation offers distinct and complementary advantages in both the differentiation of the current disease state and the prediction of future progression, making a significant advancement in molecular diagnostics for cervical cancer. In distinguishing CIN2 + from CIN1-, ZNF671 methylation demonstrated superior diagnostic performance (AUC: 0.816) with high specificity (90.58%), establishing a △Cp threshold of 0.6 as a critical diagnostic benchmark. This precision enables the accurate identification of high-grade lesions, supporting immediate clinical decision-making and treatment planning. Notably, ZNF671 methylation also exhibited enhanced predictive power for cancer progression (AUC: 0.854), with a more stringent △Cp threshold of 0.4, optimizing the identification of CIN2 + cases at the highest risk for malignant transformation. This dual functionality of ZNF671 methylation - diagnostic discrimination and progression prediction, provides clinicians with comprehensive molecular information for both immediate and long-term patient management strategies. Especially for CIN2 patients concerned about fertility preservation. Limitations of this study also should be acknowledged. First, the sample size was relatively small, particularly in the cervical cancer (14 cases) and CIN3 (23 cases) groups, which may limit the statistical power and generalizability of the findings. Second, the study was conducted at a single institution, highlighting the need for external validation in diverse populations and clinical settings to confirm the reproducibility of the results. Additionally, technical challenges associated with DNA methylation detection, including standardization of sample collection, processing, and analysis protocols, need to be addressed to ensure reliable clinical implementation. Future research should prioritize validating these findings in larger, multi-center cohorts with diverse patient populations. Furthermore, studies should assess the long-term predictive value of ZNF671 methylation levels and their correlation with disease outcomes. Lastly, further investigation is required to elucidate the biological mechanisms linking ZNF671 methylation to cervical cancer progression, which may provide critical insights for therapeutic development. Conclusions The evaluation of DNA methylation levels holds promise for improving the diagnostic accuracy of cervical cancer and its precursors. Among these, ZNF671 methylation analysis demonstrates superior performance, particularly due to its high predictive value for cancer progression. This indicates its potential as a valuable clinical tool, provided further validation is conducted across diverse populations. Integrating this approach into clinical practice may enhance risk stratification, support personalized treatment strategies, and ultimately contribute to more effective cancer prevention and management for specific patient populations. Abbreviations CC: cervical cancer; CIN1: Cervical Intraepithelial Neoplasia Grade 1; CIN2: Cervical Intraepithelial Neoplasia Grade 2; CIN3: Cervical Intraepithelial Neoplasia Grade 3; CIN2+: Cervical Intraepithelial Neoplasia Grade 2 or higher; CIN3+: Cervical Intraepithelial Neoplasia Grade 3 or higher; HPV: Human Papillomavirus; hrHPV: High-risk Human Papillomavirus; TCT: Thinprep Cytologic Test; ROC: Receiver Operating Characteristic; AUC: Area Under the Curve; HSIL: High-grade Squamous Intraepithelial Lesion; GynTect®: GynTect® Assay Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the First People's Hospital of Linping District, Hangzhou (ethics number 2020001) and conducted in accordance with the World Medical Association Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The data used and/or analyzed in this study are available from the corresponding author upon reasonable request Competing interests The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article Funding This work was supported by the Zhejiang Provincial Medical and Health Science and Technology Program (2025KY1226) Authors' contributions Zhenping Liu and Lu Liu: Designed the research framework, organized data, and wrote the manuscript. Danhua Wang: Collected data and conducted statistical analyses. Xiaoying Li and Hui Lian: Collected experimental data and provided technical support. Longying Zhu: Managed data storage and quality control. Yixuan He: Reviewed and edited the manuscript. Chunyan Qian: Guided research design and approved the manuscript. All the authors have read and approved the final version of the manuscript. Acknowledgements The authors wish to thank the colleagues from the Pathology Department for their multiple discussions and reviews of pathological results, and for their guidance throughout this research. References Bruni L, Serrano B, Roura E, Alemany L, Cowan M, Herrero R, et al. Cervical cancer screening programmes and age-specific coverage estimates for 202 countries and territories worldwide: a review and synthetic analysis. Lancet Glob Health. 2022;10(8):e1115-e27. Hoppenot C, Stampler K, Dunton C. Cervical cancer screening in high- and low-resource countries: implications and new developments. Obstet Gynecol Surv. 2012;67(10):658–67. Perkins RB, Wentzensen N, Guido RS, Schiffman M. Cervical Cancer Screening: A Review. JAMA. 2023;330(6):547–58. Ebisch RM, Siebers AG, Bosgraaf RP, Massuger LF, Bekkers RL, Melchers WJ. Triage of high-risk HPV positive women in cervical cancer screening. Expert Rev Anticancer Ther. 2016;16(10):1073–85. Bhatla N, Singhal S. Primary HPV screening for cervical cancer. Best Pract Res Clin Obstet Gynaecol. 2020;65:98–108. Koliopoulos G, Nyaga VN, Santesso N, Bryant A, Martin-Hirsch PP, Mustafa RA, et al. Cytology versus HPV testing for cervical cancer screening in the general population. Cochrane Database Syst Rev. 2017;8(8):CD008587. Swid MA, Monaco SE. Should screening for cervical cancer go to primary human papillomavirus testing and eliminate cytology? Mod Pathol. 2022;35(7):858–64. Gibb RK, Martens MG. The impact of liquid-based cytology in decreasing the incidence of cervical cancer. Rev Obstet Gynecol. 2011;4(Suppl 1):S2-S11. Pretorius RG, Belinson JL, Burchette RJ, Hu S, Zhang X, Qiao YL. Regardless of skill, performing more biopsies increases the sensitivity of colposcopy. J Low Genit Tract Dis. 2011;15(3):180–8. Wentzensen N, Schiffman M, Silver MI, Khan MJ, Perkins RB, Smith KM, et al. ASCCP Colposcopy Standards: Risk-Based Colposcopy Practice. J Low Genit Tract Dis. 2017;21(4):230–4. van der Marel J, van Baars R, Rodriguez A, Quint WG, van de Sandt MM, Berkhof J, et al. The increased detection of cervical intraepithelial neoplasia when using a second biopsy at colposcopy. Gynecol Oncol. 2014;135(2):201–7. Pruski D, Millert-Kalińska S, Lis A, Pelc E, Konopelski P, Jach R, et al. Evaluation of the Use of Methylation as a New Tool for the Diagnostics and Progression of Squamous Intraepithelial Lesions. International Journal of Molecular Sciences. 2024;25(22). El Aliani A, El-Abid H, El Mallali Y, Attaleb M, Ennaji MM, El Mzibri M. Association between Gene Promoter Methylation and Cervical Cancer Development: Global Distribution and A Meta-analysis. Cancer Epidemiol Biomarkers Prev. 2021;30(3):450–9. Salta S, Lobo J, Magalhaes B, Henrique R, Jeronimo C. DNA methylation as a triage marker for colposcopy referral in HPV-based cervical cancer screening: a systematic review and meta-analysis. Clin Epigenetics. 2023;15(1):125. Cao D, Yang Z, Dong S, Li Y, Mao Z, Lu Q, et al. PCDHGB7 hypermethylation-based Cervical cancer Methylation (CerMe) detection for the triage of high-risk human papillomavirus-positive women: a prospective cohort study. BMC Med. 2024;22(1):55. Fan CL, Hu JB, Luo T, Dong BH, Li HY, Wang WP, et al. Analysis of the diagnostic performance of PAX1/SOX1 gene methylation in cervical precancerous lesions and its role in triage diagnosis. Journal of Medical Virology. 2024;96(5). Dick S, Vink FJ, Heideman DAM, Lissenberg-Witte BI, Meijer C, Berkhof J. Risk-stratification of HPV-positive women with low-grade cytology by FAM19A4/miR124-2 methylation and HPV genotyping. Br J Cancer. 2022;126(2):259–64. Zhu H, Zhu H, Tian M, Wang D, He J, Xu T. DNA Methylation and Hydroxymethylation in Cervical Cancer: Diagnosis, Prognosis and Treatment. Front Genet. 2020;11:347. Fan C, Ma Q, Wu X, Dai X, Peng Q, Cai H. Detection of DNA Methylation in Gene Loci ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 for Diagnosis of Cervical Cancer. Cancer Manag Res. 2023;15:635–44. Shi L, Yang X, He L, Zheng C, Ren Z, Warsame JA, et al. Promoter hypermethylation analysis of host genes in cervical intraepithelial neoplasia and cervical cancers on histological cervical specimens. BMC Cancer. 2023;23(1):168. Zhang L, Zhao X, Hu S, Chen S, Zhao S, Dong L, et al. Triage performance and predictive value of the human gene methylation panel among women positive on self-collected HPV test: Results from a prospective cohort study. Int J Cancer. 2022;151(6):878–87. Zhu P, Xiong J, Yuan D, Li X, Luo L, Huang J, et al. ZNF671 methylation test in cervical scrapings for cervical intraepithelial neoplasia grade 3 and cervical cancer detection. Cell Reports Medicine. 2023;4(8). Li L, Yang WT, Zheng PS, Liu XF. SOX17 restrains proliferation and tumor formation by down-regulating activity of the Wnt/beta-catenin signaling pathway via trans-suppressing beta-catenin in cervical cancer. Cell Death Dis. 2018;9(7):741. Ozgu E, Yildiz Y, Ozgu BS, Oz M, Danisman N, Gungor T. Efficacy of a real time optoelectronic device (TruScreen) in detecting cervical intraepithelial pathologies: a prospective observational study. J Turk Ger Gynecol Assoc. 2015;16(1):41–4. Wang M, Hou B, Wang X, Han L, Shi Y, Zhang Y, et al. Diagnostic value of high-risk human papillomavirus viral load on cervical lesion assessment and ASCUS triage. Cancer Med. 2021;10(7):2482–8. Vieira-Baptista P, Costa M, Hippe J, Sousa C, Schmitz M, Silva AR, et al. Evaluation of Host Gene Methylation as a Triage Test for HPV-Positive Women-A Cohort Study. J Low Genit Tract Dis. 2024. Schmitz M, Eichelkraut K, Schmidt D, Zeiser I, Hilal Z, Tettenborn Z, et al. Performance of a DNA methylation marker panel using liquid-based cervical scrapes to detect cervical cancer and its precancerous stages. BMC Cancer. 2018;18(1). Shi L, Yang X, He L, Zheng C, Ren Z, Warsame JA, et al. Promoter hypermethylation analysis of host genes in cervical intraepithelial neoplasia and cervical cancers on histological cervical specimens. BMC Cancer. 2023;23(1). Fan C, Ma Q, Wu X, Dai X, Peng Q, Cai H. Detection of DNA Methylation in Gene Loci ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 for Diagnosis of Cervical Cancer. Cancer Management and Research. 2023;Volume 15:635–44. Wentzensen N, Walker JL, Gold MA, Smith KM, Zuna RE, Mathews C, et al. Multiple biopsies and detection of cervical cancer precursors at colposcopy. J Clin Oncol. 2015;33(1):83–9. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-6182081","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438862480,"identity":"bd0f2793-516d-4831-9855-5b1459de1c31","order_by":0,"name":"Zhenping Liu","email":"","orcid":"","institution":"The First People’s Hospital of Linping District","correspondingAuthor":false,"prefix":"","firstName":"Zhenping","middleName":"","lastName":"Liu","suffix":""},{"id":438862481,"identity":"00d91b13-9c7c-4fe1-bf9f-4f409a06eee8","order_by":1,"name":"Lu Liu","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Liu","suffix":""},{"id":438862482,"identity":"163668ee-f190-4eb0-9246-bab17e4f0e4d","order_by":2,"name":"Danhua Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYLACHgaGBAYG5gMMEmBuAtFa2BIYJBJI08JjAFVNQIvB8bOHX7ypuZNncPzM5w+WPw4z8LPnGDD83IFHy5m8NMs5x54VG5zJ3SYhkXCYQbLnjQFj7xncWswO5JgZ87AdTtxwIHcbA0iLwY0cA2bGNjxazr8BavkH1HL+zeMPIC32BLXcyDF+zNsG1HIjhwHsMAMJAlrsb7wxY5zbdzhx5o1nZhISaek8EmeeFRzsxaNFsj/H+MObb4cT+84nP/4sYWMtx9+evPHBTzxagIBNAsZilgDHEQPDAbwagAo/wFiMH/CpGwWjYBSMghELAC8nWP+A3cOmAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Danhua","middleName":"","lastName":"Wang","suffix":""},{"id":438862483,"identity":"10e643fa-3235-4420-af32-3f461a759bb6","order_by":3,"name":"Xiaoying Li","email":"","orcid":"","institution":"The First People's Hospital of Lin'an District","correspondingAuthor":false,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Li","suffix":""},{"id":438862484,"identity":"73bcf17d-89f9-4e25-9ba9-69c5804c5fac","order_by":4,"name":"Hui Lian","email":"","orcid":"","institution":"The First People’s Hospital of Linping District","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Lian","suffix":""},{"id":438862485,"identity":"098d44f7-ec93-4d01-8bb5-f792b62369e7","order_by":5,"name":"Longying Zhu","email":"","orcid":"","institution":"The First People’s Hospital of Linping District","correspondingAuthor":false,"prefix":"","firstName":"Longying","middleName":"","lastName":"Zhu","suffix":""},{"id":438862486,"identity":"6977f7c0-c6d7-4c58-89a9-01dc0864a9cc","order_by":6,"name":"Yixuan He","email":"","orcid":"","institution":"The First People’s Hospital of Linping District","correspondingAuthor":false,"prefix":"","firstName":"Yixuan","middleName":"","lastName":"He","suffix":""},{"id":438862487,"identity":"999f335d-41d5-4f25-80c8-1bd9a86dd634","order_by":7,"name":"Chunyan Qian","email":"","orcid":"","institution":"The First People’s Hospital of Linping District","correspondingAuthor":false,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2025-03-08 05:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6182081/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6182081/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81694789,"identity":"52433bb3-c3b9-42e0-abae-1bb9cb23fa0f","added_by":"auto","created_at":"2025-04-30 11:54:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113055,"visible":true,"origin":"","legend":"\u003cp\u003eGene expression heatmap from the methylation levels of genes in various cervical lesions. CIN, cervical intraepithelial neoplasia\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6182081/v1/a37910e3fffd19e305229193.png"},{"id":81694799,"identity":"807bca91-e6e4-43a0-95ba-4327ea85e5a4","added_by":"auto","created_at":"2025-04-30 11:54:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91040,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve and the associated AUC value of the ZNF671 methylation between CIN3- and Cancer. CIN3-, including CIN2 and CIN3.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6182081/v1/7bc9da79864942ab0bd4503e.png"},{"id":81695742,"identity":"e5eb53f3-b2e5-4b20-9382-3996fadb82f6","added_by":"auto","created_at":"2025-04-30 12:02:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99979,"visible":true,"origin":"","legend":"\u003cp\u003eThe level of\u003cstrong\u003e \u003c/strong\u003eZNF671 methylation in distinguishing between different grades of cervical lesions. \u003cstrong\u003eA\u003c/strong\u003e ZNF671 methylation between CIN1- and CIN2+. CIN1-,including normal and CIN1; CIN2 + , including CIN2, CIN3, and cancer. \u003cstrong\u003eB \u003c/strong\u003eZNF671 methylation between CIN3- and Cancer. CIN3-, including CIN2 and CIN3.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6182081/v1/5f7a83bdf0a7b2027a1559c0.png"},{"id":86114273,"identity":"59933424-14fb-4fd3-9c6d-642fb8e01d11","added_by":"auto","created_at":"2025-07-07 00:01:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1232905,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6182081/v1/205418cd-3aa8-4718-af07-4c8436b14f9e.pdf"},{"id":81695734,"identity":"754ef192-fc3b-4c18-a840-e781e1e344c2","added_by":"auto","created_at":"2025-04-30 12:02:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":156412,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6182081/v1/da67d0f385f4ab8f8a2948f2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"DNA Methylation Levels in Cervical Scrapes for Predicting the Risk of Cervical Lesions and Cancer","fulltext":[{"header":"Background","content":"\u003cp\u003eCervical cancer remains a major public health concern worldwide, particularly in low and middle-income countries where screening programs are less established(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Primary HPV testing and cytology are widely used in cervical cancer screening to reduce the incidence and mortality (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, women with positive HPV test results or abnormal cytological findings require a thorough evaluation through colposcopy-directed biopsy to determine the need for further interventions(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The complexity of interpretation and management presents a significant challenge for clinicians. The relatively low specificity of HPV testing can result in false-positive outcome, leading to unnecessary colposcopy and follow-up diagnostic procedures, which consume medical resources and impose a financial burden for patients(\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Additionally, the accuracy of cytology depends heavily on sample quality and the physician's clinical judgment, increasing the risk of diagnostic errors and false-negative results (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Furthermore, a second or third colposcopic biopsy may be necessary to confirm a diagnosis, as a single biopsy can fail to detect high-grade squamous intraepithelial lesions (HSIL) in 30\u0026ndash;50% of cases (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Incorporating more detailed molecular information is crucial to accurately assess cervical cancer risk, enhance diagnostic precision and guide treatment decisions.\u003c/p\u003e \u003cp\u003eIn recent years, methylation of the promoter regions of tumor suppressor genes has been identified as a mechanism that silences gene expression, thereby contributing to the pathogenesis of cervical cancer(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Compared to conventional cervical cancer screening methods, DNA methylation testing offers superior accuracy, convenience, and automation(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Furthermore, PCDHGB7(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), PAX1/SOX1(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and FAM19A4/miR124-2(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) alterations have emerged as promising biomarkers for cervical cancer screening. However, most DNA methylation-based studies have primarily focused on evaluating sensitivity and specificity of these markers as diagnostic tools rather than exploring dynamic methylation changes that could provide crucial insights into lesion severity(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous studies have highlighted the significance of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 methylation in diagnosing cervical cancer(\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). ZNF671 has demonstrated remarkable diagnostic efficacy in detecting high-grade lesions (CIN3+)(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Additionally, research has revealed the differential expression of SOX17 across normal cervical tissue, high-grade squamous intraepithelial lesions, and cervical cancer(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Previously, we conducted a comprehensive genome-wide methylation analysis of 100 cervical scrape samples with varying pathological outcomes. Our findings established a strong correlation between the methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 and cervical cancer progression, with methylation levels progressively increasing from CIN2 onwards. Given the robust diagnostic performance of these markers, further investigation into methylation changes across different cervical lesion types is warranted.\u003c/p\u003e \u003cp\u003eThe purpose of this study was to evaluate the methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 genes in cervical secretions to assess their potential for identifying cervical lesions and cancer. Additionally, the study compared these methylation markers with traditional screening methods, including HPV testing and TCT. This study aimed to evaluate the predictive value of these gene methylation patterns in cervical lesion progression, ultimately providing a more precise diagnostic framework, guiding treatment decisions, and supporting individualized patient management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study recruited 283 patients with cervical-related diseases who received treatment at the First People's Hospital of Linping District, Hangzhou, between May 2023 and June 2024. Following histopathological confirmation of cervical disease, cervical brush samples from HPV testing were collected and cryopreserved at -40\u0026deg;C until analysis. Patients with a history of prior surgery for cervical disease, sexual exposure, or non-pregnant individuals (e.g., hysterectomy, laser therapy, chemotherapy/radiotherapy, pregnancy, or extracervical malignancy) were excluded based on the predefined criteria. A total of 260 samples retained for methylation testing, and the specific sample analysis process was illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All included cases underwent HPV testing, ThinPrep cytological testing (TCT), cervicoscopic biopsy, and DNA methylation analysis. Based on pathological results, patients were categorized into cervical cancer (CC, n\u0026thinsp;=\u0026thinsp;14), CIN3 (n\u0026thinsp;=\u0026thinsp;23), CIN2 (n\u0026thinsp;=\u0026thinsp;53), CIN1 (n\u0026thinsp;=\u0026thinsp;115), and normal (n\u0026thinsp;=\u0026thinsp;55). hrHPV status, TCT results, and pathology diagnoses were collected from the original institutions. This study was approved by the Ethics Committee of the First People's Hospital of Linping District, Hangzhou (ethics number 2020001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHPV testing\u003c/h3\u003e\n\u003cp\u003eWe employed the Sansure high-risk HPV DNA Fluorescence Diagnostic Kit (Sansure, Hunan, China) for genotyping HPV DNA in cervical scrape samples, The targeted HPV types included: 6, 11, 16, 18, 26, 31, 33, 35, 39, 42, 43, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 81, and 82. HPV results were categorized into two groups: HPV16/18 -positive and hrHPV-positive.\u003c/p\u003e\n\u003ch3\u003eDNA methylation analysis by quantitative methylation-specific PCR\u003c/h3\u003e\n\u003cp\u003eMethylation analysis was performed using the remaining cervical brush samples from HPV testing. Genomic DNA was extracted from the samples using standard protocols and subsequently converted to bisulfite form utilizing the EpiTect\u0026reg; Fast Bisulfite Kit (Qiagen, Germany). Quantitative methylation-specific PCR (qMSP) was carried out on an ABI7500 Real-Time PCR system (Life Technologies; Thermo Scientific, USA) to detect the methylation of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671, in accordance with the manufacturer\u0026rsquo;s guidelines. The Acetylcholinesterase (ACHE) gene served as an internal control for normalization. The △Cp value was calculated as the difference between the Cp values of the target genes and ACHE gene, was calculated using the formula: △Cp\u0026thinsp;=\u0026thinsp;Cp target gene - Cp ACHE. A lower △Cp indicates a higher degree of methylation. Positive results for the six genes were determined based on the manufacturer-defined ΔCp cutoff values (cutoff of 9 for ASTN1, DLX1, ITGA4, RXFP3, SOX17, 10 for ZNF671). A result was classified as positive if the ΔCp value was below the respective cutoff, and the corresponding ΔCp value was recorded. For samples with undetectable methylation, the ΔCp values were substituted with the predefined cutoff values. The score for each marker was predefined: 3 for ZNF671; 1 for each of ASTN1, ITGA4, RXFP3, and SOX17. The methylation panel was considered positive if the total score of all six markers was \u0026ge;\u0026thinsp;3.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to characterize the study population. Histology and HPV genotypes characteristics were presented as counts (%) and assessed using the chi-square test. DNA methylation levels in each group were represented as median and interquartile range (IQR), with the Mann-Whitney U test applied for comparative analysis. The discrimination performances of DNA Methylation and other screening methods were evaluated by the ROC curve and AUC value, reported with corresponding 95% confidence interval. An AUC of 0.50\u0026ndash;0.69 was considered mild, 0.70\u0026ndash;0.89 was moderate, and 0.90\u0026ndash;1.00 was good. The accuracy, sensitivity, and specificity were calculated from the ROC curve according to the cut-off value that maximizes the Youden index, equal to (sensitivity\u0026thinsp;+\u0026thinsp;specificity)-1. ROC curves between different models were compared using the DeLong method. SPSS software version 22.0 (SPSS, Inc., Chicago, IL, USA), MedCalc statistical software, GraphPad Prism software (Ver 8.0.1, La Jolla, CA, USA) and R version 4.4.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used for statistical analysis, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 being statistically significant. This analysis involved a suite of packages including ggplot2, tidyverse、dplyr、compareGroups、hrbrthemes、ggthemes、RColorBrewer、gridExtra、patchwork.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy characteristics\u003c/h2\u003e \u003cp\u003eBasic participant information and the enrollment flowchart are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cb\u003eSupplemental S1\u003c/b\u003e. The study included 260 patients with a median age of 41.5 years (interquartile range [IQR], 31\u0026ndash;51 years). Histopathological classification divided patients into the following groups: normal tissue (n\u0026thinsp;=\u0026thinsp;55, 21.2%), CIN1 (n\u0026thinsp;=\u0026thinsp;115, 44.2%), CIN2 (n\u0026thinsp;=\u0026thinsp;53, 20.4%), CIN3 (n\u0026thinsp;=\u0026thinsp;23, 8.85%), and cervical cancer (n\u0026thinsp;=\u0026thinsp;14, 5.38%). HPV testing identified 8 patients (30%) as HPV16/18 positive, while 219 patients (84.2%) tested positive for other high-risk HPV types. The median DNA methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 ranged from 7.39 to 10.0, with DLX1 exhibiting the highest median methylation level (7.39), indicating the highest positivity rate among participants.\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\u003eSheet of basic information for participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Character (n\u0026thinsp;=\u0026thinsp;260)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber (%) or median [Q1; Q3]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.5 [31.0;51.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55 (21.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115 (44.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (8.85%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (5.38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA methylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASTN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.00 [6.42;9.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.39 [5.36;9.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.00 [9.00;9.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRXFP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.00 [8.88;9.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOX17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.00 [8.47;9.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.0 [8.30;10.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPV testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e219 (84.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPV16/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e182 (70.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparison of DNA methylation levels for Cervical Cancer and various CIN\u003c/h3\u003e\n\u003cp\u003eThe methylation levels of the genes ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 progressively decreased with increasing severity of cervical disease, from normal tissue to Cervical Intraepithelial Neoplasia 1 (CIN1), CIN2, CIN3, and cervical cancer. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The △Cp levels of these six genes across histological groups are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Statistical analysis revealed significant differences in gene methylation levels among the groups (Mann\u0026ndash;Whitney U test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, compared to normal tissue, the methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 were significantly lower in cervical cancer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eΔCp values of genes by groups of Histology\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCIN1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCIN2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCIN3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;55\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;115\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;53\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;23\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;14\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASTN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.00 [8.29;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.00 [8.23;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.00 [4.47;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.59 [2.42;7.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87 [-0.20;3.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.60 [7.08;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.60 [6.27;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.85 [4.75;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.44 [2.66;6.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.43 [0.90;4.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.00 [9.00;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.00 [9.00;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.00 [9.00;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.35 [5.46;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.48 [0.84;6.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRXFP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.00 [9.00;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.00 [9.00;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.00 [7.66;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.24 [5.17;8.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.19 [2.15;7.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOX17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.00 [9.00;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.00 [9.00;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.00 [7.05;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.60 [5.06;8.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.40 [2.06;5.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.0 [10.0;10.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0 [10.0;10.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.12 [3.91;10.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.58 [1.45;3.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42 [-0.41;1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eValues are Median [Q1;Q3]\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePerformance of DNA methylation and other screening methods for CIN1- and CIN2+\u003c/h3\u003e\n\u003cp\u003eWe analyzed and compared the areas under the curve (AUC) for HPV16/18, hrHPV, TCT, and the methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 in cervical scrapings using receiver operating characteristic (ROC) curves to differentiate between CIN1- and CIN2\u0026thinsp;+\u0026thinsp;lesions. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the methylation markers demonstrated relatively high diagnostic accuracy, with mean AUC values ranging from 0.713 to 0.816, surpassing common screening methods (TCT: 0.554; hrHPV: 0.617; HPV16/18: 0.653). Among these, the△Cp level of ZNF671 exhibited the highest diagnostic efficacy, achieving a Youden's index of 0.59 and an AUC of 0.816. It demonstrated a sensitivity of 68.89% and a specificity of 90.58% for CIN2\u0026thinsp;+\u0026thinsp;detection. The combined use of six markers yielded an AUC of 0.767 (95% confidence interval [CI]: 0.711\u0026ndash;0.817) for CIN2\u0026thinsp;+\u0026thinsp;detection. However, increasing the sensitivity to 72.22% resulted in a reduction in specificity to 81.18%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e6 DNA Methylation and other screening methods between normal, CIN1 and CIN2, CIN3, Cancer (N\u0026thinsp;=\u0026thinsp;260)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMethods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYouden Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[95% CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.8\u0026ndash;85.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.9\u0026ndash;40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.491\u0026ndash;0.615]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehr HPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e[21.8\u0026ndash;41.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[87.3\u0026ndash;95.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.555\u0026ndash;0.677]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPV16/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e[39.3\u0026ndash;60.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[73.8\u0026ndash;86.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.592\u0026ndash;0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASTN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e[40.3\u0026ndash;61.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[82.4\u0026ndash;92.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.654\u0026ndash;0.767]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e[51.4\u0026ndash;72.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[70.0-83.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.661\u0026ndash;0.773]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRXFP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e[40.3\u0026ndash;61.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[85.2\u0026ndash;94.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.666\u0026ndash;0.778]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOX17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e[43.6\u0026ndash;65.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[88.7\u0026ndash;96.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.692\u0026ndash;0.801]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e[58.3\u0026ndash;78.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[85.2\u0026ndash;94.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.764\u0026ndash;0.861]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e[32.9\u0026ndash;54.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[94.1\u0026ndash;99.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.648\u0026ndash;0.762]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGynTect\u0026reg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e[61.8\u0026ndash;81.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[74.5\u0026ndash;86.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.711\u0026ndash;0.817]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePerformance of DNA Methylation and other screening methods between CIN3- and Cancer\u003c/h2\u003e \u003cp\u003eIn the study cohort, 90 women were diagnosed with CIN2+. When cervical cancer was considered the outcome, risk stratification for the six methylation markers revealed similar trends, with the △Cp level of the cancer group significantly lower than that of CIN3- group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In contrast, no significant differences were observed in HPV testing and TCT screening methods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e6 DNA Methylation and other screening methods between CIN3- and Cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eScreening methods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ALL]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCIN3-\u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;90\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;76\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;14\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA methylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASTN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.40 [2.17;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.56 [3.02;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87 [-0.20;3.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.74 [2.90;7.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.31 [3.21;8.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.43 [0.90;4.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRXFP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.63 [5.56;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.00 [5.94;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.19 [2.15;7.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOX17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.96 [4.66;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.00 [5.86;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.40 [2.06;5.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.13 [1.44;10.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.81 [2.29;10.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42 [-0.41;1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.00 [4.63;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.00 [6.11;9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.48 [0.84;6.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (92.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehr HPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62 (68.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (68.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPV16/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (64.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGynTect\u0026reg; result\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (72.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are Median [Q1; Q3] or N (%)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026dagger; CIN3-, CIN2 and CIN3\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe evaluated the specificity and sensitivity of the six methylation markers. Among patients with CIN2 or CIN3 who progressed to cervical cancer, the ROC curves ranged from 0.804 to 0.854 (\u003cb\u003eSupplemental Table S2\u003c/b\u003e). ZNF671 demonstrated the highest classification performance, with an AUC of 0.854, a sensitivity of 78.57%, and a specificity of 81.58% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLevels of ZNF671 methylation inform the extent of cervical lesions\u003c/h2\u003e \u003cp\u003eThe performance of ZNF671 (ZNF671\u003csup\u003em\u003c/sup\u003e) methylation outperformed other screening methods in our study. We then evaluated whether ZNF671\u003csup\u003em\u003c/sup\u003e levels correlated with the severity of cervical lesions. Our analysis revealed that ZNF671\u003csup\u003em\u003c/sup\u003e levels were significantly lower in patients with CIN1- than those with CIN2+. To assess the prognostic significance of ZNF671m levels in distinguishing CIN1 from CIN2\u0026thinsp;+\u0026thinsp;and predicting the progression of CIN3- to cervical cancer, we determined the optimal cutoff value using the ROC curve based on the Youden index, calculated as (sensitivity\u0026thinsp;+\u0026thinsp;specificity) -1. The analysis identified that when △Cp (ZNF671) was less than 0.6, the risk of CIN2\u0026thinsp;+\u0026thinsp;increased significantly. Furthermore, the optimal △Cp (ZNF671) cutoff for predicting the progression of high-grade lesions to cervical cancer was determined to be 0.4 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we analyzed the methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 during the progression of cervical lesions. We observed a decreasing trend in methylation levels as disease severity advanced from normal tissue to cervical cancer. All six genes exhibited statistically significant differences across various CIN and invasive cervical cancer stages, allowing for a clear distinction between disease stages. ZNF671 methylation demonstrated the highest diagnostic performance, effectively differentiating CIN1- from CIN2\u0026thinsp;+\u0026thinsp;lesions and stratifying CIN3- and cervical cancer within high-grade squamous intraepithelial lesions. Our findings provide detailed insights into the extent of methylation and its clinical implications, offering a more comprehensive comparison to previous studies on these six methylation markers.\u003c/p\u003e \u003cp\u003eThe incidence of cervical cancer has significantly declined due to hrHPV testing and cytology-based screening, both of which exhibit high sensitivity for detecting high-grade lesions (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, our study revealed that these conventional strategies showed significantly lower diagnostic performance (AUC: hrHPV\u0026thinsp;=\u0026thinsp;0.617; TCT\u0026thinsp;=\u0026thinsp;0.554) compared to methylation analysis of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671. The AUC for hrHPV testing alone was 0.617. Affected by low specificity (Sensitivity (%) :31.11, Specificity (%):92.35). Leading to potential overtreatment, TCT also demonstrated deficiencies in diagnostic accuracy (AUC: 0.554), primarily due to low specificity (32.94%) despite relatively higher sensitivity (77.78), leading to potential overtreatment. Compared with TCT and hrHPV testing, HPV16/18 testing demonstrated moderate performance with AUC 0.653 in risk stratification. The diagnostic efficacy of above is consistent with previous literature reports(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In contrast, all six methylation markers achieved superior AUC values ranging from 0.713 to 0.816. underscoring the enhanced accuracy of methylation-based approaches. Among these, ZNF671 exhibited the highest AUC (0.816) and specificity (90.58%) while maintaining robust sensitivity (68.89%). This enhanced performance addresses a critical clinical need, particularly in reducing the high false-positive rates and potential overtreatment associated with current screening methods.\u003c/p\u003e \u003cp\u003ePrevious literatures report that the methylation panel (ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671) exhibits high sensitivity and specificity for detecting CIN3 and cervical cancer. For instance, one study reported a sensitivity of 78.0% and specificity of 86.0% for CIN3\u0026thinsp;+\u0026thinsp;detection(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Another study found that the GynTect\u0026reg; assay, which includes these markers, had a specificity of 94.6% for CIN3+(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Moreover, Shi et al, observed that methylation levels of these genes increase with the severity, as reflected in the positive rates of methylation markers in the GynTect\u0026reg; assay(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). However, their study did not investigate the intrinsic changes of cervical lesions. In this study, we analyzed the changes in △Cp values of the studied markers in cervical lesions. Our findings identified ZNF671 as the most effective marker, demonstrating the highest diagnostic accuracy (AUC: 0.816) for CIN2\u0026thinsp;+\u0026thinsp;detection with an optimal sensitivity of 68.89% and specificity of 90.58%. This exceptional performance was further validated in cancer prediction, where ZNF671 achieved an AUC of 0.854, with a sensitivity of 78.57% and a specificity of 81.58%. The robust performance of ZNF671 highlights its potential as an optimized biomarker for cervical cancer screening. These findings align with those of previous studies. Zhu et al. reported a sensitivity of 93% for ZNF671 in detecting cervical cancer, making it a highly reliable marker.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) while another study found that ZNF671 had significantly higher odds ratios for detecting CIN2\u0026thinsp;+\u0026thinsp;and CIN3\u0026thinsp;+\u0026thinsp;compared to other markers in the GynTect\u0026reg; assay(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, the combination of all yielded an AUC of 0.767 for CIN2\u0026thinsp;+\u0026thinsp;detection, which was slightly lower than that of ZNF671 alone. This contrasts with previously reported findings (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Possibly due to sample size limitations. In our study, CIN1 cases constituted 44.2% of the cohort, and there may be some patients have been misclassified as not having CIN2+. Since the patient involved only one colposcopic biopsy for pathological examination. Notably, most patients with colposcopy findings indicative of CIN1 underwent only a single colposcopic biopsy, which is known to have a limited ability to detect HSIL in a substantial proportion of cases(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Despite these limitations, ZNF671 testing remains a cost-effective strategy for optimizing laboratory workflows, ensuring quality control, and standardizing screening protocols.\u003c/p\u003e \u003cp\u003eFurthermore, the identification of specific △Cp thresholds of ZNF671 methylation offers distinct and complementary advantages in both the differentiation of the current disease state and the prediction of future progression, making a significant advancement in molecular diagnostics for cervical cancer. In distinguishing CIN2\u0026thinsp;+\u0026thinsp;from CIN1-, ZNF671 methylation demonstrated superior diagnostic performance (AUC: 0.816) with high specificity (90.58%), establishing a △Cp threshold of 0.6 as a critical diagnostic benchmark. This precision enables the accurate identification of high-grade lesions, supporting immediate clinical decision-making and treatment planning. Notably, ZNF671 methylation also exhibited enhanced predictive power for cancer progression (AUC: 0.854), with a more stringent △Cp threshold of 0.4, optimizing the identification of CIN2\u0026thinsp;+\u0026thinsp;cases at the highest risk for malignant transformation. This dual functionality of ZNF671 methylation - diagnostic discrimination and progression prediction, provides clinicians with comprehensive molecular information for both immediate and long-term patient management strategies. Especially for CIN2 patients concerned about fertility preservation.\u003c/p\u003e \u003cp\u003eLimitations of this study also should be acknowledged. First, the sample size was relatively small, particularly in the cervical cancer (14 cases) and CIN3 (23 cases) groups, which may limit the statistical power and generalizability of the findings. Second, the study was conducted at a single institution, highlighting the need for external validation in diverse populations and clinical settings to confirm the reproducibility of the results. Additionally, technical challenges associated with DNA methylation detection, including standardization of sample collection, processing, and analysis protocols, need to be addressed to ensure reliable clinical implementation. Future research should prioritize validating these findings in larger, multi-center cohorts with diverse patient populations. Furthermore, studies should assess the long-term predictive value of ZNF671 methylation levels and their correlation with disease outcomes. Lastly, further investigation is required to elucidate the biological mechanisms linking ZNF671 methylation to cervical cancer progression, which may provide critical insights for therapeutic development.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe evaluation of DNA methylation levels holds promise for improving the diagnostic accuracy of cervical cancer and its precursors. Among these, ZNF671 methylation analysis demonstrates superior performance, particularly due to its high predictive value for cancer progression. This indicates its potential as a valuable clinical tool, provided further validation is conducted across diverse populations. Integrating this approach into clinical practice may enhance risk stratification, support personalized treatment strategies, and ultimately contribute to more effective cancer prevention and management for specific patient populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCC: cervical cancer; CIN1: Cervical Intraepithelial Neoplasia Grade 1; CIN2: Cervical Intraepithelial Neoplasia Grade 2; CIN3: Cervical Intraepithelial Neoplasia Grade 3; CIN2+: Cervical Intraepithelial Neoplasia Grade 2 or higher; CIN3+: Cervical Intraepithelial Neoplasia Grade 3 or higher; HPV: Human Papillomavirus; hrHPV: High-risk Human Papillomavirus; TCT: Thinprep Cytologic Test; ROC: Receiver Operating Characteristic; AUC: Area Under the Curve; HSIL: High-grade Squamous Intraepithelial Lesion; GynTect\u0026reg;: GynTect\u0026reg; Assay\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the First People's Hospital of Linping District, Hangzhou (ethics number 2020001) and conducted in accordance with the World Medical Association Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used and/or analyzed in this study are available from the corresponding author upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Zhejiang Provincial Medical and Health Science and Technology Program (2025KY1226)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhenping Liu and Lu Liu: Designed the research framework, organized data, and wrote the manuscript. Danhua Wang: Collected data and conducted statistical analyses. Xiaoying Li and Hui Lian: Collected experimental data and provided technical support. Longying Zhu: Managed data storage and quality control. Yixuan He: Reviewed and edited the manuscript. Chunyan Qian: Guided research design and approved the manuscript. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the colleagues from the Pathology Department for their multiple discussions and reviews of pathological results, and for their guidance throughout this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBruni L, Serrano B, Roura E, Alemany L, Cowan M, Herrero R, et al. Cervical cancer screening programmes and age-specific coverage estimates for 202 countries and territories worldwide: a review and synthetic analysis. Lancet Glob Health. 2022;10(8):e1115-e27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoppenot C, Stampler K, Dunton C. Cervical cancer screening in high- and low-resource countries: implications and new developments. Obstet Gynecol Surv. 2012;67(10):658\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerkins RB, Wentzensen N, Guido RS, Schiffman M. Cervical Cancer Screening: A Review. JAMA. 2023;330(6):547\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbisch RM, Siebers AG, Bosgraaf RP, Massuger LF, Bekkers RL, Melchers WJ. Triage of high-risk HPV positive women in cervical cancer screening. Expert Rev Anticancer Ther. 2016;16(10):1073\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhatla N, Singhal S. Primary HPV screening for cervical cancer. Best Pract Res Clin Obstet Gynaecol. 2020;65:98\u0026ndash;108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoliopoulos G, Nyaga VN, Santesso N, Bryant A, Martin-Hirsch PP, Mustafa RA, et al. Cytology versus HPV testing for cervical cancer screening in the general population. Cochrane Database Syst Rev. 2017;8(8):CD008587.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwid MA, Monaco SE. Should screening for cervical cancer go to primary human papillomavirus testing and eliminate cytology? Mod Pathol. 2022;35(7):858\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibb RK, Martens MG. The impact of liquid-based cytology in decreasing the incidence of cervical cancer. Rev Obstet Gynecol. 2011;4(Suppl 1):S2-S11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePretorius RG, Belinson JL, Burchette RJ, Hu S, Zhang X, Qiao YL. Regardless of skill, performing more biopsies increases the sensitivity of colposcopy. J Low Genit Tract Dis. 2011;15(3):180\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWentzensen N, Schiffman M, Silver MI, Khan MJ, Perkins RB, Smith KM, et al. ASCCP Colposcopy Standards: Risk-Based Colposcopy Practice. J Low Genit Tract Dis. 2017;21(4):230\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Marel J, van Baars R, Rodriguez A, Quint WG, van de Sandt MM, Berkhof J, et al. The increased detection of cervical intraepithelial neoplasia when using a second biopsy at colposcopy. Gynecol Oncol. 2014;135(2):201\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePruski D, Millert-Kalińska S, Lis A, Pelc E, Konopelski P, Jach R, et al. Evaluation of the Use of Methylation as a New Tool for the Diagnostics and Progression of Squamous Intraepithelial Lesions. International Journal of Molecular Sciences. 2024;25(22).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Aliani A, El-Abid H, El Mallali Y, Attaleb M, Ennaji MM, El Mzibri M. Association between Gene Promoter Methylation and Cervical Cancer Development: Global Distribution and A Meta-analysis. Cancer Epidemiol Biomarkers Prev. 2021;30(3):450\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalta S, Lobo J, Magalhaes B, Henrique R, Jeronimo C. DNA methylation as a triage marker for colposcopy referral in HPV-based cervical cancer screening: a systematic review and meta-analysis. Clin Epigenetics. 2023;15(1):125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao D, Yang Z, Dong S, Li Y, Mao Z, Lu Q, et al. PCDHGB7 hypermethylation-based Cervical cancer Methylation (CerMe) detection for the triage of high-risk human papillomavirus-positive women: a prospective cohort study. BMC Med. 2024;22(1):55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan CL, Hu JB, Luo T, Dong BH, Li HY, Wang WP, et al. Analysis of the diagnostic performance of PAX1/SOX1 gene methylation in cervical precancerous lesions and its role in triage diagnosis. Journal of Medical Virology. 2024;96(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDick S, Vink FJ, Heideman DAM, Lissenberg-Witte BI, Meijer C, Berkhof J. Risk-stratification of HPV-positive women with low-grade cytology by FAM19A4/miR124-2 methylation and HPV genotyping. Br J Cancer. 2022;126(2):259\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu H, Zhu H, Tian M, Wang D, He J, Xu T. DNA Methylation and Hydroxymethylation in Cervical Cancer: Diagnosis, Prognosis and Treatment. Front Genet. 2020;11:347.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan C, Ma Q, Wu X, Dai X, Peng Q, Cai H. Detection of DNA Methylation in Gene Loci ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 for Diagnosis of Cervical Cancer. Cancer Manag Res. 2023;15:635\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi L, Yang X, He L, Zheng C, Ren Z, Warsame JA, et al. Promoter hypermethylation analysis of host genes in cervical intraepithelial neoplasia and cervical cancers on histological cervical specimens. BMC Cancer. 2023;23(1):168.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Zhao X, Hu S, Chen S, Zhao S, Dong L, et al. Triage performance and predictive value of the human gene methylation panel among women positive on self-collected HPV test: Results from a prospective cohort study. Int J Cancer. 2022;151(6):878\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu P, Xiong J, Yuan D, Li X, Luo L, Huang J, et al. ZNF671 methylation test in cervical scrapings for cervical intraepithelial neoplasia grade 3 and cervical cancer detection. Cell Reports Medicine. 2023;4(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Yang WT, Zheng PS, Liu XF. SOX17 restrains proliferation and tumor formation by down-regulating activity of the Wnt/beta-catenin signaling pathway via trans-suppressing beta-catenin in cervical cancer. Cell Death Dis. 2018;9(7):741.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzgu E, Yildiz Y, Ozgu BS, Oz M, Danisman N, Gungor T. Efficacy of a real time optoelectronic device (TruScreen) in detecting cervical intraepithelial pathologies: a prospective observational study. J Turk Ger Gynecol Assoc. 2015;16(1):41\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M, Hou B, Wang X, Han L, Shi Y, Zhang Y, et al. Diagnostic value of high-risk human papillomavirus viral load on cervical lesion assessment and ASCUS triage. Cancer Med. 2021;10(7):2482\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVieira-Baptista P, Costa M, Hippe J, Sousa C, Schmitz M, Silva AR, et al. Evaluation of Host Gene Methylation as a Triage Test for HPV-Positive Women-A Cohort Study. J Low Genit Tract Dis. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmitz M, Eichelkraut K, Schmidt D, Zeiser I, Hilal Z, Tettenborn Z, et al. Performance of a DNA methylation marker panel using liquid-based cervical scrapes to detect cervical cancer and its precancerous stages. BMC Cancer. 2018;18(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi L, Yang X, He L, Zheng C, Ren Z, Warsame JA, et al. Promoter hypermethylation analysis of host genes in cervical intraepithelial neoplasia and cervical cancers on histological cervical specimens. BMC Cancer. 2023;23(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan C, Ma Q, Wu X, Dai X, Peng Q, Cai H. Detection of DNA Methylation in Gene Loci ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 for Diagnosis of Cervical Cancer. Cancer Management and Research. 2023;Volume 15:635\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWentzensen N, Walker JL, Gold MA, Smith KM, Zuna RE, Mathews C, et al. Multiple biopsies and detection of cervical cancer precursors at colposcopy. J Clin Oncol. 2015;33(1):83\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cervical lesions, cervical cancer, risk prediction, methylation level","lastPublishedDoi":"10.21203/rs.3.rs-6182081/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6182081/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccurate assessment of cervical cancer progression and severity is crucial for precise diagnosis and timely risk prediction. DNA methylation, which can be easily readily detected using molecular methods, has emerged as a promising approach for identifying novel biomarkers for cervical cancer diagnosis. However, routine methylation evaluation in cervical lesions primarily determines its presence or absence, failing to capture the nuances of disease progression and severity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study evaluated the DNA methylation level of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 for their potential in diagnosing cervical lesions and predicting cancer risk. The study cohort comprised 14 cases of cervical cancer (CC), 23 cases of CIN3, 53 cases of CIN2, 115 cases of CIN1, and 55 normal cases. All patients underwent high-risk HPV (hrHPV) testing, ThinPrep cytological testing (TCT), colposcopic biopsy, and DNA methylation analysis. Methylation levels of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and ZNF671 progressively declined from normal tissue to CIN1, CIN2, CIN3, and cervical cancer. These markers exhibited superior performance in detecting CIN2\u0026thinsp;+\u0026thinsp;and assessing the risk of cervical cancer. The receiver operating characteristic (ROC) curves for differentiating CIN1- from CIN2\u0026thinsp;+\u0026thinsp;using these six markers ranged from 0.713 to 0.816. Outperforming common screening methods such as TCT (0.554), hrHPV testing (0.617), and HPV16/18 testing (0.653). Among the six markers, ZNF671 demonstrated the highest diagnostic accuracy, with an area under the curve (AUC) of 0.816 for detecting CIN2+. Furthermore, a △Cp level of 0.4 for ZNF671 showed high predictive value in identifying CIN2\u0026thinsp;+\u0026thinsp;lesions at risk of progressing to cervical cancer, yielding an AUC of 0.854.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings indicate that DNA methylation levels can serve as accurate diagnostic and screening tools for assessing the progression and severity of cervical disease.\u003c/p\u003e","manuscriptTitle":"DNA Methylation Levels in Cervical Scrapes for Predicting the Risk of Cervical Lesions and Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 11:54:26","doi":"10.21203/rs.3.rs-6182081/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d478be0-3fae-411c-95b7-182bc7590504","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T13:53:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-30 11:54:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6182081","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6182081","identity":"rs-6182081","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}