A Multicenter Cohort Study on DNA Methylation for Endometrial Cancer Detection in Cervical Scrapings

IF 2.9 2区医学 Q2 ONCOLOGY Cancer Medicine Pub Date : 2024-11-02 DOI:10.1002/cam4.70361

Xiao Ma, Xiaojun Chen, Jing Liang, Jingbo Zhang, Qixi Wu, Dong Wang, Xianghua Huang, Dan Zi, Dexin Chen, Hua Wan, Li Qu, Zhaoyun Jiang, Wenyu Shao, Jie Sun, Luyuan Chang, Yunchao Liu, Qin Zhang, Yanan Li, Yani Ding, Biao Tang, Fang Zhao, Hanqing Zhao, Dongyan Cao

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Abstract

Background

The increasing incidence of endometrial cancer (EC) has highlighted the need for improved early detection methods. This study aimed to develop and validate a novel DNA methylation classifier, EMPap, for EC detection using cervical scrapings.

Methods

EMPap incorporated the methylation status of BHLHE22 and CDO1, along with age and body mass index (BMI), into a logistic regression model to calculate the endometrial cancer methylation (EM) score for identifying EC in cervical scrapings. We enrolled 1297 patients with highly suspected EC, including 196 confirmed EC cases, and assessed the EMPap performance in detecting EC.

Results

EMPap demonstrated robust diagnostic accuracy, with an area under the curve of 0.93, sensitivity of 90.3%, and specificity of 89.3%. It effectively detected EC across various disease stages, grades, and histological subtypes, and consistently performed well across patient demographics and symptoms. EMPap correctly identified 87.5% of the type II ECs and 53.8% of premalignant lesions. Notably, compared with transvaginal ultrasonography (TVS) in patients with postmenopausal bleeding, EMPap exhibited superior sensitivity (100% vs. 82.0%) and specificity (85.2% vs. 38.5%). In asymptomatic postmenopausal women, EMPap maintained high sensitivity (89.5%) and negative predictive value (NPV) (98.3%).

Conclusions

This study demonstrated the potential of EMPap as an effective tool for EC detection. Despite the limited sample size, EMPap showed promise for identifying type II EC and detecting over 50% of premalignant lesions. As a DNA methylation classifier, EMPap can reduce unnecessary uterine interventions and improve diagnosis and outcomes.

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从宫颈刮片中检测子宫内膜癌的 DNA 甲基化多中心队列研究

背景：子宫内膜癌（EC）发病率的上升凸显了改进早期检测方法的必要性。本研究旨在开发和验证一种新型DNA甲基化分类器EMPap，用于使用宫颈刮片检测子宫内膜癌：方法：EMPap将BHLHE22和CDO1的甲基化状态以及年龄和体重指数（BMI）纳入一个逻辑回归模型，计算出子宫内膜癌甲基化（EM）评分，用于识别宫颈刮片中的EC。我们招募了1297名高度疑似EC患者，其中包括196例确诊EC病例，并评估了EMPap在检测EC方面的性能：结果：EMPap表现出很高的诊断准确性，曲线下面积为0.93，灵敏度为90.3%，特异度为89.3%。它能有效检测出不同疾病分期、分级和组织学亚型的心肌梗死，而且在不同患者的人口统计学和症状方面都表现出色。EMPap能正确识别87.5%的II型EC和53.8%的癌前病变。值得注意的是，在绝经后出血患者中，与经阴道超声检查（TVS）相比，EMPap的灵敏度（100% 对 82.0%）和特异性（85.2% 对 38.5%）都更胜一筹。在无症状的绝经后妇女中，EMPap保持了较高的灵敏度（89.5%）和阴性预测值（98.3%）：这项研究证明了EMPap作为一种有效的EC检测工具的潜力。尽管样本量有限，但EMPap在识别II型EC和检测超过50%的恶性前病变方面显示出了前景。作为一种DNA甲基化分类器，EMPap可减少不必要的子宫干预，改善诊断和预后。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.