Abstract：
Objectives: To develop and validate predictive models for esophageal squamous cell carcinoma (ESCC) using circulating cell-free DNA (cfDNA) terminal motif analysis. The goal was to improve the non-invasive detection of early-stage ESCC and its precancerous lesions. Methods: Between August 2021 and November 2022, we prospectively collected plasma samples from 448 individuals at the Department of Endoscopy, Cancer Hospital, Chinese Academy of Medical Sciences for cfDNA extraction, library construction, and sequencing. We analyzed 201 cases of ESCC, 46 high-grade intraepithelial neoplasia (HGIN), 46 low-grade intraepithelial neoplasia (LGIN), 176 benign esophageal lesions, and 29 healthy controls. Participants, including ESCC patients and control subjects, were randomly assigned to a training set (n=284) and a validation set (n=122). The training cohort underwent z-score normalization of cfDNA terminal motif matrices and a selection of distinctive features differentiated ESCC cases from controls. The random forest classifier, Motif-1 (M1), was then developed through principal component analysis, ten-fold cross-validation, and recursive feature elimination. M1\'s efficacy was then validated in the validation and precancerous lesion sets. Subsequently, individuals with precancerous lesions were included in the dataset and participants were randomly allocated to newly formed training (n=243), validation (n=105), and test (n=150) cohorts. Using the same procedure as M1, we trained the Motif-2 (M2) random forest model with the training cohort. The M2 model\'s accuracy was then confirmed in the validation cohort to establish the optimal threshold and further tested by performing validation in the test cohort. Results: We developed two cfDNA terminal motif-based predictive models for ESCC and associated precancerous conditions. The first model, M1, achieved a sensitivity of 90.0%, a specificity of 77.4%, and an area under the curve (AUC) of 0.884 in the validation cohort. For LGIN, HGIN, and T1aN0 stage ESCC, M1\'s sensitivities were 76.1%, 80.4%, and 91.2% respectively. Notably, the sensitivity for jointly predicting HGIN and T1aN0 ESCC reached 85.0%. Both the predictive accuracy and sensitivity increased in line with the cancer\'s progression (P＜0.001). The second model, M2, exhibited a sensitivity of 87.5%, a specificity of 77.4%, and an AUC of 0.857 in the test cohort. M2\'s sensitivities for detecting precancerous lesions and ESCC were 80.0% and 89.7%, respectively, and it showed a combined sensitivity of 89.4% for HGIN and T1aN0 stage ESCC. Conclusions: Two predictive models based on cfDNA terminal motif analysis for ESCC and its precancerous lesions are developed. They both show high sensitivity and specificity in identifying ESCC and its precancerous stages, indicating its potential for early ESCC detection.
目的： 探索并建立基于循环游离DNA（cfDNA）末端基序检测的食管鳞状细胞癌（ESCC）预测模型，并评估其用于ESCC早期检测的可行性。 方法： 前瞻性收集2021年8月至2022年11月中国医学科学院肿瘤医院201例ESCC、46例食管高级别上皮内瘤变（HGIN）、46例食管低级别上皮内瘤变（LGIN）、176例食管良性病变患者和29例健康对照的血浆样本进行cfDNA提取、文库构建与测序。将ESCC患者和对照组受试者随机分为训练集（284例）和验证集（122例）。对训练集中ESCC及对照组cfDNA末端基序矩阵标准化处理及差异特征筛选，经主成分分析降维、十折交叉验证及随机森林递归特征消除后训练并建立随机森林分类模型Motif-1（M1），在验证集和癌前病变组中验证其性能。将癌前病变组患者纳入数据集后随机分为训练集（243例）、验证集（105例）、测试集（150例），采用与M1相同的方法使用训练集训练并构建随机森林模型Motif-2（M2），在验证集中检验并确定最佳阈值，在测试集中进行性能检测。 结果： 构建了2个基于cfDNA 末端基序检测的ESCC及其癌前病变的预测模型，M1模型在验证集中的灵敏度为90.0%，特异度为77.4%，曲线下面积（AUC）为0.884；M1模型预测LGIN、HGIN及T1aN0期食管癌的灵敏度分别为76.1%、80.4%和91.2%，联合预测HGIN及T1aN0期食管癌的灵敏度为85.0%，且预测分值和灵敏度随着恶性肿瘤的临床分期上升而增加（P＜0.001）。M2模型在测试集中灵敏度为87.5%，特异度为77.4%，AUC为0.857；M2模型预测食管癌前病变及ESCC的灵敏度分别为80.0%和89.7%，联合预测HGIN及T1aN0期食管癌的灵敏度为89.4%。 结论： 构建的2种基于 cfDNA 末端基序的血浆检测模型在ESCC及其癌前病变中具有较高的灵敏度及特异度，可用于早期ESCC检测。.
目的： 探索并建立基于循环游离DNA（cfDNA）末端基序检测的食管鳞状细胞癌（ESCC）预测模型，并评估其用于ESCC早期检测的可行性。 方法： 前瞻性收集2021年8月至2022年11月中国医学科学院肿瘤医院201例ESCC、46例食管高级别上皮内瘤变（HGIN）、46例食管低级别上皮内瘤变（LGIN）、176例食管良性病变患者和29例健康对照的血浆样本进行cfDNA提取、文库构建与测序。将ESCC患者和对照组受试者随机分为训练集（284例）和验证集（122例）。对训练集中ESCC及对照组cfDNA末端基序矩阵标准化处理及差异特征筛选，经主成分分析降维、十折交叉验证及随机森林递归特征消除后训练并建立随机森林分类模型Motif-1（M1），在验证集和癌前病变组中验证其性能。将癌前病变组患者纳入数据集后随机分为训练集（243例）、验证集（105例）、测试集（150例），采用与M1相同的方法使用训练集训练并构建随机森林模型Motif-2（M2），在验证集中检验并确定最佳阈值，在测试集中进行性能检测。 结果： 构建了2个基于cfDNA 末端基序检测的ESCC及其癌前病变的预测模型，M1模型在验证集中的灵敏度为90.0%，特异度为77.4%，曲线下面积（AUC）为0.884；M1模型预测LGIN、HGIN及T1aN0期食管癌的灵敏度分别为76.1%、80.4%和91.2%，联合预测HGIN及T1aN0期食管癌的灵敏度为85.0%，且预测分值和灵敏度随着恶性肿瘤的临床分期上升而增加（P＜0.001）。M2模型在测试集中灵敏度为87.5%，特异度为77.4%，AUC为0.857；M2模型预测食管癌前病变及ESCC的灵敏度分别为80.0%和89.7%，联合预测HGIN及T1aN0期食管癌的灵敏度为89.4%。 结论： 构建的2种基于 cfDNA 末端基序的血浆检测模型在ESCC及其癌前病变中具有较高的灵敏度及特异度，可用于早期ESCC检测。.
摘要：
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