Yu Yanxiang, Xia Lei, Wang Zhouxuan, Zhu Tong, Zhao Lujun, Fan Saijun
Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China.
Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China.
iScience. 2024 Nov 22;27(12):111453. doi: 10.1016/j.isci.2024.111453. eCollection 2024 Dec 20.
Current screening methods for esophageal squamous cell carcinoma (ESCC) face challenges such as low patient compliance and high costs. This study aimed to develop a model based on oral microbiome data for identifying ESCC. By analyzing 249 oral flora samples, we identified microbial markers associated with ESCC and constructed random forest classifiers that distinguished patients with ESCC from controls, achieving an area under the ROC curve (AUC) of 0.87. Key ESCC-associated microbial markers included and . The classifier was validated within the cohort, attaining an AUC of 0.93. For comparison, traditional tumor markers carcinoembryonic antigen (CEA) and squamous cell carcinoma antigen (SCC-Ag) yielded AUCs of 0.84. Functional analysis identified pathways linked to ESCC, such as glycerol degradation and nitrate reduction. This study suggests a potential noninvasive method for detecting ESCC, offering a more accessible and accurate alternative to current screening methods.
目前用于食管鳞状细胞癌（ESCC）的筛查方法面临患者依从性低和成本高等挑战。本研究旨在开发一种基于口腔微生物组数据的模型来识别ESCC。通过分析249份口腔菌群样本，我们确定了与ESCC相关的微生物标志物，并构建了将ESCC患者与对照组区分开来的随机森林分类器，其ROC曲线下面积（AUC）达到0.87。与ESCC相关的关键微生物标志物包括 和 。该分类器在队列中得到验证，AUC为0.93。作为比较，传统肿瘤标志物癌胚抗原（CEA）和鳞状细胞癌抗原（SCC-Ag）的AUC为0.84。功能分析确定了与ESCC相关的途径，如甘油降解和硝酸盐还原。本研究提出了一种检测ESCC的潜在非侵入性方法，为当前筛查方法提供了一种更易获得且准确的替代方法。
