Liu Mengfei, Tian Hongrui, Wang Minmin, Guo Chuanhai, Xu Ruiping, Li Fenglei, Liu Anxiang, Yang Haijun, Duan Liping, Shen Lin, Wu Qi, Liu Zhen, Liu Ying, Liu Fangfang, Pan Yaqi, Hu Zhe, Chen Huanyu, Cai Hong, He Zhonghu, Ke Yang
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China.
Department of Global Health, School of Public Health, Peking University, Beijing 100191, China.
iScience. 2024 May 11;27(6):109965. doi: 10.1016/j.isci.2024.109965. eCollection 2024 Jun 21.
Using noninvasive biomarkers to identify high-risk individuals prior to endoscopic examination is crucial for optimization of screening strategies for esophageal squamous cell carcinoma (ESCC). We conducted a nested case-control study based on two community-based screening cohorts to evaluate the warning value of serum metabolites for esophageal malignancy. The serum samples were collected at enrollment when the cases had not been diagnosed. We identified 74 differential metabolites and two prominent perturbed metabolic pathways, and constructed Metabolic Risk Score (MRS) based on 22 selected metabolic predictors. The MRS generated an area under the receiver operating characteristics curve (AUC) of 0.815. The model performed well for the within-1-year interval (AUC: 0.868) and 1-to-5-year interval (AUC: 0.845) from blood draw to diagnosis, but showed limited ability in predicting long-term cases (>5 years). In summary, the MRS could serve as a potential early warning and risk stratification tool for establishing a precision strategy of ESCC screening.
在内镜检查前使用非侵入性生物标志物识别高危个体对于优化食管鳞状细胞癌（ESCC）筛查策略至关重要。我们基于两个社区筛查队列进行了一项巢式病例对照研究，以评估血清代谢物对食管恶性肿瘤的预警价值。病例未被诊断时，在入组时采集血清样本。我们鉴定出74种差异代谢物和两条显著扰动的代谢途径，并基于22个选定的代谢预测指标构建了代谢风险评分（MRS）。MRS在受试者工作特征曲线（AUC）下的面积为0.815。该模型在从采血到诊断的1年内间隔（AUC：0.868）和1至5年间隔（AUC：0.845）表现良好，但在预测长期病例（>5年）方面能力有限。总之，MRS可作为一种潜在的早期预警和风险分层工具，用于建立ESCC筛查的精准策略。
