Department of Thoracic Surgery, Baodi Clinical College, Tianjin Medical University, Tianjin, 301800, China.
State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
Esophagus. 2022 Oct;19(4):604-616. doi: 10.1007/s10388-022-00932-7. Epub 2022 Jul 6.
Discovery of noninvasive urinary biomarkers for the early diagnosis of esophageal squamous carcinoma (ESCC).
We conducted proteomic analyses of 499 human urine samples obtained from healthy individuals (n = 321) and ESCC (n = 83), bladder cancer (n = 17), breast cancer (n = 12), colorectal cancer (n = 16), lung cancer (n = 33) and thyroid cancer (n = 17) patients from multiple medical centers. Those samples were divided into a discovery set (n = 247) and an independent validation set (n = 157).
Among urinary proteins identified in the comprehensive quantitative proteomics analysis, we selected a panel of three urinary biomarkers (ANXA1, S100A8, TMEM256), and established a logistic regression model in the discovery set that can correctly classify the majority of ESCC cases in the validation sets with the area under the curve (AUC) values of 0.825. This urinary biomarker panel not only discriminates ESCC patients from healthy individuals but also differentiates ESCC from other common tumors. Notably, the panel distinguishes stage I ESCC patients from healthy individuals with AUC values of 0.886. On the analysis of stage-specific biomarkers, another combination panel of protein (ANXA1, S100A8, SOD3, TMEM256) demonstrated a good AUC value of 0.792 for stage I ESCC.
Urinary biomarker panel represents a promising auxiliary diagnostic tool for ESCC, including early-stage ESCC.
发现用于早期诊断食管鳞癌(ESCC)的非侵入性尿生物标志物。
我们对来自多个医疗中心的 499 个人体尿液样本进行了蛋白质组学分析,这些样本包括健康个体(n=321)、ESCC(n=83)、膀胱癌(n=17)、乳腺癌(n=12)、结直肠癌(n=16)、肺癌(n=33)和甲状腺癌(n=17)患者。这些样本被分为发现集(n=247)和独立验证集(n=157)。
在全面的定量蛋白质组学分析中鉴定出的尿液蛋白中,我们选择了一组三个尿液生物标志物(ANXA1、S100A8、TMEM256),并在发现集中建立了一个逻辑回归模型,该模型可以用曲线下面积(AUC)值为 0.825 的正确分类验证集中的大多数 ESCC 病例。该尿液生物标志物组合不仅可以区分 ESCC 患者和健康个体,还可以区分 ESCC 与其他常见肿瘤。值得注意的是,该组合还可以将 I 期 ESCC 患者与健康个体区分开来,AUC 值为 0.886。在对特定于阶段的生物标志物的分析中,另一个蛋白质组合面板(ANXA1、S100A8、SOD3、TMEM256)在 I 期 ESCC 中表现出 0.792 的良好 AUC 值。
尿液生物标志物组合代表了一种有前途的 ESCC 辅助诊断工具,包括早期 ESCC。