基于代谢组学的食管鳞状细胞癌生物标志物筛选研究

A Metabolomics-based Study of Screening of Biomarkers for Esophageal Squamous Cell Carcinoma

目的采用超高效液相色谱综合高分辨率轨道分析质谱(UPLC-Orbitrap-MS)技术分析食管鳞状细胞癌(ESCC)潜在生物标志物,为ESCC的早期诊断提供帮助。方法选取2018年1月至2019年5月陆军第七十一集团军医院肿瘤科收治的12例ESCC患者的临床病理标本作为研究对象,采用UPLC-Orbitrap-MS技术对12例ESCC患者的肿瘤组织和相应正常黏膜活检标本进行分析,并行单变量和多变量统计,包括Student的t检验、主成分分析和偏最小二乘判别分析(PLS-DA),以探索其代谢差异。结果共检测和鉴定出26种变化显著的代谢物,PLS-DA模型分析显示组间明显分离,具有较好的拟合度和预测值(R2Y=0.990,Q2=0.763)。采用受试者工作特征曲线及曲线下面积(AUC)对26种代谢物进行简化和重新组合,将AUC>0.85的代谢物作为简化组,进一步评价其辨别力,该组代谢组学的AUC值为0.937,表明其对ESCC的鉴别能力优于任何单一代谢物。结论ESCC生物标志物的代谢组学筛选研究为识别肿瘤病灶和相应正常组织的内源性代谢紊乱提供了一种新的策略,其检测结果较血液和尿液的代谢组学方法更精确,有助于ESCC的早期诊断。

Objective To adopt ultra-high performance liquid chromatography coupled with high resolution orbitrap mass spectrometry(UPLC-Orbitrap-MS)to discover potential biomarkers of esophageal squamous cell carcinoma(ESCC),to provide help for the early diagnosis of ESCC.Methods The tumor tissues and corresponding normal mucosa biopsy specimens of 12 ESCC patients admitted to Department of Oncology,PLA 71 st Group Army Hospital from Jan.2018 to May2019 were analyzed by UPLC-Orbitrap-MS technology.Univariate and multivariate statistics including student’s t test,principal component analysis and partial least squares discriminant analysis(PLS-DA)were used to explore the metabolic differences.Results A total of 26 metabolites with significant changes were detected and identified,and PLS-DA model analysis showed that there was significant separation between groups with good fitting degree and predictive value(R2 Y=0.990,Q2=0.763).The work characteristic curve and area under the curve(AUC)were used to simplify and recombine the 26 metabolites.The metabolites with AUC>0.85 were taken as the simplified group to further evaluate their discrimination.The AUC value of this group of metabolomics was 0.937,indicating that its ability to identify ESCC was better than any single metabolite.Conclusion Metabolomic screening of ESCC biomarkers provides a new strategy for identifying endogenous metabolic disorders in tumor lesions and corresponding normal tissues.The detection results are more accurate than those of blood and urine metabolomics,which is helpful for the early diagnosis of ESCC.