Li Xia, Zhao Lihong, Wei Mengke, Lv Jiali, Sun Yawen, Shen Xiaotao, Zhao Deli, Xue Fuzhong, Zhang Tao, Wang Jialin
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.
Tumor Preventative and Therapeutic Base of Shandong Province, Feicheng People's Hospital, Feicheng 271600, China.
J Cancer. 2021 Apr 2;12(11):3190-3197. doi: 10.7150/jca.54429. eCollection 2021.
Previous metabolomics studies have found differences in metabolic characteristics between the healthy and ESCC patients. However, few of these studies concerned the whole process of the progression of ESCC. This study aims to explore serum metabolites associated with the progression of ESCC. Serum samples from 653 participants (305 normal, 77 esophagitis, 228 LGD, and 43 HGD/ESCC) were examined by ultra-high performance liquid chromatography quadruple time-of-flight mass spectrometry (UHPLC-QTOF/MS). Principal component analysis (PCA) was first applied to obtain an overview of the clustering trend for the multidimensional data. Fuzzy c-means (FCM) clustering was then used to screen metabolites with a changing tendency in the progression of ESCC. Univariate ordinal logistic regression analysis and multiple ordinal logistic regression analysis were applied to evaluate the association of metabolites with the risk of ESCC progression, and adjusted for age, gender, BMI, tobacco smoking, and alcohol drinking status. After FCM clustering analysis, a total of 38 metabolites exhibiting changing tendency among normal, esophagitis, LGD, and HGD/ESCC patients. Final results showed 15 metabolites associated with the progression of ESCC. Ten metabolites (dopamine, L-histidine, 5-hydroxyindoleacetate, L-tryptophan, 2'-O-methylcytidine, PC (14:0/0:0), PC (O-16:1/0:0), PE (18:0/0:0), PC (16:1/0:0), PC (18:2/0:0)) were associated with decreased risk of developing ESCC. Five metabolites (hypoxanthine, inosine, carnitine (14:1), glycochenodeoxycholate, PC (P-18:0/18:3)) were associated with increased risk of developing ESCC. These results demonstrated that serum metabolites are associated with the progression of ESCC. These metabolites are capable of potential biomarkers for the risk prediction and early detection of ESCC.
以往的代谢组学研究发现,健康人和食管癌患者之间存在代谢特征差异。然而,这些研究中很少有涉及食管癌进展的全过程。本研究旨在探索与食管癌进展相关的血清代谢物。通过超高效液相色谱四极杆飞行时间质谱(UHPLC-QTOF/MS)对653名参与者(305名正常者、77名食管炎患者、228名低级别上皮内瘤变患者和43名高级别上皮内瘤变/食管癌患者)的血清样本进行检测。首先应用主成分分析(PCA)来了解多维数据的聚类趋势概况。然后使用模糊c均值(FCM)聚类来筛选在食管癌进展过程中有变化趋势的代谢物。应用单变量有序逻辑回归分析和多变量有序逻辑回归分析来评估代谢物与食管癌进展风险的关联,并对年龄、性别、体重指数、吸烟和饮酒状况进行了校正。经过FCM聚类分析,共有38种代谢物在正常、食管炎、低级别上皮内瘤变和高级别上皮内瘤变/食管癌患者中呈现变化趋势。最终结果显示,有15种代谢物与食管癌进展相关。十种代谢物(多巴胺、L-组氨酸、5-羟吲哚乙酸、L-色氨酸、2'-O-甲基胞苷、PC(14:0/0:0)、PC(O-16:1/0:0)、PE(18:0/0:0)、PC(16:1/0:0)、PC(18:2/0:0))与食管癌发生风险降低相关。五种代谢物(次黄嘌呤、肌苷、肉碱(14:1)、甘氨鹅去氧胆酸、PC(P-18:0/18:3))与食管癌发生风险增加相关。这些结果表明,血清代谢物与食管癌进展相关。这些代谢物有潜力成为食管癌风险预测和早期检测生物标志物。