College of Pharmaceutical Sciences, Zhejiang University, Yuhangtang Road #866, Hangzhou, Zhejiang Province, 310058, China; Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Banshandong Road#1, Hangzhou, Zhejiang Province, 310022, China; Cancer Hospital of the University of Chinese Academy of Sciences, Banshandong Road#1, Hangzhou, Zhejiang Province, 310022, China; Zhejiang Cancer Hospital, Banshandong Road#1, Hangzhou, Zhejiang Province, 310022, China.
Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Banshandong Road#1, Hangzhou, Zhejiang Province, 310022, China; Cancer Hospital of the University of Chinese Academy of Sciences, Banshandong Road#1, Hangzhou, Zhejiang Province, 310022, China; Zhejiang Cancer Hospital, Banshandong Road#1, Hangzhou, Zhejiang Province, 310022, China.
J Pharm Biomed Anal. 2021 Apr 15;197:113937. doi: 10.1016/j.jpba.2021.113937. Epub 2021 Feb 5.
Prognosis for esophageal squamous cell carcinoma (ESCC) is poor, so it is essential to develop a more complete understanding of the disease. The purpose of this study was to explore metabolic biomarkers and potential therapeutic targets for ESCC. An ultra-high-performance liquid chromatography coupled with high resolution mass (UPLC/MS)-based metabolomic analysis was performed in 141 ESCC cancerous tissue samples and 70 non-cancerous counterparts. The results showed that 41 differential metabolites were annotated in the training set, and 37 were validated in the test set. Single-metabolite-based receiver operating characteristic (ROC) curves as well as metabolite-based machine learning models, including Partial Least Squares (PLS), Support Vector Machine (SVM), and Random Forest (RF), were investigated for cancerous and non-cancerous tissue classification. Six most prevalent diagnostic metabolites-adenylsuccinic acid, UDP-GalNAc, maleylacetoacetic acid, hydroxyphenylacetylglycine, galactose, and kynurenine-showed testing predictive accuracies of 0.89, 0.95, 0.97, 0.89, 0.84, and 0.84, respectively. Moreover, the metabolite-based models (PLS, SVM, and RF) had testing predictive accuracies of 0.95, 0.95, and 1.00, respectively. Kaplan-Meier survival analysis and Cox proportional hazards regression analysis demonstrated that 2-hydroxymyristoylcarnitine (HR: 0.55, 95 % CI: 0.32 to 0.92), 3-hydroxyhexadecanoylcarnitine (HR: 0.49, 95 % CI: 0.29 to 0.83), and 2,3-Dinor-TXB1 (HR: 0.56, 95 % CI: 0.33 to 0.95) to be significantly associated with OS. Based on the observation of accumulation in amino acids, immunohistochemistry (IHC) staining revealed that the amino acid transporters SLC7A5/LAT1, SLC1A5/ASCT2, and SLC16A10/MCT10 were up-regulated in ESCC cancerous tissues when compared to non-cancerous equivalents. Consistently, the same panel of amino acids were downregulated in cells with SLC1A5 knockdown. Herein, it is concluded that this study not only identified several metabolites with diagnostic and/or prognostic value, but also provided accurate metabolite-based prediction models for ESCC tissue classification. Furthermore, the three up-regulated amino acid transporters were identified as potential therapeutic targets for ESCC, especially SLC1A5.
食管鳞状细胞癌 (ESCC) 的预后较差，因此深入了解该疾病至关重要。本研究旨在探索 ESCC 的代谢生物标志物和潜在治疗靶点。对 141 例 ESCC 癌组织样本和 70 例非癌对照组织进行了基于超高效液相色谱与高分辨质谱联用 (UPLC/MS) 的代谢组学分析。结果显示，在训练集中注释了 41 个差异代谢物，在测试集中验证了 37 个差异代谢物。分别对单代谢物的受试者工作特征 (ROC) 曲线以及基于代谢物的机器学习模型（包括偏最小二乘法 (PLS)、支持向量机 (SVM) 和随机森林 (RF)）进行了研究，以对癌组织和非癌组织进行分类。6 种最常见的诊断代谢物——腺苷琥珀酸、UDP-GalNAc、马来酰乙酰乙酸、对羟苯乙酰甘氨酸、半乳糖和犬尿氨酸——在测试中的预测准确率分别为 0.89、0.95、0.97、0.89、0.84 和 0.84。此外，基于代谢物的模型（PLS、SVM 和 RF）在测试中的预测准确率分别为 0.95、0.95 和 1.00。Kaplan-Meier 生存分析和 Cox 比例风险回归分析表明，2-羟十七烷酰肉碱（HR：0.55，95%CI：0.32 至 0.92）、3-羟基十六烷酰肉碱（HR：0.49，95%CI：0.29 至 0.83）和 2,3-二去甲-TXB1（HR：0.56，95%CI：0.33 至 0.95）与 OS 显著相关。基于氨基酸积累的观察，免疫组织化学 (IHC) 染色显示，与非癌对应物相比，氨基酸转运蛋白 SLC7A5/LAT1、SLC1A5/ASCT2 和 SLC16A10/MCT10 在 ESCC 癌组织中上调。同样，SLC1A5 敲低的细胞中，同样的氨基酸谱下调。综上所述，本研究不仅鉴定了一些具有诊断和/或预后价值的代谢物，还为 ESCC 组织分类提供了准确的基于代谢物的预测模型。此外，还鉴定出 3 种上调的氨基酸转运蛋白为 ESCC 的潜在治疗靶点，特别是 SLC1A5。
