Targeted Metabolomic Profiling of Plasma Samples in Gastric Cancer by Liquid Chromatography-Mass Spectrometry
Targeted Metabolomic Profiling of Plasma Samples in Gastric Cancer by Liquid Chromatography-Mass Spectrometry
复制标题
DOI：
10.1159/000526864
复制
发表时间：
2022-11
期刊：
影响因子：
3.2
通讯作者：
Taisuke Matsumoto;M. Fukuzawa;T. Itoi;M. Sugimoto;Yumi Aizawa;M. Sunamura;T. Kawai;Daiki Nemoto
中科院分区：
文献类型：
--
作者：
Taisuke Matsumoto;M. Fukuzawa;T. Itoi;M. Sugimoto;Yumi Aizawa;M. Sunamura;T. Kawai;Daiki Nemoto
由于胃癌（GC）的高死亡率是由于延迟诊断，因此早期检测对于改善患者结局至关重要。代谢失调在GC中起重要作用。虽然已经评估了用于早期检测的各种代谢物水平的生物标志物，但仍然没有统一的早期检测方法。我们进行了一项血浆代谢组学研究，以评估可能区分GC样本和非GC样本的代谢物。研究方法：于2020年3月至2020年11月期间在东京医科大学医院采集了72名GC患者和29名对照参与者（非GC组）的血液样本。采用液相色谱-飞行时间质谱法鉴别和定量亲水性代谢物。使用Mann-Whitney检验评价GC和非GC组之间代谢物浓度的差异。通过受试者工作特征曲线下面积评价每种代谢物的鉴别能力。建立了一种基于径向基函数（RBF）核函数的支持向量机（SVM）模型，用于评价多种代谢物的鉴别能力。用于SVM的变量的选择利用逐步回归方法。结果：在96种定量代谢物中，GC组和非GC组之间有8种存在显著差异。其中，N1-乙酰精胺，琥珀酸和组氨酸被用于RBF-SVM模型，以区分GC样品从非GC样品。RBF-SVM模型的曲线下面积（AUC）较高（0.915; 95% CI：0.865-0.965，p < 0.0001），表明RBF-SVM模型的性能良好。将该RBF-SVM应用于由RBF-SVM模型的AUC得到的验证数据集为（0.885; 95%CI：0.797-0.973，p < 0.0001），表明RBF-SVM模型的良好性能。RBF-SVM模型的敏感性（69.0%）优于常用肿瘤标志物癌胚抗原（CEA）（10.5%）和糖链抗原19-9（CA 19 -9）（2.86%）。RBF-SVM与CEA和CA 19 -9的相关性较低，表明其独立性。结论：我们分析了血浆代谢组学，定量代谢物的组合显示出高灵敏度的GC检测。RBF-SVM与肿瘤标志物的独立性表明，它们的互补使用将有助于GC筛选。
Introduction: As the high mortality rate of gastric cancer (GC) is due to delayed diagnosis, early detection is vital for improved patient outcomes. Metabolic deregulation plays an important role in GC. Although various metabolite-level biomarkers for early detection have been assessed, there is still no unified early detection method. We conducted a plasma metabolome study to assess metabolites that may distinguish GC samples from non-GC samples. Methods: Blood samples were collected from 72 GC patients and 29 control participants (non-GC group) at the Tokyo Medical University Hospital between March 2020 and November 2020. Hydrophilic metabolites were identified and quantified using liquid chromatography-time-of-flight mass spectrometry. Differences in metabolite concentrations between the GC and non-GC groups were evaluated using the Mann-Whitney test. The discrimination ability of each metabolite was evaluated by the area under the receiver operating characteristic curve. A radial basis function (RBF) kernel-based support vector machine (SVM) model was developed to assess the discrimination ability of multiple metabolites. The selection of variables used for the SVM utilized a step-wise regression method. Results: Of the 96 quantified metabolites, 8 were significantly different between the GC and non-GC groups. Of these, N1-acetylspermine, succinate, and histidine were used in the RBF-SVM model to discriminate GC samples from non-GC samples. The area under the curve (AUC) of the RBF-SVM model was higher (0.915; 95% CI: 0.865–0.965, p < 0.0001), indicating good performance of the RBF-SVM model. The application of this RBF-SVM to the validation dataset resulted from the AUC of the RBF-SVM model was (0.885; 95% CI: 0.797–0.973, p < 0.0001), indicating the good performance of the RBF-SVM model. The sensitivity of the RBF-SVM model was better (69.0%) than those of the common tumor markers carcinoembryonic antigen (CEA) (10.5%) and carbohydrate antigen 19-9 (CA19-9) (2.86%). The RBF-SVM showed a low correlation with CEA and CA19-9, indicating its independence. Conclusion: We analyzed plasma metabolomics, and a combination of the quantified metabolites showed high sensitivity for the detection of GC. The independence of the RBF-SVM from tumor markers suggested that their complementary use would be helpful for GC screening.