Bifarin Olatomiwa O, Gaul David A, Sah Samyukta, Arnold Rebecca S, Ogan Kenneth, Master Viraj A, Roberts David L, Bergquist Sharon H, Petros John A, Edison Arthur S, Fernández Facundo M
Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.
Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA.
Cancers (Basel). 2021 Dec 13;13(24):6253. doi: 10.3390/cancers13246253.
Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to providing metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a value of 0.58. A voting classifier model consisting of random forest, support vector machines, logistic regression, and adaptive boosting yielded an AUC of 0.96 and an accuracy of 87%. Some identified metabolites associated with renal cell carcinoma progression included 4-guanidinobutanoic acid, 7-aminomethyl-7-carbaguanine, 3-hydroxyanthranilic acid, lysyl-glycine, glycine, citrate, and pyruvate. Overall, we identified a urine metabolic phenotype associated with renal cell carcinoma stage, exploring the promise of a urine-based metabolomic assay for staging this disease.
尿液代谢组学分析除了能为疾病进展提供代谢方面的见解外，还具有用于非侵入性肾细胞癌分期的潜力。在本研究中，我们利用液相色谱 - 质谱联用（LC - MS）、核磁共振（NMR）和机器学习（ML）来发现与肾细胞癌进展相关的尿液代谢物。该研究提出了两个机器学习问题：将肾细胞癌分为早期（I期和II期）和晚期（III期和IV期）的二元分类，以及通过回归分析估计肾细胞癌肿瘤大小。共有82名已知肿瘤大小和代谢组学测量值的肾细胞癌患者用于回归任务，70名具有完整肿瘤 - 淋巴结 - 转移（TNM）分期信息的肾细胞癌患者用于十折交叉验证条件下的分类任务。由弹性网络、岭回归和支持向量回归组成的投票集成回归模型预测肾细胞癌肿瘤大小的 值为0.58。由随机森林、支持向量机、逻辑回归和自适应增强组成的投票分类器模型的曲线下面积（AUC）为0.96，准确率为87%。一些鉴定出的与肾细胞癌进展相关的代谢物包括4 - 胍基丁酸、7 - 氨基甲基 - 7 - 碳环鸟嘌呤、3 - 羟基邻氨基苯甲酸、赖氨酰 - 甘氨酸、甘氨酸、柠檬酸盐和丙酮酸。总体而言，我们确定了一种与肾细胞癌分期相关的尿液代谢表型，探索了基于尿液的代谢组学检测用于该疾病分期的前景。
