Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage.
Olatomiwa O BifarinDavid A GaulSamyukta SahRebecca S ArnoldKenneth OganViraj A MasterDavid L RobertsSharon H BergquistJohn A PetrosArthur S EdisonFacundo M FernándezPublished in: Cancers (2021)
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 R 2 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.
Keyphrases
- renal cell carcinoma
- machine learning
- mass spectrometry
- magnetic resonance
- liquid chromatography
- ms ms
- lymph node
- big data
- pet ct
- high resolution
- high resolution mass spectrometry
- high throughput
- radiation therapy
- working memory
- small molecule
- risk assessment
- magnetic resonance imaging
- climate change
- high performance liquid chromatography
- tandem mass spectrometry
- early stage
- ionic liquid
- neural network
- gas chromatography
- social media
- solid phase extraction
- data analysis