Summary of project PR001091
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001091. The data can be accessed directly via it's Project DOI: 10.21228/M8P97V This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Project ID: | PR001091 |
| Project DOI: | doi: 10.21228/M8P97V |
| Project Title: | Machine learning-enabled renal cell carcinoma status prediction using multi-platform urine-based metabolomics |
| Project Type: | multi-platform urine-based metabolomics |
| Project Summary: | Currently, Renal Cell Carcinoma (RCC) is identified through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is invasive and subject to sampling errors. Hence, there is a critical need for a non-invasive diagnostic assay. RCC is a disease of altered cellular metabolism with the tumor(s) in close proximity to the urine in the kidney suggesting metabolomic profiling would be an excellent choice for assay development. Here, we applied liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of candidate metabolic panels for RCC. The study cohort consists of 82 RCC patients and 174 healthy controls, these were separated into two sub-cohorts: model cohort and the test cohort. Discriminatory metabolic features were selected in the model cohort, using univariate, wrapper, and embedded methods of feature selection. Three ML techniques with different induction biases were used for training and hyperparameter tuning. Final assessment of RCC status prediction was made using the test cohort with the selected biomarkers and the tuned ML algorithms. A seven-metabolite panel consisting of endogenous and exogenous metabolites enabled the prediction of RCC with 88% accuracy, 94% sensitivity, and 85% specificity in the test cohort, with an AUC of 0.98. |
| Institute: | University of Georgia |
| Department: | Biochemistry and Molecular Biology |
| Laboratory: | Edison Lab |
| Last Name: | Bifarin |
| First Name: | Olatomiwa |
| Address: | 315 Riverbend Rd, Athens, GA 30602 |
| Email: | olatomiwa.bifarin25@uga.edu |
| Phone: | (706) 542-4401 Lab: 1045 |
Summary of all studies in project PR001091
| Study ID | Study Title | Species | Institute | Analysis(* : Contains Untargted data) | Release Date | Version | Samples | Download(* : Contains raw data) |
|---|---|---|---|---|---|---|---|---|
| ST001705 | Machine learning-enabled renal cell carcinoma status prediction using multi-platform urine-based metabolomics (part-I) | Homo sapiens | University of Georgia | MS* | 2021-04-27 | 1 | 256 | Uploaded data (54.8G)* |
| ST001706 | Machine learning-enabled renal cell carcinoma status prediction using multi-platform urine-based metabolomics NMR (part-II) | Homo sapiens | University of Georgia | NMR | 2021-04-27 | 1 | 258 | Uploaded data (497.8M)* |
