Summary of Study ST001923
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 PR001214. The data can be accessed directly via it's Project DOI: 10.21228/M8SH7V This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST001923 |
| Study Title | Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Progression |
| Study Summary | Every year, hundreds of thousands of cases of renal carcinoma (RCC) are reported worldwide. Accurate staging of the disease is important for treatment and prognosis purposes; however, contemporary methods such as computerized tomography (CT) and biopsies are expensive and prone to sampling errors, respectively. As such, a non-invasive diagnostic assay for staging would be beneficial. This study aims to investigate urine metabolites as potential biomarkers to stage RCC. In the study, we identified a panel of such urine metabolites with machine learning techniques. |
| Institute | University of Georgia |
| Department | Department of Biochemistry and Molecular Biology |
| Laboratory | Edison Lab |
| Last Name | Bifarin |
| First Name | Olatomiwa |
| Address | 315 Riverbend Rd, Athens, GA 30602 |
| olatomiwa.bifarin25@uga.edu | |
| Phone | 757-405-4379 |
| Submit Date | 2021-08-16 |
| Num Groups | Two |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2021-10-18 |
| Release Version | 1 |
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Subject:
| Subject ID: | SU002001 |
| Subject Type: | Human |
| Subject Species: | Homo sapiens |
| Taxonomy ID: | 9606 |
