Summary of Study ST003048
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 PR001898. The data can be accessed directly via it's Project DOI: 10.21228/M8CQ76 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 | ST003048 |
| Study Title | Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis |
| Study Type | Biomarker, Endometrial cancer, Machine learning, Mass spectrometry, Metabolite |
| Study Summary | Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, P < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics. |
| Institute | Shanghai Jiao Tong University |
| Department | School of Biomedical Engineering |
| Last Name | Liu |
| First Name | Wanshan |
| Address | 1954 Huashan Road, Shanghai, China |
| liuwanshan@sjtu.edu.cn | |
| Phone | +86-13262629289 |
| Submit Date | 2024-01-20 |
| Analysis Type Detail | MALDI |
| Release Date | 2024-01-23 |
| Release Version | 1 |
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Treatment:
| Treatment ID: | TR003172 |
| Treatment Summary: | NA |
