Summary of Study ST002120
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 PR001345. The data can be accessed directly via it's Project DOI: 10.21228/M8VM5R 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 | ST002120 |
| Study Title | Feasibility of detecting AC and SCC using UPLC-HRMS based tissue metabolomics |
| Study Summary | UPLC-HRMS analysis was performed on AC and SCC patients. OPLSDA classfication was performed on tumor vs. ANT&DNT samples. Panels of discriminant features were identified.The biomarkers identified in discovery set samples for each binary classification were confirmed by using a set of validation samples, which were run separately. Additionally, paired analysis shows the abundance of discriminant metabolic features has significant altered in tumor tissues compared to corresponding DNT and ANT samples, indicating metabolic reprogramming during tumorigenesis in AC and SCC. |
| Institute | Ocean University of China |
| Last Name | Zang |
| First Name | Xiaoling |
| Address | No.5 Yushan Road, Qingdao, Shandong, China |
| xlingzang@163.com | |
| Phone | +86 0532 82032064 |
| Submit Date | 2022-02-17 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2022-07-20 |
| Release Version | 1 |
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Project:
| Project ID: | PR001345 |
| Project DOI: | doi: 10.21228/M8VM5R |
| Project Title: | Non-Small Cell Lung Cancer Detection and Subtyping by UPLC-HRMS Based Tissue Metabolomics |
| Project Type: | non-targeted metabolomics analysis |
| Project Summary: | Non-small cell lung cancer (NSCLC) is the prevalent histological subtype of lung cancer. In this study, we performed ultraperformance liquid chromatography - high resolution mass spectrometry (UPLC-HRMS) based metabolic profiling of 227 tissue samples from 79 lung cancer patients with adenocarcinoma (AC) or squamous cell carcinoma (SCC). oPLS-DA analysis showed detections of AC, SCC and NSCLC were possible with good accuracies (91.3%, 100% and 88.3%), sensitivities (85.7%, 100% and 83.9%), and specificities (94.3%, 100% and 90.7%), respectively. Valine, sphingosine, Glu γ-methyl ester and LPC 16:0 contributed to AC detection and significantly altered in tumor. Valine, sphingosine, LPC 18:1 and leucine derivative contributed to SCC detection. Classification of AC and SCC was also possible (accuracy of 96.8%, sensitivity of 98.2% and specificity of 85.7%) with a panel of 5 metabolites, of which valine and creatine were significantly changed. The classification models were confirmed with external validation sets, showing promise for NSCLC detection and subtyping. |
| Institute: | OUC |
| Department: | Department of Medicine and Pharmacy |
| Laboratory: | Analytical chemistry |
| Last Name: | Zang |
| First Name: | Xiaoling |
| Address: | No. 5 Yushan Road, Qingdao, Shandong, China |
| Email: | zangxiaoling@ouc.edu.cn |
| Phone: | 15863037065 |
| Funding Source: | OUC |
| Project Comments: | OUC |
| Publications: | OUC |
| Contributors: | OUC |
