Summary of Study ST001269

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR000854. The data can be accessed directly via it's Project DOI: 10.21228/M8998T This work is supported by NIH grant, U2C- DK119886.


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.

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Study IDST001269
Study TitleExosomal lipids for classifying early and late stage non-small cell lung cancer
Study TypeBiomarker Discovery
Study SummaryLung cancer is the leading cause of cancer deaths in the United States. Patients with early stage lung cancer have the best prognosis with surgical removal of the tumor, but the disease is often asymptomatic until advanced disease develops, and there are no effective blood-based screening methods for early detection of lung cancer in at-risk populations. We have explored the lipid profiles of blood plasma exosomes using ultra high-resolution Fourier transform mass spectrometry (UHR-FTMS) for early detection of the prevalent non-small cell lung cancers (NSCLC). Exosomes are nanovehicles released by various cells and tumor tissues to elicit important biofunctions such as immune modulation and tumor development. Plasma exosomal lipid profiles were acquired from 39 normal and 91 NSCLC subjects (44 early stage and 47 late stage). We have applied two multivariate statistical methods, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO) to classify the data. For the RF method, the Gini importance of the assigned lipids was calculated to select 16 lipids with top importance. Using the LASSO method, 7 features were selected based on a grouped LASSO penalty. The Area Under the Receiver Operating Characteristic curve for early and late stage cancer versus normal subjects using the selected lipid features was 0.85 and 0.88 for RF and 0.79 and 0.77 for LASSO, respectively. These results show the value of RF and LASSO for metabolomics data-based biomarker development, which provide robust an independent classifiers with sparse data sets. Application of LASSO and Random Forests identifies lipid features that successfully distinguish early stage lung cancer patient from healthy individuals.
University of Kentucky
DepartmentCenter for Environmental and Systems Biochemistry
Last NameThompson
First NamePatrick
Address789 South Limestone, Lexington, Kentucky, 40536, USA,
Submit Date2019-10-17
Total Subjects95
Raw Data AvailableYes
Raw Data File Type(s)raw(Thermo)
Analysis Type DetailLC-MS
Release Date2019-10-11
Release Version1
Patrick Thompson Patrick Thompson application/zip

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Collection ID:CO001331
Collection Summary:Ten mL samples of blood were drawn into a purple top vacutainer containing K2-EDTA (Becton-Dickson), inverted twice to ensure dissolution of the EDTA, and kept on ice immediately after blood draw. The whole blood was separated into packed red cells, buffy coat, and plasma within 30 min of collection by centrifuging at 3500 g for 15 min at 4 C in a swing out rotor. All blood processing procedures were performed in a class II biosafety cabinet housed in a BSL category 2 laboratory. Plasma (0.7 mL) was aliquotted into 1.5 mL screw cap vials, flash frozen in liq. N2, and stored at-80  C until exosomal isolation. These collection and processing procedures were designed to minimize variations in plasma and exosome quality.
Sample Type:Blood (whole)
Storage Conditions:-80℃