Exosomal lipids for classifying early and late stage non-small cell lung cancer

Teresa W.M. Fan, Xiaofei Zhang, Chi Wang, Ye Yang, Woo Young Kang, Susanne Arnold, Richard M. Higashi, Jinze Liu, Andrew N. Lane

Research output: Contribution to journalArticlepeer-review

70 Scopus citations

Abstract

Lung 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.

Original languageEnglish
Pages (from-to)256-264
Number of pages9
JournalAnalytica Chimica Acta
Volume1037
DOIs
StatePublished - Dec 11 2018

Bibliographical note

Publisher Copyright:
© 2018 Elsevier B.V.

Keywords

  • Exosomal lipid profiling
  • LASSO
  • Non-small cell lung cancer
  • Random forest
  • Ultrahigh resolution Fourier transform mass spectrometry

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Spectroscopy
  • Environmental Chemistry

Fingerprint

Dive into the research topics of 'Exosomal lipids for classifying early and late stage non-small cell lung cancer'. Together they form a unique fingerprint.

Cite this