Human sweat metabolomics for lung cancer screening

Anal Bioanal Chem. 2015 Jul;407(18):5381-92. doi: 10.1007/s00216-015-8700-8. Epub 2015 May 3.


Sweat is one of the less employed biofluids for discovery of markers in spite of its increased application in medicine for detection of drugs or for diagnostic of cystic fibrosis. In this research, human sweat was used as clinical sample to develop a screening tool for lung cancer, which is the carcinogenic disease with the highest mortality rate owing to the advanced stage at which it is usually detected. In this context, a method based on the metabolite analysis of sweat to discriminate between patients with lung cancer versus smokers as control individuals is proposed. The capability of the metabolites identified in sweat to discriminate between both groups of individuals was studied and, among them, a trisaccharide phosphate presented the best independent performance in terms of the specificity/sensitivity pair (80 and 72.7%, respectively). Additionally, two panels of metabolites were configured using the PanelomiX tool as an attempt to reduce false negatives (at least 80% specificity) and false positives (at least 80% sensitivity). The first panel (80% specificity and 69% sensitivity) was composed by suberic acid, a tetrahexose, and a trihexose, while the second panel (69% specificity and 80% sensitivity) included nonanedioic acid, a trihexose, and the monoglyceride MG(22:2). Thus, the combination of the five metabolites led to a single panel providing 80% specificity and 79% sensitivity, reducing the false positive and negative rates to almost 20%. The method was validated by estimation of within-day and between-days variability of the quantitative analysis of the five metabolites.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Aged
  • Chromatography, Liquid
  • Cohort Studies
  • Female
  • Humans
  • Lung Neoplasms / chemistry
  • Lung Neoplasms / diagnosis*
  • Male
  • Metabolomics / methods*
  • Middle Aged
  • Multivariate Analysis
  • ROC Curve
  • Sweat / chemistry*
  • Tandem Mass Spectrometry / methods*