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Keywords:

  • lung cancer risk;
  • biomarkers;
  • inflammation;
  • serum amyloid A;
  • epidemiologic study

Abstract

BACKGROUND:

The authors investigated whether early stage lung cancer could be identified by proteomic analyses of plasma.

METHODS:

For the first case-control study, plasma samples from 52 patients with lung cancer and from a group of 51 controls were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. In a second case-control study, a classifier of 4 markers (mass-to-charge ratio, 11,681, 6843, 5607, and 8762) also was tested for validation on plasma from 16 consecutive patients with screen-detected cancer versus 406 healthy individuals. The most relevant marker was identified, and an enzyme-linked immunosorbent assay-based analysis revealed that signal intensity was correlated with concentration.

RESULTS:

The classifier had a sensitivity of 94.23% and a specificity of 76.47% in the first study but lost predictive value in the second study. Nevertheless, the 11,681 cluster, which was identified as serum amyloid protein A (SAA), resulted in a multiple logistic regression model that indicated a strong association with lung cancer. When both studies were considered as a together, the odds ratio (OR) for an SAA intensity ≥0.5 was 10.27 (95% confidence interval [CI], 4.64-22.74), whereas an analysis restricted to stage I cancers (TNM classification) revealed an OR of 8.45 (95% CI, 2.76-25.83) for T1 lung cancer and 21.22 (95% CI, 5.62-80.14) for T2 lung cancer.

CONCLUSIONS:

SAA levels were predictive of an elevated risk of lung cancer, supporting the general view that inflammation is implicated in lung cancer development. Cancer 2010. © 2010 American Cancer Society.