Meta-markers for the differential diagnosis of lung cancer and lung disease.

UNLABELLED: Misdiagnosis of lung cancer remains a serious problem due to the difficulty of distinguishing lung cancer from other respiratory lung diseases. As a result, the development of serum-based differential diagnostic biomarkers is in high demand. In this study, 198 clinical serum samples from non-cancer lung disease and lung cancer patients were analyzed using nLC-MRM-MS for the levels of seven lung cancer biomarker candidates. When the candidates were assessed individually, only SERPINEA4 showed statistically significant changes in the serum levels. The MRM results and clinical information were analyzed using a logistic regression analysis to select model for the best 'meta-marker', or combination of biomarkers for differential diagnosis. Also, under consideration of statistical interaction, variables having low significance as a single factor but statistically influencing on meta-marker model were selected. Using this probabilistic classification, the best meta-marker was determined to be made up of two proteins SERPINA4 and PON1 with age factor. This meta-marker showed an enhanced differential diagnostic capability (AUC=0.915) for distinguishing the two patient groups. Our results suggest that a statistical model can determine optimal meta-markers, which may have better specificity and sensitivity than a single biomarker and thus improve the differential diagnosis of lung cancer and lung disease patients. BIOLOGICAL SIGNIFICANCE: Diagnosing lung cancer commonly involves the use of radiographic methods. However, an imaging-based diagnosis may fail to differentiate lung cancer from non-cancerous lung disease. In this study, we examined several serum proteins in the sera of 198 lung cancer and non-cancerous lung disease patients by multiple-reaction monitoring. We then used a combination of variables to generate a meta-marker model that is useful as a differential diagnostic biomarker.