Statistical analysis At first, the integration values of all 110 spectral regions are analyzed by means of a student t-test with correction for multiple testing by Benjamini-Hochberg to identify those which differ significantly between lung cancer patients and controls (IBM SPSS Version 20.0, Chicago, Illinois, USA). Secondly, multivariate statistical analyses are performed using SIMCA-P+ (Version 12.0, Umetrics, Umea, Sweden) to investigate whether the metabolic composition of blood plasma allows to discriminate between lung cancer patients and controls. An unsupervised principal component analysis (PCA) was performed to identify intrinsic clusters and outliers within the dataset. After the removal of outliers (detected by a Hotelling's T2 range plot), an orthogonal partial least squares discriminant analysis (OPLS-DA), an extension of partial least squares discriminant analysis (PLS-DA) with an integrated orthogonal signal correction filter, is performed to remove variability not relevant to class separation. The predicted classification is expressed as specificity (the percentage of controls that are actually classified as controls) and sensitivity (the percentage of lung cancer patients that are actually classified as lung cancer patients). The outcome of using the integration values of the significantly different spectral regions, obtained by the student t-test with correction for multiple testing by Benjamini-Hochberg, is compared to the outcome in which the integration values of all 110 spectral regions were used.