Comparison of software tools to improve the detection of carcinogen induced changes in the rat liver proteome by analyzing SELDI-TOF-MS spectra.

A common animal model of chemical hepatocarcinogenesis was used to demonstrate the potential identification of carcinogenicity related protein signatures/biomarkers. Therefore, an animal study in which rats were treated with the known liver carcinogen N-nitrosomorpholine (NNM) or the corresponding vehicle was evaluated. Histopathological investigation as well as SELDI-TOF-MS analysis was performed. SELDI-TOF-MS is an affinity-based mass spectrometry method in which subsets of proteins from biological samples are selectively adsorbed to a chemically modified surface. The proteins are subsequently analyzed with respect to their mass-charge ratios (m/z) by a time of flight (TOF) mass spectrometry (MS) approach. As data preprocessing of SELDI-TOF-MS spectra is essential, baseline correction, normalization, peak detection, and alignment of raw spectra were performed using either the Ciphergen ProteinChip Software 3.1 or functions implemented in the library PROcess of the BioConductor Project. Baseline correction and normalization algorithms of both tools lead to comparable results, whereas results after peak detection and alignment steps differed. Variability between technical and biological replicates was investigated. A linear mixed model with factors experimental group and time point was applied for each protein peak, taking into account the different correlation structure of technical and biological replicates. Alternatively, only median intensity values of technical replicates were used. Results of both models were similar and correlated well with those of the histopathological evaluation of the study. In conclusion, statistical analyses lead to comparable results, whereas parameter settings for preprocessing proved to be crucial.