Proteomic patterns analysis with multivariate calculations as a promising tool for prompt differentiation of early stage lung tissue with cancer and unchanged tissue material

Abstract

BackgroundLung cancer diagnosis in tissue material with commonly used histological techniques is sometimes inconvenient and in a number of cases leads to ambiguous conclusions. Frequently advanced immunostaining techniques have to be employed, yet they are both time consuming and limited. In this study a proteomic approach is presented which may help provide unambiguous pathologic diagnosis of tissue material.MethodsLung tissue material found to be pathologically changed was prepared to isolate proteome with fast and non selective procedure. Isolated peptides and proteins in ranging from 3.5 to 20 kDa were analysed directly using high resolution mass spectrometer (MALDI-TOF/TOF) with sinapic acid as a matrix. Recorded complex spectra of a single run were then analyzed with multivariate statistical analysis algorithms (principle component analysis, classification methods). In the applied protocol we focused on obtaining the spectra richest in protein signals constituting a pattern of change within the sample containing detailed information about its protein composition. Advanced statistical methods were to indicate differences between examined groups.ResultsObtained results indicate changes in proteome profiles of changed tissues in comparison to physiologically unchanged material (control group) which were reflected in the result of principle component analysis (PCA). Points representing spectra of control group were located in different areas of multidimensional space and were less diffused in comparison to cancer tissues. Three different classification algorithms showed recognition capability of 100% regarding classification of examined material into an appropriate group.ConclusionThe application of the presented protocol and method enabled finding pathological changes in tissue material regardless of localization and size of abnormalities in the sample volume. Proteomic profile as a complex, rich in signals spectrum of proteins can be expressed as a single point in multidimensional space and than analysed using advanced statistical methods. This approach seems to provide more precise information about a pathology and may be considered in futer evaluation of biomarkers for clinical applications in different pathology. Multiparameter statistical methods may be helpful in elucidation of newly expressed sensitive biomarkers defined as many factors "in one point".

Lung Cancer Proteomics: Recent Advances in Biomarker Discovery

“…For instance, Toyama and colleagues demonstrated that serum protein deglycosylation improves the quantitative performance of shotgun proteomics [ 53 ]. Moreover, in a recent study a multivariate calculation method has been suggested as a tool to differentiate lung cancer tissues, even in an early stage, and control tissues [ 54 ]. A further powerful methodology in the search for novel disease biomarkers has been proposed to be the activity-based proteomics [ 55 ].…”

Interactive hyperspectral approach for exploring and interpreting DESI-MS images of cancerous and normal tissue sections

“…4,[8][9][10][11] These experiments are done in the open laboratory environment and outcomes of such studies suggest that alterations in lipid profiles potentially might serve as biochemical markers of cancer. 12 Remarkably, in vivo applications are important future perspectives that are made possible by ambient ionization techniques such as DESI-MS. 13 As many analytical results are essentially presented as images, processing techniques for representation 14 and interpretation of multi-dimensional data are necessary. An inherent feature of MS hyperspectral image acquisition is the large amount of information contained in each image.…”