Open Access Research

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

Piotr Waloszczyk1, Tomasz Janus1*, Jacek Alchimowicz2, Tomasz Grodzki2 and Krzysztof Borowiak1

Author Affiliations

1 Department of Toxicology and Molecular Pathobiochemistry, Pomeranian Medical University, 70-204 Szczecin, Poland

2 Professor A. Sokolowski Specialist Hospital Szczecin Zdunowo, Poland

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Diagnostic Pathology 2011, 6:22  doi:10.1186/1746-1596-6-22

Published: 21 March 2011



Lung 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.


Lung 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.


Obtained 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.


The 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".