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".
Highlights
Lung cancer diagnosis in tissue material with commonly used histological techniques is sometimes inconvenient and in a number of cases leads to ambiguous conclusions
Our work suggests an option of proteomic profiling of biological materials which focuses on obtaining spectra rich in proteins that may reveal the proteomic composition of tissue
The obtained supernatant of 0.6 ml was treated with 50μl of 10% trifluoracetic acid (TFA) and centrifuged again and it was transferred to Amicon Ultracel (Millipore) centrifugal filter of 3 kDa selectivity and centifuged at 13,000 rpm to decrease the volume to 50 μl
Summary
Lung cancer diagnosis in tissue material with commonly used histological techniques is sometimes inconvenient and in a number of cases leads to ambiguous conclusions. In this study a proteomic approach is presented which may help provide unambiguous pathologic diagnosis of tissue material. New, objective methods in cancer diagnosis are still needed especially in respect of their sensitive. Molecular imaging techniques described in literature as well as profiling and detailed characterisation of biomarker methods tend to prove their usefulness [10,11,12]. Molecular imaging with mass spectrometry may offer ample possibilities in practical usage yet in the future rather than at present, because of extent of time necessary for analysis, workload of instruments, and poor image resolution in comparison to other methods [13,14,15]
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