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Background Mass spectrometric analysis of the blood proteome is an emerging method of clinical proteomics. The approach exploiting multi-protein/peptide sets (fingerprints) detected by mass spectrometry that reflect overall features of a specimen's proteome, termed proteome pattern analysis, have been already shown in several studies to have applicability in cancer diagnostics. We aimed to identify serum proteome patterns specific for early stage breast cancer patients using MALDI-ToF mass spectrometry. Methods Blood samples were collected before the start of therapy in a group of 92 patients diagnosed at stages I and II of the disease, and in a group of age-matched healthy controls (104 women). Serum specimens were purified and the low-molecular-weight proteome fraction was examined using MALDI-ToF mass spectrometry after removal of albumin and other high-molecular-weight serum proteins. Protein ions registered in a mass range between 2,000 and 10,000 Da were analyzed using a new bioinformatic tool created in our group, which included modeling spectra as a sum of Gaussian bell-shaped curves. Results We have identified features of serum proteome patterns that were significantly different between blood samples of healthy individuals and early stage breast cancer patients. The classifier built of three spectral components that differentiated controls and cancer patients had 83% sensitivity and 85% specificity. Spectral components (i.e., protein ions) that were the most frequent in such classifiers had approximate m/z values of 2303, 2866 and 3579 Da (a biomarker built from these three components showed 88% sensitivity and 78% specificity). Of note, we did not find a significant correlation between features of serum proteome patterns and established prognostic or predictive factors like tumor size, nodal involvement, histopathological grade, estrogen and progesterone receptor expression. In addition, we observed a significantly (p = 0.0003) increased level of osteopontin in blood of the group of cancer patients studied (however, the plasma level of osteopontin classified cancer samples with 88% sensitivity but only 28% specificity). Conclusion MALDI-ToF spectrometry of serum has an obvious potential to differentiate samples between early breast cancer patients and healthy controls. Importantly, a classifier built on MS-based serum proteome patterns outperforms available protein biomarkers analyzed in blood by immunoassays.

Background Atherosclerosis is a major cause of cardiac events and mortality in patients suffering from chronic kidney disease (CKD). Moreover, the risk of cardiovascular disease (CVD) development in patients with CKD increases as kidney function declines. Although the close connection between atherosclerosis and kidney dysfunction is undeniable, particular risk factors and specific mechanisms that promote CVD in patients with CKD remain unclear. To gain insight into better recognition of the mechanisms of accelerated atherosclerosis in patients with CKD, we performed a comparative proteomic analysis of blood plasma from patients in various stages of CKD and thus distinct progression of atherosclerosis (n = 90), patients with advanced CVD and normal renal function (n = 30) and healthy volunteers (n = 30). Methods Plasma samples were depleted using affinity chromatography and divided into three fractions: high-abundant, low-abundant and low-molecular weight proteins. The first two fractions were analyzed by two-dimensional gel electrophoresis and mass spectrometry, the last one has been subjected to direct MS/MS analysis. A proteomic profiles for high-abundant, low-abundant and low-molecular weight proteins fractions were obtained. Differential accumulated proteins were confirmed by selected reaction monitoring analysis (SRM). The Gene Ontology (GO) function and the interaction networks of differentially expressed proteins were then analyzed. Results Forty-nine proteins (13 high- and 36 low-molecular mass) showed differences in accumulation levels. For eleven of them differential expression were confirmed by selected reaction monitoring analysis. Bioinformatic analysis showed that identified differential proteins were related to three different processes: the blood coagulation cascade, the transport, binding and metabolism of lipoproteins and inflammatory processes. Conclusions Obtained data provide an additional line of evidence that different molecular mechanisms are involved in the development of CKD- and CVD-related atherosclerosis. The abundance of some anti-atherogenic factors revealed in patients with CKD suggests that these factors are not associated with the reduction of atherosclerosis progression in CKD that is typically observed in “classical” CVD. Moreover, obtained data also suggest that mechanism of CVD acceleration may be different in initial and advanced stages of CKD. Undoubtedly, in advanced stages of CKD inflammation is highly pronounced. Electronic supplementary material The online version of this article (doi:10.1186/s12967-014-0378-8) contains supplementary material, which is available to authorized users.

Background The proteomics approach termed proteome pattern analysis has been shown previously to have potential in the detection and classification of breast cancer. Here we aimed to identify changes in serum proteome patterns related to therapy of breast cancer patients. Methods Blood samples were collected before the start of therapy, after the surgical resection of tumors and one year after the end of therapy in a group of 70 patients diagnosed at early stages of the disease. Patients were treated with surgery either independently (26) or in combination with neoadjuvant chemotherapy (5) or adjuvant radio/chemotherapy (39). The low-molecular-weight fraction of serum proteome was examined using MALDI-ToF mass spectrometry, and then changes in intensities of peptide ions registered in a mass range between 2,000 and 14,000 Da were identified and correlated with clinical data. Results We found that surgical resection of tumors did not have an immediate effect on the mass profiles of the serum proteome. On the other hand, significant long-term effects were observed in serum proteome patterns one year after the end of basic treatment (we found that about 20 peptides exhibited significant changes in their abundances). Moreover, the significant differences were found primarily in the subgroup of patients treated with adjuvant therapy, but not in the subgroup subjected only to surgery. This suggests that the observed changes reflect overall responses of the patients to the toxic effects of adjuvant radio/chemotherapy. In line with this hypothesis we detected two serum peptides (registered m/z values 2,184 and 5,403 Da) whose changes correlated significantly with the type of treatment employed (their abundances decreased after adjuvant therapy, but increased in patients treated only with surgery). On the other hand, no significant correlation was found between changes in the abundance of any spectral component or clinical features of patients, including staging and grading of tumors. Conclusions The study establishes a high potential of MALDI-ToF-based analyses for the detection of dynamic changes in the serum proteome related to therapy of breast cancer patients, which revealed the potential applicability of serum proteome patterns analyses in monitoring the toxicity of therapy.