Hemostasis System and Plasminogen Activity in Retrochorial Hematoma in the First Trimester of Pregnancy.

(1) Background: The components of the fibrinolytic system and its main component, plasminogen, play a key role in the first months of pregnancy. The effect of autoantibodies interacting with plasminogen in the formation of retrochorial hematoma is unknown. The aim of our study was to determine the role of plasminogen and IgA, IgM, and IgG, which bind to plasminogen, in retrochorial hematoma. (2) Methods: Prothrombin time (PT), thrombin time (TT), partial activated thromboplastin time (aPTT), soluble fibrin-monomer complex (SFMC), D-dimer, plasminogen activity (%Plg), plasminogen concentration (Plg), and the levels of IgG (IgG-Plg), IgM (IgM-Plg), IgA (IgA-Plg) interacting with plasminogen were determined in plasma samples of 57 women with normal pregnancy and 16 with retrochorial hematoma. (3) Results: %Plg in plasma samples from women with retrochorial hematoma was significantly lower than in plasma samples from women with normal pregnancy. The diagnostic significance of %Plg in the ROC analysis was AUC = 0.85. A direct correlation was found between aPTT and the level of autologous IgM interacting with plasminogen. (4) Conclusions: A decrease in the activity of plasminogen in the blood serum of women in the first trimester of pregnancy may indicate disturbances in the hemostasis system and the formation of retrochorial hematoma. According to the results of the study, it is possible to recommend the determination of plasminogen activity in the management of pregnant women in gynecological practice.

Proteolyzed Variant of IgG with Free C-Terminal Lysine as a Biomarker of Prostate Cancer.

The differential diagnosis of prostate cancer is problematic due to the lack of markers with high diagnostic accuracy. We previously demonstrated the increased binding of IgG to human plasminogen (PLG) in plasma of patients with prostate cancer (PC) compared to healthy controls. Heavy and light chains of PLG (PLG-H and PLG-L) were immobilized on 96-well plates and the binding of IgG to PLG-H and PLG-L was analyzed in serum from 30 prostate cancer (PC) patients, 30 patients with benign prostatic hyperplasia (BPH) and 30 healthy controls using enzyme-linked immunosorbent assay (ELISA). Our results demonstrate that IgG from PC sera bind to PLG-H but not to PLG-L. This interaction occurred through the free IgG C-terminal lysine (Lys) that becomes exposed as a result of IgG conformational changes associated with proteolysis. Circulating levels of modified IgG with exposed C-terminal Lys (IgG-Lys) were significantly higher in PC patients than in healthy controls and in BPH. We used Receiver Operating Characteristic (ROC) analysis to calculate the sensitivity (SN) and specificity (SP) of circulating IgG-Lys for differentiating PC from BPH as 77% and 90%, respectively. The area under the curve (AUC) was 0.87. We demonstrated that the diagnostic accuracy of circulating levels of IgG-Lys is much higher than diagnostic accuracy of total PSA (tPSA).

Quantification of autoantibodies to plasminogen in plasma of patients with cancer.

BACKGROUND: The investigation of autoantibodies which may play a role in the processes of angiogenesis and tumorogenesis is important in the early diagnostis of cancer. OBJECTIVE: This study aimed to investigate the levels of autoantibodies to Glu-plasminogen (Pg) in plasma of patients with tumors. METHODS: Plasma samples from healthy volunteers were compared with samples from patients with prostate cancer using 2D electrophoresis and MALDI-TOF mass spectrometry. Plasma samples from 25 patients with prostate cancer, 15 patients with benign prostatic hyperplasia (BPH), 29 patients with breast cancer, and 43 healthy volunteers were tested using ELISA to anti-Pg IgG autoantibodies. Affinity chromatography on Pg-sepharoses was used to assess the quantity of anti-Pg IgG in control plasma and plasma of prostate cancer patients. ATTESTAT program was used for nonparametric analysis. RESULTS: Using 2D electrophoresis, marker spots below 50 kD were detected in prostate cancer samples. These spots were identified as fragments of Pg and IgG. Using affinity chromatography on Pg-sepharose, the quantity of IgG bound to Pg versus total IgG was determined to be 9% in control and 27% in prostate cancer samples. The frequency of occurence of elevated levels of anti-Pg IgG was 84% in prostate cancer samples, 69% in breast cancer samples, 40% in BPH samples, and 11% in healthy plasma. CONCLUSIONS: Autoantibodies to Pg may be involved in tumorogenesis and elevated levels of anti-Pg IgG antibodies may be a risk factor for tumor development.

Ovarian cancer marker of 11.7 kDa detected by proteomics is a serum amyloid A1.

In this study, to reduce the number of major plasma components, we examined thermostable plasma fractions to search for a biomarker of ovarian cancer. An apparent cancer biomarker of 11.7 kDa was detected in these fractions using ProteinChip SELDI-TOF mass spectrometry system. This peak invariably appeared with another close peak of about 11.5 kDa, suggesting that it is a derivative of a larger mass molecule. Of 27 cancer plasma specimens, 15 (55.6%) demonstrated this peak pair, whereas only 2 of 34 controls specimens (5.8%) were shown to express it with low intensity. Using a method involving cysteine modification by 4-vinylpyridine (4-VP), 2-DE and HPLC, these peaks were identified by mass spectrometry as serum amyloid A1 (11.68 kDa) and its N-terminal arginine-truncated form (11.52 kDa).

Database search post-processing by neural network: Advanced facilities for identification of components in protein mixtures using mass spectrometric peptide mapping.

Database search post-processing by neural network was employed in peptide mapping experiments. The database search was performed using both the known algorithms and score functions, such as Bayesian, MOWSE, Z-score, correlations between calculated and actual peptide length fractional abundance, and, in addition, the probability of protein digest pattern in peptide fingerprint, all embedded in locally developed program. The new signal-processing algorithm based on neural network improves signal-noise separation and is acceptable for automatic protein identification in mixtures. Its power was tested on Helicobacter pylori protein inventory after preceding protein separation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Increase in protein identification success rate was observed, and about 100 proteins were identified with no need of human participation in database search estimation.