Mouse Fetub (Fetuin-B) ELISA Kit

Background and aims: The predictive value of traditional CV risk calculators is limited. Novel indicators of CVD progression are needed particularly in the young population. The main aim of this study was the identification of a molecular profile with added value to classical CV risk estimation. Methods: Eighty-one subjects (30-50 years) were classified in 3 groups according to their CV risk: healthy subjects; individuals with CV risk factors; and those who had suffered a previous CV event. The urine proteome was quantitatively analyzed and significantly altered proteins were identified between patients' groups, either related to CV risk or established organ damage. Target-MS and ELISA were used for confirmation in independent patients' cohorts. Systems Biology Analysis (SBA) was carried out to identify functional categories behind CVD. Results: 4309 proteins were identified, 75 of them differentially expressed. ADX, ECP, FETUB, GDF15, GUAD and NOTCH1 compose a fingerprint positively correlating with lifetime risk estimate (LTR QRISK). Best performance ROC curve was obtained when ECP, GDF15 and GUAD were combined (AUC = 0.96). SBA revealed oxidative stress response, dilated cardiomyopathy, signaling by Wnt and proteasome, as main functional processes related to CV risk. Conclusions: A novel urinary protein signature is shown, which correlates with CV risk estimation in young individuals. Pending further confirmation, this six-protein-panel could help in CV risk assessment.

Objective: Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy, and early tumor detection is the most promising approach for improving the EOC survival rate. The goal of this study was to identify the biomarkers underlying ovarian carcinogenesis. Study design: To mimic the onset and progression of human ovarian cancer, we established a rat model of ovarian neoplasm by implanting 7,12-dimethylbenz(a)anthracene (DMBA)-coated silk cloth strips onto the ovaries. Sera collected from rats bearing serous ovarian carcinoma (SOC) at baseline, 12 and 24 weeks after DMBA treatment and from controls were analyzed using iTRAQ combined with two-dimensional liquid chromatography and tandem mass spectrometry. The data were analyzed with ProteinPilot software for peptide matching, protein identification, and protein quantitation. Ingenuity pathway analysis software was used to identify the canonical pathways and biological interaction networks of differentially expressed proteins. Results: The cumulative ovarian tumor incidence rate reached 75% at 32 weeks after DMBA treatment. Out of all tumors, 94% were EOC, and 51% of the EOC cases were SOC. A total of 225 unique, non-redundant proteins were identified with 95% confidence. Twenty-seven differentially expressed proteins were significantly up- or down-regulated during the early or advanced carcinogenesis of SOC. Fifteen proteins were previously reported to be involved in ovarian cancer, and 12 proteins, including MMRN1, SERPINC1, TLN1, AHSG, PLC, APOA2, HPX, APOC1, APOC2, FERMT3, FETUB and HBB, were identified for the first time in our study. Conclusion: The discovery of these differentially expressed proteins provides valuable clues for understanding the molecular mechanism underlying the dynamic carcinogenic process of ovarian cancer. These proteins could be used as diagnostic biomarkers for early detection, disease monitoring and therapeutic targets. (C) 2012 Elsevier Ireland Ltd. All rights reserved.