Search

Schizophrenia is a psychiatric condition characterized by poor prognosis and severe symptoms that decrease the quality of life of patients. It is therefore important to develop rapid and reliable methods for early diagnosis. To achieve this aim, identification of accurate biomarkers will promote research into the mechanisms of schizophrenia and the design of effective diagnosis tools. Discriminative peptides in the serum of participants (277 schizophrenia patients and 294 healthy controls) were detected using the matrix-assisted laser desorption ionization time-of-flight tandem mass spectrometry (MALDI-TOF-MS). The diagnostic model for schizophrenia was validated using the ClinProTool software. Discrimination models were tested in the training set (200 schizophrenia patients and 200 healthy controls), and the robustness of the models was evaluated using the independent test set (77 cases and 94 controls). A total of 77 peaks were significantly different between schizophrenia patients and healthy controls with a signal-to-noise ratio of over 5. Among them, 35 peptides were down-regulated and 42 peptides were up-regulated in schizophrenia patients. With a cross-validation rate of 92.7% and a recognition capability rate of 96.5%, the supervised neural network comprising 25 discriminative peaks was found to be the most efficient model for schizophrenia diagnosis (sensitivity = 96.10%, specificity = 94.68%). Peptides at m/z = 2022.18 corresponded to complement C3f, and peptides at m/z = 1020.89, 1351.02, 1466.1 were identified as fragments of fibrinopeptide A. These two peptides may be potential biomarkers for schizophrenia in the Chinese population. The serum peptide levels present a potential clinical diagnostic tool for schizophrenia.

No ideal serum biomarker currently exists for the early diagnosis of colorectal cancer (CRC). Magnetic bead-based fractionation coupled with MALDI-TOF MS was used to screen serum samples from CRC patients, healthy controls, and other cancer patients. A diagnostic model with five proteomic features (m/z 1778.97, 1866.16, 1934.65, 2022.46, and 4588.53) was generated using Fisher algorithm with best performance. The Fisher-based model could discriminate CRC patients from the controls with 100% (46/46) sensitivity and 100% (35/35) specificity in the training set, 95.6% (43/45) sensitivity and 83.3% (35/42) specificity in the test set. We further validated the model with 94.4% (254/269) sensitivity and 75.5% (83/110) specificity in the external independent group. In other cancers group, the Fisher-based model classified 25 of 46 samples (54.3%) as positive and the other 21 as negative. With FT-ICR-MS, the proteomic features of m/z 1778.97, 1866.16, 1934.65, and 2022.46, of which intensities decreased significantly in CRC, were identified as fragments of complement C3f. Therefore, the Fisher-based model containing five proteomic features was able to effectively differentiate CRC patients from healthy controls and other cancers with a high sensitivity and specificity, and may be CRC-specific. Serum complement C3f, which was significantly decreased in CRC group, may be relevant to the incidence of CRC. J. Cell. Biochem. 114: 448–455, 2013. © 2012 Wiley Periodicals, Inc.

Gross cystic disease (GCDB) is a breast benign condition predisposing to breast cancer. Cryopreserved sera from GCDB patients, some of whom later developed a cancer (cases), were studied to identify potential risk markers. A MALDI-TOF mass spectrometry analysis found several complement C3f fragments having a significant increased abundance in cases compared to controls. After multivariate analysis, the full-length form of C3f maintained a predictive value of breast cancer risk. Higher levels of C3f in the serum of women affected by a benign condition like GCDB thus appears to be correlated to the development of breast cancer even 20 years later. Biological significance Increased complement system activation has been found in the sera of women affected by GCDB who developed a breast cancer, even twenty or more years later. C3f may predict an increased breast cancer risk in the healthy population and in women affected by predisposing conditions.