Akademik Veri Yönetim Sistemi
Current Proteomics, cilt.15, sa.3, ss.214-220, 2018 (SCI-Expanded)
Background: Early detection of breast cancer is a key to the success of breast cancer management. Serum proteome analysis using Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (SELDI-TOF-MS) generates useful information that can be utilized to describe exclusive prognostic and diagnostic biomarkers. Objective: This study aimed to use proteomics and bioinformatics to identify new biomarkers during the metastatic process of breast cancers that were classified as invasive lobular cancer or invasive ductal cancer. Method: Blood samples from 64 breast cancer patients [36 with invasive ductal cancer (14 of whom were lymph node positive); 28 with invasive lobular cancer (8 of whom were lymph node positive] were analyzed using IMAC 30 protein chips. The data acquired from the spectra were processed with univariate statistical analysis (Protein Chip Data Manager Software). Results: One-hundred-eighteen clusters were generated from the individual serum samples. Thirty-six proteins of the metastatic phenotype were found to be diagnostically accurate in cluster analysis. In the breast cancer group, a single candidate peak (m/z 1090.8) that was able to discriminate the metastatic progression was identified as a metastatic phenotype marker. Fifteen protein peaks were identified as markers to separate the histopathological subtypes as either invasive ductal cancer or invasive lobular cancer. Conclusion: In recent years, proteomic methods have rapidly become widespread in breast cancer research. This study revealed the pattern of a group of proteins that were not previously identified and are recommended as candidate markers to diagnose metastatic progression.