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
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
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.