The usefulness of multiple markers for diagnosis, prognosis, and prediction of the risk of developing diseases or their complications is now widely recognized [13, 26]. Various proteomic approaches have been applied to biomarker discovery using biological fluids. Interestingly, low-molecular-weight peptides, such as S100A8 and fibrinogen, have been recognized to play important roles in physiologic and pathologic processes and could be used as relevant biomarker candidates [27, 28]. Recently, the mass spectrum that directly detects and differentiates short peptides has offered a promising approach for peptidomic biomarker discovery [14, 15, 29–31].
MS instrumentation and analysis tools have continued to rapidly evolve and improve our ability to detect lessabundant serum proteins. Until now, the most commonly used instrument was the SELDI-TOF MS[32–35]. However, SELDI-TOF MS does not allow a direct identification of the discriminatory proteins and the debate about the reproducibility has been particularly strong. Alternative approaches for measuring polypeptides, such as the surface-enhanced laser desorption and ionization, recently reported by several groups, have several disadvantages, such as low resolution and the loss of most proteins and peptides [37–39]. MALDI is a soft ionization technique used in MS that allows the analysis of biomolecules such as proteins, peptide sugars, and large organic molecules. As a powerful tool for surveying the complex patterns of biologically informative molecules, MALDI-TOF MS protein/peptide profiling has been applied in proteomics biomarker research and has become a promising tool in cancer biomarker research [29, 40, 41].
In the present study, by integrating short peptide purification with IMAC-MBs, peak intensity detection with MALDI-TOF MS, and profile analysis with ClinProt Tools software 2.2, a series of differentially expressed short peptides in the sera of breast cancer patients has been successfully detected. A comparative case control analysis between breast cancer and healthy volunteers was performed. Peptidomic maps associated with the disease were drawn. The results show that compared with the healthy volunteers, the breast cancer patients share 24 significantly differentiated peptides, including 15 upregulated and 9 downregulated peptides. Genomic and proteomic technologies will further help us understand the intracellular signaling and gene transcription systems, as well as the protein pathways that connect the extracellular microenvironment to the serum or plasma macroenvironment of cancer . These 24 interesting significantly differentiated peptides may provide further evidence for understanding the occurrence and progress of breast cancer.
Using SVM algorithm analysis, classification models were developed to classify samples between healthy volunteers and breast cancer. A cluster of three peptides at m/z 698, 720 and 1866 achieved a recognition capacity and a cross-validation of 91.78% to discriminate breast cancer from healthy volunteers. The blinded verification of the SVM classification model proved the correct classification of 91.89% (34/37) of the breast cancer (sensitivity) and 91.67% (33/36) of the healthy volunteers(specificity). To our knowledge, this is the first study to screen for breast cancer-related short peptides in sera by combining IMAC-MBs and MALDI-TOF MS. The classification model that we have built up has potential applications in providing alternatives for breast cancer diagnosis and may provide a better understanding of breast cancer pathogenesis or help in tailoring the use of chemotherapy for each patient, finally resulting in improved patient outcomes.
In conclusion, peptidome patterns from IMAC-MB-purified serum samples were directly profiled with MALDI-TOF MS and a peptidome model that differentiated breast cancer from the healthy volunteers was constructed with high sensitivity and specificity. Despite the high sensitivity and specificity, the number of specimens analyzed in this study was relatively small, which may limit the validity of the results. The next step in our study will be to analyze larger patient cohorts and to run blinded samples to confirm the usefulness of the currently identified peptides for breast cancer diagnosis. After this confirmation, the biomarkers of the interest will then be isolated and identified and their biological role in breast cancer pathogenesis will be studied.