Identification of Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry as Predictors of Prognosis in Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) is characterized by lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expressions. Prognosis of TNBC is poor due to recurrence and cancer death even with complete tumor resection, and it remains unclear which biomarkers are clinically useful for prediction of recurrence. The aim of this study was therefore to identify biomarkers for prognosis of TNBC by using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). 51 cases of TNBC were collected, including 17 patients with recurrence or death and 34 patients with non-recurrence. Factors that were significantly associated (P < 0.05) with TNBN prognosis after statistical analysis included tumor size, menopausal status and lymph node metastasis. Seven statistical significant peaks were found after protein mass spectrometry, with corrected P < 0.05. The features were selected to predict the patient's outcome using Support Vector Machine (SVM) classifier. When all the peaks were combined to classify the two groups, we obtained high classification accuracy, sensitivity and specificity, which were 90.2%, 82.35% and 94.12%, respectively. The results indicated that the identified protein peaks are potential biomarkers for predicting the prognosis of TNBC patients.