摘要:Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based serum N-glycan analysis has gained acknowledgment for the diagnosis of breast cancer in recent years. In this study, the possibilities of expanding its application for breast cancer management and surveillance were discovered and evaluated. First, a novel MALDI-TOF platform, IDsys RT, was confirmed to be effective for breast cancer analysis, showing a maximum area under the curve of 0.91. Multiple N-glycan markers were identified and validated using this process, and they were found to be applicable for differentiating recurring breast cancer samples from healthy control or ordinary breast cancer samples. Recurrence samples were especially distinct from non-recurrence samples when N-glycan signatures were sampled in multiple time points and monitored via MALDI-TOF, throughout the therapy. These results suggested the feasibility of MALDI-TOF-based N-glycan analysis for tracking the molecular signatures of breast cancer and predicting recurrence.
其他摘要:Abstract Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based serum N -glycan analysis has gained acknowledgment for the diagnosis of breast cancer in recent years. In this study, the possibilities of expanding its application for breast cancer management and surveillance were discovered and evaluated. First, a novel MALDI-TOF platform, IDsys RT, was confirmed to be effective for breast cancer analysis, showing a maximum area under the curve of 0.91. Multiple N -glycan markers were identified and validated using this process, and they were found to be applicable for differentiating recurring breast cancer samples from healthy control or ordinary breast cancer samples. Recurrence samples were especially distinct from non-recurrence samples when N -glycan signatures were sampled in multiple time points and monitored via MALDI-TOF, throughout the therapy. These results suggested the feasibility of MALDI-TOF-based N -glycan analysis for tracking the molecular signatures of breast cancer and predicting recurrence.