Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies.

Background: Approximately 10%-15% of patients with breast cancer die of cancer metastasis or recurrence, and early diagnosis of it can improve prognosis. Breast cancer outcomes may be prognosticated on the basis of surface markers of tumor cells and serum tests. However, evaluation of a combination of clinicopathological features may offer a more comprehensive overview for breast cancer prognosis.

Fas ligand enhances malignant behavior of tumor cells through interaction with Met, hepatocyte growth factor receptor, in lipid rafts.

Many late-stage cancer cells express Fas ligand (FasL) and show high malignancy with metastatic potential. We report here a novel signaling mechanism for FasL that hijacks the Met signal pathway to promote tumor metastasis. FasL-expressing human tumor cells express a significant amount of phosphorylated Met.