Identification of Prognostic Biomarkers for Breast Cancer Metastasis Using Penalized Additive Hazards Regression Model.

Background: Breast cancer (BC) has been reported as one of the most common cancers diagnosed in females throughout the world. Survival rate of BC patients is affected by metastasis. So, exploring its underlying mechanisms and identifying related biomarkers to monitor BC relapse/recurrence using new statistical methods is essential. This study investigated the high-dimensional gene-expression profiles of BC patients using penalized additive hazards regression models.

Methods: A publicly available dataset related to the time to metastasis in BC patients (GSE2034) was used. There was information of 22 283 genes expression profiles related to 286 BC patients. Penalized additive hazards regression models with different penalties, including LASSO, SCAD, SICA, MCP and Elastic net were used to identify metastasis related genes.

Results: Five regression models with penalties were applied in the additive hazards model and jointly found 9 genes including SNU13, CLINT1, MAPK9, ABCC5, NKX3-1, NCOR2, COL2A1, and ZNF219. According the median of the prognostic index calculated using the regression coefficients of the penalized additive hazards model, the patients were labeled as high/low risk groups. A significant difference was detected in the survival curves of the identified groups. The selected genes were examined using validation data and were significantly associated with the hazard of metastasis.

Conclusion: This study showed that MAPK9, NKX3-1, NCOR1, ABCC5, and CD44 are the potential recurrence and metastatic predictors in breast cancer and can be taken into account as candidates for further research in tumorigenesis, invasion, metastasis, and epithelial-mesenchymal transition of breast cancer.