Identification of a risk prediction model for clinical prognosis in HER2 positive breast cancer patients

Background New biomarkers are needed to identify different clinical outcomes for HER2+ breast cancer (BC). Methods Differential genes of HER2+ BC were screened based on TCGA database. We used WGCNA to identify the genes related to the survival. Genetic Algorithm was used to structure risk prediction model. The prognostic model was validated in GSE data. Results We constructed a risk prediction model of 6 genes to identify prognosis of HER2+ BC, including CLEC9A, PLD4, PIM1, PTK2B, AKNAD1 and C15orf27. Kaplan-Meier curve showed that the model effectively distinguished the survival of HER2+ BC patients. The multivariate Cox regression suggested that the risk model was an independent predictor for HER2+ BC. Analysis related to immune showed that significant differences in immune infiltration between high-and low-risk groups classified by the prognostic model. Conclusions Our study identified a risk prediction model of 6 genes that could distinguish the prognosis of HER2+ BC.

Automated summary

This study identified a risk prediction model of 6 genes, including CLEC9A, PLD4, PIM1, PTK2B, AKNAD1 and C15orf27, that could distinguish the prognosis of HER2+ breast cancer. The Kaplan-Meier curve showed the model effectively distinguished the survival of HER2+ breast cancer patients. Multivariate Cox regression indicated that the risk model was an independent predictor for HER2+ breast cancer.