Identification of glycolysis related genes for prognosis prediction in patients with breast cancer

Abstract

Abstract Objective Breast cancer (BC) is a malignant tumor that threatens women’s health. Studies have found that glycolysis is related to the occurrence of BC. However, there is no systematic screening of glycolysis related genes affecting the prognosis of BC. Methods BC-related data were downloaded, and analyzed to identify glycolysis related genes that regulate BC. The glycolysis related genes associated with BC prognosis was screened by using Cox regression analysis, and a predictive model was established. The relevant clinical data and model risk scores were analyzed. The correlation graph of the model with tumor characteristics data and survival rate was obtained. Results A total of seven glycolysis related genes (AK3, PGK1, SDC3, NUP43, CACNA1H, SDC1, and IL13RA1) that affect BC patients’ prognosis were authenticated, and they were divided into high-risk and low-risk groups. For overall survival rate, patients with low-risk score were higher than patients with high-risk score. The correlation analysis of risk scores and clinical data showed that the constructed model could be used to predict the prognosis of BC patients. Conclusion The seven glycolysis related genes can predict BC prognosis, and may provide new targets for the treatment of BC.