Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival - Zhang, Dai; Zheng, Yi; Yang, Si; Li, Yiche; Wang, Meng; Yao, Jia; Deng, Yujiao; Li, Na; Wei, Bajin; Wu, Ying; Zhu, Yuyao; Li, Hongtao; et al. (Frontiers in Oncology, 2021)
To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO and multiple Cox regression analysis. Finally, an 11‑gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan-Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.