Abstract
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 1,632 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.
Highlights
Cancer is a global public health problem and the second most important cause of death in America [1]
This study aimed to evaluate the Glycolysis-related gene (GRG) expression in breast cancer (BC) based on The Cancer Genome Atlas (TCGA) data and to study the association between GRG expression and BC survival
The combined International Cancer Genomics Consortium (ICGC) cohort formed by the merger of Breast Cancer-FR and Breast Cancer-KR cohorts included 149 BC patients and the clinical information and expression profiles were obtained from ICGC database
Summary
Cancer is a global public health problem and the second most important cause of death in America [1]. The global cancer burden is estimated every year by the American Cancer Society. According to the latest data report, the numbers of breast cancer (BC) cases and deaths estimated to occur in 2019 were 271,270 and 42,260, respectively [2]. A Glycolysis-Related Gene Signature global health challenge, and the global burden is still increasing in several countries [3,4,5]. Improvement of the overall clinical outcome of patients is crucial [6]. There is an urgent need to develop effective prognostic models for predicting the overall survival (OS) in patients with BC and for guiding clinical practice
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