Abstract
BackgroundBreast cancer (BC) has a high incidence and mortality rate in females. Its conventional clinical characteristics are far from accurate for the prediction of individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC.MethodsWe analyzed the data of a training cohort from the Cancer Genome Atlas (TCGA) database and a validation cohort from the Gene Expression Omnibus (GEO) database. After the applications of Gene Set Enrichment Analysis (GSEA) and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed; the signature contained AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Furthermore, on the basis of expression levels of the six-gene signature, we constructed a risk score formula to classify the patients into high- and low-risk groups. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Later, a nomogram was developed to predict the outcomes of patients with risk score and clinical features over a period of 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues, which were taken as control.ResultsWe constructed a six-gene signature to predict the outcomes of patients with BC. The patients in the high-risk group showed poor prognosis than those in the low-risk group. The area under the curve (AUC) values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients with BC without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues; however, AK3 was an exception.ConclusionWe developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate prediction and might be useful in expanding the hypothesis in clinical research.
Highlights
In 2020, breast cancer (BC) was estimated to account for 30% of cancers in females, in which 15% of the cases lead to death [1, 2]
We sorted out 284 glycolysis-related genes through the integration of these four gene-sets
The results showed that CACNA1H, IL13RA1, NUP43, PGK1, and SDC1 were highly expressed in tumor samples, while expression of AK3 was significantly decreased (Fig. 6C)
Summary
In 2020, breast cancer (BC) was estimated to account for 30% of cancers in females, in which 15% of the cases lead to death [1, 2]. Even after a sufficient supply of oxygen, tumor cells mainly use the mechanism of glycolysis to produce energy and have a high glycolysis rate. This phenomenon is known as the Warburg effect, which is found in all types of cancer [7]. Many studies have proved that glycolysis can accelerate the proliferation, invasion, and migration of certain tumor cells and enhance drug resistance [8, 9] These glycolysis-related genes and proteins can be used as targets for prognosis or treatment in patients with BC. We aimed to develop a novel signature to predict the survival of patients with BC
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