Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer

Abstract

One of the most frequently identified tumors and a contributing cause of death in women is breast cancer (BC). Many biomarkers associated with survival and prognosis were identified in previous studies through database mining. Nevertheless, the predictive capabilities of single-gene biomarkers are not accurate enough. Genetic signatures can be an enhanced prediction method. This research analyzed data from The Cancer Genome Atlas (TCGA) for the detection of a new genetic signature to predict BC prognosis. Profiling of mRNA expression was carried out in samples of patients with TCGA BC (n = 1222). Gene set enrichment research has been undertaken to classify gene sets that vary greatly between BC tissues and normal tissues. Cox models for additive hazards regression were used to classify genes that were strongly linked to overall survival. A subsequent Cox regression multivariate analysis was used to construct a predictive risk parameter model. Kaplan-Meier survival predictions and log-rank validation have been used to verify the value of risk prediction parameters. Seven genes (PGK1, CACNA1H, IL13RA1, SDC1, AK3, NUP43, SDC3) correlated with glycolysis were shown to be strongly linked to overall survival. Depending on the 7-gene-signature, 1222 BC patients were classified into subgroups of high/low-risk. Certain variables have not impaired the prognostic potential of the seven-gene signature. A seven-gene signature correlated with cellular glycolysis was developed to predict the survival of BC patients. The results include insight into cellular glycolysis mechanisms and the detection of patients with poor BC prognosis.

Figure 1

enrichment of nine gene sets with major variations between BC tissues and noncancerous tissues by GSEA.

Figure 2

Identification of patient survival mRNAs. (a) Alteration of the selected genes in clinical samples. (b) Modification of chosen genes in various pathological forms of BC. (c) Multiple expression of seven genes selected.

Figure 3

The risk parameter-associated seven-mRNA signature predicts OS in patients with breast cancer. (a) The distribution of risk parameter of mRNA in each patient. (b) Survival days of BC patients with increasing risk parameters. (c) A heatmap of the expression profile of seven genes. Red indicates upregulated genes and light green indicates downregulated genes.

Figure 4

Time-dependence ROC curve according to the 5-year survival of the area under the AUC value.

Figure 5

Forest plot of multivariate COX regression analysis.

Figure 6

Kaplan–Meier survival study in TCGA data set for BC patients. (a) K–M survival curve for high/low risk BC patients. (b) Age, Stage, T-classification, N-classification and M-classification features involve patients survival in clinical features.

Figure 7

Kaplan–Meier curves for the prognostic value of the signature of risk parameter in each clinical feature for the patients. (a) Age, (b) stage, (c) T classification, (d) N classification, (e) M classification.