Abstract
Breast cancer (BC) is a common type of cancer and has a poor prognosis. In this study, we collected the mRNA and miRNA expression profiles of BC patients were obtained from The Cancer Genome Atlas (TCGA) to explore a novel prognostic strategy for BC patients using bioinformatics tools. We found that six glycolysis-related miRNAs (GRmiRs, including hsa-mir-1247, hsa-mir148b, hsa-mir-133a-2, has-mir-1307, hsa-mir-195 and hsa-mir-1258) were correlated with prognosis of BC samples. The risk score model was established based on 6 prognosis-associated GRmiRs. The outcome of high risk group was significantly poorer. Cox regression analysis showed that risk score was an independent prognostic factor. Differentially expressed genes identified between high and low risk groups were mainly enriched in inflammation and immune-related signaling pathways. The proportion of infiltration of 12 kinds of immune cells in high and low risk groups were significantly different. Risk score was closely associated with many immune indexes. Multiple DEGRGs and miRNAs were associated with drugs. In conclusion, glycolysis-related miRNA signature effectively predicts BC prognosis.
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