Identifying glycolysis-related LncRNAs for predicting prognosis in breast cancer patients

PURPOSE: Functions associated with glycolysis could serve as targets or biomarkers for therapy cancer. Our purpose was to establish a prognostic model that could evaluate the importance of Glycolysis-related lncRNAs in breast cancer. METHODS: Gene expressions were evaluated for breast cancer through The Cancer Genome Atlas (TCGA) database, and we calculated Pearson correlations to discover potential related lncRNAs. Differentially expressed genes were identified via criteria of FDR < 0.05 and vertical bar FC vertical bar > 2. Total samples were separated into training and validating sets randomly. Univariate Cox regression identified 14 prognostic lncRNAs in training set. A prognostic model was constructed to evaluate the accuracy in predicting prognosis. The univariate and multivariate Cox analysis were performed to verify whether lncRNA signature could be an independent prognostic factor The signature was validated in validating set. Immune infiltration levels were assessed. RESULTS: Eighty-nine differentially expressed lncRNAs were identified from 420 Glycolysis-related lncRNAs. 14 lncRNAs were correlated with prognosis in training set and were selected to establish the prognostic model. Low risk group had better prognosis in both training (p = 9.025 e-10) and validating (p = 4.272 e-3) sets. The univariate and multivariate Cox analysis revealed that risk score of glycolysis-related lncRNAs (P < 0.001) was an independent prognostic factor in both training and validating sets. The neutrophils (p = 4.214 e-13, r = 0.223), CD4+ T cells (p = 1.833 e-20, r = 0.283), CD8+ T cells (p = 7.641 e-12, r = 0.211), B cells (p = 2.502 e-10, r = 0.195) and dendritic cells (p = 5.14 e-18, r = 0.265) were negatively correlated with risk score of prognostic model. The Macrophage (p = 0.016, r = 0.0755) was positively correlated with the risk score. CONCLUSION: Our study indicated that glycolysis-related lncRNAs had a significant role to facilitate the individualized survival prediction in breast cancer patients, which would be a potential therapeutic target.