Prognosis model of patients with breast cancer based on metabolism-related LncRNAs

• DOI: 10.1007/s12672-025-02178-y
• Publication Date: 2025-03-28T06:40:23Z
• Authors: Dan Zhang; Shiwei Ma; Liling Yang; Hongyuan Liu; Han Jiang; Yan Wang
• Keywords: Breast cancer; Metabolism; CIBERSORT; Neoplasms. Tumors. Oncology. Including cancer and carcinogens; LASSO; Prognostic model; LncRNA; RC254-282; Analysis

OBJECTIVE: Metabolism-related lncRNAs may play a significant role in the occurrence and development of breast cancer. This study aims to identify metabolism-related lncRNAs with high predictive value for prognosis and to construct a model that can predict the prognosis of breast cancer individually. METHODS: Transcriptome data and clinical data of patients with breast cancer were retrieved from the TCGA database, and metabolism-related genes were sourced from the GSEA database. Metabolism-related lncRNAs in breast cancer were obtained through differential expression analysis and Pearson correlation analysis. Prognostic-related lncRNAs were further screened using Univariate Cox regression and LASSO regression. Kaplan–Meier survival analysis was performed and the survival curve of the two groups was drawn. Univariate and Multivariate Cox regression analyses were conducted to identify the independent prognostic factors, which were subsequently integrated into a nomogram for individualized prognostic prediction. RESULTS: Through differential analysis, 2135 differential lncRNAs were obtained, of which 231 were metabolism-related lncRNAs. Using Univariate Cox regression and LASSO regression, a risk prediction model incorporating 19 metabolism-related lncRNAs was constructed. The survival curve suggested that patients with high-risk scores had a poor prognosis compared to those with low-risk scores (P < 0.05). Cox regression analysis further identified that age, stage classification, distant metastasis and risk score as independent prognostic factors to construct a nomogram. KEGG pathway enrichment analysis revealed that differential lncRNAs may be related to JAK-STAT signaling pathway, MAPK signaling pathway and mTOR signaling pathway. Finally, based on the analysis of the CIBERSORT algorithm, lncRNAs used in the construction of the model had a strong correlation with CD8(+)T cells, activated CD4(+)T cells and the polarization of M2 macrophages. CONCLUSION: Bioinformatics methods were utilized to identify metabolism-related lncRNAs associated with breast cancer prognosis, and a prognostic risk model was constructed, laying a solid foundation for the study of metabolism-related lncRNAs in breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-025-02178-y.