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: Univariate; Nomogram; Lasso; KEGG; Univariate analysis

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