A Lactate-related LncRNA Model for Predicting Prognosis, Immune Landscape and Therapeutic Response in Breast Cancer

Background: Breast cancer (BC) has the highest incidence rate of all cancers globally, with high heterogeneity. Increasing evidence shows that lactate and long non-coding RNA (lncRNA) play a critical role in the occurrence, maintenance, therapeutic response, and the immune microenvironment of tumors. We aimed to construct a lactate-related lncRNAs prognostic signature (LRLPS) for BC patients to predict prognosis, tumor microenvironment, and treatment responses. Methods: : The BC data was download from the Cancer Genome Atlas (TCGA) database. The lactate-related genes were got from the Molecular Signatures Database. Difference analysis and Pearson correlation analysis were used to identify the differentially expressed lactate-related lncRNAs (LRLs). The univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were used to construct the LRLPS. We performed the Kaplan–Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and univariate and multivariate analyses to valid the LRLPS. The GO/KEGG, GSEA, CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore (IPS), pRRophetic and CellMiner databases were used to explore the different tumor immune microenvironment, and treatment responses. Results: : We totally acquired 196 differentially expressed LRLs between breast tumor and normal tissues. We constructed the LRLPS with 7 LRLs. Patients could be assigned into high-risk and low-risk groups based on the medium-risk sore in the training cohort. And it was proved that the prognosis prediction ability of the LRLPS was excellent, robust, and independent. Furthermore, a nomogram was constructed based on the LRLPS risk score and clinical factors to predict the 3-, 5-, and 10-year survival probability. The two risk groups had different immune activity. The low-risk patients had higher levels of immune infiltration and better immunotherapeutic response. Furthermore, many common chemotherapeutic drugs were more effective for low-risk patients. Conclusions: : In conclusion, we developed a novel LRLPS for BC that could predict the prognosis, immune landscape, and treatment response.