Abstract
BACKGROUND: The role of immune-related long noncoding RNAs (irlncRNAs) in breast cancer (BRCA) is still unclear. Recently, studies have performed analyses based on the expression of irlncRNAs, however, in the present study, we used a novel method that did not require the specific expression levels of lncRNAs of BRCA patients. METHODS: We downloaded transcriptome and clinical data of BRCA patients from The Cancer Genome Atlas (TCGA), obtained immune genes from the Immport database, and extracted immune genes and lncRNAs for correlation analysis. Then, the differential expression of irlncRNA pairs (IRLPs) was determined and the prognostic signature was established by the IRLPs. The immune cell abundance of the TCGA-BRCA cohort was downloaded from the Tumor IMmune Estimation Resource (TIMER) database, and the relationship between the risk score of the IRLP signature and immune cell abundance was analyzed. Finally, we explored the relationship between risk scores and drug sensitivity based on the R package pRRophetic. RESULTS: Univariate cox regression results showed that 33 IRLPs had significant effects on the overall survival (OS) of BRCA patients. Then 22 IRLPs were obtained via lasso regression for further analysis. Multivariate regression analysis obtained 12 IRLPs to establish the IRLP prognostic signature. The model showed that this IRLP signature could act as a prognostic biomarker for BRCA patients. Kaplan-Meier (KM) survival analysis indicated that low-risk patients of IRLP's signature had a better OS (P<0.001). Advanced status BRCA patients may have higher risk scores, and univariate and multivariate cox regression analyses showed that risk scores were independent prognostic factors of clinical features (P<0.001). The results of the relationship between risk scores and immune infiltration showed that M1 macrophages were higher in the low-risk group (P=0.00015), while M2 macrophages were higher in the high-risk group (P=0.0015). The high-risk group had a greater sensitivity to chemotherapeutic agents such as cisplatin, docetaxel, doxorubicin, and gemcitabine. CONCLUSIONS: In present study, we used a novel method that did not require the specific expression levels of lncRNAs of BRCA patients, which can be used as a novel model for predicting the prognosis of BRCA patients.