Construction and Validation of Immune-Related LncRNA Signature to Predict the Prognosis and Therapeutic Efficacy of Breast Cancer

Breast cancer ranks first in morbidity and mortality among women worldwide. The immune microenvironment including immune-related long non-coding RNAs (irlncRNAs) are involved in various processes in tumors. We downloaded the RNA-sequencing data and corresponding clinical characteristics of breast samples from TCGA website and obtained the irlncRNAs and identified differentially expressed irlncRNAs (DEirlncRNAs) compared tumor with normal samples. Cox and Lasso regression analysis were used to establish a risk model after DEirlncRNAs were cyclically and separately paired. 15 DEirlncRNAs pairs were used to construct the risk model and to distinguish the high- or low-risk groups based on the median value of risk score. Patients in the high-risk group had a poorer prognosis, more aggressive clinicopathologic features, lower expression of immune checkpoint inhibitors, and higher drug sensitivity. The high-risk group was positively related to cancer-promoting immune cells like M2 macrophages, and negatively associated with anti-cancer immune cells like T cells. Gene set enrichment analysis were performed to investigate the potential molecular mechanisms on tumor progression. We constructed a risk model, which based on the relative expression values of the two irlncRNAs, could predict the prognosis of breast cancer patients, as well as the efficacy of immunotherapy and chemotherapy.