A Novel lncRNA Panel for Risk Stratification and Immune Landscape in Breast Cancer Patients

Abstract

Purpose: In recent years, breast cancer (BC) has been a primary cause of mortality in women. However, the underlying mechanisms remain to be elucidated. Accumulating evidence has supported the hypothesis that long noncoding RNAs (lncRNAs) play central roles in the progression of cancer. We aimed to construct an immune-related lncRNA panel to predict the prognosis of patients with BC and evaluate the immune features. Methods: The expression profiles of patients with BC were obtained from The Cancer Genome Atlas (TCGA) database to screen the differentially expressed lncRNAs (DELs). Pearson’s correlation analysis was employed to filter the DELs related to the immune-associated genes. Univariate Cox regression, the LASSO algorithm, and multivariate Cox regression analyses were conducted to establish the model. Functional enrichment analyses and biological experiments were performed to explore the immune activity of the lncRNA panel. Results: A four-immune-related lncRNA panel (IRLP) composed of AC022196.1, ARHGAP26-AS1, DPYD-AS1 and PURPL was established in TCGA training cohort. The prognostic accuracy of the predictive model was confirmed in TCGA internal validation cohort, TCGA entire cohort and Qilu external validation cohort. Bioinformatics analyses indicated that the IRLP had a close relationship with tumour infiltrating immune cells and immunomodulatory biomarkers. The biological functions of the four immunerelated lncRNAs in BC were first investigated in vitro and in vivo. PURPL was indicated to play a central role in the regulation of macrophage recruitment and polarization via CCL2. Conclusion: Our study identified IRLP as a reliable prognostic indicator with great potential for clinical application in personalized immunotherapy.