Predicting the immune landscape of invasive breast carcinoma based on the novel signature of immune-related lncRNA

Background The composition of the population of immune-related long non-coding ribonucleic acid (irlncRNA) generates a signature, irrespective of expression level, with potential value in predicting the survival status of patients with invasive breast carcinoma. Methods The current study uses univariate analysis to identify differentially expressed irlncRNA (DEirlncRNA) pairs from RNA-Seq data from The Cancer Genome Atlas (TCGA). 36 pairs of DEirlncRNA pairs were identified. Using various algorithms to construct a model, we have compared the area under the curve and calculated the 5-year curve of Akaike information criterion (AIC) values, which allows determination of the threshold indicating the maximum value for differentiation. Through cut-off point to establish the optimal model for distinguishing high-risk or low-risk groups among breast cancer patients. We assigned individual patients with invasive breast cancer to either high risk or low risk groups depending on the cut-off point, re-evaluated the tumor immune cell infiltration, the effectiveness of chemotherapy, immunosuppressive biomarkers, and immunotherapy. Results After re-assessing patients according to the threshold, we demonstrated an effective means of distinguish the severity of the disease, and identified patients with different clinicopathological characteristics, specific tumor immune infiltration states, high sensitivity to chemotherapy,wellpredicted response to immunotherapy and thus a more favorable survival outcome. Conclusions The current study presents novel findings regarding the use of irlncRNA without the need to predict precise expression levels in the prognosis of breast cancer patients and to indicate their suitability for anti-tumor immunotherapy.