Autophagy‐related long non‐coding RNAs (lncRNAs) disorders are related to the occurrence and development of breast cancer. The purpose of this study is to explore whether autophagy‐related lncRNA can predict the prognosis of breast cancer patients. The autophagy‐related lncRNAs prognostic signature was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression. We identified five autophagy‐related lncRNAs (MAPT‐AS1, LINC01871, AL122010.1, AC090912.1, AC061992.1) associated with prognostic value, and they were used to construct an autophagy‐related lncRNA prognostic signature (ALPS) model. ALPS model offered an independent prognostic value (HR = 1.664, 1.381‐2.006), where this risk score of the model was significantly related to the TNM stage, ER, PR and HER2 status in breast cancer patients. Nomogram could be utilized to predict survival for patients with breast cancer. Principal component analysis and Sankey Diagram results indicated that the distribution of five lncRNAs from the ALPS model tends to be low‐risk. Gene set enrichment analysis showed that the high‐risk group was enriched in autophagy and cancer‐related pathways, and the low‐risk group was enriched in regulatory immune‐related pathways. These results indicated that the ALPS model composed of five autophagy‐related lncRNAs could predict the prognosis of breast cancer patients.

Limited predictable long noncoding RNA (lncRNA) signature was reported in tamoxifen resistance among estrogen receptor (ER)-positive breast cancer (BC) patients. The aim of this study was to identify and assess prognostic lncRNA signature to predict recurrence among ER-positive BC patients treated with tamoxifen. Cohorts from Gene Expression Omnibus (GEO) (n = 298) and The Cancer Genome Atlas (TCGA) (n = 160) were defined as training and validation cohort, respectively. BC relapse associated lnRNAs was identify within training cohort, and the predictable value of recurrence was assessed in both cohorts. A total of 11lncRNAs were recognized to be associated with relapse free survival (RFS) of ER-positive BC patients receiving tamoxifen, who were divided into low-risk and high-risk group on basis of relapse risk scores (RRS). Multivariate cox regression analyses revealed that the RRS is an independent prognostic biomarker in the prediction of ER-positive BC patients’ survival. GSEA indicated that high-risk group was associated with several signaling pathways in processing of BC recurrence and metastasis such as PI3K-Akt and Wnt signaling. Our 11-lncRNA based classifier is a reliable prognostic and predictive tool for disease relapse in BC patients receiving tamoxifen.

Purpose: Accumulating evidence indicated that triple‐negative breast cancer (TNBC) can stimulate stronger immune responses than other subtypes of breast cancer. We hypothesized that integrating immune‐related genomic signatures with clinicopathologic factors may yield a predictive accuracy exceeding that of the currently available system.Methods: Ten signatures that reflect specific immunogenic or immune microenvironmental features of TNBC were identified and re‐analyzed using bioinformatic methods. Then, clinically annotated TNBC (n = 711) with the corresponding expression profiles, which predicted a patient's probability of disease‐free survival (DFS) and overall survival (OS), was pooled to evaluate their prognostic values and establish a clinicopathologic‐genomic nomogram. Three and two immune features were, respectively, selected out of 10 immune features to construct nomogram for DFS and OS prediction based on multivariate backward stepwise Cox regression analyses.Results: By integrating the above immune expression signatures with prognostic clinicopathologic features, clinicopathologic‐genomic nomograms were cautiously constructed, which showed reasonable prediction accuracies (DFS: HR, 1.79; 95% CI, 1.46‐2.18, P < 0.001; AUC, 0.71; OS: HR, 1.96; 95% CI, 1.54‐2.49; P < 0.001; AUC, 0.73). The nomogram showed low‐risk subgroup had higher immune checkpoint molecules (PD‐L1, PD‐1, CTLA‐4, LAG‐3) expression and benefited from radiotherapy (HR, 0.2, 95% CI, 0.05‐0.89; P = 0.034) rather than chemotherapy (HR, 1.26, 95% CI, 0.66‐2.43; P = 0.485).Conclusions: These findings offer evidence that immune‐related genomic data provide independent and complementary prognostic information for TNBC, and the nomogram might be a practical predictive tool to identify TNBC patients who would benefit from chemotherapy, radiotherapy, and upcoming popularity of immunotherapy.