Role of androgen receptor signaling pathway-related lncRNAs in the prognosis and immune infiltration of breast cancer

• Published in: Scientific reports Vol. 12; no. 1; pp. 20631 - 17
• Main Authors: Huang, Guo, Cao, Hong, Liu, Guowen, Chen, Juan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.11.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 692/4028; 692/499; Androgen receptors; Androgens; Breast cancer; Breast Neoplasms - genetics; CD4 antigen; Cluster analysis; Female; Gene set enrichment analysis; Humanities and Social Sciences; Humans; Immunosuppressive agents; Infiltration; Lymphocytes B; Lymphocytes T; Macrophages; Metastases; multidisciplinary; Nomograms; Non-coding RNA; Prognosis; Receptors, Androgen - genetics; Regression analysis; Risk groups; RNA, Long Noncoding - genetics; Science; Science (multidisciplinary); Signal Transduction; Tumor microenvironment; Tumor Microenvironment - genetics; Tumors
• DOI: 10.1038/s41598-022-25231-0
• ISSN: 2045-2322

Androgen receptor (AR) is strong association with breast cancer (BRCA). We aimed to investigate the effect of the androgen receptor signaling pathway-related long non-coding RNAs (ARSP-related lncRNAs) on the process of subtype classification and the tumor microenvironment (TME) of breast cancer (BRCA). Our study screen ARSP-related lncRNAs for the construction of a risk model. The single-sample gene set enrichment analysis (ssGSEA) method was used to detect the differences between the immune responses generated by the patients belonging to the low- and high-risk groups. The relationship between the ARSP-related lncRNAs and TME was explored following the process of cluster analysis. The univariate Cox analysis and the Lasso regression analysis method was used to screen nine of these lncRNAs to develop a risk model. It was observed that risk score could function as an independent prognostic factor, affecting the prognoses of patients suffering from BRCA. The validity of the model was assessed by analyzing the generated calibration curves and a nomogram. Additionally, the effect of the risk score on the extent of immune cell infiltration realized in TME was explored. M2 macrophages correlated positively, whereas NK cells, CD4+ T cells, and naive B cells correlated negatively with the risk score. Results obtained using the cluster analysis indicated that immune scores correlated with clustered subtypes. Finally, the risk score and cluster subtypes were analyzed to study the sensitivity of the patients toward different drugs to identify the appropriate therapeutic agents. The prognoses of patients suffering from BRCA can be accurately predicted by ARSP-related lncRNAs.