Necroptosis-Related LncRNAs Signature and Subtypes for Predicting Prognosis and Revealing the Immune Microenvironment in Breast Cancer - Xu, Yuhao; Zheng, Qinghui; Zhou, Tao; Ye, Buyun; Xu, Qiuran; Meng, Xuli (Frontiers in Oncology, 2022)
Purpose: Necroptosis is a mode of programmed cell death which overcomes apoptotic resistance. We aimed to construct a steady necroptosis-related signature and identify subtypes for prognostic and immunotherapy sensitivity prediction. Methods: Necroptosis-related prognostic lncRNAs were selected by co-expression analysis, and were used to construct a linear stepwise regression model via univariate and multivariate Cox regression, along with least absolute shrinkage and selection operator (lasso). Quantitative reverse transcription polymerase chain reaction (RT-PCR) was used to measure the gene expression levels of lncRNAs included in the model. Based on the riskScore calculated, we separated patients into high and low risk groups. Afterwards, we performed CIBERSORT and the single sample gene set enrichment analysis (ssGSEA) methods to explore immune infiltration status. FurthermoreAfterwards, we investigated the relationships between the signature and immune landscape, genomic integrity, clinical characteristics, drug sensitivity, immunotherapy efficacy. Results: We constructed a robust 22 necroptosis-related lncRNAs model, serving as an independent prognostic factor for BRCA. The low-risk group seemed to be the immune-activated type. Meanwhile, it showed higher the tumor mutation burden (TMB), the higher riskScore. The PD-L1 combined CTLA4 immunotherapy seemed to be a promising treatment strategy. At last, patients were assigned to 4 clusters to better discern the heterogeneity among patients. Conclusions: The necroptosis-related lncRNAs signature and molecular clusters indicated superior predictive performance in prognosis and immune micronenvironment, which may also provide guidance of drug regimens for immunotherapy and provide novel insights into precision medicine.