AUTHOR=Yang Xia , Weng Xin , Yang Yajie , Jiang ZhiNong 

TITLE=Pyroptosis-Related lncRNAs Predict the Prognosis and Immune Response in Patients With Breast Cancer

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.792106

DOI=10.3389/fgene.2021.792106

ISSN=1664-8021

ABSTRACT=Background: Breast cancer (BC) is the most common malignant tumor and the leading cause of cancer-related death in female worldwide. Pyroptosis and long noncoding RNAs (lncRNAs) have been demonstrated play vital roles in the tumorigenesis and development of BC. However, the clinical significance of pyroptosis-related lncRNAs in BC remains unclear. 
Methods：Using the mRNA and lncRNA profiles of TNBC obtained from TCGA dataset, a risk model based on the pyroptosis-related lncRNAs for prognosis were constructed using univariate and multivariate cox regression model, and least absolute shrinkage and selection operator (LASSO). Patients were divided into high- and low-risk groups based on the risk model, and the prognosis value and immune response in different risk groups were analyzed. Furthermore, functional enrichment annotation, therapeutic signature and tumor mutation burden (TMB) were performed to evaluate the risk model we established.
Results: 3364 pyroptosis-related lncRNAs were identified using pearson’s correlation analysis. The risk model we constructed comprising 10 pyroptosis-related lncRNAs, which was identified as an independent predictor of overall survival (OS) in BC. Moreover, the nomogram we constructed based on the clinicopathologic features and risk model yielded favorable performance for prognosis prediction in BC. In terms of immune response and mutation status, patients in the low-risk group had a higher expression of immune checkpoint markers, and exhibited higher fractions of activated immune cells. while the high-risk group had a highly percentage of TMB. 
Conclusions: The risk model based on the pyroptosis-related lncRNAs we established may be a promising tool for predicting the prognosis and personalized therapeutic response in BC patients.