A Novel Framework to Predict Breast Cancer Prognosis Using Immune-Associated LncRNAs - Huang, Zhijian; Xiao, Chen; Zhang, Fushou; Zhou, Zhifeng; Yu, Liang; Ye, Changsheng; Huang, Weiwei; Li, Nani (Frontiers in Genetics, 2021)
Breast cancer (BC) is one of the most frequently diagnosed malignancies among females. As a huge heterogeneity of malignant tumor, it is important to seek reliable molecular biomarkers to carry out the stratification for patients with BC.We surveyed immune-related lncRNAs that may be used as potential therapeutic targets in BC.The expression data of BC and the corresponding clinical data were downloaded from the Cancer Genome Atlas (TCGA) database. Co-expression network were constructed for further analysis.Univariate Cox regression and iterative Lasso Cox regression analysis were used to establish an optimal immune-related lncRNA signature model. A nomogram was constructed to predict the prognosis of BC. These findings were further validated in two independent datasets. Kaplan-Meier analysis showed that the OS of Patients in the low-risk group had significantly better survival than those in the high-risk group, Clinical subgroup analysis showed that the predictive ability was independent of clinicopathological factors. Univariate / multivariate Cox regression analysis showed immune lncRNA signature is an important prognostic factor and an independent prognostic marker. In addition, GSEA and GSVA analysis as well as comprehensive analysis of immune cells showed that the signature was significantly correlated with the infiltration of immune cells. We successfully constructed an immune-related lncRNA signature, which has important prognostic value for breast patients.