AUTHOR=Li Huamei , Liu Hongjia , Hao Qiongyu , Liu Xianglin , Yao Yongzhong , Cao Meng 

TITLE=Oncogenic signaling pathway-related long non-coding RNAs for predicting prognosis and immunotherapy response in breast cancer

JOURNAL=Frontiers in Immunology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.891175

DOI=10.3389/fimmu.2022.891175

ISSN=1664-3224

ABSTRACT=Background: The clinical outcomes of breast cancer are unpredictable due to the high level of heterogeneity and complex immune status of tumor microenvironment. Multiple long non-coding RNA (lncRNA) signatures tended to be employed to appraise the prognosis of BC when set up. Nevertheless, predicting immunotherapy response in BC is still required. LncRNAs play a significant role in cancer development via diverse oncogenic signal pathways. Then, we attempted to construct an oncogenic signal pathway-based lncRNA signature for forecasting the prognosis and immunotherapy response by providing reliable biomarkers. 
Methods: We preliminarily retrieved the RNA sequencing data from TCGA database and extracted lncRNA profiles by matching with GENCODE. Then we identified lncRNAs from 10 oncogenic signaling pathways-related lncRNAs in the TCGA-BRCA cohort using a tool called GSVA, and  further screened by using the LASSO Cox regression model, and a signature (OncoSig) was established through the expression level of the final 29 selected lncRNAs. To verify the reliability of the signature, the time-dependent receiver operating characteristic (ROC) curve together with log-rank test were operated in two sets and the combined set for figuring out the risk threshold and validating the consistency. Afterward, we performed GESA in 782 immune cells infiltration-related genes and elucidated the combined effect of Oncosig and immune checkpoint genes on prognosis and immunotherapy. Ultimately, a pan-cancer analysis was furtherly conducted to attest the prevalence of OncoSig. 
Results: The OncoSig classified patients into the high- and low-risk group in training and test cohorts. The low-risk group manifested a significantly higher survival rate and more immune cell infiltration when compared to the high-risk group. Multivariate analysis suggested that OncoSig is an independent prognosis predictor for BC patients. In addition, the analysis of OncoSig and immune checkpoint genes clarified that a lower OncoSig score meant significantly longer survival and an improved response to immunotherapy. 
Conclusions: Our study established a trustworthy and discriminable prognostic biomarker for BC patients who have similar clinical profiles and gave a new perspective for evaluating immunotherapy response. More importantly, this finding can be generalized to vast majority of human cancers.