Comprehensive Analysis of Genome-instability Related lncRNAs and Tumor-immune Microenvironment to Discriminate the Prognosis, Immunotherapy and PARP Inhibitor Responses in Patients with Low-grade Glioma

Abstract Background: Previous study revealed that Genome-instability was correlated with tumor-immune microenvironment in cancer. We try to discriminate the prognosis, immunotherapy and poly (ADP)-ribose polymerase (PARP) inhibitor responses through comprehensive analysis of genome-instability related lncRNAs and the tumor-immune microenvironment in patients with low-grade glioma (LGG). Methods and Results: RNAseq data, genome variation profiling data and copy number variation (CNV) data were used to evaluate the genomic instability of LGG patients. Genomic unstable-like (GU-like) and genomic stable-like (GS-like) clusters were identified by hierarchical clustering analysis of 102 genome-instability related lncRNAs (GILncRNAs). GS-like cluster had a tendency to receive better clinical outcome. Patients in GU-like cluster were more likely to respond to immunotherapy, especially anti-PD-1/PD-L1 treatment. PARP inhibitors including Rucaparib and Olaparib will get better therapeutic effects for patients in GU-like cluster. Lasso and Cox regression analysis were utilized to construct the risk model based on GILncRNAs. As for the risk model constructed by 9 GILncRNAs, the overall survival, clinical outcome, immunotherapeutic response, and PARP inhibitor sensitivity were significantly different between patients of high and low-risk groups. Conclusions: The genome-instability related lncRNAs signature involved in our risk model had great advantages in predicting prognosis, immunotherapy and PARP inhibitor response.