AUTHOR=Chen Desheng , Dou Chao , Liu Haiyu , Xu Binshun , Hu Bowen , Kuang Liangwen , Yao Jiawei , Zhao Yan , Yu Shan , Li Yang , Wang Fuqing , Guo Mian 

TITLE=Comprehensive analysis: Necroptosis-related lncRNAs can effectively predict the prognosis of glioma patients

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.929233

DOI=10.3389/fonc.2022.929233

ISSN=2234-943X

ABSTRACT=Glioma is the most common and fatal primary brain tumor in human. A major role for long non-coding RNA in glioma is the regulation of gene expression and chromatin recombination, and immunotherapy is a promising cancer treatment. Therefore, it is necessary to identify Necroptsis-Related lncRNAs in glioma. In this study, we collected and evaluated the RNA-seq data of The Cancer Genome Atlas (TCGA, https ://www.ncbi.nlm.nih.gov/) glioma patients and Necroptsis-Related lncRNAs were screened. Cox regression and LASSO analysis were performed to construct a risk score formula to explore the different overall survival between high- and low-risk groups in TCGA. Gene ontology (GO) and pathway-enrichment analysis (KEGG) were performed to identify the function of screened genes. The results of immune correlation analysis showed that a variety of immune cells and immune pathways were positively associated with a patient's risk score. And the analysis of tumor microenvironment indicated that there were a large number of immune cells and stromal cells in the tumor microenvironment of glioma patients. Four Necroptsis-Related lncRNAs were concerned to be involved in survival and adopted to construct the risk score formula. Patients with high-risk score held poor survival in TCGA. Compared with current clinical data, the Area Under Curve (AUC) of different years suggested that the formula had better predictive power. we verified that necroptosis-related lncRNAs plays an extremely important role in the occurrence and development of glioma, and the constructed risk model can well predict the prognosis of glioma. The results of these studies added some valuable guidance to understanding glioma pathogenesis and treatment, and these necroptosis-related lncRNAs may be used as biomarkers and therapeutic targets for glioma prevention.