Integrated analysis of necroptosis related gene signature to predict clinical outcomes, immune status and drug sensitivity in lower grade Glioma

Abstract

BackgroundThe treatment of lower grade gliomas (LGG) is currently the most challenging dilemma in the management of intracranial tumors. Necroptosis is a type of programmed cell death that is closely associated with tumor progression, However, the role of necroptosis related genes in LGG is not yet well elucidated. MethodsOnline databases were used to obtain gene expression and clinical information. After gene differential expression analysis, a risk score model based on prognostic differentially expressed necroptosis-related genes (DENGs) were constructed to predict prognosis for LGG patients. The validity of the risk score model was then assessed with Kaplan-Meier survival curve. The prognostic DENGs included in the risk score model were then subjected to gene expression analysis, functional enrichment analysis, consensus clustering analysis, and single cell sequencing analysis. Finally, we investigated the correlation of the risk score and immune infiltration in LGG tumor microenvironment and drug sensitivity for LGG patients in different risk groups. ResultsA survival risk score model was constructed based on seven prognostic DENGs, which demonstrated satisfactory performance in predicting the prognosis of LGG patients. According to functional enrichment analyses, these seven DENGs may play a regulatory role in LGG tumorigenesis through several immune and metabolic pathways. LGG patients could be categorized into two clusters with distinct prognosis and clinicopathologic characteristics based on the expression of seven DENGs. Single-cell sequencing analysis demonstrated that the DENG signature was differentially expressed in various types of cells in LGG and may play a vital role in oncogenesis. Additionally, drug sensitivity analysis suggested that the seven-gene signature could guide clinical medication for LGG patients. ConclusionOur study developed a reliable necroptosis-related signature to predict the prognosis of LGG patients. This gene signature may also help estimate immune status and anti-cancer drug sensitivity in LGG patients. Our findings may pave the way to enhance our understanding of necroptosis in LGG.