Novel immune checkpoint-related gene model to predict prognosis and treatment responsiveness in low-grade gliomas

• DOI: 10.1016/j.heliyon.2023.e20178
• Publication Date: 2023-09-15T10:16:45Z
• Authors: Yangyang Guo; Jingxia Bao; Danfeng Lin; Kai Hong; Kenan Cen; Jie Sun; Zhepei Wang; Zhixuan Wu
• Keywords: Gene signature; Immune checkpoint

Recently, studies have shown that immune checkpoint-related genes (ICGs) are instrumental in maintaining homeostasis and can be regarded as potential therapeutic targets. However, the prognostic applications of ICGs require further elucidation low-grade glioma (LGG) cases. In present study, a unique gene signature LGG has been identified validated well based on means facilitating clinical decision-making. The RNA-seq data corresponding samples retrieved utilizing Cancer Genome Atlas (TCGA) Gene Expression Omnibus (GEO) databases. ICG-defined non-negative matrix factorization (NMF) clustering was performed to categorize patients with into two molecular subtypes different prognoses, traits, microenvironments. TCGA database, integrating 8 developed LASSO Cox method GEO database. is superior other well-recognized signatures terms predicting survival probability LGG. This 8-gene then subsequently applied high- low-risk groups, differences between them alteration frequency were observed. There remarkable variations IDH1 (91% 64%) across low-as high-risk groups. Additionally, various analyses like function enrichment, tumor microenvironment, chemotherapy drug sensitivity revealed significant populations. Overall, this may useful tool for prognosis immunotherapy outcome predictions among patients.