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

Autor: Yangyang Guo, Jingxia Bao, Danfeng Lin, Kai Hong, Kenan Cen, Jie Sun, Zhepei Wang, Zhixuan Wu
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Heliyon, Vol 9, Iss 9, Pp e20178- (2023)
Druh dokumentu: article
ISSN: 2405-8440
32066562
DOI: 10.1016/j.heliyon.2023.e20178
Popis: Recently, studies have shown that immune checkpoint-related genes (ICGs) are instrumental in maintaining immune homeostasis and can be regarded as potential therapeutic targets. However, the prognostic applications of ICGs require further elucidation in low-grade glioma (LGG) cases. In the present study, a unique prognostic gene signature in LGG has been identified and validated as well based on ICGs as a means of facilitating clinical decision-making. The RNA-seq data as well as corresponding clinical data of LGG samples have been retrieved utilizing the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ICG-defined non-negative matrix factorization (NMF) clustering was performed to categorize patients with LGG into two molecular subtypes with different prognoses, clinical traits, and immune microenvironments. In the TCGA database, a signature integrating 8 genes has been developed utilizing the LASSO Cox method and validated in the GEO database. The signature developed is superior to other well-recognized signatures in terms of predicting the survival probability of patients with LGG. This 8-gene signature was then subsequently applied to categorize patients into high- and low-risk groups, and differences between them in terms of gene alteration frequency were observed. There were remarkable variations in IDH1 (91% and 64%) across low-as well as high-risk groups. Additionally, various analyses like function enrichment, tumor immune microenvironment, and chemotherapy drug sensitivity revealed significant variations across high- and low-risk populations. Overall, this 8-gene signature may function as a useful tool for prognosis and immunotherapy outcome predictions among LGG patients.
Databáze: Directory of Open Access Journals
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