Prognostic value of γ‐aminobutyric acidergic synapse-associated signature for lower-grade gliomas - Jiang, Hongxiang; Sun, Zhiqiang; Li, Fei; Chen, Qianxue (Frontiers in Immunology, 2022)
Background: Synapse associated proteins (SAPs) founction as irreplaceable roles among the central nervous system (CNS) tumors. Recent studies reported that γ-aminobutyric acidergic (GABAergic) synapses also played critical roles in the development of gliomas. And the related biomarkers of GABAergic synapse in lower grade gliomas (LGGs) has not been reported yet. Methods: Related mRNA data of the normal brain tissue and glioma respectively derived form the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) database. And the validation set obtained form the Chinese Glioma Genome Atlas (CGGA) database. The expression pattern of γ-aminobutyric acidergic synapse-related genes (GSRGs) were evaluated with difference analysis in LGG. Then the γ-aminobutyric acidergic synapse-related risk signature (GSRS) was built with the least absolute shrinkage and selection operator (LASSO) Cox regression. According to the expression value and coefficients of identified GSRGs, the risk score of all the LGG samples were calculated. Univariate and multivariate Cox regression analyses were conducted to value the risk score related the prognostic. The correlation between characters of tumor microenvironment (TME) and risk score were explored with the single-sample gene set enrichment analysis (ssGSEA) and immunity profile in LGGs. The GSRS related pathways were explored by the Gene set variation analysis (GSVA). Real-time PCR and the Human Protein Atlas (HPA) database were applied to explore related expression of hub genes selected in GSRS. Results: Comparing with the normal brain samples, there were 25 genes of 31 GSRGs differentially expressed in LGG samples. 5-genes GSRS constructed were related to the clinicopathological features and prognosis of LGGs by the LASSO algorithm. It was revealed that the risk score level was positively related to the infiltrating level of CD4 memory resting T cells and M0 Macrophages. GSVA identified several cancer-related pathways associated with the GSRS, like P53 pathways and the JAK-STAT signaling pathway. Additionally, CA2, PTEN, OXTR, and SLC6A1, hub genes identified in GSRS, were regarded as the potential predictors in LGGs. Conclusion: The new 5-genes GSRS was identified and verified with bioinformatics methods. A new perspective of GSRS in LGG which may contribute to prognosis of LGGs.