Zheng Hui, Zhao Yutong, Zhou Hai, Tang Yuguang, Xie Zongyi
Department of Neurosurgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing 404100, China.
Brain Sci. 2023 Sep 12;13(9):1311. doi: 10.3390/brainsci13091311.
The relationship between N6-methyladenosine (m6A) regulators and anoikis and their effects on low-grade glioma (LGG) is not clear yet. The TCGA-LGG cohort, mRNAseq 325 dataset, and GSE16011 validation set were separately obtained via the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Altas (CGGA), and Gene Expression Omnibus (GEO) databases. In total, 27 m6A-related genes (m6A-RGs) and 508 anoikis-related genes (ANRGs) were extracted from published articles individually. First, differentially expressed genes (DEGs) between LGG and normal samples were sifted out by differential expression analysis. DEGs were respectively intersected with m6A-RGs and ANRGs to acquire differentially expressed m6A-RGs (DE-m6A-RGs) and differentially expressed ANRGs (DE-ANRGs). A correlation analysis of DE-m6A-RGs and DE-ANRGs was performed to obtain DE-m6A-ANRGs. Next, univariate Cox and least absolute shrinkage and selection operator (LASSO) were performed on DE-m6A-ANRGs to sift out risk model genes, and a risk score was gained according to them. Then, gene set enrichment analysis (GSEA) was implemented based on risk model genes. After that, we constructed an independent prognostic model and performed immune infiltration analysis and drug sensitivity analysis. Finally, an mRNA-miRNA-lncRNA regulatory network was constructed. There were 6901 DEGs between LGG and normal samples. Six DE-m6A-RGs and 214 DE-ANRGs were gained through intersecting DEGs with m6A-RGs and ANRGs, respectively. A total of 149 DE-m6A-ANRGs were derived after correlation analysis. Four genes, namely ANXA5, KIF18A, BRCA1, and HOXA10, composed the risk model, and they were involved in apoptosis, fatty acid metabolism, and glycolysis. The age and risk scores were finally sifted out to construct an independent prognostic model. Activated CD4 T cells, gamma delta T cells, and natural killer T cells had the largest positive correlations with risk model genes, while activated B cells were significantly negatively correlated with KIF18A and BRCA1. AT.9283, EXEL.2280, Gilteritinib, and Pracinostat had the largest correlation (absolute value) with a risk score. Four risk model genes (mRNAs), 12 miRNAs, and 21 lncRNAs formed an mRNA-miRNA-lncRNA network, containing HOXA10-hsa-miR-129-5p-LINC00689 and KIF18A-hsa-miR-221-3p-DANCR. Through bioinformatics, we constructed a prognostic model of m6A-associated anoikis genes in LGG, providing new ideas for research related to the prognosis and treatment of LGG.
N6-甲基腺苷（m6A）调节因子与失巢凋亡之间的关系及其对低级别胶质瘤（LGG）的影响尚不清楚。通过癌症基因组图谱（TCGA）、中国胶质瘤基因组图谱（CGGA）和基因表达综合数据库（GEO）分别获取了TCGA-LGG队列、mRNAseq 325数据集和GSE16011验证集。总共从已发表文章中分别提取了27个m6A相关基因（m6A-RGs）和508个失巢凋亡相关基因（ANRGs）。首先，通过差异表达分析筛选出LGG与正常样本之间的差异表达基因（DEGs）。将DEGs分别与m6A-RGs和ANRGs进行交集分析，以获得差异表达的m6A-RGs（DE-m6A-RGs）和差异表达的ANRGs（DE-ANRGs）。对DE-m6A-RGs和DE-ANRGs进行相关性分析，以获得DE-m6A-ANRGs。接下来，对DE-m6A-ANRGs进行单变量Cox分析和最小绝对收缩和选择算子（LASSO）分析，以筛选出风险模型基因，并据此获得风险评分。然后，基于风险模型基因进行基因集富集分析（GSEA）。之后，构建独立预后模型并进行免疫浸润分析和药物敏感性分析。最后，构建mRNA-miRNA-lncRNA调控网络。LGG与正常样本之间有6901个DEGs。通过将DEGs分别与m6A-RGs和ANRGs进行交集分析，分别获得了6个DE-m6A-RGs和214个DE-ANRGs。相关性分析后共得到149个DE-m6A-ANRGs。四个基因，即膜联蛋白A5（ANXA5）、驱动蛋白家族成员18A（KIF18A）、乳腺癌1号基因（BRCA1）和同源盒A10（HOXA10），组成了风险模型，它们参与细胞凋亡脂肪酸代谢和糖酵解。最终筛选出年龄和风险评分以构建独立预后模型。活化的CD4 T细胞、γδ T细胞和自然杀伤T细胞与风险模型基因的正相关性最大，而活化的B细胞与KIF18A和BRCA1显著负相关。AT.9283、EXEL.2280、吉瑞替尼和帕西诺司他与风险评分的相关性最大（绝对值）。四个风险模型基因（mRNAs）、12个miRNA和21个lncRNA形成了一个mRNA-miRNA-lncRNA网络，包括HOXA10-hsa-miR-129-5p-LINC00689和KIF18A-hsa-miR-221-3p-DANCR。通过生物信息学，我们构建了LGG中m6A相关失巢凋亡基因的预后模型，为LGG的预后和治疗相关研究提供了新思路。
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