The purpose of this study was to characterize subtype-specific patterns of mRNA and miRNA expression of gliomas using The Cancer Genome Atlas (TCGA) data to search for genetic determinants that predict prognosis in terms of overall survival and to create interaction networks for grade 2 and 3 (G2 and G3) astrocytomas, oligodendrogliomas and grade 4 (G4) glioblastoma multiforme. Based on open-access TCGA data, 5 groups were formed: astrocytoma G2 (n = 58), astrocytoma G3 (n = 128), oligodendroglioma G2 (n = 102), oligodendroglioma G3 (n = 72) and glioblastoma G4 (n = 564); normal samples of brain tissue were also analysed (n = 15). Data of patient age, sex, survival and expression patterns of mRNA and miRNA were extracted for each sample. After stratification of the data into groups, a differential analysis of expression was carried out, genes and miRNAs that affect overall survival were identified and gene set enrichment analysis (GSEA) and interaction analysis were performed. A total of 939 samples of glial tumours were analysed, for which subtype-specific expression profiles of genes and miRNAs were identified and networks of mRNA-miRNA interactions were constructed. Genes whose aberrant expression level was associated with survival were determined, and pairwise correlations between differential gene expression (DEG) and differential miRNA expression (DE miRNA) were calculated. The developed panel of genes and miRNAs allowed us to differentiate glioma subtypes and evaluate prognosis in terms of the overall survival of patients. The regulatory miRNA-mRNA pairs unique to the five glioma subtypes identified in this study can stimulate the development of new therapeutic approaches based on subtype-specific mechanisms of oncogenesis.

中文翻译：

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不同级别胶质瘤的mRNA和miRNA测序数据整合分析

本研究的目的是使用癌症基因组图谱 (TCGA) 数据来表征胶质瘤的 mRNA 和 miRNA 表达的亚型特异性模式，以搜索预测总生存期预后的遗传决定因素，并为 2 级和3 (G2 和 G3) 星形细胞瘤、少突胶质细胞瘤和 4 级 (G4) 多形性胶质母细胞瘤。基于开放获取的 TCGA 数据，形成了 5 个组：星形细胞瘤 G2（n = 58）、星形细胞瘤 G3（n = 128）、少突胶质细胞瘤 G2（n = 102）、少突胶质细胞瘤 G3（n = 72）和胶质母细胞瘤 G4（n =第564话 还分析了脑组织的正常样本（n = 15）。提取每个样本的患者年龄、性别、存活率和 mRNA 和 miRNA 表达模式的数据。将数据分组后，进行表达差异分析，确定了影响总体存活率的基因和 miRNA，并进行了基因集富集分析 (GSEA) 和相互作用分析。共分析了 939 个神经胶质瘤样本，确定了基因和 miRNA 的亚型特异性表达谱，并构建了 mRNA-miRNA 相互作用网络。确定异常表达水平与存活相关的基因，并计算差异基因表达 (DEG) 和差异 miRNA 表达 (DE miRNA) 之间的成对相关性。开发的基因组和 miRNA 使我们能够区分神经胶质瘤亚型并根据患者的总生存率评估预后。