DataSheet_1_Integrative Analysis of a Novel Eleven-Small Nucleolar RNA Prognostic Signature in Patients With Lower Grade Glioma.pdf

ObjectiveThe present study used the RNA sequencing (RNA-seq) dataset to identify prognostic snoRNAs and construct a prognostic signature of The Cancer Genome Atla (TCGA) lower grade glioma (LGG) cohort, and comprehensive analysis of this signature.
MethodsRNA-seq dataset of 488 patients from TCGA LGG cohort were included in this study. Comprehensive analysis including function enrichment, gene set enrichment analysis (GSEA), immune infiltration, cancer immune microenvironment, and connectivity map (CMap) were used to evaluate the snoRNAs prognostic signature.
ResultsWe identified 21 LGG prognostic snoRNAs and constructed a novel eleven-snoRNA prognostic signature for LGG patients. Survival analysis suggests that this signature is an independent prognostic risk factor for LGG, and the prognosis of LGG patients with a high-risk phenotype is poor (adjusted P = 0.003, adjusted hazard ratio = 2.076, 95% confidence interval = 1.290–3.340). GSEA and functional enrichment analysis suggest that this signature may be involved in the following biological processes and signaling pathways: such as cell cycle, Wnt, mitogen-activated protein kinase, janus kinase/signal transducer and activator of tran-ions, T cell receptor, nuclear factor-kappa B signaling pathway. CMap analysis screened out ten targeted therapy drugs for this signature: 15-delta prostaglandin J2, MG-262, vorinostat, 5155877, puromycin, anisomycin, withaferin A, ciclopirox, chloropyrazine and megestrol. We also found that high- and low-risk score phenotypes of LGG patients have significant differences in immune infiltration and cancer immune microenvironment.
ConclusionsThe present study identified a novel eleven-snoRNA prognostic signature of LGG and performed a integrative analysis of its molecular mechanisms and relationship with tumor immunity.

研究目的：本研究采用RNA测序（RNA sequencing, RNA-seq）数据集，从癌症基因组图谱（The Cancer Genome Atlas, TCGA）低级别胶质瘤（lower grade glioma, LGG）队列中筛选预后相关小核仁RNA（small nucleolar RNA, snoRNA），构建预后特征模型，并对该特征开展综合分析。
研究方法：本研究纳入了来自TCGA LGG队列的488例患者的RNA-seq数据集。采用功能富集分析、基因集富集分析（Gene Set Enrichment Analysis, GSEA）、免疫浸润分析、肿瘤免疫微环境分析以及连接图分析（Connectivity Map, CMap）等多种方法，对该小核仁RNA预后特征模型进行评估。
研究结果：本研究筛选出21个与LGG预后相关的小核仁RNA，并为LGG患者构建了一个包含11个小核仁RNA的新型预后特征模型。生存分析结果显示，该特征模型是LGG患者的独立预后危险因素，高风险表型的LGG患者预后较差（校正P=0.003，校正风险比=2.076，95%置信区间：1.290~3.340）。基因集富集分析与功能富集分析结果表明，该特征模型可能参与以下生物学过程与信号通路：细胞周期、Wnt信号通路、丝裂原活化蛋白激酶（mitogen-activated protein kinase, MAPK）通路、Janus激酶/信号转导与转录激活因子（janus kinase/signal transducer and activator of transcription, JAK/STAT）通路、T细胞受体信号通路以及核因子-κB（nuclear factor-kappa B, NF-κB）信号通路。连接图分析筛选出10种可靶向该特征模型的治疗药物：15-Δ前列腺素J2、MG-262、伏立诺他、5155877、嘌呤霉素、茴香霉素、睡茄内酯A、环吡酮胺、氯吡嗪以及甲地孕酮。本研究同时发现，LGG患者的高、低风险评分表型在免疫浸润与肿瘤免疫微环境方面存在显著差异。
研究结论：本研究成功构建了一个新型的LGG 11个小核仁RNA预后特征模型，并对其分子机制以及与肿瘤免疫的关联开展了综合分析。