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Abstract：
BACKGROUND: Bioinformatics analysis integrating microenvironmental factors and single cell analysis segregated the glioblastoma (GBM) subtype into 3 subtypes: proneural, classic, and mesenchymal. Mesenchymal GBM tends to have the worst survival but benefits from aggressive treatment protocols. Therefore, it is clinically meaningful to identify relevant biomarkers to distinguish the mesenchymal subtype. Moreover, in developing nations with limited resources, rigorous examinations are costly and inefficient for patient care.
METHODS: In this study, we analyzed The Cancer Genome Atlas (TCGA)-Glioblastoma and TCGA-Low-Grade Glioma RNA sequencing (RNAseq) cohorts and confirmed that the mesenchymal subtype was associated with the worst prognosis.
RESULTS: We identified periostin (POSTN) as a mesenchymal subtype biomarker with prognostic value across histologic grades and confirmed the reliability of POSTN by gene expression meta-analysis combining TCGA, Chinese Glioma Genome Atlas (CGGA) and REMBRANDT (Repository for Molecular Brain Neoplasia Data) GBM cohorts (hazard ratio, 1.71 [range, 1.47-2.07], n = 693) and LGG cohorts (hazard ratio, 2.55 [range, 1.61-4.05], n = 1226).
CONCLUSIONS: By using available online glioma databases, our study provided an insight into the expression of POSTN as an independent predictor for patients with glioma (GBM and LGG) and could be useful for diagnostic simplification to identify high-risk groups.
METHODS: In this study, we analyzed The Cancer Genome Atlas (TCGA)-Glioblastoma and TCGA-Low-Grade Glioma RNA sequencing (RNAseq) cohorts and confirmed that the mesenchymal subtype was associated with the worst prognosis.
RESULTS: We identified periostin (POSTN) as a mesenchymal subtype biomarker with prognostic value across histologic grades and confirmed the reliability of POSTN by gene expression meta-analysis combining TCGA, Chinese Glioma Genome Atlas (CGGA) and REMBRANDT (Repository for Molecular Brain Neoplasia Data) GBM cohorts (hazard ratio, 1.71 [range, 1.47-2.07], n = 693) and LGG cohorts (hazard ratio, 2.55 [range, 1.61-4.05], n = 1226).
CONCLUSIONS: By using available online glioma databases, our study provided an insight into the expression of POSTN as an independent predictor for patients with glioma (GBM and LGG) and could be useful for diagnostic simplification to identify high-risk groups.
摘要：
背景：整合微环境因素和单细胞分析的生物信息学分析将胶质母细胞瘤（GBM）亚型分为3种亚型：前神经，经典,和间充质。间充质GBM往往具有最差的存活率，但受益于积极的治疗方案。因此,鉴定相关生物标志物以区分间充质亚型具有临床意义.此外，在资源有限的发展中国家，严格的检查是昂贵和低效的病人护理。
方法：在本研究中，我们分析了癌症基因组图谱(TCGA)-胶质母细胞瘤和TCGA-低度胶质瘤RNA测序(RNAseq)队列,并证实间充质亚型与最差预后相关.
结果：我们确定骨膜素（POSTN）是一种间充质亚型生物标志物，具有不同组织学分级的预后价值，并通过结合TCGA的基因表达荟萃分析证实了POSTN的可靠性，中国胶质瘤基因组图谱（CGGA）和REMBRANDT（分子脑肿瘤数据存储库）GBM队列（风险比，1.71[范围，1.47-2.07],n=693)和LGG队列(风险比，2.55[范围，1.61-4.05],n=1226）。
结论：通过使用可用的在线神经胶质瘤数据库，我们的研究为了解POSTN的表达作为神经胶质瘤(GBM和LGG)患者的独立预测因子,可用于简化诊断以确定高危人群.
方法：在本研究中，我们分析了癌症基因组图谱(TCGA)-胶质母细胞瘤和TCGA-低度胶质瘤RNA测序(RNAseq)队列,并证实间充质亚型与最差预后相关.
结果：我们确定骨膜素（POSTN）是一种间充质亚型生物标志物，具有不同组织学分级的预后价值，并通过结合TCGA的基因表达荟萃分析证实了POSTN的可靠性，中国胶质瘤基因组图谱（CGGA）和REMBRANDT（分子脑肿瘤数据存储库）GBM队列（风险比，1.71[范围，1.47-2.07],n=693)和LGG队列(风险比，2.55[范围，1.61-4.05],n=1226）。
结论：通过使用可用的在线神经胶质瘤数据库，我们的研究为了解POSTN的表达作为神经胶质瘤(GBM和LGG)患者的独立预测因子,可用于简化诊断以确定高危人群.
