Tran Paul Minh Huy, Tran Lynn Kim Hoang, Satter Khaled Bin, Purohit Sharad, Nechtman John, Hopkins Diane I, Dos Santos Bruno, Bollag Roni, Kolhe Ravindra, Sharma Suash, She Jin Xiong
Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120, 15th St, Augusta, GA 30912, USA.
Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta University, 1120, 15th St, Augusta, GA 30912, USA.
Cancers (Basel). 2021 Jan 25;13(3):439. doi: 10.3390/cancers13030439.
Gene expression profiling has been shown to be comparable to other molecular methods for glioma classification. We sought to validate a gene-expression based glioma classification method. Formalin-fixed paraffin embedded tissue and flash frozen tissue collected at the Augusta University (AU) Pathology Department between 2000-2019 were identified and 2 mm cores were taken. The RNA was extracted from these cores after deparaffinization and bead homogenization. One hundred sixty-eight genes were evaluated in the RNA samples on the nCounter instrument. Forty-eight gliomas were classified using a supervised learning algorithm trained by using data from The Cancer Genome Atlas. An ensemble of 1000 linear support vector models classified 30 glioma samples into TP1 with classification confidence of 0.99. Glioma patients in TP1 group have a poorer survival (HR (95% CI) = 4.5 (1.3-15.4), = 0.005) with median survival time of 12.1 months, compared to non-TP1 groups. Network analysis revealed that cell cycle genes play an important role in distinguishing TP1 from non-TP1 cases and that these genes may play an important role in glioma survival. This could be a good clinical pipeline for molecular classification of gliomas.
基因表达谱分析已被证明与其他用于胶质瘤分类的分子方法相当。我们试图验证一种基于基因表达的胶质瘤分类方法。对2000年至2019年间在奥古斯塔大学（AU）病理科收集的福尔马林固定石蜡包埋组织和速冻组织进行了识别，并取了2毫米的组织芯。经过脱石蜡处理和磁珠匀浆后，从这些组织芯中提取RNA。在nCounter仪器上对RNA样本中的168个基因进行了评估。使用由癌症基因组图谱数据训练的监督学习算法对48例胶质瘤进行分类。由1000个线性支持向量模型组成的集成模型将30例胶质瘤样本分类为TP1，分类置信度为0.99。与非TP1组相比，TP1组的胶质瘤患者生存率较差（HR（95%CI）=4.5（1.3 - 15.4），P = 0.005），中位生存时间为12.1个月。网络分析表明，细胞周期基因在区分TP1和非TP1病例中起重要作用，并且这些基因可能在胶质瘤生存中起重要作用。这可能是一种用于胶质瘤分子分类的良好临床途径。
