Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma
Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma
复制标题
将基因组数据与转录组数据整合以改善成人弥漫性胶质瘤的生存预测
DOI：
10.7150/jca.44032
复制
发表时间：
2020-04
影响因子：
3.9
通讯作者：
Department of Neurosurgery Xiangya Hospital Cent
中科院分区：
文献类型：
--
作者：
Yang Qi;Xiong Yi;Chunhai Huang;Department of Neurosurgery Xiangya Hospital Cent
背景：胶质瘤是最常见的原发性中枢神经系统肿瘤。然而，弥漫性胶质瘤中基因突变与转录组之间的关系尚不清楚，目前还没有关于基因型-表型关联的系统分析。研究方法：我们在从癌症基因组图谱（TCGA）数据库获得的大型多形性胶质母细胞瘤（GBM，n=126）和低级别胶质瘤（LGG，n=481）队列中进行了多组学分析。我们使用多变量线性模型来评估驱动基因突变和全局基因表达之间的关联。我们开发了广义线性模型来评估遗传/表达因素与临床病理特征之间的关联。多因素考克斯比例风险模型用于预测总生存期。结果：弥漫型胶质瘤的基因型与遗传学、临床及病理特征之间存在潜在的相关性。至少一个驱动突变与GBM中约10%的基因表达变化相关，而LGG中约80%的基因表达变化相关。在GBM和LGG中分别观察到DRG 2和LRCC 41基因突变和表达变化之间的最强关联。此外，基因组学特征和临床病理特征之间的关联表明分子亚型或组织学亚型的不同潜在分子机制。对于预测生存率，在遗传学、转录组和临床变量中，转录组特征的贡献最大。结合现有资料，提高了对弥漫性胶质瘤患者预后判断的准确性。结论：我们的研究结果揭示了弥漫性胶质瘤患者驱动基因突变对整体基因表达的影响。当结合所有可用数据而不仅仅是转录组数据时，可以实现预测患者预后的更准确模型。
Background: Glioma is the most common type of primary central nervous system tumors. However, the relationship between gene mutations and transcriptome is unclear in diffuse glioma, and there are no systemic analyses with regard to the genotype-phenotype association currently...Methods: We performed the multi-omics analysis in large glioblastoma multiforme (GBM, n=126) and low-grade glioma (LGG, n=481) cohorts obtained from The Cancer Genome Atlas (TCGA) database. We used multivariate linear models to evaluate associations between driver gene mutations and global gene expression. We developed generalized linear models to evaluate associations between genetic/expression factors with clinicopathologic features. Multivariate Cox proportional hazards models were used to predict the overall survival...Results: The potential relationship between genotype and genetics, clinical as well as pathologic features, on diffused glioma was observed. At least one driver mutation correlated with expression changes of about 10% of genes in GBMs while about 80% of genes in LGGs. The strongest association between mutations and expression changes was observed for DRG2 and LRCC41 gene in GBMs and LGGs, respectively. Additionally, the association between genomics features and clinicopathologic features suggested the different underlying molecular mechanisms in molecular subtypes or histology subtypes. For predicting survival, among genetics, transcriptome and clinical variables, transcriptome features made the largest contribution. By combining all the available data, the accuracy in predicting the prognosis of diffuse glioma in patients was also improved...Conclusion: Our study results revealed the influences of driver gene mutations on global gene expression in diffuse glioma patients. A more accurate model in predicting the prognosis of patients was achieved when combining with all the available data than just transcriptomic data.
登录
查看更多内容
影响因子：
8.8
作者：
Miyakawa A;Ichimura K;Schmidt EE;Varmeh-Ziaie S;Collins VP
通讯作者：
Collins VP
影响因子：
14.9
作者：
Colaprico A;Silva TC;Olsen C;Garofano L;Cava C;Garolini D;Sabedot TS;Malta TM;Pagnotta SM;Castiglioni I;Ceccarelli M;Bontempi G;Noushmehr H
通讯作者：
Noushmehr H
DOI：
10.1590/1806-9282.65.3.460
发表时间：
2019-03-01
期刊：
Revista da Associação Médica Brasileira
影响因子：
--
作者：
Carvalho, Juliana Arcangelo Di Vita;Barbosa, Caroline Chaul de Lima;Marta, Gustavo Nader
通讯作者：
Marta, Gustavo Nader
DOI：
--
发表时间：
2014
期刊：
--
影响因子：
--
作者：
M. Lawrence;P. Stojanov;P. Polak;G. Kryukov;K. Cibulskis;A. Sivachenko;S. Carter;C. Stewart;
通讯作者：
M. Lawrence;P. Stojanov;P. Polak;G. Kryukov;K. Cibulskis;A. Sivachenko;S. Carter;C. Stewart;
影响因子：
11.5
作者：
Gorovets, Daniel;Kannan, Kasthuri;Huse, Jason T.
通讯作者：
Huse, Jason T.