J Cancer 2020; 11(13):3794-3802. doi:10.7150/jca.44032 This issue

Research Paper

Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma

Qi Yang1,2*, Yi Xiong1,2*, Nian Jiang1,2, Fanyuan Zeng1,2, Chunhai Huang3,4✉, Xuejun Li1,2✉

1. Department of Neurosurgery, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan 410008 P. R. China
2. Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan 410008 P. R. China
3. Department of Neurosurgery, First Affiliated Hospital of Jishou University, Jishou, Hunan, 416000 P. R. China
4. Centre for Clinical and Translational Medicine Research, Jishou University, Jishou, Hunan, 416000 P. R. China
*Authors contribute equally to this work.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Citation:
Yang Q, Xiong Y, Jiang N, Zeng F, Huang C, Li X. Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma. J Cancer 2020; 11(13):3794-3802. doi:10.7150/jca.44032. Available from https://www.jcancer.org/v11p3794.htm

File import instruction

Abstract

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.

Keywords: glioblastoma, diffused glioma, driver gene mutations, transcriptome, prognosis prediction