Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma
| Autor: | Xuejun Li, Chunhai Huang, Fanyuan Zeng, Nian Jiang, Qi Yang, Yi Xiong |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Oncology medicine.medical_specialty Genomics Gene mutation Biology driver gene mutations Transcriptome 03 medical and health sciences Diffuse Glioma 0302 clinical medicine diffused glioma Glioma Internal medicine Genotype medicine Gene Proportional hazards model glioblastoma medicine.disease 030104 developmental biology 030220 oncology & carcinogenesis prognosis prediction transcriptome Research Paper |
| Zdroj: | Journal of Cancer |
| ISSN: | 1837-9664 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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