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Prognostic factor identification by analysis of the gene expression and DNA methylation data in glioma

1 Department of Neurosurgery, The Third Hospital of Jilin University, Changchun 130033, China
2 Departments of Radiology, The Third Hospital of Jilin University, Changchun 130033, China
3 Departments of Ophthalmology, The Third Hospital of Jilin University, Changchun 130033, China

These authors contributed to this work equally.

Special Issues: Data integration and feature selection techniques for omic analysis in cancer research

Objective: This study was aimed to identify prognostic factors in glioma by analysis of the gene expression and DNA methylation data. Methods: The RNAseq and DNA methylation data associated with glioma were downloaded from GEO and TCGA databases to analyze the differentially expressed genes (DEGs) and methylated genes between tumor and normal tissues. Function and pathway analyses, co-expression network and survival analysis were performed based on these DEGs. The intersection genes of DEGs and differentially methylated genes were obtained followed by function analysis. Results: Total 2190 DEGs were identified between tumor and normal tissues, which were significantly enriched in neuron differentiation associated functions, as well as ribosome pathway. There were 6186 methylation sites (2834 up-regulated and 3352 down-regulated) with significant differences in tumor vs. normal. In the constructed co-expression network, DPP6, MAPK10 and RPL3 were hub genes. Survival analysis of 20 DEGs obtained 18 prognostic genes, among which 9 were differentially methylated, such as LHFPL tetraspan subfamily member 3 (LHFPL3), cadherin 20 (CDH20), complexin 2 (CPLX2), and tenascin R (TNR). The intersection of DEGs and differentially methylated genes (632 genes) were significantly enriched in functions of neuron differentiation. Conclusion: DPP6, MAPK10 and RPL3 may play important roles in tumorigenesis of glioma. Additionally, methylation of LHFPL3, CDH20, CPLX2, and TNR may serve as prognostic factors of glioma.
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Keywords glioma; gene; methylation, prognosis

Citation: Bo Wei, Rui Wang, Le Wang, Chao Du. Prognostic factor identification by analysis of the gene expression and DNA methylation data in glioma. Mathematical Biosciences and Engineering, 2020, 17(4): 3909-3924. doi: 10.3934/mbe.2020217


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