Abstract
Glioma is the most common primary brain tumor in humans and the most deadly. Stem cells, which are characterized by therapeutic resistance and self-renewal, play a critical role in glioma, and therefore the identification of stem cell-related genes in glioma is important. In this study, we collected and evaluated the epigenetically regulated-mRNA expression-based stemness index (EREG-mRNAsi) of The Cancer Genome Atlas (TCGA, http://www.ncbi.nlm.nih.gov/) for glioma patient samples, corrected through tumor purity. After EREG-mRNAsi correction, glioma pathological grade and survival were analyzed. The differentially expressed gene (DEG) co-expression network was constructed by weighted gene co-expression network analysis (WGCNA) in TCGA glioma samples to find modules of interest and key genes. Gene ontology (GO) and pathway-enrichment analysis were performed to identify the function of significant genetic modules. Protein–protein interaction (PPI) and co-expression network analysis of key genes was performed for further analysis. In this experiment, we found that corrected EREG-mRNAsi was significantly up-regulated in glioma samples and increased with glioma grade, with G4 having the highest stemness index. Patients with higher corrected EREG-mRNAsi scores had worse overall survival. Fifty-one DEGs in the brown gene module were found to be positively related to EREG-mRNAsi via WGCNA. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that chromosome segregation and cell cycle molecular function were the major functions in key DEGs. Among these key DEGs, BUB1 showed high connectivity and co-expression, and also high connectivity in PPI. Fifty-one key genes were verified to play a critical role in glioma stem cells. These genes may serve as primary therapeutic targets to inhibit the activity of glioma stem cells.
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The authors thank the contributors of TCGA for sharing the glioma data.
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This study was designed by Pengfei Xia. The data were extracted by Pengfei Xia, Yimin Huang and Qing Li. Yimin Huang and Guanlin Wu performed statistical analysis. The manuscript was written by Pengfei Xia and Guanlin Wu. This manuscript was originally written, and the final manuscript was approved by all the authors.
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Xia, P., Li, Q., Wu, G. et al. Identification of Glioma Cancer Stem Cell Characteristics Based on Weighted Gene Prognosis Module Co-Expression Network Analysis of Transcriptome Data Stemness Indices. J Mol Neurosci 70, 1512–1520 (2020). https://doi.org/10.1007/s12031-020-01590-z
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DOI: https://doi.org/10.1007/s12031-020-01590-z