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Fu M, Zhang J, Li W, He S, Zhang J, Tennant D, Hua W, Mao Y. Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma. J Transl Med 2021; 19:404. [PMID: 34565408 PMCID: PMC8474912 DOI: 10.1186/s12967-021-03083-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 09/16/2021] [Indexed: 11/14/2022] Open
Abstract
Background The molecular profiling of glioblastoma (GBM) based on transcriptomic analysis could provide precise treatment and prognosis. However, current subtyping (classic, mesenchymal, neural, proneural) is time-consuming and cost-intensive hindering its clinical application. A simple and efficient method for classification was imperative. Methods In this study, to simplify GBM subtyping more efficiently, we applied a random forest algorithm to conduct 26 genes as a cluster featured with hub genes, OLIG2 and CD276. Functional enrichment analysis and Protein–protein interaction were performed using the genes in this gene cluster. The classification efficiency of the gene cluster was validated by WGCNA and LASSO algorithms, and tested in GSE84010 and Gravandeel’s GBM datasets. Results The gene cluster (n = 26) could distinguish mesenchymal and proneural excellently (AUC = 0.92), which could be validated by multiple algorithms (WGCNA, LASSO) and datasets (GSE84010 and Gravandeel’s GBM dataset). The gene cluster could be functionally enriched in DNA elements and T cell associated pathways. Additionally, five genes in the signature could predict the prognosis well (p = 0.0051 for training cohort, p = 0.065 for test cohort). Conclusions Our study proved the accuracy and efficiency of random forest classifier for GBM subtyping, which could provide a convenient and efficient method for subtyping Proneural and Mesenchymal GBM. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03083-y.
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Affiliation(s)
- Minjie Fu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Neurosurgery, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
| | - Jinsen Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Neurosurgery, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
| | - Weifeng Li
- School of Computer Science, University of Birmingham, Edgartown, UK
| | - Shan He
- School of Computer Science, University of Birmingham, Edgartown, UK
| | - Jingwen Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Neurosurgery, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
| | - Daniel Tennant
- Institute of Metabolism and Systems Research, University of Birmingham, Edgartown, UK
| | - Wei Hua
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China. .,Institute of Neurosurgery, Fudan University, Shanghai, China. .,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China.
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China. .,Institute of Neurosurgery, Fudan University, Shanghai, China. .,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China.
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