Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma

Minjie Fu; Jinsen Zhang; Weifeng Li; Shan He; Daniel Tennant; Wei Hua; Ying Mao; Jingwen Zhang

doi:10.1186/s12967-021-03083-y

Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma

Minjie Fu, Jinsen Zhang, Weifeng Li, Shan He, Daniel Tennant, Wei Hua^*, Ying Mao^*, Jingwen Zhang

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Article number	404
Number of pages	13
Journal	Journal of translational medicine
Volume	19
Issue number	1
Early online date	26 Sept 2021
DOIs	https://doi.org/10.1186/s12967-021-03083-y
Publication status	Published - Dec 2021

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (82072785, 82072784) and the Shanghai Development and Reform Commission Major Project (2018SHZDZX01). The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.

Publisher Copyright:
© 2021, The Author(s).

Keywords

CD276
Glioblastoma
Molecular subtype
OLIG2
Random forest

ASJC Scopus subject areas

General Biochemistry,Genetics and Molecular Biology

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FuM2021GeneFinal published version, 8.21 MBLicence: Creative Commons: Attribution (CC BY)

Cite this

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title = "Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma",

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{\textquoteright}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{\textquoteright}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.",

keywords = "CD276, Glioblastoma, Molecular subtype, OLIG2, Random forest",

author = "Minjie Fu and Jinsen Zhang and Weifeng Li and Shan He and Daniel Tennant and Wei Hua and Ying Mao and Jingwen Zhang",

note = "Funding Information: This work was supported by the National Natural Science Foundation of China (82072785, 82072784) and the Shanghai Development and Reform Commission Major Project (2018SHZDZX01). The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication. Publisher Copyright: {\textcopyright} 2021, The Author(s).",

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AU - Fu, Minjie

AU - Zhang, Jinsen

AU - Li, Weifeng

AU - He, Shan

AU - Tennant, Daniel

AU - Hua, Wei

AU - Mao, Ying

AU - Zhang, Jingwen

N1 - Funding Information: This work was supported by the National Natural Science Foundation of China (82072785, 82072784) and the Shanghai Development and Reform Commission Major Project (2018SHZDZX01). The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication. Publisher Copyright: © 2021, The Author(s).

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N2 - 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.

AB - 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.

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Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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