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Identification of the Gene Expression Rules That Define the Subtypes in Glioma

1
School of Life Sciences, Shanghai University, Shanghai 200444, China
2
Department of Biostatistics, University of Copenhagen, Copenhagen 2099, Denmark
3
Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
4
Department of Medical Informatics, Erasmus Medical Centre, Rotterdam 3014ZK, The Netherlands
5
Department of Computer Science, Guangdong AIB Polytechnic, Guangzhou, 510507, China
6
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
7
Shanghai Key Laboratory of Pure Mathematics and Mathematical Practice (PMMP), East China Normal University, Shanghai 200241, China
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2018, 7(10), 350; https://doi.org/10.3390/jcm7100350
Received: 13 September 2018 / Revised: 9 October 2018 / Accepted: 11 October 2018 / Published: 13 October 2018
(This article belongs to the Section Molecular Medicine)
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PDF [1773 KB, uploaded 13 October 2018]
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Abstract

As a common brain cancer derived from glial cells, gliomas have three subtypes: glioblastoma, diffuse astrocytoma, and anaplastic astrocytoma. The subtypes have distinctive clinical features but are closely related to each other. A glioblastoma can be derived from the early stage of diffuse astrocytoma, which can be transformed into anaplastic astrocytoma. Due to the complexity of these dynamic processes, single-cell gene expression profiles are extremely helpful to understand what defines these subtypes. We analyzed the single-cell gene expression profiles of 5057 cells of anaplastic astrocytoma tissues, 261 cells of diffuse astrocytoma tissues, and 1023 cells of glioblastoma tissues with advanced machine learning methods. In detail, a powerful feature selection method, Monte Carlo feature selection (MCFS) method, was adopted to analyze the gene expression profiles of cells, resulting in a feature list. Then, the incremental feature selection (IFS) method was applied to the obtained feature list, with the help of support vector machine (SVM), to extract key features (genes) and construct an optimal SVM classifier. Several key biomarker genes, such as IGFBP2, IGF2BP3, PRDX1, NOV, NEFL, HOXA10, GNG12, SPRY4, and BCL11A, were identified. In addition, the underlying rules of classifying the three subtypes were produced by Johnson reducer algorithm. We found that in diffuse astrocytoma, PRDX1 is highly expressed, and in glioblastoma, the expression level of PRDX1 is low. These rules revealed the difference among the three subtypes, and how they are formed and transformed. These genes are not only biomarkers for glioma subtypes, but also drug targets that may switch the clinical features or even reverse the tumor progression. View Full-Text
Keywords: glioma; gene expression; Monte Carlo feature selection; Johnson reducer algorithm; support vector machine glioma; gene expression; Monte Carlo feature selection; Johnson reducer algorithm; support vector machine
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Cai, Y.-D.; Zhang, S.; Zhang, Y.-H.; Pan, X.; Feng, K.; Chen, L.; Huang, T.; Kong, X. Identification of the Gene Expression Rules That Define the Subtypes in Glioma. J. Clin. Med. 2018, 7, 350.

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