Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression

doi:10.1093/bib/bbaa365

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能制造技术与系统研究中心 > 多维数据分析团队

	Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression
	Rahman, Md Habibur 3; Rana, Humayan Kabir 4; Peng, Silong 5; Hu, Xiyuan 5; Chen, Chen 5; Quinn, Julian M. W.1; Moni, Mohammad Ali 2
刊名	BRIEFINGS IN BIOINFORMATICS
	2021-09-01
卷号	22 期号:5 页码:23
关键词	bioinformatics machine learning central nervous system disorders glioblastoma comorbidity pathway ontology proteins survival analysis
ISSN号	1467-5463
DOI	10.1093/bib/bbaa365
通讯作者	Moni, Mohammad Ali(m.moni@unsw.edu.au)
英文摘要	Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.
资助项目	CAS-TWAS Presidents Fellowship[2016CTF014]
WOS关键词	PARKINSONS-DISEASE ; MOLECULAR-GENETICS ; CANCER ; GLIOMA ; GLUTAMATE ; BRAIN ; CLASSIFICATION ; METAANALYSIS ; ASSOCIATION ; COMORBIDITY
WOS研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
语种	英语
出版者	OXFORD UNIV PRESS
WOS记录号	WOS:000709461800002
资助机构	CAS-TWAS Presidents Fellowship
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46331]
专题	自动化研究所_智能制造技术与系统研究中心_多维数据分析团队
通讯作者	Moni, Mohammad Ali
作者单位	1.Royal North Shore Hosp, Surg Educ & Res Training Inst, Sydney, NSW, Australia 2.Univ New South Wales, Sydney, NSW, Australia 3.Islamic Univ, Dept Comp Sci & Engn, Kushtia, Bangladesh 4.Green Univ Bangladesh, Dept Comp Sci & Engn, Dhaka, Bangladesh 5.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Rahman, Md Habibur,Rana, Humayan Kabir,Peng, Silong,et al. Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression[J]. BRIEFINGS IN BIOINFORMATICS,2021,22(5):23.
APA	Rahman, Md Habibur.,Rana, Humayan Kabir.,Peng, Silong.,Hu, Xiyuan.,Chen, Chen.,...&Moni, Mohammad Ali.(2021).Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.BRIEFINGS IN BIOINFORMATICS,22(5),23.
MLA	Rahman, Md Habibur,et al."Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression".BRIEFINGS IN BIOINFORMATICS 22.5(2021):23.