| Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression | |
Rahman, Md Habibur1; Rana, Humayan Kabir2; Peng, Silong3 ; Hu, Xiyuan3; Chen, Chen3; Quinn, Julian M. W.4; Moni, Mohammad Ali5
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| Source Publication | BRIEFINGS IN BIOINFORMATICS
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| ISSN | 1467-5463 |
| 2021-09-01 | |
| Volume | 22Issue:5Pages:23 |
| Corresponding Author | Moni, Mohammad Ali(m.moni@unsw.edu.au) |
| Abstract | 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. |
| Keyword | bioinformatics machine learning central nervous system disorders glioblastoma comorbidity pathway ontology proteins survival analysis |
| DOI | 10.1093/bib/bbaa365 |
| WOS Keyword | PARKINSONS-DISEASE ; MOLECULAR-GENETICS ; CANCER ; GLIOMA ; GLUTAMATE ; BRAIN ; CLASSIFICATION ; METAANALYSIS ; ASSOCIATION ; COMORBIDITY |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | CAS-TWAS Presidents Fellowship[2016CTF014] |
| Funding Organization | CAS-TWAS Presidents Fellowship |
| WOS Research Area | Biochemistry & Molecular Biology ; Mathematical & Computational Biology |
| WOS Subject | Biochemical Research Methods ; Mathematical & Computational Biology |
| WOS ID | WOS:000709461800002 |
| Publisher | OXFORD UNIV PRESS |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/46332 |
| Collection | 智能制造技术与系统研究中心_多维数据分析 |
| Corresponding Author | Moni, Mohammad Ali |
| Affiliation | 1.Islamic Univ, Dept Comp Sci & Engn, Kushtia, Bangladesh 2.Green Univ Bangladesh, Dept Comp Sci & Engn, Dhaka, Bangladesh 3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 4.Royal North Shore Hosp, Surg Educ & Res Training Inst, Sydney, NSW, Australia 5.Univ New South Wales, Sydney, NSW, Australia |
| Recommended Citation 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. |
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