| 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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| 发表期刊 | BRIEFINGS IN BIOINFORMATICS
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| ISSN | 1467-5463 |
| 2021-09-01 | |
| 卷号 | 22期号:5页码:23 |
| 通讯作者 | Moni, Mohammad Ali(m.moni@unsw.edu.au) |
| 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. |
| 关键词 | bioinformatics machine learning central nervous system disorders glioblastoma comorbidity pathway ontology proteins survival analysis |
| DOI | 10.1093/bib/bbaa365 |
| 关键词[WOS] | PARKINSONS-DISEASE ; MOLECULAR-GENETICS ; CANCER ; GLIOMA ; GLUTAMATE ; BRAIN ; CLASSIFICATION ; METAANALYSIS ; ASSOCIATION ; COMORBIDITY |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | CAS-TWAS Presidents Fellowship[2016CTF014] |
| 项目资助者 | CAS-TWAS Presidents Fellowship |
| WOS研究方向 | Biochemistry & Molecular Biology ; Mathematical & Computational Biology |
| WOS类目 | Biochemical Research Methods ; Mathematical & Computational Biology |
| WOS记录号 | WOS:000709461800002 |
| 出版者 | OXFORD UNIV PRESS |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/46332 |
| 专题 | 智能制造技术与系统研究中心_多维数据分析（彭思龙）-技术团队 |
| 通讯作者 | Moni, Mohammad Ali |
| 作者单位 | 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 |
| 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. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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