Analysis of cancer-related IncRNAs using gene ontology and KEGG pathways
文献类型：期刊论文
| 作者 | Chen, Lei3,4; Cai, Yu-Dong3; Zhang, Yu-Hang1; Huang, Tao1; Lu, Guohui2; , |
| 刊名 | ARTIFICIAL INTELLIGENCE IN MEDICINE
 |
| 出版日期 | 2017 |
| 卷号 | 76期号:-页码:27-36 |
| 关键词 | Cancer-related lncRNA Gene ontology KEGG pathway Minimum redundancy maximum relevance Incremental feature selection Dagging |
| ISSN号 | 0933-3657 |
| DOI | 10.1016/j.artmed.2017.02.001 |
| 文献子类 | Article |
| 英文摘要 | Background: Cancer is a disease that involves abnormal cell growth and can invade or metastasize to other tissues. It is known that several factors are related to its initiation, proliferation, and invasiveness. Recently, it has been reported that long non-coding RNAs (lncRNAs) can participate in specific functional pathways and further regulate the biological function of cancer cells. Studies on lncRNAs are therefore helpful for uncovering the underlying mechanisms of cancer biological processes. Methods: We investigated cancer-related lncRNAs using gene ontology (GO) terms and KEGG pathway enrichment scores of neighboring genes that are co-expressed with the lncRNAs by extracting important GO terms and KEGG pathways that can help us identify cancer-related lncRNAs. The enrichment theory of GO terms and KEGG pathways was adopted to encode each lncRNA. Then, feature selection methods were employed to analyze these features and obtain the key GO terms and KEGG pathways. Results: The analysis indicated that the extracted GO terms and KEGG pathways are closely related to several cancer associated processes, such as hormone associated pathways, energy associated pathways, and ribosome associated pathways. And they can accurately predict cancer-related lncRNAs. Conclusions: This study provided novel insight of how lncRNAs may affect tumorigenesis and which pathways may play important roles during it. These results could help understanding the biological mechanisms of lncRNAs and treating cancer. (C) 2017 Elsevier B.V. All rights reserved. |
| 学科主题 | Computer Science ; Engineering ; Medical Informatics |
| WOS关键词 | LONG NONCODING RNA ; DNA MISMATCH REPAIR ; AMINO-ACID-COMPOSITION ; CELL CARCINOMA-CELLS ; TUMOR-SUPPRESSOR ; OVARIAN-CANCER ; SULFATED GLYCOSAMINOGLYCANS ; HOMOLOGOUS RECOMBINATION ; REPLICATION STRESS ; COLORECTAL-CANCER |
| 语种 | 英语 |
| WOS记录号 | WOS:000397816500004 |
| 出版者 | ELSEVIER SCIENCE BV |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/883]  |
| 专题 | 中国科学院上海生命科学研究院营养科学研究所 |
| 作者单位 | 1.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200025, Peoples R China; 2.Nanchang Univ, Affiliated Hosp 1, Dept Neurosurg, Nanchang 330006, Jiangxi, Peoples R China, 3.Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China; 4.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Chen, Lei,Cai, Yu-Dong,Zhang, Yu-Hang,et al. Analysis of cancer-related IncRNAs using gene ontology and KEGG pathways[J]. ARTIFICIAL INTELLIGENCE IN MEDICINE,2017,76(-):27-36. |
| APA | Chen, Lei,Cai, Yu-Dong,Zhang, Yu-Hang,Huang, Tao,Lu, Guohui,&,.(2017).Analysis of cancer-related IncRNAs using gene ontology and KEGG pathways.ARTIFICIAL INTELLIGENCE IN MEDICINE,76(-),27-36. |
| MLA | Chen, Lei,et al."Analysis of cancer-related IncRNAs using gene ontology and KEGG pathways".ARTIFICIAL INTELLIGENCE IN MEDICINE 76.-(2017):27-36. |
入库方式： OAI收割
来源：上海营养与健康研究所
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