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论文题目: Analysis of cancer-related IncRNAs using gene ontology and KEGG pathways
英文论文题目: Analysis of cancer-related IncRNAs using gene ontology and KEGG pathways
第一作者: Chen, L; Zhang, YH; Lu, GH; Huang, T; Cai, YD
英文第一作者: Chen, L; Zhang, YH; Lu, GH; Huang, T; Cai, YD
联系作者: Huang, T (reprint author), Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200025, Peoples R China.
英文联系作者: Huang, T (reprint author), Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200025, Peoples R China.
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发表年度: 2017
卷: 76
期:
页码: 27-36
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.
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.
刊物名称: ARTIFICIAL INTELLIGENCE IN MEDICINE
英文刊物名称: ARTIFICIAL INTELLIGENCE IN MEDICINE
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学科: Computer Science, Artificial Intelligence; Engineering, Biomedical; Medical Informatics
英文学科: Computer Science, Artificial Intelligence; Engineering, Biomedical; Medical Informatics
影响因子: 2.009
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论文类别: Article
英文论文类别: Article
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