Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways

小桶 计算生物学 癌变 生物途径 癌症 生物 基因本体论 生物信息学 基因 遗传学 基因表达
作者
Chen Lei,Yuhang Zhang,Guohui Lu,Tao Huang,Yi Cai
出处
期刊:Artificial Intelligence in Medicine [Elsevier]
卷期号:76: 27-36 被引量:127
标识
DOI:10.1016/j.artmed.2017.02.001
摘要

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.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.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.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.
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