Yang Jing, Chen Lei, Kong Xiangyin, Huang Tao, Cai Yu-Dong
The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, People's Republic of China.
College of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China.
PLoS One. 2014 Sep 10;9(9):e107202. doi: 10.1371/journal.pone.0107202. eCollection 2014.
Cancer is a serious disease that causes many deaths every year. We urgently need to design effective treatments to cure this disease. Tumor suppressor genes (TSGs) are a type of gene that can protect cells from becoming cancerous. In view of this, correct identification of TSGs is an alternative method for identifying effective cancer therapies. In this study, we performed gene ontology (GO) and pathway enrichment analysis of the TSGs and non-TSGs. Some popular feature selection methods, including minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS), were employed to analyze the enrichment features. Accordingly, some GO terms and KEGG pathways, such as biological adhesion, cell cycle control, genomic stability maintenance and cell death regulation, were extracted, which are important factors for identifying TSGs. We hope these findings can help in building effective prediction methods for identifying TSGs and thereby, promoting the discovery of effective cancer treatments.
癌症是一种严重的疾病，每年导致许多人死亡。我们迫切需要设计有效的治疗方法来治愈这种疾病。肿瘤抑制基因（TSGs）是一种能够保护细胞不发生癌变的基因类型。鉴于此，正确识别肿瘤抑制基因是识别有效癌症治疗方法的一种替代方法。在本研究中，我们对肿瘤抑制基因和非肿瘤抑制基因进行了基因本体论（GO）和通路富集分析。采用了一些流行的特征选择方法，包括最小冗余最大相关性（mRMR）和增量特征选择（IFS）来分析富集特征。据此，提取了一些GO术语和KEGG通路，如生物粘附、细胞周期控制、基因组稳定性维持和细胞死亡调控，这些都是识别肿瘤抑制基因的重要因素。我们希望这些发现有助于建立有效的预测方法来识别肿瘤抑制基因，从而促进有效癌症治疗方法的发现。
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