与肺癌相关的基因共表达网络的构建与分析
The Construction and Analysis of Gene Co-Expression Network in Lung Cancer

作者: 翟媛媛 , 陈颖丽 * , 薛济先 :内蒙古大学物理科学与技术学院,内蒙古 呼和浩特;

关键词: 肺癌基因共表达网络枢纽基因WGCNALung Cancer Gene Co-Expression Network Hub Gene WGCNA

摘要:
肺癌是目前世界范围内发病率和死亡率最高的恶性肿瘤,它是一种复杂的分子网络疾病。为了进一步了解肺癌致病的分子机制,我们使用加权基因共表达网络分析(WGCNA)方法,对肺癌组织与其癌旁正常组织的差异表达基因进行分析,进而对差异表达基因进行模块的划分以及枢纽基因(hub gene)的识别,共得到了八个模块。通过计算每个模块特征向量基因(module eigengene)与样本特征的皮尔森相关系数,最终得到一个与肺癌高关联的模块(blue模块),发现blue模块的枢纽基因为碳酸酐酶4 (carbonic anhydrase 4, CA4),这一枢纽基因在模块中起着重要的作用。使用在线工具DAVID (Database for Annotation, Visualization and Integrated Discovery)对blue模块进行GO功能富集及KEGG通路分析。GO分析显示blue模块具有Rho 蛋白的信号转导调控、生物粘附、糖结合等生物功能;KEGG分析显示blue模块参与了轴突导向和O 型聚糖的生物合成通路。这些分析结果表明,文中识别的肺癌高关联模块和枢纽基因在肺癌的发生发展过程中起着潜在的重要作用。

Abstract: Lung cancer, a complex molecular network disease, is a malignant tumor with the highest inci-dence and mortality around the world at present. In order to further understand the pathogenic molecular mechanism of lung cancer, we firstly identified differentially expressed gene (DEG) between cancer tissue and the corresponding adjacent normal tissue. Then, we used weighted gene co-expression network analysis (WGCNA) to screen for the DEG. In total, eight gene modules of DEG were detected and hub genes were identified. By calculating the Pearson’s correlation coefficient between module eigengene and sample traits, we obtained the blue module which was highly associated with lung cancer, and found the hub gene of blue module was carbonic anhydrase 4. Hub gene plays an important role in the blue module. By using Database for Annotation, Visualization and Integrated Discovery (DAVID), the Gene Ontology (GO) enrichment analysis and KEGG pathway analysis were performed for blue module. The analysis of GO showed that blue module played important roles in biological functions, such as regulation of Rho protein signal transduction, biological adhesion, and carbohydrate binding. The analysis of KEGG indicated that blue module took part in the pathways of axon guidance and O-Glycan biosynthesis. The results showed that high correlations modules of lung cancer and hub gene identified in this paper played a potentially important role in the development of lung cancer.

文章引用: 翟媛媛 , 陈颖丽 , 薛济先 (2016) 与肺癌相关的基因共表达网络的构建与分析。 计算生物学, 6, 33-40. doi: 10.12677/HJCB.2016.62005

参考文献

[1] Torre, L.A., Bray, F., Siegel, R.L., Ferlay, J., Lortet-Tieulent, J. and Jemal, A. (2015) Global Cancer Statistics, 2012. CA-A Cancer Journal for Clinicians, 65, 87-108.
http://dx.doi.org/10.3322/caac.21262

[2] Jemal, A., Bray, F., Center, M.M., Ferlay, J., Ward, E. and Forman, D. (2011) Global Cancer Statistics. CA-A Cancer Journal for Clinicians, 61, 69-90.
http://dx.doi.org/10.3322/caac.20107

[3] Langfelder, P. and Horvath, S. (2008) WGCNA: An R Package for Weighted Correlation Network Analysis. BMC Bioinformatics, 9, 559-572.
http://dx.doi.org/10.1186/1471-2105-9-559

[4] 钟诗龙, 伍虹, 杨敏, 等. 用权重基因共表达网络分析识别心脏重构关键节点基因[J]. 中国药理学通报, 2011, 27(10): 1358-1362.

[5] Lin, R.C., Weeks, K.L., Kang, S., Miao, R., Xiao, H., Zhao, G., Luo, H., Du, D., Zhao, H., et al. (2011) Large-Scale Prediction of Long Non-Coding RNA Functions in a Coding-Non-Coding Gene Co-Expression Network. Nucleic Acids Research, 39, 3864-3878.
http://dx.doi.org/10.1093/nar/gkq1348

[6] Plaisier, C.L., Horvath, S., Huertas-Vazquez, A., et al. (2009) A Systems Genetics Approach Implicates USF1, FADS3, and Other Causal Candidate Genes for Familial Combined Hyperlipidemia. PLoS Genets, 5, e1000642.
http://dx.doi.org/10.1371/journal.pgen.1000642

[7] Xing, Y., Zhang, J., Lu, L., Li, D., Wang, Y., Huang, S., et al. (2016) Identification of Hub Genes of Pneumocyte Senescence Induced by Thoracic Irradiation Using Weighted Gene Coexpression Network Analysis. Molecular Medicine Reports, 13, 107-116.

[8] 王攀. 加权基因共表达网络(WGCNA)在食管鳞癌中的应用[D]: [博士学位论文]. 北京: 北京协和医院, 2014.

[9] Lu, T.P., Tsai, M.H., Lee, J.M., Hsu, C.P., Chen, P.C., Lin, C.W., et al. (2010) Identification of a Novel Biomarker, SEMA5A, for Non-Small Cell Lung Carcinoma in Nonsmoking Women. Cancer Epidemiology Biomarkers & Prevention, 19, 2590-2597.
http://dx.doi.org/10.1158/1055-9965.EPI-10-0332

[10] Zhang, B. and Horvath, S. (2005) A General Framework for Weighted Gene Co-Expression Network Analysis. Statistical Applications in Genetics and Molecular Biology, 4, 1-45.
http://dx.doi.org/10.2202/1544-6115.1128

[11] Xing, Y.X., Zhang, J.L., Lu, L., Li, D.G., et al. (2016) Identification of Hub Genes of Pneumocyte Senescence Induced by Thoracic Irradiation Using Weighted Gene Coexpression Network Analysis. Molecular Medicine Reports, 13, 107-116.

[12] 梁栋, 邢永强, 蔡禄. 肾肿瘤相关基因的共表达网络构建与分析[J]. 中国生物工程杂志, 2016, 36(2): 30-37.

[13] Lecca, P. and Re, A. (2015) Detecting Modules in Biological Networks by Edge Weight Clustering and Entropy Significance. Front Genet, 6, 265-277.
http://dx.doi.org/10.3389/fgene.2015.00265

[14] Supuran, C.T. (2016) How Many Carbonic Anhydrase Inhibition Mechanisms Exist. Journal of Enzyme Inhibition and Medicinal Chemistry, 31, 345-360.
http://dx.doi.org/10.3109/14756366.2015.1122001

[15] Chedotal, A., Kerjan, G. and Moreau-Fauvarque, C. (2005) The Brain within the Tumor: New Roles for Axon Guidance Molecules in Cancers. Cell Death Differ, 12, 1044-1056.
http://dx.doi.org/10.1038/sj.cdd.4401707

[16] Waheed, A. and Sly, W.S. (2014) Membrane Associated Carbonic Anhydrase IV (CA IV): A Personal and Historical Perspective. Subcell Biochem, 75, 157-179.
http://dx.doi.org/10.1007/978-94-007-7359-2_9

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