基于共表达网络挖掘肺癌相关模块
Identification of Lung Cancer Related Function Modules Based on Co-Expression Network

作者: 吕亚娜 :哈尔滨医科大学基础医学院,哈尔滨; 何月涵 , 苗正强 , 贾婿 , 冯陈晨 , 陈丽娜 :哈尔滨医科大学生物信息科学与技术学院,哈尔滨;

关键词: 共表达网络基因表达模块挖掘肺癌Co-Expression Network Gene Expression Module Mining Lung Cancer

摘要:

目的:识别肺癌疾病相关功能模块,对了解肺癌疾病的发病机制至关重要。方法:本文提出一个挖掘疾病相关功能模块的整合方法。采用包括正常和肺癌样本的微阵列数据,首先,应用rank-based方法构建基因共表达网络;其次,通过Qcut挖掘基因共表达模块;然后基于肺癌差异表达基因及基因模块功能一致性的联合测度,最终筛选出疾病相关功能模块。结果:研究发现,我们的方法获得7个显著疾病相关功能模块,经文献证实都与肺癌的发生发展有着密切的联系。进一步分析发现不仅能获得与传统方法功能一致的模块,而且还发现了传统方法没有获得的病毒层面的两个模块(模块351和352)。结论:我们的方法能够有效地发现新的功能模块,为探索癌症致病机理提供新的视角及依据。

Abstract: Objective: Identifying lung cancer disease-related functional modules is important to understand the mechanism of lung cancer. Methods: In this paper, we propose an integration method of mining disease-related functional mod-ule. Using microarray data of normal and lung cancer samples, firstly, rank-based method was applied to construct gene co-expression network. Secondly, gene co-expression modules were mined through Qcut, then disease-related functional modules were screened based on the joint measure of lung cancer differentially expressed genes and the functional con-sistency. Results: 7 significant disease-related functional modules were screened, which were closely linked with the development of lung cancer by literature confirmation. Further it found that our method could not only return the func-tional consistency modules, but also find two modules were associated with specific functional annotations named “virus response” that could not be identified by other methods. Conclusions: The method provided additional insights for find-ing new functional module, which will be helpful for the studies on the pathogenesis of human complex diseases.

文章引用: 吕亚娜 , 何月涵 , 苗正强 , 贾婿 , 冯陈晨 , 陈丽娜 (2013) 基于共表达网络挖掘肺癌相关模块。 生物物理学, 1, 17-24. doi: 10.12677/biphy.2013.11003

参考文献

[1] Corti A, Giovannini M, Belli C, Villa E. Immunomodulatory Agents with Antivascular Activity in the Treatment of Non-Small Cell Lung Cancer: Focus on TLR9 Agonists, IMiDs and NGR-TNF[J]. J Oncol, 2010:732680.

[2] [2] Kim SY, Hahn WC. Cancer genomics: integrating form and function[J].Carcinogenesis, 2007,28(7):1387-92.

[3] [3] Yu-Chao Wang, Bor-Sen Chen. A network-based biomarker approach for molecular investigation and diagnosis of lung cancer[J]. BMC Medical Genomics, 2011,4:2.

[4] [4] E Zahra SN, Khattak NA, Mir A. Comparative modeling and docking studies of p16ink4/Cyclin D1/Rb pathway genes in lung cancer revealed functionally interactive residue of RB1 and its functional partner E2F1[J]. Theor Biol Med Model. 2013 Jan 1;10(1):1.

[5] [5] Dupage M, Jacks T. Genetically engineered mouse models of cancer reveal new insights about the antitumor immune response[J]. Curr Opin Immunol. 2013 Mar 2.

[6] [6] Fang X, Netzer M, Baumgartner C, et al. Genetic network and gene set enrichment analysis to identify biomarkers related to cigarette smoking and lung cancer[J]. Cancer Treat Rev, 2013 Feb;39(1):77-88.

[7] [7] Mariño-Ramírez L, Tharakaraman K, Bodenreider O, et al. Identification of cis-regulatory elements in gene co-expression networks using A-GLAM[J]. Methods Mol Biol, 2009,541:1-22.

[8] [8] Huang FM, Chen HC, Khan MA, et al. CYP2A6, CYP1A1, and CYP2D6 polymorphisms in lung cancer patients from Central South China[J]. Med Oncol. 2013 Jun;30(2):521.

[9] [9] Mosca E, Barcella M, Alfieri R, et al. Systems biology of the metabolic network regulated by the Akt pathway[J]. Biotechnol Adv, 2011 Aug 12.

[10] [10] Liu H, Su J, Li J, et al. Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network[J]. BMC Syst Biol, 2011 Oct 11;5:158.

[11] [11] Cantile M, Franco R, Schiavo G, et al. The HOX Genes Network in Uro-Genital Cancers: Mechanisms and Potential Therapeutic Implications[J]. Curr Med Chem, 2011 Oct 26.

[12] [12] Pyon YS, Li X, Li J. Cancer progression analysis based on ordinal relationship of cancer stages and co-expression network modularity[J]. Int J Data Min Bioinform, 2011;5(3):233-51.

[13] [13] Prifti E, Zucker JD, Clément K, et al. Interactional and functional centrality in transcriptional co-expression networks[J]. Bioformatics, 2010,26(24):3083-9.

[14] [14] Aoki K, Ogata Y, Shibata D. Approaches for extracting practical information from gene co-expression networks in plant biology. Plant Cell Physiol, 2007,48(3):381-90.

[15] [15] Yu S, Zheng L, Li Y, et al. Causal co-expression method with module analysis to screen drugs with specific target.[J]. Gene. 2013 Apr 10;518(1):145-51.

[16] [17] Zhang J, Ni S, Xiang Y, et al. Gene co-expression analysis predicts genetic aberration loci associated with colon cancer metastasis[J]. Int J Comput Biol Drug Des. 2013;6(1-2):60-71.

[17] [18] Lehtinen S, Marsellach FX, Codlin S, et al. Stress induces remodelling of yeast interaction and co-expression networks[J]. Mol Biosyst. 2013 Mar 7.

[18] [19] López-Kleine L, Leal L, López C. Biostatistical approaches for the reconstruction of gene co-expression networks based on transcriptomic data[J]. Brief Funct Genomics. 2013 Feb 12.

[19] [20] Tsaparas P, Mariño-Ramírez L, Bodenreider O, et al. Global similarity and local divergence in human and mouse gene co-expression networks[J]. BMC Evol Biol. 2006 Sep 12;6:70.

[20] [21] Jordan IK, Mariño-Ramírez L, Wolf YI, Koonin EV. Conservation and coevolution in the scale-free human gene coexpression network[J]. Mol Biol Evol. 2004 Nov;21(11):2058-70.

[21] [22] Ruan J, Dean AK, Zhang W. A general co-expression network-based approach to gene expression analysis: comparison and applications[J]. BMC Syst Biol, 2010 Feb 2;4:8

[22] [23] Sun PG, Gao L, Han S. Prediction of human disease-related gene clusters by clustering analysis[J]. Int J Biol Sci, 2011 Jan 14;7(1):61-73.

[23] [24] Habibi M, Eslahchi C, Wong L. Protein complex prediction based on k-connected subgraphs in protein interaction network[J]. BMC Syst Biol. 2010 Sep 16;4:129.

[24] [25] Ebrahimzadeh A, Addeh J, Rahmani Z. Control chart pattern recognition using K-MICA clustering and neural networks[J]. ISA Trans, 2011 Oct 28.

[25] [26] Kim K, Kim W, Kim S. ReMark: an automatic program for clustering orthologs flexibly combining a Recursive and a Markov clustering algorithms[J]. Bioinformatics, 2011 Jun 15;27(12):1731-3.

[26] [27] Srihari S, Ning K, Leong HW. Refining Markov Clustering for protein complex prediction by incorporating core-attachment structure[J]. Genome Inform. 2009 Oct;23(1):159-68.

[27] [28] Ruan J, Zhang W. Identifying network communities with a high resolution[J]. Phys Rev E Stat Nonlin Soft Matter Phys. 2008 Jan;77(1 Pt 2):016104.

[28] [29] Mao L, Van Hemert JL, Dash S, et al. Arabidopsis gene co-expression network and its functional modules[J].BMC Bioinformatics. 2009 Oct 21;10:346.

[29] [30] Newman ME, Girvan M. Finding and evaluating community structure in networks[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2004 Feb;69(2 Pt 2):026113.

[30] [31] Selvendiran K, Sakthisekaran D. Chemopreventive effect of piperine on modulating lipid peroxidation and membrane bound enzymes in benzo(a)pyrene induced lung carcinogenesis[J]. Biomed Pharmacother. 2004 May;58(4):264-7.

[31] [32] van der Hoeven D, Cho KJ, Ma X, et al. Fendiline inhibits K-Ras plasma membrane localization and blocks K-Ras signal transmission[J]. Mol Cell Biol. 2013 Jan;33(2):237-51.

[32] [33] Thomas KJ, Jacobson MR. Defects in mitochondrial fission protein dynamin-related protein 1 are linked to apoptotic resistance and autophagy in a lung cancer model[J]. PLoS One. 2012;7(9):e45319.

[33] [34] Bian J, Wang K, Kong X, et al. Caspase- and p38-MAPK-dependent induction of apoptosis in A549 lung cancer cells by Newcastle disease virus[J]. Arch Virol, 2011.2(3):12-23

[34] [35] Lu G, Reinert JT, Pitha-Rowe I, et al. ISG15 enhances the innate antiviral response by inhibition of IRF-3 degradation[J]. Cell Mol Biol (Noisy-le-grand), 2006.52(1):29-41

[35] [36] Behr M, Schieferdecker K, Bühr P, et al. Interferon-stimulated response element (ISRE)-binding protein complex DRAF1 is activated in Sindbis virus (HR)-infected cells[J]. JInterferon Cytokine Res, 2001 21(11):981-90.

[36] [37] Nakaya T, Sato M, Hata N, et al. Gene induction pathways mediated by distinct IRFsduring viral infection[J]. Biochem Biophys ResCommun, 2001. 283(5):1150-6

[37] [38] Nomori H, Mori T, Iyama K, et al. Risk of Bronchioloalveolar Carcinoma in Patients with Human T-cell Lymphotropic Virus Type 1 (HTLV-I): Case-control Study Results[J]. Ann Thorac Cardiovasc Surg, 2011 .17(1): 19-23

[38] [39] Yin Q, Wang X, Fewell C, et al. MicroRNA miR-155 inhibits bone morphogenetic protein (BMP) signaling and BMP-mediated Epstein-Barr virus reactivation[J]. J Virol, 2010 .84(13):6318-27

[39] [40] Kashiwagi S, Kumasaka T, Bunsei N, et al. Detection of Epstein-Barr virus-encoded small RNA-expressed myofibroblasts and IgG4-producing plasma cells in sclerosing angiomatoid nodular transformation of the spleen[J]. Virchows Arch, 2010.453(3):275-82

[40] [41] Guertler A, Kraemer A, Roessler U, et al. The WST survival assay: an easy and reliable method to screen radiation-sensitive individuals[J]. Radiat Prot Dosimetry, 2011.143(2-4):487-90

[41] [42] Nordén R, Nyström K, Olofsson S. Inhibition of protein deacetylation augments herpes simplex virus type 1-activated transcription of host fucosyltransferase genes associated with virus-induced sLex expression[J]. Arch Virol, 2010 .155(3):305-13

[42] [43] Christensen CL, Gjetting T, Poulsen TT, et al. Targeted cytosine deaminase-uracil phosphoribosyl transferase suicide gene therapy induces small cell lung cancer-specific cytotoxicity and tumor growth delay[J]. Clin Cancer Res, 2010.155(3):305-13

[43] [16] Zhang J, Xiang Y, Ding L, et al. Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia[J]. BMC Bioinformatics. 2010 Oct 28;11 Suppl 9:S5.

[44] [44] Xi S, Xu H, Shan J, et al. Cigarette smoke mediates epigenetic repression of miR-487b during pulmonary carcinogenesis[J]. J Clin Invest. 2013 Mar 1;123(3):1241-61.

分享
Top