肺癌新基因的发现及致病机理研究
摘要
肺癌是一种常见的发生在肺部的恶性肿瘤,目前是因癌症死亡最常见病因之一,全球每年约有130万人死于肺癌。肺癌患者5年生存率一直维持在14%不变。在过去的20年。高通量技术应用于肺癌的研究产生了大量的数据,潜在地为我们研究肺癌的分子机制提供了重要的资源。人类肺癌数据库HLungDB是一个将肺癌有关的基因,蛋白质和miRNA以及临床资料等相应的数据整合在一起的数据库。这一平台建立的主要目的是提供一个与肺癌有关的全面综合的网络资源,以促进肺癌机理研究的发展。当前版本的数据库手动从文献中提取了描述分子和人类肺癌的关系的数据。目前,我们通过文本挖掘收集了在不同阶段的肺癌中的2585个基因和212个miRNA的相关数据。此外,我们亦整合了转录因子,转录因子结合位点,启动子,SNP等信息并存储在数据库中。由于表观遗传调控在肺癌的发生中起到了重要作用,我们也收集了相关的数据。我们希望HLungDB数据库能够丰富我们对肺癌的生物学知识的认识,并最终导致新的治疗肺癌的方案出现。HLungDB数据库可以通过(http://www.megabionet.org/bio/hlung)访问使用。建立在HLungDB数据库的基础上,我们从生物学通路角度对肺癌机理展开了系统生物学的研究分析。在我们的研究中,我们分析表达谱的数据和现有的通路有关的数据,使用因子分析和超几何分布两种方法获得了显著性表达的肺癌通路,量化了通路与通路之间的相互作用程度,构建了肺癌通路的复杂网络。我们也衡量了通路的活化和抑制情况,试图找出关键通路之间通过哪些基因发生交互作用。在我们的结果中,有的通路已经在肺癌中被广泛的证实,如细胞外基质受体相互作用,p53信号通路,细胞粘附分子通路,黏着斑通路,细胞周期通路。补体和凝血级联通路也出现在我们的结果中,同时很多间接证据也表明,它和肺癌密切相关。我们构建的生物学通路交互作用网络也展示了几个子网络密切和肺癌关联。通过综合分析通路的激活抑制状态的结果,我们提出一个假设：细胞周期通路,P53信号通路,MAPK信号通路可能通过GADD45b来发生交互作用。从通路角度出发的肺癌有关的分析,一定可以促进肺癌生物学的研究发展。
Lung cancer is currently the most common cause of cancer death. About 130 million people die of lung cancer every year in the word.5-year survival rate of patients with lung cancer has remained unchanged at 14% for many years. In the past 20 years, high-throughput technology for lung cancer produced a large amount of data. It potentially provides the resources for lung cancer molecular mechanisms. Human lung cancer database HLungDB is a lung cancer platform related to genes, proteins and miRNA and corresponding clinical data. The main function of the platform is to provide a comprehensive cancer related network resources to promote the progress of lung cancer mechanism research. We manually extracted the molecular and human lung cancer data from the literature. Currently, we collected 2585 genes and 212 miRNA related data at different stages of lung cancer by text mining. In addition, we also integrated the transcription factor, transcription factor binding sites, promoter, SNP and other information. Since epigenetic regulation plays an important role in lung cancer, we also collected relevant data. We hope this database could enrich our biological knowledge about lung cancer and ultimately lead to new treatment for lung cancer. HLungDB database is available through (http://www.megabionet.org/bio/hlung). Next, we explore the potential mechanisms related to lung cancer from the point of view in the biological pathway cross-talk by integrating the microarray data and HLungDB data. By using factor analysis and hyper-geometric distribution methods, we analyzed the expression profile data and the biological pathway data to obtain significant lung cancer pathways and quantify the interaction degree between the pathways, then constructed a network of the lung cancer pathways. We also analyzed the overrepresentation of the pathways and detected the potential key genes involving cross-talk between pathways. As a result, we identified lung cancer related pathways which have been widely studied, such as ECM-receptor interaction pathway, p53 signaling pathway, cell adhesion molecules (CAMs) pathway, focal adhesion pathway, cell cycle pathway. In addition, we also detected a new lung cancer related pathway, complement and coagulation cascades. Many indirect evidences indicate that the new identified pathway indeed closely link to lung cancer. The lung cancer biological pathway network also demonstrated some sub-networks associated with lung cancer. By studying the overrepresentated pathways in the network, we proposed a hypothesis that cross-talk takes place among cell cycle, p53 signaling pathway and MAPK signaling pathway through gene GADD45b in coordination with other proteins. Our results related to lung cancer tumorigenesis can no doubt facilitate future lung cancer investigations.
Lung cancer is currently the most common cause of cancer death. About 130 million people die of lung cancer every year in the word.5-year survival rate of patients with lung cancer has remained unchanged at 14% for many years. In the past 20 years, high-throughput technology for lung cancer produced a large amount of data. It potentially provides the resources for lung cancer molecular mechanisms. Human lung cancer database HLungDB is a lung cancer platform related to genes, proteins and miRNA and corresponding clinical data. The main function of the platform is to provide a comprehensive cancer related network resources to promote the progress of lung cancer mechanism research. We manually extracted the molecular and human lung cancer data from the literature. Currently, we collected 2585 genes and 212 miRNA related data at different stages of lung cancer by text mining. In addition, we also integrated the transcription factor, transcription factor binding sites, promoter, SNP and other information. Since epigenetic regulation plays an important role in lung cancer, we also collected relevant data. We hope this database could enrich our biological knowledge about lung cancer and ultimately lead to new treatment for lung cancer. HLungDB database is available through (http://www.megabionet.org/bio/hlung). Next, we explore the potential mechanisms related to lung cancer from the point of view in the biological pathway cross-talk by integrating the microarray data and HLungDB data. By using factor analysis and hyper-geometric distribution methods, we analyzed the expression profile data and the biological pathway data to obtain significant lung cancer pathways and quantify the interaction degree between the pathways, then constructed a network of the lung cancer pathways. We also analyzed the overrepresentation of the pathways and detected the potential key genes involving cross-talk between pathways. As a result, we identified lung cancer related pathways which have been widely studied, such as ECM-receptor interaction pathway, p53 signaling pathway, cell adhesion molecules (CAMs) pathway, focal adhesion pathway, cell cycle pathway. In addition, we also detected a new lung cancer related pathway, complement and coagulation cascades. Many indirect evidences indicate that the new identified pathway indeed closely link to lung cancer. The lung cancer biological pathway network also demonstrated some sub-networks associated with lung cancer. By studying the overrepresentated pathways in the network, we proposed a hypothesis that cross-talk takes place among cell cycle, p53 signaling pathway and MAPK signaling pathway through gene GADD45b in coordination with other proteins. Our results related to lung cancer tumorigenesis can no doubt facilitate future lung cancer investigations.
引文
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23. Pappot, H., Pedersen, A.N., Brunner, N. and Christensen, I.J. (2006) The complex between urokinase (uPA) and its type-1 inhibitor (PAI-1) in pulmonary adenocarcinoma:relation to prognosis. Lung Cancer,51,193-200.
24. Di Bernardo, M.C., Matakidou, A., Eisen, T. and Houlston, R.S. (2009) Plasminogen activator inhibitor variants PAI-1 A15T and PAI-2 S413C influence lung cancer prognosis. Lung Cancer, 65,237-241.
25. Currie, M.S., Vala, M., Pisetsky, D.S., Greenberg, C.S., Crawford, J. and Cohen, H.J. (1990) Correlation between erythrocyte CR1 reduction and other blood proteinase markers in patients with malignant and inflammatory disorders. Blood,75,1699-1704.
26. Borensztajn, K., Peppelenbosch, M.P. and Spek, C.A. Coagulation Factor Xa inhibits cancer cell migration via LIMK1-mediated cofilin inactivation. Thromb Res.
27. Sierko, E., Wojtukiewicz, M.Z., Zimnoch, L. and Kisiel, W. Expression of tissue factor pathway inhibitor (TFPI) in human breast and colon cancer tissue. Thromb Haemost,103,.198-204.
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30. Yang, P., Ebbert, J.O., Sun, Z. and Weinshilboum, R.M. (2006) Role of the glutathione metabolic pathway in lung cancer treatment and prognosis:a review. J Clin Oncol,24, 1761-1769.
31. Parent, J.M. (2009) Waiting for gadd45b. Epilepsy Curr,9,170-172.
32. Chi, H., Lu, B., Takekawa, M., Davis, R.J. and Flavell, R.A. (2004) GADD45beta/GADD45gamma and MEKK4 comprise a genetic pathway mediating STAT4-independent IFNgamma production in T cells. EMBO J,23,1576-1586.
33. Takekawa, M., Tatebayashi, K., Itoh, F., Adachi, M., Imai, K. and Saito, H. (2002) Smad-dependent GADD45beta expression mediates delayed activation of p38 MAP kinase by TGF-beta. EMBOJ,21,6473-6482.
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2. Fong, K.M., Sekido, Y., Gazdar, A.F. and Minna, J.D. (2003) Lung cancer.9:Molecular biology of lung cancer:clinical implications. Thorax,58,892-900.
3. Aviel-Ronen, S., Blackhall, F.H., Shepherd, F.A. and Tsao, M.S. (2006) K-ras mutations in non-small-cell lung carcinoma:a review. Clin Lung Cancer,8,30-38.
4. Herbst, R.S., Heymach, J.V. and Lippman, S.M. (2008) Lung cancer. N Engl J Med,359, 1367-1380.
5. Devereux, T.R., Taylor, J.A. and Barrett, J.C. (1996) Molecular mechanisms of lung cancer. Interaction of environmental and genetic factors. Giles F. Filley Lecture. Chest,109,14S-19S.
6. Ji, H., Ramsey, M.R., Hayes, D.N., Fan, C., McNamara, K., Kozlowski, P., Torrice, C., Wu, M.C., Shimamura, T., Perera, S.A. et al. (2007) LKB1 modulates lung cancer differentiation and metastasis. Nature,448,807-810.
7. Son, J.W., Kang, H.K., Chae, M.H., Choi, J.E., Park, J.M., Lee, W.K., Kim, C.H., Kim, D.S., Kam, S., Kang, YM. et al. (2006) Polymorphisms in the caspase-8 gene and the risk of lung cancer. Cancer Genet Cytogenet,169,121-127.
8. Amberger, J., Bocchini, C.A., Scott, A.F. and Hamosh, A. (2009) McKusick's Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res,37, D793-796.
9. He, X., Chang, S., Zhang, J., Zhao, Q., Xiang, H., Kusonmano, K., Yang, L., Sun, Z.S., Yang, H. and Wang, J. (2008) MethyCancer:the database of human DNA methylation and cancer. Nucleic Acids Res,36,D836-841.
10. Jiang, Q., Wang, Y., Hao, Y, Juan, L., Teng, M., Zhang, X., Li, M., Wang, G. and Liu, Y. (2009) miR2Disease:a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res,37, D98-104.
11. Gu,D., Scaringe, W.A., Li, K., Saldivar, J.S., Hill, K.A., Chen, Z., Gonzalez, K.D. and Sommer, S.S. (2007) Database of somatic mutations in EGFR with analyses revealing indel hotspots but no smoking-associated signature. Hum Mutat,28,760-770.
12. Landi, M.T., Dracheva, T., Rotunno, M., Figueroa, J.D., Liu, H., Dasgupta, A., Mann, F.E., Fukuoka, J., Hames, M., Bergen, A.W. et al. (2008) Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival. PLoS One,3; e1651.
13. Stearman, R.S., Dwyer-Nield, L., Zerbe, L., Blaine, S.A., Chan, Z., Bunn, P.A., Jr., Johnson, G.L., Hirsch, F.R., Merrick, D.T., Franklin, W.A. et al. (2005) Analysis of orthologous gene expression between human pulmonary adenocarcinoma and a carcinogen-induced murine model. Am J Pathol,167,1763-1775.
14. Wachi, S., Yoneda, K. and Wu, R. (2005) Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinformatics,21, 4205-4208.
15. Rohrbeck, A., Neukirchen, J., Rosskopf, M., Pardillos, G.G., Geddert, H., Schwalen, A., Gabbert, H.E., von Haeseler, A., Pitschke, G., Schott, M. et al. (2008) Gene expression profiling for molecular distinction and characterization of laser captured primary lung cancers. J Transl Med,6,69.
16. Wrage, M.,Ruosaari, S., Eijk, P.P., Kaifi, J.T., Hollmen, J., Yekebas, E.F., Izbicki, J.R., Brakenhoff, R.H., Streichert, T, Riethdorf, S. et al. (2009) Genomic profiles associated with early micrometastasis in lung cancer:relevance of 4q deletion. Clin Cancer Res,15,1566-1574.
17. Spira, A., Beane, J.E., Shah, V., Steiling, K., Liu, G., Schembri, F., Gilman, S., Dumas, Y.M., Calner, P., Sebastiani, P. et al. (2007) Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer. Nat Med,13,361-366.
18. Keshava Prasad, T.S., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, B., Venugopal, A. et al. (2009) Human Protein Reference Database--2009 update. Nucleic Acids Res,37, D767-772.
19. Draghici, S., Khatri, P., Tarca, A.L., Amin, K., Done, A., Voichita, C., Georgescu, C. and Romero, R. (2007) A systems biology approach for pathway level analysis. Genome Res,17,1537-1545.
20. Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J. and Church, G.M. (1999) Systematic determination of genetic network architecture. Nat Genet,22,281-285.
21. Draghici, S., Khatri, P., Martins, R.P., Ostermeier, G.C. and Krawetz, S.A. (2003) Global functional profiling of gene expression. Genomics,81,98-104.
22. Gharib, T.G., Chen, G., Huang, C.C., Misek, D.E., lannettoni, M.D., Hanash, S.M., Orringer, M.B. and Beer, D.G. (2004) Genomic and proteomic analyses of vascular endothelial growth factor and insulin-like growth factor-binding protein 3 in lung adenocarcinomas. Clin Lung Cancer,5, 307-312.
23. Pappot, H., Pedersen, A.N., Brunner, N. and Christensen, I.J. (2006) The complex between urokinase (uPA) and its type-1 inhibitor (PAI-1) in pulmonary adenocarcinoma:relation to prognosis. Lung Cancer,51,193-200.
24. Di Bernardo, M.C., Matakidou, A., Eisen, T. and Houlston, R.S. (2009) Plasminogen activator inhibitor variants PAI-1 A15T and PAI-2 S413C influence lung cancer prognosis. Lung Cancer, 65,237-241.
25. Currie, M.S., Vala, M., Pisetsky, D.S., Greenberg, C.S., Crawford, J. and Cohen, H.J. (1990) Correlation between erythrocyte CR1 reduction and other blood proteinase markers in patients with malignant and inflammatory disorders. Blood,75,1699-1704.
26. Borensztajn, K., Peppelenbosch, M.P. and Spek, C.A. Coagulation Factor Xa inhibits cancer cell migration via LIMK1-mediated cofilin inactivation. Thromb Res.
27. Sierko, E., Wojtukiewicz, M.Z., Zimnoch, L. and Kisiel, W. Expression of tissue factor pathway inhibitor (TFPI) in human breast and colon cancer tissue. Thromb Haemost,103,.198-204.
28. Francesconi, M., Remondini, D., Neretti, N., Sedivy, J.M., Cooper, L.N., Verondini, E., Milanesi, L. and Castellani, G. (2008) Reconstructing networks of pathways via significance analysis of their intersections. BMC Bioinformatics,9 Suppl 4, S9.
29. Fan, T.W., Lane, A.N., Higashi, R.M., Farag, M.A., Gao, H., Bousamra, M. and Miller, D.M. (2009) Altered regulation of metabolic pathways in human lung cancer discerned by (13)C stable isotope-resolved metabolomics (SIRM). Mol Cancer,8,41.
30. Yang, P., Ebbert, J.O., Sun, Z. and Weinshilboum, R.M. (2006) Role of the glutathione metabolic pathway in lung cancer treatment and prognosis:a review. J Clin Oncol,24, 1761-1769.
31. Parent, J.M. (2009) Waiting for gadd45b. Epilepsy Curr,9,170-172.
32. Chi, H., Lu, B., Takekawa, M., Davis, R.J. and Flavell, R.A. (2004) GADD45beta/GADD45gamma and MEKK4 comprise a genetic pathway mediating STAT4-independent IFNgamma production in T cells. EMBO J,23,1576-1586.
33. Takekawa, M., Tatebayashi, K., Itoh, F., Adachi, M., Imai, K. and Saito, H. (2002) Smad-dependent GADD45beta expression mediates delayed activation of p38 MAP kinase by TGF-beta. EMBOJ,21,6473-6482.
34. Major, M.B. and Jones, D.A. (2004) Identification of a gadd45beta 3' enhancer that mediates SMAD3-and SMAD4-dependent transcriptional induction by transforming growth factor beta. J Biol Chem,279,5278-5287.
35. De Smaele, E., Zazzeroni, F., Papa, S., Nguyen, D.U., Jin, R., Jones, J., Cong, R. and Franzoso, G. (2001) Induction of gadd45beta by NF-kappaB downregulates pro-apoptotic JNK signalling. Nature,414,308-313.
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