胃癌血管特异性短肽GEBP11受体信息的初步筛选
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摘要
【背景】
     肿瘤的血管生成与肿瘤的生长、侵袭、转移和预后有着密切的联系。对肿瘤血管生成的研究为肿瘤的诊断和治疗提供了新的策略和良好前景。然而,由于缺乏肿瘤血管靶向性分子,通过肿瘤血管诊断治疗肿瘤尚未取得令人满意的结果。研究证实,在肿瘤微环境的作用下肿瘤血管内皮细胞的遗传组成受到影响,表达异质性分子[1, 2]。异质性分子的存在将为肿瘤的靶向诊断及治疗提供特异性靶标。肿瘤血管的异质性取决于肿瘤的异质性,由肿瘤的类型及微环境决定[3, 4]。胃癌细胞与血管内皮细胞的体外共培养可以模拟肿瘤微环境,诱导胃癌血管异质性分子的表达。前期工作中,我们通过建立胃癌细胞SGC7901和人脐静脉内皮细胞的体外共培养模型,利用噬菌体随机环七肽库体外筛选获得能特异结合于共培养内皮细胞的短肽GEBP11。通过初步鉴定证实,GEBP11具有良好的胃癌血管靶向性[5]和血管生成抑制活性。筛选GEBP11的结合受体对深入研究GEBP11实现靶向性和血管抑制活性的机制具有重要意义。因此,本课题围绕筛选GEBP11结合受体的相关信息展开研究,旨在为获得GEBP11的结合受体奠定坚实的基础。
     【目的】
     1.获得GEBP11结合受体在多种胃癌细胞系、多种正常组织和多种器官肿瘤组织血管的表达分布信息;2.研究GEBP11干预后Co-HUVECs分子群的表达变化,鉴定GEBP11影响的信号通路,为受体筛选提供参考信息;3.筛选GEBP11的结合受体,为阐明GEBP11的机制和功能奠定基础。
     【方法】
     1.通过免疫组织化学染色的方法检测GEBP11在多种正常组织、多器官肿瘤组织血管上的结合特性,通过免疫细胞化学染色的方法检测GEBP11在不同胃癌细胞系上的结合特性;
     2.通过RT-PCR和Western blot对基因表达谱分析的结果进行验证,并对基因表达谱分析的结果进行深入的生物信息学分析,鉴定GEBP11影响的信号通路;
     3.通过免疫沉淀和质谱分析的方法初步筛选GEBP11的受体。
     【结果】
     1. GEBP11受体的分布信息
     1) GEBP11结合受体在多种细胞系上的表达分布
     免疫细胞化学染色结果显示,GEBP11在Co-HUVECs和MKN45染色呈阳性,在GES染色弱阳性,在MKN28、AGS、SGC7901染色呈阴性;URP、PBS对照在各细胞系染色均呈阴性。
     2) GEBP11的结合受体在多种器官肿瘤中的表达分布
     GEBP11在多器官肿瘤组织的免疫化学染色显示,GEBP11的结合受体在胃癌、胰腺癌组织血管呈强阳性表达;在食管癌、结肠癌、肝细胞肝癌、子宫癌组织血管呈弱阳性表达;在直肠癌、肺癌、膀胱癌组织血管呈阴性表达。上述9种肿瘤对应的癌旁组织中,GEBP11的结合受体在子宫癌和膀胱癌的癌旁组织血管呈阳性表达;其余均为阴性表达。GEBP11的结合受体在肝细胞肝癌、膀胱尿路上皮癌组织呈阳性表达,在慢性结肠粘膜炎和慢性直肠粘膜炎的腺体、肾组织、膀胱粘膜等癌旁组织中呈阳性表达,在其余肿瘤和癌旁组织实质呈阴性表达。
     3) GEBP11的结合受体在正常组织中的表达分布
     GEBP11在食管、肝、胰腺、垂体、甲状腺、甲状旁腺、心肌、肺、肾、胸腺、骨髓、脾脏、乳腺、卵巢、扁桃体、睾丸、前列腺、涎腺、胃、小肠、结肠等各种正常组织血管染色均为阴性,在食管粘膜细胞、胃腺体、结肠腺体、肾小管、睾丸实质染色阳性。
     2. GEBP11干预后Co-HUVECs表达变化的分子群和影响的信号通路
     经验证,RT-PCR与Western blot的结果与基因表达谱一致;差异基因筛选、GoTerm和Pathway分析为GEBP11结合受体的筛选提供了信息。
     3. GEBP11受体的筛选
     通过免疫沉淀-质谱分析的方法筛选获得了12个GEBP11结合受体的候选分子;通过与基因表达谱结果结合分析发现候选蛋白ZCCHC11经GEBP11处理后表达上调约3倍。
     【结论】
     1. GEBP11的受体表达于一些低分化胃癌细胞系和某些肿瘤组织血管,而在正常组织血管无表达。
     2. RT-PCR和Western blot结果与基因表达谱一致,差异基因筛选、GoTerm和Pathway分析为GEBP11结合受体的筛选提供了信息。
     3.筛选获得了12个GEBP11结合受体的候选蛋白,其中候选蛋白ZCCHC11经GEBP11处理后表达上调约3倍。
【Background】
     Tumor angiogenesis is related closely to growth, invasion, metastatic and prognosis of tumor. The researches of tumor angiogenesis provide new strategy and perspective for the diagnosis and treatment of tumor. However, the status of diagnosis and treatment of tumor by angiogenesis is not satisfactory due to the deficiency of target molecules. Researches indicated that the genetic component of vascular endothelial cells changed in the tumor microenvironment and the endothelial cells expressed heterogeneity molecules, which would probably provide target molecules for diagnosis and therapeutics of tumor. The heterogeneity of tumor vascular depends on that of tumor and decided by the type and microenvironment of tumor. The co-culture of gastric cancer cells and vascular endothelial cells in vitro could simulate tumor microenvironment and induce the expression of the heterogeneity molecules. In previous studies, we obtained a peptide GEBP11 specifically binding to the co-cultured human umbilical vein endothelial cells (Co-HUVECs) through establishing an in vitro co-culture model of gastric cancer cell line SGC7901 and HUVECs and screening in phage display peptides library. Initial studies suggested that GEBP11 peptide has the ability in targeting to gastric cancer vascular and the biological activity in inhibiting angiogenesis. It is important to screen the receptor of GEBP11 for the mechanistic study of the targeting and angiogenesis inhibiting activities of GEBP11. Therefore, the aim of this study is screening the information about the receptor of GEBP11 and setting a foundation for obtaining the receptor.
     【Objectives】
     1. To obtain the localizing information of the receptor of GEBP11 in gastric cancer cell lines, normal tissues and multiple organ cancers; 2. To detect the changed genes of Co-HUVECs and pathways influenced after treated by GEBP11, which could provide information for screening the receptor; 3. To screen the receptor of GEBP11 and settle a foundation for elucidating the mechanism and function of GEBP11.
     【Methods】
     1. Immunohistochemistry and immunocytochemistry were performed to detect the expression of the receptor of GEBP11 in normal tissues, multiple organ cancers and gastric cancer cell lines.
     2. The result of gene array was verified by RT-PCR and Western blot; bioinformatics was used to analyze the different genes and pathways in Co-HUVECs after interfered by GEBP11.
     3. The receptor of GEBP11 was screened by immunoprecipitation and mass spectroscopy.
     【Results】
     1. The expression of the receptor of GEBP11
     1) The expresstion of the receptor of GEBP11 in cell lines.
     The result of immunocytochemistry revealed that GEBP11 was stained on the cytoplasm of Co-HUVECs and MKN45, but not MKN28, SGC7901, AGS, GES.
     2) The expresstion of the receptor of GEBP11 in multiple organ cancers.
     The result of immunohistochemistry showed that GEBP11 was stained on the vascular of gastric cancer, pancreatic cancer and hysterocarcinoma, but not other cancers including cancer of lung, bladder, suprarenal epithelioma, colon, esophagus, rectum, liver.
     3) The expresstion of the receptor of GEBP11 in normal tissues.
     The result of immunohistochemistry showed that GEBP11 was not stained on the vascular of normal tissues including esophagus, liver, pancreas, hypophysis, thyroid, parathyroid gland, cardiac muscle, lung, kidney, thymus, marrow, spleen, mammary gland, ovary, tonsil, testis, sialaden, stomach, small intestine, colon.
     2. The changed genes of Co-HUVECs and pathways after treat by GEBP11
     The results of RT-PCR and Western blot were consistent with gene microarray. The screening of differential genes and analysis of GoTerm and pathway provided information for the receptor of GEBP11.
     3. The screening of the receptor of GEBP11
     We obtained 12 candidant proteins by using the methods of immunoprecipitation and mass spectrography. Compared with the result of gene microarray, we found that the expression of candidant protein ZCCHC11 was upregulated about 3 folds after treated by GEBP11.
     【Conclusions】
     1. The receptor of GEBP11 expressed in some poorly differentiated gastric cancer cell lines and vascular of some other cancers, but not that of normal tissues.
     2. The results of RT-PCR and Western blot were consistent with gene microarray. The screening of differential genes and analysis of GoTerm and pathway provided information for the receptor of GEBP11.
     3. We obtained 12 candidant proteins by using the methods of immunoprecipitation and mass spectrography. Candiant protein ZCCHC11 was upregulated about 3folds after treated by GEBP11.
引文
1. Ruoslahti E, Rajotte D. An address system in the vasculature of normal tissues and tumors. Annu Rev Immunol. 2000;18:813-827.
    2. Yu JL, Rak JW, Carmeliet P, et al. Heterogeneous vascular dependence of tumor cell populations. Am J Pathol. 2001;158(4):1325-1334.
    3. Bergers G, Benjamin LE. Tumorigenesis and the angiogenic switch. Nat Rev Cancer, 2003, 3 (6):401-410.
    4. Ozawa CR,Banfi A, GlazerNL, et al. Microenvironmental VEGF concentration, not total dose, determines a threshold between normal and aberrant angiogenesis. J Clin Invest, 2004, 113 (4):516-527.
    5. Shuhui Liang, Tao Lin, Jie Ding,et al.Screening and identification of vascular-endothelial-cell-specific binding peptide in gastric cancer. J Mol Med. 2006 Sep;84(9):764-773.
    6. American Cancer Society. Global Cancer Facts & Figures. Atlanta, Georgia: American Cancer Society;2007.
    7. Goede V, Schmidt T, Kimmina S, Kozian D, Augustin HG. Analysis of blood vessel maturation processes during cyclic ovarian angiogenesis. Lab Invest. 1998;78(11): 1385-1394
    8. Carmeliet, P. Angiogenesis in life, disease and medicine. Nature. 2005;438:932-936.
    9. George DY, Samuel D, Nicholas WG, et al. Vascular-specific growth factors and blood vessel formation. Nature. 2000;407:242-248
    10. Carmeliet P, Jain RK. Angiogenesis in cancer and other disease. Nature. 2000 Sep
    14;407(6801):249-57.
    11. Hanahan, D., Weinberg, R. A. The hallmarks of cancer. Cell, 100: 57–70, 2000.
    12. Folkman, J. Clinical applications of research on angiogenesis. N. Engl. J. Med.333: 1757–1763, 1995.
    13. Folkman J. Tumor angiogenesis: therapeutic implications. N Engl J Med. 1971;285: 1182-1186
    14. Boehm, T., Folkman, J., Browder, T., and O’Reilly, M. S. Antiangiogenic therapy of experimental cancer does not induce acquired drug resistance. Nature (Lond.), 390:404–407, 1997.
    15. Folkman J. What is the evidence that tumors are angiogenesis dependent? J Natl Cancer Inst. 1990;82(1):4-6
    16. Taylor, S. Folkman, J. Protamine is an inhibitor of angiogenesis. Nature. 1982;297:307-312.
    17. Goldman E. The growth of malignant disease in man and the lower animals with special reference to the vascular system. Lancet. 1907;2:1236-1240
    18. Alguire GH. The transparent chamber technique as a tool in experimental tumortherapy. In: Symposia: approaches to tumour chemotherapy. Washington DC: American Association for the Advancement of Science. 1947:13-26
    19. Tannock IF. The relationship between cell proliferation and the vascular system in a transplanted mouse mammary tumour. Br J Cancer.1968;22:258-273
    20. Jain RK, di Tomaso E, Duda DG, et al. Angiogenesis in brain tumors. Nat Rev Neurosci 2007;8:610.
    21. Carmeliet P, Jain RK. Angiogenesis in cancer and other diseases. Nature 2000;407:249.
    22. Pinter E, Haigh J, Nagy A, et al. Hyperglycemia-induced vasculopathy in the murine conceptus is mediated via reductions of VEGF-A expression and VEGF receptor activation. Am J Pathol 2001;158:1199.
    23. Asahara T, Takahashi T, Masuda H, et al. VEGF contributes to postnatal neovascularization by mobilizing bone marrow–derived endothelial progenitor cells. EMBO J 1999;18:3964.
    24. Rafii S, Lyden D, Benezra R, et al. Vascular and haematopoietic stem cells: Novel targets for antiangiogenesis therapy? Nat Rev Cancer 2002;2:826.
    25. Patan S, Tanda S, Roberge S, et al. Vascular morphogenesis and remodeling in a human tumor xenograft: Blood vessel formation and growth after ovariectomy and tumor implantation. Circ Res 2001;89:732.
    26. Jain RK. Normalization of tumor vasculature: An emerging concept in antiangiogenic therapy. Science 2005;307:58.
    27. Celec P, Yonemitsu Y. Vascular endothelial growth factor basic science and its clinical implications. Pathophysiology 2004;11:69.
    28. Ryan HE, Lo J, Johnson RS. HIF-1 alpha is required for solid tumor formation and embryonic vascularization. EMBO J 1998;17:3005.
    29. Tabruyn SP, Griffioen AW. Molecular pathways of angiogenesis inhibition. Biochem Biophys Res Commun 2007;355:1.
    30. Duda DG, Jain RK, Willett CG. Antiangiogenics: The potential role of integrating this novel treatment modality with chemoradiation for solid tumors. J Clin Oncol 2007;25:4033.
    31. Zurita AJ, Arap W, Pasqualini R. Mapping tumor vascular diversity by screening phage display libraries. J Control Release. 2003;91(1-2):183-186
    32. Arap W, Haedicke W. Targeting the prostate for destruction through a vascular address. Proc Natl Acad Sci USA. 2002;99:1527-1531
    33. Arap W, Pasqualini R, Ruoslahti E. Cancer treatment by targeted drug delivery to tumor vasculature in a mouse model. Science. 1998;279 (5349):377-380
    34. Jason A. Hoffman, Enrico Giraudo, Mallika Singh. et al. Progressive vascular changes in a transgenic mouse model of squamous cell carcinoma. Cancer Cell. 2003;4(5);383-391
    35. Lee K, Erturk E, Mayer R, et al. Efficacy of antitumor chemotherapy in C3H miceenhanced by the antiangiogenesis steroid, cortisone acetate. Cancer Res 1987; 47:5021.
    36. Tseng JE, Glisson BS, Khurl FR, et al. Phase II study of the antiangiogenesis agent thalidomide in recurrent or metastatic squamous cell carcinoma of the head and neck. Cancer 2001;92:2364.
    37. Vacca A, Iurlaro M, Ribatti D, et al. Antiangiogenesis is produced by nontoxic doses of vinblastine. Blood 1999;94:4143.
    38. Browder T, Butterfield CE, Kraling BM, et al. Antiangiogenic scheduling of chemotherapy improves efficacy against experimental drug-resistant cancer. Cancer Res 2000;60:1878.
    39. Hanahan D, Bergers G, Bergsland E. Less is more, regularly: Metronomic dosing of cytotoxic drugs can target tumor angiogenesis in mice. J Clin Invest 2002;105:1045.
    40. Kerbel RS, Kamen BA. The antiangiogenic basis of metronomic chemotherapy. Nat Rev Cancer 2004;4:423.
    41. Folkman J. Angiogenesis: An organizing principle for drug discovery? Nat Rev Drug Discov 2007;6:273.
    42. Willett CG, Boucher Y, di Tomaso E, et al. Direct evidence that the VEGF-specific antibody bevacizumab has antivascular effects in human rectal cancer. Nat Med 2004;10:145.
    43. Sun Y, Wang J, Liu Y, et al. Results of phase III trial of rh-endostatin (YH-16) in advanced non-small-cell lung cancer (NSCLC) patients. J Clin Oncol 2005;23:7138.
    44. Duda DG, Sunamura M, Lozonschi L, et al. Direct in vitro evidence and in vivo analysis of the antiangiogenesis effects of interleukin 12. Cancer Res 2000;60:1111.
    45. Zhao HJ, Ramos CF, Brooks JD, Peehl DM. Distinctive gene expression of prostatic stromal cells cultured from diseased versus normal tissues. J Cell Physiol 2007;210:111–21.
    46. Finak G, Sadekova S, Pepin F, Hallett M, Meterissian S, Halwani F, Khetani K, Souleimanova M, Zabolotny B, Omeroglu A, Park M. Gene expression signatures of morphologically normal breast tissue identify basal-like tumors. Breast Cancer Res 2006;8:R58.
    47. Chang HY, Sneddon JB, Alizadeh AA, Sood R, West RB, Montgomery K, Chi JT, van de Rijn M, Botstein D, Brown PO. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol 2004;2:206–14.
    48. Irizarry RA, Warren D, Spencer F, Kim IF, Biswal S, et al. Multiple-laboratory comparison of microarray platforms. Nat Methods 20052:345–350.
    49. Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, et al. The MicroArray Quality Control (MAQC) project shows interand intraplatform reproducibility of gene expression measurements. Nat Biotechnol 200624:1151–1161.
    50. Altman NS, Hua J (2006) Extending the loop design for two-channel microarray experiments. Genet Res 88: 153–163.
    51. Patterson TA, Lobenhofer EK, Fulmer Smentek SB, Collins PJ, Chu TM, et al. Performance comparison of one-color and two color platforms within the MicroArray QualityControl (MAQC) project. Nat Biotechnol 200624:1140–1150.
    52. Sarwal M, Chua MS, Kambham N, Hsieh SC, Satterwhite T, Masek M, Salvatierra O Jr Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. N Engl J Med 2003349:125–138
    53. Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, Lightfoot S, Menzel W, Granzow M, Ragg T Pediatr Nephrol. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol2009 7:3
    54. Imbeaud S, Graudens E, Boulanger V, Barlet X, Zaborski P, EvenoE, Mueller O, Schroeder A, Auffray C Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces. Nucleic Acids Res 2005,33:56
    55. Van Gelder RN, von Zastrow ME, Yool A, Dement WC, Barchas JD, Eberwine JH Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc Natl Acad Sci USA 199087:1663–1667
    56. Wang E, Miller LD, Ohnmacht GA, Liu ET, Marincola FM High-fidelity mRNA amplification for gene profiling. Nat Biotechnol 200018:457–459
    57. Nygaard V, Hovig E Options available for profiling small samples: a review of sample amplification technology when combined with microarray profiling. Nucleic Acids Res 200634:996-1014
    58. Simon RM, McShane LM, Korn EL, Radmacher MD. Design and Analysis of DNA Microarray Investigations Springer. 2003
    59. Khatri P, Draghici S. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics2005 21:3587–3595.
    60. Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC. A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 2004.20: 93–99.
    61. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005,102:15545–15550.
    62. Tomfohr J, Lu J, Kepler TB. Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics,2005, 6:225.
    63. Fodor SP, Read JL, Pirrung MC, Stryer L, Lu AT, Solas D Light-directed, spatially addressable parallel chemical synthesis. Science, 1991,251:767–773
    64. Chee M, Yang R, Hubbell E, Berno A, Huang XC, Stern D, Winkler J, Lockhart DJ, Morris MS, Fodor SP . Accessing genetic information with high-density DNA arrays.Science 1996,274:610–614
    65. Hsieh HB, Fitch J, White D, Torres F, Roy J, Matusiak R, Krivacic B, Kowalski B, Bruce R, Elrod S .Ultra-highthroughput microarray generation and liquid dispensing using multiple disposable piezoelectric ejectors. J Biomol Screen 20049:85–94
    66. Gunderson KL, Kruglyak S, Graige MS, Garcia F, Kermani BG, Zhao C, Che D, Dickinson T, Wickham E, Bierle J, Doucet D, Milewski M, Yang R, Siegmund C, Haas J, Zhou L, Oliphant A, Fan JB, Barnard S, Chee MS.Decoding randomly ordered DNA arrays. Genome Res 2004,14:870–877
    67. Kapranov P, Sementchenko VI, Gingeras TR. Beyond expression profiling: next generation uses of high density oligonucleotide arrays. Brief Funct Genomic Proteomic 20032:47–56
    68. Mansfield ES, Sarwal MM .Arraying the orchestration of allograft pathology. Am J Transplant 2004,4:853–862
    69. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999,286:531–537
    70. van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002,415:530–536
    71. Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, D’Amico AV, Richie JP, Lander ES, Loda M, Kantoff PW, Golub TR, Sellers WR.Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 2002,1:203–209
    72. Wang T, Hopkins D, Schmidt C, Silva S, Houghton R, Takita H, Repasky E, Reed SG.Identification of genes differentially over-expressed in lung squamous cell carcinoma using combination of cDNA subtraction and microarray analysis. Oncogene 2000,19:1519–1528
    73. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ.Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA 1999,96:6745–6750
    74. Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang CH, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov JP, Poggio T, Gerald W, Loda M, Lander ES, Golub TR Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA 2001,98:15149–15154
    75. Brachat A, Pierrat B, Xynos A, Brecht K, Simonen M, Brungger A, Heim J.A microarray-based, integrated approach to identify novel regulators of cancer drugresponse and apoptosis. Oncogene 2002,21:8361–8371
    76. Cutler DJ, Zwick ME, Carrasquillo MM, Yohn CT, Tobin KP, Kashuk C, Mathews DJ, Shah NA, Eichler EE, Warrington JA, Chakravarti A. High-throughput variation detection and genotyping using microarrays. Genome Res 2001,11:1913–1925
    77. Yan PS, Chen CM, Shi H, Rahmatpanah F, Wei SH, Caldwell CW, Huang TH. Dissecting complex epigenetic alterations in breast cancer using CpG island microarrays. Cancer Res 2001,61:8375–8380
    78. Pollack JR, Perou CM, Alizadeh AA, Eisen MB, Pergamenschikov A, Williams CF, Williams CF, Jeffrey SS, Botstein D, Brown PO. Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nat Genet1999,23:41–46
    79. Relogio A, Ben-Dov C, Baum M, Ruggiu M, Gemund C, Benes V, Darnell RB, Valcárcel J.Alternative splicing microarrays reveal functional expression of neuron-specific regulators in Hodgkin lymphoma cells. J Biol Chem 2005,280:4779–4784
    80. Wang D, Coscoy L, Zylberberg M, Avila PC, Boushey HA, Ganem D, DeRisi JL. Microarray-based detection and genotyping of viral pathogens. Proc Natl Acad Sci USA 2002,99:15687–15692
    81. Conejero-Goldberg C, Wang E, Yi C, Goldberg TE, Jones Brando L, Marincola FM, Webster MJ, Torrey EF.Infectious pathogen detection arrays: viral detection in cell lines and postmortem brain tissue. Biotechniques 2005,39:741–751
    82. Ji R, Cheng Y, Yue J, Yang J, Liu X, Chen H, Dean DB, Zhang C.MicroRNA expression signature and antisense-mediated depletion reveal an essential role of microRNA in vascular neointimal lesion formation. Circ Res 2007,100:1579–1588
    83. Fields S, Song O.A novel genetic system to detect protein–protein interactions. Nature 1989,340: 245–246.
    84. Fashena SJ, Serebriiskii I, Golemis EA.The continued evolution of two-hybrid screening approaches in yeast: How to outwit different preys with different baits. Gene 2000,250: 1–14.
    85. Causier B.Studying the interactome with the yeast two-hybrid system and mass spectrometry. Mass Spectrom Rev 2004,23: 350–367.
    86. Auerbach D, Thaminy S, Hottiger MO, Stagljar I.The post-genomic era of interactive proteomics: Facts andperspectives. Proteomics,2002,2: 611–623.
    87. Van Criekinge W, Beyaert R.Yeast two-hybrid: State of the art. Biol Proced Online 1999,2: 1V38.
    88. Toby GG, Golemis EA.Using the yeast interaction trap and other two-hybrid–based approaches to study protein–protein interactions. Methods 2001,24: 201–217.
    89. Lee JW, Lee SK. Mammalian two-hybrid assay for detecting protein–protein interactions in vivo. Methods Mol Biol 2004,261: 327–336.
    90. Walhout AJ, Vidal M.High-throughput yeast two-hybrid assays for large-scale proteininteraction mapping. Methods 2001,24: 297–306.
    91. Aronheim A, Zandi E, Hennemann H, Elledge SJ, Karin M. Isolation of an AP-1 repressor by a novel method for detecting protein–protein interactions. Mol Cell Biol 1997,17: 3094–3102.
    92. Mohler WA, Blau HM.Gene expression and cell fusion analyzed by lacZ complementation in mammalian cells. Proc Natl Acad Sci U S A 1996,93:12423–12427.
    93. Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, et al. A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 2000,403: 623–627.
    94. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, et al. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci U S A 2001,98: 4569–4574.
    95. Deeds EJ, Ashenberg O, Shakhnovich EI.A simple physical model for scaling in protein–protein interaction networks. Proc Natl Acad Sci U S A 2006,103: 311–316.
    96. Di Tullio A, Reale S, De Angelis F.Molecular recognition by mass spectrometry. J Mass Spectrom 2005,40: 845–865.
    97. Aebersold R, Mann M.Mass spectrometry-based proteomics. Nature 2003,422: 198–
    207.
    98. Pieles U, Zurcher W, Schar M, Moser HE.Matrix-assisted laser desorption ionization time-of-flight mass spectrometry: A powerful tool for the mass and sequence analysis of natural and modified oligonucleotides. Nucleic Acids Res 1993,21: 3191–3196.
    99. Karas M, Hillenkamp F.Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Anal Chem 1988,60: 2299–2301.
    100. Yates JR III, Eng JK, McCormack AL, Schieltz D.Method to correlate tandem mass spectra of modified peptides to amino acid sequences in the protein database. Anal Chem 1995,67: 1426–1436.
    101. Taylor JA, Johnson RS.Sequence database searches via de novo peptide sequencing by tandem mass spectrometry. Rapid Commun Mass Spectrom 1997,11: 1067–1075.
    102. Pevzner PA, Dancik V, Tang CL.Mutation-tolerant protein identification by mass spectrometry. J Comput Biol 2000,7: 777–787.
    103. Geer LY, Markey SP, Kowalak JA, Wagner L, Xu M, et al. Open mass spectrometry search algorithm. J Proteome Res 2004,3: 958–964.
    104. Gould KL, Ren L, Feoktistova AS,et al. Tandem affinity purification and identification of protein complex components. Method,2004,33,239-244.
    105. Rohila JS, Chen M, Cerny R, et al.Improved tandem affinity purification tag and method for isolation of protein heterocomplexes from plants. Plant J 2004,38,172-181
    106. Gavin AC, Bosche M, Krause R, et al.Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002,415, 141-147.
    107. Ho Y, Gruhler A, Heilbut A, et al.Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002,415,180-183
    108. Li S, Armstrong CM, Bertin N, Ge H, Milstein S, et al. A map of the interactome network of the metazoan C. elegans. Science 2004,303: 540–543.
    109. Gavin AC, Aloy P, Grandi P, Krause R, Boesche M, et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 2006,440: 631–636.
    110. Krogan NJ, Cagney G, Yu H, Zhong G, Guo X, et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 2006,440: 637–643.
    111. Jansen R, Greenbaum D, Gerstein M.Relating whole-genome expression data with protein–protein interactions. Genome Res 2002,12: 37–46.
    112. Deane CM, Salwinski L, Xenarios I, Eisenberg D.Protein interactions: Two methods for assessment of the reliability of high throughput observations. Mol Cell Proteomics 2002,1: 349–356.
    113. Kemmeren P, van Berkum NL, Vilo J, Bijma T, Donders R, et al. Protein interaction verification and functional annotation by integrated analysis of genome-scale data. Mol Cell 2002,9: 1133–1143.
    114. Kim SK, Lund J, Kiraly M, Duke K, Jiang M, et al. A gene expression map for Caenorhabditis elegans. Science 2001,293: 2087–2092.
    115. Troyanskaya OG, Garber ME, Brown PO, Botstein D, Altman RB.Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 2002,18: 1454–1461.
    116. Fraser HB, Hirsh AE, Wall DP, Eisen MB.Coevolution of gene expression among interacting proteins. Proc Natl Acad Sci U S A 2004,101: 9033–9038.
    117. Stuart JM, Segal E, Koller D, Kim SK.A gene-coexpression network for global discovery of conserved genetic modules. Science 2003,302: 249–255.
    118. Rutherford SL.From genotype to phenotype: Buffering mechanisms and the storage of genetic information. Bioessays 2000,22: 1095–1105.
    119. Hartman J, Garvik B, Hartwell L.Principles for the buffering of genetic variation. Science 2001,291: 1001–1004.
    120. Bender A, Pringle JR.Use of a screen for synthetic lethal and multicopy suppressee mutants to identify two new genes involved in morphogenesis in Saccharomyces cerevisiae. Mol Cell Biol 1991,11: 1295–1305.
    121. Ooi SL, Pan X, Peyser BD, Ye P, Meluh PB, et al. Global syntheticlethality analysis and yeast functional profiling. Trends Genet 2006 22: 56–63.
    122. Brown JA, Sherlock G, Myers CL, Burrows NM, Deng C, et al. Global analysis of gene function in yeast by quantitative phenotypic profiling. Mol Syst Biol 2006,2: 0001.
    123. Tong AH, Evangelista M, Parsons AB, Xu H, Bader GD, et al. Systematic geneticanalysis with ordered arrays of yeast deletion mutants.Science 2001,294: 2364–2368.
    124. Lippincott-Schwartz J, Patterson GH.Development and use of fluorescent protein markers in living cells. Science 2003,300: 87–91.
    125. Piehler J.New methodologies for measuring protein interactions in vivo and in vitro. Curr Opin Struct Biol 2005,15: 4–14.
    126. Yan Y, Marriott G.Analysis of protein interactions using fluorescence technologies. Curr Opin Chem Biol 2003,7: 635–640.
    127. Karlsson R.SPR for molecular interaction analysis: A review of emerging application areas. J Mol Recognit 2004,17: 151–161.
    128. Cooper MA.Label-free screening of bio-molecular interactions. Anal Bioanal Chem 2003,377: 834–842.
    129. Velazquez Campoy A, Freire E .ITC in the post-genomic era...?Priceless. Biophys Chem 2005,115: 115–124.
    130. Yang Y, Wang H, Erie DA.Quantitative characterization of biomolecular assemblies and interactions using atomic force microscopy. Methods 2003,29: 175–187.
    131. Margittai M, Widengren J, Schweinberger E, Schroder GF, Felekyan S, et al. Single-molecule fluorescence resonance energy transfer reveals a dynamic equilibrium between closed and open conformations of syntaxin 1. Proc Natl Acad Sci U S A 2003 ,100: 15516–15521.
    132. Butland G, Peregrin-Alvarez JM, Li J, Yang W, Yang X, et al. Interaction network containing conserved and essential protein complexes in Escherichia coli. Nature 2005,433: 531–537.
    133. Guldener U, Munsterkotter M, Oesterheld M, Pagel P, Ruepp A, et al. MPact: The MIPS protein interaction resource on yeast. Nucleic Acids Res 2006,34: D436–D441.
    134. Sprinzak E, Sattath S, Margalit H.How reliable are experimental protein–protein interaction data? J Mol Biol 2003,327: 919–923.
    135. Masters SC.Co-immunoprecipitation from transfected cells. Methods Mol Biol 2004,261: 337–350.
    136.郅敏。利用随机噬菌体环七肽库筛选人胃癌血管特异性结合短肽的实验研究。西安交通大学博士学位论文。2004 May
    137.梁树辉。胃癌血管内皮细胞特异结合短肽的噬菌体肽库筛选。第四军医大学硕士学位论文。2005 May
    138.梁树辉。胃癌血管特异结合肽GEBP11的鉴定及其对血管的生成抑制作用。第四军医大学博士学位论文。2009 May
    139.惠晓丽。肿瘤血管靶向肽GX1的体内外鉴定。第四军医大学博士学位论文。2008 May
    140.贺莉,吴开春,惠晓丽等.应用免疫沉淀-质谱法筛选肿瘤血管靶向肽CGNSNPKSC的结合受体[J].现代肿瘤医学,2008,16(8):1259-1263.

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