上皮性浆液性卵巢癌的蛋白质组学研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
卵巢癌是致死率最高的妇科恶性肿瘤,严重威胁妇女的生命和健康。浆液性卵巢癌是其中最主要的病理类型。目前卵巢癌的发病、转移机理仍不清楚。近年来诸多研究提示,肿瘤是一种分子病,肿瘤的特异表现很可能是由深层次的基因改变造成的,而其产物——蛋白质是细胞功能的最终执行者,故本论文的目的在于,从蛋白质表达水平的角度来探讨卵巢癌的发生发展机制。首先,通过双向电泳技术获得了10例浆液性卵巢癌组织和10例正常卵巢上皮组织进行了总蛋白表达胶图,用PDQUEST软件进行对比分析,找出在浆液性卵巢癌组织和正常卵巢上皮组织中表达差异2倍以上并有统计学意义(P<0.01)的蛋白质点。挖取并富集这些差异蛋白质点进行MALDI-TOF-TOF质谱鉴定。其次,选取了其中的6个蛋白质:GGCT, GMFB, HDGF, Rho GDI 2, BANF1,和Galectin-1,采用western blot的方法检测它们在20例浆液性卵巢癌组织和20正常卵巢上皮组织中的表达情况,以检验双向电泳及质谱鉴定的准确性。接着,为了观察相应的基因表达情况,又采用了实时荧光定量RT-PCR的方法检测了相应的6个基因:GGCT, GMFB, HDGF, ARHGDIB, BANF1,和Galectin-1在同样20例浆液性卵巢癌组织和20正常卵巢上皮组织中的表达情况。然后,选取了一个在人类肿瘤中研究较少的蛋白质-GMFB,扩大样本量,采用免疫组织化学的方法,检测了其在245例各种不同程度的卵巢上皮性病变(44例正常卵巢上皮组织,51例良性浆液性囊腺瘤,40例交界性浆液性腺瘤,110例浆液性卵巢癌)中的表达情况,分析了GMFB蛋白质表达与浆液性卵巢癌临床及组织病理学参数的关系,并采用了单因素和多因素模型的方法,进一步判断了GMFB蛋白质表达与浆液性卵巢癌患者预后的关系。最后,本研究基于RNAi干扰的方法,从细胞水平进一步探讨了GMFB对浆液性卵巢癌SKOV3细胞增殖的影响。本研究最终在每张胶图上平均约分离到1800个蛋白质点,找出63个差异表达的蛋白质点,并成功鉴定到其中的61个,代表54个独立的蛋白质。Western blot验证表明相对于正常卵巢上皮组织,GGCT, GMFB, HDGF, Rho GDI 2在浆液性卵巢癌组织中表达上调(P<0.001),而BANF1,和Galectin-1在浆液性卵巢癌组织中表达下调(P<0.001),其表达变化趋势与前期双向电泳及质谱结果完全吻合,一定程度上证实了前期工作的可靠性。与于正常卵巢上皮组织比较,HDGF和ARHGDIB基因在浆液性卵巢癌组织中表达上调(P<0.0001), BANF1和Galectin-1基因在浆液性卵巢癌组织中表达下调(P<0.0001),它们的表达变化趋势与蛋白质表达变化趋势相同;然而,GGCT和GMFB基因在正常卵巢上皮组织和浆液性卵巢癌组织中的表达未发现明显差异。免疫组化结果发现,GMFB蛋白质在正常卵巢上皮组织及三种卵巢上皮性病变中表达有显著差异(P<0.0001),且从正常卵巢上皮组织,到良性浆液性囊腺瘤,交界性浆液性腺瘤,再到浆液性卵巢癌,表达逐渐上调,并与浆液性卵巢癌患者的临床分期相关(P=0.012)。生存分析提示,GMFB与浆液性卵巢癌患者的总体生存率(P=0.003)和无瘤生存率(P=0.010)相关,而且多元回归模型分析揭示GMFB是浆液性卵巢癌患者总体生存(P=0.006)和无瘤生存(P=0.026)的独立预后因子。RNAi干扰技术成功沉默GMFB在SKOV3细胞株中的表达,基因沉默组肿瘤细胞增殖力明显下降(P<0.05)。总之,蛋白质组学技术是发现及筛选肿瘤相关蛋白质的有力手段。GGCT, GMFB, HDGF, Rho GDI 2, BANF1,和Galectin-1等差异蛋白质可能与浆液性卵巢癌的发生发展相关,也可能是潜在的分子标志物。GMFB可能通过促进细胞增殖而参与浆液性卵巢癌发生发展过程,它同时也是一个新发现的浆液性卵巢癌独立预后因子,还可能是潜在的有前途的肿瘤靶向治疗靶点。
     第一部分上皮性浆液性卵巢癌组织和正常卵巢上皮组织中差异表达蛋白质的分离和鉴定
     目的:
     分离和鉴定浆液性卵巢癌组织和正常卵巢上皮组织之间差异表达的蛋白质。
     方法:
     提取10例浆液性卵巢癌组织和10例正常卵巢上皮组织的总蛋白,利用双向电泳技术进行蛋白分离,采用硝酸银染色法进行凝胶染色,PDQUEST软件分析电泳图谱,找出在两组中表达差异2倍以上并有统计学意义(P<0.01)的蛋白质点。手工挖取并富集这些差异蛋白质胶点,采用硝酸银染色兼容的串联质谱方法,MALDI-TOF-TOF MS,对这些蛋白质进行鉴定。
     结果:
     共得到60张双向电泳图谱,每张约有1800个蛋白质点,找到63个差异表达的蛋白质点,成功鉴定出其中的61个,分别代表54个互不相同的蛋白质。
     结论:
     运用双向电泳结合串联质谱技术筛选到的54个在正常卵巢上皮组织和浆液性卵巢癌组织中差异表达的蛋白质可能与浆液性卵巢癌的发生、发展有关。
     蛋白质组学技术是发现和筛选肿瘤相关蛋白质的有效手段。
     第二部分部分差异表达蛋白质的验证及其基因水平表达的研究
     目的:进一步检验其中6个蛋白质,GGCT, GMFB, HDGF, Rho GDI 2, BANF1,和Galectin-1,在正常卵巢上皮组织和浆液性卵巢癌组织中的差异表达情况,进而验证双向电泳及质谱技术的准确性;检测相应的6个基因在两组组织中的表达情况,并与蛋白质表达改变趋势进行比较。
     方法:
     收集20例正常卵巢上皮新鲜组织和20例浆液性卵巢癌新鲜组织,分别提取总蛋白和总RNA, Western blot方法检测蛋白质表达,real time RT-PCR方法检测mRNA表达。
     结果:
     GGCT, GMFB, HDGF, Rho GDI 2在浆液性卵巢癌组织中表达上调,BANF1和Galectin-1在浆液性卵巢癌组织中表达下调。HDGF, ARHGDIB基因在浆液性卵巢癌组织中表达上调,BANF1和Galectin-1基因在浆液性卵巢癌组织中表达下调,GGCT和GMFB在浆液性卵巢癌组织和正常卵巢上皮组织中表达没有显著差异。
     结论:
     1、6个蛋白质表达验证结果与双向电泳及质谱结果完全相符,前期差异蛋白质分离鉴定过程可信。
     2、HDGF,ARHGDIB,BANF1,Galectin-1基因表达变化趋势与蛋白质相同;GGC与GMFB基因水平没有明显变化,提示此两种蛋白表达可能受翻译或翻译后水平机制的调控。
     第三部分GMFB在浆液性卵巢肿瘤中的表达分析及预后相关分析
     目的:
     扩大样本量,检测GMFB在卵巢上皮组织,良性浆液性囊腺瘤,交界性浆液性腺瘤及浆液性卵巢癌中的表达;分析GMFB与浆液性卵巢癌患者临床病理参数及生存率的关系。
     方法:
     制作44例正常卵巢上皮组织,51例良性浆液性囊腺瘤,40例交界性浆液性腺瘤,110例浆液性卵巢癌石蜡切片,收集该组浆液性卵巢癌患者的临床病理资料及生存资料,采用免疫组织化学的方法检测GMFB的表达,采用多种统计学方法进行结果分析。
     结果:
     GMFB蛋白质在正常卵巢上皮组织及三种卵巢上皮性病变中表达有显著差异,且从正常卵巢上皮组织,到良性浆液性囊腺瘤,交界性浆液性腺瘤,再到浆液性卵巢癌,表达逐渐上调,并与浆液性卵巢癌患者的临床分期相关。高GMFB表达与浆液性卵巢癌患者的低总体生存率和低无瘤生存率相关,多元回归模型分析揭示GMFB是浆液性卵巢癌患者总体生存和无瘤生存(P=0.026)的独立预后因子。
     结论:
     1、GMFB可能参与浆液性卵巢癌的发生发展过程。
     2、GMFB是浆液性卵巢癌独立预后因子,还可能是潜在的的肿瘤分子标志物和靶向治疗靶点。
     第四部分GMFB表达对SKOV3细胞株增殖的影响
     目的:
     探讨GMFB对SKOV3细胞增殖力的影响,初步阐明GMFB在浆液性卵巢癌中的作用机制。
     方法:
     合成特异性的siRNA,转染入SKOV3细胞株中,沉默其中GMFB的表达,观察沉默前后肿瘤细胞增殖能力的变化。
     结果:
     GMFB基因沉默组SKOV3细胞增殖力明显下降。
     结论:
     GMFB可能通过促进肿瘤细胞增殖从而在浆液性卵巢癌发展过程中起作用。Vll
Serous ovarian carcinoma is the most common subtype of epithelial ovarian cancer which remains the leading cause of death from gynecologic malignancy. The pathogenesis of serous ovarian carcinoma remains largely unknown. Recently, the viewpoint that malignancy is a kind of molecular disease is generally accepted. Authors believe that altered expression of certain clusters of genes/proteins probably contribute to the gain and maintain of specific features of malignant tumors. Quantitative changes in protein expression but not gene expression can eventually reflect the phenotypic biologic properties of ovarian cancer. Although a large number of studies have demonstrated various molecules including genes and proteins associated with the development or progress of cancers, few of them are recognized to be specific for epithelial ovarian cancer. In this study, we applied proteomic techniques to analyze the protein expression profiles of serous ovarian carcinoma and normal ovarian epithelium tissue aiming to characterize tumor-specific changes in the proteome of serous ovarian cancer, which may bring out new valuable diagnostic biomarkers and/or promising therapeutic targets for serous ovarian cancer, and further more, may provide new insight into carcinogenesis of serous ovarian caner. Using two-dimensional electrophoresis and silver staining, combined with PDQUEST analysis, approximately 1800 proteins spots were detected in normal ovarian epithelium and serous ovarian carcinoma tissue groups. Compared with the normal control group,63 protein spots exhibited significantly differential expression, including 38 up-regulated spots and 25 down-regulated spots.60 of them were successfully identified by MALDI-TOF/TOF MS, representing 54 unambiguous and unique proteins. To further confirm the protein alterations in SOC revealed by proteomic analysis, BANF1, galectin-1, GMFB, GGCT, HDGF, and RhoGDI 2 were selected for validation. The expression changes of these selected proteins were consistent with the 2-DE and silver-staining results, which validated that the differential expressions of proteins obtained from proteomic analysis were convincing. Corresponding gene expression analysis of these proteins was also performed using real-time quantitative reverse transcription-polymerase chain reaction (RT-PCR). The levels of ARHGDIB and HDGF mRNA expression were upregulated and those of BANF1 and LAGLS1 mRNA were downregulated in serous ovarian carcinoma compared with normal epithelium. But no changes of GMFB and GGCT mRNA expression were found in serous ovarian carcinoma, compared with its normal counterpart, which was not consistent with the alterations of protein expression.
     Additionally, we analyzed glia maturation factor beta (GMFB) protein expression by immunohistochemistry in 246 patients with various degrees of ovarian epithelial lesions including 45 normal ovarian epithelia,51 benign ovarian serous adenomas,40 borderline ovarian serous adenomas, and 110 serous ovarian carcinomas. GMFB expression was found to be gradually elevated from normal epithelium, to benign serous adenoma, to borderline serous adenoma, to serous ovarian carcinoma tissues, and was positively correlated with FIGO stage (P=0.012). High GMFB expression was associated with poor disease-free survival (P=0.010) and overall survival (P=0.003), while multivariate analysis revealed GMFB to be an independent prognostic factor for disease-free survival (P=0.026) and overall survival (P=0.006) in patients with SOC. Using RNAi technique, GMFB expression in SKOV3 cell line was silenced and the cells consequently exhibited significantly decreased proliferation (P<0.05).We therefore propose that proteins identified here may be involved in the development or progression of serous ovarian carcinoma, and GMFB can be considered as a prognostic predictor for SOC patients, as well as a potential promising therapy target.
     PART I Identification of differentially expressed proteins between normal ovarian epithelium and serous ovarian carcinoma tissues
     Objective:
     To identify the differentially expressed proteins between normal ovarian epithelium and serous ovarian carcinoma tissues.
     Methods:
     Total proteins were extracted from 10 cases of serous ovarian carcinoma and 10 cases of normal ovarian epithelia. The proteins were separated using Two-dimensional electrophoresis and the gels were subjected to modified silver staining method compatible with MS. The stained gels were scanned using the high-resolution scanner GS-800 calibrated densitometer followed by analysis with PDQUEST analysis. Student's t-test statistical analysis with 99% significance level has been applied to the replicate groups. Significantly differentially expressed protein spots match the threshold (>2 fold) and statistical analysis standard were selected.
     Results:
     Approximately 1800 proteins spots were detected in both groups (Figure 1 A and 1B). Compared with the normal control group,63 protein spots exhibited significantly differential expression, including 38 up-regulated spots and 25 down-regulated spots.60 spots were successfully identified by MALDI-TOF/TOFMS, representing 54 unambiguous and unique proteins.
     Conclusions:
     1、The differentially expressed proteins identified here may involve in the carcinogenesis of serous ovarian carcinoma.
     2、Proteome technique is fairly powerful for the detection of tumor-specific proteins.
     PART II Validation of differentially expressed proteins and quantization of corresponding gene expression level of selected proteins.
     Objective:
     To further confirm the protein alterations in serous ovarian carcinoma revealed by proteomic analysis and to compare protein expression with their corresponding mRNA expression.
     Methods:
     Six proteins of interest, BANF1, galectin-1, GMFB, GGCT, HDGF, and RhoGDI 2 were selected. Total proteins and RNA were extracted from 20 cases of serous ovarian carcinoma and 20 cases of normal ovarian epithelia. Protein expressions were examined using western blot and gene expression levels were quantified employing real time RT-PCR.
     Results:
     GMFB, GGCT, HDGF, and RhoGDI 2 protein expression were upregulated in serous ovarian carcinoma; BANF1, galectin-1 protein expression were decreased in serous ovarian carcinoma; HDGF, and ARHGDIB mRNA expression were elevated in serous ovarian carcinoma; BANF1, galectin-1 mRNA expression were downregualted in serous ovarian carcinoma; no changes of GMFB and GGCT mRNA expression were found in serous ovarian carcinoma.
     Conclusions:
     1、The expression changes of these selected proteins were consistent with the 2-DE and silver-staining results, which validated that the differential expressions of proteins obtained from proteomic analysis were convincing.
     2、GMFB and GGCT mRNA expression alterations were not consistant with that of proteins, suggesting that the expressions of the two proteins are likely to be influenced by translation and post-translational mechanisms in these tissues.
     PARTⅢGMFB expression in various serous ovarian epithelial lesions and survival analysis
     Objective:
     To analyze GMFB protein expression in various degrees of ovarian epithelial lesions and to determine the correlation between GMFB protein expression with clinicopathologic features and survival of patients with serous ovarian carcinoma.
     Methods:
     Employing immunohistochemistry to detect GMFB expression in 45 normal ovarian epithelia,51 benign ovarian serous adenomas,40 borderline ovarian serous adenomas, and 110 serous ovarian carcinomas; Collection the clinicopathologic features and survival data of these patients with serous ovarian carcinoma; using diverse statistical processes to analyze the results.
     Results:
     GMFB expression was found to be gradually elevated from normal epithelium, to benign serous adenoma, to borderline serous adenoma, to serous ovarian carcinoma tissues, and was positively correlated with FIGO stage. High GMFB expression was associated with poor disease-free survival and overall survival, while multivariate analysis revealed GMFB to be an independent prognostic factor for disease-free survival and overall survival in patients with SOC.
     Conculsions:
     1、GMFB may contribute to the initiation and development of serous ovarian carcinoma.
     2、GMFB can be considered as a prognostic predictor for SOC patients, as well as a potential promising therapy target.
     PART IV The effect of GMFB expression upon proliferation of SKOV3 cell line
     Objective:
     To investigate the effect of GMFB expression upon proliferation of SKOV3 cell line.
     Methods:
     Using sequencing-targeting siRNA to silence GMFB expression in SKOV3 cell line and cellular proliferation was detected.
     Results:
     GMFB expression silenced cells showed decreased celluar proliferation.
     Conclusions:
     GMFB may contribute to the progression of serous ovarian carcinoma due to promotion of cellular proliferation.
引文
[1]Mok SC, Kwong J, Welch WR, et al. Etiology and pathogenesis of epithelial ovarian cancer. Dis Markers 2007; 23 (5-6):367-76.
    [2]FerlayJ, BrayF, PisaniP, et al. GLOBOCAN 2002:cancer accidence, mortality and prevalenceworldwide [Internet]. IARC.Cancer-BaseNo.5.version2.0.Lyon: IARCPress; 2004 [cited2006Apr6]. Available from:http://www-dep.iarc.fr/.
    [3]Legge F, Ferrandin G, Salutari V, et al. Biological characterization of ovarian cancer prognostic and the rapeutic implications. Ann Onco 2005; 16(Suppl.4):95-101.
    [4]McCorkle R, Pasacreta J, Tang ST, The silent killer:psychological issues in ovarian cancer. Holist Nurs Pract 2003;17(6):300-8.
    [5]Hanahan D, Weinberg RA. The hallmarks of cancer. Cell.2000;100(1):57-70.
    [6]Heppner GH. Tumor heterogeneity. Cancer Res 1984; 44(6):2259-65.
    [7]Nordling C. A new theory on cancer-inducing mechanism. Br J Cancer 1953;7 (1): 68-72.
    [8]Knudson A. Mutation and cancer:statistical study of retinoblastoma. Proc Natl Acad Sci U S A 1971; 68(4):820-3.
    [9]son L, Seilhamer J. A comparison of selected mRNA and protein abundances in human liver. Electrophoresis 1997; 18,533-7.
    [10]SP, Rochon Y, Franza BR,et al. Correlation between protein and mRNA abundance in yeast. Mol Cell Biol 1999;19:1720-30.
    [11]Chen G, Gharib TG, Huang CC, et al. Discordant protein and mRNA expression in lung adenocarcinomas. Mol Cell Proteomics 2002;1(4):304-13.
    [12]Anderson NL, Anderson NG Proteome and proteomics:new technologies, new concepts, and new words. Electrophoresis 1998;19 (11):1853-61.
    [13]Blackstock WP, Weir MP. Proteomics:quantitative and physical mapping of cellular proteins. Trends Biotechnol 1999;17(3):121-7.
    [14]Wilkins MR, Pasquali C, Ron D, et al. From Proteins to Proteomes:Large Scale Protein Identification by Two-Dimensional Electrophoresis and Arnino Acid Analysis. Nature Biotechnology 1996; 14(1):61-5.
    [15]Kobel M, Kalloger SE, Boyd N, et al. Ovarian carcinoma subtypes are different diseases:implications for biomarker studies. PLoS Med 2008;5(12):e232.
    [1]Gorg A, Obermaier C, noguth G, et al. The current state of two-dimonsional electrophoresis with immobilized pH gradients. Electrophoresis 2000; 21:1037-53.
    [2]Gharahdaghi F, Weinberg CR, Meagher DA, et al. Mass spectrometric identification of proteins from silver-stained polyacrylamide gel:a method for the removal of silver ions to enhance sensitivity. Electrophoresis 1999; 20:601-5.
    [3]American Cancer Society, I. American Cancer Society, Cancer Facts and Figures-2005. www.cancer.org/docroot/STT/stt O.asp,2005.
    [4]Bell DA, Scully RE. Clinical perspective on borderline tumors of the ovary, Current Topics in Obstetrics and Gynecology, Elsevier Science Publishing Co., Inc,1991,119-33.
    [5]Gagne JP, Gagne P, Hunter JM, et al. Proteome profiling of human epithelial ovarian cancer cell line TOV-112D. Mol Cell Biochem 2005;275:25-55.
    [6]Gagne JP, Ethier C, Gagne P, et al. Comparative proteome analysis of human epithelial ovarian cancer. Proteome Sci 2007;5:16.
    [7]Morita A, Miyagi E, Yasumitsu H, et al. Proteomic search for potential diagnostic markers and therapeutic targets for ovarian clear cell adenocarcinoma. Proteomics 2006;6:5880-90.
    [8]Ismail RS, Baldwin RL, Fang J, et al. Differential gene expression between normal and tumorderived ovarian epithelial cells. Cancer Res 2000;60:6744-9.
    [9]Tonin PN, Hudsno TJ, Rodier F, et al. Microarray analysis of gene expression mirrors the biology of ovarian cancer model. Oncogene 2001;20:6617-26.
    [10]An HJ, Kim DS, Park YK, et al. Comparative proteomics of ovarian epithelial tumors. J Proteome Res 2006;5:1082-90.
    [11]Bengtsson S, Krogh M, Szigyarto CA, et al. Large-scale proteomics analysis of human ovarian cancer for biomarkers. J.Proteome Res 2007;6:1440-50.
    [12]Li XQ, Zhang SL, Cai Z, Zhou Y, Ye TM, Chiu JF. Proteomic identification of tumor-associated protein in ovarian serous cystadenocarinoma. Cancer Lett 2009;275:109-16.
    [1]Oakley AJ, Yamada T, Liu D,et al. The identification and structural characterization of C7orf24 as gamma-glutamyl cyclotransferase. An essential enzyme in the gamma-glutamyl cycle. J Biol Chem 2008;283:2031-42.
    [2]Masuda Y, Maeda S, Watanabe A, et al. A novel 21-kDa cytochrome c-releasing factor is generated upon treatment of human leukemia U937 cells with geranylgeraniol. Biochem Biophys Res Commun.2006;346:454-60.
    [3]Kageyama S, Iwaki H, Inoue H,et al. A novel tumor-related protein, C7orf24, identified by proteome differential display of bladder urothelial carcinoma. Proteomics Clin Appl 2007; 1:192-9.
    [4]Lim R, Mitsunobu K. Brain cells in culture:morphological transformation by a protein. Scinece 1974; 185:63-6.
    [5]Lim R, Hicklin DJ, Ryken TC, et al. Endogenous immunoreactive glia maturation factor-like molecule in astrocytes and glioma cells. Brain Res 1987;430:49-57.
    [6]Lim R, Hicklin DJ, Miller JF, et al. Distribution of immunoreactive glia maturation factor-like molecule in organs and tissues. Brain Res 1987; 430:93-100.
    [7]Lim R, Zaheer A. In vitro enhancement of p38 mitogen-activated protein kinase activity by phosphorylated glia maturation factor. J Biol Chem 1996; 271:22953-6.
    [8]Zaheer S, Wu Y, Bassett J, et al. Glia maturation factor regulation of STAT expression:a novel mechanism in experimental autoimmune encephalomyelitis. Neurochem Res 2007;32:2123-31.
    [9]Zaheer A, Zaheer S, Sahu SK. A novel role of glia maturation factor:induction of granulocyte-macrophage colony-stimulating factor and pro-inflammatory cytokines. J Neurochem 2007; 101:364-76.
    [10]Patel S, Sinha A, Singh MP. Identification of differentially expressed proteins in striatum of maneb-and paraquat-induced Parkinson's disease phenotype in mouse. Neurotoxicol Teratol 2007;29:578-85.
    [11]Nakamura H, Izumoto Y, Kambe H, et al. Molecular cloning of complementary DNA for a novol human hepatoma-derived growth factor. Its homology with high mobility group-1 protein.J Biol Chem 1994;269:25143-9.
    [12]Nakamura H, Izumoto Y, Kambe H, et al. Molecular cloning of complementary DNA for a novel human hepatoma-derived growth factor. Its homology with high mobility group-1 protein.J Biol Chem 1994; 269:25143-9.
    [13]Izumoto Y, Kuroda T, Harada H, et al. Hepatoma-derived growth factor belongs to a gene family in mice showing significant homology in the amino terminus.Biochem Biophys Res Commun 1997; 238:26-32.
    [14]Kishima Y,Yamamoto H.Izumoto Y,et al. Hepatoma-derived growth factor stimulates cell growth after translocation to the nucleus by nuclear localization signals. J Biol Chem 2002; 277:10315-22.
    [15]Everett AD,Stoops T,McNamara CA. Nuclear targeting is required for hepatoma-derived growth factor-stimulated mitogenesis in vascular smooth muscle cells. J Biol Chem 2001;276:37564-8.
    [16]Okuda Y,Nakamura H,Yoshida K,et al. Hepatoma-derived growth factor induces tumorigenesis in vivo through both direct angiogenic activity and induction of vascular endothelial growth factor. Cancer Sci 2003; 94:1034-41.
    [17]Enomoto H,Yoshida K,Kishima Y,et al. Hepatoma-derived growth factor is highly expressed in developing liver and promotes fetal hepatocyte proliferation. Hepatology 2002;36:1519-27.
    [18]Everett AD,Lobe DR,Matsumura ME, et al. Hepatoma-derived growth factor stimulates smooth muscle cell growth and is expressed in vascular development. J Clin Invest 2000; 105:567-75
    [19]Enomoto H, Yoshida K, Kishima Y,et al. Hepatoma-derived growth factor is highly expressed in developing liver and promotes fetal hepatocyte proliferation. Hepatology 2002; 36:1519-27.
    [20]Lepourcelet M, Tou L, Cai L, et al. Insights into developmental mechanisms and cancers in the mammalian intestine derived from serial analysis of gene expression and study of the hepatoma-derived growth factor (HDGF). Development 2005; 132:415-27.
    [21]Kishima Y, Yoshida K, Enomoto H, et al. Antisense oligonucleotides of hepatoma-derived growth factor (HDGF) suppress the proliferation of hepatoma cells. Hepatogastroenterology 2002; 49:1639-44.
    [22]Okuda Y, Nakamura H, Yoshida K, et al. Hepatoma-derived growth factor induces tumorigenesis in vivo through both direct angiogenic activity and induction of vascular endothelial growth factor. Cancer Sci 2003; 94:1034-41.
    [23]Uyama H, Tomita Y, Nakamura H, et al. Hepatoma-derived growth factor is a novel prognostic factor for patients with pancreatic cancer. Clin Cancer Res 2006; 12:6043-48.
    [24]Hu TH, Lin JW, Chen HH, et al. The expression and prognostic role of hepatoma-derived growth factor in colorectal stromal tumors. Dis Colon Rectum 2009; 52: 319-36.
    [25]Chang KC, Tai MH, Lin JW, et al. Hepatoma-derived growth factor is a novel prognostic factor for gastrointestinal stromal tumors. Int J Cancer 2007; 121: 1059-65.
    [26]Yoshida K, Tomita Y, Okuda Y, et al. Hepatoma-derived growth factor is a novel prognostic factor for hepatocellular carcinoma. Ann Surg Oncol 2006; 13:159-67.
    [27]Yamamoto S, Tomita Y, Hoshida Y, et al. Expression of hepatoma-derived growth factor is correlated with lymph node metastasis and prognosis of gastric carcinoma. Clin Cancer Res 2006; 12:117-22.
    [28]Ota T, Maeda M, Suto S, et al. LyGDI functions in cancer metastasis by anchoring Rho proteins to the cell membrane. Mol Carcinog 2004; 39:206-20.
    [29]Zhang Y, Zhang B. D4-GDI, a Rho GTPase regulator, promotes breast cancer cell invasiveness. Cancer Res 2006; 66:5592-8.
    [30]Gildea JJ, Seraj MJ, Oxford G, et al. RhoGDI2 is an invasion and metastasis suppressor gene in human cancer. Cancer Res 2002; 62:6418-23.
    [31]Titus B, Frierson HFJr, Conaway M, et al. Endothelin axis is a target of the lung metastasis suppressor gene RhoGDI2.Cancer Res 2005; 65:7320-7.
    [32]Schunke D, Span P, Ronneburg H, et al. Cyclooxygenase-2 is a target gene of rho GDP dissociation inhibitor beta in breast cancer cells. Cancer Res 2007; 67: 0694-702.
    [33]Haraguchi T, Koujin T, Osakada H,et al. Nuclear localization of barrier-to-autointegration factor is correlated with progression of S phase in human cells. J Cell Sci 2007; 20:67-77.
    [34]Choufani G, Nagy N, Saussez S, et al.The levels of expression of galectin-1, galectin-3, and the Thomsen-Friedenreich antigen and their binding sites decrease as clinical aggressiveness increases in head and neck cancers. Cancer 1999; 86: 2353-63.
    [35]Shen J, Person MD, Zhu J, et al. Protein expression profiles in pancreatic adenocarcinoma compared with normal pancreatic tissue and tissue affected by pancreatitis as detected by two-dimensional gel electrophoresis and mass spectrometry. Cancer Res 2004; 64:9018-26.
    [36]Pan S, Chen R, Reimel BA, et al. Quantitative proteomics investigation of pancreatic intraepithelial neoplasia. Electrophoresis 2009; 30:1132-44.
    [37]Watanabe M, Takemasa I, Kawaguchi N, et al. An application of the 2-nitrobenzenesulfenyl (NBS) method to proteomic profiling of human colorectal carcinoma:A novel approach for biomarker discovery. Proteomics Clin Appl 2008; 2:925-35.
    [38]Rubinstein N, Alvarez M, Zwirner NW. et al. Targeted inhibition of galectin-1 gene expression in tumor cells results in heightened T cell-mediated rejection:a potential mechanism of tumor-immune-privilege Cancer Cell 2004; 5:241-51.
    [1]Sarbia M, Loberg C,Wolter M, et al. Expression of bc1-2 and amplification of c-myc are frequent in basaloid squamous cell carcinomas of the esophagus. Am J Pathol 1999; 155:1027-32.
    [2]Hao XP, PretlowTG, Rao JS, et al. Bata-Catenin expression is altered in human colonic aberrant crypt foci. Cancer Res 2001;61:8085-8.
    [3]Zhang L, Ding F, Cao W, et al. Stomatin-like Protein 2 is Overexpressed in Cancer and Involved in Regulating Cell Growth and Cell Adhesion in Human Esophageal Squamous Cell Carcinoma. Clin Cancer Res 2006; 12:1639-46.
    [4]Lim R, Mitsunobu K. Brain cells in culture:morphological transformation by a protein. Science.;185,.63-66.
    [5]Lim R, Miller JF, Zaheer A. Purification and characterization of glia maturation factor-β:a growth regulator for neurons and glia. Proc Natl Acad Sci.1989; 86, 3901-3905.
    [6]Lim R, Zaheer A, Lane WS. Complete amino acid sequence of bovine glia maturation factorβ. Proc Natl Acad Sci.1990; 87,5233-7.
    [7]Kaplan R, Zaheer A, Jaye M, Lim R. Molecular cloning and expression of biologically active human glia maturation factor-beta. J Neurochem.1991 Aug;57(2):483-90.
    [8]Halazonetis TD, Gorgoulis VG, Bartek J. An oncogene-induced DNA damage model for cancer development. Science 2008; 319:1352-5.
    [9]Bartkova J, Horejsi Z, Koed K, et al. DNA damage response as a candidate anti-cancer barrier in early human tumorigenesis. Nature 2005;434:864-70.
    [10]Gorgoulis VG, Vassiliou LV, Karakaidos P, et al. Activation of the DNA damage checkpoint and genomic instability in human precancerous lesions. Nature 2005; 434:907-13.
    [11]Fleming JS, Beaugie CR, Haviv I, et al. Incessant ovulation, inflammation and epithelial ovarian carcinogenesis:Revisiting old hypotheses. Mol Cell Endocrinol 2006; 247:4-21.
    [12]Bonello N, McKie K, Jasper M, et al:Inhibition of nitric oxide:Effects on interleukin-1 betaenhanced ovulation rate, steroid hormones, and ovarian leukocyte distribution at ovulation in the rat. Biol Reprod 1996; 54:436-445.
    [13]Ness RB, Cottreau C. Possible role of ovarian epithelial inflammation in ovarian cancer. J Natl Cancer Inst 1999; 91:1459-67.
    [14]Landen CN Jr, Birrer MJ, Sood AK:Early events in the pathogenesis of epithelial ovarian cancer. J Clin Oncol 2008; 26:995-1005.
    [15]Shih IeM, Kurman RJ:Ovarian tumorigenesis:A proposed model based on morphological and molecular genetic analysis. Am J Pathol 2004;164:1511-8.
    [16]Shih IeM, Kurman RJ:Molecular pathogenesis of ovarian borderline tumors:New insights and old challenges. Clin Cancer Res 2005; 11:7273-9.
    [1]Lim R, Miller JF, Hicklin DJ, et al. Mitogenic activity of glia maturation factor: interaction with insulin and insulin-like growth factor-Ⅱ. Exp Cell Res 1974; 159:335-43.
    [2]Lim R, Turriff DE, Troy SS.Response of glioblasts to a morphological transforming factor:cinematographic and chemical correlations. Brain Res 1976; 113:165-70.
    [3]Brockes JP, Lemke GE, Balzer DR. Purification and preliminary characterization of a glial growth factor from the bovine pituitary. J Biol Chem 1980; 255:8374-7.
    [4]Lim R, Nakagawa S, Arnason BG, et al. Glia maturation factor promotes contact inhibition in cancer cells. Proc NatI Acad Sci 1981; 78:4373-7.
    [5]Lim R, Hicklin DJ, Ryken TC, et al. Suppression of glioma growth in vitro and in vivo by glia maturation factor. Cancer Res 1986; 46:5241-7.
    [6]Lim R, Zhong WX, Zaheer A. Antiproliferative function of glia maturation factorβ. cell regulation 1990; 1:741-6.
    [1]Jemal A, Siegel R, Ward E, et al. Cancer statistics,2006. CA Cancer J Clin 2006;56:106-30.
    [2]Markman M. The myth of measurable disease in ovarian cancer. J Clin Oncol 2003;21:3013-5.
    [3]Markman M. Limitations to the use of the CA-125 antigen level in ovarian cancer. Curr Oncol Rep 2003;5:263-4.
    [4]McGuire WP III, Markman M. Primary ovarian cancer chemotherapy:current standards of care. Br J Cancer 2003; 89(Suppl.3):S3-8.
    [5]Auersperg N, Ota T, Mitchell GW. Early events in ovarian epithelial carcinogenesis:progress and problems in experimental approaches. Int J Gynecol Cancer 2002; 12:691-703.
    [6]Colgan TJ. Challenges in the early diagnosis and staging of Fallopian-tube carcinomas associated with BRCA mutations. Int J Gynecol Pathol 2003;22:109-20.
    [7]Lee Y, Medeiros F, Kindelberger D, et al. Advances in the recognition of tubal intraepithelial carcinoma:applications to cancer screening and the pathogenesis of ovarian cancer. Adv Anat Pathol 2006; 13:1-7.
    [8]Cravatt BF, Simon GM, Yates JR Ⅲ. The biological impact of mass-spectrometry-based proteomics. Nature 2007;450:991-1000.
    [9]Bast RC Jr, Xu FJ, Yu YH, et al. CA 125:the past and the future. Int J Biol Markers 1998; 13:179-87.
    [10]Hunter VJ, Daly L, Helms M, et al. The prognostic significance of CA 125 half-life in patients with ovarian cancer who have received primary chemotherapy after surgical cytoreduction. Am J Obstet Gynecol 1990; 163:1164-7.
    [11]Hunter VJ, Weinberg JB, Haney AF,et al.CA 125 in peritoneal fluid and serum from patients with benign gynecologic conditions and, ovarian cancer. Gynecol Oncol 1990;36:161-5.
    [12]Visintin I, Feng Z, Longton G, et al. Diagnostic markers for early detection of ovarian cancer. Clin Cancer Res 2008;14:1065-72.
    [13]Gramolini A, Peterman S, Kislinger T. Mass spectrometry-based proteomics:a useful tool for biomarker discovery? Clin Pharmacol Ther 2008;83:758-60.
    [14]Faca V, Krasnoselsky A, Hanash S. Innovative proteomic approaches for cancer biomarker discovery. Biotechniques 2007;43:273-81.
    [15]Pitteri SJ, Hanash SM. Proteomic approaches for cancer biomarker discovery in plasma. Expert Rev. Proteomics 2007;4:589-90.
    [16]Adachi J, Kumar C, Zhang Y, et al. The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins. Genome Biol 2006; 7:R80.
    [17]Omenn GS, States DJ, Adamski M, et al. Overview of the HUPO Plasma Proteome Project:results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics 2005;5:3226-45.
    [18]States DJ, Omenn GS, Blackwell TW, et al. Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat Biotechnol 2006;24:333-8.
    [19]Huang L, Fang X. Immunoaffinity fractionation of plasma proteins by chicken Ig Y antibodies. Methods Mol Biol 2008;425:41-51.
    [20]Faca V, Pitteri SJ, Newcomb L, et al. Contribution of protein fractionation to depth of analysis of the serum and plasma proteomes. J Proteome Res 2007;6:3558-65.
    [21]Whiteaker JR, Zhang H, Eng JK, et al. Head-to-head comparison of serum fractionation techniques. J Proteome Res 2007;6:828-36.
    [22]Zhang H, Li XJ, Martin DB, et al. Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat Biotechnol 2003; 21:660-6.
    [23]Lowenthal MS, Mehta AI, Frogale K, et al. Analysis of albumin-associated peptides and proteins from ovarian cancer patients. Clin Chem 2005;51:1933-45.
    [24]Mehta Al, Ross S, Lowenthal MS, et al. Biomarker amplification by serum carrier protein binding. Dis Markers 2003; 19:1-10.
    [25]Horth P, Miller CA, Preckel T, Wenz C. Efficient fractionation and improved protein identification by peptide OFFGEL electrophoresis. Mol. Cell Proteomics 2006; 5:1968-74.
    [26]Zhang H, Yi EC, Li XJ, et al. High throughput quantitative analysis of serum proteins using glycopeptide capture and liquid chromatography mass spectrometry. Mol Cell Proteomics 2005; 4:144-55.
    [27]Zhou Y, Aebersold R, Zhang H. Isolation of N-linked glycopeptides from plasma. Anal Chem 2007;79:5826-37.
    [28]Stahl-Zeng J, Lange V, Ossola R et al. High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol. Cell Proteomics 2007;6:1809-17.
    [29]Liotta LA, Lowenthal M, Mehta A et al. Importance of communication between producers and consumers of publicly available experimental data. J Natl Cancer Inst 2005; 97:310-4.
    [30]Lopez MF, Mikulskis A, Kuzdzal S et al. A novel, high-throughput workflow for discovery and identification of serum carrier protein-bound peptide biomarker candidates in ovarian cancer samples. Clin Chem 2007; 53:1067-74.
    [31]Bast RC Jr, Badgwell D, Lu Z, et al. New tumor markers:CA125 and beyond. Int J Gynecol Cancer 2005; 15:274-81.
    [32]Drukier AK, Ossetrova N, Schors E, et al. High-sensitivity blood-based detection of breast cancer by multi photon detection diagnostic proteomics. J Proteome Res 2006;5:1906-15.
    [33]Jackson D, Craven RA, Hutson RC, et al. Proteomic profiling identifies afamin as a potential biomarker for ovarian cancer. Clin. Cancer Res 2007; 13:7370-9.
    [34]Zhang Z, Bast RC Jr, Yu Y, et al. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer. Cancer Res 2004; 64:5882-90.
    [35]Zhang Z, Yu Y, Xu F, et al. Combining multiple serum tumor markers improves detection of stage I epithelial ovarian cancer. Gynecol Oncol 2007;107:526-31.
    [36]Arshadi N, Jurisica I. Integrating case-based reasoning systems with data mining techniques for discovering and using disease biomarkers. IEEE Trans Knowl Data Eng 2005;17:1127-1137.
    [37]Baggerly KA, Coombes KR, Neeley ES.'Run batch effects potentially compromise the usefulness of genomic signatures for ovarian cancer. J Clin Oncol 2008;26:1186-7.
    [38]Baggerly KA, Edmonson SR, Morris JS, et al. High-resolution serum proteomic patterns for ovarian cancer detection. Endocr Relat Cancer 2004; 11:583-4.
    [39]Gortzak-Uzan L, Ignatchenko A, Evangelou AI, et al. A proteome resource of ovarian cancer ascites:integrated proteomic and bioinformatic analyses to identify putative biomarkers. J Proteome Res 2008;7:339-51.
    [40]Mills GB, May C, McGill M, et al. A putative new growth factor in ascitic fluid from ovarian cancer patients:identification, characterization, and mechanism of action. Cancer Res 1988; 48:1066-71.
    [41]Whiteaker JR, Zhang H, Zhao L, et al. Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer. J Proteome Res 2007;6:3962-75.
    [42]Wolf-Yadlin A, Hautaniemi S, Lauffenburger DA, et al. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc. Natl Acad Sci USA 2007; 104:5860-5.
    [43]Wang H, Kachman MT, Schwartz DR, et al. Comprehensive proteome analysis of ovarian cancers using liquid phase separation, mass mapping and tandem mass spectrometry:a strategy for identification of candidate cancer biomarkers. Proteomics 2004;4:2476-95.
    [44]Wang H, Kachman MT, Schwartz DR, et al. A protein molecular weight map of ES2 clear cell ovarian carcinoma cells using a two-dimensional liquid separations/mass mapping technique. Electrophoresis 2002;23:3168-81.
    [45]Wang Y, Wu R, Cho KR et al. Classification of cancer cell lines using an automated two-dimensional liquid mapping method with hierarchical clustering techniques. Mol Cell Proteomics 2006;5;43-52.
    [46]Kim H, Lubman DM. Micro-proteome analysis using micro-chromatofocusing in intact protein separations. J Chromatogr A 2008; 1194:3-10.
    [47]Zhu Y, Wu R, Sangha N, et al. Classifications of ovarian cancer tissues by proteomic patterns. Proteomics 2006;6:5846-56.
    [48]Castronovo V, Kischel P, Guillonneau F, et al. Identification of specific reachable molecular targets in human breast cancer using a versatile ex vivo proteomic method. Proteomics 2007;7:1188-96.
    [49]Castronovo V, Waltregny D, Kischel P, et al. A chemical proteomics approach for the identification of accessible antigens expressed in human kidney cancer. Mol. Cell Proteomics 2006;5(11):2083-91.
    [50]Faca VM, Ventura AP, Fitzgibbon MP, et al. Proteomic analysis of ovarian cancer cells reveals dynamic processes of protein secretion and shedding of extra-cellular domains. PLoS ONE 2008;6:e2425.
    [51]Kulasingam V, Diamandis EP. Proteomics analysis of conditioned media from three breast cancer cell lines:a mine for biomarkers and therapeutic targets. Mol Cell Proteomics 2007;6:1997-2011.
    [52]Sardana G, Marshall J, Diamandis EP. Discovery of candidate tumor markers for prostate cancer via proteomic analysis of cell culture-conditioned medium. Clin Chem 2007;53:429-37.
    [53]Chen JY, Yan Z, Shen C, et al. A systems biology approach to the study of cisplatin drug resistance in ovarian cancers. J Bioinform Comput Biol 2007;5:383-405.
    [54]Le Moguen K, Lincet H, Deslandes E, et al. Comparative proteomic analysis of cisplatin sensitive IGROV1 ovarian carcinoma cell line and its resistant counterpart IGROV1-R10. Proteomics 2006;6:5183-92.
    [55]Gagne JP, Ethier C, Gagne P, et al. Comparative proteome analysis of human epithelial ovarian cancer. Proteome Sci 2007;5:16.
    [56]Sheehan KM, Calvert VS, Kay EW, et al. Use of reverse phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma. Mol Cell Proteomics 2005;4:346-55.
    [57]Sodek KL, Evangelou AI, Ignatchenko A, et al. Identification of pathways associated with invasive behavior by ovarian cancer cells using multidimensional protein identification technology (MudPIT). Mol BioSyst 2008;4:762-73.