非小细胞肺癌血清蛋白质标记物的检测与鉴定
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摘要
第一部分：非小细胞肺癌血清蛋白质标记物的检测
目的检测非小细胞肺癌血清蛋白质组,筛选特异性的蛋白质标记物,构建可用于非小细胞肺癌早期检测和诊断的判别模型。
方法应用表面增强激光解析电离飞行时间质谱(SELDI-TOF-MS)技术检测112例非小细胞肺癌和123例对照血清样本的蛋白质质谱,并结合支持向量机(SVM)对数据进行分析。
结果经检测及生物信息学分析处理,发现P<0.01的差异性m/z峰22个。从差异性显著的蛋白质峰的任意组合中,运用SVM筛选出预测值Youden指数最高的组合模型,获得m/z峰值位于6628、9191和11412的蛋白质标记物3个。与正常健康组相比,m/z峰值位于6628的蛋白质在非小细胞肺癌中明显低表达,而峰值位于9191和11412的蛋白质呈明显高表达,且表达强度与分期也有一定的相关性。盲法测试结果显示,联合3种潜在蛋白质标记物,该判别模型区分非小细胞肺癌和对照组的敏感性为96.56%,特异性为94.79%,阳性预测值为95.0%。
结论m/z峰值位于6628、9191和11412的蛋白质可以作为非小细胞肺癌潜在的生物学标记物。SELDI-TOF-MS蛋白质芯片技术为非小细胞肺癌血清蛋白质标记物的检测提供了有效的手段。
第二部分：非小细胞肺癌血清蛋白质标记物的鉴定
目的结合蛋白组学技术对m/z峰值位于6628、9191、11412的蛋白质标记物进行结构鉴定。
方法运用HPLC对3种蛋白质标记物进行分离纯化,随后采用MALDI-TOF-MS质谱技术对分离后的不同时间点的蛋白质纯化液进行检测,以跟踪目标蛋白质所在的样本。最后,对纯化后的蛋白质标记物进行酶解,并应用nano-LC-ESI-MS/MS进行测序。
结果m/z峰值位于6628的蛋白质经鉴定为载脂蛋白C-Ⅰ；m/z峰值位于9191、11412的蛋白质分别为触珠蛋白α1链、S100A4蛋白。
结论联合运用HPLC、MALDI-TOF-MS和nano-LC-ESI-MS/MS等蛋白组学技术,可有效地对肿瘤蛋白质标记物进行结构鉴定。m/z峰值位于6628、9191和11412的蛋白质分别被鉴定为载脂蛋白C-Ⅰ,触珠蛋白α1链和S100A4蛋白。
第三部分：非小细胞肺癌血清蛋白质标记物的验证
目的运用抗体芯片技术对鉴定出的载脂蛋白C-Ⅰ、触珠蛋白α1链和S100A4蛋白质进行免疫学检测,以验证鉴定结果的可靠性。
方法分别运用螯合有抗人载脂蛋白C-Ⅰ抗体、抗人触珠蛋白α抗体和抗人S100A4抗体的蛋白质芯片对非小细胞肺癌患者和对照血清样本进行检测。
结果m/z峰值位于6628的蛋白质可被偶联有抗载脂蛋白C-I抗体的芯片捕获,而m/z峰值位于9191和11412的蛋白质可分别与偶联有抗触珠蛋白α抗体和抗S100A4抗体的芯片相结合。
结论3种蛋白质均可被抗体芯片特异性捕获,证明了前期鉴定结果的可靠性。载脂蛋白C-Ⅰ、触珠蛋白α1链和S100A4蛋白在非小细胞肺癌患者和对照组血清中存在显著的差异性表达,三者联合应用对非小细胞肺癌的早期检测和诊断具有良好的应用价值和广阔的发展前景。
Part I Detection of Serum protein Biomarkers of Non-small Cell Lung Cancer
Objective To detect the specific serum protein biomarkers and establish the discriminant model for early screening and diagnosis of non-small cell lung cancer (NSCLC).
Methods Serum proteomic profiles of 112 NSCLC and 123 controls were analyzed using surfaced enhanced laser desorption/ionization time of flight mass spectroscopy (SELDI-TOF-MS), combing a support vector machine (SVM) classifier to deal with the data.
Results After detecting and analyzing by bioinformation,22 peaks with p value <0.01 were obtained. From the random combination of protein peaks with remakable variation, SVM screened out the combined model with maximum Youden index of the predicted value, identifying 3 markers positioned at 6628,9191 and 11412. In the NSCLC group, the 6628 Da protein was remarkably decreased while 9191 and 11412 Da proteins were significantly elevated. In addition, the levels of these proteins were associated with the clinical stages. Combining these 3 potential markers, the sensitivity, specificity and positive predictive value of the model were 96.56%,94.79% and 95.0% respectively in the blind testing set.
Conclusions These proteins with m/z of 6628,9191 and 11412 could be as potential biomarks of NSCLC. SELDI-TOF-MS has been proved an effective method for serum protein biomarkers detection of NSCLC.
PartⅡIdentification of Serum protein Biomarkers of Non-small Cell Lung Cancer
Objective To identify the candidate protein biomarkers with m/z of 6628,9191 and 11412 using proteomics technologies.
Methods The HPLC was used to purify the 3 candidate protein biomarkers, and then the MALDI-TOF-MS technology was applied to detect each fraction to trace the candidate protein biomarkers. After digestion with modified trypsin, the peptide mixture was analyzed by nano-LC-MS/MS.
Results The candidate biomarker with m/z of 6628 was identified as apolipoprotein C-I, while another two biomarkers were identified as haptoglobin alpha-1 chain (9191 Da) and S100A4 (11412 Da).
Conclusions An efficient strategy, including HPLC purification, MALDI-TOF-MS trace and LC-MS/MS identification, has been proved a powerful tool to identify the target proteins. The candidate biomarker with m/z of 6628,9191 and 11412 were identified as apolipoprotein C-I, haptoglobin alpha-1 chain and S100A4.
PartⅢConfirmation of Serum protein Biomarkers of Non-small Cell Lung Cancer
Objective To confirm the identity of apolipoprotein C-I, haptoglobin alpha-1 chain and S100A4 with the ProteinChip-array-based immunoassay.
Methods Serum samples with NSCLC and controls were analyzed using specific antibody arrays which were prepared by covalently coupling the appropriate antibodies to preactivated ProteinChip arrays.
Results The anti-apolipoprotein C-I array captured apolipoprotein C-I (6628 Da), the anti-haptoglobin alpha-chain antibody specifically captured the haptoglobin alpha-chain (9191 Da) protein, and the S100A4 antibody specifically captured the S100A4 protein (11412 Da).
Conclusions These 3 biomarkers could be specifically captured by the corresponding antibody arrays, which proved the reliability of results with identification. The expression of apolipoprotein C-I, haptoglobin alpha-1 chain and S100A4 in patients with NSCLC are significantly different from nomal controls, which have a good value and broad application prospects for early screening and diagnosis of non-small cell lung cancer.
目的检测非小细胞肺癌血清蛋白质组,筛选特异性的蛋白质标记物,构建可用于非小细胞肺癌早期检测和诊断的判别模型。
方法应用表面增强激光解析电离飞行时间质谱(SELDI-TOF-MS)技术检测112例非小细胞肺癌和123例对照血清样本的蛋白质质谱,并结合支持向量机(SVM)对数据进行分析。
结果经检测及生物信息学分析处理,发现P<0.01的差异性m/z峰22个。从差异性显著的蛋白质峰的任意组合中,运用SVM筛选出预测值Youden指数最高的组合模型,获得m/z峰值位于6628、9191和11412的蛋白质标记物3个。与正常健康组相比,m/z峰值位于6628的蛋白质在非小细胞肺癌中明显低表达,而峰值位于9191和11412的蛋白质呈明显高表达,且表达强度与分期也有一定的相关性。盲法测试结果显示,联合3种潜在蛋白质标记物,该判别模型区分非小细胞肺癌和对照组的敏感性为96.56%,特异性为94.79%,阳性预测值为95.0%。
结论m/z峰值位于6628、9191和11412的蛋白质可以作为非小细胞肺癌潜在的生物学标记物。SELDI-TOF-MS蛋白质芯片技术为非小细胞肺癌血清蛋白质标记物的检测提供了有效的手段。
第二部分：非小细胞肺癌血清蛋白质标记物的鉴定
目的结合蛋白组学技术对m/z峰值位于6628、9191、11412的蛋白质标记物进行结构鉴定。
方法运用HPLC对3种蛋白质标记物进行分离纯化,随后采用MALDI-TOF-MS质谱技术对分离后的不同时间点的蛋白质纯化液进行检测,以跟踪目标蛋白质所在的样本。最后,对纯化后的蛋白质标记物进行酶解,并应用nano-LC-ESI-MS/MS进行测序。
结果m/z峰值位于6628的蛋白质经鉴定为载脂蛋白C-Ⅰ；m/z峰值位于9191、11412的蛋白质分别为触珠蛋白α1链、S100A4蛋白。
结论联合运用HPLC、MALDI-TOF-MS和nano-LC-ESI-MS/MS等蛋白组学技术,可有效地对肿瘤蛋白质标记物进行结构鉴定。m/z峰值位于6628、9191和11412的蛋白质分别被鉴定为载脂蛋白C-Ⅰ,触珠蛋白α1链和S100A4蛋白。
第三部分：非小细胞肺癌血清蛋白质标记物的验证
目的运用抗体芯片技术对鉴定出的载脂蛋白C-Ⅰ、触珠蛋白α1链和S100A4蛋白质进行免疫学检测,以验证鉴定结果的可靠性。
方法分别运用螯合有抗人载脂蛋白C-Ⅰ抗体、抗人触珠蛋白α抗体和抗人S100A4抗体的蛋白质芯片对非小细胞肺癌患者和对照血清样本进行检测。
结果m/z峰值位于6628的蛋白质可被偶联有抗载脂蛋白C-I抗体的芯片捕获,而m/z峰值位于9191和11412的蛋白质可分别与偶联有抗触珠蛋白α抗体和抗S100A4抗体的芯片相结合。
结论3种蛋白质均可被抗体芯片特异性捕获,证明了前期鉴定结果的可靠性。载脂蛋白C-Ⅰ、触珠蛋白α1链和S100A4蛋白在非小细胞肺癌患者和对照组血清中存在显著的差异性表达,三者联合应用对非小细胞肺癌的早期检测和诊断具有良好的应用价值和广阔的发展前景。
Part I Detection of Serum protein Biomarkers of Non-small Cell Lung Cancer
Objective To detect the specific serum protein biomarkers and establish the discriminant model for early screening and diagnosis of non-small cell lung cancer (NSCLC).
Methods Serum proteomic profiles of 112 NSCLC and 123 controls were analyzed using surfaced enhanced laser desorption/ionization time of flight mass spectroscopy (SELDI-TOF-MS), combing a support vector machine (SVM) classifier to deal with the data.
Results After detecting and analyzing by bioinformation,22 peaks with p value <0.01 were obtained. From the random combination of protein peaks with remakable variation, SVM screened out the combined model with maximum Youden index of the predicted value, identifying 3 markers positioned at 6628,9191 and 11412. In the NSCLC group, the 6628 Da protein was remarkably decreased while 9191 and 11412 Da proteins were significantly elevated. In addition, the levels of these proteins were associated with the clinical stages. Combining these 3 potential markers, the sensitivity, specificity and positive predictive value of the model were 96.56%,94.79% and 95.0% respectively in the blind testing set.
Conclusions These proteins with m/z of 6628,9191 and 11412 could be as potential biomarks of NSCLC. SELDI-TOF-MS has been proved an effective method for serum protein biomarkers detection of NSCLC.
PartⅡIdentification of Serum protein Biomarkers of Non-small Cell Lung Cancer
Objective To identify the candidate protein biomarkers with m/z of 6628,9191 and 11412 using proteomics technologies.
Methods The HPLC was used to purify the 3 candidate protein biomarkers, and then the MALDI-TOF-MS technology was applied to detect each fraction to trace the candidate protein biomarkers. After digestion with modified trypsin, the peptide mixture was analyzed by nano-LC-MS/MS.
Results The candidate biomarker with m/z of 6628 was identified as apolipoprotein C-I, while another two biomarkers were identified as haptoglobin alpha-1 chain (9191 Da) and S100A4 (11412 Da).
Conclusions An efficient strategy, including HPLC purification, MALDI-TOF-MS trace and LC-MS/MS identification, has been proved a powerful tool to identify the target proteins. The candidate biomarker with m/z of 6628,9191 and 11412 were identified as apolipoprotein C-I, haptoglobin alpha-1 chain and S100A4.
PartⅢConfirmation of Serum protein Biomarkers of Non-small Cell Lung Cancer
Objective To confirm the identity of apolipoprotein C-I, haptoglobin alpha-1 chain and S100A4 with the ProteinChip-array-based immunoassay.
Methods Serum samples with NSCLC and controls were analyzed using specific antibody arrays which were prepared by covalently coupling the appropriate antibodies to preactivated ProteinChip arrays.
Results The anti-apolipoprotein C-I array captured apolipoprotein C-I (6628 Da), the anti-haptoglobin alpha-chain antibody specifically captured the haptoglobin alpha-chain (9191 Da) protein, and the S100A4 antibody specifically captured the S100A4 protein (11412 Da).
Conclusions These 3 biomarkers could be specifically captured by the corresponding antibody arrays, which proved the reliability of results with identification. The expression of apolipoprotein C-I, haptoglobin alpha-1 chain and S100A4 in patients with NSCLC are significantly different from nomal controls, which have a good value and broad application prospects for early screening and diagnosis of non-small cell lung cancer.
引文
[1]Jemal A, Siegel R, Ward E, et al. Cancer statistics,2006. CA Cancer J Clin,2006,56(2):106～130
[2]Pathak AK, Bhutani M, Mohan A, et al. Non small cell lung cancer (NSCLC):current status and future prospects. Indian J Chest Dis Allied Sci,2004,46(3):191～203
[3]Massion PP, Caprioli RM. Proteomic strategies for the characterization and the early detection of lung cancer. J Thorac Oncol,2006,1(9):1027～1039
[4]Hammerschmidt S, Wirtz H. Lung cancer:current diagnosis and treatment. Dtsch Arztebl Int,2009, 106(49):809～818
[5]Ghosal R, Kloer P, Lewis KE. A review of novel biological tools used in screening for the early detection of lung cancer. Postgrad Med J,2009,85(1005):358～363
[6]Molina R, Auge JM, Filella X, et al. Pro-gastrin-releasing peptide (proGRP) in patients with benign and malignant diseases:comparison with CEA, SCC, CYFRA 21-1 and NSE in patients with lung cancer. Anticancer Res,2005,25(3A):1773～1778
[7]Ardizzoni A. MA, Cafferata M, Tiseo R, et al. Decline in serum carcinoembryonic antigen and cytokeratin 19 fragment during chemotherapy predicts objective response and survival in patients with advanced nonsmall cell lung cancer. Cancer,2006,107(12):2842～2849
[8]Greenberg AK, Lee MS. Biomarkers for lung cancer:clinical uses. Curr Opin Pulm Med,2007, 13(4):249～255
[9]Whelan LC, Power KA, McDowell DT, et al. Applications of SELDI-MS technology in oncology. J Cell Mol Med,2008,12(5A):1535-1547
[10]Tomancova A, Sedo O, Zdrahal Z, et al. Proteomic analysis of cancer cell. Klin Onkol,2009,22(5): 210～217
[11]Wright GL Jr. SELDI proteinchip MS:a platform for biomarker discovery and cancer diagnosis. Expert Rev Mol Diagn,2002,2(6):549～563
[12]Kumar C, Mann M. Bioinformatics analysis of mass spectrometry-based proteomics data sets. FEBS Lett,2009,583(11):1703～171
[1]Granville CA, Dennis PA. An overview of lung cancer genomics and proteomics. Am J Respir Cell Mol Biol,2005,32(3):169～176
[2]Jemal A, Siegel R, Ward E, et al. Cancer statistics,2006. CA Cancer J Clin,2006 56(2): 106～130
[3]Molina JR, Yang P, Cassivi SD, et al. Non-small cell lung cancer:epidemiology, risk factors, treatment and survivorship. Mayo Clin Pro,2008,83(5):584～594
[4]Mulshine JL, Sullivan DC. Clinical practice. Lung cancer screening. N Engl J Med, 2005,352(26):2714～2720
[5]Wistuba Ⅱ, Gazdar AF. Lung cancer preneoplasia. Annu Rev Pathol,2006,1:331～348
[6]Kerr KM, Galler JS, Hagen JA, et al. The role of DNA methylation in the development and progression of lung adenocarcinoma. Dis Markers,2007,23(1-2):5～30
[7]Rajcevic U, Niclou SP, Jimenez CR. Proteomics strategies for target identification and biomarker discovery in cancer. Front Biosci,2009,14:3292～3303
[8]Dalton WS, Friend SH. Cancer biomarkers-an invitation to the table. Science,2006, 312(5777):1165～1168
[9]Larson DR, Singer RH, Zenklusen D. A single molecule view of gene expression. Trends Cell Biol,2009,19(11):630～637
[10]Suarez E, Burguete A, Mclachlan GJ. Microarray data analysis for differential expression:a tutorial. P R Health Sci J,2009,28(2):89～104
[11]Coate LE, John T, Tsao MS, et al. Molecular predictive and prognostic markers in non-small-cell lung cancer. Lancet Oncol,2009,10(10):1001～1010
[12]Hampton R, Walker M, Marshall J, et al. Differential expression of carcinoembryonic antigen (CEA) splice variants in whole blood of colon cancer patients and healthy volunteers:implication for the detection of circulating colon cancer cells. Oncogene, 2002,21(51):7817～7823
[13]Buccheri G, Torchio P, Ferrigno D. Clinical equivalence of two cytokeratin markers in non-small cell lung cancer:a study of tissue polypeptide antigen and cytokeratin 19 fragments. Chest,2003,124(2):622～632
[14]Barak V, Goike H, Panaretakis KW, et al. Clinical utility of cytokeratins as tumor markers. Clin Biochem,2004,37(7):529～540
[15]Molina R, Auge JM, Filella X, et al. Pro-gastrin-releasing peptide (proGRP) in patients with benign and malignant diseases:comparison with CEA, SCC, CYFRA 21-1 and NSE in patients with lung cancer. Anticancer Res,2005,25(3A):1773～1778
[16]Greenberg AK, Lee MS. Biomarkers for lung cancer:clinical uses. Curr Opin Pulm Med,2007,13(4):249～255
[17]Westermeier R, Schickle H. The current state of the art in high-resolution two-dimensional electrophoresis. Arch Physiol Biochem,2009,115(5):279～285
[18]Chen H, Fan ZH. Two-dimensional protein separation in microfluidic devices. Electrophoresis,2009,30(5):758～765
[19]Lopez JL. Two-dimensional electrophoresis in proteome expression analysis. J Chromatogr B Analyt Technol Biomed Life Sci,2007,849(1-2):190～202
[20]Schneider U. Protein concentration by hydrophilic interaction chromatography combined with solid phase extraction. Methods Mol Biol,2008,424:63～69
[21]Gorg A, Weiss W, Dunn MJ. Current two-dimensional electrophoresis technology for proteomics. Proteomics,2004,4(12):3665～3685
[22]Byvatov E, Schneider G. Support vector machine applications in bioinformatics. Appl Bioinformatics,2003,2(2):67～77
[23]Jorissen RN, Gilson MK. Virtual screening of molecular databases using a support vector machine. J Chem Inf Model,2005,45(3):549～561
[1]Lu X, Zhao X, Bai C, et al. LC-MS-based metabonomics analysis. J Chromatogr B Analyt Technol Biomed Life Sci,2008,866(1-2):64～76
[2]Qian WJ, Jacobs JM, Liu T, et al. Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications. Mol Cell Proteomics,2006,5(10):1727～1744
[3]Mbeunkui F, Fodstad O, Pannell LK. Secretory protein enrichment and analysis:an optimized approach applied on cancer cell lines using 2D LC-MS/MS. J Proteome Res, 2006,5(4):899～906
[4]Dwivedi RC, Spicer V, Harder M, et al. Practical implementation of 2D HPLC scheme with accurate peptide retention prediction in both dimensions for high-throughput bottom-up proteomics. Anal Chem,2008,80(18):7036～7042
[5]Herebian D, Choi JH, Abd El-Aty AM, et al. Metabolite analysis in Curcuma domestica using various GC-MS and LC-MS separation and detection techniques. Biomed Chromatogr,2009,23(9):951～965
[6]Lasaosa M, Jakoby T, Delmotte N, et al. Rapid selection of peptide containing fractions in off-line 2-D HPLC in shotgun proteome analysis by screening with MALDI-TOF-MS. Proteomics,2009,9(19):4577～4581
[7]Hardouin J, Joubert-Caron R, Caron M. HPLC-chip-mass spectrometry for protein signature identifications. J Sep Sci,2007,30(10):1482～1487
[8]Kumarathasan P, Mohottalage S, Goegan P, et al. An optimized protein in-gel digest method for reliable proteome characterization by MALDI-TOF-MS analysis. Anal Biochem,2005,346(1):85～89
[9]Fuchs B, Schiller J, MALDI-TOF MS analysis of lipids from cells, tissues and body fluids. Subcell Biochem,2008,49:541～565
[10]Lee J, Soper SA, Murray KK. Development of an efficient on-chip digestion system for protein analysis using MALDI-TOF-MS. Analyst,2009,134(12):2426～2433
[11]Diamandis EP. Peptidomics for cancer diagnosis:present and future. J Proteome Res, 2006,5(9):2079～2082
[12]Mathur S, Park JD, Kim DH, et al. A method for screening enzyme inhibitors using size exclusion chromatography and ESI-LC-MS/MS; J Biomol Screen,2005,10(1): 30～35
[13]Pan C, Ye M, Liu Y, et al. Enrichment of phosphopeptides by Fe3+-immobilized mesoporous nanoparticles of MCM-41 for MALDI and nano-LC-MS/MS analysis. J Proteome Res,2006,5(11):3114～3124
[14]Wu H Y, Tseng VS, Chen LC, et al. Combining alkaline phosphatase treatment and hybrid linear ion trap/Orbitrap high mass accuracy liquid chromatography-mass spectrometry data for the efficient and confident identification of protein phosphorylation. Anal Chem,2009,81(18):7778～7787
[15]Zaikin VG, Halket JM. Derivatization in mass spectrometry--8.Soft ionization mass spectrometry of small molecules. Eur J Mass Spectrom,2006,12(2):79～115
[16]Qian WJ, Liu T, Monroe ME, et al. Probability-based evaluation of peptide and protein identification from tandem mass spectrometry and SEQUEST analysis:the human proteome. J Proteome Res,2005,4(1):53～62
[1]Pepe MS, Cai T, Longton G. Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics,2006,62(1):221～229
[2]Wingren C, Borrebaeck CA. Antibody microarray analysis of directly labeled complex proteomes. Curr Opin Biotechnol,2008,19(1):55～61
[3]Brown WV, Levy RI, Fredrickson DS. Further characterization of the apolipoproteins from the human plasma very low density lipoproteins. J Biol Chem,1970,245(24): 6588～6594
[4]Engwegen JY, Helgason HH, Cats A, et al. Identification of serum proteins discriminating colorectal cancer patients and healthy controls using surface-enhanced laser desorption ionisation-time of flight mass spectrometry. World J Gastroenterol, 2006,12(10):1536～1544
[5]Gobel T, Vorderwulbecke S, Hauck K, et al. New multi protein patterns differentiate liver fibrosis stages and hepatocellular carcinoma in chronic hepatitis C serum samples. World J Gastroenterol,2006,12(47):7604～7612
[6]Valko M, Leibfritz D, Moncol J, et al. Free radicals and antioxidants in normal physiological functions and human disease. Int J Biochem Cell Biol,2007,39(1): 44～84
[7]Tseng CF, Lin CC, Huang HY, et al. Antioxidant role of human haptoglobin. Proteomics, 2004,4(8):2221～2228
[8]Minneci PC, Deans KJ, Zhi H, et al. Hemolysis-associated endothelial dysfunction mediated by accelerated NO inactivation by decompartmentalized oxyhemoglobin. J Clin Invest,2005,115(12):3409～3417
[9]Saeed SA, Ahmad N, Ahmed S. Dual inhibition of cyclooxygenase and lipoxygenase by human haptoglobin:its polymorphism and relation to hemoglobin binding. Biochem Biophys Res Commun,2007,353(4):915-920
[10]Quaye Ik, Ababio G, Amoah AG. Haptoglobin2-2 phenotype is a risk factor for type 2 diabetes in Ghana. J Atheroscler Thromb,2006,13(2):90-94
[11]Abdullah M, Schultz H, Kahler D, et al. Expression of the acute phase protein haptoglobin in human lung cancer and tumor-free lung tissues. Pathol Res Pract,2009, 205(9):639～647
[12]Sun ZL, Zhu Y, Wang FQ, et al. Serum proteomic-based analysis of pancreatic carcinoma for the identification of potential cancer biomarkers. Biochim Biophys Acta, 2007,1774(6):764～771
[13]Huang HL, Stasyk T, Morandell S, et al. Biomarker discovery in breast cancer serum using 2-D differential gel electrophoresis/MALDI-TOF/TOF and data validation by routine clinical assays. Electrophoresis,2006,27(8):1641～1650
[14]Vallely KM, Rustandi RR, Ellis KC, et al. Solution structure of human Mtsl(S100A4) as determined by NMR spectroscopy. Biochemistry,2002,41(42):12670～12680
[15]Koch M, Diez J, Fritz G. Crystal structure of Ca2+-free S100A2 at 1.6-A resolution. J Mol Biol,2008,378(4):933～942
[16]Chen XL, Wang LC, Zhang WG, et al. Correlations of S100A4 and MMP9 expressions to infiltration, metastasis and prognosis of non-small cell lung cancer. Nan Fang Yi Ke Da Xue Bao,2008,28(7):1254～1258
[17]Shi Y, Zou M, Collison K, et al. Ribonucleic acid interference targeting S100A4 (Mtsl) suppresses tumor growth and metastasis of anaplastic thyroid carcinoma in a mouse model. J Clin Endocrinol Metab,2006,91(6):2373～2379
[18]Grigorian M, Lukanidin E. Activator of metastasis in cancer cells, Mtsl/S100A4 protein binds to tumor suppressor protein p53. Genetika,2003,39(7):900～908
[1]Larson DR, Singer RH, Zenklusen D. A single molecule view of gene expression. Trends Cell Biol,2009,19(11):630～637
[2]Suarez E, Burguete A, Mclachlan GJ. Microarray data analysis for differential expression:a tutorial. P R Health Sci J,2009,28(2):89～104
[3]Ciordia S, de Los Rios V, Albar JP. Contributions of advanced proteomics technologies to cancer diagnosis. Clin Transl Oncol,2006,8(8):566～580
[4]Jain KK. Innovations, challenges and future prospects of oncoproteomics. Mol Oncol, 2008,2(2):153～160
[5]Westermeier R, Schickle H. The current state of the art in high-resolution two-dimensional electrophoresis. Arch Physiol Biochem,2009,115(5):279～285
[6]Chen H, Fan ZH. Two-dimensional protein separation in microfluidic devices. Electrophoresis,2009,30(5):758～765
[7]Lopez JL. Two-dimensional electrophoresis in proteome expression analysis. J Chromatogr B Analyt Technol Biomed Life Sci,2007,849(1-2):190～202
[8]Schneider U. Protein concentration by hydrophilic interaction chromatography combined with solid phase extraction. Methods Mol Biol,2008,424:63～69
[9]Hirano H. Two-dimensional electrophoresis and proteomics. Seikagaku,2004,76(10): 1320～1327
[10]Wittmann-Liebold B, Graack HR, Pohl T. Two-dimensional gel electrophoresis as tool for proteomics studies in combination with protein identification by mass spectrometry. Proteomics,2006,6(17):4688～4703
[11]Warren EN, Jiang J, Parker CE, et al. Absolute quantitation of cancer-related proteins using an MS-based peptide chip. Biotechniques,2005, Suppl:7～11
[12]Gaspari M, Abbonante V, Cuda G. Gel-free sample preparation for the nanoscale LC-MS/MS analysis and identification of low-nanogram protein samples. J Sep Sci, 2007,30(14):2210～2216
[13]Qian WJ, Jacobs JM, Liu T, et al. Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications. Mol Cell Proteomics,2006,5(10):1727～1744
[14]Min HK, Hyung SW, Shin JW, et al. Ultrahigh-pressure dual online solid phase extraction/capillary reversed-phase liquid chromatography/tandem mass spectrometry (DO-SPE/cRPLC/MS/MS):a versatile separation platform for high-throughput and highly sensitive proteomic analyses. Electrophoresis,2007,28(6):1012～1021
[15]Guo T, Lee CS, Wang W, et al. Capillary separations enabling tissue proteomics-based biomarker discovery. Electrophoresis,2006,27(18):3523～3532
[16]Hoffman SA, Joo WA, Echan LA, et al. Higher dimensional(Hi-D) separation strategies dramatically improve the potential for cancer biomarker detection in serum and plasma. J Chromatogr B Analyt Technol Biomed Life Sci,2007,849(1-2):43～52
[17]Marcus K, Schafer H, Klaus S, et al. A new fast method for nanoLC-MALDI-TOF/TOF-MS analysis using monolithic columns for peptide preconcentration and separation in proteomic studies. J Proteome Res,2007,6(2): 636～643
[18]Imami K, Monton MR, Ishihama Y, et al. Simple on-line sample preconcentration technique for peptides based on dynamic pH junction in capillary electrophoresis mass spectrometry. J Chromatogr A,2007,1148(2):250～255
[19]Hu Q, Cooks RG, Noll RJ. Phase-enhanced selective ion ejection in an orbitrap mass spectrometer. J Am Soc Mass Spectrom,2007,18(6):980～983
[20]Venable JD, Wohlschlegel J, McClatchy DB, et al. Relative quantification of stable isotope labeled peptides using a linear ion trap-Orbitrap hybrid mass spectrometer. Anal Chem,2007,79(8):3056～3064
[21]Macek B, Waanders LF, Olsen JV, et al. Top-down protein sequencing and MS3 on a hybrid linear quadrupole ion trap-orbitrap mass spectrometer. Mol Cell Proteomics, 2006,5(5):949～958
[22]Lim MS, Elenitoba-Johnson KS. Proteomics in pathology research. Lab Invest,2004, 84(10):1227～1244
[23]Everley PA, Bakalarski CE, Elias JE, et al. Enhanced analysis of metastatic prostate cancer using stable isotopes and high mass accuracy instrumentation. J Proteome Res, 2006,5(5):1224～1231
[24]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(9):R80
[25]Melanson JE, Chisholm KA, Pinto DM. Targeted comparative proteomics by liquid chromatography/matrix-assisted laser desorption/ionization triple-quadrupole mass spectrometry. Rapid Commun Mass Spectrom,2006,20(5):904～910
[26]Liu H, Sadygov RG, Yates JR 3rd. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem,2004,76(14): 4193～4201
[27]Ishihama Y, Oda Y, Tabata T, et al. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol Cell Proteomics,2005,4(9):1265～1272
[28]Mayya V, Rezual K, Wu L, et al. Absolute quantification of multisite phosphorylation by selective reaction monitoring mass spectrometry:determination of inhibitory phosphorylation status of cyclin-dependent kinases. Mol Cell Proteomics,2006,5(6): 1146～1157
[29]Yu Z, Weinberger PM, Sasaki C, et al. Phosphorylation of Akt (Ser473) predicts poor clinical outcome in oropharyngeal squamous cell cancer. Cancer Epidemiol Biomarkers Prev,2007,16(3):553～558
[30]Cazares LH, Diaz JI, Drake RR, et al. MALDI/SELDI protein profiling of serum for the identification of cancer biomarkers. Methods Mol Biol,2008,428:125～140
[31]Wong SC, Chan CM, Ma BB, et al. Advanced proteomic technologies for cancer biomarker discovery. Expert Rev Proteomics,2009,6(2):123～134
[32]MacAleese L, Stauber J, Heeren RM. Perspectives for imaging mass spectrometry in the proteomics landscape. Proteomics,2009,9(4):819～834
[33]Espejel F, Roa JC. Surface enhanced laser desorption/ionization (SELDI):proteomics technology and its application in oncology. Med Clin (Barc),2008,131(8):312～317
[34]Falk R, Ramstrom M, Stahl S, et al. Approaches for systematic proteome exploration. Biomol Eng,2007,24(2):155～168
[35]Seibert V, Ebert MP, Buschmann T. Advances in clinical cancer proteomics: SELDI-Tof-mass spectrometry and biomarker discovery. Brief Funct Genomic Proteomic,2005,4(1):16～26
[36]Ajikumar PK, Ng JK, Tang YC, et al. Carboxyl-terminated dendrimer-coated bioactive interface for protein microarray:high-sensitivity detection of antigen in complex biological samples. Langmuir,2007,23(10):5670～5677
[37]Xu R, Gan X, Fang Y, et al. A simple, rapid, and sensitive integrated protein microarray for simultaneous detection of multiple antigens and antibodies of five human hepatitis viruses(HBV, HCV, HDV, HEV and HGV). Anal Biochem,2007, 362(1):69～75
[38]Song S, Li B, Wang L, et al. A cancer protein microarray platform using antibody fragments and its clinical applications. Mol Biosyst,2007,3(2):151～158
[39]Pepe MS, Cai T, Longton G. Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics,2006,62(1):221～229
[40]Cannataro M. Computational proteomics:management and analysis of proteomics data. Brief Bioinform,2008,9(2):97～101
[41]Zhang X, Lu X, Shi Q, et al. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics,2006,7:
197
[42]Wang Y, Hanley R, Klemke RL. Computational methods for comparison of large genomic and proteomic datasets reveal protein markers of metastatic cancer. J Proteome Res,2006,5(4):907～915
[43]Waybright TJ, Veenstra TD. Bringing protein biomarker discovery to the clinic. Expert Rev Mol Diagn,2009,9(4):305～307
[44]Engkilde K, Jacobsen S, Sondergaard I. Multivariate data analysis of proteome data. Methods Mol Biol,2007,355:195～210
[45]Nettikadan S, Radke K, Johnson J, et al. Detection and quantification of protein biomarkers from fewer than 10 cells. Mol Cell Proteomics,2006,5(5):895～901
[46]Ackermann BL, Berna MJ. Coupling immunoaffinity techniques with MS for quantitative analysis of low-abundance protein biomarkers. Expert Rev Proteomics, 2007,4(2):175～186
[47]Chen G, Pramanik BN. Application of LC/MS to proteomics studies:current status and future prospects. Drug Discov Today,2009,14(9～10):465～471
[48]Feuerstein I, Rainer M, Bernardo K, et al. Derivatized cellulose combined with MALDI-TOF MS:a new tool for serum protein profiling. J Proteome Res,2005,4(6): 2320～2326
[49]Findeisen P, Neumaier M. Mass spectrometry-based clinical proteomics profiling: current status and future directions. Expert Rev Proteomics,2009,6(5):457～459
[50]Najam-UI-Haq M, Rainer M, Heigl N, et al. Nano-structured support materials, their characterisation and serum protein profiling through MALDI/TOF-MS. Amino Acids, 2008,34(2):279～286
[51]Bouamrani A, Ternier J, Ratel D, et al. Direct-tissue SELDI-TOF mass spectrometry analysis:a new application for clinical proteomics. Clin Chem,2006,52(11): 2103～2106
[52]Hwang SI, Thumar J, Lundgren DH, et al. Direct cancer tissue proteomics:a method to identify candidate cancer biomarkers from formalin-fixed paraffin-embedded archival tissues. Oncogene,2007,26(1):65～76
[53]Danna EA, Nolan GP. Transcending the biomarker mindset:deciphering disease mechanisms at the single cell level. Curr Opin Chem Biol,2006,10(1):20～27
[54]Irish JM, Kotecha N, Nolan GP. Mapping normal and cancer cell signaling networks: towards single-cell proteomics. Nat Rev Cancer,2006,6(2):146～155
[55]Veenstra TD, Zhou M. Tissue proteomics and metabolomics:an excellent start and a promising future. J Proteome Res,2009,8(4):1617
[56]Griffiths WJ, Wang Y. Mass spectrometry:from proteomics to metabolomics and lipidomics. Chem Soc Rev,2009,38(7):1882～1896
[57]Downes MR, Byrne JC, Dunn MJ, et al. Application of proteomic strategies to the identification of urinary biomarkers for prostate cancer:a review. Biomarkers,2006, 11(5):406～416
[58]Friedman KM, Fox BA. The promising future of proteomics in cancer diagnosis and treatment. Eur J Gastroenterol Hepatol,2005,17(7):701～703
[59]Smith L, Lind MJ, Welham KJ, et al. Cancer proteomics and its application to discovery of therapy response markers in human cancer. Cancer,2006,107(2):232～241
[60]Simpson RJ, Bernhard OK, Greening DW, et al. Proteomics-driven cancer biomarker discovery:looking to the future. Curr Opin Chem Biol,2008,12(1):72～77
[61]Junqueira M, Spirin V, Balbuena TS, et al. Protein identification pipeline for the homology-driven proteomics. J Proteomics,2008,71(3):346～356
[62]Li J, Zimmerman LJ, Park BH, et al. Network-assisted protein identification and data interpretation in shotgun proteomics. Mol Syst Biol,2009,5:303
[1]Dalton WS, Friend SH. Cancer biomarkers-an invitation to the table. Science,2006, 312(5777):1165～1168
[2]Rajcevic U, Niclou SP, Jimenez CR. Proteomics strategies for target identification and biomarker discovery in cancer. Front Biosci,2009,14:3292～3303
[3]Druker BJ. Imatinib mesylate in the treatment of chronic myeloid leukaemia. Expert Opin Pharmacother,2003,4(6):963～971
[4]Leget GA, Czuczman MS. Use of rituximab, the new FDA-approved antibody. Curr Opin Oncol,1998,10(6):548～551
[5]Arteaga CL, Moulder SL, Yakes FM. HER (erbB) tyrosine kinase inhibitors in the treatment of breast cancer. Semin Oncol,2002,29(3 suppl 11):4～10
[6]Baylin SB. DNA methylation and gene silencing in cancer. Nat Clin Pract Oncol,2005, 2 suppl 1:S4～11
[7]Oh P, Li Y, Yu J, et al. Subtractive proteomic mapping of the endothelial surface in lung and solid tumours for tissue-specific therapy. Nature,2004,429(6992):629～635
[8]Sung HJ, Cho JY. Biomarkers for the lung cancer diagnosis and their advances in proteomics. BMB Rep,2008,41(9):615～625
[9]Park HJ, Kim BG, Lee SJ, et al. Proteomic profiling of endothelial cells in human lung cancer. J Proteome Res,2008,7(3):1138～1150
[10]Jemal A, Siegel R, Ward E, et al. Cancer statistics,2006. CA Cancer J Clin,2006, 56(2):106～130.
[11]Mulshine JL, Sullivan DC. Clinical practice. Lung cancer screening. N Engl J Med, 2005,352(26):2714～2720
[12]Wistuba Ⅱ, Gazdar AF. Lung cancer preneoplasia. Annu Rev Pathol,2006,1:331～348
[13]Kerr KM, Galler JS, Hagen JA, et al. The role of DNA methylation in the development and progression of lung adenocarcinoma. Dis Markers,2007,23(1-2):5～30
[14]Brambilla C, Fievet F, Jeanmart M, et al. Early detection of lung cancer:role of biomarkers. Eur Respir J Suppl,2003,39:36s～44s
[15]Wood LD, Parsons DW, Jones S, et al. The genomic landscapes of human breast and colorectal cancer. Science,2007,318(5853):1108～1113
[16]Senchenko VN, Liu J, Loginov W, et al. Discovery of frequent homozygous deletions in chromosome 3p21.3 LUCA and AP20 regions in renal, lung and breast carcinomas. Oncogene,2004,23(34):5719～5728
[17]Ocak S, Sos ML, Thomas RK, et al. High-throughput molecular analysis in lung cancer: insights into biology and potential clinical applications. Eur Respir J,2009,34(2): 489～506
[18]Zabarovsky ER, Lerman MI, Minna JD. Tumor suppressor genes on chromosome 3p involved in the pathogenesis of lung and other cancers. Oncogene,2002,21(45): 6915～6935
[19]Xue X, Zhu YM, Woll PJ. Circulating DNA and lung cancer. Ann N Y Acad Sci,2006, 1075:154～164
[20]Olivier M, Hussain SP, Caron de Fromentel C, et al. TP53 mutation spectra and load:a tool for generating hypotheses on the etiology of cancer. IARC Sci Publ,2004, (157): 247-270
[21]Wistuba Ⅱ, Behrens C, Milchgrub S, et al. Sequential molecular abnormalities are involved in the multistage development of squamous cell lung carcinoma. Oncogene, 1999,18(3):643～650
[22]Furonaka O, Takeshima Y, Awaya H, et al. Aberrant methylation of pl4(ARF), p15(INK4b) and p16(INK4a) genes and location of the primary site in pulmonary squamous cell carcinoma. Pathol Int,2004,54(8):549～555
[23]Kurakawa E, Shimamoto T, Utsumi K, et al. Hypermethylation of p16 (INK4a) and p15 (INK4b) genes in non-small cell lung cancer. Int J Oncol,2001,19(2):277～281
[24]Zochbauer-Muller S, Fong KM, Virmani AK, et al. Aberrant promoter methylation of multiple genes in non-small cell lung cancer. Cancer Res,2001,61(1):249～255
[25]Planchard D, Loriot Y, Besse B. Impact of KRAS in standard treatment of non-small cell lung cancer (NSCLC) patients in 2009:prognostic and predictive value. Bull Cancer,2009; 96(Suppl):S57～68
[26]Gao W, Mady HH, Melhem MF, et al. Analysis of p53 mutations in histologically normal lung tissues and lung tumors from non-small cell lung cancer patients. Mol Carcinog,2009,48(7):633～641
[27]Simpson RJ, Bernhard OK, Greening DW, et al. Proteomics-driven cancer biomarker discovery:looking to the future. Curr Opin Chem Biol,2008,12(1):72～77
[28]Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Science, 2001,291(5507):1304～1351
[29]Coate LE, John T, Tsao MS, et al. Molecular predictive and prognostic markers in non-small-cell lung cancer. Lancet Oncol,2009,10(10):1001～1010
[30]Molina R, Auge JM, Filella X, et al. Pro-gastrin-releasing peptide (proGRP) in patients with benign and malignant diseases:comparison with CEA, SCC, CYFRA 21-1 and NSE in patients with lung cancer. Anticancer Res,2005,25(3A):1773～1778
[31]Ardizzoni A. MA, Cafferata M, Tiseo R, et al. Decline in serum carcinoembryonic antigen and cytokeratin 19 fragment during chemotherapy predicts objective response and survival in patients with advanced nonsmall cell lung cancer. Cancer,2006,107(12): 2842～2849
[32]Hampton R, Walker M, Marshall J, et al. Differential expression of carcinoembryonic antigen (CEA) splice variants in whole blood of colon cancer patients and healthy volunteers:implication for the detection of circulating colon cancer cells. Oncogene, 2002,21(51):7817～7823
[33]Buccheri G, Torchio P, Ferrigno D. Clinical equivalence of two cytokeratin markers in non-small cell lung cancer:a study of tissue polypeptide antigen and cytokeratin 19 fragments. Chest,2003,124(2):622～632
[34]Barak V, Goike H, Panaretakis KW, et al. Clinical utility of cytokeratins as tumor markers. Clin Biochem,2004,37(7):529～540
[35]Greenberg AK, Lee MS. Biomarkers for lung cancer:clinical uses. Curr Opin Pulm Med,2007,13(4):249～255
[36]Dziadziuszko R, Witta SE, Cappuzzo F, et al. Epidermal growth factor receptor messenger RNA expression, gene dosage, and gefitinib sensitivity in non-small cell lung cancer. Clin Cancer Res,2006,12(10):3078～3084
[37]Nishio K, Arao T, Shimoyama T, et al. Translational studies for target-based drugs. Cancer Chemother Pharmacol,2005,56(Suppl 1):90～93
[38]Cho WC, Yip TT, Yip C, et al. Identification of serum amyloid a protein as a potentially useful biomarker to monitor relapse of nasopharyngeal cancer by serum proteomic profiling. Clin Cancer Res,2004,10(1 Pt 1):43～52
[39]Maciel CM, Junqueira M, Paschoal ME, et al. Differential proteomic serum pattern of low molecular weight proteins expressed by adenocarcinoma lung cancer patients. J Exp Ther Oncol 2005;5(1):31-38.
[40]Huang LJ, Chen SX, Huang Y, et al. Proteomics-based identification of secreted protein dihydrodiol dehydrogenase as a novel serum markers of non-small cell lung cancer. Lung Cancer,2006,54(1):87～94
[41]Heo SH, Lee SJ, Ryoo HM, et al. Identification of putative serum glycoprotein biomarkers for human lung adenocarcinoma by multilectin affinity chromatography and LC-MS/MS. Proteomics,2007,7(23):4292～4302
[42]Liotta LA, Petricoin EF. Serum peptidome for cancer detection:spinning biologic trash into diagnostic gold. J Clin Invest,2006,116(1):26y-30
[43]Villanueva J, Shaffer DR, Philip J, et al. Differential exoprotease activities confer tumor-specific serum peptidome patterns. J Clin Invest,2006,116(1):271～284
[44]de Hoog CL, Mann M. Proteomics. Annu Rev Genomics Hum Genet,2004,5: 267～293
[45]Conrad DH, Goyette J, Thomas PS. Proteomics as a method for early detection of cancer:a review of proteomics, exhaled breath condensate, and lung cancer screening. J Gen Intern Med,2008,23(Suppl 1):78～84
[46]Alessandro R, Fontana S, Kohn E, et al. Proteomic strategies and their application in cancer research. Tumori,2005,91(6):447～455
[47]Hanash SM, Pitteri SJ, Faca VM. Mining the plasma proteome for cancer biomarkers. Nature,2008,452(7187):571～579
[48]Janecki DJ, Bemis KG, Tegeler TJ, et al. A multiple reaction monitoring method for absolute quantification of the human liver alcohol dehydrogenase ADH1C1 isoenzyme. Anal Biochem,2007,369(1):18～26
[49]Anderson NL, Anderson NG, Haines LR, et al. Mass spectrometric quantitation of peptides and proteins using stable isotope standards and capture by anti-peptide antibodies (SISCAPA). J Proteome Res,2004,3(2):235～244
[50]Keshishian H, Addona T, Burgess M, et al. Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol Cell Proteomics,2007,6(12):2212～2229
[2]Pathak AK, Bhutani M, Mohan A, et al. Non small cell lung cancer (NSCLC):current status and future prospects. Indian J Chest Dis Allied Sci,2004,46(3):191～203
[3]Massion PP, Caprioli RM. Proteomic strategies for the characterization and the early detection of lung cancer. J Thorac Oncol,2006,1(9):1027～1039
[4]Hammerschmidt S, Wirtz H. Lung cancer:current diagnosis and treatment. Dtsch Arztebl Int,2009, 106(49):809～818
[5]Ghosal R, Kloer P, Lewis KE. A review of novel biological tools used in screening for the early detection of lung cancer. Postgrad Med J,2009,85(1005):358～363
[6]Molina R, Auge JM, Filella X, et al. Pro-gastrin-releasing peptide (proGRP) in patients with benign and malignant diseases:comparison with CEA, SCC, CYFRA 21-1 and NSE in patients with lung cancer. Anticancer Res,2005,25(3A):1773～1778
[7]Ardizzoni A. MA, Cafferata M, Tiseo R, et al. Decline in serum carcinoembryonic antigen and cytokeratin 19 fragment during chemotherapy predicts objective response and survival in patients with advanced nonsmall cell lung cancer. Cancer,2006,107(12):2842～2849
[8]Greenberg AK, Lee MS. Biomarkers for lung cancer:clinical uses. Curr Opin Pulm Med,2007, 13(4):249～255
[9]Whelan LC, Power KA, McDowell DT, et al. Applications of SELDI-MS technology in oncology. J Cell Mol Med,2008,12(5A):1535-1547
[10]Tomancova A, Sedo O, Zdrahal Z, et al. Proteomic analysis of cancer cell. Klin Onkol,2009,22(5): 210～217
[11]Wright GL Jr. SELDI proteinchip MS:a platform for biomarker discovery and cancer diagnosis. Expert Rev Mol Diagn,2002,2(6):549～563
[12]Kumar C, Mann M. Bioinformatics analysis of mass spectrometry-based proteomics data sets. FEBS Lett,2009,583(11):1703～171
[1]Granville CA, Dennis PA. An overview of lung cancer genomics and proteomics. Am J Respir Cell Mol Biol,2005,32(3):169～176
[2]Jemal A, Siegel R, Ward E, et al. Cancer statistics,2006. CA Cancer J Clin,2006 56(2): 106～130
[3]Molina JR, Yang P, Cassivi SD, et al. Non-small cell lung cancer:epidemiology, risk factors, treatment and survivorship. Mayo Clin Pro,2008,83(5):584～594
[4]Mulshine JL, Sullivan DC. Clinical practice. Lung cancer screening. N Engl J Med, 2005,352(26):2714～2720
[5]Wistuba Ⅱ, Gazdar AF. Lung cancer preneoplasia. Annu Rev Pathol,2006,1:331～348
[6]Kerr KM, Galler JS, Hagen JA, et al. The role of DNA methylation in the development and progression of lung adenocarcinoma. Dis Markers,2007,23(1-2):5～30
[7]Rajcevic U, Niclou SP, Jimenez CR. Proteomics strategies for target identification and biomarker discovery in cancer. Front Biosci,2009,14:3292～3303
[8]Dalton WS, Friend SH. Cancer biomarkers-an invitation to the table. Science,2006, 312(5777):1165～1168
[9]Larson DR, Singer RH, Zenklusen D. A single molecule view of gene expression. Trends Cell Biol,2009,19(11):630～637
[10]Suarez E, Burguete A, Mclachlan GJ. Microarray data analysis for differential expression:a tutorial. P R Health Sci J,2009,28(2):89～104
[11]Coate LE, John T, Tsao MS, et al. Molecular predictive and prognostic markers in non-small-cell lung cancer. Lancet Oncol,2009,10(10):1001～1010
[12]Hampton R, Walker M, Marshall J, et al. Differential expression of carcinoembryonic antigen (CEA) splice variants in whole blood of colon cancer patients and healthy volunteers:implication for the detection of circulating colon cancer cells. Oncogene, 2002,21(51):7817～7823
[13]Buccheri G, Torchio P, Ferrigno D. Clinical equivalence of two cytokeratin markers in non-small cell lung cancer:a study of tissue polypeptide antigen and cytokeratin 19 fragments. Chest,2003,124(2):622～632
[14]Barak V, Goike H, Panaretakis KW, et al. Clinical utility of cytokeratins as tumor markers. Clin Biochem,2004,37(7):529～540
[15]Molina R, Auge JM, Filella X, et al. Pro-gastrin-releasing peptide (proGRP) in patients with benign and malignant diseases:comparison with CEA, SCC, CYFRA 21-1 and NSE in patients with lung cancer. Anticancer Res,2005,25(3A):1773～1778
[16]Greenberg AK, Lee MS. Biomarkers for lung cancer:clinical uses. Curr Opin Pulm Med,2007,13(4):249～255
[17]Westermeier R, Schickle H. The current state of the art in high-resolution two-dimensional electrophoresis. Arch Physiol Biochem,2009,115(5):279～285
[18]Chen H, Fan ZH. Two-dimensional protein separation in microfluidic devices. Electrophoresis,2009,30(5):758～765
[19]Lopez JL. Two-dimensional electrophoresis in proteome expression analysis. J Chromatogr B Analyt Technol Biomed Life Sci,2007,849(1-2):190～202
[20]Schneider U. Protein concentration by hydrophilic interaction chromatography combined with solid phase extraction. Methods Mol Biol,2008,424:63～69
[21]Gorg A, Weiss W, Dunn MJ. Current two-dimensional electrophoresis technology for proteomics. Proteomics,2004,4(12):3665～3685
[22]Byvatov E, Schneider G. Support vector machine applications in bioinformatics. Appl Bioinformatics,2003,2(2):67～77
[23]Jorissen RN, Gilson MK. Virtual screening of molecular databases using a support vector machine. J Chem Inf Model,2005,45(3):549～561
[1]Lu X, Zhao X, Bai C, et al. LC-MS-based metabonomics analysis. J Chromatogr B Analyt Technol Biomed Life Sci,2008,866(1-2):64～76
[2]Qian WJ, Jacobs JM, Liu T, et al. Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications. Mol Cell Proteomics,2006,5(10):1727～1744
[3]Mbeunkui F, Fodstad O, Pannell LK. Secretory protein enrichment and analysis:an optimized approach applied on cancer cell lines using 2D LC-MS/MS. J Proteome Res, 2006,5(4):899～906
[4]Dwivedi RC, Spicer V, Harder M, et al. Practical implementation of 2D HPLC scheme with accurate peptide retention prediction in both dimensions for high-throughput bottom-up proteomics. Anal Chem,2008,80(18):7036～7042
[5]Herebian D, Choi JH, Abd El-Aty AM, et al. Metabolite analysis in Curcuma domestica using various GC-MS and LC-MS separation and detection techniques. Biomed Chromatogr,2009,23(9):951～965
[6]Lasaosa M, Jakoby T, Delmotte N, et al. Rapid selection of peptide containing fractions in off-line 2-D HPLC in shotgun proteome analysis by screening with MALDI-TOF-MS. Proteomics,2009,9(19):4577～4581
[7]Hardouin J, Joubert-Caron R, Caron M. HPLC-chip-mass spectrometry for protein signature identifications. J Sep Sci,2007,30(10):1482～1487
[8]Kumarathasan P, Mohottalage S, Goegan P, et al. An optimized protein in-gel digest method for reliable proteome characterization by MALDI-TOF-MS analysis. Anal Biochem,2005,346(1):85～89
[9]Fuchs B, Schiller J, MALDI-TOF MS analysis of lipids from cells, tissues and body fluids. Subcell Biochem,2008,49:541～565
[10]Lee J, Soper SA, Murray KK. Development of an efficient on-chip digestion system for protein analysis using MALDI-TOF-MS. Analyst,2009,134(12):2426～2433
[11]Diamandis EP. Peptidomics for cancer diagnosis:present and future. J Proteome Res, 2006,5(9):2079～2082
[12]Mathur S, Park JD, Kim DH, et al. A method for screening enzyme inhibitors using size exclusion chromatography and ESI-LC-MS/MS; J Biomol Screen,2005,10(1): 30～35
[13]Pan C, Ye M, Liu Y, et al. Enrichment of phosphopeptides by Fe3+-immobilized mesoporous nanoparticles of MCM-41 for MALDI and nano-LC-MS/MS analysis. J Proteome Res,2006,5(11):3114～3124
[14]Wu H Y, Tseng VS, Chen LC, et al. Combining alkaline phosphatase treatment and hybrid linear ion trap/Orbitrap high mass accuracy liquid chromatography-mass spectrometry data for the efficient and confident identification of protein phosphorylation. Anal Chem,2009,81(18):7778～7787
[15]Zaikin VG, Halket JM. Derivatization in mass spectrometry--8.Soft ionization mass spectrometry of small molecules. Eur J Mass Spectrom,2006,12(2):79～115
[16]Qian WJ, Liu T, Monroe ME, et al. Probability-based evaluation of peptide and protein identification from tandem mass spectrometry and SEQUEST analysis:the human proteome. J Proteome Res,2005,4(1):53～62
[1]Pepe MS, Cai T, Longton G. Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics,2006,62(1):221～229
[2]Wingren C, Borrebaeck CA. Antibody microarray analysis of directly labeled complex proteomes. Curr Opin Biotechnol,2008,19(1):55～61
[3]Brown WV, Levy RI, Fredrickson DS. Further characterization of the apolipoproteins from the human plasma very low density lipoproteins. J Biol Chem,1970,245(24): 6588～6594
[4]Engwegen JY, Helgason HH, Cats A, et al. Identification of serum proteins discriminating colorectal cancer patients and healthy controls using surface-enhanced laser desorption ionisation-time of flight mass spectrometry. World J Gastroenterol, 2006,12(10):1536～1544
[5]Gobel T, Vorderwulbecke S, Hauck K, et al. New multi protein patterns differentiate liver fibrosis stages and hepatocellular carcinoma in chronic hepatitis C serum samples. World J Gastroenterol,2006,12(47):7604～7612
[6]Valko M, Leibfritz D, Moncol J, et al. Free radicals and antioxidants in normal physiological functions and human disease. Int J Biochem Cell Biol,2007,39(1): 44～84
[7]Tseng CF, Lin CC, Huang HY, et al. Antioxidant role of human haptoglobin. Proteomics, 2004,4(8):2221～2228
[8]Minneci PC, Deans KJ, Zhi H, et al. Hemolysis-associated endothelial dysfunction mediated by accelerated NO inactivation by decompartmentalized oxyhemoglobin. J Clin Invest,2005,115(12):3409～3417
[9]Saeed SA, Ahmad N, Ahmed S. Dual inhibition of cyclooxygenase and lipoxygenase by human haptoglobin:its polymorphism and relation to hemoglobin binding. Biochem Biophys Res Commun,2007,353(4):915-920
[10]Quaye Ik, Ababio G, Amoah AG. Haptoglobin2-2 phenotype is a risk factor for type 2 diabetes in Ghana. J Atheroscler Thromb,2006,13(2):90-94
[11]Abdullah M, Schultz H, Kahler D, et al. Expression of the acute phase protein haptoglobin in human lung cancer and tumor-free lung tissues. Pathol Res Pract,2009, 205(9):639～647
[12]Sun ZL, Zhu Y, Wang FQ, et al. Serum proteomic-based analysis of pancreatic carcinoma for the identification of potential cancer biomarkers. Biochim Biophys Acta, 2007,1774(6):764～771
[13]Huang HL, Stasyk T, Morandell S, et al. Biomarker discovery in breast cancer serum using 2-D differential gel electrophoresis/MALDI-TOF/TOF and data validation by routine clinical assays. Electrophoresis,2006,27(8):1641～1650
[14]Vallely KM, Rustandi RR, Ellis KC, et al. Solution structure of human Mtsl(S100A4) as determined by NMR spectroscopy. Biochemistry,2002,41(42):12670～12680
[15]Koch M, Diez J, Fritz G. Crystal structure of Ca2+-free S100A2 at 1.6-A resolution. J Mol Biol,2008,378(4):933～942
[16]Chen XL, Wang LC, Zhang WG, et al. Correlations of S100A4 and MMP9 expressions to infiltration, metastasis and prognosis of non-small cell lung cancer. Nan Fang Yi Ke Da Xue Bao,2008,28(7):1254～1258
[17]Shi Y, Zou M, Collison K, et al. Ribonucleic acid interference targeting S100A4 (Mtsl) suppresses tumor growth and metastasis of anaplastic thyroid carcinoma in a mouse model. J Clin Endocrinol Metab,2006,91(6):2373～2379
[18]Grigorian M, Lukanidin E. Activator of metastasis in cancer cells, Mtsl/S100A4 protein binds to tumor suppressor protein p53. Genetika,2003,39(7):900～908
[1]Larson DR, Singer RH, Zenklusen D. A single molecule view of gene expression. Trends Cell Biol,2009,19(11):630～637
[2]Suarez E, Burguete A, Mclachlan GJ. Microarray data analysis for differential expression:a tutorial. P R Health Sci J,2009,28(2):89～104
[3]Ciordia S, de Los Rios V, Albar JP. Contributions of advanced proteomics technologies to cancer diagnosis. Clin Transl Oncol,2006,8(8):566～580
[4]Jain KK. Innovations, challenges and future prospects of oncoproteomics. Mol Oncol, 2008,2(2):153～160
[5]Westermeier R, Schickle H. The current state of the art in high-resolution two-dimensional electrophoresis. Arch Physiol Biochem,2009,115(5):279～285
[6]Chen H, Fan ZH. Two-dimensional protein separation in microfluidic devices. Electrophoresis,2009,30(5):758～765
[7]Lopez JL. Two-dimensional electrophoresis in proteome expression analysis. J Chromatogr B Analyt Technol Biomed Life Sci,2007,849(1-2):190～202
[8]Schneider U. Protein concentration by hydrophilic interaction chromatography combined with solid phase extraction. Methods Mol Biol,2008,424:63～69
[9]Hirano H. Two-dimensional electrophoresis and proteomics. Seikagaku,2004,76(10): 1320～1327
[10]Wittmann-Liebold B, Graack HR, Pohl T. Two-dimensional gel electrophoresis as tool for proteomics studies in combination with protein identification by mass spectrometry. Proteomics,2006,6(17):4688～4703
[11]Warren EN, Jiang J, Parker CE, et al. Absolute quantitation of cancer-related proteins using an MS-based peptide chip. Biotechniques,2005, Suppl:7～11
[12]Gaspari M, Abbonante V, Cuda G. Gel-free sample preparation for the nanoscale LC-MS/MS analysis and identification of low-nanogram protein samples. J Sep Sci, 2007,30(14):2210～2216
[13]Qian WJ, Jacobs JM, Liu T, et al. Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications. Mol Cell Proteomics,2006,5(10):1727～1744
[14]Min HK, Hyung SW, Shin JW, et al. Ultrahigh-pressure dual online solid phase extraction/capillary reversed-phase liquid chromatography/tandem mass spectrometry (DO-SPE/cRPLC/MS/MS):a versatile separation platform for high-throughput and highly sensitive proteomic analyses. Electrophoresis,2007,28(6):1012～1021
[15]Guo T, Lee CS, Wang W, et al. Capillary separations enabling tissue proteomics-based biomarker discovery. Electrophoresis,2006,27(18):3523～3532
[16]Hoffman SA, Joo WA, Echan LA, et al. Higher dimensional(Hi-D) separation strategies dramatically improve the potential for cancer biomarker detection in serum and plasma. J Chromatogr B Analyt Technol Biomed Life Sci,2007,849(1-2):43～52
[17]Marcus K, Schafer H, Klaus S, et al. A new fast method for nanoLC-MALDI-TOF/TOF-MS analysis using monolithic columns for peptide preconcentration and separation in proteomic studies. J Proteome Res,2007,6(2): 636～643
[18]Imami K, Monton MR, Ishihama Y, et al. Simple on-line sample preconcentration technique for peptides based on dynamic pH junction in capillary electrophoresis mass spectrometry. J Chromatogr A,2007,1148(2):250～255
[19]Hu Q, Cooks RG, Noll RJ. Phase-enhanced selective ion ejection in an orbitrap mass spectrometer. J Am Soc Mass Spectrom,2007,18(6):980～983
[20]Venable JD, Wohlschlegel J, McClatchy DB, et al. Relative quantification of stable isotope labeled peptides using a linear ion trap-Orbitrap hybrid mass spectrometer. Anal Chem,2007,79(8):3056～3064
[21]Macek B, Waanders LF, Olsen JV, et al. Top-down protein sequencing and MS3 on a hybrid linear quadrupole ion trap-orbitrap mass spectrometer. Mol Cell Proteomics, 2006,5(5):949～958
[22]Lim MS, Elenitoba-Johnson KS. Proteomics in pathology research. Lab Invest,2004, 84(10):1227～1244
[23]Everley PA, Bakalarski CE, Elias JE, et al. Enhanced analysis of metastatic prostate cancer using stable isotopes and high mass accuracy instrumentation. J Proteome Res, 2006,5(5):1224～1231
[24]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(9):R80
[25]Melanson JE, Chisholm KA, Pinto DM. Targeted comparative proteomics by liquid chromatography/matrix-assisted laser desorption/ionization triple-quadrupole mass spectrometry. Rapid Commun Mass Spectrom,2006,20(5):904～910
[26]Liu H, Sadygov RG, Yates JR 3rd. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem,2004,76(14): 4193～4201
[27]Ishihama Y, Oda Y, Tabata T, et al. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol Cell Proteomics,2005,4(9):1265～1272
[28]Mayya V, Rezual K, Wu L, et al. Absolute quantification of multisite phosphorylation by selective reaction monitoring mass spectrometry:determination of inhibitory phosphorylation status of cyclin-dependent kinases. Mol Cell Proteomics,2006,5(6): 1146～1157
[29]Yu Z, Weinberger PM, Sasaki C, et al. Phosphorylation of Akt (Ser473) predicts poor clinical outcome in oropharyngeal squamous cell cancer. Cancer Epidemiol Biomarkers Prev,2007,16(3):553～558
[30]Cazares LH, Diaz JI, Drake RR, et al. MALDI/SELDI protein profiling of serum for the identification of cancer biomarkers. Methods Mol Biol,2008,428:125～140
[31]Wong SC, Chan CM, Ma BB, et al. Advanced proteomic technologies for cancer biomarker discovery. Expert Rev Proteomics,2009,6(2):123～134
[32]MacAleese L, Stauber J, Heeren RM. Perspectives for imaging mass spectrometry in the proteomics landscape. Proteomics,2009,9(4):819～834
[33]Espejel F, Roa JC. Surface enhanced laser desorption/ionization (SELDI):proteomics technology and its application in oncology. Med Clin (Barc),2008,131(8):312～317
[34]Falk R, Ramstrom M, Stahl S, et al. Approaches for systematic proteome exploration. Biomol Eng,2007,24(2):155～168
[35]Seibert V, Ebert MP, Buschmann T. Advances in clinical cancer proteomics: SELDI-Tof-mass spectrometry and biomarker discovery. Brief Funct Genomic Proteomic,2005,4(1):16～26
[36]Ajikumar PK, Ng JK, Tang YC, et al. Carboxyl-terminated dendrimer-coated bioactive interface for protein microarray:high-sensitivity detection of antigen in complex biological samples. Langmuir,2007,23(10):5670～5677
[37]Xu R, Gan X, Fang Y, et al. A simple, rapid, and sensitive integrated protein microarray for simultaneous detection of multiple antigens and antibodies of five human hepatitis viruses(HBV, HCV, HDV, HEV and HGV). Anal Biochem,2007, 362(1):69～75
[38]Song S, Li B, Wang L, et al. A cancer protein microarray platform using antibody fragments and its clinical applications. Mol Biosyst,2007,3(2):151～158
[39]Pepe MS, Cai T, Longton G. Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics,2006,62(1):221～229
[40]Cannataro M. Computational proteomics:management and analysis of proteomics data. Brief Bioinform,2008,9(2):97～101
[41]Zhang X, Lu X, Shi Q, et al. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics,2006,7:
197
[42]Wang Y, Hanley R, Klemke RL. Computational methods for comparison of large genomic and proteomic datasets reveal protein markers of metastatic cancer. J Proteome Res,2006,5(4):907～915
[43]Waybright TJ, Veenstra TD. Bringing protein biomarker discovery to the clinic. Expert Rev Mol Diagn,2009,9(4):305～307
[44]Engkilde K, Jacobsen S, Sondergaard I. Multivariate data analysis of proteome data. Methods Mol Biol,2007,355:195～210
[45]Nettikadan S, Radke K, Johnson J, et al. Detection and quantification of protein biomarkers from fewer than 10 cells. Mol Cell Proteomics,2006,5(5):895～901
[46]Ackermann BL, Berna MJ. Coupling immunoaffinity techniques with MS for quantitative analysis of low-abundance protein biomarkers. Expert Rev Proteomics, 2007,4(2):175～186
[47]Chen G, Pramanik BN. Application of LC/MS to proteomics studies:current status and future prospects. Drug Discov Today,2009,14(9～10):465～471
[48]Feuerstein I, Rainer M, Bernardo K, et al. Derivatized cellulose combined with MALDI-TOF MS:a new tool for serum protein profiling. J Proteome Res,2005,4(6): 2320～2326
[49]Findeisen P, Neumaier M. Mass spectrometry-based clinical proteomics profiling: current status and future directions. Expert Rev Proteomics,2009,6(5):457～459
[50]Najam-UI-Haq M, Rainer M, Heigl N, et al. Nano-structured support materials, their characterisation and serum protein profiling through MALDI/TOF-MS. Amino Acids, 2008,34(2):279～286
[51]Bouamrani A, Ternier J, Ratel D, et al. Direct-tissue SELDI-TOF mass spectrometry analysis:a new application for clinical proteomics. Clin Chem,2006,52(11): 2103～2106
[52]Hwang SI, Thumar J, Lundgren DH, et al. Direct cancer tissue proteomics:a method to identify candidate cancer biomarkers from formalin-fixed paraffin-embedded archival tissues. Oncogene,2007,26(1):65～76
[53]Danna EA, Nolan GP. Transcending the biomarker mindset:deciphering disease mechanisms at the single cell level. Curr Opin Chem Biol,2006,10(1):20～27
[54]Irish JM, Kotecha N, Nolan GP. Mapping normal and cancer cell signaling networks: towards single-cell proteomics. Nat Rev Cancer,2006,6(2):146～155
[55]Veenstra TD, Zhou M. Tissue proteomics and metabolomics:an excellent start and a promising future. J Proteome Res,2009,8(4):1617
[56]Griffiths WJ, Wang Y. Mass spectrometry:from proteomics to metabolomics and lipidomics. Chem Soc Rev,2009,38(7):1882～1896
[57]Downes MR, Byrne JC, Dunn MJ, et al. Application of proteomic strategies to the identification of urinary biomarkers for prostate cancer:a review. Biomarkers,2006, 11(5):406～416
[58]Friedman KM, Fox BA. The promising future of proteomics in cancer diagnosis and treatment. Eur J Gastroenterol Hepatol,2005,17(7):701～703
[59]Smith L, Lind MJ, Welham KJ, et al. Cancer proteomics and its application to discovery of therapy response markers in human cancer. Cancer,2006,107(2):232～241
[60]Simpson RJ, Bernhard OK, Greening DW, et al. Proteomics-driven cancer biomarker discovery:looking to the future. Curr Opin Chem Biol,2008,12(1):72～77
[61]Junqueira M, Spirin V, Balbuena TS, et al. Protein identification pipeline for the homology-driven proteomics. J Proteomics,2008,71(3):346～356
[62]Li J, Zimmerman LJ, Park BH, et al. Network-assisted protein identification and data interpretation in shotgun proteomics. Mol Syst Biol,2009,5:303
[1]Dalton WS, Friend SH. Cancer biomarkers-an invitation to the table. Science,2006, 312(5777):1165～1168
[2]Rajcevic U, Niclou SP, Jimenez CR. Proteomics strategies for target identification and biomarker discovery in cancer. Front Biosci,2009,14:3292～3303
[3]Druker BJ. Imatinib mesylate in the treatment of chronic myeloid leukaemia. Expert Opin Pharmacother,2003,4(6):963～971
[4]Leget GA, Czuczman MS. Use of rituximab, the new FDA-approved antibody. Curr Opin Oncol,1998,10(6):548～551
[5]Arteaga CL, Moulder SL, Yakes FM. HER (erbB) tyrosine kinase inhibitors in the treatment of breast cancer. Semin Oncol,2002,29(3 suppl 11):4～10
[6]Baylin SB. DNA methylation and gene silencing in cancer. Nat Clin Pract Oncol,2005, 2 suppl 1:S4～11
[7]Oh P, Li Y, Yu J, et al. Subtractive proteomic mapping of the endothelial surface in lung and solid tumours for tissue-specific therapy. Nature,2004,429(6992):629～635
[8]Sung HJ, Cho JY. Biomarkers for the lung cancer diagnosis and their advances in proteomics. BMB Rep,2008,41(9):615～625
[9]Park HJ, Kim BG, Lee SJ, et al. Proteomic profiling of endothelial cells in human lung cancer. J Proteome Res,2008,7(3):1138～1150
[10]Jemal A, Siegel R, Ward E, et al. Cancer statistics,2006. CA Cancer J Clin,2006, 56(2):106～130.
[11]Mulshine JL, Sullivan DC. Clinical practice. Lung cancer screening. N Engl J Med, 2005,352(26):2714～2720
[12]Wistuba Ⅱ, Gazdar AF. Lung cancer preneoplasia. Annu Rev Pathol,2006,1:331～348
[13]Kerr KM, Galler JS, Hagen JA, et al. The role of DNA methylation in the development and progression of lung adenocarcinoma. Dis Markers,2007,23(1-2):5～30
[14]Brambilla C, Fievet F, Jeanmart M, et al. Early detection of lung cancer:role of biomarkers. Eur Respir J Suppl,2003,39:36s～44s
[15]Wood LD, Parsons DW, Jones S, et al. The genomic landscapes of human breast and colorectal cancer. Science,2007,318(5853):1108～1113
[16]Senchenko VN, Liu J, Loginov W, et al. Discovery of frequent homozygous deletions in chromosome 3p21.3 LUCA and AP20 regions in renal, lung and breast carcinomas. Oncogene,2004,23(34):5719～5728
[17]Ocak S, Sos ML, Thomas RK, et al. High-throughput molecular analysis in lung cancer: insights into biology and potential clinical applications. Eur Respir J,2009,34(2): 489～506
[18]Zabarovsky ER, Lerman MI, Minna JD. Tumor suppressor genes on chromosome 3p involved in the pathogenesis of lung and other cancers. Oncogene,2002,21(45): 6915～6935
[19]Xue X, Zhu YM, Woll PJ. Circulating DNA and lung cancer. Ann N Y Acad Sci,2006, 1075:154～164
[20]Olivier M, Hussain SP, Caron de Fromentel C, et al. TP53 mutation spectra and load:a tool for generating hypotheses on the etiology of cancer. IARC Sci Publ,2004, (157): 247-270
[21]Wistuba Ⅱ, Behrens C, Milchgrub S, et al. Sequential molecular abnormalities are involved in the multistage development of squamous cell lung carcinoma. Oncogene, 1999,18(3):643～650
[22]Furonaka O, Takeshima Y, Awaya H, et al. Aberrant methylation of pl4(ARF), p15(INK4b) and p16(INK4a) genes and location of the primary site in pulmonary squamous cell carcinoma. Pathol Int,2004,54(8):549～555
[23]Kurakawa E, Shimamoto T, Utsumi K, et al. Hypermethylation of p16 (INK4a) and p15 (INK4b) genes in non-small cell lung cancer. Int J Oncol,2001,19(2):277～281
[24]Zochbauer-Muller S, Fong KM, Virmani AK, et al. Aberrant promoter methylation of multiple genes in non-small cell lung cancer. Cancer Res,2001,61(1):249～255
[25]Planchard D, Loriot Y, Besse B. Impact of KRAS in standard treatment of non-small cell lung cancer (NSCLC) patients in 2009:prognostic and predictive value. Bull Cancer,2009; 96(Suppl):S57～68
[26]Gao W, Mady HH, Melhem MF, et al. Analysis of p53 mutations in histologically normal lung tissues and lung tumors from non-small cell lung cancer patients. Mol Carcinog,2009,48(7):633～641
[27]Simpson RJ, Bernhard OK, Greening DW, et al. Proteomics-driven cancer biomarker discovery:looking to the future. Curr Opin Chem Biol,2008,12(1):72～77
[28]Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Science, 2001,291(5507):1304～1351
[29]Coate LE, John T, Tsao MS, et al. Molecular predictive and prognostic markers in non-small-cell lung cancer. Lancet Oncol,2009,10(10):1001～1010
[30]Molina R, Auge JM, Filella X, et al. Pro-gastrin-releasing peptide (proGRP) in patients with benign and malignant diseases:comparison with CEA, SCC, CYFRA 21-1 and NSE in patients with lung cancer. Anticancer Res,2005,25(3A):1773～1778
[31]Ardizzoni A. MA, Cafferata M, Tiseo R, et al. Decline in serum carcinoembryonic antigen and cytokeratin 19 fragment during chemotherapy predicts objective response and survival in patients with advanced nonsmall cell lung cancer. Cancer,2006,107(12): 2842～2849
[32]Hampton R, Walker M, Marshall J, et al. Differential expression of carcinoembryonic antigen (CEA) splice variants in whole blood of colon cancer patients and healthy volunteers:implication for the detection of circulating colon cancer cells. Oncogene, 2002,21(51):7817～7823
[33]Buccheri G, Torchio P, Ferrigno D. Clinical equivalence of two cytokeratin markers in non-small cell lung cancer:a study of tissue polypeptide antigen and cytokeratin 19 fragments. Chest,2003,124(2):622～632
[34]Barak V, Goike H, Panaretakis KW, et al. Clinical utility of cytokeratins as tumor markers. Clin Biochem,2004,37(7):529～540
[35]Greenberg AK, Lee MS. Biomarkers for lung cancer:clinical uses. Curr Opin Pulm Med,2007,13(4):249～255
[36]Dziadziuszko R, Witta SE, Cappuzzo F, et al. Epidermal growth factor receptor messenger RNA expression, gene dosage, and gefitinib sensitivity in non-small cell lung cancer. Clin Cancer Res,2006,12(10):3078～3084
[37]Nishio K, Arao T, Shimoyama T, et al. Translational studies for target-based drugs. Cancer Chemother Pharmacol,2005,56(Suppl 1):90～93
[38]Cho WC, Yip TT, Yip C, et al. Identification of serum amyloid a protein as a potentially useful biomarker to monitor relapse of nasopharyngeal cancer by serum proteomic profiling. Clin Cancer Res,2004,10(1 Pt 1):43～52
[39]Maciel CM, Junqueira M, Paschoal ME, et al. Differential proteomic serum pattern of low molecular weight proteins expressed by adenocarcinoma lung cancer patients. J Exp Ther Oncol 2005;5(1):31-38.
[40]Huang LJ, Chen SX, Huang Y, et al. Proteomics-based identification of secreted protein dihydrodiol dehydrogenase as a novel serum markers of non-small cell lung cancer. Lung Cancer,2006,54(1):87～94
[41]Heo SH, Lee SJ, Ryoo HM, et al. Identification of putative serum glycoprotein biomarkers for human lung adenocarcinoma by multilectin affinity chromatography and LC-MS/MS. Proteomics,2007,7(23):4292～4302
[42]Liotta LA, Petricoin EF. Serum peptidome for cancer detection:spinning biologic trash into diagnostic gold. J Clin Invest,2006,116(1):26y-30
[43]Villanueva J, Shaffer DR, Philip J, et al. Differential exoprotease activities confer tumor-specific serum peptidome patterns. J Clin Invest,2006,116(1):271～284
[44]de Hoog CL, Mann M. Proteomics. Annu Rev Genomics Hum Genet,2004,5: 267～293
[45]Conrad DH, Goyette J, Thomas PS. Proteomics as a method for early detection of cancer:a review of proteomics, exhaled breath condensate, and lung cancer screening. J Gen Intern Med,2008,23(Suppl 1):78～84
[46]Alessandro R, Fontana S, Kohn E, et al. Proteomic strategies and their application in cancer research. Tumori,2005,91(6):447～455
[47]Hanash SM, Pitteri SJ, Faca VM. Mining the plasma proteome for cancer biomarkers. Nature,2008,452(7187):571～579
[48]Janecki DJ, Bemis KG, Tegeler TJ, et al. A multiple reaction monitoring method for absolute quantification of the human liver alcohol dehydrogenase ADH1C1 isoenzyme. Anal Biochem,2007,369(1):18～26
[49]Anderson NL, Anderson NG, Haines LR, et al. Mass spectrometric quantitation of peptides and proteins using stable isotope standards and capture by anti-peptide antibodies (SISCAPA). J Proteome Res,2004,3(2):235～244
[50]Keshishian H, Addona T, Burgess M, et al. Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol Cell Proteomics,2007,6(12):2212～2229