脑胶质瘤蛋白表达谱筛选信号传导通路及相关分析
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摘要
目的：脑胶质瘤(Glioma)是中枢神经系统最常见的肿瘤，约占所有中枢神经系统肿瘤的50%。目前为止，尚没有效方法治愈本疾病。尽管经过多年的研究已经发现了多个与胶质瘤发生、发展及预后相关的因素，但目前仍缺乏有实际意义的胶质瘤分子标志物。因此迫切需要找到有效的分子标记物去指导诊断与治疗。我们实验的目的是筛选胶质瘤与正常脑组织表达差异的蛋白质，从中优选出可以帮助脑胶质瘤诊断和治疗的分子标记物，探讨这些蛋白差异对胶质瘤信号传导通路的影响，为下一步机制研究奠定基础。
材料与方法：实验标本选取2008-2010年间在吉林大学第一医院及吉林大学中日联谊医院手术全切的胶质瘤标本77例，对照组采用同期收集的正常非肿瘤脑组织28例。应用新型蛋白通路芯片技术（PPA），共选用135种磷酸化和非磷酸化抗体对上述所有样本进行总蛋白表达情况分析并应用聚类分析鉴定差异蛋白。通过Ingenuity Pathway Analysis（IPA）分析软件系统研究差异蛋白对脑胶质瘤信号传导网络的影响。对全部胶质瘤患者进行了Ki-67免疫组织化学染色，比较不同程度ki-67的表达与蛋白表达的关系。通过SPSS统计软件分析肿瘤蛋白表达差异与患者的年龄、性别、KPS评分、肿瘤大小、病理级别、组织学类型等临床特征之间的关系。最后根据随访结果对全部病例进行COX单因素，COX多因素及K-M生存曲线分析，找到影响生存的独立风险因素。并使用因子分析方法定义出不同因子对生存之间的影响。
结果：我们的研究发现，在135个抗体中有表达的蛋白有63个，表达率为47%。在正常对照的脑组织和胶质瘤组织中共有31种蛋白存在差异表达，其中最具差异表达的有21种蛋白。应用此21个蛋白组成的分类模型对脑胶质瘤正常脑组织的分类预测的准确率为91%，通过聚类分析，这21个差异蛋白可清楚的将肿瘤组织和正常组织分开为两类。我们用IPA系统分析了31个有差异性表达的蛋白数据，结果发现，这31个蛋白与细胞发育，细胞生长及增殖，细胞死亡和生存，细胞循环，以及细胞运动相关；此外这些蛋白也与一些疾病和功能紊乱相关：如癌症，呼吸系统疾病，生殖系统疾病，血液系统疾病以及内分泌系统疾病。由IPA系统进行的信号传导网络图绘制，绘制了5条细胞信号传导通络。通过免疫组织化学染色可见77个胶质瘤患者Ki-67染色存在明显差异。另外在不同年龄、KPS评分、WHO病理级别、Ki-67指数等不同临床指标之间，存在蛋白差异表达；研究发现FAS和Cdk4在年龄大于等于50岁的脑胶质瘤患者中高表达；PCNA，Cdk2，Cdk6，和Cdk4在高分级患者中表达升高，cPKCα在高分级患者中表达降低；PCNA，Cdk2和Cdk6的高表达与Ki-67指数大于15%有显著性意义；而cPKCα，Akt，VSV-G，FAS和Cdk6的高表达与高KPS评分显著性相关。我们的结果还表明PCNA和CDK2的表达水平是影响脑胶质瘤预后的风险因素，而且通过因子分析显示PCNA和CDK2的共同表达可以作为一个因子成为影响患者预后的独立风险因素。基于Cdk2和PCNA的风险评分能够作为脑胶质瘤患者的预后的评估指标，且高风险评分患者预后差。同时基于肿瘤大小、年龄、Ki-67和分级建立的另外因子2与KPS评分也可作为评价预后的独立影响因素存在。
结论：
1．通过新型高通量蛋白芯片技术（PPA）对较大样本量的脑胶质瘤标本进行蛋白表达测定是可行且可靠的。通过本方法测定在胶质瘤组织中存在大量的信号传导通路及蛋白表达的异常。
2．在肿瘤组织与正常组织存在差异性表达的蛋白与细胞发育，细胞生长及增殖，细胞死亡和生存，细胞循环，以及细胞运动等相关；此外这些蛋白也与一些疾病和功能紊乱相关：如癌症，呼吸系统疾病，生殖系统疾病，血液系统疾病以及内分泌系统疾病。
3．在所有差异性蛋白中，FAS和Cdk4可能与患者年龄因素相关，cPKCα、PCNA、Cdk2、Cdk6和Cdk4可能与病理分级有，PCNA，Cdk2和Cdk6与Ki-67指数相关。这些蛋白可被选作与临床诊断及治疗相关的分子标志
4．PCNA和CDK2两个蛋白表达组合后可作为一个因子共同成为判断预后的独立性影响因素。
Objective: Glioma is recognized as the most common diagnosed tumors of centralnervous system，representing50%of all central nervous system tumors. Until now，it isstill not curable. Even though researches in the past many years had discovered manyelements that inducing and developing Glioma，useful biomarkers are still missing.Therefore，it is imperative to find effective biomarkers to provide guidance for diagnosisand treatment. The purposes of our experiment are to screen diagnostic and prognosticprotein targets for glioma，to find its molecular biological mechanisms，to identifythese proteins involved in signal transduction pathways and to establish foundation forfurther studies.
Methods: Total proteins were extracted from77gliomas and28normal tissues inthe first hospital and China-Japan union hospital of Jilin university between year2008and2010. The expression level of135proteins and phosphoproteins were analyzed usingProtein Pathway Array (PPA) method. Differentially expressed proteins andphossphoproteins were identified between glioma tissues and normal control tissuesutilizing cluster analysis. We performed proteomic-wide expression analysis usingIngenuity Pathway Analysis (IPA) software and investigated the significant protein andphosphoproteins expression in gliomas signal network. We did ICH staining in77casesof gliomas including Ki-67staining and observed with light microscopy. We obtainedrelationship between different degree of Ki-67and protein expression，the relationshipbetween gliomas protein expression profiling and patients’ clinical features such as age，gender，geographic regions and gliomas diagnostic level. The overall survival factors of77cases of gliomas was analyzed to determine which risk factors affect survival.
Results: Our study revealed that63of the135antibodies had protein expressions，with the ratio being47%. In the normal control group consisting of brain tissues andglioma tissues，differential expressions were presented in31proteins，among which21were the most obvious. The classification model composed of the21proteins performed ahigh accuracy rate of91%，when conducting the prediction of the classification betweenthe brain glioma and the normal brain tissues. Though clustering analysis，the21proteins distinctly identified tumor issues from the normal ones. We analyzed31proteinswith differential expression by IPA system and found that they are closely linked tocellular growth，proliferation，death，circulation and even motion. In addition，theseproteins are related to some diseases and disorders，such as cancers， diseases ofrespiratory system， reproductive system，blood and endocrine. The signal transductionnetwork diagram made by IPA system had found changes of5cell signal transductionpathways. Immunohistochemistry dyeing witnessed significant differences among Ki-67dyeings of77glioma patients. Differentially expressed proteins were also found inpatients with different ages， genders， KPS grading， sizes of tumors， WHOpathological levels and pathogenic sites. The study also discovered high expression ofFAS and Cdk4in glioma patients older than50. PCNA，Cdk2，Cdk6，and Cdk4hadincreasing expressions in high grade patients， while cPKCα decreased. The highexpression of PCNA， Cdk2,Cdk6and the Ki-67index(larger than15%) showedprofound meanings. Moreover，the high expressions of cPKCα，Akt，VSV-G，FASand Cdk6were remarkably relevant to the high KPS grading. Our results demonstratedthat the expression levels of PCNA and CDK2affected the risk of glioma prognosis.Factorial analysis showed that the co-expression of PCNK and CDK2could become aindependent risk factor of prognosis. The risk score based on Cdk2and PCNA could beused as evaluation indicators of prognosis in patients with glioma，and patients with highrisk scores had poor prognosis. Meanwhile，Another factor2and KPS also acted as theindependent factor evaluating prognosis.
Conclusion:
1. It is feasible to detect the protein expression of high quantity glioma specimensand the normal control brain tissues through new high-throughput protein chip technology (PPA). This method could be adapted to detect vast disorders of signal transductionpathway and protein expression in glioma tissues.
2. The differently expressed proteins between tumor and normal tissues are relevantto the growth， development， proliferation， death， circulation and motion. Inaddition，these proteins are related to some diseases and disorders，such as cancers，diseases of respiratory system，reproductive system，blood and endocrine.
3. In all these different proteins，FAS and Cdk4were related to patients’ ages，cPKCα、PCNA、Cdk2、Cdk6and Cdk4may be connected to pathological grade.Another three protein: PCNA，Cdk2and Cdk6are linked to Immunohistochemicalindicators Ki-67. The proteins could be selected as molecular marks in clinical diagnosisand treatment.
4. The combination of PCNK and CDK2could act as one independent factor toestimate prognosis.
材料与方法：实验标本选取2008-2010年间在吉林大学第一医院及吉林大学中日联谊医院手术全切的胶质瘤标本77例，对照组采用同期收集的正常非肿瘤脑组织28例。应用新型蛋白通路芯片技术（PPA），共选用135种磷酸化和非磷酸化抗体对上述所有样本进行总蛋白表达情况分析并应用聚类分析鉴定差异蛋白。通过Ingenuity Pathway Analysis（IPA）分析软件系统研究差异蛋白对脑胶质瘤信号传导网络的影响。对全部胶质瘤患者进行了Ki-67免疫组织化学染色，比较不同程度ki-67的表达与蛋白表达的关系。通过SPSS统计软件分析肿瘤蛋白表达差异与患者的年龄、性别、KPS评分、肿瘤大小、病理级别、组织学类型等临床特征之间的关系。最后根据随访结果对全部病例进行COX单因素，COX多因素及K-M生存曲线分析，找到影响生存的独立风险因素。并使用因子分析方法定义出不同因子对生存之间的影响。
结果：我们的研究发现，在135个抗体中有表达的蛋白有63个，表达率为47%。在正常对照的脑组织和胶质瘤组织中共有31种蛋白存在差异表达，其中最具差异表达的有21种蛋白。应用此21个蛋白组成的分类模型对脑胶质瘤正常脑组织的分类预测的准确率为91%，通过聚类分析，这21个差异蛋白可清楚的将肿瘤组织和正常组织分开为两类。我们用IPA系统分析了31个有差异性表达的蛋白数据，结果发现，这31个蛋白与细胞发育，细胞生长及增殖，细胞死亡和生存，细胞循环，以及细胞运动相关；此外这些蛋白也与一些疾病和功能紊乱相关：如癌症，呼吸系统疾病，生殖系统疾病，血液系统疾病以及内分泌系统疾病。由IPA系统进行的信号传导网络图绘制，绘制了5条细胞信号传导通络。通过免疫组织化学染色可见77个胶质瘤患者Ki-67染色存在明显差异。另外在不同年龄、KPS评分、WHO病理级别、Ki-67指数等不同临床指标之间，存在蛋白差异表达；研究发现FAS和Cdk4在年龄大于等于50岁的脑胶质瘤患者中高表达；PCNA，Cdk2，Cdk6，和Cdk4在高分级患者中表达升高，cPKCα在高分级患者中表达降低；PCNA，Cdk2和Cdk6的高表达与Ki-67指数大于15%有显著性意义；而cPKCα，Akt，VSV-G，FAS和Cdk6的高表达与高KPS评分显著性相关。我们的结果还表明PCNA和CDK2的表达水平是影响脑胶质瘤预后的风险因素，而且通过因子分析显示PCNA和CDK2的共同表达可以作为一个因子成为影响患者预后的独立风险因素。基于Cdk2和PCNA的风险评分能够作为脑胶质瘤患者的预后的评估指标，且高风险评分患者预后差。同时基于肿瘤大小、年龄、Ki-67和分级建立的另外因子2与KPS评分也可作为评价预后的独立影响因素存在。
结论：
1．通过新型高通量蛋白芯片技术（PPA）对较大样本量的脑胶质瘤标本进行蛋白表达测定是可行且可靠的。通过本方法测定在胶质瘤组织中存在大量的信号传导通路及蛋白表达的异常。
2．在肿瘤组织与正常组织存在差异性表达的蛋白与细胞发育，细胞生长及增殖，细胞死亡和生存，细胞循环，以及细胞运动等相关；此外这些蛋白也与一些疾病和功能紊乱相关：如癌症，呼吸系统疾病，生殖系统疾病，血液系统疾病以及内分泌系统疾病。
3．在所有差异性蛋白中，FAS和Cdk4可能与患者年龄因素相关，cPKCα、PCNA、Cdk2、Cdk6和Cdk4可能与病理分级有，PCNA，Cdk2和Cdk6与Ki-67指数相关。这些蛋白可被选作与临床诊断及治疗相关的分子标志
4．PCNA和CDK2两个蛋白表达组合后可作为一个因子共同成为判断预后的独立性影响因素。
Objective: Glioma is recognized as the most common diagnosed tumors of centralnervous system，representing50%of all central nervous system tumors. Until now，it isstill not curable. Even though researches in the past many years had discovered manyelements that inducing and developing Glioma，useful biomarkers are still missing.Therefore，it is imperative to find effective biomarkers to provide guidance for diagnosisand treatment. The purposes of our experiment are to screen diagnostic and prognosticprotein targets for glioma，to find its molecular biological mechanisms，to identifythese proteins involved in signal transduction pathways and to establish foundation forfurther studies.
Methods: Total proteins were extracted from77gliomas and28normal tissues inthe first hospital and China-Japan union hospital of Jilin university between year2008and2010. The expression level of135proteins and phosphoproteins were analyzed usingProtein Pathway Array (PPA) method. Differentially expressed proteins andphossphoproteins were identified between glioma tissues and normal control tissuesutilizing cluster analysis. We performed proteomic-wide expression analysis usingIngenuity Pathway Analysis (IPA) software and investigated the significant protein andphosphoproteins expression in gliomas signal network. We did ICH staining in77casesof gliomas including Ki-67staining and observed with light microscopy. We obtainedrelationship between different degree of Ki-67and protein expression，the relationshipbetween gliomas protein expression profiling and patients’ clinical features such as age，gender，geographic regions and gliomas diagnostic level. The overall survival factors of77cases of gliomas was analyzed to determine which risk factors affect survival.
Results: Our study revealed that63of the135antibodies had protein expressions，with the ratio being47%. In the normal control group consisting of brain tissues andglioma tissues，differential expressions were presented in31proteins，among which21were the most obvious. The classification model composed of the21proteins performed ahigh accuracy rate of91%，when conducting the prediction of the classification betweenthe brain glioma and the normal brain tissues. Though clustering analysis，the21proteins distinctly identified tumor issues from the normal ones. We analyzed31proteinswith differential expression by IPA system and found that they are closely linked tocellular growth，proliferation，death，circulation and even motion. In addition，theseproteins are related to some diseases and disorders，such as cancers， diseases ofrespiratory system， reproductive system，blood and endocrine. The signal transductionnetwork diagram made by IPA system had found changes of5cell signal transductionpathways. Immunohistochemistry dyeing witnessed significant differences among Ki-67dyeings of77glioma patients. Differentially expressed proteins were also found inpatients with different ages， genders， KPS grading， sizes of tumors， WHOpathological levels and pathogenic sites. The study also discovered high expression ofFAS and Cdk4in glioma patients older than50. PCNA，Cdk2，Cdk6，and Cdk4hadincreasing expressions in high grade patients， while cPKCα decreased. The highexpression of PCNA， Cdk2,Cdk6and the Ki-67index(larger than15%) showedprofound meanings. Moreover，the high expressions of cPKCα，Akt，VSV-G，FASand Cdk6were remarkably relevant to the high KPS grading. Our results demonstratedthat the expression levels of PCNA and CDK2affected the risk of glioma prognosis.Factorial analysis showed that the co-expression of PCNK and CDK2could become aindependent risk factor of prognosis. The risk score based on Cdk2and PCNA could beused as evaluation indicators of prognosis in patients with glioma，and patients with highrisk scores had poor prognosis. Meanwhile，Another factor2and KPS also acted as theindependent factor evaluating prognosis.
Conclusion:
1. It is feasible to detect the protein expression of high quantity glioma specimensand the normal control brain tissues through new high-throughput protein chip technology (PPA). This method could be adapted to detect vast disorders of signal transductionpathway and protein expression in glioma tissues.
2. The differently expressed proteins between tumor and normal tissues are relevantto the growth， development， proliferation， death， circulation and motion. Inaddition，these proteins are related to some diseases and disorders，such as cancers，diseases of respiratory system，reproductive system，blood and endocrine.
3. In all these different proteins，FAS and Cdk4were related to patients’ ages，cPKCα、PCNA、Cdk2、Cdk6and Cdk4may be connected to pathological grade.Another three protein: PCNA，Cdk2and Cdk6are linked to Immunohistochemicalindicators Ki-67. The proteins could be selected as molecular marks in clinical diagnosisand treatment.
4. The combination of PCNK and CDK2could act as one independent factor toestimate prognosis.
引文
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