跨平台整合微阵列数据筛选与胶质瘤级别相关基因的实验研究
摘要
原发性中枢神经系统肿瘤发病率约2-5/10万,85%为颅内肿瘤,最常见的肿瘤是胶质瘤,约占40%,其次是脑膜瘤约13%-19%,还有垂体瘤,神经鞘瘤,颅咽管瘤等。大量研究表明,神经系统肿瘤和其他部位的肿瘤一样是多基因异常导致的,并且肿瘤的恶性程度,病人的预后均与基因异常密切相关。寻找并验证能够用于临床的与病人肿瘤恶性程度和预后相关的标志基因一直是研究的热点。本研究通过整合不同平台的基因微阵列数据寻找与病人肿瘤恶性程度密切相关的标志基因并应用RT-PCR和Western blot进行验证。探讨有丝分裂检验点通路,MCM基因家族与胶质瘤WHO分级的关系。研究结果显示：(1)BUB1, BUB1B, BUB3, TTK, CDC20, CENPE在IV级肿瘤中表达较正常脑组织明显增高；BUB1和TTK在低级别胶质瘤(Ⅰ-Ⅱ级)中较正常组织明显增高；BUB1B, CDC20, CENPE, MAD1L1和MAD2L1的表达在低级别肿瘤和Ⅳ级肿瘤之间有明显差异；BUB1B和CDC20在不同级别之间都有明显差异。(2)MCMs基因的表达水平随着肿瘤级别增加而增高,MCM2-MCM5,MCM7和MCM10在WHOⅠ-Ⅱ级,Ⅲ级和Ⅳ级之间均有明显差异(p<0.05)；MCM6在低级别胶质瘤(WHOⅠ-Ⅱ级)和高级别胶质瘤(Ⅲ-Ⅳ级)中有明显差异(p<0.05)。(3)目前国内外尚未见跨平台整合分析胶质瘤微阵列数据寻找与肿瘤级别相关基因的报道。
Central nervous system neoplasms are the kind of common human neoplasms. Glioma tumors are the most common and among the most deadly of central nervous system (CNS) neoplasms. It has high malignancy, strong invasion and recurrence so that the patients have a poor outcome. Thus it is an important thing how to manage patients after operation. In present, we have only depended on the traditional histological classification. The doctor suggested the patients to use different therapy according to the report of tumor slide. But there have some problem, and have not got the satisfied results. In the past years, human try to use the molecular biological technology to research the tumor. Tumor will be classified by the genes. Some hospital uses the model of genes to help patients to select the different therapy. Microarray technology could help us to realize the tumor on the level of the genome. Integrate the microarray of tumor, we could got more tumor samples and reduce the bias. Otherwise, multiple genes affect the regression of the tumor. That is not enough to use single gene to evaluate the grade, prognosis of the tumor. So multiple genes build model is better than single gene model.
In the view of the above research background, this study took microarray datasets that come from GEO database. We integrated the gliomas microarray datasets focus on the tumor grade. Cluster analysis the candidate genes by pathway database. We focus on the several cell cycle pathway, include SAC gene, MCMs. These genes are relative with tumor grade. Validate the microarray results by RT-PCR in tumor samples. In this study, the experiments were carried out as follows:
(1) Integrate microarray datatsets to search candidate genes by WebArrayDB
Objective:Search candidate genes that are correlate with tumor grade. Methods:Data screening microarray datasets in GEO database. Integrated and analysis GSE4412, GSE16011, GSE12907. Include:WHOI-II 49;Ⅲ111;Ⅳ218; Normal 7. Cluster analysis the candidate genes (1-500). Results:Found some interesting pathway and genes that are correlated with glioma grade. Conclusion:SAC gene, MCMs gene that were correlated with glioma WHO grade.
(2) Validate the candidate genes
1) Over-expression levels of major mitotic spindle checkpoint genes are highly correlated to grade classification of gliomas
Objective:Investigate the expression of SAC gene expression in glioma tissues, as well as its relationship with clinical pathological grade. Methods:Extracted RNA of forty glioma tissue samples and 6 normal brain tissues. Semi-quantitative RT-PCR method validated SAC gene mRNA expression in glioma tissue samples and normal brain tissue. Results:SAC genes are generally up-regulated in glioma samples, and the increase in the expression of SAC genes correlate closely with glioma WHO grade. ANCOVA analysis reveals the significant over-expressions of BUB 1 and TTK in low-grade gliomas. By mathematically modeling, we identified CDC20 as the most important gene for identification of WHO grades in gliomas.
Conclusion:Our research suggests that SAC genes can be used for diagnosis, grade classification in gliomas.
2) Minichromosome Maintenance (MCM) Family as potential diagnostic and prognostic tumor markers for human gliomas
Objective:Investigate the expression of MCMs gene expression in glioma tissues, as well as its relationship with clinical pathological grade. Methods:Extracted RNA of forty glioma tissue samples and 6 normal brain tissues. RT-PCR method validated MCMs gene mRNA expression in glioma tissue samples and normal brain tissue. Western blot test the protein expression of MCMs gene. Results:We found that MCMs expression was significantly up-regulated in glioma samples. MCM2-7 and MCM10 expression were associated with glioma grade. Conclusions:Our research suggests that MCMs can be used for diagnosis, grade classification in gliomas.
Central nervous system neoplasms are the kind of common human neoplasms. Glioma tumors are the most common and among the most deadly of central nervous system (CNS) neoplasms. It has high malignancy, strong invasion and recurrence so that the patients have a poor outcome. Thus it is an important thing how to manage patients after operation. In present, we have only depended on the traditional histological classification. The doctor suggested the patients to use different therapy according to the report of tumor slide. But there have some problem, and have not got the satisfied results. In the past years, human try to use the molecular biological technology to research the tumor. Tumor will be classified by the genes. Some hospital uses the model of genes to help patients to select the different therapy. Microarray technology could help us to realize the tumor on the level of the genome. Integrate the microarray of tumor, we could got more tumor samples and reduce the bias. Otherwise, multiple genes affect the regression of the tumor. That is not enough to use single gene to evaluate the grade, prognosis of the tumor. So multiple genes build model is better than single gene model.
In the view of the above research background, this study took microarray datasets that come from GEO database. We integrated the gliomas microarray datasets focus on the tumor grade. Cluster analysis the candidate genes by pathway database. We focus on the several cell cycle pathway, include SAC gene, MCMs. These genes are relative with tumor grade. Validate the microarray results by RT-PCR in tumor samples. In this study, the experiments were carried out as follows:
(1) Integrate microarray datatsets to search candidate genes by WebArrayDB
Objective:Search candidate genes that are correlate with tumor grade. Methods:Data screening microarray datasets in GEO database. Integrated and analysis GSE4412, GSE16011, GSE12907. Include:WHOI-II 49;Ⅲ111;Ⅳ218; Normal 7. Cluster analysis the candidate genes (1-500). Results:Found some interesting pathway and genes that are correlated with glioma grade. Conclusion:SAC gene, MCMs gene that were correlated with glioma WHO grade.
(2) Validate the candidate genes
1) Over-expression levels of major mitotic spindle checkpoint genes are highly correlated to grade classification of gliomas
Objective:Investigate the expression of SAC gene expression in glioma tissues, as well as its relationship with clinical pathological grade. Methods:Extracted RNA of forty glioma tissue samples and 6 normal brain tissues. Semi-quantitative RT-PCR method validated SAC gene mRNA expression in glioma tissue samples and normal brain tissue. Results:SAC genes are generally up-regulated in glioma samples, and the increase in the expression of SAC genes correlate closely with glioma WHO grade. ANCOVA analysis reveals the significant over-expressions of BUB 1 and TTK in low-grade gliomas. By mathematically modeling, we identified CDC20 as the most important gene for identification of WHO grades in gliomas.
Conclusion:Our research suggests that SAC genes can be used for diagnosis, grade classification in gliomas.
2) Minichromosome Maintenance (MCM) Family as potential diagnostic and prognostic tumor markers for human gliomas
Objective:Investigate the expression of MCMs gene expression in glioma tissues, as well as its relationship with clinical pathological grade. Methods:Extracted RNA of forty glioma tissue samples and 6 normal brain tissues. RT-PCR method validated MCMs gene mRNA expression in glioma tissue samples and normal brain tissue. Western blot test the protein expression of MCMs gene. Results:We found that MCMs expression was significantly up-regulated in glioma samples. MCM2-7 and MCM10 expression were associated with glioma grade. Conclusions:Our research suggests that MCMs can be used for diagnosis, grade classification in gliomas.
引文
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