Integrate Microarray Datasets And Data Screen Candidate Genes That Are Correlated With WHO Grade Of Glioma

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 WebArrayDBObjective: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;â…¢111;â…£218; 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 gliomasObjective: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 gliomasObjective: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.