Identification of idh1 mutation-related gene signature of glioblastoma multiforme
Zhou, Zhaoran (2014)
2014
Bioinformatiikka - Bioinformatics
BioMediTech - BioMediTech
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Hyväksymispäivämäärä
2014-06-06
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:uta-201407091975
https://urn.fi/URN:NBN:fi:uta-201407091975
Tiivistelmä
Background: Glioblastoma multiforme (GBM) is a type of commonly occurred malignant astrocytoma with an extremely poor prognosis. GBMs display a remarkable genetic variability, and it is essential to study the genomic alterations and pathway dysregulations based on the different tumor entities.
The gene IDH1 encodes cytosolic isocitrate dehydrogenase 1, which catalyzes the reactions of oxidative decarboxylation of isocitrate to Alpha-ketoglutarate. Different types of mutation of IDH1 has been found in gliomas and GBMs, especially in secondary GBMs. Among the IDH1 mutations, R132H mutation is the most prominent one. IDH1 mutation in GBMs is correlated with a longer survival time, and no IDH1 mutations are reported in many other tumor types. Thus IDH1 is hypothesized as crucial in the pathogenesis of GBMs, and it is regarded as a potential drug target.
The fundamental goal of this study is to identify a gene signature correlated with IDH1 mutation in GBMs. And related genes and biological pathways are also studied.
Methods: Most of the work of data collection and analysis are achieved with R packages. The step-down maxT method is adopted to perform the multiple testing procedure in order to find differently expressed genes. The p-values of statistical tests are corrected by controlling FWER. The clustering result is explicated as heatmap, and clinical data is elucidated with boxplot and Kaplan Meier-plot. Analysis of GO and KEGG pathways are used to extract more information from the genes. And the results are visualized as graphs in Cytoscape.
Results: A framework is created for identifying gene signatures as well as studying biological pathways. The expression data from 548 samples are collected, and 58 genes out of 12042 genes are identified as differently expressed. Finally a gene signature with 50 genes are proposed.
Conclusion: Microarray technology and statistics methods are effective for the studying of alterations in gene expression and biological pathways. The gene signature proposed by this study can distinguish samples harboring IDH1 mutation from GBMs. And future researches are necessary to corroborate and extend the results.
The gene IDH1 encodes cytosolic isocitrate dehydrogenase 1, which catalyzes the reactions of oxidative decarboxylation of isocitrate to Alpha-ketoglutarate. Different types of mutation of IDH1 has been found in gliomas and GBMs, especially in secondary GBMs. Among the IDH1 mutations, R132H mutation is the most prominent one. IDH1 mutation in GBMs is correlated with a longer survival time, and no IDH1 mutations are reported in many other tumor types. Thus IDH1 is hypothesized as crucial in the pathogenesis of GBMs, and it is regarded as a potential drug target.
The fundamental goal of this study is to identify a gene signature correlated with IDH1 mutation in GBMs. And related genes and biological pathways are also studied.
Methods: Most of the work of data collection and analysis are achieved with R packages. The step-down maxT method is adopted to perform the multiple testing procedure in order to find differently expressed genes. The p-values of statistical tests are corrected by controlling FWER. The clustering result is explicated as heatmap, and clinical data is elucidated with boxplot and Kaplan Meier-plot. Analysis of GO and KEGG pathways are used to extract more information from the genes. And the results are visualized as graphs in Cytoscape.
Results: A framework is created for identifying gene signatures as well as studying biological pathways. The expression data from 548 samples are collected, and 58 genes out of 12042 genes are identified as differently expressed. Finally a gene signature with 50 genes are proposed.
Conclusion: Microarray technology and statistics methods are effective for the studying of alterations in gene expression and biological pathways. The gene signature proposed by this study can distinguish samples harboring IDH1 mutation from GBMs. And future researches are necessary to corroborate and extend the results.