2002 Fiscal Year Final Research Report Summary
Microarray Analysis of Gene Expression Profiles in Primary and Secondary Glloblastomas
| Project/Area Number |
12557114
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
Cerebral neurosurgery
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| Research Institution | HOKKAIDO UNIVERSITY |
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Principal Investigator |
MORIUCHI Tetsuya Hokkaido Univ. Institute for Genetic Medicine, Prof., 遺伝子病制御研究所, 教授 (20174394)
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| Co-Investigator(Kenkyū-buntansha) |
HOSOKAWA Masuo Health Sci. Univ. of Hokkaido, Sch. Of Nursing & Soc. Sci., Prof., 看護福祉学部, 教授 (20001901)
TADA Mitsuhiro Hokkaido Univ. Institute for Genetic Medicine, Asso. Prof., 遺伝子病制御研究所, 助教授 (10241316)
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| Project Period (FY) |
2000 – 2002
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| Keywords | glioblastoma / de novo glioblastoma / secondary glioblastoma / PI3K-Akt pathway / p53 functional loss / cDNA array / transcriptome / pattern classification |
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| Research Abstract |
Glioblastoma multiforme is classified into two subsets : one is 'primary (de novo) glioblastoma' which arises in relatively elderly persons without a preceding lesion, and another is 'secondary glioblastoma' which arises in young persons with a preceding benign astrocytoma. The present study aimed to analyze mRNA expression profiles of the both subsets, disclose their underlying molecular pathological natures, and know the mechanism which causes the biological and clinical differences between the both subsets.
We obtained the following results through this study : 1) We developed a yeast-based stop codon assay to identify PTEN gene mutations that inversely correlate with p53 mutations to classify glioblastomas (Oncogene).
2) We developed an original DNA array consisting of 1300 genes. We analyzed 119 cancer cell lines including glioblastoma cells with the array, and obtained successful results.
3) We analyzed glioblastoma cell lines including U251MG, SF268, SF295, SF539, SNB-75, and SNB-78, for their p53-, APC- and PTEN-mutational states and their expression profiles. We identified a total of 85 genes that associated with the p53 mutational status.
4) We found that application of feature subset selection algorithms and neural networks to extract characteristic patterns of gene expression. We are now undertaking studies on a number of clinical cases of glioblastoma.
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