Background: As the histological diagnosis of glioma is oft en diffi cult, the patients outcome will
fail to match the predicted biological behavior.Th erefore, it is clinically important to identify the
molecular prognosis predictors for gliomas.
Purpose: Our aim was to identify prognostic gene signature forgliomas based on gene expression
Materials and Methods: We selected 3456 genes expressed in gliomas, including 3012 genes
found in a gliomal expressed sequence tag collection. Th e expression levels of these genes in
152 gliomas (100 glioblastomas, 21 anaplastic astrocytomas, 19 diff use astrocytomas, and 12
anaplastic oligodendrogliomas) were measured using adaptor-tagged competitive polymerase
chain reaction, a high-throughput reverse transcription?polymerase chain reaction technique.
We applied unsupervised and supervised principal component analyses to elucidate the
prognostic molecular features of the gliomas. Th e prognostic gene scores(PGS) were determined
by expression levels of 58 prognostic genes identifi ed by Cox regression analysis.Th e prognosis
predictability of the PGSwas tested in independent sample sets.
Results: Th e grobalgene expression data matrix was signifi cantly correlated with the histological
grades, oligo-astro histology, and prognosis. Using 110 gliomas, we identifi ed PGS based on the
expression profi le of 58 genes, resulting in a scheme that reliably classifi ed the glioblastomas
into two distinct prognostic subgroups. Th e prognosis predictability of PGS was then tested
with another 42 cases. Multivariate Cox analysis of the glioblastoma patients using other clinical
prognostic factors, including age and the extent of surgical resection, indicated that the PGS was
a strong and independent prognostic parameter.
Th e clinical utility of the PGS was demonstrated in another 55 cases of anaplastic glioma.
Conclusion: Th e gene expression profi ling identifi ed clinically informative prognostic molecular
features in astrocytic and oligodendroglial tumors that were more reliable than the traditional
histological classifi cation scheme.
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