4.7 Article

An integrated analysis of germline and somatic, genetic and epigenetic alterations at 9p21.3 in glioblastoma

期刊

CANCER
卷 118, 期 1, 页码 232-240

出版社

WILEY-BLACKWELL
DOI: 10.1002/cncr.26250

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glioblastoma; CDKN2A; CDKN2B; copy number alterations; microRNAs; DNA methylation

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  1. Wake Forest University School of Medicine

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BACKGROUND: Glioblastoma multiforme (GBM) is the most prevalent and deadly brain tumor. A variety of germline and somatic, genetic and epigenetic alterations at 9p21.3, which encode CDKN2A/CDKN2B tumor suppressor genes, have been isolatedly reported to be associated with GBM risk and prognosis. METHODS: To obtain a comprehensive view of these events, we leveraged the wide-spectrum GBM data available from The Cancer Genome Atlas project and performed an integrated analysis by systematically evaluating 9p21.3-related germline single-nucleotide polymorphisms, somatic copy number alterations (CNAs), DNA methylation, and microRNAs (miRNAs) with regard to CDKN2A/CDKN2B expression and patient prognosis in GBM. RESULTS: Our multivariate analysis indicated that expression of CDKN2A and CDKN2B was both strongly affected by CNAs (P = 1.00 x 10(-4) and 2.37 x 10(-14)). The miRNAs hsa-mir-126, hsa-mir-517a, and hsa-mir-125b exhibited significant negative correlations with CDKN2A expression (P = 0.003, 0.041, and 0.050). Survival analysis showed that complete 9p21.3 loss and low CDKN2B expression were associated with worse prognosis for both tumor progression/recurrence-free survival (P = .041 and .019) and patient overall survival (P = .043 and .021) after adjustment for age and treatment, and that higher methylation at cg17449661 predicted poorer overall survival (P = .048). CONCLUSION: Representing one of the first attempts to systematically integrate various levels of alterations associated with the often complex cancer genomes and phenotypes, our study provided a holistic view and a mechanistic explanation over the functional connections of multiple 9p21.3-related events in GBM, as well as clinically useful biomarker information for predicting disease outcomes. Cancer 2012;118:232-40. (C) 2011 American Cancer Society.

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