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FBLN4 as candidate gene associated with long-term and short-term survival with primary glioblastoma

Fubin Li,Yiping Li,Kewei Zhang,Ye Li,P. He,Yujia Liu,Hongyan Yuan,Honghua Lu,Jinxiang Liu,Songtian Che,Zhenjun Li,L. Bie

Published 2017 in OncoTargets and Therapy

ABSTRACT

Background Glioblastoma multiforme (GBM) is the most common malignant and lethal type of primary central nervous system tumor in humans. In spite of its high lethality, a small percentage of patients have a relatively good prognosis, with median survival times of 36 months or longer. The identification of clinical subsets of GBM associated with distinct molecular genetic profiles has made it possible to design therapies tailored to treat individual patients. Methods We compared microarray data sets from long-term survivors (LTSs) and short-term survivors (STSs) to screen for prognostic biomarkers in GBM patients using the WebArrayDB platform. We focused on FBLN4, IGFBP-2, and CHI3L1, all members of a group of 10 of the most promising, differentially regulated gene candidates. Using formalin-fixed paraffin-embedded GBM samples, we corroborated the relationship between these genes and patient outcomes using methylation-specific polymerase chain reaction (PCR) for MGMT methylation status and quantitative reverse transcription PCR for expression of these genes. Results Expression levels of the mRNAs of these 3 genes were higher in the GBM samples than in normal brain samples and these 3 genes were significantly upregulated in STSs compared to the levels in LTS samples (P<0.01). Furthermore, Kaplan–Meier analysis showed that the expression patterns of FBLN4 and IGFBP-2 serve as independent prognostic indicators for overall survival (P<0.01 and P<0.05, respectively). Conclusion To our knowledge, this is the first report describing FBLN4 as a prognostic factor for GBM patient survival, demonstrating that increased GBM survival time correlates with decreased FBLN4 expression. Understanding FBLN4 expression patterns could aid in the creation of powerful tools to predict clinical prognoses of GBM patients.

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