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Identification of a panel of genes as a prognostic biomarker for glioblastoma

BACKGROUND: Glioblastoma multiforme (GBM) is a fatal disease without effective therapy. Identification of new biomarkers for prognosis would enable more rational selections of strategies to cure patients with GBM and prevent disease relapse. METHODS: Seven datasets derived from GBM patients using mi...

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Autores principales: Wang, Fengfei, Zheng, Zheng, Guan, Jitian, Qi, Dan, Zhou, Shuang, Shen, Xin, Wang, Fushun, Wenkert, David, Kirmani, Batool, Solouki, Touradj, Fonkem, Ekokobe, Wong, Eric T., Huang, Jason H., Wu, Erxi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284420/
https://www.ncbi.nlm.nih.gov/pubmed/30341039
http://dx.doi.org/10.1016/j.ebiom.2018.10.024
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author Wang, Fengfei
Zheng, Zheng
Guan, Jitian
Qi, Dan
Zhou, Shuang
Shen, Xin
Wang, Fushun
Wenkert, David
Kirmani, Batool
Solouki, Touradj
Fonkem, Ekokobe
Wong, Eric T.
Huang, Jason H.
Wu, Erxi
author_facet Wang, Fengfei
Zheng, Zheng
Guan, Jitian
Qi, Dan
Zhou, Shuang
Shen, Xin
Wang, Fushun
Wenkert, David
Kirmani, Batool
Solouki, Touradj
Fonkem, Ekokobe
Wong, Eric T.
Huang, Jason H.
Wu, Erxi
author_sort Wang, Fengfei
collection PubMed
description BACKGROUND: Glioblastoma multiforme (GBM) is a fatal disease without effective therapy. Identification of new biomarkers for prognosis would enable more rational selections of strategies to cure patients with GBM and prevent disease relapse. METHODS: Seven datasets derived from GBM patients using microarray or next generation sequencing in R2 online database (http://r2.amc.nl) were extracted and then analyzed using JMP software. The survival distribution was calculated according to the Kaplan-Meier method and the significance was determined using log-rank statistics. The sensitivity of a panel of GBM cell lines in response to temozolomide (TMZ), salinomycin, celastrol, and triptolide treatments was evaluated using MTS and tumor-sphere formation assay. FINDINGS: We identified that CD44, ATP binding cassette subfamily C member 3 (ABCC3), and tumor necrosis factor receptor subfamily member 1A (TNFRSF1A) as highly expressed genes in GBMs are associated with patients' poor outcomes and therapy resistance. Furthermore, these three markers combined with MGMT, a conventional GBM marker, can classify GBM patients into five new subtypes with different overall survival time in response to treatment. The four-gene signature and the therapy response of GBMs to a panel of therapeutic compounds were confirmed in a panel of GBM cell lines. INTERPRETATION: The data indicate that the four-gene panel can be used as a therapy response index for GBM patients and potential therapeutic targets. These results provide important new insights into the early diagnosis and the prognosis for GBM patients and introduce potential targets for GBM therapeutics. FUND: Baylor Scott & White Health Startup Fund (E.W.); Collaborative Faculty Research Investment Program (CFRIP) of Baylor University, Baylor Scott & White Health, and Baylor College of Medicine (E.W., T.S., J.H.H.); NIH R01 NS067435 (J.H.H.); Scott & White Plummer Foundation Grant (J.H.H.); National Natural Science Foundation of China 816280007 (J.H.H. and Fu.W.).
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spelling pubmed-62844202018-12-13 Identification of a panel of genes as a prognostic biomarker for glioblastoma Wang, Fengfei Zheng, Zheng Guan, Jitian Qi, Dan Zhou, Shuang Shen, Xin Wang, Fushun Wenkert, David Kirmani, Batool Solouki, Touradj Fonkem, Ekokobe Wong, Eric T. Huang, Jason H. Wu, Erxi EBioMedicine Research paper BACKGROUND: Glioblastoma multiforme (GBM) is a fatal disease without effective therapy. Identification of new biomarkers for prognosis would enable more rational selections of strategies to cure patients with GBM and prevent disease relapse. METHODS: Seven datasets derived from GBM patients using microarray or next generation sequencing in R2 online database (http://r2.amc.nl) were extracted and then analyzed using JMP software. The survival distribution was calculated according to the Kaplan-Meier method and the significance was determined using log-rank statistics. The sensitivity of a panel of GBM cell lines in response to temozolomide (TMZ), salinomycin, celastrol, and triptolide treatments was evaluated using MTS and tumor-sphere formation assay. FINDINGS: We identified that CD44, ATP binding cassette subfamily C member 3 (ABCC3), and tumor necrosis factor receptor subfamily member 1A (TNFRSF1A) as highly expressed genes in GBMs are associated with patients' poor outcomes and therapy resistance. Furthermore, these three markers combined with MGMT, a conventional GBM marker, can classify GBM patients into five new subtypes with different overall survival time in response to treatment. The four-gene signature and the therapy response of GBMs to a panel of therapeutic compounds were confirmed in a panel of GBM cell lines. INTERPRETATION: The data indicate that the four-gene panel can be used as a therapy response index for GBM patients and potential therapeutic targets. These results provide important new insights into the early diagnosis and the prognosis for GBM patients and introduce potential targets for GBM therapeutics. FUND: Baylor Scott & White Health Startup Fund (E.W.); Collaborative Faculty Research Investment Program (CFRIP) of Baylor University, Baylor Scott & White Health, and Baylor College of Medicine (E.W., T.S., J.H.H.); NIH R01 NS067435 (J.H.H.); Scott & White Plummer Foundation Grant (J.H.H.); National Natural Science Foundation of China 816280007 (J.H.H. and Fu.W.). Elsevier 2018-10-16 /pmc/articles/PMC6284420/ /pubmed/30341039 http://dx.doi.org/10.1016/j.ebiom.2018.10.024 Text en © 2018 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research paper
Wang, Fengfei
Zheng, Zheng
Guan, Jitian
Qi, Dan
Zhou, Shuang
Shen, Xin
Wang, Fushun
Wenkert, David
Kirmani, Batool
Solouki, Touradj
Fonkem, Ekokobe
Wong, Eric T.
Huang, Jason H.
Wu, Erxi
Identification of a panel of genes as a prognostic biomarker for glioblastoma
title Identification of a panel of genes as a prognostic biomarker for glioblastoma
title_full Identification of a panel of genes as a prognostic biomarker for glioblastoma
title_fullStr Identification of a panel of genes as a prognostic biomarker for glioblastoma
title_full_unstemmed Identification of a panel of genes as a prognostic biomarker for glioblastoma
title_short Identification of a panel of genes as a prognostic biomarker for glioblastoma
title_sort identification of a panel of genes as a prognostic biomarker for glioblastoma
topic Research paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284420/
https://www.ncbi.nlm.nih.gov/pubmed/30341039
http://dx.doi.org/10.1016/j.ebiom.2018.10.024
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