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A cancer tissue-specific FAM72 expression profile defines a novel glioblastoma multiform (GBM) gene-mutation signature

Authors
Rahane, Chinmay SatishKutzner, ArneHeese, Klaus
Issue Date
Jan-2019
Publisher
SPRINGER
Keywords
Cancer; Glia; Glioblastoma; Neuron; SRGAP2; Stem cells; TCGA
Citation
JOURNAL OF NEURO-ONCOLOGY, v.141, no.1, pp.57 - 70
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF NEURO-ONCOLOGY
Volume
141
Number
1
Start Page
57
End Page
70
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/148505
DOI
10.1007/s11060-018-03029-3
ISSN
0167-594X
Abstract
Introduction Glioblastoma multiform (GBM) is a neural stem cell (NSC)-derived malignant brain tumor with complex genetic alterations challenging clinical treatments. FAM72 is a NSC-specific protein comprised of four paralogous genes (FAM72 A-D) in the human genome, but its functional tumorigenic significance is unclear. Methods We conducted an in-depth expression and somatic mutation data analysis of FAM72 (A-D) in GBM using the comprehensive human clinical cancer study database cBioPortal [including The Cancer Genome Atlas (TCGA)]. Results We established a FAM72 transcription profile across TCGA correlated with the expression of the proliferative marker MKI67 and a tissue-specific gene-mutation signature represented by pivotal genes involved in driving the cell cycle. FAM72 paralogs are overexpressed in cancer cells, specifically correlating with the mitotic cell cycle genes ASPM, KIF14, KIF23, CENPE, CENPE, CEP55, SGO1, and BUB1, thereby contributing to centrosome and mitotic spindle formation. FAM72 expression correlation identifies a novel GBM-specific gene set (SCN9A, MXRA5, ADAM29, KDR, LRP1B, and PIK3C2G) in the de novo pathway of primary GBM predestined as viable targets for therapeutics. Conclusion Our newly identified primary GBM-specific gene-mutation signature, along with FAM72, could thus provide a new basis for prognostic biomarkers for diagnostics of GBM and could serve as potential therapeutic targets.
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서울 의생명공학전문대학원 > 서울 의생명공학전문대학원 > 1. Journal Articles
서울 공과대학 > 서울 정보시스템학과 > 1. Journal Articles

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GRADUATE SCHOOL OF BIOMEDICAL SCIENCE AND ENGINEERING (DEPARTMENT OF BIOMEDICAL SCIENCE)
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