Dataset Information

Phospholipase C Beta 1: a Candidate Signature Gene for Proneural Subtype High-Grade Glioma.

ABSTRACT: Phospholipase C beta 1 (PLCβ1) expresses in gliomas and cultured glial cells, but its expression is barely detectable in normal glial cells. We analyzed data from Gene Expression Omnibus (GEO-GDSxxx), The Cancer Genome Atlas (TCGA), and the Repository for Molecular Brain Neoplasia Data (REMBRANDT) to explore the potential role of PLCβ1 as a biomarker in high-grade glioma (HGG). PLCβ1 expression is significantly higher in grade III gliomas than that in grade IV gliomas from GDS1815 (n = 24 vs. 76), GDS1962 (n = 19 vs. 81), and GDS1975 (n = 26 vs. 59). In GDS1815, PLCβ1 expression correlates with several known proneural (PN) signature genes; its expression from PN subtype (n = 15) is significantly higher than that from mesenchymal (Mes) subtype (n = 33) HGG. In GDS1962, PLCβ1 expression is the highest in nontumor brain tissue (n = 23) and is significantly higher than its expression in grade II gliomas [astrocytomas (n = 7) and oligodendrogliomas (n = 37)]. A Kaplan-Meier survival curve from a REMBRANDT cohort demonstrates that glioma patients with intermediate PLCβ1 expression (n = 103) survived significantly longer than PLCβ1 downregulated (2X) groups (n = 226). From TCGA data, PLCβ1 RNA-Seq signal inversely correlates with the pathological grades, and PLCβ1 expression in PN (n = 8) is of significantly higher levels than that in Mes (n = 8) subtypes of glioblastoma. The top 50 % of PLCβ1 expression subgroup (n = 294) of gliomas (grades II to IV merged) survived significantly longer than the low 50 percentile of the PLCβ1 expression subgroup (n = 293). p values are less than 0.05 for all these analyses. We conclude that PLCβ1 is a candidate signature gene for PN subtype HGG, and its expression inversely correlates with glioma pathological grade and is a potential prognostic factor.

SUBMITTER: Lu G

PROVIDER: S-EPMC5085994 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Phospholipase C Beta 1: a Candidate Signature Gene for Proneural Subtype High-Grade Glioma.

Lu Guangrong G Chang Jeffrey T JT Liu Zheyu Z Chen Yong Y Li Min M Zhu Jay-Jiguang JJ

Molecular neurobiology 20151128 9

Phospholipase C beta 1 (PLCβ1) expresses in gliomas and cultured glial cells, but its expression is barely detectable in normal glial cells. We analyzed data from Gene Expression Omnibus (GEO-GDSxxx), The Cancer Genome Atlas (TCGA), and the Repository for Molecular Brain Neoplasia Data (REMBRANDT) to explore the potential role of PLCβ1 as a biomarker in high-grade glioma (HGG). PLCβ1 expression is significantly higher in grade III gliomas than that in grade IV gliomas from GDS1815 (n = 24 vs. 76 ...[more]

PMID: 26614510

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Project description:BackgroundLower grade glioma (LGG) are a heterogeneous tumor that may develop into high-grade malignant glioma seriously shortens patient survival time. The clinical prognostic biomarker of lower-grade glioma is still lacking. The aim of our study is to explore novel biomarkers for LGG that contribute to distinguish potential malignancy in low-grade glioma, to guide clinical adoption of more rational and effective treatments.MethodsThe RNA-seq data for LGG was downloaded from UCSC Xena and the Chinese Glioma Genome Atlas (CGGA). By a robust likelihood-based survival model, least absolute shrinkage and selection operator regression and multivariate Cox regression analysis, we developed a three-gene signature and established a risk score to predict the prognosis of patient with LGG. The three-gene signature was an independent survival predictor compared to other clinical parameters. Based on the signature related risk score system, stratified survival analysis was performed in patients with different age group, gender and pathologic grade. The prognostic signature was validated in the CGGA dataset. Finally, weighted gene co-expression network analysis (WGCNA) was carried out to find the co-expression genes related to the member of the signature and enrichment analysis of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted for those co-expression network. To prove the efficiency of the model, time-dependent receiver operating characteristic curves of our model and other models are constructed.ResultsIn this study, a three-gene signature (WEE1, CRTAC1, SEMA4G) was constructed. Based on the model, the risk score of each patient was calculated with LGG (low-risk vs. high-risk, hazard ratio (HR) = 0.198 (95% CI [0.120-0.325])) and patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Furthermore, the model was validated in the CGGA dataset. Lastly, by WGCNA, we constructed the co-expression network of the three genes and conducted the enrichment of GO and KEGG. Our study identified a three-gene model that showed satisfactory performance in predicting the 1-, 3- and 5-year survival of LGG patients compared to other models and may be a promising independent biomarker of LGG.

Project description:Tumor heterogeneity of high-grade glioma (HGG) is recognized by four clinically relevant subtypes based on core gene signatures. However, molecular signaling in glioma stem cells (GSCs) in individual HGG subtypes is poorly characterized. Previously we identified and characterized two mutually exclusive GSC subtypes with distinct activated signaling pathways and biological phenotypes. One GSC subtype presented with a gene signature resembling Proneural (PN) HGG, whereas the other was similar to Mesenchymal (Mes) HGG. Classical HGG-derived GSCs were sub-classified as either one of these two subtypes. Differential mRNA expression analysis of PN and Mes GSCs identified 5,796 differentially expressed genes, revealing a pronounced correlation with the corresponding PN or Mes HGGs. Mes GSCs displayed more aggressive phenotypes in vitro and as intracranial xenografts in mice. Further, Mes GSCs were markedly resistant to radiation compared with PN GSCs. Expression of ALDH1A3 - one of the most up-regulated Mes representative genes and a universal cancer stem cell marker in non-brain cancers - was associated with self-renewal and a multi-potent stem cell population in Mes but not PN samples. Moreover, inhibition of ALDH1A3 attenuated the growth of Mes but not PN GSCs in vitro. Lastly, radiation treatment of PN GSCs up-regulated Mes-associated markers and down-regulated PN-associated markers, whereas inhibition of ALDH1A3 attenuated an irradiation-induced gain of Mes identity in PN GSCs in vitro. Taken together, our data suggest that two subtypes of GSCs, harboring distinct metabolic signaling pathways, represent intertumoral glioma heterogeneity and highlight previously unidentified roles of ALDH1A3-associated signaling that promotes aberrant proliferation of Mes HGGs and GSCs. Inhibition of ALDH1A3-mediated pathways therefore might provide a promising therapeutic approach for a subset of HGGs with the Mes signature. Here, we describe the gene expression analysis, including pre-processing methods for the data published by Mao and colleagues in PNAS [1], integration of microarray data from this study with The Cancer Genome Atlas (TCGA) glioblastoma data and also with another published study.

Dataset Information

Phospholipase C Beta 1: a Candidate Signature Gene for Proneural Subtype High-Grade Glioma.

Publications

Phospholipase C Beta 1: a Candidate Signature Gene for Proneural Subtype High-Grade Glioma.

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