Oncotarget

Clinical Research Papers:

A radiosensitivity gene signature in predicting glioma prognostic via EMT pathway

Jin Meng _, Ping Li, Qing Zhang, Zhangru Yang and Shen Fu

PDF  |  HTML  |  Supplementary Files  |  How to cite  |  Order a Reprint

Oncotarget. 2014; 5:4683-4693. https://doi.org/10.18632/oncotarget.2088

Metrics: PDF 1964 views  |   HTML 2916 views  |   ?  


Abstract

Jin Meng1, Ping Li1, Qing Zhang2,3, Zhangru Yang1 and Shen Fu1,2,3

1 Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, China

2 Radiation Oncology Center, Fudan University Shanghai Cancer Center (FUSCC), Shanghai, China

3 Radiation Oncology Dept, Shanghai Proton and Heavy Ion Center (SPHIC), Shanghai, China

Correspondence:

Shen Fu, email:

Keywords: radiosensitivity, glioma, glioblastoma multiforme, gene signature, EMT

Received: May 1, 2014 Accepted: June 9, 2014 Published: June 10, 2014

Abstract

A 31-gene signature derived by integrating four different microarray experiments, has been found to have a potential for predicting radiosensitivity of cancer cells, but it was seldom validated in clinical cancer samples. We proposed that the gene signature may serve as a predictive biomarker for estimating the overall survival of radiation-treated patients. The significance of gene signature was tested in two previously published datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Altas (TCGA), respectively. In GEO data set, patients predicted to be radiosensitive(RS) had an improved overall survival when compared with radioresistant(RR) patients in either radiotherapy(RT)-treated or non radiotherapy(RT)-treated subgroups(p<0.0001 in the RT-treated group). Multivariate Cox regression analysis showed that the gene signature is the strongest predictor(p=0.0093) in the RT-treated subgroup of patients. However, it does not remain significant (p=0.7668) in non radiotherapy-treated group when adjusting for age and Karnofsky performance score (KPS) as covariates. Similarly, in the TCGA data set, radiotherapy-treated glioblastoma multiforme(GBM) patients assigned to RS group had an improved overall survival compared with RR group(p<0.0001). Geneset enrichment analysis(GSEA) analysis revealed that enrichment of epithelial mesenchymal transition(EMT) pathway was observed with radioresistant phenotype. These results suggest that the signature is a predictive biomarker for radiation-treated glioma patients’ prognostic.


Creative Commons License All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.
PII: 2088