title: A radiosensitivity gene signature in predicting glioma prognostic via EMT pathway.
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.
authors: Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, China.
authors: Jin
authors: J
authors: Meng
authors: Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, China.
authors: Ping
authors: P
authors: Li
authors: Radiation Oncology Center, Fudan University Shanghai Cancer Center (FUSCC), Shanghai, China. Radiation Oncology Dept, Shanghai Proton and Heavy Ion Center (SPHIC), Shanghai, China.
authors: Qing
authors: Q
authors: Zhang
authors: Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, China.
authors: Zhangru
authors: Z
authors: Yang
authors: Radiation Oncology Center, Fudan University Shanghai Cancer Center (FUSCC), Shanghai, China. Radiation Oncology Dept, Shanghai Proton and Heavy Ion Center (SPHIC), Shanghai, China.
authors: Shen
authors: S
authors: Fu
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000235:genetics; D001932:Brain Neoplasms / Q000235:genetics / Q000532:radiotherapy*; D002648:Child; D016000:Cluster Analysis; D058750:Epithelial-Mesenchymal Transition / Q000235:genetics / Q000528:radiation effects*; D005260:Female; D015972:Gene Expression Regulation, Neoplastic / Q000528:radiation effects*; D005910:Glioma / Q000235:genetics / Q000532:radiotherapy*; D006801:Humans; D008297:Male; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data; D011379:Prognosis; D016016:Proportional Hazards Models; D011836:Radiation Tolerance / Q000235:genetics / Q000528:radiation effects; D016019:Survival Analysis; D059467:Transcriptome / Q000528:radiation effects*; D055815:Young Adult
chemical_list: D014408:Biomarkers, Tumor