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.

Targeting WISP1 to sensitize esophageal squamous cell carcinoma to irradiation

“…However, the prognosis is very poor [2, 3]. The mechanisms of radioresistance are known to alter in different cell types and even in different subpopulations within the tumor [21-24]. Although there have been several studies performed to explore the radiosensitizing targets, it still remained a tough problem to overcome ESCC radioresistance [25-32].…”

Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy

“…RSI predicted improved RFS exclusively in patients who received adjuvant radiotherapy in two larger independent cohorts of over 500 breast cancer patients in total. The 31 gene signature investigated in glioma was found to be predictive of longer overall survival in radiotherapy treated patients only in the GSE16011 cohort and in the TCGA cohort in which all patients received radiotherapy [30]. Further work is necessary to conclude that this is definitely a predictive rather than a prognostic marker, as it has only been evaluated in one cohort that allowed comparison between radiotherapy and non-radiotherapy treated patients and was associated with overall survival in both groups on univariate analysis.…”

“…In the subgroup analysis, overall survival was superior for radiosensitive patients treated with radiotherapy and without radiotherapy in a univariate analysis, but only remained significant in the multivariate analysis in the radiotherapy treated group (P ¼ 0.0093 radiotherapy, P ¼ 0.202 no radiotherapy). In the second more homogeneous cohort of 463 glioblastoma multiforme patients from The Cancer Genome Atlas (TCGA), superior overall survival was observed in radiosensitive patients compared with radioresistant (P ¼ 0.000687) and remained significant in the multivariate analysis (P ¼ 0.033) [30]. The multivariate analysis for the GSE16011 cohort suggests that the gene signature may predict overall survival in radiotherapy treated glioma patients only.…”

High expression of N-myc (and STAT) interactor predicts poor prognosis and promotes tumor growth in human glioblastoma

“…Recently, substantial efforts have been made in the identification of molecular subtypes [13, 14] and biomarkers associated with GBM patients' survival [15-17], to better understand the pathogenesis of GBM. Several public resources, such as the Repository of Molecular Brain Neoplasia Data (Rembrandt) database [18] and The Cancer Genome Atlas (TCGA) network [19] have provided insight into the molecular carcinogenesis of GBM, affording opportunities for researchers to correlate gene expression with multidimensional clinical and molecular features of patients [15, 16, 20-22]. Based on gene expression studies of GBM tissues, TCGA network identified several distinct molecular subtypes of GBM, including classical, mesenchymal, proneural and neural [23].…”