A radiosensitivity gene signature in predicting glioma prognostic via EMT pathway.

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

doi:10.18632/oncotarget.2088

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.

Highlights

Gliomas represent approximately 30% of primary brain tumors, and 80% of malignant tumors
The survival fraction at 2 Gy (SF2) was used as a measure of cellular radiosensitivity. This gene set was identified with multiple microarray platforms using significant analysis of microarrays (SAM), and gene set analysis was carried out to explore the biological processes and signaling pathways of radiosensitivity
In the multivariate Cox regression analysis to assess for independent predictors of the relation between the gene signature and clinicopathologic features, we found that the gene signature is the strongest predictor(p=0.0093) in the subgroup of patients with radiotherapy, whereas it does not remain significant (p=0.202) in the non RT group when taking age and Karnofsky performance score (KPS) into account.(Table 3) Taken together, the radiosensitivity gene signature is mainly predictive in patients treated with radiation therapy

Summary

Introduction

Gliomas represent approximately 30% of primary brain tumors, and 80% of malignant tumors. According to the WHO classification, they can be further subclassified into grades: I (pilocytic astrocytomas, PA), II (low grade), III (anaplastic) and IV (glioblastoma multiforme, GBM), depending on the malignant features present.[2, 3]. The response to therapy and outcome of glioma patients varies between different histological subtypes and grades.[3, 4] Most patients with WHO grade II tumours survive more than 5 years, whereas the median survival time for patients with grade III tumours is 2–3 years. Despite of the standard multimodal care for patients— surgical resection followed by adjuvant radiation therapy combined with chemotherapy, most patients with glioblastoma(WHO grade IV) succumb to the disease within one year. Tumor grade is a critical factor which influences the choice of therapy modalities, the use of adjuvant radiation and chemotherapy protocols. Tumor grade is a critical factor which influences the choice of therapy modalities, the use of adjuvant radiation and chemotherapy protocols. [3]

Methods

Results

Conclusion