Chromatin accessibility analysis identifies GSTM1 as a prognostic marker in human glioblastoma patients

Abstract

Abstract Background Glioblastoma (GBM) is a malignant human brain tumor that has an extremely poor prognosis. Classic mutations such as IDH (isocitrate dehydrogenase) mutations, EGFR (epidermal growth factor receptor) alternations, and MGMT (O6-methylguanine-methyltransferase) promoter hypermethylation have been used to stratify patients and provide prognostic significance. Epigenetic perturbations have been demonstrated in glioblastoma tumorigenesis. Despite the genetic markers used in the management of glioblastoma patients, new biomarkers that could predict patient survival independent of known biomarkers remain to be identified. Methods ATAC-seq (assay for transposase accessible chromatin followed by sequencing) and RNA-seq have been used to profile chromatin accessible regions using glioblastoma patient samples with short-survival versus long-survival. Cell viability, cell cycle, and Western blot analysis were used to characterize the cellular phenotypes and identify signaling pathways. Results Analysis of chromatin accessibility by ATAC-seq coupled with RNA-seq methods identified the GSTM1 (glutathione S-transferase mu-1) gene, which featured higher chromatin accessibility in GBM tumors with short survival. GSTM1 was confirmed to be a significant prognostic marker to predict survival using a different GBM patient cohort. Knockdown of GSTM1 decreased cell viability, caused cell cycle arrest, and decreased the phosphorylation levels of the NF-kB (nuclear factor kappa B) p65 subunit and STAT3 (signal transducer and activator of transcription 3) (pSer727). Conclusions This report demonstrates the use of ATAC-seq coupled with RNA-seq to identify GSTM1 as a prognostic marker of GBM patient survival. Activation of phosphorylation levels of NF-kB p65 and STAT3 (pSer727) by GSTM1 is shown. Analysis of chromatin accessibility in patient samples could generate an independent biomarker that can be used to predict patient survival.