A DNA Methylation Prognostic Signature of Glioblastoma: Identification of NPTX2-PTEN-NF-κB Nexus

Abstract

Glioblastoma (GBM) is the most common, malignant adult primary tumor with dismal patient survival, yet the molecular determinants of patient survival are poorly characterized. Global methylation profile of GBM samples (our cohort; n ¼ 44) using high-resolution methylation microarrays was carried out. Cox regression analysis identified a 9-gene methylation signature that predicted survival in GBM patients. A risk-score derived from methylation signature predicted survival in univariate analysis in our and The Cancer Genome Atlas (TCGA) cohort. Multivariate analysis identified methylation risk score as an independent survival predictor in TCGA cohort. Methylation risk score stratified the patients into low-risk and high-risk groups with significant survival difference. Network analysis revealed an activated NF-kB pathway association with high-risk group. NF-kB inhibition reversed glioma chemoresistance, and RNA interference studies identified interleukin-6 and intercellular adhesion molecule-1 as key NF-kB targets in imparting chemoresistance. Promoter hypermethylation of neuronal pentraxin II (NPTX2), a risky methylated gene, was confirmed by bisulfite sequencing in GBMs. GBMs and glioma cell lines had low levels of NPTX2 transcripts, which could be reversed upon methylation inhibitor treatment. NPTX2 overexpression induced apoptosis, inhibited proliferation and anchorage-independent growth, and rendered glioma cells chemosensitive. Furthermore, NPTX2 repressed NF-kB activity by inhibiting AKT through a p53-PTENdependent pathway, thus explaining the hypermethylation and downregulation of NPTX2 in NF-kB-activated highrisk GBMs. Taken together, a 9-gene methylation signature was identified as an independent GBM prognosticator and could be used for GBM risk stratification. Prosurvival NF-kB pathway activation characterized high-risk patients with poor prognosis, indicating it to be a therapeutic target. Cancer Res; 73(22); 6563-73. Ó2013 AACR.

Methylation Silencing of ULK2, an Autophagy Gene, Is Essential for Astrocyte Transformation and Tumor Growth

“…Thus, the differences in DNA methylation within GBM have been addressed extensively. In contrast to these studies, while there have been few studies on differential methylation in GBM in comparison to normal brain samples, the role of DNA methy- lation in glioma development needs further investigation (9,20,34,35). In this study, we have compared methylation of GBM as a single group and also as G-CIMPϩ and G-CIMP-groups separately with normal brain samples and identified both common and group-specific differentially methylated genes.…”

“…Infinium array interrogates 27,578 CpG sites covering 14,495 genes. DNA methylation analysis was performed using 44 GBM and 8 normal samples as described before (20). Briefly, intensities obtained from the Infinium array was used to calculate the beta value, the methylation index, using the following formula: ␤ value ϭ (signal intensity of M Probe)/[(signal intensity of M ϩ U probes) ϩ100].…”

“…Normal human astrocytes were a kind gift from Dr. Abhijit Guha. Wildtype iBMK and ATG5 Ϫ/Ϫ iBMK cells were a kind gift from Prof. Eileen White, Cancer Institute of NJ (20) and were grown in DMEM with 10% FBS, penicillin, and streptomycin. ULK2 overexpression construct (Myc and DDK tagged) and vector control was obtained from Origene.…”

Downregulation of microRNA-630 inhibits cell proliferation and invasion and enhances chemosensitivity in human ovarian carcinoma

“…Several mechanisms such as genetic mutation, promoter methylation, and post-transcriptional modification may contribute to PTEN inactivation. PTEN can encode a series of specificity proteins, which regulate cellular processes such as cell growth, survival, proliferation, and migration (Schneider et al, 2011;Sarkar et al, 2013;Shukla et al, 2013). Moreover, PTEN is one of the most frequently inactivated tumor suppressors in a wide range of human cancers, including gastric, osteosarcoma, hepatocellular carcinomas, as well as ovarian cancer (Wu et al, 2008a;Yang et al, 2013;Sui et al, 2014).…”

Network-based approaches for drug response prediction and targeted therapy development in cancer

“…Several cancer-type specific signatures and classifiers were proposed based on genomic [8], miRNA [9], mutational [10], methylation [11] and gene expression [12] data. In addition, the signatures associated with clinical profile containing treatment response information, lead to appearance of response predictors to standalone drugs.…”