Identification of potential serum biomarkers of glioblastoma: serum osteopontin levels correlate with poor prognosis

Background: The aim of this study is to identify serum biomarkers with classification and prognosis utility for astrocytoma, in particular glioblastoma (GBM). Methods: Our previous glioma microarray database was mined to identify genes that encode secreted or membrane-localized proteins. Subsequent analysis was done using significant analysis of microarrays, followed by reverse transcription-quantitative PCR (RT-qPCR) and immunohistochemical validation in tumor tissues, ELISA and Western blot validation in sera, and correlation with survival of GBM patients. Results: Significant analysis of microarrays identified 31 upregulated and 3 downregulated genes specifically in GBMs. RT-qPCR validation on an independent set of samples confirmed the GBM-specific differential expression of several genes, including three upregulated (CALU, CXCL9, and TIMP1) and two downregulated (GPX3 and TIMP3) novel genes. With respect to osteopontin (OPN), we show the GBM-specific upregulation by RT-qPCR and immunohistochemical staining of tumor tissues. Elevated serum OPN levels in GBM patients were also shown by ELISA and Western blot. GBM patients with high serum OPN levels had poorer survival than those with low serum OPN levels (median survival 9 versus 22 months respectively; P = 0.0001). Further, we also show high serum TIMP1 levels in GBM patients compared with grade II/III patients by ELISA and downregulation of serum GPX3 and TIMP3 proteins in GBMs compared with normal control by Western blot analysis. Conclusions: Several novel potential serum biomarkers of GBM are identified and validated. High serum OPN level is found as a poor prognostic indicator in GBMs. Impact: Identified serum biomarkers may have potential utility in astrocytoma classification and GBM prognosis

Data from: An eighteen serum cytokine signature for discriminating glioma from normal healthy individuals

Glioblastomas (GBM) are largely incurable as they diffusely infiltrate adjacent brain tissues and are difficult to diagnose at early stages. Biomarkers derived from serum, which can be obtained by minimally invasive procedures, may help in early diagnosis, prognosis and treatment monitoring. To develop a serum cytokine signature, we profiled 48 cytokines in sera derived from normal healthy individuals (n = 26) and different grades of glioma patients (n = 194). We divided the normal and grade IV glioma/GBM serum samples randomly into equal sized training and test sets. In the training set, the Prediction Analysis for Microarrays (PAM) identified a panel of 18 cytokines that could discriminate GBM sera from normal sera with maximum accuracy (95.40%) and minimum error (4.60%). The 18-cytokine signature obtained in the training set discriminated GBM sera from normal sera in the test set as well (accuracy 96.55%; error 3.45%). Interestingly, the 18-cytokine signature also differentiated grade II/Diffuse Astrocytoma (DA) and grade III/Anaplastic Astrocytoma (AA) sera from normal sera very efficiently (DA vs. normal–accuracy 96.00%, error 4.00%; AA vs. normal–accuracy 95.83%, error 4.17%). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using 18 cytokines resulted in the enrichment of two pathways, cytokine-cytokine receptor interaction and JAK-STAT pathways with high significance. Thus our study identified an 18-cytokine signature for distinguishing glioma sera from normal healthy individual sera and also demonstrated the importance of their differential abundance in glioma biology

Data from: An eighteen serum cytokine signature for discriminating glioma from normal healthy individuals

Glioblastomas (GBM) are largely incurable as they diffusely infiltrate adjacent brain tissues and are difficult to diagnose at early stages. Biomarkers derived from serum, which can be obtained by minimally invasive procedures, may help in early diagnosis, prognosis and treatment monitoring. To develop a serum cytokine signature, we profiled 48 cytokines in sera derived from normal healthy individuals (n = 26) and different grades of glioma patients (n = 194). We divided the normal and grade IV glioma/GBM serum samples randomly into equal sized training and test sets. In the training set, the Prediction Analysis for Microarrays (PAM) identified a panel of 18 cytokines that could discriminate GBM sera from normal sera with maximum accuracy (95.40%) and minimum error (4.60%). The 18-cytokine signature obtained in the training set discriminated GBM sera from normal sera in the test set as well (accuracy 96.55%; error 3.45%). Interestingly, the 18-cytokine signature also differentiated grade II/Diffuse Astrocytoma (DA) and grade III/Anaplastic Astrocytoma (AA) sera from normal sera very efficiently (DA vs. normal–accuracy 96.00%, error 4.00%; AA vs. normal–accuracy 95.83%, error 4.17%). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using 18 cytokines resulted in the enrichment of two pathways, cytokine-cytokine receptor interaction and JAK-STAT pathways with high significance. Thus our study identified an 18-cytokine signature for distinguishing glioma sera from normal healthy individual sera and also demonstrated the importance of their differential abundance in glioma biology

Glioblastoma-derived Macrophage Colony-stimulating Factor (MCSF) Induces Microglial Release of Insulin-like Growth Factor-binding Protein 1 (IGFBP1) to Promote Angiogenesis

Glioblastoma (grade IV glioma/GBM) is the most common primary adult malignant brain tumor with poor prognosis. To characterize molecular determinants of tumor-stroma interaction in GBM, we profiled 48 serum cytokines and identified macrophage colony-stimulating factor (MCSF) as one of the elevated cytokines in sera from GBM patients. Both MCSF transcript and protein were up-regulated in GBM tissue samples through a spleen tyrosine kinase (SYK)-dependent activation of the PI3K-NF kappa B pathway. Ectopic overexpression and silencing experiments revealed that glioma-secreted MCSF has no role in autocrine functions and M2 polarization of macrophages. In contrast, silencing expression of MCSF in glioma cells prevented tube formation of human umbilical vein endothelial cells elicited by the supernatant from monocytes/microglial cells treated with conditioned medium from glioma cells. Quantitative proteomics based on stable isotope labeling by amino acids in cell culture showed that glioma-derived MCSF induces changes in microglial secretome and identified insulin-like growth factor-binding protein 1 (IGFBP1) as one of the MCSF-regulated proteins secreted by microglia. Silencing IGFBP1 expression in microglial cells or its neutralization by an antibody reduced the ability of supernatants derived from microglial cells treated with glioma cell-conditioned medium to induce angiogenesis. In conclusion, this study shows up-regulation of MCSF in GBM via a SYK-PI3K-NF kappa B-dependent mechanism and identifies IGFBP1 released by microglial cells as a novel mediator of MCSF-induced angiogenesis, of potential interest for developing targeted therapy to prevent GBM progression

An Eighteen Serum Cytokine Signature for Discriminating Glioma from Normal Healthy Individuals

<div><p>Glioblastomas (GBM) are largely incurable as they diffusely infiltrate adjacent brain tissues and are difficult to diagnose at early stages. Biomarkers derived from serum, which can be obtained by minimally invasive procedures, may help in early diagnosis, prognosis and treatment monitoring. To develop a serum cytokine signature, we profiled 48 cytokines in sera derived from normal healthy individuals (n = 26) and different grades of glioma patients (n = 194). We divided the normal and grade IV glioma/GBM serum samples randomly into equal sized training and test sets. In the training set, the <i>P</i>rediction <i>A</i>nalysis for <i>M</i>icroarrays (PAM) identified a panel of 18 cytokines that could discriminate GBM sera from normal sera with maximum accuracy (95.40%) and minimum error (4.60%). The 18-cytokine signature obtained in the training set discriminated GBM sera from normal sera in the test set as well (accuracy 96.55%; error 3.45%). Interestingly, the 18-cytokine signature also differentiated grade II/Diffuse Astrocytoma (DA) and grade III/Anaplastic Astrocytoma (AA) sera from normal sera very efficiently (DA vs. normal–accuracy 96.00%, error 4.00%; AA vs. normal–accuracy 95.83%, error 4.17%). <i>K</i>yoto <i>E</i>ncyclopedia of <i>G</i>enes and <i>G</i>enomes (KEGG) pathway analysis using 18 cytokines resulted in the enrichment of two pathways, cytokine-cytokine receptor interaction and JAK-STAT pathways with high significance. Thus our study identified an 18-cytokine signature for distinguishing glioma sera from normal healthy individual sera and also demonstrated the importance of their differential abundance in glioma biology.</p></div

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

Glioblastoma (GBM) is the most aggressive type of brain tumor and shows very poor prognosis. Here, using genome-wide methylation analysis, we show that G-CIMP+ and G-CIMP-subtypes enrich distinct classes of biological processes. One of the hypermethylated genes in GBM, ULK2, an upstream autophagy inducer, was found to be down-regulated in GBM. Promoter hypermethylation of ULK2 was confirmed by bisulfite sequencing. GBM and glioma cell lines had low levels of ULK2 transcripts, which could be reversed upon methylation inhibitor treatment. ULK2 promoter methylation and transcript levels showed significant negative correlation. Ectopic overexpression of ULK2-induced autophagy, which further enhanced upon nutrient starvation or temozolomide chemotherapy. ULK2 also inhibited the growth of glioma cells, which required autophagy induction as kinase mutant of ULK2 failed to induce autophagy and inhibit growth. Furthermore, ULK2 induced autophagy and inhibited growth in Ras-transformed immortalized Baby Mouse Kidney (iBMK) ATG5(+/+) but not in autophagy-deficient ATG5(-/-) cells. Growth inhibition due to ULK2 induced high levels of autophagy under starvation or chemotherapy utilized apoptotic cell death but not at low levels of autophagy. Growth inhibition by ULK2 also appears to involve catalase degradation and reactive oxygen species generation. ULK2 overexpression inhibited anchorage independent growth, inhibited astrocyte transformation in vitro and tumor growth in vivo. Of all autophagy genes, we found ULK2 and its homologue ULK1 were only down-regulated in all grades of glioma. Thus these results altogether suggest that inhibition of autophagy by ULK1/2 down-regulation is essential for glioma development

Levels of 18 discriminatory cytokines in normal and GBM sera of the training set.

<p>^<b>a</b>Cytokine profiling (n = 48) was carried out using bead array method. The levels of 18 discriminatory cytokines of the training set was converted to differential log 2 ratio by dividing the individual sample value with mean of all normal samples for a given cytokine. In this table, median of differential log 2 ratio for a given cytokine and standard error are shown.</p><p>^#The abundance of cytokines in GBM sera when compared to normal sera. “High” refers to cytokine present in elevated levels and “Low” refers to cytokine present in lower levels in GBM sera when compared to normal sera.</p><p>^<b>b</b>The TCGA microarray data which is publically available was used to check the transcript levels of 18 differentially abundant cytokines. Non-parametric t-test was conducted with FDR correction using log 2 ratio of normal brain tissue and GBM tumor tissue to identify significant differentially regulated cytokines at transcript level. The regulation, p value and log 2 fold change are provided in the table. “Up” refers to up-regulated and “Down” refers to down-regulated in GBM when compared to normal brain.</p><p>NS refers to non-significant.</p><p>Levels of 18 discriminatory cytokines in normal and GBM sera of the training set.</p

A DNA methylation prognostic signature of glioblastoma: identification of NPTX2-PTEN-NF-kappa B nexus

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 = 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-kappa B pathway association with high-risk group. NF-kappa B inhibition reversed glioma chemoresistance, and RNA interference studies identified interleukin-6 and intercellular adhesion molecule-1 as key NF-kappa B 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-kappa B activity by inhibiting AKT through a p53-PTEN-dependent pathway, thus explaining the hypermethylation and downregulation of NPTX2 in NF-kappa B-activated high-risk 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-kappa B pathway activation characterized high-risk patients with poor prognosis, indicating it to be a therapeutic target. (C) 2013 AACR

Serum cytokine levels, PCA and cross validated probabilities in test set.

<p><b>A.</b> Heat map of supervised one-way hierarchical clustering of 18 PAM-identified cytokines in normal and GBM sera of the test set. A dual-color code was used, with red and green indicating high and low abundance, respectively. The white line separates normal from GBM samples. <b>B.</b> PCA was performed using serum levels of 18 PAM-identified cytokines of normal and GBM sera in the test set. A scatter plot was generated using first three principal components for each sample. The color code of the samples is as indicated. <b>C.</b> The graph shows detailed probabilities of 10-fold cross-validation for the samples of test set based on the serum levels of 18 PAM-identified cytokines. The probability of a given sample as normal (green color) and GBM (red color) are shown. This was predicted by the PAM program, based on which type of sample (normal <i>vs</i>. GBM) probability is higher. The original histological type of the samples is indicated above the graph.</p