Meta-analysis of glioblastoma multiforme versus anaplastic astrocytoma identifies robust gene markers

Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma

Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.

Nuclear respiratory factor 1 transcriptomic signatures as prognostic indicators of recurring aggressive mesenchymal glioblastoma and resistance to therapy in White American females

PURPOSE

The mechanisms contributing to recurrence of glioblastoma (GBM), an aggressive neuroepithelial brain tumor, remain unknown. We have recently shown that nuclear respiratory factor 1 (NRF1) is an oncogenic transcription factor and its transcriptional activity is associated with the progression and prognosis of GBM. Herein, we extend our efforts to (1) identify influential NRF1-driven gene and microRNA (miRNA) expression for the aggressiveness of mesenchymal GBM; and (2) understand the molecular basis for its poor response to therapy.

METHODS

Clinical data and RNA-Seq from four independent GBM cohorts were analyzed by Bayesian Network Inference with Java Objects (BANJO) and Markov chain Monte Carlo (MCMC)-based gene order to identify molecular drivers of mesenchymal GBM as well as prognostic indicators of poor response to radiation and chemotherapy.

RESULTS

We are the first to report sex-specific NRF1 motif enriched gene signatures showing increased susceptibility to GBM. Risk estimates for GBM were increased by greater than 100-fold with the joint effect of NRF1-driven gene signatures-CDK4, DUSP6, MSH2, NRF1, and PARK7 in female GBM patients and CDK4, CASP2, H6PD, and NRF1 in male GBM patients. NRF1-driven causal Bayesian network genes were predictive of poor survival and resistance to chemoradiation in IDH1 wild-type mesenchymal GBM patients. NRF1-regulatable miRNAs were also associated with poor response to chemoradiation therapy in female IDH1 wild-type mesenchymal GBM. Stable overexpression of NRF1 reprogramed human astrocytes into neural stem cell-like cells expressing SOX2 and nestin. These cells differentiated into neurons and form tumorospheroids.

CONCLUSIONS

In summary, our novel discovery shows that NRF1-driven causal genes and miRNAs involved in cancer cell stemness and mesenchymal features contribute to cancer aggressiveness and recurrence of aggressive therapy-resistant glioblastoma.

Reviewing and assessing existing meta-analysis models and tools

Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.

Pathway-based meta-analysis for partially paired transcriptomics analysis

Pathway-based differential expression analysis allows the incorporation of biological domain knowledge into transcriptomics analysis to enhance our understanding of disease mechanisms. To integrate information among multiple studies at the pathway level, pathway-based meta-analysis can be performed. Paired or partially paired samples are common in biomedical research. However, there are currently no existing pathway-based meta-analysis methods appropriate for paired or partially paired study designs. In this study, we developed a pathway-based meta-analysis approach for paired or partially paired samples. Meta-analysis on the transcriptomics profiles were conducted using p-value-based, rank-based, and effect size-based algorithms. The application of our approach was demonstrated using partially paired data from psoriasis transcriptomics studies. Upon combining six transcriptomics studies, genes related to the cell cycle and DNA replication pathways are found to be highly perturbed in psoriatic lesional skin samples. Results were validated externally with independent RNA-Seq data. Comparison with existing pathway meta-analysis methods revealed consistent results, with our method showing higher detection power. This study demonstrated the utility of our newly developed pathway-based meta-analysis that allows the incorporation of partially paired or paired samples. The proposed framework can be applied to omics data including but not limited to transcriptomics data.

Statistical genomics in rare cancer

Rare cancers make of more than 20% of cancer cases. Due to the rare nature, less research has been conducted on rare cancers resulting in worse outcomes for patients with rare cancers compared to common cancers. The ability to study rare cancers is impaired by the ability to collect a large enough set of patients to complete an adequately powered genomic study. In this manuscript we outline analytical approaches and public genomic datasets that have been used in genomic studies of rare cancers. These statistical analysis approaches and study designs include: gene set / pathway analyses, pedigree and consortium studies, meta-analysis or horizontal integration, and integration of multiple types of genomic information or vertical integration. We also discuss some of the publicly available resources that can be leveraged in rare cancer genomic studies.

Ranking genomic features using an information-theoretic measure of epigenetic discordance

Background

Establishment and maintenance of DNA methylation throughout the genome is an important epigenetic mechanism that regulates gene expression whose disruption has been implicated in human diseases like cancer. It is therefore crucial to know which genes, or other genomic features of interest, exhibit significant discordance in DNA methylation between two phenotypes. We have previously proposed an approach for ranking genes based on methylation discordance within their promoter regions, determined by centering a window of fixed size at their transcription start sites. However, we cannot use this method to identify statistically significant genomic features and handle features of variable length and with missing data.

Results

We present a new approach for computing the statistical significance of methylation discordance within genomic features of interest in single and multiple test/reference studies. We base the proposed method on a well-articulated hypothesis testing problem that produces p- and q-values for each genomic feature, which we then use to identify and rank features based on the statistical significance of their epigenetic dysregulation. We employ the information-theoretic concept of mutual information to derive a novel test statistic, which we can evaluate by computing Jensen-Shannon distances between the probability distributions of methylation in a test and a reference sample. We design the proposed methodology to simultaneously handle biological, statistical, and technical variability in the data, as well as variable feature lengths and missing data, thus enabling its wide-spread use on any list of genomic features. This is accomplished by estimating, from reference data, the null distribution of the test statistic as a function of feature length using generalized additive regression models. Differential assessment, using normal/cancer data from healthy fetal tissue and pediatric high-grade glioma patients, illustrates the potential of our approach to greatly facilitate the exploratory phases of clinically and biologically relevant methylation studies.

Conclusions

The proposed approach provides the first computational tool for statistically testing and ranking genomic features of interest based on observed DNA methylation discordance in comparative studies that accounts, in a rigorous manner, for biological, statistical, and technical variability in methylation data, as well as for variability in feature length and for missing data.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2777-6) contains supplementary material, which is available to authorized users.

Current Challenges and Opportunities in Treating Glioblastoma

Glioblastoma multiforme (GBM), the most common and aggressive primary brain tumor, has a high mortality rate despite extensive efforts to develop new treatments. GBM exhibits both intra- and intertumor heterogeneity, lending to resistance and eventual tumor recurrence. Large-scale genomic and proteomic analysis of GBM tumors has uncovered potential drug targets. Effective and “druggable” targets must be validated to embark on a robust medicinal chemistry campaign culminating in the discovery of clinical candidates. Here, we review recent developments in GBM drug discovery and delivery. To identify GBM drug targets, we performed extensive bioinformatics analysis using data from The Cancer Genome Atlas project. We discovered 20 genes, BOC, CLEC4GP1, ELOVL6, EREG, ESR2, FDCSP, FURIN, FUT8-AS1, GZMB, IRX3, LITAF, NDEL1, NKX3-1, PODNL1, PTPRN, QSOX1, SEMA4F, TH, VEGFC, and C20orf166AS1 that are overexpressed in a subpopulation of GBM patients and correlate with poor survival outcomes. Importantly, nine of these genes exhibit higher expression in GBM versus low-grade glioma and may be involved in disease progression. In this review, we discuss these proteins in the context of GBM disease progression. We also conducted computational multi-parameter optimization to assess the blood-brain barrier (BBB) permeability of small molecules in clinical trials for GBM treatment. Drug delivery in the context of GBM is particularly challenging because the BBB hinders small molecule transport. Therefore, we discuss novel drug delivery methods, including nanoparticles and prodrugs. Given the aggressive nature of GBM and the complexity of targeting the central nervous system, effective treatment options are a major unmet medical need. Identification and validation of biomarkers and drug targets associated with GBM disease progression present an exciting opportunity to improve treatment of this devastating disease.

Gene expression profiles for predicting antibody‑mediated kidney allograft rejection: Analysis of GEO datasets

Antibody‑mediated rejections (AMRs) are one of the most challenging complications that result in the deterioration of renal allograft function and graft loss in a large majority of cases. The purpose of the present study was to characterize a meta‑signature of differentially expressed RNAs associated with AMR in cases of kidney transplantation. Gene Expression Omnibus (GEO) dataset searches up to September 11, 2017, using Medical Subject Heading terms and keywords associated with kidney transplantation, AMR and mRNA arrays were downloaded from the GEO dataset. Using a computational analysis, a meta‑signature was determined that characterized the significant intersection of differentially expressed genes (DEGs). Gene‑set and network analyses were also performed to identify gene sets and sub‑networks associated with the AMR‑related traits. A statistically significant mRNA meta‑signature of upregulated and downregulated gene expression levels that were significantly associated with AMR was identified. C‑X‑C motif chemokine ligand 10 (CXCL10), CXCL9 and guanylate binding protein 1 were the most significantly associated with AMR. DEGs were efficiently identified and were found to be able to predict the occurrence of AMR according to a meta‑analysis approach from publicly available datasets. These methods and results can be applied for a more accurate diagnosis of AMR in transplant cases.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T

OBJECTIVE

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach.

MATERIALS AND METHODS

Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification.

RESULTS

A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features.

CONCLUSION

In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort.

MMP2 is associated with glioma malignancy and patient outcome

Gliomas are fast growing and usually manifest in an aggressive infiltrative model. MMP2 overexpression is associated with brain tumor malignancy and metastasis formation. The aim of this study was to investigate the influence of MMP2 on glioma formation and clinical outcomes by performing analysis at the DNA, RNA, and protein levels. Methylation status and mRNA level were evaluated in 162 samples; the MMP2 protein level was analyzed in 28 patient preoperative and postoperative blood samples using protein microarray analysis and conventional ELISA. The MMP2 MSP analysis revealed a gradually increasing gene promoter demethylation frequency, and the Kaplan-Meier analysis showed that the methylated gene promoter is related to longer overall survival (Log-rank test X ² = 12.508, df = 1, P < 0.0001). Relative mRNA expression was significantly downregulated when the promoter was methylated. Pairwise comparison analysis showed statistically significant (Mann-Whitney test, P < 0.05) differences in the MMP2 expression median when comparing different glioma grades. The Kaplan-Meier analysis revealed that low MMP2 expression was associated with better survival (Log-rank test X ² = 7.732, df = 1, P = 0.005). At the protein level, MMP2 expression in patient sera showed no differences between malignancy grades and patient preoperative and postoperative states, while the ELISA assay showed the tendency of accumulating MMP2 protein in higher malignancy patient sera samples. The Kaplan-Meier analysis showed the tendency of having a shorter survival time with a higher MMP2 protein level in patient sera. MMP2 has a significant role in glioma pathogenesis and could be used as a potential molecular marker for tumor progression.

A Dexamethasone-regulated Gene Signature Is Prognostic for Poor Survival in Glioblastoma Patients

BACKGROUND

Dexamethasone is reported to induce both tumor-suppressive and tumor-promoting effects. The purpose of this study was to identify the genomic impact of dexamethasone in glioblastoma stem cell (GSC) lines and its prognostic value; furthermore, to identify drugs that can counter these side effects of dexamethasone exposure.

METHODS

We utilized 3 independent GSC lines with tumorigenic potential for this study. Whole-genome expression profiling and pathway analyses were done with dexamethasone-exposed and control cells. GSCs were also co-exposed to dexamethasone and temozolomide. Risk scores were calculated for most affected genes, and their associations with survival in The Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data databases. In silico Connectivity Map analysis identified camptothecin as antagonist to dexamethasone-induced negative effects.

RESULTS

Pathway analyses predicted an activation of dexamethasone network (z-score: 2.908). Top activated canonical pathways included "role of breast cancer 1 in DNA damage response" (P=1.07E-04). GSCs were protected against temozolomide-induced apoptosis when coincubated with dexamethasone. Altered cellular functions included cell movement, cell survival, and apoptosis with z-scores of 2.815, 5.137, and -3.122, respectively. CCAAT/enhancer binding protein beta (CEBPB) was activated in a dose dependent manner specifically in slow-dividing "stem-like" cells. CEBPB was activated in dexamethasone-treated orthotopic tumors. Patients with high risk scores had significantly shorter survival. Camptothecin was validated as potential partial neutralizer of dexamethasone-induced oncogenic effects.

CONCLUSIONS

Dexamethasone exposure induces a genetic program and CEBPB expression in GSCs that adversely affects key cellular functions and response to therapeutics. High risk scores associated with these genes have negative prognostic value in patients. Our findings further suggest camptothecin as a potential neutralizer of adverse dexamethasone-mediated effects.

Estimation of random effects and identifying heterogeneous genes in meta-analysis of gene expression studies

Combining effect sizes from individual studies using random-effects meta-analysis models are commonly applied in high-dimensional gene expression data. However, unknown study heterogeneity can arise from inconsistencies in sample quality and experimental conditions. High heterogeneity of effect sizes can reduce statistical power of the models. In this study, we describe three hypothesis-testing frameworks for meta-analysis of microarray data, and review several existing meta-analytic techniques that have been used in the genomic setting. These include P-value-based methods, rank-based methods and effect-size-based methods. We then discuss limitations of some of these methods and describe random-effects-based methods in detail. We introduce two methods for estimating the inter-study variance in random-effects meta-analytic models and another method for identifying heterogeneous genes for gene expression data. We compared various methods with the standard and existing meta-analytic techniques in the genomic framework. We demonstrate our results through a series of simulations and application in Alzheimer's gene expression data.

A Transcriptional Program for Detecting TGFβ-Induced EMT in Cancer

Most cancer deaths are due to metastasis, and epithelial-to-mesenchymal transition (EMT) plays a central role in driving cancer cell metastasis. EMT is induced by different stimuli, leading to different signaling patterns and therapeutic responses. TGFβ is one of the best-studied drivers of EMT, and many drugs are available to target this signaling pathway. A comprehensive bioinformatics approach was employed to derive a signature for TGFβ-induced EMT which can be used to score TGFβ-driven EMT in cells and clinical specimens. Considering this signature in pan-cancer cell and tumor datasets, a number of cell lines (including basal B breast cancer and cancers of the central nervous system) show evidence for TGFβ-driven EMT and carry a low mutational burden across the TGFβ signaling pathway. Furthermore, significant variation is observed in the response of high scoring cell lines to some common cancer drugs. Finally, this signature was applied to pan-cancer data from The Cancer Genome Atlas to identify tumor types with evidence of TGFβ-induced EMT. Tumor types with high scores showed significantly lower survival rates than those with low scores and also carry a lower mutational burden in the TGFβ pathway. The current transcriptomic signature demonstrates reproducible results across independent cell line and cancer datasets and identifies samples with strong mesenchymal phenotypes likely to be driven by TGFβ.Implications: The TGFβ-induced EMT signature may be useful to identify patients with mesenchymal-like tumors who could benefit from targeted therapeutics to inhibit promesenchymal TGFβ signaling and disrupt the metastatic cascade. Mol Cancer Res; 15(5); 619-31. ©2017 AACR.

Preoperative grading of supratentorial gliomas using high or standard b-value diffusion-weighted MR imaging at 3T

PURPOSE

The goal of this study was to compare diffusion-weighted magnetic resonance imaging (DW-MRI) using high b-value (b=3000s/mm²) to DW-MRI using standard b-value (b=1000s/mm²) in the preoperative grading of supratentorial gliomas.

MATERIALS AND METHODS

Fifty-three patients with glioma had brain DW-MRI at 3T using two different b-values (b=1000s/mm² and b=3000s/mm²). There were 35 men and 18 women with a mean age of 40.5±17.1 years (range: 18-79 years). Mean, minimum, maximum, and range of apparent diffusion coefficient (ADC) values for solid tumor ROIs (ADC_mean, ADC_min, ADC_max, and ADC_diff), and the normalized ADC (ADC_ratio) were calculated. A Kruskal-Wallis statistic with Bonferroni correction for multiple comparisons was applied to detect significant ADC parameter differences between tumor grades by including or excluding 19 patients with an oligodendroglioma. Receiver operating characteristic curve analysis was conducted to define appropriate cutoff values for grading gliomas.

RESULTS

No differences in ADC derived parameters were found between grade II and grade III gliomas. Mean ADC values using standard b-value were 1.17±0.27×10^-3mm²/s [range: 0.63-1.61], 1.05±0.22×10^-3mm²/s [range: 0.73-1.33], and 0.86±0.23×10^-3mm²/s [range: 0.52-1.46] for grades II, III and IV gliomas, respectively. Using high b-value, mean ADC values were 0.89±0.24×10^-3mm²/s [range: 0.42-1.25], 0.82±0.20×10^-3mm²/s [range: 0.56-1.10], and 0.59±0.17×10^-3mm²/s [range: 0.40-1.01] for grades II, III and IV gliomas, respectively. ADC_mean, ADC_ratio, ADC_max, and ADC_min were different between grade II and grade IV gliomas at both standard and high b-values. Differences in ADC_mean, ADC_max, and ADC_diff were found between grade III and grade IV only using high b-value.

CONCLUSION

ADC parameters derived from DW-MRI using a high b-value allows a better differential diagnosis of gliomas, especially for differentiating grades III and IV, than those derived from DW-MRI using a standard b-value.

Prediction of Possible Biomarkers and Novel Pathways Conferring Risk to Post-Traumatic Stress Disorder

Post-traumatic stress disorder is one of the common mental ailments that is triggered by exposure to traumatic events. Till date, the molecular factors conferring risk to the development of PTSD is not well understood. In this study, we have conducted a meta-analysis followed by hierarchical clustering and functional enrichment, to uncover the potential molecular networks and critical genes which play an important role in PTSD. Two datasets of expression profiles from Peripheral Blood Mononuclear Cells from 62 control samples and 63 PTSD samples were included in our study. In PTSD samples of GSE860 dataset, we identified 26 genes informative when compared with Post-deploy PTSD condition and 58 genes informative when compared with Pre-deploy and Post-deploy PTSD of GSE63878 dataset. We conducted the meta-analysis using Fisher, roP, Stouffer, AW, SR, PR and RP methods in MetaDE package. Results from the rOP method of MetaDE package showed that among these genes, the following showed significant changes including, OR2B6, SOX21, MOBP, IL15, PTPRK, PPBPP2 and SEC14L5. Gene ontology analysis revealed enrichment of these significant PTSD-related genes for cell proliferation, DNA damage and repair (p-value ≤ 0.05). Furthermore, interaction network analysis was performed on these 7 significant genes. This analysis revealed highly connected functional interaction networks with two candidate genes, IL15 and SEC14L5 highly enriched in networks. Overall, from these results, we concluded that these genes can be recommended as some of the potential targets for PTSD.

Anaplastic astrocytoma: prognostic factors and survival in 4807 patients with emphasis on receipt and impact of adjuvant therapy

To determine the receipt and impact of adjuvant therapy on overall survival (OS) for anaplastic astrocytoma (AA). Data were extracted from the National Cancer Data Base (NCDB). Chi square test, Kaplan-Meier method, and Cox regression models were employed in SPSS 22.0 (Armonk, NY: IBM Corp.) for data analyses. 4807 patients with AA diagnosed from 2004 to 2013 who underwent surgery were identified. 3243 (67.5 %) received adjuvant chemoRT, 525 (10.9 %) adjuvant radiotherapy (RT) alone, 176 (3.7 %) adjuvant chemotherapy alone and 863 (18.0 %) received no adjuvant therapy. Patients were more likely to receive adjuvant chemoRT if they were diagnosed in 2009-2013 (p = 0.022), were ≤ 50 years (p < 0.001), were male (p = 0.043), were Asian or White race (p < 0.001), had private insurance (p < 0.001), had income ≥$38,000 (p < 0.001), or underwent total resection (p < 0.003). Those who received adjuvant chemoRT had significantly better 5-year OS than the other adjuvant treatment types (41.8 % vs. 31.2 % vs. 29.8 % vs. 27.4 %, p < 0.001). This significant 5-year OS benefit was also observed regardless of age at diagnosis. Of those undergoing adjuvant chemoRT, those receiving ≥59.4 Gy had significantly better 5-year OS than those receiving <59.4 Gy (44.4 % vs. 25.9 %, p < 0.001). There was no significant difference in OS when comparing 59.4 Gy to higher RT doses. On multivariate analysis, receipt of adjuvant chemoRT, age at diagnosis, extent of disease, and insurance status were independent prognostic factors for OS. Adjuvant chemoRT is an independent prognostic factor for improved OS in AA and concomitant chemoRT should be considered for all clinically suitable patients who have undergone surgery for the disease.

A semi-parametric statistical model for integrating gene expression profiles across different platforms

BACKGROUND

Determining differentially expressed genes (DEGs) between biological samples is the key to understand how genotype gives rise to phenotype. RNA-seq and microarray are two main technologies for profiling gene expression levels. However, considerable discrepancy has been found between DEGs detected using the two technologies. Integration data across these two platforms has the potential to improve the power and reliability of DEG detection.

METHODS

We propose a rank-based semi-parametric model to determine DEGs using information across different sources and apply it to the integration of RNA-seq and microarray data. By incorporating both the significance of differential expression and the consistency across platforms, our method effectively detects DEGs with moderate but consistent signals. We demonstrate the effectiveness of our method using simulation studies, MAQC/SEQC data and a synthetic microRNA dataset.

CONCLUSIONS

Our integration method is not only robust to noise and heterogeneity in the data, but also adaptive to the structure of data. In our simulations and real data studies, our approach shows a higher discriminate power and identifies more biologically relevant DEGs than eBayes, DEseq and some commonly used meta-analysis methods.

Broad targeting of resistance to apoptosis in cancer

Apoptosis or programmed cell death is natural way of removing aged cells from the body. Most of the anti-cancer therapies trigger apoptosis induction and related cell death networks to eliminate malignant cells. However, in cancer, de-regulated apoptotic signaling, particularly the activation of an anti-apoptotic systems, allows cancer cells to escape this program leading to uncontrolled proliferation resulting in tumor survival, therapeutic resistance and recurrence of cancer. This resistance is a complicated phenomenon that emanates from the interactions of various molecules and signaling pathways. In this comprehensive review we discuss the various factors contributing to apoptosis resistance in cancers. The key resistance targets that are discussed include (1) Bcl-2 and Mcl-1 proteins; (2) autophagy processes; (3) necrosis and necroptosis; (4) heat shock protein signaling; (5) the proteasome pathway; (6) epigenetic mechanisms; and (7) aberrant nuclear export signaling. The shortcomings of current therapeutic modalities are highlighted and a broad spectrum strategy using approaches including (a) gossypol; (b) epigallocatechin-3-gallate; (c) UMI-77 (d) triptolide and (e) selinexor that can be used to overcome cell death resistance is presented. This review provides a roadmap for the design of successful anti-cancer strategies that overcome resistance to apoptosis for better therapeutic outcome in patients with cancer.

Reactive oxygen species production has a critical role in hypoxia-induced Stat3 activation and angiogenesis in human glioblastoma

Glioblastoma is the most aggressive primary brain tumor with hypoxia-associated morphologic features including pseudopalisading necrosis and endothelial hyperplasia. It has been known that hypoxia can activate signal transducer and activator of transcription 3 (Stat3) and subsequently induce angiogenesis. However, the molecular mechanism underlying hypoxia-induced Stat3 activation has not been defined. In this study, we explored the possible implication of reactive oxygen species (ROS) in hypoxia-driven Stat3 activation in human glioblastoma. We found that hypoxic stress increased ROS production as well as Stat3 activation and that ROS inhibitors (diphenyleneiodonium, rotenone and myxothiazol) and an antioxidant (N-acetyl-L-cysteine) blocked Stat3 activation under hypoxic conditions. To determine a major route of ROS production, we tested whether nicotinamide adenine dinucleotide phosphate oxidase 4 (Nox4) is involved in hypoxia-induced ROS production. Nox4 expression was found to be increased at both mRNA and protein levels in hypoxic glioblastoma cells. In addition, siRNA-mediated knockdown of Nox4 expression abolished hypoxia induced Stat3 activation and vascular endothelial growth factor expression, which is associated with tumor cells' ability to trigger tube formation of endothelial cells in vitro. Our findings indicate that elevated ROS production plays a crucial role for Stat3 activation and angiogenesis in hypoxic glioblastoma cells.

Elimination of cancer stem-like side population in human glioblastoma cells accompanied with stemness gene suppression by Korean herbal recipe MSC500

BACKGROUND

High-grade gliomas are the most common and invasive malignant brain tumors in adults, and they are almost universally fatal because of drug resistance and recurrence. In spite of the progress in adjuvant therapy (like temozolomide) and irradiation after surgery, no effective salvage therapy is currently available for relapsed patients. A Korean herbal recipe MSC500 has been reported to have beneficial therapeutic effects in patients with high-grade gliomas who are relapsed or refractory to conventional treatments. But the underlying molecular mechanisms remain unclear.

METHODS

As Cancer stem cell (CSC) plays a pivotal role in the resistance to conventional cancer therapy, we explored the effects of MSC500 on the CSC-like side population (SP) in GBM8401 human glioblastoma multiforme cells.

RESULTS

Compared with the parental cells, the SP cells were more resistant to temozolomide but sensitive to MSC500. The mRNA levels of stemness genes such as Nanog, CD133, and ABCG2 were much higher in the SP cells, and so was E-cadherin, which was reported to correlate with the aggressiveness of glioblastoma multiforme. Treatment with MSC500 decreased the proportion of SP cells and high ALDH activity cells from 1.6% to 0.3% and from 0.9% to 0.1%, respectively, accompanied with suppression of the aforementioned stemness genes and E-cadherin, as well as other CSC markers such as ABCB5, Oct-4, Sox-2, β-catenin, Gli-1, and Notch-1.

CONCLUSION

Our results suggest the potential role of MSC500 as an integrative and complementary therapeutic for advanced or refractory high-grade glioma patients.

mRNA expression levels of hypoxia-induced and stem cell-associated genes in human glioblastoma

The roles of hypoxia-induced and stem cell-associated genes in the development of malignancy and tumour progression are well known. However, there are a limited number of studies analysing the impact of mRNA expression levels of hypoxia-induced and stem cell-associated genes in the tissues of brain tumours and glioblastoma patients. In this study, tumour tissues from patients with glioblastoma multiforme and tumour adjacent tissues were analysed. We investigated mRNA expression levels of hypoxia-inducible factor-1α (HIF-1α), hypoxia-inducible factor-2α (HIF-2α), carbonic anhydrase 9 (CA9), vascular endothelial growth factor (VEGF), glucose transporter-1 (GLUT-1) and osteopontin (OPN), and stem cell-associated genes survivin, epidermal growth factor receptor (EGFR), human telomerase reverse transcriptase (hTERT), Nanog and octamer binding transcription factor 4 (OCT4) using quantitative real-time polymerase chain reaction (qRT-PCR). Our data revealed higher mRNA expression levels of hypoxia-induced and stem cell-associated genes in tumour tissue than levels in the tumour adjacent tissues in patients with glioblastoma multiforme. A strong positive correlation between the mRNA expression levels of HIF-2α, CA9, VEGF, GLUT-1 and OPN suggests a specific hypoxia-associated profile of mRNA expression in glioblastoma multiforme. Additionally, the results indicate the role of stem-cell-related genes in tumour hypoxia. Kaplan-Maier analysis revealed that high mRNA expression levels of hypoxia-induced markers showed a trend towards shorter overall survival in glioblastoma patients (P=0.061). Our data suggest that mRNA expression levels of hypoxia-induced genes are important tumour markers in patients with glioblastoma multiforme.

Identification of suitable genes contributes to lung adenocarcinoma clustering by multiple meta-analysis methods

BACKGROUND

With the widespread application of high-throughput technology, numerous meta-analysis methods have been proposed for differential expression profiling across multiple studies.

OBJECTIVES

We identified the suitable differentially expressed (DE) genes that contributed to lung adenocarcinoma (ADC) clustering based on seven popular multiple meta-analysis methods.

METHODS

Seven microarray expression profiles of ADC and normal controls were extracted from the ArrayExpress database. The Bioconductor was used to perform the data preliminary preprocessing. Then, DE genes across multiple studies were identified. Hierarchical clustering was applied to compare the classification performance for microarray data samples. The classification efficiency was compared based on accuracy, sensitivity and specificity.

RESULTS

Across seven datasets, 573 ADC cases and 222 normal controls were collected. After filtering out unexpressed and noninformative genes, 3688 genes were remained for further analysis. The classification efficiency analysis showed that DE genes identified by sum of ranks method separated ADC from normal controls with the best accuracy, sensitivity and specificity of 0.953, 0.969 and 0.932, respectively. The gene set with the highest classification accuracy mainly participated in the regulation of response to external stimulus (P = 7.97E-04), cyclic nucleotide-mediated signaling (P = 0.01), regulation of cell morphogenesis (P = 0.01) and regulation of cell proliferation (P = 0.01).

CONCLUSIONS

Evaluation of DE genes identified by different meta-analysis methods in classification efficiency provided a new perspective to the choice of the suitable method in a given application. Varying meta-analysis methods always present varying abilities, so synthetic consideration should be taken when providing meta-analysis methods for particular research.

The driver and passenger effects of isocitrate dehydrogenase 1 and 2 mutations in oncogenesis and survival prolongation

Mutations in isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) are key events in the development of glioma, acute myeloid leukemia (AML), chondrosarcoma, intrahepatic cholangiocarcinoma (ICC), and angioimmunoblastic T-cell lymphoma. They also cause D-2-hydroxyglutaric aciduria and Ollier and Maffucci syndromes. IDH1/2 mutations are associated with prolonged survival in glioma and in ICC, but not in AML. The reason for this is unknown. In their wild-type forms, IDH1 and IDH2 convert isocitrate and NADP(+) to α-ketoglutarate (αKG) and NADPH. Missense mutations in the active sites of these enzymes induce a neo-enzymatic reaction wherein NADPH reduces αKG to D-2-hydroxyglutarate (D-2HG). The resulting D-2HG accumulation leads to hypoxia-inducible factor 1α degradation, and changes in epigenetics and extracellular matrix homeostasis. Such mutations also imply less NADPH production capacity. Each of these effects could play a role in cancer formation. Here, we provide an overview of the literature and discuss which downstream molecular effects are likely to be the drivers of the oncogenic and survival-prolonging properties of IDH1/2 mutations. We discuss interactions between mutant IDH1/2 inhibitors and conventional therapies. Understanding of the biochemical consequences of IDH1/2 mutations in oncogenesis and survival prolongation will yield valuable information for rational therapy design: it will tell us which oncogenic processes should be blocked and which "survivalogenic" effects should be retained.

Loss of CDC14B expression in clear cell renal cell carcinoma: meta-analysis of microarray data sets

OBJECTIVES

To discover significant differentially expressed genes (DEGs) in clear cell renal cell carcinoma (ccRCC) that might be unidentified by single microarray analysis.

METHODS

The effect sizes of five ccRCC microarray data sets were combined using a random-effects model. The most downregulated gene was validated in paired 80 ccRCC tissues by immunohistochemistry.

RESULTS

CDC14B was the most downregulated gene among 1,761 DEGs. CDC14B was strongly expressed in the apical proximal tubules in the nonneoplastic tissues, while it was completely absent in 10 (12.5%) of 80 or downregulated in 70 (87.5%) of 80 ccRCC cases. The complete loss of CDC14B correlated with high T stage (P = .038), advanced TNM stage (P = .027), tumor recurrence (P = .038), and shorter recurrence-free survival (P = .046) compared with the partial loss of CDC14B.

CONCLUSIONS

Microarray meta-analysis is a useful tool for pathologists. CDC14B expression is downregulated in ccRCC, suggesting its role in renal carcinogenesis.

Loss of Sh3gl2/endophilin A1 is a common event in urothelial carcinoma that promotes malignant behavior

Urothelial carcinoma (UC) causes substantial morbidity and mortality worldwide. However, the molecular mechanisms underlying urothelial cancer development and tumor progression are still largely unknown. Using informatics analysis, we identified Sh3gl2 (endophilin A1) as a bladder urothelium-enriched transcript. The gene encoding Sh3gl2 is located on chromosome 9p, a region frequently altered in UC. Sh3gl2 is known to regulate endocytosis of receptor tyrosine kinases implicated in oncogenesis, such as the epidermal growth factor receptor (EGFR) and c-Met. However, its role in UC pathogenesis is unknown. Informatics analysis of expression profiles as well as immunohistochemical staining of tissue microarrays revealed Sh3gl2 expression to be decreased in UC specimens compared to nontumor tissues. Loss of Sh3gl2 was associated with increasing tumor grade and with muscle invasion, which is a reliable predictor of metastatic disease and cancer-derived mortality. Sh3gl2 expression was undetectable in 19 of 20 human UC cell lines but preserved in the low-grade cell line RT4. Stable silencing of Sh3gl2 in RT4 cells by RNA interference 1) enhanced proliferation and colony formation in vitro, 2) inhibited EGF-induced EGFR internalization and increased EGFR activation, 3) stimulated phosphorylation of Src family kinases and STAT3, and 4) promoted growth of RT4 xenografts in subrenal capsule tissue recombination experiments. Conversely, forced re-expression of Sh3gl2 in T24 cells and silenced RT4 clones attenuated oncogenic behaviors, including growth and migration. Together, these findings identify loss of Sh3gl2 as a frequent event in UC development that promotes disease progression.

A 16-gene signature distinguishes anaplastic astrocytoma from glioblastoma

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, GSE1993 and GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma.

Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline

BACKGROUND

As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations.

RESULTS

We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS(A): DE genes with non-zero effect sizes in all studies, (2) HS(B): DE genes with non-zero effect sizes in one or more studies and (3) HS(r): DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively.

CONCLUSIONS

The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HS(A), HS(B), and HS(r)). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author's publication website.

Identifying novel glioma associated pathways based on systems biology level meta-analysis

Background

With recent advances in microarray technology, including genomics, proteomics, and metabolomics, it brings a great challenge for integrating this "-omics" data to analysis complex disease. Glioma is an extremely aggressive and lethal form of brain tumor, and thus the study of the molecule mechanism underlying glioma remains very important. To date, most studies focus on detecting the differentially expressed genes in glioma. However, the meta-analysis for pathway analysis based on multiple microarray datasets has not been systematically pursued.

Results

In this study, we therefore developed a systems biology based approach by integrating three types of omics data to identify common pathways in glioma. Firstly, the meta-analysis has been performed to study the overlapping of signatures at different levels based on the microarray gene expression data of glioma. Among these gene expression datasets, 12 pathways were found in GeneGO database that shared by four stages. Then, microRNA expression profiles and ChIP-seq data were integrated for the further pathway enrichment analysis. As a result, we suggest 5 of these pathways could be served as putative pathways in glioma. Among them, the pathway of TGF-beta-dependent induction of EMT via SMAD is of particular importance.

Conclusions

Our results demonstrate that the meta-analysis based on systems biology level provide a more useful approach to study the molecule mechanism of complex disease. The integration of different types of omics data, including gene expression microarrays, microRNA and ChIP-seq data, suggest some common pathways correlated with glioma. These findings will offer useful potential candidates for targeted therapeutic intervention of glioma.

Systematic review of protein biomarkers of invasive behavior in glioblastoma

Glioblastoma (GBM) is an aggressive and incurable brain tumor with a grave prognosis. Recurrence is inevitable even with maximal surgical resection, in large part because GBM is a highly invasive tumor. Invasiveness also contributes to the failure of multiple cornerstones of GBM therapy, including radiotherapy, temozolomide chemotherapy, and vascular endothelial growth factor blockade. In recent years there has been significant progress in the identification of protein biomarkers of invasive phenotype in GBM. In this article, we comprehensively review the literature and survey a broad spectrum of biomarkers, including proteolytic enzymes, extracellular matrix proteins, cell adhesion molecules, neurodevelopmental factors, cell signaling and transcription factors, angiogenic effectors, metabolic proteins, membrane channels, and cytokines and chemokines. In light of the marked variation seen in outcomes in GBM patients, the systematic use of these biomarkers could be used to form a framework for better prediction, prognostication, and treatment selection, as well as the identification of molecular targets for further laboratory investigation and development of nascent, directed therapies.

Distinct genomic aberrations between low-grade and high-grade gliomas of Chinese patients

Background

Glioma is a type of tumor that develops in the central nerve system, mainly the brain. Alterations of genomic sequence and sequence segments (such as copy number variations or CNV and copy neutral loss of heterozygosities or cnLOH) are thought to be a major determinant of the tumor grade.

Methods

We mapped genomic variations between low-grade and high-grade gliomas (LGG and HGG) in Chinese population based on Illumina’s Beadchip and validated the results using real-time qPCR.

Results

At the cytoband level, we discovered: (1) unique losses in LGG on 5q, 8p and 11q, and in HGG on 6q, 11p, 13q and 19q; (2) unique gains in the LGG on 1p and in HGG at 5p, 7p, 7q and 20q; and (3) cnLOH in HGG only on 3q, 8q, 10p, 14q, 15q, 17p, 17q, 18q and 21q. Subsequently, we confirmed well-characterized oncogenes among tumor-related loci (such as EGFR and KIT) and detected novel genes that gained chromosome sequences (such as AASS, HYAL4, NDUFA5 and SPAM1) in both LGG and HGG. In addition, we found gains, losses, and cnLOH in several genes, including VN1R2, VN1R4, and ZNF677, in multiple samples. Mapping grade-associated pathways and their related gene ontology (GO) terms, we classified LGG-associated functions as “arachidonic acid metabolism”, “DNA binding” and “regulation of DNA-dependent transcription” and the HGG-associated as “neuroactive ligand-receptor interaction”, “neuronal cell body” and “defense response to bacterium”.

Conclusion

LGG and HGG appear to have different molecular signatures in genomic variations and our results provide invaluable information for the diagnosis and treatment of gliomas in patients with variable duration or diverse tumor differentiation.

Application of 62Cu-diacetyl-bis (N4-methylthiosemicarbazone) PET imaging to predict highly malignant tumor grades and hypoxia-inducible factor-1α expression in patients with glioma

BACKGROUND AND PURPOSE

Hypoxic tissue evaluation in glioma is important for predicting treatment response and establishing antihypoxia therapy. In this preliminary study, (62)Cu-ATSM PET was used to determine its validity as a biomarker for distinguishing tumor grade and tissue hypoxia.

MATERIALS AND METHODS

(62)Cu-ATSM PET was performed in 22 patients with glioma, and the (62)Cu-ATSM SUV(max) and T/B ratio were semiquantitatively evaluated. (62)Cu-ATSM uptake distribution was qualitatively evaluated and compared with MR imaging findings. HIF-1α expression, a hypoxia marker, was compared with (62)Cu-ATSM uptake values.

RESULTS

The (62)Cu-ATSM SUV(max) and T/B ratio were significantly higher in grade IV than in grade III gliomas (P = .014 and .018, respectively), whereas no significant differences were found between grade III and grade II gliomas. At a T/B ratio cutoff threshold of 1.8, (62)Cu-ATSM uptake was predictive of HIF-1α expression, with 92.3% sensitivity and 88.9% specificity. The mean T/B ratio was also significantly higher in HIF-1α-positive glioma tissue than in HIF-1α-negative tissue (P = .001). Using this optimal threshold of T/B ratio, (62)Cu-ATSM PET showed regional uptake in 61.9% (13/21) of tumors within the contrast-enhanced region on MR imaging, which was significantly correlated with presence of a necrotic component (P = .002).

CONCLUSIONS

Our results demonstrated that (62)Cu-ATSM uptake is relatively high in grade IV gliomas and correlates with the MR imaging findings of necrosis. Moreover, the (62)Cu-ATSM T/B ratio showed significant correlation with HIF-1α expression. Thus, (62)Cu-ATSM appears to be a suitable biomarker for predicting highly malignant grades and tissue hypoxia in patients with glioma.

Moesin is a glioma progression marker that induces proliferation and Wnt/β-catenin pathway activation via interaction with CD44

Moesin is an ERM family protein that connects the actin cytoskeleton to transmembrane receptors. With the identification of the ERM family protein NF2 as a tumor suppressor in glioblastoma, we investigated roles for other ERM proteins in this malignancy. Here, we report that overexpression of moesin occurs generally in high-grade glioblastoma in a pattern correlated with the stem cell marker CD44. Unlike NF2, moesin acts as an oncogene by increasing cell proliferation and stem cell neurosphere formation, with its ectopic overexpression sufficient to shorten survival in an orthotopic mouse model of glioblastoma. Moesin was the major ERM member activated by phosphorylation in glioblastoma cells, where it interacted and colocalized with CD44 in membrane protrusions. Increasing the levels of moesin competitively displaced NF2 from CD44, increasing CD44 expression in a positive feedback loop driven by the Wnt/β-catenin signaling pathway. Therapeutic targeting of the moesin-CD44 interaction with the small-molecule inhibitor 7-cyanoquinocarcinol (DX-52-1) or with a CD44-mimetic peptide specifically reduced the proliferation of glioblastoma cells overexpressing moesin, where the Wnt/β-catenin pathway was activated. Our findings establish moesin and CD44 as progression markers and drugable targets in glioblastoma, relating their oncogenic effects to activation of the Wnt/β-catenin pathway.

Increased age of transformed mouse neural progenitor/stem cells recapitulates age-dependent clinical features of human glioma malignancy

Increasing age is the most robust predictor of greater malignancy and treatment resistance in human gliomas. However, the adverse association of clinical course with aging is rarely considered in animal glioma models, impeding delineation of the relative importance of organismal versus progenitor cell aging in the genesis of glioma malignancy. To address this limitation, we implanted transformed neural stem/progenitor cells (NSPCs), the presumed cells of glioma origin, from 3- and 18-month-old mice into 3- and 20-month host animals. Transplantation with progenitors from older animals resulted in significantly shorter (P ≤ 0.0001) median survival in both 3-month (37.5 vs. 83 days) and 20-month (38 vs. 67 days) hosts, indicating that age-dependent changes intrinsic to NSPCs rather than host animal age accounted for greater malignancy. Subsequent analyses revealed that increased invasiveness, genomic instability, resistance to therapeutic agents, and tolerance to hypoxic stress accompanied aging in transformed NSPCs. Greater tolerance to hypoxia in older progenitor cells, as evidenced by elevated HIF-1 promoter reporter activity and hypoxia response gene (HRG) expression, mirrors the upregulation of HRGs in cohorts of older vs. younger glioma patients revealed by analysis of gene expression databases, suggesting that differential response to hypoxic stress may underlie age-dependent differences in invasion, genomic instability, and treatment resistance. Our study provides strong evidence that progenitor cell aging is responsible for promoting the hallmarks of age-dependent glioma malignancy and that consideration of progenitor aging will facilitate development of physiologically and clinically relevant animal models of human gliomas.

Hypoxia and hypoxia-inducible factors in glioblastoma multiforme progression and therapeutic implications

Glioblastoma multiforme (GBM) is the most malignant and aggressive primary brain tumor in humans, with a uniformly poor prognosis. Hypoxia is a predominant feature in GBM and its microenvironment; it is associated with the tumor growth, progression and resistance to conventional therapy of cancers. Hypoxia-inducible factors (HIFs) are the master regulators of the transcriptional response to hypoxia in tumor cells and their microenvironment. Numerous studies indicated that hypoxia and HIFs played pivotal roles in the initiation, progression, therapy resistance and recurrence of GBM and maintained the phenotype of glioma stem cells (GSCs), which makes the prognosis of GBM patients worse. This review summarized the current research advance of hypoxia and HIFs in GBM progression and therapeutic implications, which will provide a better understanding of the contribution of hypoxia and HIFs to GBM initiation and progression and highlight that HIFs might be taken as the attractive molecular target approaches for GBM therapeutics.

A systems biology approach to the global analysis of transcription factors in colorectal cancer

Background

Biological entities do not perform in isolation, and often, it is the nature and degree of interactions among numerous biological entities which ultimately determines any final outcome. Hence, experimental data on any single biological entity can be of limited value when considered only in isolation. To address this, we propose that augmenting individual entity data with the literature will not only better define the entity’s own significance but also uncover relationships with novel biological entities.

To test this notion, we developed a comprehensive text mining and computational methodology that focused on discovering new targets of one class of molecular entities, transcription factors (TF), within one particular disease, colorectal cancer (CRC).

Methods

We used 39 molecular entities known to be associated with CRC along with six colorectal cancer terms as the bait list, or list of search terms, for mining the biomedical literature to identify CRC-specific genes and proteins. Using the literature-mined data, we constructed a global TF interaction network for CRC. We then developed a multi-level, multi-parametric methodology to identify TFs to CRC.

Results

The small bait list, when augmented with literature-mined data, identified a large number of biological entities associated with CRC. The relative importance of these TF and their associated modules was identified using functional and topological features. Additional validation of these highly-ranked TF using the literature strengthened our findings. Some of the novel TF that we identified were: SLUG, RUNX1, IRF1, HIF1A, ATF-2, ABL1, ELK-1 and GATA-1. Some of these TFs are associated with functional modules in known pathways of CRC, including the Beta-catenin/development, immune response, transcription, and DNA damage pathways.

Conclusions

Our methodology of using text mining data and a multi-level, multi-parameter scoring technique was able to identify both known and novel TF that have roles in CRC. Starting with just one TF (SMAD3) in the bait list, the literature mining process identified an additional 116 CRC-associated TFs. Our network-based analysis showed that these TFs all belonged to any of 13 major functional groups that are known to play important roles in CRC. Among these identified TFs, we obtained a novel six-node module consisting of ATF2-P53-JNK1-ELK1-EPHB2-HIF1A, from which the novel JNK1-ELK1 association could potentially be a significant marker for CRC.

Differential expression of HIF-1 in glioblastoma multiforme and anaplastic astrocytoma

Hypoxia is an important factor mediating tumor progression and therapeutic resistance, in part through proteome changes mediated by the transcription factor hypoxia-inducible factor (HIF)-1. Since glioblastoma multiforme is the epitome of a highly aggressive tumor entity, while lower-grade astrocytomas often show a prolonged clinical course, a profound difference in the extent of hypoxic tissue areas and corresponding magnitude of HIF-1 activity may exist between these entities. In this study, to address this question, serial sections of 11 glioblastomas and 10 anaplastic astrocytomas were immunostained for HIF-1α, glucose transporter (GLUT)-1, carbonic anhydrase (CA) IX (i.e., hypoxia-related markers), Ki67 (proliferation), phosphorylated ribosomal protein S6 [p-rpS6; mammalian target of rapamycin (mTOR) activity] and CD34 (microvascular endothelium). Digital scans of whole tumor sections were registered to achieve geometric correspondence for subsequent morphometric operations. HIF-1α-, GLUT-1- and CA IX-positive staining was found in all 11 glioblastomas, showing a preferential expression in tissue areas adjacent to necroses. A considerable spatial overlap between GLUT-1 and CA IX, and a colocalization of these proteins with areas of enlarged mean diffusion distances were observed. Conversely, 8 of the 10 anaplastic astrocytomas were completely negative for hypoxia-related markers. The glioblastomas also showed significantly greater heterogeneity of intercapillary distances, larger diffusion-limited tissue fractions, significantly higher mTOR activity and a trend for higher proliferation rates. Microregionally, mTOR and proliferation showed a significant spatial overlap with areas of shorter mean diffusion distances. In conclusion, diffusion-limited hypoxia, leading to the expression of hypoxia-related markers is a pivotal element of the glioblastoma phenotype and may be driven by dysregulated growth and proliferation in normoxic subregions.

Comprehensive literature review and statistical considerations for microarray meta-analysis

With the rapid advances of various high-throughput technologies, generation of ‘-omics’ data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.

MetaQC: objective quality control and inclusion/exclusion criteria for genomic meta-analysis

Genomic meta-analysis to combine relevant and homogeneous studies has been widely applied, but the quality control (QC) and objective inclusion/exclusion criteria have been largely overlooked. Currently, the inclusion/exclusion criteria mostly depend on ad-hoc expert opinion or naïve threshold by sample size or platform. There are pressing needs to develop a systematic QC methodology as the decision of study inclusion greatly impacts the final meta-analysis outcome. In this article, we propose six quantitative quality control measures, covering internal homogeneity of coexpression structure among studies, external consistency of coexpression pattern with pathway database, and accuracy and consistency of differentially expressed gene detection or enriched pathway identification. Each quality control index is defined as the minus log transformed P values from formal hypothesis testing. Principal component analysis biplots and a standardized mean rank are applied to assist visualization and decision. We applied the proposed method to 4 large-scale examples, combining 7 brain cancer, 9 prostate cancer, 8 idiopathic pulmonary fibrosis and 17 major depressive disorder studies, respectively. The identified problematic studies were further scrutinized for potential technical or biological causes of their lower quality to determine their exclusion from meta-analysis. The application and simulation results concluded a systematic quality assessment framework for genomic meta-analysis.

Choline metabolism, proliferation, and angiogenesis in nonenhancing grades 2 and 3 astrocytoma

PURPOSE

To study choline metabolism in biopsies from nonenhancing Grade 2 (AS2) and Grade 3 (AS3) astrocytomas to determine whether (1) phosphocholine (PC) dominates in AS3, and (2) PC is associated with proliferation or angiogenesis. PC and glycerophosphocholine (GPC) are involved in phospholipid metabolism that accompanies mitosis. PC is the predominant peak in Grade 4 astrocytoma (GBM) while GPC dominates in AS2.

MATERIALS AND METHODS

We used high resolution magic angle spinning magnetic resonance spectroscopy to compare the concentrations of 10 metabolites in 41 biopsies (16 AS2 and 25 AS3) from 24 tumors. Immunohistochemistry was performed on paired biopsies to determine the cell density, Ki-67 proliferation index, and vascular endothelial growth factor (VEGF) angiogenic marker expression.

RESULTS

AS3 had higher PC than AS2; however, the PC:GPC was less than 1 in all cases irrespective of tumor grade. Within tumors, GPC increased with Ki-67 and PC and tCho increased with cell density. There was no association between any choline compound and VEGF.

CONCLUSION

These data suggest that PC:GPC less than 1 is not unique to low grade glioma. Furthermore, the PC concentration that is a marker of aggressive glial tumors is not tightly linked to cell proliferation or angiogenesis in nonenhancing astrocytomas.

Cell cycle and aging, morphogenesis, and response to stimuli genes are individualized biomarkers of glioblastoma progression and survival

BACKGROUND

Glioblastoma is a complex multifactorial disorder that has swift and devastating consequences. Few genes have been consistently identified as prognostic biomarkers of glioblastoma survival. The goal of this study was to identify general and clinical-dependent biomarker genes and biological processes of three complementary events: lifetime, overall and progression-free glioblastoma survival.

METHODS

A novel analytical strategy was developed to identify general associations between the biomarkers and glioblastoma, and associations that depend on cohort groups, such as race, gender, and therapy. Gene network inference, cross-validation and functional analyses further supported the identified biomarkers.

RESULTS

A total of 61, 47 and 60 gene expression profiles were significantly associated with lifetime, overall, and progression-free survival, respectively. The vast majority of these genes have been previously reported to be associated with glioblastoma (35, 24, and 35 genes, respectively) or with other cancers (10, 19, and 15 genes, respectively) and the rest (16, 4, and 10 genes, respectively) are novel associations. Pik3r1, E2f3, Akr1c3, Csf1, Jag2, Plcg1, Rpl37a, Sod2, Topors, Hras, Mdm2, Camk2g, Fstl1, Il13ra1, Mtap and Tp53 were associated with multiple survival events.Most genes (from 90 to 96%) were associated with survival in a general or cohort-independent manner and thus the same trend is observed across all clinical levels studied. The most extreme associations between profiles and survival were observed for Syne1, Pdcd4, Ighg1, Tgfa, Pla2g7, and Paics. Several genes were found to have a cohort-dependent association with survival and these associations are the basis for individualized prognostic and gene-based therapies. C2, Egfr, Prkcb, Igf2bp3, and Gdf10 had gender-dependent associations; Sox10, Rps20, Rab31, and Vav3 had race-dependent associations; Chi3l1, Prkcb, Polr2d, and Apool had therapy-dependent associations. Biological processes associated glioblastoma survival included morphogenesis, cell cycle, aging, response to stimuli, and programmed cell death.

CONCLUSIONS

Known biomarkers of glioblastoma survival were confirmed, and new general and clinical-dependent gene profiles were uncovered. The comparison of biomarkers across glioblastoma phases and functional analyses offered insights into the role of genes. These findings support the development of more accurate and personalized prognostic tools and gene-based therapies that improve the survival and quality of life of individuals afflicted by glioblastoma multiforme.

Role of YKL-40 in the angiogenesis, radioresistance, and progression of glioblastoma

Glioblastoma is one of the most fatal cancers, characterized by a strong vascularized phenotype. YKL-40, a secreted glycoprotein, is overexpressed in patients with glioblastomas and has potential as a novel tumor biomarker. The molecular mechanisms of YKL-40 in glioblastoma development, however, are poorly understood. Here, we aimed to elucidate the role YKL-40 plays in the regulation of VEGF expression, tumor angiogenesis, and radioresistance. YKL-40 up-regulated VEGF expression in glioblastoma cell line U87, and both YKL-40 and VEGF synergistically promote endothelial cell angiogenesis. Interestingly, long term inhibition of VEGF up-regulated YKL-40. YKL-40 induced coordination of membrane receptor syndecan-1 and integrin αvβ5, and triggered a signaling cascade through FAK(397) to ERK-1 and ERK-2, leading to elevated VEGF and enhanced angiogenesis. In addition, γ-irradiation of U87 cells increased YKL-40 expression that protects cell death through AKT activation and also enhances endothelial cell angiogenesis. Blockade of YKL-40 activity or expression decreased tumor growth, angiogenesis, and metastasis in xenografted animals. Immunohistochemical analysis of human glioblastomas revealed a correlation between YKL-40, VEGF, and patient survival. These findings have shed light on the mechanisms by which YKL-40 promotes tumor angiogenesis and malignancy, and thus provide a therapeutic target for tumor treatment.

Complementary information from magnetic resonance imaging and (18)F-fluoromisonidazole positron emission tomography in the assessment of the response to an antiangiogenic treatment in a rat brain tumor model

INTRODUCTION

No direct proof has been brought to light in a link between hypoxic changes in glioma models and the effects of antiangiogenic treatments. Here, we assessed the sensitivity of the detection of hypoxia through the use of (18)F-fluoromisonidazole positron emission tomography ([(18)F]-FMISO PET) in response to the evolution of the tumor and its vasculature.

METHODS

Orthotopic glioma tumors were induced in rats after implantation of C6 or 9L cells. Sunitinib was administered from day (D) 17 to D24. At D17 and D24, multiparametric magnetic resonance imaging was performed to characterize tumor growth and vasculature. Hypoxia was assessed by [(18)F]-FMISO PET.

RESULTS

We showed that brain hypoxic volumes are related to glioma volume and its vasculature and that an antiangiogenic treatment, leading to an increase in cerebral blood volume and a decrease in vessel permeability, is accompanied by a decrease in the degree of hypoxia.

CONCLUSIONS

We propose that [(18)F]-FMISO PET and multiparametric magnetic resonance imaging are pertinent complementary tools in the evaluation of the effects of an antiangiogenic treatment in glioma.

A widespread peroxiredoxin-like domain present in tumor suppression- and progression-implicated proteins

BACKGROUND

Peroxide turnover and signalling are involved in many biological phenomena relevant to human diseases. Yet, all the players and mechanisms involved in peroxide perception are not known. Elucidating very remote evolutionary relationships between proteins is an approach that allows the discovery of novel protein functions. Here, we start with three human proteins, SRPX, SRPX2 and CCDC80, involved in tumor suppression and progression, which possess a conserved region of similarity. Structure and function prediction allowed the definition of P-DUDES, a phylogenetically widespread, possibly ancient protein structural domain, common to vertebrates and many bacterial species.

RESULTS

We show, using bioinformatics approaches, that the P-DUDES domain, surprisingly, adopts the thioredoxin-like (Thx-like) fold. A tentative, more detailed prediction of function is made, namely, that of a 2-Cys peroxiredoxin. Incidentally, consistent overexpression of all three human P-DUDES genes in two public glioblastoma microarray gene expression datasets was discovered. This finding is discussed in the context of the tumor suppressor role that has been ascribed to P-DUDES proteins in several studies. Majority of non-redundant P-DUDES proteins are found in marine metagenome, and among the bacterial species possessing this domain a trend for a higher proportion of aquatic species is observed.

CONCLUSIONS

The new protein structural domain, now with a broad enzymatic function predicted, may become a drug target once its detailed molecular mechanism of action is understood in detail.

Information technology solutions for integration of biomolecular and clinical data in the identification of new cancer biomarkers and targets for therapy

The quest for new cancer biomarkers and targets for therapy requires not only the aggregation and analysis of heterogeneous biomolecular data but also integration of clinical data. In this review we highlight information technology solutions for the integration of biomolecular and clinical data and focus on a solution at the departmental level, i.e., decentralized and medium-scale solution for groups of labs working on a specific topic. Both, hardware and software requirements are described as well as bioinformatics methods and tools for the data analysis. The highlighted IT solutions include storage architecture, high-performance computing, and application servers. Additionally, following computational approaches for data integration are reviewed: data aggregation, integrative data analysis including methodological aspects as well as examples, biomolecular pathways and network reconstruction, and mathematical modelling. Finally, a case study in cancer immunology including the used computational methods is shown, demonstrating how IT solutions for integrating biomolecular and clinical data can help to identify new cancer biomarkers for improving diagnosis and predicting clinical outcome.