The prognostic value of a seven-microRNA classifier as a novel biomarker for the prediction and detection of recurrence in glioma patients

Recent insights into the microRNA-dependent modulation of gliomas from pathogenesis to diagnosis and treatment

Gliomas are the most lethal primary brain tumors in adults. These highly invasive tumors have poor 5-year survival for patients. Gliomas are principally characterized by rapid diffusion as well as high levels of cellular heterogeneity. However, to date, the exact pathogenic mechanisms, contributing to gliomas remain ambiguous. MicroRNAs (miRNAs), as small noncoding RNAs of about 20 nucleotides in length, are known as chief modulators of different biological processes at both transcriptional and posttranscriptional levels. More recently, it has been revealed that these noncoding RNA molecules have essential roles in tumorigenesis and progression of multiple cancers, including gliomas. Interestingly, miRNAs are able to modulate diverse cancer-related processes such as cell proliferation and apoptosis, invasion and migration, differentiation and stemness, angiogenesis, and drug resistance; thus, impaired miRNAs may result in deterioration of gliomas. Additionally, miRNAs can be secreted into cerebrospinal fluid (CSF), as well as the bloodstream, and transported between normal and tumor cells freely or by exosomes, converting them into potential diagnostic and/or prognostic biomarkers for gliomas. They would also be great therapeutic agents, especially if they could cross the blood–brain barrier (BBB). Accordingly, in the current review, the contribution of miRNAs to glioma pathogenesis is first discussed, then their glioma-related diagnostic/prognostic and therapeutic potential is highlighted briefly.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Plasma microRNA-720 may predict prognosis and diagnosis in glioma patients

We enrolled 122 patients with glioma who received surgery treatment in our hospital from June 2010 to May 2012, and 60 healthy individuals. We found that the plasma miR-720 in the glioma group was significantly higher than that in the healthy control group (3.19 ± 1.26 vs 0.98 ± 0.65, P<0.001). The sensitivity and specificity were 71.3% (95%CI: 62.4–79.1%) and 83.3% (71.5–91.7%), respectively. The results indicated that the plasma miR-720 level was associated with tumor grade (t = 104.418, P<0.001). The advanced tumor tended to have higher miR-720 expression level. No significant association was found between miR-720 and age, sex, tumor size, KPS and tumor position (P=0.438, 0.514, 0.518, 0.058, 0.226). The multivariate cox analysis indicated that the high expression of miR-720 (HR = 1.48, 95%CI: 1.12–2.97, P=0.023) was independently predictors of adverse prognosis in patients with glioma. The high expression of miR-720 was also associated with recurrence or development in patients with glioma (HR = 1.47, 95%CI: 1.18–3.14, P=0.012). Plasma miR-720 has a moderate diagnostic ability in early diagnostic of glioma and may be a potential tumor biomarker. The high plasma miR-720 was related to adverse prognosis in patients with glioma and could be a prognosis predictor of glioma patients.

Prognostic value of test(s) for O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation for predicting overall survival in people with glioblastoma treated with temozolomide

BACKGROUND

Glioblastoma is an aggressive form of brain cancer. Approximately five in 100 people with glioblastoma survive for five years past diagnosis. Glioblastomas that have a particular modification to their DNA (called methylation) in a particular region (the O6-methylguanine-DNA methyltransferase (MGMT) promoter) respond better to treatment with chemotherapy using a drug called temozolomide.

OBJECTIVES

To determine which method for assessing MGMT methylation status best predicts overall survival in people diagnosed with glioblastoma who are treated with temozolomide.

SEARCH METHODS

We searched MEDLINE, Embase, BIOSIS, Web of Science Conference Proceedings Citation Index to December 2018, and examined reference lists. For economic evaluation studies, we additionally searched NHS Economic Evaluation Database (EED) up to December 2014.

SELECTION CRITERIA

Eligible studies were longitudinal (cohort) studies of adults with diagnosed glioblastoma treated with temozolomide with/without radiotherapy/surgery. Studies had to have related MGMT status in tumour tissue (assessed by one or more method) with overall survival and presented results as hazard ratios or with sufficient information (e.g. Kaplan-Meier curves) for us to estimate hazard ratios. We focused mainly on studies comparing two or more methods, and listed brief details of articles that examined a single method of measuring MGMT promoter methylation. We also sought economic evaluations conducted alongside trials, modelling studies and cost analysis.

DATA COLLECTION AND ANALYSIS

Two review authors independently undertook all steps of the identification and data extraction process for multiple-method studies. We assessed risk of bias and applicability using our own modified and extended version of the QUality In Prognosis Studies (QUIPS) tool. We compared different techniques, exact promoter regions (5'-cytosine-phosphate-guanine-3' (CpG) sites) and thresholds for interpretation within studies by examining hazard ratios. We performed meta-analyses for comparisons of the three most commonly examined methods (immunohistochemistry (IHC), methylation-specific polymerase chain reaction (MSP) and pyrosequencing (PSQ)), with ratios of hazard ratios (RHR), using an imputed value of the correlation between results based on the same individuals.

MAIN RESULTS

We included 32 independent cohorts involving 3474 people that compared two or more methods. We found evidence that MSP (CpG sites 76 to 80 and 84 to 87) is more prognostic than IHC for MGMT protein at varying thresholds (RHR 1.31, 95% confidence interval (CI) 1.01 to 1.71). We also found evidence that PSQ is more prognostic than IHC for MGMT protein at various thresholds (RHR 1.36, 95% CI 1.01 to 1.84). The data suggest that PSQ (mainly at CpG sites 74 to 78, using various thresholds) is slightly more prognostic than MSP at sites 76 to 80 and 84 to 87 (RHR 1.14, 95% CI 0.87 to 1.48). Many variants of PSQ have been compared, although we did not see any strong and consistent messages from the results. Targeting multiple CpG sites is likely to be more prognostic than targeting just one. In addition, we identified and summarised 190 articles describing a single method for measuring MGMT promoter methylation status.

AUTHORS' CONCLUSIONS

PSQ and MSP appear more prognostic for overall survival than IHC. Strong evidence is not available to draw conclusions with confidence about the best CpG sites or thresholds for quantitative methods. MSP has been studied mainly for CpG sites 76 to 80 and 84 to 87 and PSQ at CpG sites ranging from 72 to 95. A threshold of 9% for CpG sites 74 to 78 performed better than higher thresholds of 28% or 29% in two of three good-quality studies making such comparisons.

An integrated approach to biomarker discovery reveals gene signatures highly predictive of cancer progression

Current cancer biomarkers present variability in their predictive power and demonstrate limited clinical efficacy, possibly due to the lack of functional relevance of biomarker genes to cancer progression. To address this challenge, a biomarker discovery pipeline was developed to integrate gene expression profiles from The Cancer Genome Atlas and essential survival gene datasets from The Cancer Dependency Map, the latter of which catalogs genes driving cancer progression. By applying this pipeline to lung adenocarcinoma, lung squamous cell carcinoma, and glioblastoma, genes highly associated with cancer progression were identified and designated as progression gene signatures (PGSs). Analysis of area under the receiver operating characteristics curve revealed that PGSs predicted patient survival more accurately than previously identified cancer biomarkers. Moreover, PGSs stratified patients with high risk for progressive disease indicated by worse prognostic outcomes, increased frequency of cancer progression, and poor responses to chemotherapy. The robust performance of these PGSs were recapitulated in four independent microarray datasets from Gene Expression Omnibus and were further verified in six freshly dissected tumors from glioblastoma patients. Our results demonstrate the power of an integrated approach to cancer biomarker discovery and the possibility of implementing PGSs into clinical biomarker tests.

High expression of microRNA 221 is a poor predictor for glioma

BACKGROUND

MicroRNA 221 has been found to be a good marker for several cancers. Some studies also focused on the relationship between microRNA 221 and glioma. However, the results are controversial. We aimed to systematically evaluate the prognostic role of microRNA 221 in glioma through performing a meta-analysis.

METHODS

The articles which were included in our study were searched on the Web of Science, EMBASE, PubMed, Cochrane Library and China National Knowledge Infrastructure. The basic characteristics and relevant data were extracted. Hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled to evaluate the prognostic role of microRNA 221 in glioma.

RESULTS

Eight studies with 1069 patients were included. We systematically evaluated the role of microRNA 221 for overall survival (OS) and disease free survival (DFS) in glioma patients (HR for OS = 1.66, 95% CI, 1.34-2.04; HR for DFS = 1.14, 95% CI, 1.02-1.26). Subgroup analyses were performed according to the nation of the studies, the origin of the samples, the stage of the tumors, the cut-off value, and the method for detecting the microRNA 221. No significant publication bias was found (P = .133).

CONCLUSION

In conclusion, high expression of microRNA 221 was related to poor prognosis of glioma. These findings may assist future exploration on microRNA 221 and help predict the prognosis of glioma. However, due to the significant heterogeneity of these studies, more studies are warranted.

Three-Dimensional Nuclear Telomere Profiling as a Biomarker for Recurrence in Oligodendrogliomas: A Pilot Study

Mechanisms of recurrence in oligodendrogliomas are poorly understood. Recurrence might be driven by telomere dysfunction-mediated genomic instability. In a pilot study, we investigated ten patients with oligodendrogliomas at the time of diagnosis (first surgery) and after recurrence (second surgery) using three-dimensional nuclear telomere analysis performed with quantitative software TeloView^® (Telo Genomics Corp, Toronto, Ontario, Canada). 1p/19q deletion status of each patient was determined by fluorescent in situ hybridization on touch preparation slides. We found that a very specific 3D telomeric profile was associated with two pathways of recurrence in oligodendrogliomas independent of their 1p/19q status: a first group of 8 patients displayed significantly different 3D telomere profiles between both surgeries (p < 0.0001). Their recurrence happened at a mean of 231.375 ± 117.42 days and a median time to progression (TTP) of 239 days, a period defined as short-term recurrence; and a second group of three patients displayed identical 3D telomere profiles between both surgery samples (p > 0.05). Their recurrence happened at a mean of 960.666 ± 86.19 days and a median TTP of 930 days, a period defined as long-term recurrence. Our results suggest a potential link between nuclear telomere architecture and telomere dysfunction with time to recurrence in oligodendrogliomas, independently of the 1p/19q status.

Prognostic Role of MicroRNA 222 in Patients with Glioma: A Meta-analysis

Background

Several studies have focused on the prognostic role of microRNA 222 in glioma. But different conclusions were drawn by these studies. We aimed to systematically evaluate the role of microRNA 222 in glioma by conducting a meta-analysis.

Methods

A systematic literature search until January 2020 was conducted in Web of Science, EMBASE, Cochrane Library, PubMed, and China National Knowledge Infrastructure. The general characteristics and relevant data of nine articles were extracted. Hazard ratios (HRs) with 95% confidence intervals (CIs) were applied to evaluate the prognostic role of microRNA 222 in glioma. The primary outcomes were overall survival (OS) and disease-free survival (DFS).

Results

Nine articles (11 data sets) with 1564 patients were included. We systematically evaluated the role of microRNA 222 for OS and DFS in glioma patients (HR for OS = 1.72; 95% CI, 1.31-2.26; p = 0.001; HR for DFS = 1.02; 95% CI, 0.86-1.22; p = 0.032). Subgroup analyses were performed according to the sources of patients, the types of the samples, the stages of the tumors, the methods for detecting the microRNA 222, and the sample size. No significant publication bias was found.

Conclusion

In conclusion, our study provided evidence that a high expression of microRNA 222 was related to worse overall survival in glioma patients. However, given the limited study number, more high-quality studies are warranted in the future.

High-throughput microRNA profile in adult and pediatric primary glioblastomas: the role of miR-10b-5p and miR-630 in the tumor aggressiveness

Glioblastoma (GBM) is the most common primary malignant neoplasm of the central nervous system and, despite the standard therapy; the patients' prognoses remain dismal. The miRNA expression profiles have been associated with patient prognosis, suggesting that they may be helpful for tumor diagnosis and classification as well as predictive of tumor response to treatment. We described the microRNA expression profile of 29 primary GBM samples (9 pediatric GBMs) and 11 non-neoplastic white matter samples as controls (WM) by microarray analysis and we performed functional in vitro assays on these 2 most differentially expressed miRNAs. Hierarchical clustering analysis showed 3 distinct miRNA profiles, two of them in the GBM samples and a group consisting only of cerebral white matter. When adult and pediatric GBMs were compared to WM, 37 human miRNAs were found to be differentially expressed, with miR-10b-5p being the most overexpressed and miR-630 the most underexpressed. The overexpression of miR-630 was associated with reduced cell proliferation and invasion in the U87 GBM cell line, whereas the inhibition of miR-10b-5p reduced cell proliferation and colony formation in the U251 GBM cell line, suggesting that these miRNAs may act as tumor-suppressive and oncogenic miRNAs, respectively. The present study highlights the distinct epigenetic profiling of adult and pediatric GBMs and underscores the biological importance of mir-10b-5p and miR-630 for the pathobiology of these lethal tumors.

Expression of microRNA-210 and the prognosis in glioma patients: a meta-analysis

Aim: To evaluate the predictive power of tumor microRNA-210 (miR-210) on overall survival (OS) in glioma patients. Materials & methods: Studies were identified through searching PubMed, Embase and China National Knowledge Internet electronic databases. Meta-analyses were performed with a random- or fixed-effect model according to the heterogeneity. Results: Six studies were included. Results showed that increased miR-210 expression in tumor independently predicted poor OS in glioma patients (hazard ratio [HR]: 1.38; p = 0.001). Subgroup analyses showed that the prognostic efficacy of tumor miR-210 levels for OS was stronger in overall patients with glioma (HR: 2.22; p < 0.001) than in those with glioblastoma (HR: 1.13; p = 0.01). Conclusion: Expression of miR-210 may predict poor survival in patients with glioma.

Micro RNA Molecules as Modulators of Treatment Resistance, Immune Checkpoints Controllers and Sensitive Biomarkers in Glioblastoma Multiforme

Based on genome sequencing, it is estimated that over 90% of genes stored in human genetic material are transcribed, but only 3% of them contain the information needed for the production of body proteins. This group also includes micro RNAs representing about 1%–3% of the human genome. Recent studies confirmed the hypothesis that targeting molecules called Immune Checkpoint (IC) open new opportunities to take control over glioblastoma multiforme (GBM). Detection of markers that indicate the presence of the cancer occupies a very important place in modern oncology. This function can be performed by both the cancer cells themselves as well as their components and other substances detected in the patients’ bodies. Efforts have been made for many years to find a suitable marker useful in the diagnosis and monitoring of gliomas, including glioblastoma.

Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis

The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified among GSE14520, GSE22058, and ICGC databases. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of DEGs, and genes of key modules were examined to identify hub genes using univariate Cox regression in the ICGC cohort. Expression levels and time-dependent receiver operating characteristic (ROC) and area under the curve (AUC) were determined to estimate the prognostic competence of the hub genes. These hub genes were also validated in the Gene Expression Profiling Interactive Analysis (GEPIA) and TCGA databases. TIMER algorithm and GSCALite database were applied to analyze the association of the hub genes with immunocytotic infiltration and their pathway enrichment. Altogether, 276 DEGs were identified and WGCNA described a unique and significantly DEGs-associated co-expression module containing 148 genes, with 10 hub genes selected by univariate Cox regression in the ICGC cohort (BIRC5, FOXM1, CENPA, KIF4A, DTYMK, PRC1, IGF2BP3, KIF2C, TRIP13, and TPX2). Most of the genes were validated in the GEPIA databases, except IGF2BP3. The results of multivariate Cox regression analysis indicated that the abovementioned hub genes are all independent predictors of HCC. The 10 genes were also confirmed to be associated with immune cell infiltration using the TIMER algorithm. Moreover, four-gene signature was developed, including BIRC5, CENPA, FOXM1, DTYMK. These hub genes and the model demonstrated a strong prognostic capability and are likely to be a therapeutic target for HCC. Moreover, the association of these genes with immune cell infiltration improves our understanding of the occurrence and development of HCC.

11PS04 is a new chemical entity identified by microRNA-based biosensing with promising therapeutic potential against cancer stem cells

Phenotypic drug discovery must take advantage of the large amount of clinical data currently available. In this sense, the impact of microRNAs (miRs) on human disease and clinical therapeutic responses is becoming increasingly well documented. Accordingly, it might be possible to use miR-based signatures as phenotypic read-outs of pathological status, for example in cancer. Here, we propose to use the information accumulating regarding the biology of miRs from clinical research in the preclinical arena, adapting it to the use of miR biosensors in the earliest steps of drug screening. Thus, we have used an amperometric dual magnetosensor capable of monitoring a miR-21/miR-205 signature to screen for new drugs that restore these miRs to non-tumorigenic levels in cell models of breast cancer and glioblastoma. In this way we have been able to identify a new chemical entity, 11PS04 ((3aR,7aS)-2-(3-propoxyphenyl)-7,7a-dihydro-3aH-pyrano[3,4-d]oxazol-6(4H)-one), the therapeutic potential of which was suggested in mechanistic assays of disease models, including 3D cell culture (oncospheres) and xenografts. These assays highlighted the potential of this compound to attack cancer stem cells, reducing the growth of breast and glioblastoma tumors in vivo. These data demonstrate the enhanced chain of translatability of this strategy, opening up new perspectives for drug-discovery pipelines and highlighting the potential of miR-based electro-analytical sensors as efficient tools in modern drug discovery.

Survivin is a prognostic indicator in glioblastoma and may be a target of microRNA-218

Accumulating evidence has revealed that survivin expression is associated with a malignant phenotype and poor prognosis in glioma. Survivin is also a potential target of microRNA (miRNA/miR)-218. The aim of the present study was to investigate the expression and function of survivin in glioblastoma, and to examine the association between survivin and miR-218. For that purpose, survivin mRNA levels were analyzed in 144 frozen samples of glioblastoma using whole-genome RNA sequencing. In vitro cell proliferation, migration, invasion and apoptosis assays were performed, and survivin expression was detected by western blotting. The results revealed that the mRNA expression levels of survivin were negatively and significantly associated with overall survival in glioblastoma. Further in vitro analyses suggested that miR-218 may inhibit the expression of survivin. Expression of miR-218 in the LN229 cell line was significantly lower than that in the immortalized human gliocyte HEB cell line. miR-218 markedly inhibited tumor cell proliferation, migration and invasion capacities, and decreased apoptosis. miR-218 also inhibited the expression of survivin. These results indicated that survivin may be a target of miR-218 and could serve as a predictive biomarker.

Prognostic Significance of MicroRNAs in Glioma: A Systematic Review and Meta-Analysis

PURPOSE

Different microRNAs (miRs) have been demonstrated to relate with the outcome of glioma patients, while the conclusions are inconsistent. We perform a meta-analysis to clarify the relationship between different miRs and prognosis of glioma.

METHODS

Related studies were retrieved from PubMed, Embase, and Cochrane Library. Pooled hazard ratios (HRs) of different miRs expression for survival and 95% confidence intervals (CIs) were calculated using random-effects model.

RESULTS

A total of 15 miRs with 4708 glioma patients were ultimately included. Increased expression of miR-15b (HR, 1.584; 95% CI, 1.199-2.092), 21 (HR, 1.591; 95% CI, 1.278-1.981), 148a (HR, 1.122; 95% CI, 1.023-1.231), 196 (HR, 1.877; 95% CI, 1.033-3.411), 210 (HR, 1.251; 95% CI, 1.010-1.550), and 221 (HR, 1.269; 95% CI, 1.054-1.527) or decreased expression of miR-106a (HR, 0.809; 95% CI, 0.655-0.998) and 124 (HR, 0.833; 95% CI, 0.729-0.952) was correlated with poor outcome of glioma patients.

CONCLUSIONS

miR-15b, 21, 148a, 196, 210, 221, 106a, and 124 are valuable biomarkers for the prognosis of glioma which might be used in clinical settings.

Non-Coding RNAs in Glioma

Glioma is the most aggressive brain tumor of the central nervous system. The ability of glioma cells to migrate, rapidly diffuse and invade normal adjacent tissue, their sustained proliferation, and heterogeneity contribute to an overall survival of approximately 15 months for most patients with high grade glioma. Numerous studies indicate that non-coding RNA species have critical functions across biological processes that regulate glioma initiation and progression. Recently, new data emerged, which shows that the cross-regulation between long non-coding RNAs and small non-coding RNAs contribute to phenotypic diversity of glioblastoma subclasses. In this paper, we review data of long non-coding RNA expression, which was evaluated in human glioma tissue samples during a five-year period. Thus, this review summarizes the following: (I) the role of non-coding RNAs in glioblastoma pathogenesis, (II) the potential application of non-coding RNA species in glioma-grading, (III) crosstalk between lncRNAs and miRNAs (IV) future perspectives of non-coding RNAs as biomarkers for glioma.

Aberrant miRNAs Regulate the Biological Hallmarks of Glioblastoma

GBM is the highest incidence in primary intracranial malignancy, and it remains poor prognosis even though the patient is gave standard treatment. Despite decades of intense research, the complex biology of GBM remains elusive. In view of eight hallmarks of cancer which were proposed in 2011, studies related to the eight biological capabilities in GBM have made great progress. From these studies, it can be inferred that miRs, as a mode of post-transcriptional regulation, are involved in regulating these malignant biological hallmarks of GBM. Herein, we discuss state-of-the-art research on how aberrant miRs modulate the eight hallmarks of GBM. The upregulation of 'oncomiRs' or the genetic loss of tumor suppressor miRs is associated with these eight biological capabilities acquired during GBM formation. Furthermore, we also discuss the applicable clinical potential of these research results. MiRs may aid in the diagnosis and prognosis of GBM. Moreover, miRs are also therapeutic targets of GBM. These studies will develop and improve precision medicine for GBM in the future.

Loss of MycBP may be associated with the improved survival in 1P co-deletion of lower grade glioma patients

OBJECTIVES

The chromosome 1p/19q co-deletion is a favorable prognostic factor in patients with low grade glioma. In the current analysis, we examined MycBP expression in low grade glioma. MycBP lies on chromosome 1p.

PATIENTS AND METHODS

We evaluated the association between MycBP and overall survival in the TCGA Lower Grade Glioma (LGG) dataset in TCGA (The Cancer Genome Atlas).

RESULTS

Loss of MycBP copy number segment expression coincides with co-deletion of 1 P. The deleterious effect of MycBP on survival is significant (p = 0.00006306, hazard ratio 2.02, 95% CI 1.4-2.9). Patients with astrocytoma have the poorest survival of low grade glioma patients. MycBP mRNA is significantly overexpressed in astrocytomas when compared to normal brain (2.156 fold change, p = 0.0000488).

CONCLUSION

Our report that Chromosome 1 P co-deletion may confer better survival in patients with lower grade glioma in part because of loss of MycBP corroborates other studies of the importance of MycBP in glioma development. Further work with microRNAs may lead to new treatments.

The Role of miRNAs in Angiogenesis, Invasion and Metabolism and Their Therapeutic Implications in Gliomas

MicroRNAs (miRNAs) are small, non-coding, endogenous RNA molecules that function in gene silencing by post-transcriptional regulation of gene expression. The dysregulation of miRNA plays a pivotal role in cancer tumorigenesis, including the development and progression of gliomas. Their small size, stability and ability to target multiple oncogenes have simultaneously distinguished miRNAs as attractive candidates for biomarkers and novel therapeutic targets for glioma patients. In this review, we summarize the most frequently cited miRNAs known to contribute to gliomagenesis and progression by regulating the defining hallmarks of gliomas, including angiogenesis, invasion, and cell metabolism. We also discuss their promising potential as prognostic and predictive biomarkers and novel therapeutic targets, in addition to the challenges that must be overcome before their translation from bench to bedside.

Evaluating the Prognostic Accuracy of Biomarkers for Glioblastoma Multiforme Using The Cancer Genome Atlas Data

Background:

Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumor. Previous studies on GBM biomarkers focused on the effect of the biomarkers on overall survival (OS). Until now, no study has been published that evaluates the performance of biomarkers for prognosing OS. We examined the performance of microRNAs, gene expressions, gene signatures, and methylation that were previously identified to be prognostic. In addition, we investigated whether using clinical risk factors in combination with biomarkers can improve the prognostic performance.

Methods:

The Cancer Genome Atlas, which provides both biomarkers and OS information, was used in this study. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic accuracy.

Results:

For prognosis of OS by 2 years from diagnosis, the area under the ROC curve (AUC) of microRNAs, Mir21 and Mir222, was 0.550 and 0.625, respectively. When age was included in the risk prediction score of these biomarkers, the AUC increased to 0.719 and 0.701, respectively. The SAMSN1 gene expression attains an AUC of 0.563, and the “8-gene” signature identified by Bao achieves an AUC of 0.613.

Conclusions:

Although some biomarkers are significantly associated with OS, the ability of these biomarkers for prognosing OS events is limited. Incorporating clinical risk factors, such as age, can greatly improve the prognostic performance.