DNA methylation signatures for 2016 WHO classification subtypes of diffuse gliomas

Rapid and non-invasive renal injury diagnosis unlocked by a glimpse into urinary protein particle size and charge

Urinary protein, an important marker for early detection of kidney injury, would change in type and content dynamically with the degree of kidney injury due to the particle size and charge selectivity of the glomerular filtration system, making it significantly valuable for accurate classification and early diagnosis. In this study, we developed a fluorescence sensor (Ami-AuNP/DNAs) based on charge interaction to rapidly identify the progression of kidney injury. When the positively charged Ami-AuNP combines with negatively charged DNAs, fluorescence quenching occurs, and urine proteins that appear compete with the DNAs, leading to fluorescence recovery. Based on these signal changes, PCA and PSO-BP neural network analysis were used to successfully identified kidney injury progression in 197 animal kidney injury and 62 clinical chronic kidney disease urine samples through a simple urine sample drop. Additionally, the sensor could also evaluate the effect of Huangkui capsule on kidney injury in adriamycin nephropathy model mice. Accordingly, this method transforms complex biological signals in vivo into macroscopic visual optical signals, amplifying differences of urinary protein, making up for the deficiency of the traditional method in hysteresis and low accuracy, and promoting urinary protein as the potential noninvasive biomarker for evaluating kidney injury.

Segmentation method of magnetic resonance imaging brain tumor images based on improved UNet network

Background

Glioma is a primary malignant craniocerebral tumor commonly found in the central nervous system. According to research, preoperative diagnosis of glioma and a full understanding of its imaging features are very significant. Still, the traditional segmentation methods of image dispensation and machine wisdom are not acceptable in glioma segmentation. This analysis explores the potential of magnetic resonance imaging (MRI) brain tumor images as an effective segmentation method of glioma.

Methods

This study used 200 MRI images from the affiliated hospital and applied the 2-dimensional residual block UNet (2DResUNet). Features were extracted from input images using a 2×2 kernel size (64-kernel) 1-step 2D convolution (Conv) layer. The 2DDenseUNet model implemented in this study incorporates a ResBlock mechanism within the UNet architecture, as well as a Gaussian noise layer for data augmentation at the input stage, and a pooling layer for replacing the conventional 2D convolutional layers. Finally, the performance of the proposed protocol and its effective measures in glioma segmentation were verified.

Results

The outcomes of the 5-fold cross-validation evaluation show that the proposed 2DResUNet and 2DDenseUNet structure has a high sensitivity despite the slightly lower evaluation result on the Dice score. At the same time, compared with other models used in the experiment, the DM-DA-UNet model proposed in this paper was significantly improved in various indicators, increasing the reliability of the model and providing a reference and basis for the accurate formulation of clinical treatment strategies. The method used in this study showed stronger feature extraction ability than the UNet model. In addition, our findings demonstrated that using generalized die harm and prejudiced cross entropy as loss functions in the training process effectively alleviated the class imbalance of glioma data and effectively segmented glioma.

Conclusions

The method based on the improved UNet network has obvious advantages in the MRI brain tumor portrait segmentation procedure. The result showed that we developed a 2D residual block UNet, which can improve the incorporation of glioma segmentation into the clinical process.

Evaluation of DNA Methylation Array for Glioma Tumor Profiling and Description of a Novel Epi-Signature to Distinguish IDH1/IDH2 Mutant and Wild-Type Tumors

Molecular biomarkers, such as IDH1/IDH2 mutations and 1p19q co-deletion, are included in the histopathological and clinical criteria currently used to diagnose and classify gliomas. IDH1/IDH2 mutation is a common feature of gliomas and is associated with a glioma-CpG island methylator phenotype (CIMP). Aberrant genomic methylation patterns can also be used to extrapolate information about copy number variation in a tumor. This project's goal was to assess the feasibility of DNA methylation array for the simultaneous detection of glioma biomarkers as a more effective testing strategy compared to existing single analyte tests.

METHODS

Whole-genome methylation array (WGMA) testing was performed using 48 glioma DNA samples to detect methylation aberrations and chromosomal gains and losses. The analyzed samples include 39 tumors in the discovery cohort and 9 tumors in the replication cohort. Methylation profiles for each sample were correlated with IDH1 p.R132G mutation, immunohistochemistry (IHC), and previous 1p19q clinical testing to assess the sensitivity and specificity of the WGMA assay for the detection of these variants.

RESULTS

We developed a DNA methylation signature to specifically distinguish a IDH1/IDH2 mutant tumor from normal samples. This signature is composed of 11 CpG sites that were significantly hypermethylated in the IDH1/IDH2 mutant group. Copy number analysis using WGMA data was able to identify five of five positive samples for 1p19q co-deletion and was concordant for all negative samples.

CONCLUSIONS

The DNA methylation signature presented here has the potential to refine the utility of WGMA to predict IDH1/IDH2 mutation status of gliomas, thus improving diagnostic yield and efficiency of laboratory testing compared to single analyte IDH1/IDH2 or 1p19q tests.

Chromatin insulation dynamics in glioblastoma: challenges and future perspectives of precision oncology

Glioblastoma (GBM) is the most aggressive primary brain tumor, having a poor prognosis and a median overall survival of less than two years. Over the last decade, numerous findings regarding the distinct molecular and genetic profiles of GBM have led to the emergence of several therapeutic approaches. Unfortunately, none of them has proven to be effective against GBM progression and recurrence. Epigenetic mechanisms underlying GBM tumor biology, including histone modifications, DNA methylation, and chromatin architecture, have become an attractive target for novel drug discovery strategies. Alterations on chromatin insulator elements (IEs) might lead to aberrant chromatin remodeling via DNA loop formation, causing oncogene reactivation in several types of cancer, including GBM. Importantly, it is shown that mutations affecting the isocitrate dehydrogenase (IDH) 1 and 2 genes, one of the most frequent genetic alterations in gliomas, lead to genome-wide DNA hypermethylation and the consequent IE dysfunction. The relevance of IEs has also been observed in a small population of cancer stem cells known as glioma stem cells (GSCs), which are thought to participate in GBM tumor initiation and drug resistance. Recent studies revealed that epigenomic alterations, specifically chromatin insulation and DNA loop formation, play a crucial role in establishing and maintaining the GSC transcriptional program. This review focuses on the relevance of IEs in GBM biology and their implementation as a potential theranostic target to stratify GBM patients and develop novel therapeutic approaches. We will also discuss the state-of-the-art emerging technologies using big data analysis and how they will settle the bases on future diagnosis and treatment strategies in GBM patients.

Clinical and molecular immune characterization of ERBB2 in glioma

ERBB2 is a well-studied oncogene that promotes progression of multiple cancers, especially in breast cancer. However, the expression status of ERBB2, the values of ERBB2 on prognosis, and its molecular characterization in glioma have not been well examined. We explored the expression of ERBB2 and its clinical and molecular immune characterization in human glioma samples using the extracted genetic and clinical data from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. Immunohistochemistry (IHC) performed on the tissue microarray slide was used to validate the expression and clinical and prognostic values of ERBB2 in glioma. Higher ERBB2 expression was found in patients with higher grades gliomas than in patients with lower grades gliomas. Besides, patients with higher ERBB2 expression showed poor prognosis. The IHC and clinical data next validated the expression pattern and prognostic value of ERBB2 in glioma. Further analysis showed that there was a strong positive correlation between ERBB2 and common immune checkpoints as well as immune markers of various immune cells in both TCGA and CGGA databases, and the IHC data further validated the positive correlation between ERBB2 and PD-L1 expression. Besides, analysis of ERBB2-related immune genes and signatures showed the significant role of ERBB2 in mediating tumor immune response in glioma. To sum up, our findings summarize the expression pattern and clinical characteristics of ERBB2 in glioma, which may be useful for expanding our understanding of the critical role of ERBB2 in antitumor therapy in glioma.

NEAT1 Is a Novel Oncogenic LncRNA and Correlated with miR-143 in Pediatric Oligodendrogliomas

INTRODUCTION

The noncoding RNAs (ncRNAs) play a role in biological processes of various cancers including gliomas. The majority of these transcripts are uniquely expressed in differentiated tissues or specific glioma types. Pediatric oligodendroglioma (POG) is a rare subtype of diffuse glioma and accounts for <1% of pediatric brain tumors. Because histologically POG resembles adult OG, the same treatment is applied as adults. However, the significance in predicting outcomes in POG patients is unclear. In this study, we aimed to investigate the prognostic significance of expression -profiles of microRNA (miRNA) and long noncoding RNA -(LncRNA) in POGs.

METHODS

We investigated the levels of 13 known miRNAs and 6 LncRNAs in tumor samples from 9 patients with primary POG by using RT-PCR and analyzed their association with outcomes.

RESULTS

The expression levels of miR-21, miR-106a, miR-10b, and LncRNA NEAT1 were higher, and the expression level of miR-143 was lower in POG tissues compared with normal brain tissues (p = 0.006, p = 0.032, p = 0.034, p = 0.002, and p = 0.001, respectively). High levels of NEAT1 and low expression of miR-143 were associated with decreased probability of short disease-free survival (p = 0.018 and p = 0.022, respectively).

DISCUSSION

NEAT1 and miR-143 levels could serve as reciprocal prognostic predictors of disease progression in patients with POG. New treatment models to regulate the expression levels of NEAT1 and miR-143 will bring a new approach to the therapy of POG.

Current Opinion on Molecular Characterization for GBM Classification in Guiding Clinical Diagnosis, Prognosis, and Therapy

Glioblastoma (GBM) is highly invasive and the deadliest brain tumor in adults. It is characterized by inter-tumor and intra-tumor heterogeneity, short patient survival, and lack of effective treatment. Prognosis and therapy selection is driven by molecular data from gene transcription, genetic alterations and DNA methylation. The four GBM molecular subtypes are proneural, neural, classical, and mesenchymal. More effective personalized therapy heavily depends on higher resolution molecular subtype signatures, combined with gene therapy, immunotherapy and organoid technology. In this review, we summarize the principal GBM molecular classifications that guide diagnosis, prognosis, and therapeutic recommendations.

Beyond IDH-Mutation: Emerging Molecular Diagnostic and Prognostic Features in Adult Diffuse Gliomas

Diffuse gliomas are among the most common adult central nervous system tumors with an annual incidence of more than 16,000 cases in the United States. Until very recently, the diagnosis of these tumors was based solely on morphologic features, however, with the publication of the WHO Classification of Tumours of the Central Nervous System, revised 4th edition in 2016, certain molecular features are now included in the official diagnostic and grading system. One of the most significant of these changes has been the division of adult astrocytomas into IDH-wildtype and IDH-mutant categories in addition to histologic grade as part of the main-line diagnosis, although a great deal of heterogeneity in the clinical outcome still remains to be explained within these categories. Since then, numerous groups have been working to identify additional biomarkers and prognostic factors in diffuse gliomas to help further stratify these tumors in hopes of producing a more complete grading system, as well as understanding the underlying biology that results in differing outcomes. The field of neuro-oncology is currently in the midst of a "molecular revolution" in which increasing emphasis is being placed on genetic and epigenetic features driving current diagnostic, prognostic, and predictive considerations. In this review, we focus on recent advances in adult diffuse glioma biomarkers and prognostic factors and summarize the state of the field.

Molecular Classification of Diffuse Gliomas

Technological advances in the field of molecular genetics have improved the ability to classify brain tumors into subgroups with distinct clinical features and important therapeutic implications. The World Health Organization’s newest update on classification of gliomas (2016) incorporated isocitrate dehydrogenase 1 and 2 mutations, ATRX loss, 1p/19q codeletion status, and TP53 mutations to allow for improved classification of glioblastomas, low-grade and anaplastic gliomas. This paper reviews current advances in the understanding of diffuse glioma classification and the impact of molecular markers and DNA methylation studies on survival of patients with these tumors. We also discuss whether the classification and grading of diffuse gliomas should be based on histological findings, molecular markers, or DNA methylation subgroups in future iterations of the classification system.

Identification of Prognostic Signatures of Alternative Splicing in Glioma

Alternative splicing (AS) is a ubiquitous mechanism in which pre-mRNA can be spliced into divergent variants and involved in carcinogenesis and progression in several cancers. In the present study, we systematically profiled prognostic AS signatures involving both low grade glioma (LGG) and glioblastoma (GBM) and investigated the association of AS signatures with tumor grade and IDH1 status in glioma. Percent spliced in (PSI) values and corresponding clinical data were obtained from TCGA SpliceSeq and TCGA data portal, respectively. Prognostic AS signatures were identified using univariate and stepwise multivariate Cox regression. Heatmap analysis was performed based on prognostic AS signatures. A prognostic signature was established with 69 and 88 AS events, including specific splicing events of MUTYH, STEAP3, and CTNNB1, in LGG and GBM cohorts, respectively. The area under the curve (AUC) of the prediction model was 0.968 at 2000 days of overall survival (OS) in the LGG cohort and 0.966 at 450 days of OS in the GBM cohort. In addition, these prognostic AS signatures could complement current molecular classification, such as IDH1 mutation, 1p/19q codeletion, and ATRX loss, of glioma and further identify potential subgroups of glioma with the same molecular features. In conclusion, our study systematically profiled prognostic AS events involving both low grade glioma and glioblastoma for the first time, which also shed light on the crosstalk between AS signatures and molecular features of glioma.

Comprehensive Transcriptomic Analysis and Experimental Validation Identify lncRNA HOXA-AS2/miR-184/COL6A2 as the Critical ceRNA Regulation Involved in Low-Grade Glioma Recurrence

Purpose

The recurrence and metastasis of glioma are closely related to complex regulatory networks among protein-coding genes, lncRNAs and microRNAs. The aim of this study was to investigate core genes, lncRNAs, miRNAs and critical ceRNA regulatory mechanisms, which are involved in lower-grade glioma (LGG) recurrence.

Materials and Methods

We employed multiple datasets from Chinese Glioma Genome Atlas (CGGA) database and The Cancer Genome Atlas (TCGA) to perform comprehensive transcriptomic analysis. Further in vitro experiments including cell proliferation assay, luciferase reporter assay, and Western blot were performed to validate our results.

Results

Recurrent LGG and glioblastoma (GBM) showed different transcriptome characteristics with less overlap of differentially expressed protein-coding genes (DEPs), lncRNAs (DELs) and miRNAs (DEMs) compared with primary samples. There were no overlapping gene in ontology (GO) terms related to GBM recurrence in the TCGA and CGGA databases, but there were overlaps associated with LGG recurrence. GO analysis and protein–protein interaction (PPI) network analysis identified three core genes: TIMP1, COL1A1 and COL6A2. By hierarchical cluster analysis of them, LGGs could be clustered as Low_risk and High_risk group. The High_risk group with high expression of TIMP1, COL1A1, and COL6A2 showed worse prognosis. By coexpression networks analysis, competing endogenous RNA (ceRNA) network analysis, cell proliferation assay and luciferase reporter assay, we confirmed that lncRNA HOXA-AS2 functioned as a ceRNA for miR-184 to regulate expression of COL6A2, which induced cell proliferation of low-grade glioma.

Conclusion

In this study, we revealed a 3-hub protein-coding gene signature to improve prognostic prediction in LGG, and identified a critical ceRNA regulation involved in LGG recurrence.

Therapeutic Targeting of Cancer Stem Cells in Human Glioblastoma by Manipulating the Renin-Angiotensin System

Patients with glioblastoma (GB), a highly aggressive brain tumor, have a median survival of 14.6 months following neurosurgical resection and adjuvant chemoradiotherapy. Quiescent GB cancer stem cells (CSCs) invariably cause local recurrence. These GB CSCs can be identified by embryonic stem cell markers, express components of the renin-angiotensin system (RAS) and are associated with circulating CSCs. Despite the presence of circulating CSCs, GB patients rarely develop distant metastasis outside the central nervous system. This paper reviews the current literature on GB growth inhibition in relation to CSCs, circulating CSCs, the RAS and the novel therapeutic approach by repurposing drugs that target the RAS to improve overall symptom-free survival and maintain quality of life.

Specific glioblastoma multiforme prognostic-subtype distinctions based on DNA methylation patterns

DNA methylation is an important regulator of gene expression, and plays a significant role in carcinogenesis in the brain. Here, we explored specific prognosis-subtypes based on DNA methylation status using 138 Glioblastoma Multiforme (GBM) samples from The Cancer Genome Atlas (TCGA) database. The methylation profiles of 11,637 CpG sites that significantly correlated with survival in the training set were employed for consensus clustering. We identified three GBM molecular subtypes, and their survival curves were distinct from each other. Furthermore, ten feature CpG sites were obtained on conducting a weighted gene co-expression network analysis (WGCNA) of the CpG sites. We were able to classify the samples into high- and low-methylation groups, and classified the prognosis information of the samples after cluster analysis of the training set samples using the hierarchical clustering algorithm. Similar results were obtained in the test set and clinical GBM specimens. Finally, we found that a positive relationship existed between methylation level and sensitivity to temozolomide (or radiotherapy) or anti-migration ability of GBM cells. Taken together, these results suggest that the model constructed in this study could help explain the heterogeneity of previous molecular subgroups in GBM and can provide guidance to clinicians regarding the prognosis of GBM.

DNA methylation, transcriptome and genetic copy number signatures of diffuse cerebral WHO grade II/III gliomas resolve cancer heterogeneity and development

Background

Diffuse lower WHO grade II and III gliomas (LGG) are slowly progressing brain tumors, many of which eventually transform into a more aggressive type. LGG is characterized by widespread genetic and transcriptional heterogeneity, yet little is known about the heterogeneity of the DNA methylome, its function in tumor biology, coupling with the transcriptome and tumor microenvironment and its possible impact for tumor development.

Methods

We here present novel DNA methylation data of an LGG-cohort collected in the German Glioma Network containing about 85% isocitrate dehydrogenase (IDH) mutated tumors and performed a combined bioinformatics analysis using patient-matched genome and transcriptome data.

Results

Stratification of LGG based on gene expression and DNA-methylation provided four consensus subtypes. We characterized them in terms of genetic alterations, functional context, cellular composition, tumor microenvironment and their possible impact for treatment resistance and prognosis. Glioma with astrocytoma-resembling phenotypes constitute the largest fraction of nearly 60%. They revealed largest diversity and were divided into four expression and three methylation groups which only partly match each other thus reflecting largely decoupled expression and methylation patterns. We identified a novel G-protein coupled receptor and a cancer-related ‘keratinization’ methylation signature in in addition to the glioma-CpG island methylator phenotype (G-CIMP) signature. These different signatures overlap and combine in various ways giving rise to diverse methylation and expression patterns that shape the glioma phenotypes. The decrease of global methylation in astrocytoma-like LGG associates with higher WHO grade, age at diagnosis and inferior prognosis. We found analogies between astrocytoma-like LGG with grade IV IDH-wild type tumors regarding possible worsening of treatment resistance along a proneural-to-mesenchymal axis. Using gene signature-based inference we elucidated the impact of cellular composition of the tumors including immune cell bystanders such as macrophages.

Conclusions

Genomic, epigenomic and transcriptomic factors act in concert but partly also in a decoupled fashion what underpins the need for integrative, multidimensional stratification of LGG by combining these data on gene and cellular levels to delineate mechanisms of gene (de-)regulation and to enable better patient stratification and individualization of treatment.

Electronic supplementary material

The online version of this article (10.1186/s40478-019-0704-8) contains supplementary material, which is available to authorized users.

Integrating multiple-level molecular data to infer the distinctions between glioblastoma and lower-grade glioma

Glioblastomas (GBMs) and lower-grade gliomas (LGGs) are the most common malignant brain tumors. Despite extensive studies that have suggested that there are differences between the two in terms of clinical profile and treatment, their distinctions on a molecular level had not been systematically analyzed. Here, we investigated the distinctions between GBM and LGG based on multidimensional data, including somatic mutations, somatic copy number variants (SCNVs), gene expression, lncRNA expression and DNA methylation levels. We found that GBM patients had a higher mutation frequency and SCNVs than LGG patients. Differential mRNAs and lncRNAs between GBM and LGG were identified and a differential mRNA-lncRNA network was constructed and analyzed. We also discovered some differential DNA methylation sites could distinguish between GBM and LGG samples. Finally, we identified some key GBM- and LGG-specific genes featuring multiple-level molecular alterations. These specific genes participate in diverse functions; moreover, GBM-specific genes are enriched in the glioma pathway. Overall, our studies explored the distinctions between GMB and LGG using a comprehensive genomics approach that may provide novel insights into studying the mechanism and treatment of brain tumors.

The intertwined fates of inflammation and coagulation in glioma

Inflammation and coagulation are two intertwined pathways with evolutionary ties being traced back to the hemocyte, a single cell type in invertebrates that has functions in both the inflammatory and coagulation pathways. These systems have functioned together throughout evolution to provide a solid defence against infection, damaged cells and irritants. While these systems work in harmony the majority of the time, they can also become dysregulated or corrupted by tumours, enhancing tumour proliferation, invasion, dissemination and survival. This review aims to give a brief overview of how these systems work in harmony and how dysregulation of these systems aids in the development and progression of cancer, using glioma as an example.

Blood DNA methylation as a potential biomarker of dementia: A systematic review

Dementia is a major public health issue with rising prevalence rates, but many individuals remain undiagnosed. Accurate and timely diagnosis is key for the optimal targeting of interventions. A noninvasive, easily measurable peripheral biomarker would have greatest utility in population-wide diagnostic screening. Epigenetics, including DNA methylation, is implicated in dementia; however, it is unclear whether epigenetic changes can be detected in peripheral tissue. This study aimed to systematically review the evidence for an association between dementia and peripheral DNA methylation. Forty-eight studies that measured DNA methylation in peripheral blood were identified, and 67% reported significant associations with dementia. However, most studies were underpowered and limited by their case-control design. We emphasize the need for future longitudinal studies on large well-characterized populations, measuring epigenetic patterns in asymptomatic individuals. A biomarker detectable in the preclinical stages of the disease would have the greatest utility in future intervention and treatment trials.

Identification and targeting of an FGFR fusion in a pediatric thalamic "central oligodendroglioma"

Approximately 1–5% of pediatric intracranial tumors originate in the thalamus. While great strides have been made to identify consistent molecular markers in adult oligodendrogliomas, such as the 1p/19q co-deletion, it is widely recognized that pediatric oligodendrogliomas have a vastly different molecular make-up. While pediatric thalamic or “central oligodendrogliomas” are histologically similar to peripheral pediatric oligodendrogliomas, they are behaviorally distinct and likely represent a cohesive, but entirely different entity. We describe a case of a 10-year-old girl who was diagnosed with an anaplastic glioma with features consistent with the aggressive entity often diagnosed as central or thalamic oligodendroglioma. We performed whole-exome (paired tumor and germline DNA) and transcriptome (tumor RNA) sequencing, which demonstrated an FGFR3-PHGDH fusion. We describe this fusion and our rationale for pursuing personalized, targeted therapy for the patient’s tumor that may potentially play a role in the treatment of similar cases.