Classifying lower grade glioma cases according to whole genome gene expression

Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas

PURPOSE

To investigate the feasibility of using radiomics analysis of quantitative maps from synthetic MRI to preoperatively predict diffuse glioma grades, isocitrate dehydrogenase (IDH) subtypes, and 1p/19q codeletion status.

METHODS

Data from 124 patients with diffuse glioma were used for analysis (n = 87 for training, n = 37 for testing). Quantitative T1, T2, and proton density (PD) maps were obtained using synthetic MRI. Enhancing tumour (ET), non-enhancing tumour and necrosis (NET), and peritumoral edema (PE) regions were segmented followed by manual fine-tuning. Features were extracted using PyRadiomics and then selected using Levene/T, BorutaShap and maximum relevance minimum redundancy algorithms. A support vector machine was adopted for classification. Receiver operating characteristic curve analysis and integrated discrimination improvement analysis were implemented to compare the performance of different radiomics models.

RESULTS

Radiomics models constructed using features from multiple tumour subregions (ET + NET + PE) in the combined maps (T1 + T2 + PD) achieved the highest AUC in all three prediction tasks, among which the AUC for differentiating lower-grade and high-grade diffuse gliomas, predicting IDH mutation status and predicting 1p/19q codeletion status were 0.92, 0.95 and 0.86 respectively. Compared with those constructed on individual T1, T2, and PD maps, the discriminant ability of radiomics models constructed on the combined maps separately increased by 11, 17 and 10% in predicting glioma grades, 35, 52 and 19% in predicting IDH mutation status, and 16, 15 and 14% in predicting 1p/19q codeletion status (p < 0.05).

CONCLUSION

Radiomics analysis of quantitative maps from synthetic MRI provides a new quantitative imaging tool for the preoperative prediction of grades and molecular subtypes in diffuse gliomas.

Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression

Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan-Meier plots and hazard ratio analysis on the three types of cancer-GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine.

Protein Kinase Inhibitors as a New Target for Immune System Modulation and Brain Cancer Management

High-grade brain tumors are malignant tumors with poor survival and remain the most difficult tumors to treat. An important contributing factor to the development and progression of brain tumors is their ability to evade the immune system. Several immunotherapeutic strategies including vaccines and checkpoint inhibitors have been studied to improve the effectiveness of the immune system in destroying cancer cells. Recent studies have shown that kinase inhibitors, capable of inhibiting signal transduction cascades that affect cell proliferation, migration, and angiogenesis, have additional immunological effects. In this review, we explain the beneficial therapeutic effects of novel small-molecule kinase inhibitors and explore how, through different mechanisms, they increase the protective antitumor immune response in high-grade brain tumors.

Development and validation of a prognostic gene expression signature for lower-grade glioma following surgery and adjuvant radiotherapy

BACKGROUND AND PURPOSE

Standard of care for lower-grade glioma (LGG) is maximal safe resection and risk-adaptive adjuvant therapy. While patients who benefit the most from adjuvant chemotherapy have been elucidated in prospective randomized studies, comparable insights for adjuvant radiotherapy (RT) are lacking. We sought to identify and validate patterns of gene expression that are associated with differential outcomes among LGG patients treated by RT from two large genomics databases.

MATERIALS AND METHODS

Patients from The Cancer Genome Atlas (TCGA) with LGG (WHO grade II-III glioma) treated by surgery and adjuvant RT were randomized 1:1 to a discovery cohort or an internal validation cohort. Using the discovery cohort only, associations between tumor RNA-seq expression and progression-free survival (PFS) as well as overall survival (OS) were evaluated with adjustment for clinicopathologic covariates. A Genomic Risk Score (GRS) was then constructed from the expression levels of top genes also screened for involvement in glioma carcinogenesis. The prognostic value of GRS was further assessed in the internal validation cohort of TCGA and a second distinct database, compiled by the Chinese Glioma Genome Association (CGGA).

RESULTS

From TCGA, 289 patients with LGG received adjuvant RT alone (38 grade II, 30 grade III) or chemoradiotherapy (CRT) (51 grade II, 170 grade III) between 2009 and 2015. From CGGA, 178 patients with LGG received adjuvant RT alone (40 grade II, 13 grade III) or CRT (41 grade II, 84 grade III) between 2004 and 2016. The genes comprising GRS are involved in MAP kinase activity, T cell chemotaxis, and cell cycle transition: MAP3K15, MAPK10, CCL3, CCL4, and ADAMTS1. High GRS, defined as having a GRS in the top third, was significantly associated with poorer outcomes independent of age, sex, glioma histology, WHO grade, IDH mutation, 1p/19q co-deletion, and chemotherapy status in the discovery cohort (PFS HR 1.61, 95% CI 1.10-2.36, P=0.014; OS HR 2.74, 95% CI 1.68-4.47, P<0.001). These findings were replicated in the internal validation cohort (PFS HR 1.58, 95% CI 1.05-2.37, P=0.027; OS HR 1.84, 95% CI 1.13-3.00, P=0.015) and the CGGA external validation cohort (OS HR 1.72, 95% CI 1.27-2.34, P<0.001). Association between GRS and outcomes was observed only among patients who underwent RT, in both TCGA and CGGA.

CONCLUSION

This study successfully identified an expression signature of five genes that stratified outcomes among LGG patients who received adjuvant RT, with two rounds of validation leveraging independent genomics databases. Expression levels of the highlighted genes were associated with PFS and OS only among patients whose treatment included RT, but not among those with omission of RT, suggesting that expression of these genes may be predictive of radiation treatment response. While additional prospective studies are warranted, interrogation of these genes may be considered in the multidisciplinary management of LGG.

TP53 Mutation Is a Prognostic Factor in Lower Grade Glioma and May Influence Chemotherapy Efficacy

BACKGROUND

Identification of prognostic biomarkers in cancers is a crucial step to improve overall survival (OS). Although mutations in tumour protein 53 (TP53) is prevalent in astrocytoma, the prognostic effects of TP53 mutation are unclear.

METHODS

In this retrospective study, we sequenced TP53 exons 1 to 10 in a cohort of 102 lower-grade glioma (LGG) subtypes and determined the prognostic effects of TP53 mutation in astrocytoma and oligodendroglioma. Publicly available datasets were analysed to confirm the findings.

RESULTS

In astrocytoma, mutations in TP53 codon 273 were associated with a significantly increased OS compared to the TP53 wild-type (HR (95% CI): 0.169 (0.036-0.766), p = 0.021). Public datasets confirmed these findings. TP53 codon 273 mutant astrocytomas were significantly more chemosensitive than TP53 wild-type astrocytomas (HR (95% CI): 0.344 (0.13-0.88), p = 0.0148). Post-chemotherapy, a significant correlation between TP53 and YAP1 mRNA was found (p = 0.01). In O (6)-methylguanine methyltransferase (MGMT) unmethylated chemotherapy-treated astrocytoma, both TP53 codon 273 and YAP1 mRNA were significant prognostic markers. In oligodendroglioma, TP53 mutations were associated with significantly decreased OS.

CONCLUSIONS

Based on these findings, we propose that certain TP53 mutant astrocytomas are chemosensitive through the involvement of YAP1, and we outline a potential mechanism. Thus, TP53 mutations may be key drivers of astrocytoma therapeutic efficacy and influence survival outcomes.

A pediatric cerebral tumor with MN1 alteration and pathological features mimicking carcinoma metastasis: may the terminology "high grade neuroepithelial tumor with MN1 alteration" still be relevant?

INTRODUCTION

Astroblastoma, MN1-altered (old name: high-grade neuroepithelial tumor/HGNET with MN1 alteration) is a recently described central nervous system tumor mostly affecting pediatric patients and profoundly young girls. Differential pathological diagnoses of these tumors include ependymoma, pleomorphic xanthoastrocytoma, embryonal tumor with multilayered rosettes, meningioma, and even glioblastoma. As the treatment approaches to these tumors differ, it is essential to increase the awareness about these tumors in the neurosurgical community.

CLINICAL PRESENTATION

A 7-year-old female patient admitted with a 7-day history of headache, nausea, and vomiting. A contrasted MRI scan revealed a left parietal 4 × 4 × 5 cm mass with central necrosis and peripheral contrast enhancement. The tumor's histopathological findings were suggestive of a metastatic carcinoma with unknown primary, yet further genetic analysis revealed MN1 alteration. Peculiarly, the tumor pathomorphological features were not compatible with astroblastomas and exerted features strongly indicating a metastatic cancer; however, systemic PET and whole-body MRI failed to detect a primary malignancy.

OUTCOME AND CONCLUSIONS

Eighteen months after gross-total tumor resection, an in-field and out-field multifocal recurrence developed which required a second surgery and subsequent chemo-radiotherapy. The patient is doing well for 1 year after the second treatment regimen at the time of this report. Despite the final cIMPACT6 classification in 2020 advised to define all MN1 altered brain tumors as astroblastomas, there exist prognostic differences in MN1-altered tumors with and without morphological features of astroblastoma. Rare morphological variants of MN1-altered tumors shall be recognized for their future prognostic and clinical classification. HGNET with MN1 alteration seems still be a more proper definition of such malignancies as an umbrella term.

Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67; P < 0.001) and its genes (n = 1092) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

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.

Selective Nonmethylated CpG DNA Recognition Mechanism of Cysteine Clamp Domains

Methylation of DNA at CpG sites is a major mark for epigenetic regulation, but how transcription factors are influenced by CpG methylation is not well understood. Here, we report the molecular mechanisms of how the TCF (T-cell factor) and GEF (glucose transporter 4 enhancer factor) families of proteins selectively target unmethylated DNA sequences with a C-clamp type zinc finger domain. The structure of the C-clamp domain from human GEF family protein HDBP1 (C-clamp^HDBP1) in complex with DNA was determined using NMR spectroscopy, which adopts a unique zinc finger fold and selectively binds RCCGG (R = A/G) DNA sequences with an "Arg···Trp-Lys-Lys" DNA recognition motif inserted in the major groove. The CpG base pairs are central to the binding due to multiple hydrogen bonds formed with the backbone carbonyl groups of Trp378 and Lys379, as well as the side chain ε-amino groups of Lys379 and Lys380 from C-clamp^HDBP1. Consequently, methylation of the CpG dinucleotide almost abolishes the binding. Homology modeling reveals that the C-clamp domain from human TCF1E (C-clamp^TCF1E) binds DNA through essentially the same mechanism, with a similar "Arg···Arg-Lys-Lys" DNA recognition motif. The substitution of tryptophan by arginine makes C-clamp^HDBP1 prefer RCCGC DNA sequences. The two signature DNA recognition motifs are invariant in the GEF and TCF families of proteins, respectively, from fly to human. The recognition of the CpG dinucleotide through two consecutive backbone carbonyl groups is the same as that of the CXXC type unmethylated CpG DNA binding domains, suggesting a common mechanism shared by unmethylated CpG binding proteins.

Prognostic Role of EYA4 in Lower Grade Glioma with IDH1 Mutation and 1p19q Co-Deletion

BACKGROUND

Eyes absent 4 (EYA4) participates in an important role in various cancers. Patients with low EYA4 expression have significantly favorable prognosis compared with those with high EYA4 expression. However, the expression and role of EYA4 in lower grade glioma (LGG) has not been fully elucidated.

METHODS

The R2 and UCSC Xena browser based on data from 284 cases in GSE16011 from Gene Expression Omnibus datasets and 530 cases of patients with LGG in The Cancer Genome Atlas database were extracted for bioinformatic analyses. The EYA4 expression in different subtypes of LGG was detected. Kaplan-Meier survival curves were generated to explore the association between EYA4 expression and overall survival (OS) in both datasets.

RESULTS

Patients with LGG with lower EYA4 expression had significantly longer 5- and 10-year OS in 2 datasets (P < 0.001). By matching histological subtypes and gene expression profiles of patients with LGG, oligoastrocytoma and oligodendroglioma groups had lower EYA4 expression and longer OS compared with the astrocytoma group (P < 0.05). Patients with IDH1 mutations and 1p19q co-deletion had longer 5- and 10-year OS (P < 0.001), and EYA4 expression was significantly downregulated in these patients (P < 0.001).

CONCLUSIONS

This study suggests that EYA4 can be used as a prognostic marker and provide a potential therapeutic target in patients with LGG with IDH1 mutation and 1p19q co-deletion.

Transcriptome-wide Mendelian randomization study prioritising novel tissue-dependent genes for glioma susceptibility

Genome-wide association studies (GWAS) have discovered 27 loci associated with glioma risk. Whether these loci are causally implicated in glioma risk, and how risk differs across tissues, has yet to be systematically explored. We integrated multi-tissue expression quantitative trait loci (eQTLs) and glioma GWAS data using a combined Mendelian randomisation (MR) and colocalisation approach. We investigated how genetically predicted gene expression affects risk across tissue type (brain, estimated effective n = 1194 and whole blood, n = 31,684) and glioma subtype (all glioma (7400 cases, 8257 controls) glioblastoma (GBM, 3112 cases) and non-GBM gliomas (2411 cases)). We also leveraged tissue-specific eQTLs collected from 13 brain tissues (n = 114 to 209). The MR and colocalisation results suggested that genetically predicted increased gene expression of 12 genes were associated with glioma, GBM and/or non-GBM risk, three of which are novel glioma susceptibility genes (RETREG2/FAM134A, FAM178B and MVB12B/FAM125B). The effect of gene expression appears to be relatively consistent across glioma subtype diagnoses. Examining how risk differed across 13 brain tissues highlighted five candidate tissues (cerebellum, cortex, and the putamen, nucleus accumbens and caudate basal ganglia) and four previously implicated genes (JAK1, STMN3, PICK1 and EGFR). These analyses identified robust causal evidence for 12 genes and glioma risk, three of which are novel. The correlation of MR estimates in brain and blood are consistently low which suggested that tissue specificity needs to be carefully considered for glioma. Our results have implicated genes yet to be associated with glioma susceptibility and provided insight into putatively causal pathways for glioma risk.

MN1 overexpression with varying tumor grade is a promising predictor of survival of glioma patients

Gliomas have substantial mortality to incidence rate ratio and a dismal clinical course. Newer molecular insights, therefore, are imperative to refine glioma diagnosis, prognosis and therapy. Meningioma 1 (MN1) gene is a transcriptional co-regulator implicated in other malignancies, albeit its significance in glioma pathology remains to be explored. IGFBP5 is regulated transcriptionally by MN1 and IGF1 and is associated with higher glioma grade and shorter survival time, prompting us to ascertain their correlation in these tumors. We quantified the expression of MN1, IGFBP5 and IGF1 in 40 glioma samples and examined their interrelatedness. MN1 mRNA-protein inter-correlation and the gene's copy number were evaluated in these tumors. Publicly available TCGA datasets were used to examine the association of MN1 expression levels with patient survival and for validating our findings. We observed MN1 overexpression correlated with low-grade (LGGs) and not high-grade gliomas and is not determined by the copy number alteration of the gene. Notably, gliomas with upregulated MN1 have better overall survival (OS) and progression-free survival (PFS). IGFBP5 expression associated inversely with MN1 expression levels in gliomas but correlated positively with IGF1 expression in only LGGs. This suggests a potential grade-specific interplay between repressive and activating roles of MN1 and IGF1, respectively, in the regulation of IGFBP5. Thus, MN1 overexpression, a promising predictor of OS and PFS in gliomas, may serve as a prognostic biomarker in clinical practice to categorize patients with survival advantage.

A Novel 10-Gene Signature Predicts Poor Prognosis in Low Grade Glioma

AIM

Low grade glioma (LGG) is a lethal brain cancer with relatively poor prognosis in young adults. Thus, this study was performed to develop novel molecular biomarkers to effectively predict the prognosis of LGG patients and finally guide treatment decisions.

METHODS

survival-related genes were determined by Kaplan-Meier survival analysis and multivariate Cox regression analysis using the expression and clinical data of 506 LGG patients from The Cancer Genome Atlas (TCGA) database and independently validated in a Chinese Glioma Genome Atlas (CGGA) dataset. A prognostic risk score was established based on a linear combination of 10 gene expression levels using the regression coefficients of the multivariate Cox regression models. GSEA was performed to analyze the altered signaling pathways between the high and low risk groups stratified by median risk score.

RESULTS

We identified a total of 1489 genes significantly correlated with patients' prognosis in LGG. The top 5 protective genes were DISP2, CKMT1B, AQP7, GPR162 and CHGB, the top 5 risk genes were SP1, EYA3, ZSCAN20, ITPRIPL1 and ZNF217 in LGG. The risk score was predictive of poor overall survival and relapse-free survival in LGG patients. Pathways of small cell lung cancer, pathways in cancer, chronic myeloid leukemia, colorectal cancer were the top 4 most enriched pathways in the high risk group. SP1, EYA3, ZSCAN20, ITPRIPL1, ZNF217 and GPR162 were significantly up-regulated, while DISP2, CKMT1B, AQP7 were down-regulated in 523 LGG tissues as compared to 1141 normal brain controls.

CONCLUSIONS

The 10-gene signature may become novel prognostic and diagnostic biomarkers to considerably improve the prognostic prediction in LGG.

HOX gene cluster (de)regulation in brain: from neurodevelopment to malignant glial tumours

HOX genes encode a family of evolutionarily conserved homeodomain transcription factors that are crucial both during development and adult life. In humans, 39 HOX genes are arranged in four clusters (HOXA, B, C, and D) in chromosomes 7, 17, 12, and 2, respectively. During embryonic development, particular epigenetic states accompany their expression along the anterior-posterior body axis. This tightly regulated temporal-spatial expression pattern reflects their relative chromosomal localization, and is critical for normal embryonic brain development when HOX genes are mainly expressed in the hindbrain and mostly absent in the forebrain region. Epigenetic marks, mostly polycomb-associated, are dynamically regulated at HOX loci and regulatory regions to ensure the finely tuned HOX activation and repression, highlighting a crucial epigenetic plasticity necessary for homeostatic development. HOX genes are essentially absent in healthy adult brain, whereas they are detected in malignant brain tumours, namely gliomas, where HOX genes display critical roles by regulating several hallmarks of cancer. Here, we review the major mechanisms involved in HOX genes (de)regulation in the brain, from embryonic to adult stages, in physiological and oncologic conditions. We focus particularly on the emerging causes of HOX gene deregulation in glioma, as well as on their functional and clinical implications.

Prognostic Roles of Central Carbon Metabolism-Associated Genes in Patients With Low-Grade Glioma

Purpose: Metabolic alterations are crucial for tumor progression and response to therapy. The comprehensive model of combined central carbon metabolism-associated genes that contribute to the outcomes of glioma and astrocytoma is not well understood. Method: We studied the profiles of 63 genes involved in central carbon metabolism in 514 relatively low-grade glioma patients. The different distributions of gene expression in gliomas and astrocytoma were identified. The differential gene expression between each cohort and the correlations with prognosis were detected. Finally, we built a tentative model to detect the prognostic roles of carbon metabolism-associated genes in astrocytoma. Result: Two primary clusters and four subclusters with significantly different overall survival were identified in low-grade glioma. The differences of histological diagnoses, grade, tumor site, and age were detected between each cluster. Comparing with other histological types, patients with astrocytoma exhibited the worst prognosis. Between astrocytoma patients with poor and favorable prognoses, expression profiles of 11 genes were significantly discrepant. We detected that 18 genes were respectively correlated with overall survival in astrocytoma; moreover, four genes (RAF1, AKT3, IDH1, and FGFR1) were detected as dependent variables for the prediction of the survival status of astrocytoma patients and were capable to predict the survival. Conclusion: Central carbon metabolism-associated genes are differentially expressed in all patients with glioma and histological subtype astrocytoma. The gene expression profile is significantly associated with clinical manifestations. These results suggested that both the multigene expression patterns and individual central carbon metabolism-associated genes were potentially capable to predict the prognosis of patients with low-grade glioma.

Comprehensive analysis of genes based on chr1p/19q co-deletion reveals a robust 4-gene prognostic signature for lower grade glioma

Purpose: The chr1p/19q co-deletion is a favorable prognostic factor in patients with lower grade glioma. The aim of this study was to reveal key genes for prognosis and establish prognostic gene signatures based on genes encoded by chr1p/19q. Materials and methods: The data was downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between lower grade glioma tissue and normal brain were identified. The univariate COX regression, robust likelihood-base survival analysis (rbsurv) and multivariate COX regression analysis were used to establish the 4-gene-signature based on the DEGs. The receiver operating characteristic (ROC) curve and the Kaplan-Mere curve were used to verify the prediction accuracy of the signature. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were also performed to explore the reasons for good prognosis in patients with chr1p/19q deletion. Results: A total of 1346 DEGs were identified between lower grade glioma samples and normal brain samples in GSE16011, including 56 up-regulated mRNAs located on chr1p and 20 up-regulated mRNAs located on chr19q. We established a 4-gene-signature that was significantly associated with survival based on the 76 gene. The AUC of the 4-gene-signature for 5-year OS in TCGA and CGGA was 0.837 and 0.876, respectively, which was superior compared to other parameters such as chr1p/19q co-deletion, IDH mutant, WHO grade and histology type, especially in chr1p/19q non-co-deletion patients. GSEA and KEGG analysis suggested that the prolongation of chr1p/19q in patients could be associated with cell cycle and DNA mismatch repairing. Conclusions: We established a robust 4-gene-signature based on the chr1p/19q and we explored the potential function of these newly identified survival-associated genes by bioinformatics analysis. The 4-gene from the signature are promising molecular targets to be used in the future.

Use of Genome-Scale Integrated Analysis to Identify Key Genes and Potential Molecular Mechanisms in Recurrence of Lower-Grade Brain Glioma

BACKGROUND The aim of this study was to identify gene signals for lower-grade glioma (LGG) and to assess their potential as recurrence biomarkers. MATERIAL AND METHODS An LGG-related mRNA sequencing dataset was downloaded from The Cancer Genome Atlas (TCGA) Informix. Multiple bioinformatics analysis methods were used to identify key genes and potential molecular mechanisms in recurrence of LGG. RESULTS A total of 326 differentially-expressed genes (DEGs), were identified from 511 primary LGG tumor and 18 recurrent samples. Gene ontology (GO) analysis revealed that the DEGs were implicated in cell differentiation, neuron differentiation, negative regulation of neuron differentiation, and cell proliferation in the forebrain. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database suggests that DEGs are associated with proteoglycans in cancer, the Wnt signaling pathway, ECM-receptor interaction, the PI3K-Akt signaling pathway, transcriptional deregulation in cancer, and the Hippo signaling pathway. The hub DEGs in the protein-protein interaction network are apolipoprotein A2 (APOA2), collagen type III alpha 1 chain (COL3A1), collagen type I alpha 1 chain (COL1A1), tyrosinase (TYR), collagen type I alpha 2 chain (COL1A2), neurotensin (NTS), collagen type V alpha 1 chain (COL5A1), poly(A) polymerase beta (PAPOLB), insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1), and anomalous homeobox (ANHX). GSEA revealed that the following biological processes may associated with LGG recurrence: cell cycle, DNA replication and repair, regulation of apoptosis, neuronal differentiation, and Wnt signaling pathway. CONCLUSIONS Our study demonstrated that hub DEGs may assist in the molecular understanding of LGG recurrence. These findings still need further molecular studies to identify the assignment of DEGs in LGG.

Identification of potential biomarkers related to glioma survival by gene expression profile analysis

BACKGROUND

Recent studies have proposed several gene signatures as biomarkers for different grades of gliomas from various perspectives. However, most of these genes can only be used appropriately for patients with specific grades of gliomas.

METHODS

In this study, we aimed to identify survival-relevant genes shared between glioblastoma multiforme (GBM) and lower-grade glioma (LGG), which could be used as potential biomarkers to classify patients into different risk groups. Cox proportional hazard regression model (Cox model) was used to extract relative genes, and effectiveness of genes was estimated against random forest regression. Finally, risk models were constructed with logistic regression.

RESULTS

We identified 104 key genes that were shared between GBM and LGG, which could be significantly correlated with patients' survival based on next-generation sequencing data obtained from The Cancer Genome Atlas for gene expression analysis. The effectiveness of these genes in the survival prediction of GBM and LGG was evaluated, and the average receiver operating characteristic curve (ROC) area under the curve values ranged from 0.7 to 0.8. Gene set enrichment analysis revealed that these genes were involved in eight significant pathways and 23 molecular functions. Moreover, the expressions of ten (CTSZ, EFEMP2, ITGA5, KDELR2, MDK, MICALL2, MAP 2 K3, PLAUR, SERPINE1, and SOCS3) of these genes were significantly higher in GBM than in LGG, and comparing their expression levels to those of the proposed control genes (TBP, IPO8, and SDHA) could have the potential capability to classify patients into high- and low- risk groups, which differ significantly in the overall survival. Signatures of candidate genes were validated, by multiple microarray datasets from Gene Expression Omnibus, to increase the robustness of using these potential prognostic factors. In both the GBM and LGG cohort study, most of the patients in the high-risk group had the IDH1 wild-type gene, and those in the low-risk group had IDH1 mutations. Moreover, most of the high-risk patients with LGG possessed a 1p/19q-noncodeletion.

CONCLUSION

In this study, we identified survival relevant genes which were shared between GBM and LGG, and those enabled to classify patients into high- and low-risk groups based on expression level analysis. Both the risk groups could be correlated with the well-known genetic variants, thus suggesting their potential prognostic value in clinical application.

Correlation of alteration of HLA-F expression and clinical characterization in 593 brain glioma samples

Background

Human gliomas are highly fatal tumors with a significant feature of immune suppression. The association of the immune system in gliomas is gradually revealed, and immunotherapy is expected to improve the survival of glioma patients. In-depth understanding of the immune microenvironment of gliomas and their associated immunotherapy was increased exponentially in recent years. Gliomas provide clinical targets for immunotherapy during the search of key regulators of immune response. Our study focused on the human leukocyte antigen (HLA) system that is responsible for regulating the immune system, and discovered the relationship between HLA-F expression and clinical prognosis in gliomas.

Methods

A total of 593 patients with gliomas were included in our research. Of these, 325 patients were from the Chinese Glioma Genome Atlas (CGGA) and 268 were from the GSE 16011 set. Kaplan-Meier (KM) analysis was performed to explore the prognostic value of HLA-F. t test analysis was used to find the distribution difference in various groups. R language packages are used for other statistical computations and figure drawing.

Results

HLA-F was negatively correlated with overall survival (OS) in all grades of glioma and glioblastoma (GBM). Moreover, HLA-F was enriched in GBM and isocitrate dehydrogenase 1 wild-type (IDH1 wt) group and considered HLA-F as a mesenchymal subtype marker. Pearson correlation test showed that HLA-F was correlated with other HLA-I molecules.

Conclusion

HLA-F expression was positively correlated with malignant phenotype and negatively correlated with OS, indicating that HLA-F could predict the immune state of gliomas and might be a clinical target of glioma immunotherapy.

Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas

PURPOSE

To perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas.

METHODS

This retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient. We performed a radiomics analysis by extracting 647 MRI-based features and applied the random forest algorithm to generate a radiomics signature for predicting 1p/19q co-deletion in the training cohort (n = 184). The clinical model consisted of pertinent clinical factors, and was built using a logistic regression algorithm. A combined model, incorporating both the radiomics signature and related clinical factors, was also constructed. The receiver operating characteristics curve was used to evaluate the predictive performance. We further validated the predictability of the three developed models using a time-independent validation cohort (n = 93).

RESULTS

The radiomics signature was constructed as an independent predictor for differentiating 1p/19q co-deletion genotypes, which demonstrated superior performance on both the training and validation cohorts with areas under curve (AUCs) of 0.887 and 0.760, respectively. These results outperformed the clinical model (AUCs of 0.580 and 0.627 on training and validation cohorts). The AUCs of the combined model were 0.885 and 0.753 on training and validation cohorts, respectively, which indicated that clinical factors did not present additional improvement for the prediction.

CONCLUSION

Our study highlighted that an MRI-based radiomics signature can effectively identify the 1p/19q co-deletion in histopathologically diagnosed lower-grade gliomas, thereby offering the potential to facilitate non-invasive molecular subtype prediction of gliomas.

Identification of a DNA Repair-Related Multigene Signature as a Novel Prognostic Predictor of Glioblastoma

BACKGROUND

Glioblastoma (GBM) is an extremely challenging malignancy to treat. Although temozolomide (TMZ) is a standard treatment regimen, many patients with GBM develop chemoresistance. The aim of this study was to identify a DNA repair-related gene signature to better stratify patients treated with TMZ.

METHODS

We selected 89 cases of primary GBM (pGBM) from the Chinese Glioma Genome Atlas RNA-seq dataset as the training cohort, whereas The Cancer Genome Atlas RNA-seq and Gene Set Enrichment (GSE) 16011 mRNA array sets were used as validation cohorts. Regression analysis and linear risk score assessment were performed to build a DNA repair-related signature. We used Kaplan-Meier analysis to evaluate the predictive value of the signature for overall survival (OS) in the different groups. Multivariate Cox regression analysis was used to determine whether the 5-gene signature could independently predict OS.

RESULTS

Using our 5-gene signature panel of APEX1, APRT, PARP2, PMS2L2, and POLR2L, we divided patients with pGBM into high- and low-risk groups. Patients with a low-risk score were predicted to have favorable survival and greater benefit from TMZ therapy compared with patients from the high-risk group (P < 0.05). Moreover, receiver operating characteristic curves showed that the multigene signature was the most sensitive and specific model for survival prediction (P < 0.05).

CONCLUSIONS

Among patients with pGBM, classification based on a risk score determined using a 5-gene panel indicated different OS and reaction to TMZ. The findings in this study demonstrate that this unique 5-gene signature could be a novel model to predict OS and provide accurate therapy for patients with pGBM.

TLR4 interaction with LPS in glioma CD133+ cancer stem cells induces cell proliferation, resistance to chemotherapy and evasion from cytotoxic T lymphocyte-induced cytolysis

Despite advances in treatment modalities, 5-year survival among glioma patients remains poor. Glioma cancer stem cells (CSCs) exhibit high tumorigenic activity and are associated with resistance to treatment and tumor recurrence. Because overexpression of toll-like receptor 4 (TLR4) correlated with cancer development, we investigated LPS-induced TLR4 signaling in glioma CD133-positive (CD133+) CSCs. The proliferation of CD133+ CSCs isolated from CSCs derived from the U251 and SF295 glioma cell lines and from human glioma samples was upregulated on a time- and concentration-dependent basis by LPS stimulation, with increases in CD133, NANOG, and NESTIN mRNA and protein levels. Also elevated was cytokine expression, which was coupled to phosphorylation of mitogen-activated protein kinase, and activation of cyclins and cyclin-dependent kinase complexes. TLR4 knockdown reduced LPS-induced CD133+ CSC proliferation, whereas Adriamycin-induced CD133+ CSC apoptosis was moderately inhibited by treatment with LPS, implying a protective effect of LPS. The capacity of glioma CD133+ CSC-reactive cytotoxic T lymphocyte to selectively kill CD133+ CSCs was reduced by LPS, and this effect was not apparent after TLR4 knockdown in CD133+ CSCs. These data suggest TLR4 signaling is a factor in CD133+ CSC immune evasion, and thus disruption of TLR4 signaling is a potential therapeutic strategy in glioma.

MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis

OBJECTIVE

To identify the magnetic resonance imaging (MRI) features associated with epidermal growth factor (EGFR) expression level in lower grade gliomas using radiomic analysis.

METHODS

270 lower grade glioma patients with known EGFR expression status were randomly assigned into training (n=200) and validation (n=70) sets, and were subjected to feature extraction. Using a logistic regression model, a signature of MRI features was identified to be predictive of the EGFR expression level in lower grade gliomas in the training set, and the accuracy of prediction was assessed in the validation set.

RESULTS

A signature of 41 MRI features achieved accuracies of 82.5% (area under the curve [AUC] = 0.90) in the training set and 90.0% (AUC = 0.95) in the validation set. This radiomic signature consisted of 25 first-order statistics or related wavelet features (including range, standard deviation, uniformity, variance), one shape and size-based feature (spherical disproportion), and 15 textural features or related wavelet features (including sum variance, sum entropy, run percentage).

CONCLUSIONS

A radiomic signature allowing for the prediction of the EGFR expression level in patients with lower grade glioma was identified, suggesting that using tumour-derived radiological features for predicting genomic information is feasible.

KEY POINTS

• EGFR expression status is an important biomarker for gliomas. • EGFR in lower grade gliomas could be predicted using radiogenomic analysis. • A logistic regression model is an efficient approach for analysing radiomic features.