The effect Of vascular related CeRNA genes and corresponding imaging biomarkers on survival in lower grade glioma

BACKGROUND & AIMS

To investigate the differential expression of vascular related ceRNA regulatory genes in LGG with different mutations of IDH1 and MGMT, and to verify imaging gene markers that can be closely associated with vascular related ceRNA regulatory genes.

METHOD

Five hundred fifteen patients with LGG were collected from TCGA database. CeRNA network analysis, GO analysis and Cox risk regression were used to find vascular ceRNA regulatory genes and their genetic markers related to survival. The preoperative MRI image data and postoperative tumor tissues of 14 patients with WHO grade III glioma were collected for full transcriptome analysis. The correlation between image characteristics of LGG and survival related vascular ceRNA regulatory genes was compared using nonparametric U test and Pearson correlation coefficient analysis.

RESULTS

Vascular related genes ranked first in the functional enrichment analysis of differentially expressed genes in LGG. EPHA2, ETS1, YAP1 and MEIS1 could significantly affect the survival of patients in each group of LGG. The volume of enhanced region was negatively correlated with IDH1 (r = -0.622, P = 0.009) mutation and TMEM100 (r = -0.535, P = 0.024), and positively correlated with MEIS1 (r = 0.551, P = 0.021), rCBFmax value was negatively correlated with TMEM100 (r = -0.492, P = 0.037).

CONCLUSIONS

Under different IDH1 mutations, lncRNA-dominated vascular-related ceRNA regulatory genes were the first differentially expressed subset of each group, and could be used as an effective risk factor affecting the survival of LGG. The image characteristics of LGG was an ideal image gene marker. It was a reliable imaging biological marker which can truly reflect the pathophysiological characteristics of glioma.

An alternative extension of telomeres related prognostic model to predict survival in lower grade glioma

OBJECTIVE

The alternative extension of the telomeres (ALT) mechanism is activated in lower grade glioma (LGG), but the role of the ALT mechanism has not been well discussed. The primary purpose was to demonstrate the significance of the ALT mechanism in prognosis estimation for LGG patients.

METHOD

Gene expression and clinical data of LGG patients were collected from the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) cohort, respectively. ALT-related genes obtained from the TelNet database and potential prognostic genes related to ALT were selected by LASSO regression to calculate an ALT-related risk score. Multivariate Cox regression analysis was performed to construct a prognosis signature, and a nomogram was used to represent this signature. Possible pathways of the ALT-related risk score are explored by enrichment analysis.

RESULT

The ALT-related risk score was calculated based on the LASSO regression coefficients of 22 genes and then divided into high-risk and low-risk groups according to the median. The ALT-related risk score is an independent predictor of LGG (HR and 95% CI in CGGA cohort: 5.70 (3.79, 8.58); in TCGA cohort: 1.96 (1.09, 3.54)). ROC analysis indicated that the model contained ALT-related risk score was superior to conventional clinical features (AUC: 0.818 vs 0.729) in CGGA cohorts. The results in the TCGA cohort also shown a powerful ability of ALT-related risk score (AUC: 0.766 vs 0.691). The predicted probability and actual probability of the nomogram are consistent. Enrichment analysis demonstrated that the ALT mechanism was involved in the cell cycle, DNA repair, immune processes, and others.

CONCLUSION

ALT-related risk score based on the 22-gene is an important factor in predicting the prognosis of LGG patients.

SLC10A3 Is a Prognostic Biomarker and Involved in Immune Infiltration and Programmed Cell Death in Lower Grade Glioma

BACKGROUND

The association between SLC10A3 (solute carrier family 10 member 3) and lower grade glioma (LGG) remains unclear.

METHODS

We used public databases and bioinformatics analysis to analyze SLC10A3. These included The Cancer Genome Atlas, Genotype-Tissue Expansion, Chinese Glioma Genome Atlas, Human Protein Atlas, GeneCards, cBioPortal, Search Tool for the Retrieval of Interacting Genes/Proteins, Gene Expression Profiling Interactive Analysis, Tumor Immune Estimation Resource, Tumor-Immune System Interaction Database, receiver operating characteristic curve analysis, Kaplan-Meier analysis, Cox analysis, nomograms, calibration plots, gene ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analysis, gene set enrichment analysis, single-sample gene set enrichment analysis, and Spearman's correlation analysis.

RESULTS

SLC10A3 was upregulated in adrenocortical carcinoma, glioblastoma, and LGG and was associated with good overall survival (OS) in adrenocortical carcinoma and poor OS in LGG and glioblastoma. SLC10A3 was increased with increased World Health Organization grade, upregulated in isocitrate dehydrogenase-wild type, 1p/19q (chromosome arms 1p and 19q) non-co-deleted, and higher in astrocytoma. Patients with LGG were grouped by the occurrence of the clinical outcome endpoints (i.e., OS, disease-specific survival [DSS], and progression-free interval events). Genetic alterations in SLC10A3 were associated with poor progression-free survival in LGG. Most of clinical characteristics were associated with the SLC10A3 expression level. SLC10A3 with diagnostic and prognostic value (OS, DSS, and progression-free interval) was an independent prognostic factor in LGG. Moreover, Nomograms (WHO grade, 1p/19q codeletion, age and SLC10A3) had moderately accurate predictive for OS and DSS. Functional analysis showed that SLC10A3 might participate in the transport of multiple substances, neurogenic signaling, immune response, and programmed cell death in LGG. SLC10A3 correlated with immune infiltration in LGG and moderately correlated with the gene signature of pyroptosis, lysosome-dependent cell death, necroptosis, apoptosis, ferroptosis, alkaliptosis, and autophagy-dependent cell death.

CONCLUSIONS

SLC10A3 is a potential diagnostic and prognostic biomarker for LGG and might be associated with substance transport, neurogenic signaling, immune infiltration, and programmed cell death in LGG.

Machine learning-based identification of lower grade glioma stemness subtypes discriminates patient prognosis and drug response

Glioma stem cells (GSCs) remodel their tumor microenvironment to sustain a supportive niche. Identification and stratification of stemness related characteristics in patients with glioma might aid in the diagnosis and treatment of the disease. In this study, we calculated the mRNA stemness index in bulk and single-cell RNA-sequencing datasets using machine learning methods and investigated the correlation between stemness and clinicopathological characteristics. A glioma stemness-associated score (GSScore) was constructed using multivariate Cox regression analysis. We also generated a GSC cell line derived from a patient diagnosed with glioma and used glioma cell lines to validate the performance of the GSScore in predicting chemotherapeutic responses. Differentially expressed genes (DEGs) between GSCs with high and low GSScores were used to cluster lower-grade glioma (LGG) samples into three stemness subtypes. Differences in clinicopathological characteristics, including survival, copy number variations, mutations, tumor microenvironment, and immune and chemotherapeutic responses, among the three LGG stemness-associated subtypes were identified. Using machine learning methods, we further identified genes as subtype predictors and validated their performance using the CGGA datasets. In the current study, we identified a GSScore that correlated with LGG chemotherapeutic response. Through the score, we also identified a novel classification of the LGG subtype and associated subtype predictors, which might facilitate the development of precision therapy.

DKK3 expression is associated with immunosuppression and poor prognosis in glioblastoma, in contrast to lower-grade gliomas

PURPOSE

We previously reported that expression of dickkopf-3 (DKK3), which is involved in the Wnt/β-catenin pathway, is significantly associated with prognosis in patients with glioblastoma multiforme (GBM). The aim of this study was to compare the association of DKK3 with other Wnt/β-catenin pathway-related genes and immune responses between lower grade glioma (LGG) and GBM.

METHODS

We obtained the clinicopathological data of 515 patients with LGG (World Health Organization [WHO] grade II and III glioma) and 525 patients with GBM from the Cancer Genome Atlas (TCGA) database. We performed Pearson's correlation analysis to investigate the relationships between Wnt/β-catenin-related gene expression in LGG and GBM. Linear regression analysis was performed to identify the association between DKK3 expression and immune cell fractions in all grade II to IV gliomas.

RESULTS

A total of 1,040 patients with WHO grade II to IV gliomas were included in the study. As the grade of glioma increased, DKK3 showed a tendency to be more strongly positively correlated with the expression of other Wnt/β-catenin pathway-related genes. DKK3 was not associated with immunosuppression in LGG but was associated with downregulation of immune responses in GBM. We hypothesized that the role of DKK3 in the Wnt/β-catenin pathway might be different between LGG and GBM.

CONCLUSION

According to our findings, DKK3 expression had a weak effect on LGG but a significant effect on immunosuppression and poor prognosis in GBM. Therefore, DKK3 expression seems to play different roles, through the Wnt/β-catenin pathway, between LGG and GBM.

The prognostic value of DAAM2 in lower grade glioma, liver cancer, and breast cancer

PURPOSE

Dishevelled-associated activator of morphogenesis 2 (DAAM2) is a formin protein and has a potential role in the tumor metastasis. The prognostic value of DAAM2 in pan-cancer is investigated in this study.

METHODS

TCGA and GTEx database were downloaded to perform bioinformatics analysis and ROC curves. Then we explored protein-protein interaction and GO-KEGG enrichment to figure out the protein pathways associated with DAAM2 and studied DAAM2-related immune infiltration and methylation. Fifteen pairs of BRCA clinical samples were enrolled to determine the expression and distribution of DAAM2 in BRCA sections by immunohistochemistry. Finally, BRCA cells were transfected with siRNA targeting DAAM2 and subsequently subject to cell proliferation, migration, and invasion assays.

RESULTS

DAAM2 was closely related to the diagnosis and clinical characteristics of lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), and breast cancer (BRCA). Survival curve analysis demonstrated DAAM2 served as a potential prognostic indicator of LGG and LIHC (P = 0.0029 and P = 0.025, respectively). DAAM2 was mainly participated in signaling pathways mediating cytoskeleton regulation and tumor development. The correlation of DAAM2 with tumor-infiltrating immune cells (TIICs) and methylation levels was conducive to the prediction of novel biomarkers of pan-carcinoma. DAAM2 was highly expressed in BRCA tissues than that in paracancerous tissues. The proliferation, invasion, and migration of BRCA cells were inhibited by DAAM2 siRNA.

CONCLUSION

DAAM2 had a specific value in foretelling the prognosis of LGG, LIHC, and BRCA. High expression level of DAAM2 has longer survival rates in LGG and LIHC. The knockdown of DAAM2 retards the proliferation, invasion, and migration of BRCA cells. This study provides a novel sight of DAAM2 into the exploration of a potential biomarker in pan-cancer.

Integrated Bioinformatic Analysis of the Correlation of HOXA10 Expression with Survival and Immune Cell Infiltration in Lower Grade Glioma

Homeobox A10 (HOXA10) encodes a transcription factor that regulates developmental processes. Whether HOXA10 mRNA levels in lower grade glioma (LGG) correlate with survival and immune cell infiltration has not been evaluated. The differential expression of HOXA10 in different tumors and their corresponding normal tissues was evaluated by exploring public datasets. The correlations between HOXA10 and survival, tumor immune cell infiltration, diverse gene mutation characteristics, and tumor mutation burden in LGG were also investigated using several independent datasets. Pathway enrichment analysis was conducted to identify HOXA10-associated signaling pathways. We found that HOXA10 expression levels did not significantly differ between LGG tumors and normal tissues. Upon assessing the association between HOXA10 expression and immune cell infiltration in LGG, as expected, HOXA10 gene mRNA levels were positively associated with B-cell and dendritic cell infiltration levels in public online datasets. Different HOXA10 expression groups showed diverse gene mutation characteristics and TMB, and low HOXA10 expression was closely related to improved LGG patient survival. Pathway enrichment analysis of HOXA10-associated genes indicated that the cell cycle signaling pathway may participate in affecting the outcomes of LGG patients. Our findings showed that HOXA10 expression was associated with LGG prognosis and tumor immunity.

Isocitrate-dehydrogenase-mutant lower grade glioma in elderly patients: treatment and outcome in a molecularly characterized contemporary cohort

PURPOSE

Lower-grade glioma (LGG) is rare among patients above the age of 60 ("elderly"). Previous studies reported poor outcome, likely due to the inclusion of isocitrate dehydrogenase (IDH) wildtype astrocytomas and advocated defensive surgical and adjuvant treatment. This study set out to question this paradigm analyzing a contemporary cohort of patients with IDH mutant astrocytoma and oligodendroglioma WHO grade 2 and 3.

METHODS

Elderly patients treated in our department for a supratentorial, hemispheric LGG between 2009 and 2019 were retrospectively analyzed for patient-, tumor- and treatment-related factors and progression-free survival (PFS) and compared to patients aged under 60. Inclusion required the availability of subtype-defining molecular data and pre- and post-operative tumor volumes.

RESULTS

207 patients were included, among those 21 elderlies (10%). PFS was comparable between elderly and younger patients (46 vs. 54 months; p = 0.634). Oligodendroglioma was more common in the elderly (76% vs. 46%; p = 0.011). Most patients underwent tumor resection (elderly: 81% vs. younger: 91%; p = 0.246) yielding comparable residual tumor volumes (elderly: 7.8 cm³; younger: 4.1 cm³; p = 0.137). Adjuvant treatment was administered in 76% of elderly and 61% of younger patients (p = 0.163). Uni- and multi-variate survival analyses identified a tumor crossing the midline, surgical strategy, and pre- and post-operative tumor volumes as prognostic factors.

CONCLUSION

Elderly patients constitute a small fraction of molecularly characterized LGGs. In contrast to previous reports, favorable surgical and survival outcomes were achieved in our series comparable to those of younger patients. Thus, intensified treatment including maximal safe resection should be advocated in elderly patients whenever feasible.

A dissected LMO2 functional analysis and clinical relevance in brain gliomas

Brain glioma is one of the cancer types with worst prognosis, and LMO2 has been reported to play oncogenic functions in brain gliomas. Herein, analysis of datasets from The Cancer Genome Atlas (TCGA) indicated that higher LMO2 level in patient samples indicated worse prognosis in lower grade gliomas (LGG) but not glioblastoma multiforme (GBM). Further, in tumor tissues consisting of a variety of cell types, LMO2 level indicated intratumoral endothelium and pattern recognition receptor (PRR) response in both LGGs and GBMs, and additionally indicated cytotoxic T-lymphocyte, M2 macrophage infiltration and fibroblast specifically in LGGs. Moreover, only in LGGs these aspects were significantly associated with patient survival, in either risky or protective manner, and these dissected associations can give a better prediction on patient prognosis than LMO2 alone. This study not only provided more detailed understandings of LMO2 functional representatives in brain gliomas but also demonstrated that dealing with certain gene (LMO2 in this study) in transcriptome data with the Weighted Gene Co-Expression Network Analysis (WGCNA) method was a robust strategy for dissecting exact and reasonable gene functions/associations in a complicated tumor environment.

Pre-surgery immune profiles of adult glioma patients

INTRODUCTION

Although immunosuppression is a known characteristic of glioma, no previous large studies have reported peripheral blood immune cell profiles prior to patient surgery and chemoradiation. This report describes blood immune cell characteristics and associated variables prior to surgery among typical glioma patients seen at a large University practice.

METHODS

We analyzed pre-surgery blood samples from 139 glioma patients diagnosed with a new or recurrent grade II/III glioma (LrGG, n = 64) or new glioblastoma (GBM, n = 75) and 454 control participants without glioma. Relative cell fractions of CD4, CD8, B-cells, Natural Killer cells, monocytes, and neutrophils, were estimated via a validated deconvolution algorithm from blood DNA methylation measures from Illumina EPIC arrays.

RESULTS

Dexamethasone use at time of blood draw varied by glioma type being highest among patients with IDH wild-type (wt) GBM (75%) and lowest for those with oligodendroglioma (14%). Compared to controls, glioma patients showed statistically significant lower cell fractions for all immune cell subsets except for neutrophils which were higher (all p-values < 0.001), in part because of the higher prevalence of dexamethasone use at time of blood draw for IDHwt GBM. Patients who were taking dexamethasone were more likely to have a low CD4 count (< 200, < 500), increased neutrophils, low absolute lymphocyte counts, higher total cell count and higher NLR.

CONCLUSION

We show that pre-surgery blood immune profiles vary by glioma subtype, age, and more critically, by use of dexamethasone. Our results highlight the importance of considering dexamethasone exposures in all studies of immune profiles and of obtaining immune measures prior to use of dexamethasone, if possible.

Seizures in patients with IDH-mutated lower grade gliomas

PURPOSE

Most patients with Lower Grade Gliomas (LGG) present with epileptic seizures. Since the advent of molecular diagnostics, more homogenous sub-entities have emerged, including the isocitrate dehydrogenase-mutated (IDH-mutated) astrocytomas and 1p19q-codeleted oligodendrogliomas. We aimed to describe the occurrence of seizures in patients with molecularly defined LGG pre- and postoperatively and to analyze factors affecting seizure status postoperatively.

METHODS

A population-based cohort of 130 adult patients with IDH-mutated WHO grade 2 or 3 astrocytomas and oligodendrogliomas was assessed pertaining to seizure burden before and after surgery.

RESULTS

Fifty-four (79.4%) patients with astrocytoma and 45 (72.6%) patients with oligodendroglioma had a history of seizures before surgery. At 12 months postoperatively, 51/67 (76.1%) patients with astrocytoma and 47/62 (75.8%) patients with oligodendrogliomas were seizure free. In a multivariable logistic regression analysis, lower extent of resection (EOR) (OR 0.98; 95% CI 0.97-1.00, p = 0.01) and insular tumor location (OR 5.02; 95% CI 1.01-24.87, p = 0.048) were associated with presence of seizures within 1 year postoperatively in the entire LGG cohort. In sub-entities, EOR was in a similar manner associated with seizures postoperatively in astrocytomas (OR 0.98; 95% CI 0.96-0.99, p < 0.01) but not in oligodendrogliomas (p = 0.34).

CONCLUSION

Our results are well in line with data published for non-molecularly defined LGG with a large proportion of patients being seizure free at 1 year postoperative. Better seizure outcome was observed with increased EOR in astrocytomas, but this association was absent in oligodendrogliomas.

Comparative profiling of immune genes improves the prognoses of lower grade gliomas

OBJECTIVE

Lower grade gliomas (LGGs), classified as World Health Organization (WHO) grade II and grade III gliomas, comprise a heterogeneous group with a median survival time ranging from 4-13 years. Accurate prediction of the survival times of LGGs remains a major challenge in clinical practice.

METHODS

We reviewed the expression data of 865 LGG patients from 5 transcriptomics cohorts. The comparative profile of immune genes was analyzed for signature identification and validation. In-house RNAseq and microarray data from the Chinese Glioma Genome Atlas (CGGA) dataset were used as training and internal validation cohorts, respectively. The samples from The Cancer Genome Atlas (TCGA) and GSE16011 cohorts were used as external validation cohorts, and the real-time PCR of frozen LGG tissue samples (n = 36) were used for clinical validation.

RESULTS

A total of 2,214 immune genes were subjected to pairwise comparison to generate 2,449,791 immune-related gene pairs (IGPs). A total of 402 IGPs were identified with prognostic values for LGGs. The HOXA9-related and CRH-related scores facilitated identification of patients with different prognoses. An immune signature based on 10 IGPs was constructed to stratify patients into low and high risk groups, exhibiting different clinical outcomes. A nomogram, combining immune signature, 1p/19q status, and tumor grade, was able to predict the overall survival (OS) with c-indices of 0.85, 0.80, 0.80, 0.79, and 0.75 in the training, internal validation, external validation, and tissue sample cohorts, respectively.

CONCLUSIONS

This study was the first to report a comparative profiling of immune genes in large LGG cohorts. A promising individualized immune signature was developed to estimate the survival time for LGG patients.

Identification of tumor antigens and immune subtypes in lower grade gliomas for mRNA vaccine development

BACKGROUND

As an important part of tumor immunotherapy for adjunct, therapeutic tumor vaccines have been effective against multiple solid cancers, while their efficacy against lower grade glioma (LGG) remains undefined. Immunophenotyping of tumors is an essential tool to evaluate the immune function of patients with immunodeficiency or autoimmunity. Therefore, this study aims to find the potential tumor antigen of LGG and identify the suitable population for cancer vaccination based on the immune landscape.

METHOD

The genomic and clinical data of 529 patients with LGG were obtained from TCGA, the mRNA_seq data of normal brain tissue were downloaded from GTEx. Differential expression gene and mutation analysis were performed to screen out potential antigens, K-M curves were carried out to investigate the correlation between the level of potential antigens and OS and DFS of patients. TIMER dataset was used to explore the correlation between genes and immune infiltrating cells. Immunophenotyping of 529 tumor samples was based on the single-sample gene sets enrichment analysis. Cibersort and Estimate algorithm were used to explore the tumor immune microenvironment characteristics in each immune subtype. Weighted gene co-expression network analysis (WGCNA) clustered immune-related genes and screened the hub genes, and pathway enrichment analyses were performed on the hub modules related to immune subtype in the WGCNA.

RESULTS

Selecting for the mutated, up-regulated, prognosis- and immune-related genes, four potential tumor antigens were identified in LGG. They were also significantly positively associated with the antigen-presenting immune cells (APCs). Three robust immune subtypes, IS1, IS2 and IS3, represented immune status "desert", "immune inhibition", and "inflamed" respectively, which might serve as a predictive parameter. Subsequently, clinicopathological features, including the codeletion status of 1p19q, IDH mutation status, tumor mutation burden, tumor stemness, etc., were significantly different among subtypes.

CONCLUSION

FCGBP, FLNC, TLR7, and CSF2RA were potential antigens for developing cancer vaccination, and the patients in IS3 were considered the most suitable for vaccination in LGG.

TERT promoter mutation status is necessary and sufficient to diagnose IDH-wildtype diffuse astrocytic glioma with molecular features of glioblastoma

The Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) update 3 recommends that histologic grade II and III IDH-wildtype diffuse astrocytic gliomas that harbor EGFR amplification, the combination of whole chromosome 7 gain and whole chromosome 10 loss (7 + /10 -), or TERT promoter (pTERT) mutations should be considered as glioblastomas (GBM), World Health Organization grade IV. In this retrospective study, we examined the utility of molecular classification based on pTERT status and copy-number alterations (CNAs) in IDH-wildtype lower grade gliomas (LGGs, grade II, and III). The impact on survival was evaluated for the pTERT mutation and CNAs, including EGFR gain/amplification, PTEN loss, CDKN2A homozygous deletion, and PDGFRA gain/amplification. We analyzed 46 patients with IDH-wildtype/pTERT-mutant (mut) LGGs and 85 with IDH-wildtype/pTERT-wildtype LGGs. EGFR amplification and a combination of EGFR gain and PTEN loss (EGFR + /PTEN -) were significantly more frequent in pTERT-mut patients (p < 0.0001). Cox regression analysis showed that the pTERT mutation was a significant predictor of poor prognosis (hazard ratio [HR] 2.79, 95% confidence interval [CI] 1.55-4.89, p = 0.0008), but neither EGFR amplification nor EGFR + /PTEN - was an independent prognostic factor in IDH-wildtype LGGs. PDGFRA gain/amplification was a significant poor prognostic factor in IDH-wildtype/pTERT-wildtype LGGs (HR 2.44, 95% CI 1.09-5.27, p = 0.03, Cox regression analysis). The IDH-wildtype LGGs with either pTERT-mut or PDGFRA amplification were mostly clustered with GBM by DNA methylation analysis. Thus, our study suggests that analysis of pTERT mutation status is necessary and sufficient to diagnose IDH-wildtype diffuse astrocytic gliomas with molecular features of glioblastoma. The PDGFRA status may help further delineate IDH-wildtype/pTERT-wildtype LGGs. Methylation profiling showed that IDH-wildtype LGGs without molecular features of GBM were a heterogeneous group of tumors. Some of them did not fall into existing categories and had significantly better prognoses than those clustered with GBM.

Low expression of CDHR1 is an independent unfavorable prognostic factor in glioma

Background: Analysis of the differentially expressed genes between lower grade glioma (LGG) and glioblastoma (GBM) will identify genes involved in a more aggressive phenotype of glioma. Methods: Differentially expressed genes between GBM and LGG were identified using published datasets. Kaplan-Meier estimator was used to determine the overall survival of different groups of glioma patients. The biological functions of CDHR1 in glioma were tested using CCK-8 and trans-well assays. Results: CCDC109B, CD58, CLIC1, EFEMP2, EMP3, LAMC1, LGALS1, PDLIM1 and TNFRSF1A were over-expressed, while, CDHR1 was down-regulated in GBM in The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GSE4412 and GSE43378 datasets. Compared with normal brain tissues, CDHR1 was down-regulated in glioma tissues. And low expression of CDHR1 was an unfavorable prognostic factor in glioma. Moreover, CDHR1 was lowly expressed in mesenchymal GBM subtype and lower expression of CDHR1 was associated with the worse clinical prognosis of GBM. Furthermore, CDHR1 was down-regulated in astrocytoma LGG subtype and low expression of CDHR1 was a bad prognosis of LGG. CDHR1 expression levels were also associated with IDH mutation. IDH mutant LGG or GBM patients were with higher CDHR1 expression. High expression of CDHR1 was a favorable prognosis in IDH mutant or IDH wild type LGG patients. CHDR1 expression was associated with MGMT methylation and CDHR1 was down-regulated in chemotherapy un-responsive LGG patients. CDHR1 was an independent prognostic factor and negatively associated with EMP3 expression. Glioma patients with low CDHR1 and high EMP3 expression had worse clinical outcomes. At last, we showed that over-expression of CDHR1 could inhibit glioma cell growth and invasion. Conclusion: Low expression of CDHR1 was an independent unfavorable prognostic factor in glioma.

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.

Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors

PURPOSE

This is a radiomics study investigating the ability of texture analysis of MRF maps to improve differentiation between intra-axial adult brain tumors and to predict survival in the glioblastoma cohort.

METHODS

Magnetic resonance fingerprinting (MRF) acquisition was performed on 31 patients across 3 groups: 17 glioblastomas, 6 low-grade gliomas, and 8 metastases. Using regions of interest for the solid tumor and peritumoral white matter on T1 and T2 maps, second-order texture features were calculated from gray-level co-occurrence matrices and gray-level run length matrices. Selected features were compared across the three tumor groups using Wilcoxon rank-sum test. Receiver operating characteristic curve analysis was performed for each feature. Kaplan-Meier method was used for survival analysis with log rank tests.

RESULTS

Low-grade gliomas and glioblastomas had significantly higher run percentage, run entropy, and information measure of correlation 1 on T1 than metastases (p < 0.017). The best separation of all three tumor types was seen utilizing inverse difference normalized and homogeneity values for peritumoral white matter in both T1 and T2 maps (p < 0.017). In solid tumor T2 maps, lower values in entropy and higher values of maximum probability and high-gray run emphasis were associated with longer survival in glioblastoma patients (p < 0.05). Several texture features were associated with longer survival in glioblastoma patients on peritumoral white matter T1 maps (p < 0.05).

CONCLUSION

Texture analysis of MRF-derived maps can improve our ability to differentiate common adult brain tumors by characterizing tumor heterogeneity, and may have a role in predicting outcomes in patients with glioblastoma.

A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomas

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We treat gray-level co-occurrence matrices (GLCM) as two-dimensional functional data objects.

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We develop a Bayesian functional regression method that relates GLCMs to IDH mutation status.

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The method accounts for multiple imaging sequences.

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The method outperforms other competing methods that use only GLCM derived summary statistics.

In cancer radiomics, textural features evaluated from image intensity-derived gray-level co-occurrence matrices (GLCMs) have been studied to evaluate gray-level spatial dependence within the regions of interest in the brain. Most of these analysis work with summary statistics (or texture-based features) constructed using the GLCM entries, and potentially overlook other structural properties in the GLCM. In our proposed Bayesian framework, we treat each GLCM as a realization of a two-dimensional stochastic functional process observed with error at discrete time points. The latent process is then combined with the outcome model to evaluate the prediction performance. We use simulation studies to assess the performance of our method and apply it to data collected from individuals with lower grade gliomas. We found our approach to outperform competing methods that use only summary statistics to predict isocitrate dehydrogenase (IDH) mutation status.

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign

PURPOSE

The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG.

METHODS

Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10^-3mm²/s (V_ADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach.

RESULTS

V_ADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with V_ADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited V_ADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign.

CONCLUSION

Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with V_ADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. V_ADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

Prognostic significance of age related genes in patients with lower grade glioma

Objective: To analyze the prognostic effects of age in different tumor types and determine the prognostic significance of age related genes in patients with lower grade glioma (LGG).

Methods: The relationships between age and tumor overall survival were determined by Kaplan-Meier survival analysis using The Cancer Genome Atlas (TCGA) dataset. The age related genes were identified using TCGA RNA-seq data. Univariate and multivariate cox regression were used to determine the prognostic significance of age related genes. The results derived from TCGA dataset were further validated using Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA) datasets.

Results: Age at initial pathologic diagnosis was most associated with the overall survival of LGG patients than other types of tumor patients. Age related genes EMP3, IGFBP2, TIMP1 and SERPINE1 were highly expressed in old LGG patients. The hypo-methylations of EMP3 and SERPINE1 were contributing to the high expressions of EMP3 and SERPINE1 in old LGG patients. Also, EMP3, IGFBP2, TIMP1 and SERPINE1 were highly expressed in LGG tumor tissues, compared with normal brain tissues. Moreover, high expressions of IGFBP2, EMP3, TIMP1 and SERPINE1 were associated with the worse prognosis of LGG patients. Furthermore, we demonstrated that EMP3 and SERPINE1 were connected with each other and the combination of EMP3 and SERPINE1 had better prognostic effects in glioma patients.

Conclusions: Age related genes IGFBP2, EMP3, TIMP1 and SERPINE1 have significant prognostic effects in LGG patients.

Conventional magnetic resonance imaging-based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas

PURPOSE

Telomerase reverse transcriptase (TERT) promoter mutation status is an important biomarker for the precision diagnosis and prognosis prediction of lower grade glioma (LGG). This study aimed to construct a radiomic signature to noninvasively predict the TERT promoter status in LGGs.

METHODS

Eighty-three local patients with pathology-confirmed LGG were retrospectively included as a training cohort, and 33 patients from The Cancer Imaging Archive (TCIA) were used as for independent validation. Three types of regions of interest (ROIs), which covered the tumor, peri-tumoral area, and tumor plus peri-tumoral area, were delineated on three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted and T2-weighted images. One hundred seven shape, first-order, and texture radiomic features from each modality under each ROI were extracted and selected through least absolute shrinkage and selection operator. Radiomic signatures were constructed with multiple classifiers and evaluated using receiver operating characteristic (ROC) analysis. The tumors were also stratified according to IDH status.

RESULTS

Three radiomic signatures, namely, tumoral radiomic signature, tumoral plus peri-tumoral radiomic signature, and fusion radiomic signature, were built, all of which exhibited good accuracy and balanced sensitivity and specificity. The tumoral signature displayed the best performance, with area under the ROC curves (AUC) of 0.948 (0.903-0.993) in the training cohort and 0.827 (0.667-0.988) in the validation cohort. In the IDH subgroups, the AUCs of the tumoral signature ranged from 0.750 to 0.940.

CONCLUSION

The MRI-based radiomic signature is reliable for noninvasive evaluation of TERT promoter mutations in LGG regardless of the IDH status. The inclusion of peri-tumoral area did not significantly improve the performance.

Engrailed 1 overexpression as a potential prognostic marker in Lower Grade Glioma

Background

Engrailed 1 (EN1), as a member of homeobox-containing transcription factors, participates in the development of the brain. High expressions of EN1 exist in various tumors. However, the role of EN1 in lower grade glioma (LGG) is still unknown.

Methods

Coefficients of Cox regression were examined by data mining among 13 cancer types using OncoLnc to validate EN1 expressions in LGG patients from The Cancer Genome Atlas database (TCGA). Bioinformatic analysis was performed by using R2 and the UCSC Xena browser based on the data from 273 glioma cases in GSE16011 from GEO datasets and 530 cases of LGG patients in TCGA. Cases in GSE16011 were divided into two groups according to IDH1 mutation status. Cases in TCGA-LGG were classified to subtypes according to histopathological results, IDH1 mutation status and 1p19q status. The Kaplan–Meier survival curves were performed to analyze the relationship between EN1 expressions and clinicopathological characteristics and survival time respectively.

Results

Cox regression results showed that LGG was ranked statistically first among 13 different cancer types according to the false discovery rate (FDR) correction. Results from GSE16011 showed that: glioma, LGG and LGG with IDH1 mutation patients with high EN1 expressions had significantly shorter 5, 10, and 15-year overall survival time (OS) (p < 0.001). Similar results from TCGA-LGG showed that LGG patients with high EN1 expressions had significantly shorter 15-year OS, irrespective of IDH1 mutation and 1p19q co-deletion (p < 0.001). The astrocytoma subgroup showed highest levels of EN1 expression and shortest 5, 10 and 15-year OS compared with oligoastrocytoma and oligodendroglioma (p < 0.05).

Conclusion

EN1 can be used as a prognostic marker in LGG patients, combined with IDH1 mutation and 1p19q co-deletion.

Prognostic Role of Chicken Ovalbumin Upstream Promoter Transcription Factor II in Isocitrate Dehydrogenase-Mutant Glioma with 1p19q Co-Deletion

BACKGROUND

Chicken ovalbumin upstream promoter transcription factor II is known to play a crucial role in the tumor microenvironment. However, the role of NR2F2 in gliomas is unknown.

METHODS

The genomic and clinical data of 530 cases of lower grade gliomas (LGGs) patients and 167 cases of glioblastoma (GBM) patients in The Cancer Genome Atlas (TCGA) were extracted for analysis. R2 and UCSC Xena browser were used for Kaplan-Meier survival in the GSE16011 dataset and TCGA dataset, respectively. GraphPad Prism 7 was used to compare the differences in NR2F2 expression between various groups and subtypes.

RESULTS

LGG patients with low NR2F2 expression had a significantly favorable outcome compared with those with high NR2F2 expression (p < 0.05). By matching histological subtypes and gene expression profiles of LGG patients, grade II glioma group showed lowest levels of NR2F2 expression compared with grade III gliomas and GBM. Patients diagnosed with astrocytoma have highest expression of NR2F2 but lowest OS (p < 0.05). In LGGs, NR2F2 expression was significantly downregulated in patient group with IDH mutation and 1p19q co-deletion (p < 0.05).

CONCLUSION

Our study suggests that NR2F2 can be used as a prognostic marker in LGG patients with IDH mutation and 1p19 co-deletion.

A surgical strategy for lower grade gliomas using intraoperative molecular diagnosis

Lower grade gliomas are both treated and diagnosed via surgical resection. Maximum tumor resection is currently the standard of care; however, this risks the loss of brain function. Glioma can be genetically subdivided into three different types, based on isocitrate dehydrogenase (IDH) mutation status and the presence of 1p/19q codeletion, which have radically different prognoses and responses to adjuvant therapies. Therefore, the means to identify the subtype and evaluate the surrounding tissues during surgery would be advantageous. In this study, we have developed a new surgical strategy for lower grade glioma based on the fourth edition of the World Health Organization Brain Tumor Classification, involving intraoperative molecular diagnosis. High-resolution melting analysis was used to evaluate IDH mutational status, while rapid immunohistochemistry of p53 and alpha-thalassemia/mental retardation syndrome X-linked (ATRX) was used to evaluate the 1p/19q codeletion status, allowing genetic classification during surgery. In addition, intraoperative flow cytometry was used to evaluate the surgical cavity for additional tumor lesions, allowing maximal resection while mitigating the risk of functional losses. This strategy allows the rapid intraoperative diagnosis and mapping of lower grade gliomas, and its clinical use could dramatically improve its prognosis.

Modulation of transcriptional activity in brain lower grade glioma by alternative splicing

Proteins that modify the activity of transcription factors (TFs) are often called modulators and play a vital role in gene transcriptional regulation. Alternative splicing is a critical step of gene processing, and differentially spliced isoforms may have different functions. Alternative splicing can modulate gene function by adding or removing certain protein domains and thereby influence the activity of a protein. The objective of this study is to investigate the role of alternative splicing in modulating the transcriptional regulation in brain lower grade glioma (LGG), especially transcription factor ELK1, which is closely related to various disorders, including Alzheimer’s disease and Down syndrome. The results showed that changes in the exon inclusion ratio of proteins APP and STK16 are associated with changes in the expression correlation between ELK1 and its targets. In addition, the structural features of the two modulators are strongly associated with the pathological impact of exon inclusion. The results of our analysis suggest that alternatively spliced proteins have different functions in modifying transcription factors and can thereby induce the dysregulation of multiple genes.

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.