Management for Different Glioma Subtypes: Are All Low-Grade Gliomas Created Equal?

Identification and validation of a novel Parkinson-Glioma feature gene signature in glioma and Parkinson's disease

Introduction

The prognosis for glioma is generally poor, and the 5-year survival rate for patients with this disease has not shown significant improvement over the past few decades. Parkinson's disease (PD) is a prevalent movement disorder, ranking as the second most common neurodegenerative disease after Alzheimer's disease. Although Parkinson's disease and glioma are distinct diseases, they may share certain underlying biological pathways that contribute to their development.

Objective

This study aims to investigate the involvement of genes associated with Parkinson's disease in the development and prognosis of glioma.

Methods

We obtained datasets from the TCGA, CGGA, and GEO databases, which included RNA sequencing data and clinical information of glioma and Parkinson's patients. Eight machine learning algorithms were used to identify Parkinson-Glioma feature genes (PGFGs). PGFGs associated with glioma prognosis were identified through univariate Cox analysis. A risk signature was constructed based on PGFGs using Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) method. We subsequently validated its predictive ability using various methods, including ROC curves, calibration curves, KM survival analysis, C-index, DCA, independent prognostic analysis, and stratified analysis. To validate the reproducibility of the results, similar work was performed on three external test datasets. Additionally, a meta-analysis was employed to observe the heterogeneity and consistency of the signature across different datasets. We also compared the differences in genomic variations, functional enrichment, immune infiltration, and drug sensitivity analysis based on risk scores. This exploration aimed to uncover potential mechanisms of glioma occurrence and prognosis.

Results

We identified 30 PGFGs, of which 25 were found to be significantly associated with glioma survival. The prognostic signature, consisting of 19 genes, demonstrated excellent predictive performance for 1-, 2-, and 3-year overall survival (OS) of glioma. The signature emerged as an independent prognostic factor for glioma overall survival (OS), surpassing the predictive performance of traditional clinical variables. Notably, we observed differences in the tumor microenvironment (TME), levels of immune cell infiltration, immune gene expression, and drug resistance analysis among distinct risk groups. These findings may have significant implications for the clinical treatment of glioma patients.

Conclusion

The expression of genes related to Parkinson's disease is closely associated with the immune status and prognosis of glioma patients, potentially regulating glioma pathogenesis through multiple mechanisms. The interaction between genes associated with Parkinson's disease and the immune system during glioma development provides novel insights into the molecular mechanisms and targeted therapies for glioma.

A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma

Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathologists. Here we sought to develop and validate a deep learning model capable of precisely distinguishing PCNSL from non-PCNSL lesions, especially glioma, using hematoxylin and eosin (H&E)-stained frozen whole-slide images. Also, we compared its performance against pathologists of varying expertise. Additionally, a human-machine fusion approach integrated both model and pathologic diagnostics. In external cohorts, LGNet achieved AUROCs of 0.965 and 0.972 in distinguishing PCNSL from glioma and AUROCs of 0.981 and 0.993 in differentiating PCNSL from non-PCNSL lesions. Outperforming several pathologists, LGNet significantly improved diagnostic performance, further augmented to some extent by fusion approach. LGNet's proficiency in frozen section analysis and its synergy with pathologists indicate its valuable role in intraoperative diagnosis, particularly in discriminating PCNSL from glioma, alongside other lesions.

Long Non-coding RNA Prader Willi/Angelman Region RNA 6 Suppresses Glioma Development by Modulating MicroRNA-106a-5p

As one of the most frequent intracranial tumors, glioma showed invasive development and poor prognosis. lncRNAs have been illustrated to serve as biomarkers in various cancers. Whether the long non-coding RNA Prader Willi/Angelman region RNA 6 (PWAR6) was involved in glioma development and the underlying mechanism was investigated. PWAR6 in glioma was evaluated by polymerase chain reaction and its clinical significance was assessed with a series of statistical analyses. The biological function of PWAR6 was investigated with the cell counting kit 8 and Transwell assay. The potential underlying mechanism was studied with the luciferase reporter assay. The significant downregulation of PWAR6 was observed in glioma, which showed a close relationship with the major clinicopathological features and poor prognosis of patients. PWAR6 restrained cell growth, migration and invasion of glioma, which was alleviated by the overexpression of microRNA-106a-5p (miR-106a-5p). PWAR6 functioned as a prognostic biomarker and tumor suppressor of glioma through regulating miR-106a-5p.

Proteomic landscape of primary and metastatic brain tumors for heterogeneity discovery

PURPOSE

Despite recent advancements in our understanding of driver gene mutations and heterogeneity within brain tumors, whether primary or metastatic (also known as secondary), our comprehension of proteomic changes remains inadequate. The aim of this study is to provide an informative source for brain tumor researches, and distinguish primary brain tumors and secondary brain tumors from extracranial origins based on proteomic analysis.

EXPERIMENTAL DESIGN

We assembled the most frequent brain tumors as follows: gliomas from WHO grade 2 to 4, with IDH1 mutations and wildtypes; brain metastases (BrMs) originating from lung cancer (LC), breast cancer (BC), ovarian cancer (OC), and colorectal cancer (CC). A total of 29 tissue samples were analyzed by label free quantitative mass spectrometry-based proteomics.

RESULTS

In total, 8165 protein groups were quantified, of which 4383 proteins were filtered at 50% valid intensity values for downstream analysis. Proteomic analysis of BrMs reveals conserved features shared among multiple origins. While proteomic heterogeneities were found for discriminating different grades of gliomas, as well as IDH1 mutant and wildtype gliomas. In addition, notable distinctions were observed at the pathway level between BrMs and gliomas. Specifically, BrMs exhibited characteristic pathways focused on proliferation and immunomodulation after colonizing the brain, whereas gliomas primarily engaged in invasion processes.

CONCLUSIONS AND CLINICAL RELEVANCE

We characterized an extensive proteomic landscape of BrMs and gliomas. These findings have promising implications for the development of targeted therapies for BrMs and gliomas.

The CD2-CD58 axis: A novel marker predicting poor prognosis in patients with low-grade gliomas and potential therapeutic approaches

INTRODUCTION

Low-grade gliomas (LGGs) are currently considered a premalignant condition for high-grade gliomas (HGGs) and are characterized by a relatively intact immune system. Immunotherapeutic modalities may offer a safe and effective treatment option for these patients. However, the CD2-CD58 axis, an important component of the immunological synapse, remains unknown in LGG.

METHODS

RNA-seq data from TCGA databases were analyzed. Immune cell infiltration was determined using a single-sample gene set enrichment analysis (ssGSEA) based on integrated immune gene sets from published studies. Kaplan-Meier survival analysis, univariate and multivariate logistic analysis, and the ESTIMATE algorithm were employed to evaluate the impact of the CD2-CD58 axis on adult LGG patients.

RESULTS

The expression of the CD2-CD58 axis was found to be elevated with increasing of WHO grade (p < .05). Uni- and multi-variable logistic analysis demonstrated that age, WHO grade, and CD58 levels were associated with poor prognosis in LGG patients with (p < .01). MetaSape pathways analysis revealed the involvement of CD58 in regulating T cell activation, leukocyte-mediated immunity, and the positive regulation of cell activation in WHO grade II and III. CD58 expression correlated with infiltrations of CD4+ lymphocytes, NK cells, and macrophages cells. The ESTIMATE algorithm indicated that patients with high CD58 expression had significantly higher immune scores compared with low CD58 expression in WHO grade II/III, but no statistical difference was observed in WHO grade IV (p < .05). Furthermore, correlation analysis demonstrated the significant association between CD58 and CD274 (r = 0.581, p < .001), HAVCR2 (r = 0.58i7, p < .001), and LGALS9 (r = 0.566, p < .001). Immunohistochemical staining further confirmed the relationship of CD58, HAVCR2, WHO grade, and prognosis in grade II and III patients.

CONCLUSION

Overall, our findings highlight the significant association between the CD2-CD58 axis and poor survival in LGG patients. High CD58 expression is implicated in T cell-mediated immune responses as an immunosuppressive factor and affect inhibitory immune checkpoint genes.

The evaluation of six genes combined value in glioma diagnosis and prognosis

PURPOSE

Glioma is the most common and fatal type of brain tumour. Owing to its aggressiveness and lethality, early diagnosis and prediction of patient survival are very important. This study aimed to identify key genes and biomarkers for glioma that can guide clinicians in making rapid diagnosis and prognostication.

METHODS

Data mining of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data, and Genotype-Tissue Expression Project brain expression data revealed significantly differentially expressed genes (DEGs), and the risk scores of individual patients were calculated. WGCNA was utilized to screen for genes most related to clinical diagnosis. Prognostic genes associated with glioma were selected via combining the LASSO regression with univariate and multivariate Cox regression and protein-protein interaction network analyses. Then, a nomogram was constructed. And CGGA dataset was utilized to validated. The protein expression levels of the signature were detected using the human protein atlas. Drug response prediction was carried out using the package "pRRophetic".

RESULTS

A six-gene signature (KLF6, CHI3L1, SERPINE1, ANGPT2, TGFBR1, and PTX3) was identified and used to stratify patients into low- and high-risk groups. Survival, ROC curve, and Cox analyses clarified that the six hub genes were a favourable independent prognostic factor for patients with glioma. A nomogram was set up by integrating clinical parameters with risk signatures, showing high precision for predicting 2-, 3-, 4-, 5-years survival. In addition, the expression of most genes was consistent with protein expression. Furthermore, the sensitivity to the top ten drugs in the GDSC database of the high-risk group was significantly higher than the low-risk group.

CONCLUSION

Based on genetic profiles and clinicopathological features, including age, grade, isocitrate dehydrogenase mutation status, we constructed a comprehensive prognostic model for patients with glioma. These signatures can be regarded as biomarkers to predict the prognosis of gliomas, possibly providing more therapeutic strategies for future clinical research.

Novel immune checkpoint-related gene model to predict prognosis and treatment responsiveness in low-grade gliomas

Recently, studies have shown that immune checkpoint-related genes (ICGs) are instrumental in maintaining immune homeostasis and can be regarded as potential therapeutic targets. However, the prognostic applications of ICGs require further elucidation in low-grade glioma (LGG) cases. In the present study, a unique prognostic gene signature in LGG has been identified and validated as well based on ICGs as a means of facilitating clinical decision-making. The RNA-seq data as well as corresponding clinical data of LGG samples have been retrieved utilizing the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ICG-defined non-negative matrix factorization (NMF) clustering was performed to categorize patients with LGG into two molecular subtypes with different prognoses, clinical traits, and immune microenvironments. In the TCGA database, a signature integrating 8 genes has been developed utilizing the LASSO Cox method and validated in the GEO database. The signature developed is superior to other well-recognized signatures in terms of predicting the survival probability of patients with LGG. This 8-gene signature was then subsequently applied to categorize patients into high- and low-risk groups, and differences between them in terms of gene alteration frequency were observed. There were remarkable variations in IDH1 (91% and 64%) across low-as well as high-risk groups. Additionally, various analyses like function enrichment, tumor immune microenvironment, and chemotherapy drug sensitivity revealed significant variations across high- and low-risk populations. Overall, this 8-gene signature may function as a useful tool for prognosis and immunotherapy outcome predictions among LGG patients.

A 10-Year Survival-Trend Analysis of Low-Grade Glioma and Treatment Patterns from an LMIC

Objectives  The 2021 WHO Classification of Central Nervous System Tumors taxonomy laid further stress on molecular classification and prognostication of glial tumors in comparison to histopathological grading. Research shows that low-grade gliomas (LGGs) can go through malignant differentiation and lead to severe disability and death. Data from various populations will be necessary to ascertain the exact interplay between genotypic predictors of LGG and outcomes. Materials and Methods  To assess the molecular pathology for glial tumors in the Pakistani population, the Shaukat Khanum Memorial Cancer Hospital carried out a retrospective chart review of electronic health records from 2008 to 2018, with immunohistochemistry analysis findings from 2010 to 2018. Patients with a pathological diagnosis of a glioma were included. Statistical Analysis  Analysis was performed using IBM SPSS Statistics Version 23 and STATA Version 16. A p -value of less than 0.05 was considered statistically significant with 95% confidence intervals reported. Results  In all, 281 operable tumors were recorded. The most common procedure was a subtotal resection, and astrocytomas (64.77%) were the most common tumors. Radiation therapy and PCV (procarbazine, CCNU, and vincristine) was received by 85 patients, while radiation therapy and temozolomide were administered to 15 patients. Conclusions  Isocitrate dehydrogenase (IDH) wild-type LGG had a lower survival time, while improved survival times were seen for alpha-thalassemia X-linked intellectual disability syndrome (ATRX) retained and 1p19q co-deleted LGGs. Further studies are required to gain a better understanding of lower-grade glial tumor treatment and survival in Pakistan.

COP1 facilitates the proliferation, invasion, and migration of glioma cells by ubiquitination of DLG3 protein

OBJECTIVE

Glioma is a heterogeneous group of brain tumors that remains largely incurable. Constitutive photomorphogenic 1 (COP1) acts as an E3 ligase for tumor regulation. This study explored the mechanism of COP1 in glioma cell proliferation, invasion, and migration.

METHODS

COP1 and discs large homolog 3 (DLG3) expressions in glioma cells were determined using RT-qPCR or Western blotting, followed by transfection of si-COP1 or si-DLG3 into LN229 cells. Glioma cell proliferation, invasion, and migration were measured using CCK-8, EdU staining, and Transwell assays. The binding of COP1 and DLG3 was verified using co-immunoprecipitation. The ubiquitination level of DLG3 protein was tested after MG132 treatment. Functional rescue experiments were performed to validate the role of DLG3 in the regulation of glioma cells by COP1.

RESULTS

COP1 was highly expressed in glioma cells. COP1 silencing repressed glioma cell proliferation, invasion, and migration. COP1 bound to DLG3 protein and enhanced the ubiquitination of DLG3. DLG3 silencing reversed the inhibitory effect of COP1 silencing on glioma cell proliferation, invasion, and migration.

CONCLUSION

COP1 facilitated the proliferation, invasion, and migration of glioma cells by ubiquitination of DLG3 protein.

Multiparametric MRI radiomics for the differentiation of brain glial cell hyperplasia from low-grade glioma

BACKGROUND

Differentiating between low-grade glioma and brain glial cell hyperplasia is crucial for the customized clinical treatment of patients.

OBJECTIVE

Based on multiparametric MRI imaging and clinical risk factors, a radiomics-clinical model and nomogram were constructed for the distinction of brain glial cell hyperplasia from low-grade glioma.

METHODS

Patients with brain glial cell hyperplasia and low-grade glioma who underwent surgery at the First Affiliated Hospital of Soochow University from March 2016 to March 2022 were retrospectively included. In this study, A total of 41 patients of brain glial cell hyperplasia and 87 patients of low-grade glioma were divided into training group and validation group randomly at a ratio of 7:3. Radiomics features were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1-enhanced). Then, LASSO, SVM, and RF models were created in order to choose a model with a greater level of efficiency for calculating each patient's Rad-score (radiomics score). The independent risk factors were identified via univariate and multivariate logistic regression analysis to filter the Rad-score and clinical risk variables in turn. A radiomics-clinical model was next built of which effectiveness was assessed.

RESULTS

Brain glial cell hyperplasia and low-grade gliomas from the 128 cases were randomly divided into 10 groups, of which 7 served as training group and 3 as validation group. The mass effect and Rad-score were two independent risk variables used in the construction of the radiomics-clinical model, and their respective AUCs for the training group and validation group were 0.847 and 0.858. The diagnostic accuracy, sensitivity, and specificity of the validation group were 0.821, 0.750, and 0.852 respectively.

CONCLUSION

Combining with radiomics constructed by multiparametric MRI images and clinical features, the radiomics-clinical model and nomogram that were developed to distinguish between brain glial cell hyperplasia and low-grade glioma had a good performance.

Interactive Effects of Molecular, Therapeutic, and Patient Factors on Outcome of Diffuse Low-Grade Glioma

PURPOSE

In patients with diffuse low-grade glioma (LGG), the extent of surgical tumor resection (EOR) has a controversial role, in part because a randomized clinical trial with different levels of EOR is not feasible.

METHODS

In a 20-year retrospective cohort of 392 patients with IDH-mutant grade 2 glioma, we analyzed the combined effects of volumetric EOR and molecular and clinical factors on overall survival (OS) and progression-free survival by recursive partitioning analysis. The OS results were validated in two external cohorts (n = 365). Propensity score analysis of the combined cohorts (n = 757) was used to mimic a randomized clinical trial with varying levels of EOR.

RESULTS

Recursive partitioning analysis identified three survival risk groups. Median OS was shortest in two subsets of patients with astrocytoma: those with postoperative tumor volume (TV) > 4.6 mL and those with preoperative TV > 43.1 mL and postoperative TV ≤ 4.6 mL. Intermediate OS was seen in patients with astrocytoma who had chemotherapy with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL in addition to oligodendroglioma patients with either preoperative TV > 43.1 mL and residual TV ≤ 4.6 mL or postoperative residual volume > 4.6 mL. Longest OS was seen in astrocytoma patients with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL who received no chemotherapy and oligodendroglioma patients with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL. EOR ≥ 75% improved survival outcomes, as shown by propensity score analysis.

CONCLUSION

Across both subtypes of LGG, EOR beginning at 75% improves OS while beginning at 80% improves progression-free survival. Nonetheless, maximal resection with preservation of neurological function remains the treatment goal. Our findings have implications for surgical strategies for LGGs, particularly oligodendroglioma.

Prognostic significance of TMEM131L in glioma and establishment of oxidative stress prognostic model

Gliomas are the most aggressive of all brain tumors. In this study, it was found that there is a significant expression of transmembrane-like 131 (TMEM131L) in glioma tissues. The relevance of TMEM131L in the diagnosis and clinical prognosis of GBM and LGG was verified by additional clinical correlation and survival analysis. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve reflected the diagnostic effect of TMEM131L on the clinicopathologic features of glioma. As a unique molecular marker for the poor prognosis of overall survival (OS), PFI, and DSS in patients with GCB and LGG, TMEM131L might be employed, according to time-dependent ROC curves and Kaplan–Meier survival analysis at 1, 3, and 5 years. The potential methylation sites of TMEM131L were selected by correlation analysis between TMEM131L and DNA methylation sites. Meanwhile, TMEM131L was significantly correlated with matrix, immunity, and estimated scores of GBM and LGG. The CIBERSORT analysis revealed a significant correlation between immune checkpoint and infiltration of 22 different kinds of immune cells. Coexpression genes of TMEM131L associated with oxidative stress phenotype were screened by the LASSO logistic regression analysis. Nomogram and calibration curves further confirmed that the prognostic model composed of SYT1, CREB3L3, ITPR1, RASGRF2, PDX1, and RASGRF1 has good stability and potential application value for poor prognosis in patients with glioma.

Benign Glioma

Benign glioma broadly refers to a heterogeneous group of slow-growing glial tumors with low proliferative rates and a more indolent clinical course. These tumors may also be described as "low-grade" glioma (LGG) and are classified as WHO grade I or II lesions according to the Classification of Tumors of the Central Nervous System (CNS) (Louis et al. in Acta Neuropathol 114:97-109, 2007). Advances in molecular genetics have improved understanding of glioma tumorigenesis, leading to the identification of common mutation profiles with significant treatment and prognostic implications. The most recent WHO 2016 classification system has introduced several notable changes in the way that gliomas are diagnosed, with a new emphasis on molecular features as key factors in differentiation (Wesseling and Capper in Neuropathol Appl Neurobiol 44:139-150, 2018). Benign gliomas have a predilection for younger patients and are among the most frequently diagnosed tumors in children and young adults (Ostrom et al. in Neuro Oncol 22:iv1-iv96, 2020). These tumors can be separated into two clinically distinct subgroups. The first group is of focal, well-circumscribed lesions that notably are not associated with an increased risk of malignant transformation. Primarily diagnosed in pediatric patients, these WHO grade I tumors may be cured with surgical resection alone (Sturm et al. in J Clin Oncol 35:2370-2377, 2017). Recurrence rates are low, and the prognosis for these patients is excellent (Ostrom et al. in Neuro Oncol 22:iv1-iv96, 2020). Diffuse gliomas are WHO grade II lesions with a more infiltrative pattern of growth and high propensity for recurrence. These tumors are primarily diagnosed in young adult patients, and classically present with seizures (Pallud et al. Brain 137:449-462, 2014). The term "benign" is a misnomer in many cases, as the natural history of these tumors is with malignant transformation and recurrence as grade III or grade IV tumors (Jooma et al. in J Neurosurg 14:356-363, 2019). For all LGG, surgery with maximal safe resection is the treatment of choice for both primary and recurrent tumors. The goal of surgery should be for gross total resection (GTR), as complete tumor removal is associated with higher rates of tumor control and seizure freedom. Chemotherapy and radiation therapy (RT), while not typically a component of first-line treatment in most cases, may be employed as adjunctive therapy in high-risk or recurrent tumors and in some select cases. The prognosis of benign gliomas varies widely; non-infiltrative tumor subtypes generally have an excellent prognosis, while diffusely infiltrative tumors, although slow-growing, are eventually fatal (Sturm et al. in J Clin Oncol 35:2370-2377, 2017). This chapter reviews the shared and unique individual features of the benign glioma including diffuse glioma, pilocytic astrocytoma and pilomyxoid astrocytoma (PMA), subependymal giant cell astrocytoma (SEGA), pleomorphic xanthoastrocytoma (PXA), subependymoma (SE), angiocentric glioma (AG), and chordoid glioma (CG). Also discussed is ganglioglioma (GG), a mixed neuronal-glial tumor that represents a notable diagnosis in the differential for other LGG (Wesseling and Capper 2018). Ependymomas of the brain and spinal cord, including major histologic subtypes, are discussed in other chapters.

Necroptosis-related lncRNAs: establishment of a gene module and distinction between the cold and hot tumors in glioma

Background

Gliomas are the most common primary tumors of the central nervous system and portend a poor prognosis. The efficacy of emerging and promising immunotherapies varies significantly among individuals. Distinction and transformation of cold and hot tumors may improve the antitumor efficacy of immunotherapy.

Methods and Results

In this study, we constructed a necroptosis-related lncRNA module based on public databases. The association of this module with survival was assessed using the Cox regression, Kaplan-Meier survival analysis, and nomogram, external validation was also conducted in another public database. Furthermore, we performed gene set enrichment analysis (GSEA), immune checkpoint and tumor microenvironment analysis, and in vitro qRT-PCR validation. Finally, we clustered all samples into 2 clusters based on the expression of model lncRNAs and identified cluster 1 as cold tumors with fewer infiltrating T cells.

Conclusions

Identifying cold and hot tumors by necroptosis-related lncRNAs can help available immunotherapeutic strategies to achieve efficacy in the precise treatment of individuals. Prior treatment failure can be overcome by targeting necroptosis-related lncRNAs.

An in silico approach to the identification of diagnostic and prognostic markers in low-grade gliomas

Low-grade gliomas (LGG) are central nervous system Grade I tumors, and as they progress they are becoming one of the deadliest brain tumors. There is still great need for timely and accurate diagnosis and prognosis of LGG. Herein, we aimed to identify diagnostic and prognostic biomarkers associated with LGG, by employing diverse computational approaches. For this purpose, differential gene expression analysis on high-throughput transcriptomics data of LGG versus corresponding healthy brain tissue, derived from TCGA and GTEx, respectively, was performed. Weighted gene co-expression network analysis of the detected differentially expressed genes was carried out in order to identify modules of co-expressed genes significantly correlated with LGG clinical traits. The genes comprising these modules were further used to construct gene co-expression and protein-protein interaction networks. Based on the network analyses, we derived a consensus of eighteen hub genes, namely, CD74, CD86, CDC25A, CYBB, HLA-DMA, ITGB2, KIF11, KIFC1, LAPTM5, LMNB1, MKI67, NCKAP1L, NUSAP1, SLC7A7, TBXAS1, TOP2A, TYROBP, and WDFY4. All detected hub genes were up-regulated in LGG, and were also associated with unfavorable prognosis in LGG patients. The findings of this study could be applicable in the clinical setting for diagnosing and monitoring LGG.

Identification of prognosis-related gene features in low-grade glioma based on ssGSEA

Low-grade gliomas (LGG) are commonly seen in clinical practice, and the prognosis is often poor. Therefore, the determination of immune-related risk scores and immune-related targets for predicting prognoses in patients with LGG is crucial. A single-sample gene set enrichment analysis (ssGSEA) was performed on 22 immune gene sets to calculate immune-based prognostic scores. The prognostic value of the 22 immune cells for predicting overall survival (OS) was assessed using the least absolute shrinkage and selection operator (LASSO) and univariate and multivariate Cox analyses. Subsequently, we constructed a validated effector T-cell risk score (TCRS) to identify the immune subtypes and inflammatory immune features of LGG patients. We divided an LGG patient into a high-risk-score group and a low-risk-score group based on the optimal cutoff value. Kaplan-Meier survival curve showed that patients in the low-risk-score group had higher OS. We then identified the differentially expressed genes (DEGs) between the high-risk-score group and low-risk-score group and obtained 799 upregulated genes and 348 downregulated genes. The analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) show that DEGs were mainly concentrated in immune-related processes. In order to further explore the immune-related genes related to prognosis, we constructed a protein-protein interaction (PPI) network using Cytoscape and then identified the 50 most crucial genes. Subsequently, nine DEGs were found to be significantly associated with OS based on univariate and multivariate Cox analyses. It was further confirmed that CD2, SPN, IL18, PTPRC, GZMA, and TLR7 were independent prognostic factors for LGG through batch survival analysis and a nomogram prediction model. In addition, we used an RT-qPCR assay to validate the bioinformatics results. The results showed that CD2, SPN, IL18, PTPRC, GZMA, and TLR7 were highly expressed in LGG. Our study can provide a reference value for the prediction of prognosis in LGG patients and may help in the clinical development of effective therapeutic agents.

Comprehensive Prognostic Analysis of Immune Implication Value and Oxidative Stress Significance of NECAP2 in Low-Grade Glioma

Adaptin ear-binding coat-associated protein 2 (NECAP2) belongs to the family of proteins encoding adaptin-ear-binding coat-associated proteins. However, its immune effect on tumors and its microenvironment are still unclear. Here, we systematically evaluated the differences (variations) in NECAP2 expression for low-grade glioma (LGG) and pan-cancer in the LGG dataset of The Cancer Genome Atlas (TCGA) utilizing bioinformatics methods. We found for the first time that NECAP2 level was elevated in gliomas and that this upregulation increased as the tumor grade increased. In addition, Pearson correlations of NECAP2 with five immune pathways and significant gene mutations associated with NECAP2 were also analyzed. Univariate survival and multivariate Cox analyses were used to compare the clinical characteristics and survival of the patients. Glioma patients with NECAP2 overexpression have a remarkably higher risk of developing malignant behavior and a worse prognosis. The correlation between the expression levels of NECAP2 and the prognosis of glioma patients was identified. Kaplan-Meier curves showed that patients with upregulated NECAP2 expression exhibited an unfavorable prognosis. Western blotting showed that NECAP2 was overexpressed in glioma patients. IHC staining results illustrated an elevation in the NECAP2 protein expression level with the development of tumor malignancy. Additionally, qRT-PCR verified that oxidative stress in glioma tissues reduced the expression of stress-related genes and oxidative stress capacity compared to normal tissues, which may be associated with tumor evasion of immune surveillance and tumor progression. In vitro wound-healing and Transwell assay confirmed that NECAP2 promotes glioma cell migration and invasion. Our study also thoroughly examined the immune significance of NECAP2 in the TCGA-LGG samples, using CIBERSORT and ESTIMATE to explore the correlation between NECAP2 and cancer immune infiltration. The NECAP2 expression levels were correlated with the infiltration degree of immune cells such as neutrophils, CD4+ T cells, macrophages, CD8+ T cells, and B cells. Therefore, our results indicate that NECAP2 strongly correlates with the overall immune infiltration level of glioma and could independently serve as a prognostic biological marker for glioma patients.

A Novel Insight into Paraptosis-Related Classification and Signature in Lower-Grade Gliomas

Lower-grade gliomas (LGG) are the most common intracranial malignancies that readily evolve to high-grade gliomas and increase drug resistance. Paraptosis is defined as a nonapoptotic form of programmed cell death, which is gradually focused on patients with gliomas to develop treatment options. However, the specific role of paraptosis in LGG and its correlation is still vague. In this study, we first establish the novel paraptosis-based prognostic model for LGG patients. The relevant data of LGG patients were acquired from The Cancer Genome Atlas database, and we found that LGG patients could be divided into three different clusters based on paraptosis via consensus cluster analysis. Through least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis, 10-paraptosis-related gene (PRG) signatures (CDK4, TNK2, DSTYK, CDKN3, CCR4, CASP9, HSPA5, RGR, LPAR1, and PDCD6IP) were identified to separate LGG patients into high- and low-risk subgroups successfully. The Kaplan-Meier analysis and time-dependent receiver-operating characteristic showed that the performances of predicting overall survival (OS) were dramatically high. The parallel results were reappeared and verified by using the Chinese Glioma Genome Atlas and Gene Expression Omnibus databases. Independent prognostic analysis and nomogram construction implied that risk scores could be considered the independent factor to predict OS. Enrichment analysis indicated that immune-related biological processes were generally enriched, and different immune statuses were highly infiltrated in high-risk group. We also confirmed the potential relationship of 10-PRG signatures and drug sensitivity of Food and Drug Administration-approved drugs. In summary, our findings provide a novel knowledge of paraptosis status and crucial direction to further explore the role of PRG signatures in LGG.

Foreboding lncRNA markers of low-grade gliomas dependent on metabolism

At present, there is no systematic study on the signature of long-chain noncoding RNAs (lncRNAs) involved in metabolism that can fully predict the prognosis in patients with low-grade gliomas (LGGs). Therefore, consistent metabolic-related lncRNA signatures need to be established. The Cancer Genome Atlas (TCGA) was used to identify the expression profile of lncRNAs containing 529 LGGs samples. LncRNAs and genes related to metabolism are used to establish a network in the form of coexpression to screen lncRNAs related to metabolism. LncRNA was more clearly described by univariate Cox regression. Moreover, lncRNA signatures were explored by multivariate Cox regression and lasso regression. The risk score was established according to the signature and it was an unattached prognostic marker according to Cox regression analysis. Functional enrichment of lncRNAs was shown by employing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Univariate Cox retrospective analysis showed that 543 metabolism-related lncRNAs were independent prognostic factors of LGG, and multivariate Cox regression analysis confirmed that 19 metabolism-related lncRNAs were prognostic genes of LGG. In the risk model, the low-risk group had a higher Overall survival (OS) than the high-risk group (P < .001). Univariate Cox regression analysis of risk score and clinical factors showed that risk score was an independent prognostic factor (P < .001, HR = 1.047, 95% CI: 1.038-1.056). Multivariate Cox results showed that risk score could predict the prognosis of LGG (P < .001, HR = 1.036, 95% CI: 1.026-1.045). ROC curve analysis showed that risk score could predict the prognosis of LGG. The areas of 1-year, 3-years, and 5 years are 0.891, 0.904 and 0.832. GO and KEGG analysis showed that metabolism-related lncRNAs was mainly concentrated in the pathways related to tumor metabolism. In order to find a more stable and reliable target for the treatment of LGG, we established 19 metabolic-related lncRNAs prognostic model, and determined that it can predict the prognosis of LGG patients. This provides a new solution approach to the poor prognosis of patients with LGG and may reverse the trend of LGG's transformation to high-grade gliomas.

Thioredoxin System and miR-21, miR-23a/b and let-7a as Potential Biomarkers for Brain Tumor Progression: Preliminary Case Data

BACKGROUND

The thioredoxin system and microRNAs (miRNAs) are potential targets for both cancer progression and treatment. However, the role of miRNAs and their relation with the expression profile of thioredoxin system in brain tumor progression remains unclear.

METHODS

In this study, we aimed to determine the expression profiles of redox components Trx-1, TrxR-1 and PRDX-1, and oncogenic miR-21, miR-23a/b and let-7a and oncosuppressor miR-125 in different brain tumor tissues and their association with increasing tumor grade. We studied Trx-1, TrxR-1, and PRDX-1 messenger RNA expression levels by quantitative real-time polymerase chain reaction and protein levels by Western blot and miR-23a, miR-23b, miR-125a, miR-21, and let-7a miRNA expression levels by quantitative real-time polymerase chain reaction in 16 glioma, 15 meningioma, 5 metastatic, and 2 benign tumor samples. We also examined Trx-1, TrxR-1, and PRDX-1 protein levels in serum samples of 36 patients with brain tumor and 37 healthy volunteers by enzyme-linked immunosorbent assay.

RESULTS

We found that Trx-1, TrxR-1, and PRDX-1 presented high messenger RNA expression but low protein expression in low-grade brain tumor tissues, whereas they showed higher protein expression in sera of patients with low-grade brain tumors. miR-23b, miR-21, miR-23a, and let-7a were highly expressed in low-grade brain tumor tissues and positively correlated with the increase in thioredoxin system activity.

CONCLUSIONS

Our findings showed that Trx-1, TrxR-1, miR-21, miR-23a/b, and let-7a might be used for brain tumor diagnosis in the clinic. Further prospective studies including molecular pathway analyses are required to validate the miRNA/Trx system regulatory axis in brain tumor progression.

Mechanism of RIP2 enhancing stemness of glioma cells induces temozolomide resistance

AIMS

We aimed to investigate the role of receptor-interacting protein 2 (RIP2) in regulation of stemness of glioma cells and chemotherapy resistance.

METHODS

Plasmid transfection was used to overexpress RIP2. Chemical inhibitors were used to inhibit RIP2 or NF-κB activity. Cancer stemness of glioma cells was investigated by sphere formation assays, clone formation assays, and xenograft tumor formation assays. The expression of RIP2, p-NF-κB, IκBα, CD133, or SOX-2 was detected by Western blotting and immunofluorescence. Apoptosis was detected by flow cytometry. Immunohistochemical staining was used to detect the expression of RIP2, CD133, and SOX-2 in xenograft tumor tissue. The effect of the RIP2/NF-κB pathway on temozolomide (TMZ) resistance was evaluated by xenograft tumor assay.

RESULTS

Transfection with RIP2 plasmid enhanced the sphere formation capability of U251 cells, clone formation capability, and xenograft tumor formation capability. RIP2 could mediate TMZ resistance by upregulating the expression of CD133 and SOX-2 by activating the NF-κB pathway. Both RIP2 inhibitor GSK583 and the NF-κB inhibitor SC75741 could reverse the resistance of U251 cells to TMZ.

CONCLUSION

RIP2 mediates TMZ resistance by regulating the maintenance of stemness in glioma cells through NF-κB. Interventions targeting the RIP2/NF-κB pathway may be a new strategy for TMZ-resistant gliomas.

Development and Validation of a Liquid-Liquid Phase Separation-Related Gene Signature as Prognostic Biomarker for Low-Grade Gliomas

Aim

To explore whether the liquid-liquid phase separation- (LLPS-) related genes were potential prognostic markers that could contribute to the further classification of low-grade gliomas (LGGs).

Methods

The LLPS-related genes were subjected to functional enrichment analysis. The univariable, least absolute shrinkage and selection operator, and multivariable stepwise Cox regression analyses were performed to develop an LLPS-related gene signature (GS) in the discovery data set. The biological characteristics of the high-risk LGG were explored using gene set enrichment analysis. Two independent external data sets were used to validate the LLPS-related GS.

Results

LLPS-related genes are involved in multiple important cancer-related biological processes and pathways in LGG. Nine LLPS-related genes were identified to construct the LLPS-related GS, which was significantly associated with the prognosis of LGG patients. The LLPS-related GS could successfully divide patients with LGG into high- and low-risk groups, and the high-risk group showed a poorer prognosis than the low-risk group. Furthermore, the LLPS-related GS was independent of IDH and 1p19q status. Several cancer-related pathways may be more active in high-risk LGGs, such as IL6 JAK STAT3 signaling pathway. The LLPS-related GS was successfully validated with two independent external data sets.

Conclusion

We developed and validated a novel LLPS-related GS for risk stratification of LGG. Our findings may provide more precise management for LGGs and a useful reference for LLPS mechanism to link LGG studies.

The expression of Hexokinase 2 and its hub genes are correlated with the prognosis in glioma

BACKGROUND

Hexokinase 2 (HK2) is an enzyme that catalyses the conversion of glucose to glucose-6-phosphate, which has been found to be associated with malignant tumour growth. However, the potential immunological and clinical significance of HK2, especially in terms of prognostic prediction for patients with glioma, has not been fully elucidated.

METHODS

To investigate the expression, immunological and clinical significance of HK2 in patients with glioma, several databases, including ONCOMINE, TIMER2.0, GEPIA, CGGA, UCSC, LinkedOmics, Metascape, STRING, GSCA, and TISIDB, as well as biochemical, cellular, and pathological analyses, were used in this study. In addition, we performed univariate, multivariate Cox regression and nomogram analyses of the hub genes positively and negatively correlated with HK2 to explore the potential regulatory mechanism in the initiation and development of glioma.

RESULTS

Our results demonstrated that HK2 was highly expressed in most malignant cancers. HK2 expression was significantly higher in lower grade glioma (LGG) and glioblastoma (GBM) than in adjacent normal tissue. In addition, HK2 expression was significantly correlated with clinical parameters, histological manifestations, and prognosis in glioma patients. Specifically, the data from The Cancer Genome Atlas downloaded from UCSC Xena database analysis showed that high expression of HK2 was strongly associated with poor prognosis in glioma patients. The LinkedOmics database indicated that HK2-related genes were mainly enriched in immune-related cells. In LGG and GBM tissues, HK2 expression is usually correlated with recognized immune checkpoints and the abundance of multiple immune infiltrates. Similarly, the Metascape database revealed that HK2-related genes were mainly enriched and annotated in immune-related pathways and immune cells. Further investigations also confirmed that the inhibition of HK2 expression remarkably suppressed metastasis and vasculogenic mimicry (VM) formation in glioma cells through regulating the gene expression of inflammatory and immune modulators.

CONCLUSION

HK2 expression was closely associated with the malignant properties of glioma through activating multiple immune-related signalling pathways to regulate immune responses and the infiltration of immune cells. Thus, HK2 and its hub genes may be a potential target for the treatment of glioma.

Transcriptome Analysis and Single-Cell Sequencing Analysis Constructed the Ubiquitination-Related Signature in Glioma and Identified USP4 as a Novel Biomarker

Background

Glioma, the most frequent malignant tumor of the neurological system, has a poor prognosis and treatment problems. Glioma’s tumor microenvironment is also little known.

Methods

We downloaded glioma data from the TCGA database. The patients in the TCGA database were split into two groups, one for training and the other for validation. The ubiquitination genes were then evaluated in glioma using COX and Lasso regression to create a ubiquitination-related signature. We assessed the signature’s predictive usefulness and role in the immune microenvironment after it was generated. Finally, in vitro experiment were utilized to check the expression and function of the signature’s key gene, USP4.

Results

This signature can be used to categorize glioma patients. Glioma patients can be separated into high-risk and low-risk groups in both the training and validation cohorts, with the high-risk group having a significantly worse prognosis (P<0.05). Following further investigation of the immune microenvironment, it was discovered that this risk grouping could serve as a guide for glioma immunotherapy. The activity, invasion and migration capacity, and colony formation ability of U87-MG and LN229 cell lines were drastically reduced after the important gene USP4 in signature was knocked down in cell tests. Overexpression of USP4 in the A172 cell line, on the other hand, greatly improved clonogenesis, activity, invasion and migration.

Conclusions

Our research established a foundation for understanding the role of ubiquitination genes in gliomas and identified USP4 as a possible glioma biomarker.

Deep Convolutional Neural Network-Based Brain Magnetic Resonance Imaging Applied in Glioma Diagnosis and Tumor Region Identification

The aim of this study was to explore the application value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on a convolutional neural network (CNN) algorithm in glioma diagnosis and tumor segmentation. 66 patients with gliomas who were diagnosed and treated in the hospital were selected as the research objects. The patients were rolled into the high-grade glioma group (HGG, 46 cases) and the low-grade glioma group (LGG, 20 cases) according to the World Health Organization glioma grading standard. All patients received a conventional plain scan and a DCE-MRI. Parameters such as volume transfer constant (Ktrans), rate constant (Kep), extracellular volume (Ve), and mean plasma volume (Vp) were calculated, and the parameters of patients of each grade were analyzed. The efficacy of each parameter in diagnosing glioma was analyzed through a receiver operating characteristic curve. All images were segmented by the CNN algorithm. The CNN algorithm showed good performance in DCE-MRI image segmentation. The mean, standard deviation, kurtosis, and skewness of Ktrans and Ve, the standard deviation and skewness of Kep, and the mean and standard deviation of Vp were statistically considerable in differentiating HGG and LGG $\left(P<0.05\right)$ . ROC analysis showed that the standard deviation of Ktrans (0.885) had the highest diagnostic accuracy in distinguishing HGG and LGG. The values of Ktrans, Ve, and Vp were positively correlated with Ki-67 (r = 0.346, P = 0.014; r = 0.335, P = 0.017; r = 0.323, P = 0.022). In summary, the CNN-based DCE-MRI technology had high application value in glioma diagnosis and tumor segmentation.

Construction of a Risk Model to Predict the Prognosis and Immunotherapy of Low-Grade Glioma Ground on 7 Ferroptosis-Related Genes

Purpose

Ferroptosis is closely associated with tumors. The purpose of this study was to investigate the correlation between ferroptosis and prognosis of low grade glioma (LGG) via construction and verification of a risk model.

Patients and Methods

The data of LGG were downloaded from public databases. Through LASSO analysis of characteristic genes, a gene signature was constructed. Patients into were divided two groups based on risk score. Subsequently, survival, clinical phenotype, functional enrichment, immune cell infiltration and somatic mutation analysis were performed. In addition, whether ferroptosis-related genes (FRGs) signature can predict the patient's response to anti-PD-1/PD-L1 immunotherapy was also investigated.

Results

FRGs signature had strong prognostic assessment ability, and high risk score was associated with poor overall survival (OS) of LGG. The high risk score group had higher degree of immune cell infiltration, stronger stromal activity, higher immune score, and high expression of immune checkpoint. In low risk score group anti-PD-1/PD-L1 immunotherapy has significant therapeutic advantages and clinical response. Genes and frequency of somatic mutations and clinical phenotypes in the high and low risk score groups were significantly different.

Conclusion

A prognostic model based on 7 FRGs can be used to predict the prognosis and immunotherapeutic response of LGG.

Exploration of CT Images Based on the BN-U-net-W Network Segmentation Algorithm in Glioma Surgery

This study aimed to explore the application value of computed tomography (CT) imaging features based on the deep learning batch normalization (batch normalization, BN) U-net-W network image segmentation algorithm in evaluating and diagnosing glioma surgery. 72 patients with glioma who were admitted to hospital were selected as the research subjects. They were divided into a low-grade group (grades I-II, N = 27 cases) and high-grade group (grades III-IV, N = 45 cases) according to postoperative pathological examination results. The CT perfusion imaging (CTPI) images of patients were processed by using the deep learning-based BN-U-net-W network image segmentation algorithm. The application value of the algorithm was comprehensively evaluated by comparing the average Dice coefficient, average recall rate, and average precision of the BN-U-net-W network image segmentation algorithm with the U-net and BN-U-net network algorithms. The results showed that the Dice coefficient, recall, and precision of the BN-U-net-W network were 86.31%, 88.43%, and 87.63% respectively, which were higher than those of the U-net and BN-U-net networks, and the differences were statistically significant (P < 0.05). Cerebral blood flow (CBF), cerebral blood volume (CBV), and capillary permeability (PMB) in the glioma area were 56.85 mL/(min·100 g), 18.03 mL/(min·100 g), and 8.57 mL/100 g, respectively, which were significantly higher than those of normal brain tissue, showing statistically significant differences (P < 0.05). The mean transit time (MTT) difference between the two was not statistically significant (P > 0.05). The receiver operating characteristic (ROC) curves of CBF, CBV, and PMB in CTPI parameters of glioma had area under the curve (AUC) of 0.685, 0.724, and 0.921, respectively. PMB parameters were significantly higher than those of CBF and CVB, and the differences were statistically obvious (P < 0.05). It showed that the BN-U-net-W network model had a better image segmentation effect, and CBF, CBV, and PMB showed better sensitivity in diagnosing glioma tissue and normal brain tissue and high-grade and low-grade gliomas, among which PBM showed the highest predictability.

RFC2: a prognosis biomarker correlated with the immune signature in diffuse lower-grade gliomas

Diffuse lower-grade gliomas (LGG) represent the highly heterogeneous and infiltrative neoplasms in the central nervous system (CNS). Replication factor C 2 (RFC2) is a subunit of the RFC complex that modulates DNA replication and repair. However, the prognosis value of RFC2 and its association with the immune signature of tumor microenvironment (TME) in LGG remains unknown. Based on Oncomine, TCGA, GTEx, TIMER, GEPIA, and HPA databases, we evaluated RFC2 expression levels and its clinical prognostic value in LGG and other cancers. Then we analyzed the correlations between RFC2 expression and tumor mutation burden (TMB), tumor microsatellite instability (MSI), and mismatch repair (MMR) genes across cancers. And CIBERSORT and ESTIMATE algorithms were conducted to estimate the association of RFC2 with immune cell infiltration of LGG. Additionally, we performed the functional enrichment analyses of RFC2 in LGG. Then functional experiments were employed to further validate the oncogenic role of RFC2 in LGG. Our results showed that RFC2 was widely highly expressed in most types of cancer. And its expression was closely related to the clinicopathological features and prognosis in LGG and other cancer types. RFC2 levels were also correlated with TMB and MSI across various cancers. Furthermore, RFC2 was positively associated with the infiltration levels of immune cells and immune checkpoint genes in LGG. Additionally, in vitro experiments revealed that RFC2 played an oncogenic role in LGG progression. In conclusion, our findings revealed that RFC2 could serve as a reliable biomarker to predict the prognosis and immune signature for LGG.

Procollagen-Lysine, 2-Oxoglutarate 5-Dioxygenase Family: Novel Prognostic Biomarkers and Tumor Microenvironment Regulators for Lower-Grade Glioma

Lower-grade glioma (LGG) is a group of tumors arising from the cells of the central nervous system. Although various therapy interventions are used, the prognosis remains different. Novel biomarkers are needed for the prognosis of disease and novel therapeutic strategies in LGG. The procollagen-lysine, 2-oxoglutarate 5-dioxygenase (PLOD) family contains three members and is related to multiple cancers, yet it was not investigated in LGG. Data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) cohorts were used to analyze the role of PLOD in LGG. As the PLOD family is involved in processes, such as tumor formation and cancer metastasis, we focused on its relationship to the tumor microenvironment (TME) in LGG. A high expression of the PLOD family relates to poor prognosis and high infiltration of immune cells within the TME. The expression level of the PLOD family might become a novel biomarker for prognosis and is a potential target for individual treatment decisions in LGG.

Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition

Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.

LINC01087 indicates a poor prognosis of glioma patients with preoperative MRI

Long intergenic non-coding RNA 01,087 (LINC01087) has been concerned as an oncogene in breast cancer, while its mechanism in glioma has been little surveyed. Thus, we searched the prognostic value and functional action of LINC01087 in glioma. Glioma patients after preoperative MRI diagnosis were enrolled, and LINC01087, microRNA (miR)-1277-5p, and alkaline ceramidase 3 (ACER3) levels were tested in glioma cancer tissue. The correlation between LINC01087 expression and the survival of patients were analyzed. LINC01087, miR-1277-5p, and ACER3 levels in U251 cells were altered via transfection, and cell malignant phenotypes were monitored. The relationship between miR-1277-5p and LINC01087 or ACER3 was detected. The LINC01087 and ACER3 expression was in up-regulation and the miR-1277-5p expression was in down-regulation in clinical glioma samples. High expression of LINC01087 was associated with poor prognosis of glioma patients with preoperative MRI. LINC01087 silencing restrained tumor malignancy in glioma cells. Mechanistically, LINC01087 directly interacted with miR-1277-5p. ACER3 was a known target of miR-1277-5p. Moreover, rescue assays reveal that miR-1277-5p overexpression (or ACER3 overexpression) reversed the effects of LINC01087 upregulation (or miR-1277-5p upregulation) on glioma cells. LINC01087 has prognostic significance in glioma and silencing LINC01087 deters glioma development through elevating miR-1277-5p to reduce ACER3 expression.

PODNL1 Methylation Serves as a Prognostic Biomarker and Associates with Immune Cell Infiltration and Immune Checkpoint Blockade Response in Lower-Grade Glioma

Lower-grade glioma (LGG) is a diffuse infiltrative tumor of the central nervous system, which lacks targeted therapy. We investigated the role of Podocan-like 1 (PODNL1) methylation in LGG clinical outcomes using the TCGA-LGG transcriptomics dataset. We identified four PODNL1 CpG sites, cg07425555, cg26969888, cg18547299, and cg24354933, which were associated with unfavorable overall survival (OS) and disease-free survival (DFS) in univariate and multivariate analysis after adjusting for age, gender, tumor-grade, and IDH1-mutation. In multivariate analysis, the OS and DFS hazard ratios ranged from 0.44 to 0.58 (p < 0.001) and 0.62 to 0.72 (p < 0.001), respectively, for the four PODNL1 CpGs. Enrichment analysis of differential gene and protein expression and analysis of 24 infiltrating immune cell types showed significantly increased infiltration in LGGs and its histological subtypes with low-methylation levels of the PODNL1 CpGs. High PODNL1 expression and low-methylation subgroups of the PODNL1 CpG sites were associated with significantly increased PD-L1, PD-1, and CTLA4 expressions. PODNL1 methylation may thus be a potential indicator of immune checkpoint blockade response, and serve as a biomarker for determining prognosis and immune subtypes in LGG.

History of atopy confers improved outcomes in IDH mutant and wildtype lower grade gliomas

PURPOSE

A history of atopy or allergy has been shown to be protective against the development of glioma, however the effect of atopy on patient outcomes, especially in conjunction with the survival benefit associated with IDH mutation, has not yet been investigated, and is the focus of the study we present here.

METHODS

Low grade glioma (LGG) data from the TCGA was downloaded, along with IDH, TERT, 1p/19q and ATRX mutational status and genetic alterations. History of asthma, eczema, hay fever, animal, or food allergies, as documented in TCGA, was used to determine patient atopy status. Patients with missing variables were excluded from the study.

RESULTS

374 LGG studies were included. Patients with a history of atopy demonstrated longer overall survival (OS) compared to those without (145.3 vs. 81.5 months, p = 00.0195). IDH mutant patients with atopy had longer OS compared those without atopy (158.8 vs. 85 months, p = 0.035). Multivariate cox regression analysis demonstrated that the effects of atopy on survival were independent of IDH and histological grade, (p = 0.002, HR 0.257, 95% 0.109-0.604), (p =  < 0.001, HR 0.217, 95% 0.107-0.444), and (p = 0.004, HR 2.72, 95% 1.373-5.397), respectively. In terms of treatment outcomes, patients with atopy did not differ in treatment response compared to their counterpart. Pathway analysis demonstrated an upstream activation of the BDNF pathway (p = 0.00027).

CONCLUSION

A history of atopy confers a survival benefit in patients with diffuse low-grade glioma. Activation of the BDNF pathway may drive the observed differences.

Prognostic and Predictive Biomarkers in Gliomas

Gliomas are the most common central nervous system tumors. New technologies, including genetic research and advanced statistical methods, revolutionize the therapeutic approach to the patient and reveal new points of treatment options. Moreover, the 2021 World Health Organization Classification of Tumors of the Central Nervous System has fundamentally changed the classification of gliomas and incorporated many molecular biomarkers. Given the rapid progress in neuro-oncology, here we compile the latest research on prognostic and predictive biomarkers in gliomas. In adult patients, IDH mutations are positive prognostic markers and have the greatest prognostic significance. However, CDKN2A deletion, in IDH-mutant astrocytomas, is a marker of the highest malignancy grade. Moreover, the presence of TERT promoter mutations, EGFR alterations, or a combination of chromosome 7 gain and 10 loss upgrade IDH-wildtype astrocytoma to glioblastoma. In pediatric patients, H3F3A alterations are the most important markers which predict the worse outcome. MGMT promoter methylation has the greatest clinical significance in predicting responses to temozolomide (TMZ). Conversely, mismatch repair defects cause hypermutation phenotype predicting poor response to TMZ. Finally, we discussed liquid biopsies, which are promising diagnostic, prognostic, and predictive techniques, but further work is needed to implement these novel technologies in clinical practice.

Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images

We developed end-to-end deep learning models using whole slide images of adults diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to predict prognosis and the mutation status of a somatic biomarker, isocitrate dehydrogenase (IDH) 1/2. The models, which utilize ResNet-18 as a backbone, were developed and validated on 296 patients from The Cancer Genome Atlas (TCGA) database. To account for the small sample size, repeated random train/test splits were performed for hyperparameter tuning, and the out-of-sample predictions were pooled for evaluation. Our models achieved a concordance- (C-) index of 0.715 (95% CI: 0.569, 0.830) for predicting prognosis and an area under the curve (AUC) of 0.667 (0.532, 0.784) for predicting IDH mutations. When combined with additional clinical information, the performance metrics increased to 0.784 (95% CI: 0.655, 0.880) and 0.739 (95% CI: 0.613, 0.856), respectively. When evaluated on the WHO grade 3 gliomas from the TCGA dataset, which were not used for training, our models predicted survival with a C-index of 0.654 (95% CI: 0.537, 0.768) and IDH mutations with an AUC of 0.814 (95% CI: 0.721, 0.897). If validated in a prospective study, our method could potentially assist clinicians in managing and treating patients with diffusely infiltrating gliomas.

Glioma stem cell-derived exosomal miR-944 reduces glioma growth and angiogenesis by inhibiting AKT/ERK signaling

In this study, we investigated the regulatory role of exosomal microRNA-944 (miR-944) derived from glioma stem cells (GSCs) in glioma progression and angiogenesis. Bioinformatics analysis showed that miR-944 levels were significantly lower in high-grade gliomas (HGGs) than low-grade gliomas in the Chinese Glioma Genome Atlas and The Cancer Genome Atlas datasets. The overall survival rates were significantly shorter for glioma patients expressing low miR-944 levels than high miR-944 levels. GSC-derived exosomal miR-944 significantly decreased in vitro proliferation, migration, and tube formation by human umbilical vein endothelial cells (HUVECs). Targetscan and dual luciferase reporter assays demonstrated that miR-944 directly targets the 3’UTR of VEGFC. In vivo mouse studies demonstrated that injection of agomiR-944 directly into tumors 3 weeks after xenografting glioma cells significantly reduced tumor growth and angiogenesis. GSC-derived exosomal miR-944 significantly reduced VEGFC levels and suppressed activation of AKT/ERK signaling pathways in HUVECs and xenograft glioma cell tumors. These findings demonstrate that GSC-derived exosomal miR-944 inhibits glioma growth, progression, and angiogenesis by suppressing VEGFC expression and inhibiting the AKT/ERK signaling pathway.

Association of supratotal resection with progression-free survival, malignant transformation, and overall survival in lower-grade gliomas

BACKGROUND

Supratotal resection is advocated in lower-grade gliomas (LGGs) based on theoretical advantages but with limited verification of functional risk and data on oncological outcomes. We assessed the association of supratotal resection in molecularly defined LGGs with oncological outcomes.

METHODS

Included were 460 presumptive LGGs; 404 resected; 347 were LGGs, 319 isocitrate dehydrogenase (IDH)-mutated, 28 wildtype. All patients had clinical, imaging, and molecular data. Resection aimed at supratotal resection without any patient or tumor a priori selection. The association of extent of resection (EOR), categorized on volumetric fluid attenuated inversion recovery images as residual tumor volume, along with postsurgical management with progression-free survival (PFS), malignant (M)PFS, and overall survival (OS) assessed by univariate, multivariate, and propensity score analysis. The study mainly focused on IDH-mutated LGGs, the "typical LGGs."

RESULTS

Median follow-up was 6.8 years (interquartile range, 5-8). Out of 319 IDH-mutated LGGs, 190 (59.6%) progressed, median PFS: 4.7 years (95% CI: 4-5.3). Total and supratotal resection obtained in 39% and 35% of patients with IDH1-mutated tumors. In IDH-mutated tumors, most patients in the partial/subtotal group progressed, 82.4% in total, only 6 (5.4%) in supratotal. Median PFS was 29 months (95% CI: 25-36) in subtotal, 46 months (95% CI: 38-48) in total, while at 92 months, PFS in supratotal was 94.0%. There was no association with molecular subtypes and grade. At random forest analysis, PFS strongly associated with EOR, radiotherapy, and previous treatment. In the propensity score analysis, EOR associated with PFS (hazard ratio, 0.03; 95% CI: 0.01-0.13). MPFS occurred in 32.1% of subtotal total groups; 1 event in supratotal. EOR, grade III, previous treatment correlated to MPFS. At random forest analysis, OS associated with EOR as well.

CONCLUSIONS

Supratotal resection strongly associated with PFS, MPFS, and OS in LGGs, regardless of molecular subtypes and grade, right from the beginning of clinical presentation.

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

Purpose

To evaluate isocitrate dehydrogenase (IDH) status in clinically diagnosed grade II~IV glioma patients using the 2016 World Health Organization (WHO) classification based on MRI parameters.

Materials and Methods

One hundred and seventy-six patients with confirmed WHO grade II~IV glioma were retrospectively investigated as the study set, including lower-grade glioma (WHO grade II, n = 64; WHO grade III, n = 38) and glioblastoma (WHO grade IV, n = 74). The minimum apparent diffusion coefficient (ADCmin) in the tumor and the contralateral normal-appearing white matter (ADCn) and the rADC (ADCmin to ADCn ratio) were defined and calculated. Intraclass correlation coefficient (ICC) analysis was carried out to evaluate interobserver and intraobserver agreement for the ADC measurements. Interobserver agreement for the morphologic categories was evaluated by Cohen’s kappa analysis. The nonparametric Kruskal-Wallis test was used to determine whether the ADC measurements and glioma subtypes were related. By univariable analysis, if the differences in a variable were significant (P<0.05) or an image feature had high consistency (ICC >0.8; κ >0.6), then it was chosen as a predictor variable. The performance of the area under the receiver operating characteristic curve (AUC) was evaluated using several machine learning models, including logistic regression, support vector machine, Naive Bayes and Ensemble. Five evaluation indicators were adopted to compare the models. The optimal model was developed as the final model to predict IDH status in 40 patients with glioma as the subsequent test set. DeLong analysis was used to compare significant differences in the AUCs.

Results

In the study set, six measured variables (rADC, age, enhancement, calcification, hemorrhage, and cystic change) were selected for the machine learning model. Logistic regression had better performance than other models. Two predictive models, model 1 (including all predictor variables) and model 2 (excluding calcification), correctly classified IDH status with an AUC of 0.897 and 0.890, respectively. The test set performed equally well in prediction, indicating the effectiveness of the trained classifier. The subgroup analysis revealed that the model predicted IDH status of LGG and GBM with accuracy of 84.3% (AUC = 0.873) and 85.1% (AUC = 0.862) in the study set, and with the accuracy of 70.0% (AUC = 0.762) and 70.0% (AUC = 0.833) in the test set, respectively.

Conclusion

Through the use of machine-learning algorithms, the accurate prediction of IDH-mutant versus IDH-wildtype was achieved for adult diffuse gliomas via noninvasive MR imaging characteristics, including ADC values and tumor morphologic features, which are considered widely available in most clinical workstations.

FABP5 enhances malignancies of lower-grade gliomas via canonical activation of NF-κB signaling

Low‐grade gliomas (LGGs) are grade III gliomas based on the WHO classification with significant genetic heterogeneity and clinical properties. Traditional histological classification of gliomas has been challenged by the improvement of molecular stratification; however, the reproducibility and diagnostic accuracy of LGGs classification still remain poor. Herein, we identified fatty acid binding protein 5 (FABP5) as one of the most enriched genes in malignant LGGs and elevated FABP5 revealed severe outcomes in LGGs. Functionally, lentiviral suppression of FABP5 reduced malignant characters including proliferation, cloning formation, immigration, invasion and TMZ resistance, contrarily, the malignancies of LGGs were enhanced by exogenous overexpression of FABP5. Mechanistically, epithelial‐mesenchymal transition (EMT) was correlated to FABP5 expression in LGGs and tumour necrosis factor α (TNFα)‐dependent NF‐κB signalling was involved in this process. Furthermore, FABP5 induced phosphorylation of inhibitor of nuclear factor kappa‐B kinase α (IKKα) thus activated nuclear factor kappa‐B (NF‐κB) signalling. Taken together, our study indicated that FABP5 enhances malignancies of LGGs through canonical activation of NF‐κB signalling, which could be used as individualized prognostic biomarker and potential therapeutic target of LGGs.

MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift

Low-grade gliomas (LGGs) are tumors that affect mostly adults. These neoplasms are comprised mainly of oligodendrogliomas and diffuse astrocytomas. LGGs remain vexing to current management and therapeutic modalities although they exhibit more favorable survival rates compared with high-grade gliomas (HGGs). The specific genetic subtypes that these tumors exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of an LGG pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). The introduction of radiomics as a high throughput quantitative imaging technique that allows for improved diagnostic, prognostic and predictive indices has created more interest for such techniques in cancer research and especially in neurooncology (MRI-based classification of LGGs, predicting Isocitrate dehydrogenase (IDH) and Telomerase reverse transcriptase (TERT) promoter mutations and predicting LGG associated seizures). Radiogenomics refers to the linkage of imaging findings with the tumor/tissue genomics. Numerous applications of radiomics and radiogenomics have been described in the clinical context and management of LGGs. In this review, we describe the recently published studies discussing the potential application of radiomics and radiogenomics in LGGs. We also highlight the potential pitfalls of the above-mentioned high throughput computerized techniques and, most excitingly, explore the use of machine learning artificial intelligence technologies as standalone and adjunct imaging tools en route to enhance a personalized MRI-based tumor diagnosis and management plan design.

Application of sulfur-doped graphene quantum dots@gold-carbon nanosphere for electrical pulse-induced impedimetric detection of glioma cells

Glioma is the predominant brain tumor with high death rate. The successful development of biosensor to achieve an efficient detection of glioma cells at low concentration remains a great challenge for the personalized glioma therapy. Herein, an ultrasensitive pulse induced electrochemically impedimetric biosensor for glioma cells detection has been successfully fabricated. The 4-11 nm sulfur-doped graphene quantum dots (S-GQDs) are homogeneously deposited onto gold nanoparticles decorated carbon nanospheres (Au-CNS) by Au-thiol linkage to form S-GQDs@Au-CNS nanocomposite which acts as dual functional probe for enhancing the electrochemical activity as well as conjugating the angiopep-2 (Ang-2) for glioma cell detection. Moreover, the application of an externally electrical pulse at +0.6 V expend the surface of glioma cells to accelerate the attachment of glioma cells onto the Ang-2-conjugated S-GQDs@Au-CNS nanocomposite, resulting in the enhanced sensitivity toward glioma cell detection. An ultrasensitive impedimetric detection of glioma cells with a wide linear range of 100-100,000 cells mL^-1 and a limit of detection of 40 cells mL^-1 is observed. Moreover, the superior selectivity with long-term stability of the developed biosensor in human serum matrix corroborates the feasibility of using S-GQDs@Au-CNS based nanomaterials as the promising sensing probe for practical application to facilitate the ultrasensitive and highly selective detection of cancer cells.

Prediction of Lower Grade Insular Glioma Molecular Pathology Using Diffusion Tensor Imaging Metric-Based Histogram Parameters

Objectives

To explore whether a simplified lesion delineation method and a set of diffusion tensor imaging (DTI) metric-based histogram parameters (mean, 25th percentile, 75th percentile, skewness, and kurtosis) are efficient at predicting the molecular pathology status (MGMT methylation, IDH mutation, TERT promoter mutation, and 1p19q codeletion) of lower grade insular gliomas (grades II and III).

Methods

40 lower grade insular glioma patients in two medical centers underwent preoperative DTI scanning. For each patient, the entire abnormal area in their b-non (b0) image was defined as region of interest (ROI), and a set of histogram parameters were calculated for two DTI metrics, fractional anisotropy (FA) and mean diffusivity (MD). Then, we compared how these DTI metrics varied according to molecular pathology and glioma grade, with their predictive performance individually and jointly assessed using receiver operating characteristic curves. The reliability of the combined prediction was evaluated by the calibration curve and Hosmer and Lemeshow test.

Results

The mean, 25th percentile, and 75th percentile of FA were associated with glioma grade, while the mean, 25th percentile, 75th percentile, and skewness of both FA and MD predicted IDH mutation. The mean, 25th percentile, and 75th percentile of FA, and all MD histogram parameters significantly distinguished TERT promoter status. Similarly, all MD histogram parameters were associated with 1p19q status. However, none of the parameters analyzed for either metric successfully predicted MGMT methylation. The 25th percentile of FA yielded the highest prediction efficiency for glioma grade, IDH mutation, and TERT promoter mutation, while the 75th percentile of MD gave the best prediction of 1p19q codeletion. The combined prediction could enhance the discrimination of grading, IDH and TERT mutation, and also with a good fitness.

Conclusions

Overall, more invasive gliomas showed higher FA and lower MD values. The simplified ROI delineation method presented here based on the combination of appropriate histogram parameters yielded a more practical and efficient approach to predicting molecular pathology in lower grade insular gliomas. This approach could help clinicians to determine the extent of tumor resection required and reduce complications, enabling more precise treatment of insular gliomas in combination with radiotherapy and chemotherapy.

Prognostic Value of an Autophagy-Related Five-Gene Signature for Lower-Grade Glioma Patients

Background: Molecular characteristics can be good indicators of tumor prognosis and have been introduced into the classification of gliomas. The prognosis of patients with newly classified lower-grade gliomas (LGGs, including grade 2 and grade 3 gliomas) is highly heterogeneous, and new molecular markers are urgently needed.

Methods: Autophagy related genes (ATGs) were obtained from Human Autophagy Database (HADb). From the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), gene expression profiles including ATG expression information and patient clinical data were downloaded. Cox regression analysis, receiver operating characteristic (ROC) analysis, Kaplan–Meier analysis, random survival forest algorithm (RSFVH) and stratification analysis were performed.

Results: Through univariate Cox regression analysis, we found a total of 127 ATGs associated with the prognosis of LGG patients from TCGA dataset and a total of 131 survival-related ATGs from CGGA dataset. Using TCGA dataset as the training group (n = 524), we constructed a five-ATG signature (including BAG1, BID, MAP1LC3C, NRG3, PTK6), which could divide LGG patients into two risk groups with significantly different overall survival (Log Rank P < 0.001). Then we confirmed in the independent CGGA dataset that the five-ATG signature had the ability to predict prognosis (n = 431, Log Rank P < 0.001). We further discovered that the predictive ability of the five-ATG signature was better than the existing clinical indicators and IDH mutation status. In addition, the five-ATG signature could further classify patients after receiving radiotherapy or chemotherapy into groups with different prognosis.

Conclusions: We identified a five-ATG signature that could be a reliable prognostic marker and might be therapeutic targets for autophagy therapy for LGG patients.

Nanodelivery Systems Targeting Epidermal Growth Factor Receptors for Glioma Management

A paradigm shift in treating the most aggressive and malignant form of glioma is continuously evolving; however, these strategies do not provide a better life and survival index. Currently, neurosurgical debulking, radiotherapy, and chemotherapy are the treatment options available for glioma, but these are non-specific in action. Patients invariably develop resistance to these therapies, leading to recurrence and death. Receptor Tyrosine Kinases (RTKs) are among the most common cell surface proteins in glioma and play a significant role in malignant progression; thus, these are currently being explored as therapeutic targets. RTKs belong to the family of cell surface receptors that are activated by ligands which in turn activates two major downstream signaling pathways via Rapidly Accelerating Sarcoma/mitogen activated protein kinase/extracellular-signal-regulated kinase (Ras/MAPK/ERK) and phosphatidylinositol 3-kinase/a serine/threonine protein kinase/mammalian target of rapamycin (PI3K/AKT/mTOR). These pathways are critically involved in regulating cell proliferation, invasion, metabolism, autophagy, and apoptosis. Dysregulation in these pathways results in uncontrolled glioma cell proliferation, invasion, angiogenesis, and cancer progression. Thus, RTK pathways are considered a potential target in glioma management. This review summarizes the possible risk factors involved in the growth of glioblastoma (GBM). The role of RTKs inhibitors (TKIs) and the intracellular signaling pathways involved, small molecules under clinical trials, and the updates were discussed. We have also compiled information on the outcomes from the various endothelial growth factor receptor (EGFR)-TKIs-based nanoformulations from the preclinical and clinical points of view. Aided by an extensive literature search, we propose the challenges and potential opportunities for future research on EGFR-TKIs-based nanodelivery systems.

Glutamate Is a Noninvasive Metabolic Biomarker of IDH1-Mutant Glioma Response to Temozolomide Treatment

Although lower grade gliomas are driven by mutations in the isocitrate dehydrogenase 1 (IDH1) gene and are less aggressive than primary glioblastoma, they nonetheless generally recur. IDH1-mutant patients are increasingly being treated with temozolomide, but early detection of response remains a challenge and there is a need for complementary imaging methods to assess response to therapy prior to tumor shrinkage. The goal of this study was to determine the value of magnetic resonance spectroscopy (MRS)-based metabolic changes for detection of response to temozolomide in both genetically engineered and patient-derived mutant IDH1 models. Using 1H MRS in combination with chemometrics identified several metabolic alterations in temozolomide-treated cells, including a significant increase in steady-state glutamate levels. This was confirmed in vivo, where the observed 1H MRS increase in glutamate/glutamine occurred prior to tumor shrinkage. Cells labeled with [1-13C]glucose and [3-13C]glutamine, the principal sources of cellular glutamate, showed that flux to glutamate both from glucose via the tricarboxylic acid cycle and from glutamine were increased following temozolomide treatment. In line with these results, hyperpolarized [5-13C]glutamate produced from [2-13C]pyruvate and hyperpolarized [1-13C]glutamate produced from [1-13C]α-ketoglutarate were significantly higher in temozolomide-treated cells compared with controls. Collectively, our findings identify 1H MRS-detectable elevation of glutamate and hyperpolarized 13C MRS-detectable glutamate production from either pyruvate or α-ketoglutarate as potential translatable metabolic biomarkers of response to temozolomide treatment in mutant IDH1 glioma. SIGNIFICANCE: These findings show that glutamate can be used as a noninvasive, imageable metabolic marker for early assessment of tumor response to temozolomide, with the potential to improve treatment strategies for mutant IDH1 patients.