Value of long non-coding RNA HAS2-AS1 as a diagnostic and prognostic marker of glioma / El valor de ARNncl HAS2-AS1 como marcador diagnóstico y pronóstico del glioma

Background: Glioma presents high incidence and poor prognosis, and therefore more effective treatments are needed. Studies have confirmed that long non-coding RNAs (lncRNAs) basically regulate various human diseases including glioma. It has been theorized that HAS2-AS1 serves as an lncRNA to exert an oncogenic role in varying cancers. This study aimed to assess the value of lncRNA HAS2-AS1 as a diagnostic and prognostic marker for glioma. Methods: The miRNA expression data and clinical data of glioma were downloaded from the TCGA database for differential analysis and survival analysis. In addition, pathological specimens and specimens of adjacent normal tissue from 80 patients with glioma were used to observe the expression of HAS2-AS1. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic ability and prognostic value of HAS2-AS1 in glioma. Meanwhile, a Kaplan–Meier survival curve was plotted to evaluate the survival of glioma patients with different HAS2-AS1 expression levels. Results: HAS2-AS1 was significantly upregulated in glioma tissues compared with normal tissue. The survival curves showed that overexpression of HAS2-AS1 was associated with poor overall survival (OS) and progression-free survival (PFS). Several clinicopathological factors of glioma patients, including tumor size and WHO grade, were significantly correlated with HAS2-AS1 expression in tissues. The ROC curve showed an area under the curve (AUC) value of 0.863, indicating that HAS2-AS1 had good diagnostic value. The ROC curve for the predicted OS showed an AUC of 0.906, while the ROC curve for predicted PFS showed an AUC of 0.88. Both suggested that overexpression of HAS2-AS1 was associated with poor prognosis.Conclusions: Normal tissues could be clearly distinguished from glioma tissues based on HAS2-AS1 expression. Moreover, overexpression of HAS2-AS1 indicated poor prognosis in glioma patients.(AU)

Introducción: Los gliomas presentan una alta incidencia y un mal pronóstico, por lo que es necesario un tratamiento más efectivo. Algunos estudios han confirmado que los ARN no codificantes de cadena larga (ARNncl) regulan diferentes enfermedades, entre las que se incluyen los gliomas. Se ha postulado que HAS2-AS1 actúa como un ARNncl, con un efecto oncogénico en diferentes tipos de cáncer. Este estudio tiene como objetivo analizar el valor del ARNncl HAS2-AS1 como marcador diagnóstico y pronóstico de glioma. Métodos: Descargamos los datos clínicos y de expresión de micro-ARN de la base de datos del Atlas del Genoma del Cáncer (TCGA) para realizar el análisis diferencial y de supervivencia. También analizamos la expresión de HAS2-AS1 en muestras patológicas y muestras de tejido adyacente normal de 80 pacientes con glioma. Para analizar la capacidad diagnóstica y el valor pronóstico de HAS2-AS1 en el glioma, recurrimos a la curva ROC. También utilizamos curvas de Kaplan-Meier para evaluar la supervivencia de los pacientes con glioma con diferentes niveles de expresión de HAS2-AS1. Resultados: La expresión de HAS2-AS1 era significativamente mayor en las muestras patológicas que en el tejido normal. Las curvas de supervivencia demostraron que la sobreexpresión de HAS2-AS1 estaba relacionada con una menor supervivencia general y supervivencia libre de progresión. Algunos factores clínico-patológicos de los pacientes con glioma, como el tamaño del tumor y su grado, según la clasificación de la OMS, mostraron una correlación significativa con la expresión de HAS2-AS1 en los tejidos afectados. La curva ROC mostró un área bajo la curva de 0,863, lo que indica que la expresión de HAS2-AS1 posee un importante valor diagnóstico. El área bajo la curva de la supervivencia general estimada fue de 0,906; para la supervivencia libre de progresión estimada, de 0,88. Ambos valores muestran que la sobreexpresión de HAS2-AS1 se asocia con un mal pronóstico...(AU)

Clinical characteristics and prognostic factors of adult brainstem gliomas: A retrospective analysis of histologically-proven 40 cases.

To illustrate the clinical characteristics and prognostic factors of adult patients pathologically confirmed with brainstem gliomas (BSGs). Clinical data of 40 adult patients pathologically diagnosed with BSGs admitted to Beijing Shijitan Hospital from 2009 to 2022 were recorded and retrospectively analyzed. The primary parameters included relevant symptoms, duration of symptoms, Karnofsky performance status (KPS), tumor location, type of surgical resection, diagnosis, treatment, and survival. Univariate and multivariate analyses were evaluated by Cox regression models. The gliomas were located in the midbrain of 9 patients, in the pons of 14 cases, in the medulla of 5 cases, in the midbrain and pons of 6 cases and invading the medulla and pons of 6 cases, respectively. The proportion of patients with low-grade BSGs was 42.5%. Relevant symptoms consisted of visual disturbance, facial paralysis, dizziness, extremity weakness, ataxia, paresthesia, headache, bucking, dysphagia, dysacousia, nausea, dysphasia, dysosmia, hypomnesia and nystagmus. 23 (57.5%) patients accepted stereotactic biopsy, 17 (42.5%) patients underwent surgical resection. 39 patients received radiotherapy and 34 cases were treated with temozolomide. The median overall survival (OS) of all patients was 26.2 months and 21.5 months for the median progression-free survival (PFS). Both duration of symptoms (P = .007) and tumor grading (P = .002) were the influencing factors for OS, and tumor grading was significantly associated with PFS (P = .001). Duration of symptoms for more than 2 months and low-grade are favorable prognostic factors for adult patients with BSGs.

The diagnostic challenge of lack of choline level elevation on 1H-MR spectroscopy in grade II-III gliomas.

The accurate diagnosis of brain tumour is very important in modern neuro-oncology medicine. Magnetic resonance spectroscopy (MRS) is supposed to be a promising tool for detecting cancerous lesions. However, the interpretation of MRS data is complicated by the fact that not all cancerous lesions exhibit elevated choline (Cho) levels. The main goal of our study was to investigate the lack of Cho lesion /Cho ref elevation in the population of grade II-III gliomas. 89 cases of gliomas grade II and III were used for the retrospective analysis - glioma (astrocytoma or oligodendroglioma) grade II (74 out of 89 cases [83%]) and III (15 out of 89 cases [17%]) underwent conventional MRI extended by MRS before treatment. Histopathological diagnosis was obtained either by biopsy or surgical resection. Gliomas were classified to the group of no-choline elevation when the ratio of choline measured within the tumour (Cho lesion ) to choline from NABT (Cho ref ) were equal to or lower than 1. Significant differences were observed between ratios of Cho lesion /Cr lesion calculated for no-choline elevation and glial tumour groups as well as in the NAA lesion /Cr lesion ratio between the no-choline elevation group and glial tumour group. With consistent data concerning choline level elevation and slightly lower NAA value, the Cho lesion /NAA lesion ratio is significantly higher in the WHO II glial tumour group compared to the no-choline elevation cases ( p < 0.000). In the current study the results demonstrated possibility of lack of choline elevation in patients with grade II-III gliomas, so it is important to remember that the lack of elevated choline levels does not exclude neoplastic lesion.

Ensemble learning prediction framework for EGFR amplification status of glioma based on terahertz spectral features.

Epidermal growth factor receptor (EGFR) plays a pivotal role in the initiation and progression of gliomas. In particular, in glioblastoma, EGFR amplification emerges as a catalyst for invasion, proliferation, and resistance to radiotherapy and chemotherapy. Current approaches are not capable of providing rapid diagnostic results of molecular pathology. In this study, we propose a terahertz spectroscopic approach for predicting the EGFR amplification status of gliomas for the first time. A machine learning model was constructed using the terahertz response of the measured glioma tissues, including the absorption coefficient, refractive index, and dielectric loss tangent. The novelty of our model is the integration of three classical base classifiers, i.e., support vector machine, random forest, and extreme gradient boosting. The ensemble learning method combines the advantages of various base classifiers, this model has more generalization ability. The effectiveness of the proposed method was validated by applying an individual test set. The optimal performance of the integrated algorithm was verified with an area under the curve (AUC) maximum of 85.8 %. This signifies a significant stride toward more effective and rapid diagnostic tools for guiding postoperative therapy in gliomas.

TGIF2 is a potential biomarker for diagnosis and prognosis of glioma.

Background: TGFB-induced factor homeobox 2 (TGIF2), a member of the Three-Amino-acid-Loop-Extension (TALE) superfamily, has been implicated in various malignant tumors. However, its prognostic significance in glioma, impact on tumor immune infiltration, and underlying mechanisms in glioma development remain elusive. Methods: The expression of TGIF2 in various human normal tissues, normal brain tissues, and gliomas was investigated using HPA, TCGA, GTEx, and GEO databases. The study employed several approaches, including Kaplan-Meier analysis, ROC analysis, logistic regression, Cox regression, GO analysis, KEGG analysis, and GSEA, to explore the relationship between TGIF2 expression and clinicopathologic features, prognostic value, and potential biological functions in glioma patients. The impact of TGIF2 on tumor immune infiltration was assessed through Estimate, ssGSEA, and Spearman analysis. Genes coexpressed with TGIF2 were identified, and the protein-protein interaction (PPI) network of these coexpressed genes were constructed using the STRING database and Cytoscape software. Hub genes were identified using CytoHubba plugin, and their clinical predictive value was explored. Furthermore, in vitro experiments were performed by knocking down and knocking out TGIF2 using siRNA and CRISPR/Cas9 gene editing, and the role of TGIF2 in glioma cell invasion and migration was analyzed using transwell assay, scratch wound-healing assay, RT-qPCR, and Western blot. Results: TGIF2 mRNA was found to be upregulated in 21 cancers, including glioma. High expression of TGIF2 was associated with malignant phenotypes and poor prognosis in glioma patients, indicating its potential as an independent prognostic factor. Furthermore, elevated TGIF2 expression positively correlated with cell cycle regulation, DNA synthesis and repair, extracellular matrix (ECM) components, immune response, and several signaling pathways that promote tumor progression. TGIF2 showed correlations with Th2 cells, macrophages, and various immunoregulatory genes. The hub genes coexpressed with TGIF2 demonstrated significant predictive value. Additionally, in vitro experiments revealed that knockdown and knockout of TGIF2 inhibited glioma cell invasion, migration and suppressed the epithelial-mesenchymal transition (EMT) phenotype. Conclusion: TGIF2 emerges as a potential biomarker for glioma, possibly linked to tumor immune infiltration and EMT.

Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review.

Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.

Pathway-based stratification of gliomas uncovers four subtypes with different TME characteristics and prognosis.

Increasing evidences have found that the interactions between hypoxia, immune response and metabolism status in tumour microenvironment (TME) have clinical importance of predicting clinical outcomes and therapeutic efficacy. This study aimed to develop a reliable molecular stratification based on these key components of TME. The TCGA data set (training cohort) and two independent cohorts from CGGA database (validation cohort) were enrolled in this study. First, the enrichment score of 277 TME-related signalling pathways was calculated by gene set variation analysis (GSVA). Then, consensus clustering identified four stable and reproducible subtypes (AFM, CSS, HIS and GLU) based on TME-related signalling pathways, which were characterized by differences in hypoxia and immune responses, metabolism status, somatic alterations and clinical outcomes. Among the four subtypes, HIS subtype had features of immunosuppression, oxygen deprivation and active energy metabolism, resulting in a worst prognosis. Thus, for better clinical application of this acquired stratification, we constructed a risk signature by using the LASSO regression model to identify patients in HIS subtype accurately. We found that the risk signature could accurately screen out the patients in HIS subtype and had important reference value for individualized treatment of glioma patients. In brief, the definition of the TME-related subtypes was a valuable tool for risk stratification in gliomas. It might serve as a reliable prognostic classifier and provide rational design of individualized treatment, and follow-up scheduling for patients with gliomas.

Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks.

The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages. The first stage is responsible for tumor detection and classification by categorizing brain MRI images into normal, high-grade glioma (glioblastoma), and low-grade glioma. The uniqueness of the proposed network lies in its use of different levels of features, including local and global paths. The second stage is responsible for tumor segmentation, and skip connections and residual units are used during this step. Using 1800 images extracted from the BraTS 2017 dataset, the detection and classification stage was found to achieve a maximum accuracy of 99%. The segmentation stage was then evaluated using the Dice score, specificity, and sensitivity. The results showed that the suggested deep-learning-based system ranks highest among a variety of different strategies reported in the literature.

Demographic bias in misdiagnosis by computational pathology models.

Despite increasing numbers of regulatory approvals, deep learning-based computational pathology systems often overlook the impact of demographic factors on performance, potentially leading to biases. This concern is all the more important as computational pathology has leveraged large public datasets that underrepresent certain demographic groups. Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predict IDH1 mutations in gliomas. For example, when using common modeling approaches, we observed performance gaps (in area under the receiver operating characteristic curve) between white and Black patients of 3.0% for breast cancer subtyping, 10.9% for lung cancer subtyping and 16.0% for IDH1 mutation prediction in gliomas. We found that richer feature representations obtained from self-supervised vision foundation models reduce performance variations between groups. These representations provide improvements upon weaker models even when those weaker models are combined with state-of-the-art bias mitigation strategies and modeling choices. Nevertheless, self-supervised vision foundation models do not fully eliminate these discrepancies, highlighting the continuing need for bias mitigation efforts in computational pathology. Finally, we demonstrate that our results extend to other demographic factors beyond patient race. Given these findings, we encourage regulatory and policy agencies to integrate demographic-stratified evaluation into their assessment guidelines.

Dual-space disentangled-multimodal network (DDM-net) for glioma diagnosis and prognosis with incomplete pathology and genomic data.

Objective. Effective fusion of histology slides and molecular profiles from genomic data has shown great potential in the diagnosis and prognosis of gliomas. However, it remains challenging to explicitly utilize the consistent-complementary information among different modalities and create comprehensive representations of patients. Additionally, existing researches mainly focus on complete multi-modality data and usually fail to construct robust models for incomplete samples.Approach. In this paper, we propose adual-space disentangled-multimodal network (DDM-net)for glioma diagnosis and prognosis. DDM-net disentangles the latent features generated by two separate variational autoencoders (VAEs) into common and specific components through a dual-space disentangled approach, facilitating the construction of comprehensive representations of patients. More importantly, DDM-net imputes the unavailable modality in the latent feature space, making it robust to incomplete samples.Main results. We evaluated our approach on the TCGA-GBMLGG dataset for glioma grading and survival analysis tasks. Experimental results demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods, with a competitive AUC of 0.952 and a C-index of 0.768.Significance. The proposed model may help the clinical understanding of gliomas and can serve as an effective fusion model with multimodal data. Additionally, it is capable of handling incomplete samples, making it less constrained by clinical limitations.

Signature Construction and Disulfidptosis-Related Molecular Cluster Identification for Better Prediction of Prognosis in Glioma.

Disulfidptosis is a newly discovered form of regulatory cell death. However, the identification of disulfidptosis-related molecular subtypes and potential biomarkers in gliomas and their prognostic predictive potential need to be further elucidated. RNA sequencing profiles and the relevant clinical data were obtained from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Disulfidptosis-related clusters were identified by unsupervised clustering analysis. Immune cell infiltration analysis and drug sensitivity analysis were used to explore the differences between clusters. Gene set enrichment analysis (GSEA) of differential genes between clusters was performed to explore the potential biological functions and signaling. A disulfidptosis-related scoring system (DRSS) was constructed based on a combined COX and LASSO analysis. Mendelian randomization (MR) analyses were used to further explore the causal relationship between levels of genes in DRSS and an increased risk of glioma. A prognosis nomogram was constructed based on the DRSS and 3 clinical features (age, WHO stage, and IDH status). The accuracy and stability of the prognosis nomogram were also validated in different cohorts. We identified two clusters that exhibited different prognoses, drug sensitivity profiles, and tumor microenvironment infiltration profiles. The overall survival (OS) of Cluster2 was significantly better than Cluster1. Cluster1 had an overall greater infiltration of immune cells compared to Cluster2. However, the Monocytes, activated B cells had higher infiltration abundance in Cluster2. GSEA results showed significant enrichment of immune-related biological processes in Cluster1, while Cluster2 was more enriched for functions related to neurotransmission and regulation. PER3, RAB34, NKX3-2, GPX7, FRA10AC1, and TGIF1 were finally included to construct DRSS. DRSS was independently related to prognosis. There was a significant difference in overall survival between the low-risk score group and the high-risk score group. Among six genes in DRSS, GPX7 levels were demonstrated to have a causal relationship with an increased risk of glioma. GPX7 may become a more promising biomarker for gliomas. The prognosis nomogram constructed based on the DRSS and three clinical features has considerable potential for predicting the prognosis of patients with glioma. Free online software for implementing this nomogram was established:  https://yekun-zhuang.shinyapps.io/DynNomapp/ . Our study established a novel glioma classification based on the disulfidptosis-related molecular subtypes. We constructed the DRSS and the prognosis nomogram to accurately stratify the prognosis of glioma patients. GPX7 was identified as a more promising biomarker for glioma. We provide important insights into the treatment and prognosis of gliomas.

Value of long non-coding RNA HAS2-AS1 as a diagnostic and prognostic marker of glioma.

BACKGROUND: Glioma presents high incidence and poor prognosis, and therefore more effective treatments are needed. Studies have confirmed that long non-coding RNAs (lncRNAs) basically regulate various human diseases including glioma. It has been theorized that HAS2-AS1 serves as an lncRNA to exert an oncogenic role in varying cancers. This study aimed to assess the value of lncRNA HAS2-AS1 as a diagnostic and prognostic marker for glioma. METHODS: The miRNA expression data and clinical data of glioma were downloaded from the TCGA database for differential analysis and survival analysis. In addition, pathological specimens and specimens of adjacent normal tissue from 80 patients with glioma were used to observe the expression of HAS2-AS1. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic ability and prognostic value of HAS2-AS1 in glioma. Meanwhile, a Kaplan-Meier survival curve was plotted to evaluate the survival of glioma patients with different HAS2-AS1 expression levels. RESULTS: HAS2-AS1 was significantly upregulated in glioma tissues compared with normal tissue. The survival curves showed that overexpression of HAS2-AS1 was associated with poor overall survival (OS) and progression-free survival (PFS). Several clinicopathological factors of glioma patients, including tumor size and WHO grade, were significantly correlated with HAS2-AS1 expression in tissues. The ROC curve showed an area under the curve (AUC) value of 0.863, indicating that HAS2-AS1 had good diagnostic value. The ROC curve for the predicted OS showed an AUC of 0.906, while the ROC curve for predicted PFS showed an AUC of 0.88. Both suggested that overexpression of HAS2-AS1 was associated with poor prognosis. CONCLUSIONS: Normal tissues could be clearly distinguished from glioma tissues based on HAS2-AS1 expression. Moreover, overexpression of HAS2-AS1 indicated poor prognosis in glioma patients. Therefore, HAS2-AS1 could be used as a diagnostic and prognostic marker for glioma.

Cerebrospinal Fluid Liquid Biopsies in the Evaluation of Adult Gliomas.

PURPOSE OF REVIEW: This review aims to discuss recent research regarding the biomolecules explored in liquid biopsies and their potential clinical uses for adult-type diffuse gliomas. RECENT FINDINGS: Evaluation of tumor biomolecules via cerebrospinal fluid (CSF) is an emerging technology in neuro-oncology. Studies to date have already identified various circulating tumor DNA, extracellular vesicle, micro-messenger RNA and protein biomarkers of interest. These biomarkers show potential to assist in multiple avenues of central nervous system (CNS) tumor evaluation, including tumor differentiation and diagnosis, treatment selection, response assessment, detection of tumor progression, and prognosis. In addition, CSF liquid biopsies have the potential to better characterize tumor heterogeneity compared to conventional tissue collection and CNS imaging. Current imaging modalities are not sufficient to establish a definitive glioma diagnosis and repeated tissue sampling via conventional biopsy is risky, therefore, there is a great need to improve non-invasive and minimally invasive sampling methods. CSF liquid biopsies represent a promising, minimally invasive adjunct to current approaches which can provide diagnostic and prognostic information as well as aid in response assessment.

Expression patterns and prognostic role of m6A RNA methylation regulators in Non-small Cell Lung Cancer.

To investigate the expression pattern and prognostic role of m6A RNA methylation regulators in non-small cell lung cancer (NSCLC), we downloaded data from 422 patients from The Cancer Genome Atlas (TCGA) database. The relationship between the expression levels of m6A RNA methylation regulators and clinicopathological variables of NSCLC was analysed using R language. By analysing glioma data in TCGA, we found that a prognostic risk score model could be constructed based on 18 genes with m6A methylation modification. m6A gene alterations were significantly associated with tumour grade and tumour stage. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression models were used to identify 2 m6A RNA methylation modifiers: IFG2BP2, and METTL14 to construct risk profiles. Based on the risk profile, patients were divided into high-risk and low-risk groups. The overall survival rate of the low-risk group was significantly higher than that of the high-risk group. The results suggest that the prognostic risk score model constructed by m6A methylation regulators can predict the prognosis of glioma patients. IFG2BP2 and METTL14 may be the key m6A methylation regulators involved in the development of NSCLC and can be used as the molecular markers for the prognosis of NSCLC.

[Supratentorial neuroepithelial tumor with PLAGL1 gene fusion - a new type of morphologically variable pediatric brain neoplasm defined by a distinct DNA methylation class. A case report and literature review]. / Supratentorial'naya neiroepitelial'naya opukhol' so sliyaniem gena PLAGL1 u detei ­ novyi tip opukholi mozga s raznoobraznoi morfologicheskoi kartinoi i otlichitel'nym metilyatsionnym profilem(sluchai iz praktiki i obzor literatury).

BACKGROUND: Methylation analysis has become a powerful diagnostic tool in modern neurooncology. This technique is valuable to diagnose new brain tumor types. OBJECTIVE: To describe the MRI and histological pattern of neuroepithelial tumor with PLAGL1 gene fusion. MATERIAL AND METHODS: We present a 6-year-old patient with small right frontal intraaxial tumor causing drug resistant epilepsy. Despite indolent preoperative clinical course and MRI features suggesting glioneuronal tumor, histological evaluation revealed characteristics of high-grade glioma, ependymoma and neuroblastoma. RESULTS: Methylation analysis of tumor DNA confirmed a new type of a recently discovered neoplasm - neuroepithelial tumor with PLAGL1 fusion (NET PLAGL1). PCR confirmed fusion of PLAGL1 and EWSR1 genes. No seizures were observed throughout the follow-up period. There was no tumor relapse a year after surgery. CONCLUSION: Methylation analysis in neurooncology is essential for unclear tumor morphology or divergence between histological and clinical data. In our case, this technique confirmed benign nature of tumor, and we preferred follow-up without unnecessary adjuvant treatment.

Identification and Validation of a PEX5-Dependent Signature for Prognostic Prediction in Glioma.

Gliomas, the most prevalent and lethal form of brain cancer, are known to exhibit metabolic alterations that facilitate tumor growth, invasion, and resistance to therapies. Peroxisomes, essential organelles responsible for fatty acid oxidation and reactive oxygen species (ROS) homeostasis, rely on the receptor PEX5 for the import of metabolic enzymes into their matrix. However, the prognostic significance of peroxisomal enzymes for glioma patients remains unclear. In this study, we elucidate that PEX5 is indispensable for the cell growth, migration, and invasion of glioma cells. We establish a robust prognosis model based on the expression of peroxisomal enzymes, whose localization relies on PEX5. This PEX5-dependent signature not only serves as a robust prognosis model capable of accurately predicting outcomes for glioma patients, but also effectively distinguishes several clinicopathological features, including the grade, isocitrate dehydrogenase (IDH) mutation, and 1p19q codeletion status. Furthermore, we developed a nomogram that integrates the prognostic model with other clinicopathological factors, demonstrating highly accurate performance in estimating patient survival. Patients classified into the high-risk group based on our prognostic model exhibited an immunosuppressive microenvironment. Finally, our validation reveals that the elevated expression of GSTK1, an antioxidant enzyme within the signature, promotes the cell growth and migration of glioma cells, with this effect dependent on the peroxisomal targeting signal recognized by PEX5. These findings identify the PEX5-dependent signature as a promising prognostic tool for gliomas.

Identification of CREB5 as a prognostic and immunotherapeutic biomarker in glioma through multi-omics pan-cancer analysis.

BACKGROUND: The functional relevance of cyclic adenosine monophosphate (cAMP)-response element-binding protein 5 (CREB5) in cancers remains elusive, despite its significance as a member of the CREB family. The current research aims to explore the role of CREB5 in multiple cancers. METHODS: Pan-cancer analysis was performed to explore the expression patterns, prognostic value, mutational landscape as well as single-cell omic, immunologic, and drug sensitivity profiles of CREB5. Furthermore, we incorporated five distinct machine learning algorithms and determined that the least absolute shrinkage and selection operator-COX (LASSO-COX) algorithm, which exhibited the highest C index, was the optimal selection. Subsequently, we constructed a prognostic model centered around CREB5-associated genes. To elucidate the biological function of CREB5 in glioma cells, several assays including cell counting kit-8 (CCK-8), wound healing, transwell, flow cytometric were performed. RESULTS: CREB5 was overexpressed in pan-cancer and was linked to unfavorable prognosis, particularly in glioma. Furthermore, genetic alterations were determined in various types of cancer, and modifications in the CREB5 gene were linked to the prognosis. The single-cell omics and enrichment analyses showed that CREB5 was predominantly expressed in malignant glioma cells and was critically involved in the regulation of various oncogenic processes. Elevated levels of CREB5 were strongly linked with the infiltration of cancer-associated fibroblasts and the Th1 subset of CD4+ T cells. The validated CREB5-associated prognostic model reliably predicted the prognosis and drug response of glioma patients. The in vitro experiments showed that CREB5 promoted glioma cell proliferation, invasion, migration, and gap phase 2/mitotic (G2/M) phase arrest and recruited M2 macrophages into glioma cells. CONCLUSION: CREB5 has the potential to act as an oncogene and a biological marker in multiple cancers, particularly glioma.

Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning.

Purpose To develop and externally test a scan-to-prediction deep learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pediatric low-grade glioma. Materials and Methods This retrospective study included two pediatric low-grade glioma datasets with linked genomic and diagnostic T2-weighted MRI data of patients: Dana-Farber/Boston Children's Hospital (development dataset, n = 214 [113 (52.8%) male; 104 (48.6%) BRAF wild type, 60 (28.0%) BRAF fusion, and 50 (23.4%) BRAF V600E]) and the Children's Brain Tumor Network (external testing, n = 112 [55 (49.1%) male; 35 (31.2%) BRAF wild type, 60 (53.6%) BRAF fusion, and 17 (15.2%) BRAF V600E]). A deep learning pipeline was developed to classify BRAF mutational status (BRAF wild type vs BRAF fusion vs BRAF V600E) via a two-stage process: (a) three-dimensional tumor segmentation and extraction of axial tumor images and (b) section-wise, deep learning-based classification of mutational status. Knowledge-transfer and self-supervised approaches were investigated to prevent model overfitting, with a primary end point of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, a novel metric, center of mass distance, was developed to quantify the model attention around the tumor. Results A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest classification performance with an AUC of 0.82 (95% CI: 0.72, 0.91), 0.87 (95% CI: 0.61, 0.97), and 0.85 (95% CI: 0.66, 0.95) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively, on internal testing. On external testing, the pipeline yielded an AUC of 0.72 (95% CI: 0.64, 0.86), 0.78 (95% CI: 0.61, 0.89), and 0.72 (95% CI: 0.64, 0.88) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively. Conclusion Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pediatric low-grade glioma mutational status prediction in a limited data scenario. Keywords: Pediatrics, MRI, CNS, Brain/Brain Stem, Oncology, Feature Detection, Diagnosis, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.

The diagnostic efficiency of integration of 2HG MRS and IVIM versus individual parameters for predicting IDH mutation status in gliomas in clinical scenarios: A retrospective study.

PURPOSE: Currently, there remains a scarcity of established preoperative tests to accurately predict the isocitrate dehydrogenase (IDH) mutation status in clinical scenarios, with limited research has explored the potential synergistic diagnostic performance among metabolite, perfusion, and diffusion parameters. To address this issue, we aimed to develop an imaging protocol that integrated 2-hydroxyglutarate (2HG) magnetic resonance spectroscopy (MRS) and intravoxel incoherent motion (IVIM) by comprehensively assessing metabolic, cellular, and angiogenic changes caused by IDH mutations, and explored the diagnostic efficiency of this imaging protocol for predicting IDH mutation status in clinical scenarios. METHODS: Patients who met the inclusion criteria were categorized into two groups: IDH-wild type (IDH-WT) group and IDH-mutant (IDH-MT) group. Subsequently, we quantified the 2HG concentration, the relative apparent diffusion coefficient (rADC), the relative true diffusion coefficient value (rD), the relative pseudo-diffusion coefficient (rD*) and the relative perfusion fraction value (rf). Intergroup differences were estimated using t-test and Mann-Whitney U test. Finally, we performed receiver operating characteristic (ROC) curve and DeLong's test to evaluate and compare the diagnostic performance of individual parameters and their combinations. RESULTS: 64 patients (female, 21; male, 43; age, 47.0 ± 13.7 years) were enrolled. Compared with IDH-WT gliomas, IDH-MT gliomas had higher 2HG concentration, rADC and rD (P < 0.001), and lower rD* (P = 0.013). The ROC curve demonstrated that 2HG + rD + rD* exhibited the highest areas under curve (AUC) value (0.967, 95%CI 0.889-0.996) for discriminating IDH mutation status. Compared with each individual parameter, the predictive efficiency of 2HG + rADC + rD* and 2HG + rD + rD* shows a statistically significant enhancement (DeLong's test: P < 0.05). CONCLUSIONS: The integration of 2HG MRS and IVIM significantly improves the diagnostic efficiency for predicting IDH mutation status in clinical scenarios.

Development of a rapid and comprehensive genomic profiling test supporting diagnosis and research for gliomas.

A prompt and reliable molecular diagnosis for brain tumors has become crucial in precision medicine. While Comprehensive Genomic Profiling (CGP) has become feasible, there remains room for enhancement in brain tumor diagnosis due to the partial lack of essential genes and limitations in broad copy number analysis. In addition, the long turnaround time of commercially available CGPs poses an additional obstacle to the timely implementation of results in clinics. To address these challenges, we developed a CGP encompassing 113 genes, genome-wide copy number changes, and MGMT promoter methylation. Our CGP incorporates not only diagnostic genes but also supplementary genes valuable for research. Our CGP enables us to simultaneous identification of mutations, gene fusions, focal and broad copy number alterations, and MGMT promoter methylation status, with results delivered within a minimum of 4 days. Validation of our CGP, through comparisons with whole-genome sequencing, RNA sequencing, and pyrosequencing, has certified its accuracy and reliability. We applied our CGP for 23 consecutive cases of intracranial mass lesions, which demonstrated its efficacy in aiding diagnosis and prognostication. Our CGP offers a comprehensive and rapid molecular profiling for gliomas, which could potentially apply to clinical practices and research primarily in the field of brain tumors.