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)

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.

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.

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.

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.

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.

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.

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.

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.

[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.

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.

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.

Treatment outcome of IDH1/2 wildtype CNS WHO grade 4 glioma histologically diagnosed as WHO grade II or III astrocytomas.

BACKGROUND: Isocitrate dehydrogenase (IDH)1/2 wildtype (wt) astrocytomas formerly classified as WHO grade II or III have significantly shorter PFS and OS than IDH mutated WHO grade 2 and 3 gliomas leading to a classification as CNS WHO grade 4. It is the aim of this study to evaluate differences in the treatment-related clinical course of these tumors as they are largely unknown. METHODS: Patients undergoing surgery (between 2016-2019 in six neurosurgical departments) for a histologically diagnosed WHO grade 2-3 IDH1/2-wt astrocytoma were retrospectively reviewed to assess progression free survival (PFS), overall survival (OS), and prognostic factors. RESULTS: This multi-center study included 157 patients (mean age 58 years (20-87 years); with 36.9% females). The predominant histology was anaplastic astrocytoma WHO grade 3 (78.3%), followed by diffuse astrocytoma WHO grade 2 (21.7%). Gross total resection (GTR) was achieved in 37.6%, subtotal resection (STR) in 28.7%, and biopsy was performed in 33.8%. The median PFS (12.5 months) and OS (27.0 months) did not differ between WHO grades. Both, GTR and STR significantly increased PFS (P < 0.01) and OS (P < 0.001) compared to biopsy. Treatment according to Stupp protocol was not associated with longer OS or PFS compared to chemotherapy or radiotherapy alone. EGFR amplification (P = 0.014) and TERT-promotor mutation (P = 0.042) were associated with shortened OS. MGMT-promoter methylation had no influence on treatment response. CONCLUSIONS: WHO grade 2 and 3 IDH1/2 wt astrocytomas, treated according to the same treatment protocols, have a similar OS. Age, extent of resection, and strong EGFR expression were the most important treatment related prognostic factors.

The development of a custom RNA-sequencing panel for the identification of predictive and diagnostic biomarkers in glioma.

PURPOSE: Various molecular profiles are needed to classify malignant brain tumors, including gliomas, based on the latest classification criteria of the World Health Organization, and their poor prognosis necessitates new therapeutic targets. The Todai OncoPanel 2 RNA Panel (TOP2-RNA) is a custom-target RNA-sequencing (RNA-seq) using the junction capture method to maximize the sensitivity of detecting 455 fusion gene transcripts and analyze the expression profiles of 1,390 genes. This study aimed to classify gliomas and identify their molecular targets using TOP2-RNA. METHODS: A total of 124 frozen samples of malignant gliomas were subjected to TOP2-RNA for classification based on their molecular profiles and the identification of molecular targets. RESULTS: Among 55 glioblastoma cases, gene fusions were detected in 11 cases (20%), including novel MET fusions. Seven tyrosine kinase genes were found to be overexpressed in 15 cases (27.3%). In contrast to isocitrate dehydrogenase (IDH) wild-type glioblastoma, IDH-mutant tumors, including astrocytomas and oligodendrogliomas, barely harbor fusion genes or gene overexpression. Of the 34 overexpressed tyrosine kinase genes, MDM2 and CDK4 in glioblastoma, 22 copy number amplifications (64.7%) were observed. When comparing astrocytomas and oligodendrogliomas in gene set enrichment analysis, the gene sets related to 1p36 and 19q were highly enriched in astrocytomas, suggesting that regional genomic DNA copy number alterations can be evaluated by gene expression analysis. CONCLUSIONS: TOP2-RNA is a highly sensitive assay for detecting fusion genes, exon skipping, and aberrant gene expression. Alterations in targetable driver genes were identified in more than 50% of glioblastoma. Molecular profiling by TOP2-RNA provides ample predictive, prognostic, and diagnostic biomarkers that may not be identified by conventional assays and, therefore, is expected to increase treatment options for individual patients with glioma.

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.

Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation.

BACKGROUND: Glioma is a primary brain tumor and the assessment of its molecular profile in a minimally invasive manner is important in determining treatment strategies. Among the molecular abnormalities of gliomas, mutations in the isocitrate dehydrogenase (IDH) gene are strong predictors of treatment sensitivity and prognosis. In this study, we attempted to non-invasively diagnose glioma development and the presence of IDH mutations using multivariate analysis of the plasma mid-infrared absorption spectra for a comprehensive and sensitive view of changes in blood components associated with the disease and genetic mutations. These component changes are discussed in terms of absorption wavenumbers that contribute to differentiation. METHODS: Plasma samples were collected at our institutes from 84 patients with glioma (13 oligodendrogliomas, 17 IDH-mutant astrocytoma, 7 IDH wild-type diffuse glioma, and 47 glioblastomas) before treatment initiation and 72 healthy participants. FTIR-ATR spectra were obtained for each plasma sample, and PLS discriminant analysis was performed using the absorbance of each wavenumber in the fingerprint region of biomolecules as the explanatory variable. This data was used to distinguish patients with glioma from healthy participants and diagnose the presence of IDH mutations. RESULTS: The derived classification algorithm distinguished the patients with glioma from healthy participants with 83% accuracy (area under the curve (AUC) in receiver operating characteristic (ROC) = 0.908) and diagnosed the presence of IDH mutation with 75% accuracy (AUC = 0.752 in ROC) in cross-validation using 30% of the total test data. The characteristic changes in the absorption spectra suggest an increase in the ratio of ß-sheet structures in the conformational composition of blood proteins of patients with glioma. Furthermore, these changes were more pronounced in patients with IDH-mutant gliomas. CONCLUSIONS: The plasma infrared absorption spectra could be used to diagnose gliomas and the presence of IDH mutations in gliomas with a high degree of accuracy. The spectral shape of the protein absorption band showed that the ratio of ß-sheet structures in blood proteins was significantly higher in patients with glioma than in healthy participants, and protein aggregation was a distinct feature in patients with glioma with IDH mutations.

Identification and validation of a novel 9-gene signature of non-specific classification to predict prognosis in glioma patients.

This study aimed to identify and validate a 9-gene signature for predicting overall survival (OS) in glioma patients. Analysis of multiple gene expression datasets led to the identification of 135 candidate genes associated with OS in glioma patients. Further analysis revealed that IGFBP2, PBK, NRXN3, TGIF1, DNAJA4, and LGALS3BP were identified as risk factors for OS, while ENAH, PPP2R2C, and SPHKAP were found to be protective factors. Multifaceted validation using different databases confirmed their differential expression patterns in glioma tissues compared to normal brain tissue. By utilizing LASSO regression and multivariate Cox regression analysis, a risk score was developed based on the expression levels of the 9 crucial genes. The risk score showed a significant correlation with OS in both training and validation cohorts and yielded superior predictive accuracy compared to individual gene expression. Moreover, a predictive nomogram incorporating the risk score, WHO grade, age, IDH mutation, and 1p/19q co-deletion was constructed and validated, which exhibited high predictive capabilities for survival rates at different time points. Enrichment analysis revealed the involvement of extracellular matrix-related pathways and immune system signaling in glioma prognosis. Furthermore, the risk score showed a strong correlation with immune cell infiltration and immune checkpoint expression, suggesting its potential role in the tumor immune microenvironment. In conclusion, our study provides a robust 9-gene signature and a predictive nomogram for evaluating the prognosis of glioma patients, offering valuable insights into personalized treatment strategies.

PBX3 as a biomarker for the early diagnosis and prediction of prognosis of glioma.

BACKGROUND: Increasing evidence have elucidated that PBX3 played a crucial role in cancer initiation and progression. PBX3 was differentially expressed in many cancer types. However, PBX3 potential involvement in gliomas remains to be explored. METHODS: The expression level of PBX3 in glioma tissues and glioma cells, and its correlation with clinical features were analyzed by data from TCGA, GEPIA, CGGA and CCLE. Univariable survival and Multivariate Cox analysis was used to compare several clinical characteristics with survival. We also analyzed the correlation between PBX3 expression level and survival outcome and survival time of LGG and GBM patients by using linear regression equation. GSEA was used to generate an ordered list of all genes related to PBX3 expression and screening of genes co-expressed with PBX3 mRNA by "limma" package. RESULTS: The results showed that PBX3 was highly expressed in gliomas and its expression increased with the increase of malignancy. Survival analysis found that PBX3 is more valuable in predicting the OS and PFI of LGG patients than that of GBM. For further study, TCGA and CGGA data were downloaded for univariate Cox analysis and multivariate Cox analysis which showed that the expression of PBX3 was independent influencing factors for poor prognosis of LGG patients. Meanwhile, Receiver operating characteristic (ROC) curve showed that PBX3 was a predictor of overall survival rate and progression-free survival rate of LGG. Linear regression model analysis indicated that the higher expression of PBX3 the higher the risk of death of LGG patients, and the higher expression of PBX3 the higher the risk of disease progression of LGG patients. Next, TCGA data were downloaded for GSEA and Co-expression analyses, which was performed to study the function of PBX3. CONCLUSION: PBX3 may be involved in the occurrence and development of glioma, and has potential reference value for the early diagnosis and prediction of prognosis of glioma.

Prognostic factors analysis of diffuse midline glioma.

PURPOSE: This study retrospectively analyzes cases of diffuse midline glioma treated with radiotherapy, with the aim of investigating the prognosis of the tumor and its influencing factors. METHODS: From January 2018 to November 2022, we treated 64 patients who were pathologically diagnosed with diffuse midline glioma. Among them, 41 underwent surgical resection, and 23 underwent biopsy procedures. All patients received postoperative radiotherapy. We followed up with the patients to determine the overall survival rate and conducted univariate and multivariate analyses on relevant indicators. RESULTS: The median survival time for the entire patient group was 33.3 months, with overall survival rates of 92.9%, 75.4%, and 45.0% at 1 year, 2 years, and 3 years, respectively. Univariate and multivariate analyses indicated that older patients had a better prognosis. CONCLUSION: Patient age is an independent prognostic factor for patients with diffuse midline glioma undergoing radiation therapy.

Decoding the puzzle: A multidisciplinary systematic review of adult brainstem glioma.

Adult brainstem gliomas (BSGs) are a group of rare central nervous system tumors with varying prognoses and controversial standard treatment strategies. To provide an overview of current trends, a systematic review using the PRISMA guidelines, Class of evidence (CE) and strength of recommendation (SR), was conducted. The review identified 27 studies. Surgery was found to have a positive impact on survival, particularly for focal lesions with CE II SR C. Stereotactic image-guided biopsy was recommended when resective surgery was not feasible with CE II and SR B. The role of systemic treatments remains unclear. Eight studies provided molecular biology data. This review gathers crucial literature on diagnosis and management of adult BSGs. It provides evidence-based guidance with updated recommendations for diagnosing and treating, taking into account recent molecular and genetic advancements. The importance of brain biopsy is emphasized to optimize treatment using emerging genetic-molecular findings and explore potential targeted therapies.