Prognosis Of Glioma Research Articles

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 a dual-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.

Profile of miRNAs in small extracellular vesicles released from glioblastoma cells treated by boron neutron capture therapy.

Boron neutron capture therapy (BNCT) is a tumor cell-selective particle-radiation therapy. In BNCT, administered p-boronophenylalanine (BPA) is selectively taken up by tumor cells, and the tumor is irradiated with thermal neutrons. High-LET α-particles and recoil 7Li, which have a path length of 5-9μm, are generated by the capture reaction between 10B and thermal neutrons and selectively kill tumor cells that have uptaken 10B. Although BNCT has prolonged the survival time of malignant glioma patients, recurrences are still to be resolved. miRNAs, that are encapsulated in small extracellular vesicles (sEVs) in body fluids and exist stably may serve critical role in recurrence. In this study, we comprehensively investigated microRNAs (miRNAs) in sEVs released from post-BNCT glioblastoma cells. Glioblastoma U87 MG cells were treated with 25 ppm of BPA in the culture media and irradiated with thermal neutrons. After irradiation, they were plated into dishes and cultured for 3 days in the 5% CO2 incubator. Then, sEVs released into the medium were collected by column chromatography, and miRNAs in sEVs were comprehensively investigated using microarrays. An increase in 20 individual miRNAs (ratio > 2) and a decrease in 2 individual miRNAs (ratio < 0.5) were detected in BNCT cells compared with non-irradiated cells. Among detected miRNAs, 20 miRNAs were associated with worse prognosis of glioma in Kaplan Meier Survival Analysis of overall survival in TCGA. These miRNA after BNCT may proceed tumors, modulate radiation resistance, or inhibit invasion and affect the prognosis of glioma.

UBA2 as a Prognostic Biomarker and Potential Therapeutic Target in Glioma.

Gliomas are characterized by aggressive behavior, leading to severe disability and high mortality. Ubiquitin-like modifier activating enzyme 2 (UBA2) is a subunit of the E1-activating enzyme involved in the SUMOylation (SUMO, small ubiquitin-related modifier) of numerous proteins. Although the abnormality of UBA2 is linked to the progression of various tumor types, the role of UBA2 in glioma is still unknown. A bioinformatic analysis using several public databases was conducted to examine the expression level, clinicopathological correlations, and prognostic significance of UBA2 in glioma. The correlation between UBA2 expression and drug sensitivity in cancers was also explored. Multiple cellular experiments were conducted to validate the role of UBA2 in glioma. Analysis of multiple databases and cellular experiments revealed that UBA2 was overexpressed in glioma tissues and cell lines, respectively. UBA2 expression in gliomas correlated with World Health Organization (WHO) grade, IDH gene status, 1p19q deletion, histological type, and immune cell infiltration in glioma. UBA2 expression in carcinomas also correlated with drug sensitivity. Kaplan-Meier analysis revealed that high expression of UBA2 predicted poorer survival in glioma patients. A nomogram model containing UBA2 expression was constructed for clinical practice. Knockdown of UBA2 was observed to suppress glioma cell progression and sensitize glioma cells to irradiation in vitro. Overall, this research showed that UBA2 might be involved not only in the development of glioma but also in the regulation of immunity, drug sensitivity, and radiosensitivity. Therefore, UBA2 may be a potential target for therapy and a candidate biomarker for glioma diagnosis and prognosis.

Cell Membrane Fragment-Wrapped Parenteral Nanoemulsions: A New Drug Delivery Tool to Target Gliomas.

Poor prognosis in high-grade gliomas is mainly due to fatal relapse after surgical resection in the absence of efficient chemotherapy, which is severely hampered by the blood-brain barrier. However, the leaky blood-brain-tumour barrier forms upon tumour growth and vascularization, allowing targeted nanocarrier-mediated drug delivery. The homotypic targeting ability of cell-membrane fragments obtained from cancer cells means that these fragments can be exploited to this aim. In this experimental work, injectable nanoemulsions, which have a long history of safe clinic usage, have been wrapped in glioma-cell membrane fragments via co-extrusion to give targeted, homogeneously sized, sterile formulations. These systems were then loaded with three different chemotherapeutics, in the form of hydrophobic ion pairs that can be released into the target site thanks to interactions with physiological components. The numerous assays performed in two-dimensional (2D) and three-dimensional (3D) cell models demonstrate that the proposed approach is a versatile drug-delivery platform with chemo-tactic properties towards glioma cells, with adhesive interactions between the target cell and the cell membrane fragments most likely being responsible for the effect. This approach's promising translational perspectives towards personalized nanomedicine mean that further in vivo studies are foreseen for the future.

A Study on Prognosis of Diffuse Glioma Based on Clinical Factors and Magnetic Resonance Imaging Radiomics

To construct an optimal prognostic model to assess the prognosis of patients with diffuse glioma. Preoperative magnetic resonance imaging and clinical data were retrospectively collected from 266 patients (training cohort : validation cohort = 7:3) with pathologically confirmed diffuse gliomas. A radiomics prognostic model (R-model) based on the radiomics features was constructed. A prognostic model based on clinical factors (C-model) and a fusion model (F-model) were also constructed. Based on the optimal model of three models, the nomogram was constructed. Finally, a "Prognosis Calculator for Diffuse Glioma" was constructed based on the nomogram. The c-index of the R-, C-, and F-models in the validation cohort was 0.742, 0.796, and 0.814, respectively. In the validation cohort, the 1-year AUC of the R-, C-, and F-models was 0.749, 0.806, and 0.836, respectively; the 3-year AUC was 0.896, 0.966, and 0.963, respectively. In the training cohort, validation cohort, all cohorts, and different grades of glioma cohorts, F-model (optimal model) could identify low- and high-risk groups well. The "Prognosis Calculator for Diffuse Glioma" was available at https://github.com/HDCurry/prognosis. Among the three models, the F-model (radiomics combined with clinical factors) had optimal predictive efficacy and could more accurately assess the prognosis of diffuse glioma. The "Prognosis Calculator for Diffuse Glioma" constructed based on this model could assist clinicians in more easily and accurately assessing the prognosis of patients with diffuse glioma, thus enabling them to make more reasonable treatment strategies.

Association Between Pretreatment 11C-Methionine Positron Emission Tomography Metrics, Histology, and Prognosis in 125 Newly Diagnosed Patients with Adult-Type Diffuse Glioma Based on the World Health Organization 2021Classification

To clarify the relationships between 11C-methionine (MET) positron emission tomography (PET) metrics and the histology, genetics, and prognosis of adult-type diffuse glioma (ADG) based on the World Health Organization (WHO) 2021 classification. A total of 125 newly diagnosed patients with ADG were enrolled. We compared the maximum standardized uptake value (SUVmax), tumor-to-normal background ratio (TNR), metabolic tumor volume (MTV), and total lesion methionine uptake (TLMU) to the histology and genetics of the patients with ADG. We also evaluated the prognoses of the 93 surgically treated patients. The patients with isocitrate dehydrogenase wild ADG showed significantly higher MET-PET metrics (P < 0.05 for all parameters), significantly shorter overall survival and progression-free survival (P < 0.0001 for both) than those of the patients with isocitrate dehydrogenase mutant (IDHm) ADG. In the IDHm ADG group, the SUVmax, MTV, and TLMU values were significantly higher in patients with IDHm grade (G) 4 astrocytoma than patients with IDHm G2/3 astrocytoma (P < 0.05 for all), but not than patients with G2-3 oligodendroglioma. The progression-free survival was significantly shorter in the patients with G4 astrocytoma versus the patients with G2/3 astrocytoma and G3 oligodendroglioma (P < 0.05 for both). The SUVmax and TNR values were significantly higher in recurrent patients than nonrecurrent patients (P < 0.01 for both), but no significant differences were found in MTV or TLMU values. MET-PET metrics well reflect the histological subtype, WHO grade and prognosis of ADG based on the 2021 WHO classification, with the exception of oligodendroglial tumors. Volumetric parameters were not significantly associated with recurrence, unlike the SUVmax and TNR.

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.

Identification and classification of glioma subtypes based on RNA-binding proteins

BackgroundGlioma is a common and aggressive primary malignant cancer known for its high morbidity, mortality, and recurrence rates. Despite this, treatment options for glioma are currently restricted. The dysregulation of RBPs has been linked to the advancement of several types of cancer, but their precise role in glioma evolution is still not fully understood. This study sought to investigate how RBPs may impact the development and prognosis of glioma, with potential implications for prognosis and therapy. MethodsRNA-seq profiles of glioma and corresponding clinical data from the CGGA database were initially collected for analysis. Unsupervised clustering was utilized to identify crucial tumor subtypes in glioma development. Subsequent time-series analysis and MS model were employed to track the progression of these identified subtypes. RBPs playing a significant role in glioma progression were then pinpointed using WGCNA and Lasso Cox regression models. Functional analysis of these key RBP-related genes was conducted through GSEA. Additionally, the CIBERSORT algorithm was utilized to estimate immune infiltrating cells, while the STRING database was consulted to uncover potential mechanisms of the identified biomarkers. ResultsSix tumor subgroups were identified and found to be highly homogeneous within each subgroup. The progression stages of these tumor subgroups were determined using time-series analysis and a MS model. Through WGCNA, Lasso Cox, and multivariate Cox regression analysis, it was confirmed that BCLAF1 is correlated with survival in glioma patients and is closely linked to glioma progression. Functional annotation suggests that BCLAF1 may impact glioma progression by influencing RNA splicing, which in turn affects the cell cycle, Wnt signaling pathway, and other cancer development pathways. ConclusionsThe study initially identified six subtypes of glioma progression and assessed their malignancy ranking. Furthermore, it was determined that BCLAF1 could serve as an RBP-related prognostic marker, offering significant implications for the clinical diagnosis and personalized treatment of glioma.

Three Liquid-Liquid Phase Separation-Related Genes Associated with Prognosis in Glioma.

Dysregulated liquid-liquid phase separation (LLPS) instigates tumorigenesis through biomolecular condensate dysfunction. However, the association between LLPS-associated genes and glioma remains underexplored. Differentially expressed genes (DEGs) of glioma were obtained from the GSE50161 dataset, including 34 glioma and 13 normal samples. We analyzed differentially expressed LLPS-related genes in glioma from public databases. These genes informed refined molecular subtyping on the TCGA-glioma dataset. CIBERSORT assessed immune cell infiltration across three subclusters. A prognostic model was devised using univariate and lasso Cox regressions on intersecting genes. Prognostic gene expression was validated in glioma cells via RT-qPCR. A total of 673 differentially expressed LLPS-associated genes were identified in glioma. Three distinct molecular subtypes (C1, C2, and C3) of glioma were obtained with a marked variance in the expression of immune checkpoint genes PD1 and PDL1. Differences in immune cell infiltration were observed across subtypes. In addition, a tri-gene prognostic signature (TAGLN2, NTNG2, and IGF2BP2) was derived with significant survival differences between high and low-risk groups. The prognostic model displayed impressive AUC values for 1, 3, and 5-year survival in both training and validation sets. Further analysis highlighted a notable correlation between the three prognostic genes and immune cells in glioma samples. Furthermore, we found the upregulation of TAGLN2 and IGF2BP2 and the downregulation of NTNG2 in glioma tumors and cells. This study innovatively uncovers the significant role of LLPS-related genes in glioma tumor grading and prognosis. The constructed tri-gene prognostic model holds promise for enhancing personalized prognosis assessments and optimizing immunotherapy strategies for glioma patients.

Nuclear autoantigenic sperm protein facilitates glioblastoma progression and radioresistance by regulating the ANXA2/STAT3 axis.

Although radiotherapy is a core treatment modality for various human cancers, including glioblastoma multiforme (GBM), its clinical effects are often limited by radioresistance. The specific molecular mechanisms underlying radioresistance are largely unknown, and the reduction of radioresistance is an unresolved challenge in GBM research. We analyzed and verified the expression of nuclear autoantigenic sperm protein (NASP) in gliomas and its relationship with patient prognosis. We also explored the function of NASP in GBM cell lines. We performed further mechanistic experiments to investigate the mechanisms by which NASP facilitates GBM progression and radioresistance. An intracranial mouse model was used to verify the effectiveness of combination therapy. NASP was highly expressed in gliomas, and its expression was negatively correlated with the prognosis of glioma. Functionally, NASP facilitated GBM cell proliferation, migration, invasion, and radioresistance. Mechanistically, NASP interacted directly with annexin A2 (ANXA2) and promoted its nuclear localization, which may have been mediated by phospho-annexin A2 (Tyr23). The NASP/ANXA2 axis was involved in DNA damage repair after radiotherapy, which explains the radioresistance of GBM cells that highly express NASP. NASP overexpression significantly activated the signal transducer and activator of transcription 3 (STAT3) signaling pathway. The combination of WP1066 (a STAT3 pathway inhibitor) and radiotherapy significantly inhibited GBM growth invitro and invivo. Our findings indicate that NASP may serve as a potential biomarker of GBM radioresistance and has important implications for improving clinical radiotherapy.

Comprehensive analysis of CYBB as a prognostic marker and therapeutic target in glioma: A bioinformatics approach

BackgroundIn the central nervous system, glioma is the most common malignant tumor, and patients have a poor prognosis. Identification of novel marker genes and establishment of prognostic models are important for early diagnosis and prognosis determination. MethodsDownload glioma data from the CGGA and TCG databases. Application of bioinformatics to analyze the impact of CYBB on the clinicopathological characteristics, immunological features and prognosis of gliomas. Using single-cell sequencing data from 7 glioblastoma patients in the CGGA database, the role of CYBB in the tumor microenvironment was analyzed. In addition, a prognostic model was constructed based on CYBB high and low differentially expressed genes and mitochondrial genes. ResultsThe expression of CYBB is closely related to various clinical features, immune cell infiltration level, immune checkpoint and survival time of patients. A 10-gene prediction model was constructed based on the differentially expressed genes of low and high CYBB and mitochondria-related genes. Glioma patients with higher risk scores had significantly lower survival probabilities. Receiver operating characteristic curves and nomograms were plotted over time to show the predictive accuracy and predictive value of the 10-gene prognostic model. ConclusionsOur study shows that CYBB is strongly correlated with clinical characteristics features and prognosis of glioma patients, and can be used as a potential therapeutic target. Prognostic models based on CYBB and mitochondrial genes have good performance in predicting prognosis of glioma patients.

Use of circulating tumor cells and microemboli to predict diagnosis and prognosis in diffuse glioma.

Circulating tumor cell (CTC) detection is a promising noninvasive technique that can be used to diagnose cancer, monitor progression, and predict prognosis. In this study, the authors aimed to investigate the clinical utility of CTCs in the management of diffuse glioma. Sixty-three patients with newly diagnosed diffuse glioma were included in this multicenter clinical cohort. The authors used a platform based on isolation by size of epithelial tumor cells (ISET) to detect and analyze CTCs and circulating tumor microemboli (CTMs) in the peripheral blood of patients both before and after surgery. Least absolute shrinkage and selector operation (LASSO) and Cox regression analyses were used to verify whether CTCs and CTMs are independent prognostic factors for diffuse glioma. CTC levels were closely related to the degree of malignancy, WHO grade, and pathological subtypes. Receiver operating characteristic curve analysis revealed that a high CTC level was a predictor for glioblastoma. The results also showed that CTMs originate from the parental tumor rather than from the circulation and are an independent prognostic factor for diffuse glioma. The postoperative CTC level is related to the peripheral immune system and patient survival. Cox regression analysis showed that postoperative CTC levels and CTM status are independent prognostic factors for diffuse glioma, and CTC- and CTM-based survival models had high accuracy in internal validation. The authors revealed a correlation between CTCs and clinical characteristics and demonstrated that CTCs and CTMs are independent predictors for the diagnosis and prognosis of diffuse glioma. Their CTC- and CTM-based survival models can enable clinicians to evaluate patients' response to surgery as well as their outcomes.

Clinical significance of immune-related antigen CD58 in gliomas and analysis of its potential core related gene clusters

BackgroundThe clinical significance of immune-related antigen CD58 in gliomas remains uncertain. The aim of this study was to examine the clinical importance and possible core related genes of CD58 in gliomas. MethodsPan-cancer analysis was to observe the association between CD58 and different tumors, glioma RNA sequencing data and clinical sample analyses were used to observe the relationship between CD58 and glioma, shRNA interference models were to observe the impact of CD58 on glioma cell function, and four glioma datasets and two online analysis platforms were used to explore the core related genes affecting the correlation between CD58 and glioma. ResultsHigh CD58 expression was associated with worse prognosis in various tumors and higher malignancy in glioma. Down regulation of CD58 expression was linked to decreased proliferation, increased apoptosis, and reduced metastasis in glioma cells. The pathways involved in CD58-related effects were enriched for immune cell adhesion and immune factor activation, and the core genes were CASP1, CCL2, IL18, MYD88, PTPRC, and TLR2. The signature of CD58 and its core-related genes showed superior predictive power for glioma prognosis. ConclusionHigh CD58 expression is correlated with more malignant glioma types, and also an independent risk factor for mortality in glioma. CD58 and its core-related genes may serve as novel biomarkers for diagnosing and treating glioma.

MODERN IMAGING TECHNIQUES IN THE ASSESSMENT&#x0D; OF MALIGNANCY IN GLIAL BRAIN TUMORS:&#x0D; LITERATURE REVIEW

Relevance: Gliomas represent a major group of primary central nervous system tumors, accounting for approximately 80% of all&#x0D; malignant brain neoplasms. Recent advances in molecular diagnostics, including the identification of mutations in the isocitrate dehydrogenase(IDH) gene, offer new opportunities to improve the classification of gliomas and to understand the mechanisms of their development. IDH&#x0D; mutations, which are found in a large proportion of gliomas, are associated with a better prognosis and represent an important biomarker&#x0D; for the development of targeted therapeutic strategies. Diagnostic techniques, particularly magnetic resonance imaging (MRI), also play&#x0D; an important role, providing additional ways to assess the morphological and biological characteristics of gliomas, contributing to a more&#x0D; accurate determination of tumor malignancy and molecular profile, which is key to treatment decisions.&#x0D; The purpose: To study the possibilities of magnetic resonance imaging in assessing the degree of malignancy of glial brain tumors.&#x0D; Method: A literature review was performed using the keywords: glioma, IDH mutation, IDH wild-type, MRI, magnetic resonance&#x0D; spectroscopy (MRS), apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), diffusion kurtosis&#x0D; image (DKI), Susceptibility Weighted Imaging (SWI) in PubMed, BMC Medicine, and Google Scholar databases. The review includes the results&#x0D; of an analysis of 45 literature reviews on the diagnostic capabilities of molecular diagnostics and MRI to assess the malignancy of gliomas.&#x0D; Results: This review highlights the importance of current approaches to the diagnosis of gliomas, demonstrating that different grades of&#x0D; malignancy and IDH mutation status are associated with unique features on MR imaging, including differences in localization, signal, and&#x0D; enhancement patterns. DTI and DWI extend the ability of MRI to assess tissue microstructure, allowing for more precise tumor characterization.&#x0D; Studies combining MRI morphologic features, ADC and DTI parameters offer a non-invasive approach to glioma prognosis, highlighting the&#x0D; importance of integrated use of imaging modalities to improve diagnosis and therapy.&#x0D; Conclusion: The use of magnetic resonance imaging allows for more accurate morphologic and metabolic characterization of gliomas&#x0D; in assessing the degree of malignancy, allowing physicians to make more accurate prognostic assessments and select the most appropriate&#x0D; treatment strategy.&#x0D; Keywords: Glioma, IDH mutation, wild-type IDH, MRI, magnetic resonance spectroscopy, ADC, DWI, DTI, DKI, SWI

Bulk and single cells transcriptomes with experimental validation identify USP18 as a novel glioma prognosis and proliferation indicator.

The mechanism by which ubiquitin-specific protease 18 (USP18) (enzyme commission: 3.4.19.12) inhibition in cancer promotes cell pyroptosis via the induction of interferon (IFN)-stimulated genes has been recently demonstrated. It is also known that USP18 influences the epithelial-mesenchymal transition of glioma cells. In the present study, the upregulation of USP18 in glioma was revealed through bulk transcriptome analysis, which was associated with poor prognosis in patients with glioma. Furthermore, USP18 levels affected the response to immunotherapy in patients with glioma. Single-cell transcriptome and enrichment analyses demonstrated that USP18 was associated with type 1 IFN responses in glioma T cells. To demonstrate the effect of USP18 expression levels on glioma cells, USP18 expression was knocked down in U251 and U87MG ATCC cell lines. A subsequent Cell Counting Kit-8 assay revealed that glioma cell viability was significantly decreased 4 days after USP18 knockdown. In addition, the knockdown of USP18 expression significantly inhibited the clonogenicity of U251 and U87MG ATCC cells. In conclusion, the present study demonstrated that knockdown of USP18 expression inhibited the proliferation of glioma cells, which may be mediated by the effect of USP18 on the IFN-I response.

Abstract 361: Targeting the suppressor of ferroptosis to improve the treament effeciency of glioblastoma

Abstract Glioma is the most frequent tumor in central nervous system (CNS), which makes up about 30% of CNS tumors. The current standard strategy for glioma treatment is surgery plus adjuvant chemotherapy and concurrent radiotherapy. Even though, the prognosis of glioma is still poor. It’s encouraging that temozolomide (TMZ) prolongs the overall survival of glioma patients, but the following TMZ resistance is really frustrating. It’s urgent to figure out efficient ways to improve the long term prognosis of patients, such as antagonizing the TMZ resistance. Ferroptosis was a newly discovered type of regulated cell death characterized by iron-dependent aberrant lipid peroxidation and membrane damage in the last decade. Ferroptosis-associated iron transport proteins modulate the process precisely through controlling the iron content. ZIP14, also known as SLC39A14, is an eight-transmembrane protein which could transport divalent metal ion like Mn2+, Zn2+ and Fe2+. Our preliminary data showed that the expression level of ZIP14 was higher in tumor tissues than paired normal tissue, which was corelated with poor prognosis of glioma. Knock down ZIP14 could induce cell death, and which could be rescued by inhibitors for ferroptosis but not for apoptosis or necrosis. Knock down ZIP14 decreased the ferroptosis inhibition by Ferroptosis suppress protein 1 (FSP1) which through reduction of lipid peroxidation, and sensitized cells to TMZ. Furthermore, we found that ZIP14 formed a complex with Ubiquinone. And it was known that FSP1 is critical in the switch of ubiquinone to ubiquinol. Therefore, we propose that ZIP14 suppress lipid peroxidation through blockage of the transition of ubiquinone to ubiquinol. While ZIP14 was knocked down, the complex with Ubiquinone wad damaged, and could not further change to Ubiquinol, so the suppression to lipid peroxidation was released, and then, ferroptosis was upregulated. In this study, we are going to figure out the molecular mechanism about how ZIP14 suppress ferroptosis and find out efficient small molecule inhibitors to activate ferroptosis by abolishing ZIP14 and improve the prognosis of glioma patients. Citation Format: Jing Wang. Targeting the suppressor of ferroptosis to improve the treament effeciency of glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 361.

Abstract 5200: Multiparametric MRI along with machine learning informs on molecular underpinnings, prognosis, and treatment response in pediatric low grade glioma

Abstract We report a comprehensive radiogenomic analysis, including multiparametric MRI and RNA sequencing of 502 tumors across 13 histological types of pediatric low-grade glioma (PLGG). Through an unsupervised analysis of treatment-naïve imaging, we were able to identify 3 distinct imaging clusters that were associated with different molecular pathways according to transcriptomic pathway analysis. Using xCell enrichment scores as input for clustering, we discovered three distinct immunological groups based on immune cell infiltration. The results suggested the presence of an ‘immune-hot” cluster within the tumors with a poorer prognosis, which may potentially benefit from emerging immunotherapies. We subsequently generated a radiomic signature that was able to predict immunological profiles. We also developed a clinicoradiomic predictive model that can assess patients’ risk of progression and predict treatment response. Finally, we evaluated the transcriptomic pathways that were most predictive of clinicoradiomic risk scores and identified the biological pathways most predictive of risk of progression in PLGG, when adjusting for molecular subtype, race, reported gender, age at diagnosis, and the anatomic location of the tumor. This is the first large-scale radiogenomic analysis in PLGG that can aid in prognostication, assessing patients’ risk of progression, predicting treatment response in conventional therapies, and early stratification of patients to identify potential candidates for targeted therapies. Citation Format: Ali Nabavizadeh, Adam Kraya, Komal Rathi, Meen Chul Kim, Nastaran Khalili, Ariana Familiar, Varun Kesherwani, Hannah Anderson, Deep Gandhi, Aria Mahtabfar, Sina Bagheri, Xiaoyan Huang, Yuankun Zhu, Alexander Sickler, Saksham Phul, Sabine Mueller, Mateusz P Koptyra, Philip B Storm, Jeff Ware, Yuanquan Song, Michael J Fisher, Arastoo Vossough, Christos Davatzikos, Adam C Resnick, Anahita Fathi Kazerooni. Multiparametric MRI along with machine learning informs on molecular underpinnings, prognosis, and treatment response in pediatric low grade glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5200.

Revealing the role of SPP1+ macrophages in glioma prognosis and therapeutic targeting by investigating tumor-associated macrophage landscape in grade 2 and 3 gliomas

BackgroundGlioma is a highly heterogeneous brain tumor categorized into World Health Organization (WHO) grades 1–4 based on its malignancy. The suppressive immune microenvironment of glioma contributes significantly to unfavourable patient outcomes. However, the cellular composition and their complex interplays within the glioma environment remain poorly understood, and reliable prognostic markers remain elusive. Therefore, in-depth exploration of the tumor microenvironment (TME) and identification of predictive markers are crucial for improving the clinical management of glioma patients.ResultsOur analysis of single-cell RNA-sequencing data from glioma samples unveiled the immunosuppressive role of tumor-associated macrophages (TAMs), mediated through intricate interactions with tumor cells and lymphocytes. We also discovered the heterogeneity within TAMs, among which a group of suppressive TAMs named TAM-SPP1 demonstrated a significant association with Epidermal Growth Factor Receptor (EGFR) amplification, impaired T cell response and unfavourable patient survival outcomes. Furthermore, by leveraging genomic and transcriptomic data from The Cancer Genome Atlas (TCGA) dataset, two distinct molecular subtypes with a different constitution of TAMs, EGFR status and clinical outcomes were identified. Exploiting the molecular differences between these two subtypes, we developed a four-gene-based prognostic model. This model displayed strong associations with an elevated level of suppressive TAMs and could be used to predict anti-tumor immune response and prognosis in glioma patients.ConclusionOur findings illuminated the molecular and cellular mechanisms that shape the immunosuppressive microenvironment in gliomas, providing novel insights into potential therapeutic targets. Furthermore, the developed prognostic model holds promise for predicting immunotherapy response and assisting in more precise risk stratification for glioma patients.Graphical abstract

Association of TNFRSF19 with a TNF family-based prognostic model and subtypes in gliomas using machine learning

PurposeTNF family members (TFMs) play a crucial role in different types of cancers, with TNF Receptor Superfamily Member 19 (TNFRSF19) standing out as a particularly important member in this category. Further research is necessary to investigate the potential impact of TFMs on prognosis prediction and to elucidate the function and potential therapeutic targets linked to TNFRSF19 expression in gliomas. MethodsThree databases provided the data on gene expression and clinical information. Fourteen prognostic members were found through univariate Cox analysis and subsequently utilized to construct TFMs-based model in LASSO and multivariate Cox analyses. TFMs-based subtypes based on the expression profile were identified using an unsupervised clustering method. Machine learning algorithm identified key genes linked to prognostic model and subtype. A sequence of immune infiltrations was evaluated using the ssGSEA and ESTIMATE algorithms. Immunohistochemistry was used to examine the patterns of expression and the clinical significance of TNFRSF19. ResultsOur development of a prognostic model and subtypes based on the TNF family was successful, resulting in accurate predictions of prognosis. The findings indicate that TNFRSF19 exhibited strong performance. Upregulation of TNFRSF19 was correlated with malignant phenotypes and poor prognosis, which was confirmed through immunohistochemistry. TNFRSF19 played a role in reshaping the immunosuppressive microenvironment in gliomas, and multiple drug-targeted TNFRSF19 molecules were identified. ConclusionsThe TMF-based prognostic model and subtype can facilitate treatment decisions for glioma. TNFRSF19 is an outstanding representative of a predictor of prognosis and immunotherapy effect in gliomas.

Integrative analysis of bulk RNA-seq and scRNA-seq data indicates the prognostic and immunologic values of SERPINH1 in glioma.

SERPINH1 is abnormally expressed in multiple cancers and is associated with malignant progression. However, few reports detail its role in the etiopathogenesis of glioma. Hence, the aim of this article was to investigate the potential value of SERPINH1 in glioma using an integrative analysis. Data of RNA-seq and scRNA-seq was obtained and evaluated using online databases. The expression of SERPINH1 was confirmed by qRT-PCR and immunohistochemistry. The prognostic value of SERPINH1 was evaluated using univariate and multivariate Cox regression analyses. SERPINH1-related signaling pathways and the interaction of SERPINH1 with immunity were also investigated. SERPINH1 exhibited a markedly elevated expression in glioma compared to normal brain tissues in the online databases. Similar results were confirmed by qRT-PCR and immunohistochemistry. SERPINH1 was found to be an independent prognosis factor, and high expression of SERPINH1 indicated poor survival. Moreover, a nomogram was constructed to predict prognosis more accurately and intuitively. GSEA analysis showed that SERPINH1 was involved in seven signaling pathways, including JAK-STAT pathway. Further analysis indicated SERPINH1 was significantly associated with immunity, especially in low-grade glioma. Additionally, an examination of scRNA-seq data revealed that SERPINH1 was primarily expressed in T cells of the CD4+ and CD8+ subsets. SERPINH1 is a key biomarker of glioma prognosis and is immunologically relevant, which provides additional options for targeted therapy of glioma.