Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives

Journal of Hematology & Oncology, Journal Year: 2024, Volume and Issue: 17(1)

Published: May 8, 2024

Abstract Glioblastoma (GBM), the predominant and primary malignant intracranial tumor, poses a formidable challenge due to its immunosuppressive microenvironment, thereby confounding conventional therapeutic interventions. Despite established treatment regimen comprising surgical intervention, radiotherapy, temozolomide administration, exploration of emerging modalities such as immunotherapy integration medicine engineering technology therapy, efficacy these approaches remains constrained, resulting in suboptimal prognostic outcomes. In recent years, intensive scrutiny inhibitory milieu within GBM has underscored significance cellular constituents microenvironment their interactions with cells neurons. Novel immune targeted therapy strategies have emerged, offering promising avenues for advancing treatment. One pivotal mechanism orchestrating immunosuppression involves aggregation myeloid-derived suppressor (MDSCs), glioma-associated macrophage/microglia (GAM), regulatory T (Tregs). Among these, MDSCs, though constituting minority (4–8%) CD45 + GBM, play central component fostering evasion propelling tumor progression, angiogenesis, invasion, metastasis. MDSCs deploy intricate mechanisms that adapt dynamic (TME). Understanding interplay between provides compelling basis This review seeks elucidate inherent explore existing targets, consolidate insights into MDSC induction contribution immunosuppression. Additionally, comprehensively surveys ongoing clinical trials potential strategies, envisioning future where targeting could reshape landscape GBM. Through synergistic other modalities, this approach can establish multidisciplinary, multi-target paradigm, ultimately improving prognosis quality life patients

Language: Английский

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Cuproptosis-related lncRNAs and genes: Potential markers for glioblastoma prognosis and treatment

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0315927 - e0315927

Published: Feb. 6, 2025

Despite the availability of various treatment options, glioblastoma (GBM) remains an extremely aggressive form glioma with a poor prognosis. In recent studies, regulatory cell death (RCD) has been identified as effective mechanism to suppress glioma. Cuproptosis, caused by intracellular copper, is novel RCD process that affects chemotherapy efficacy and progression; however, precise function cuproptosis-related lncRNAs (CRLs) genes (CRGs) in GBM uncertain. To determine whether CRLs CRGs have prognostic significance, cohort TCGA build risk model. Two high-risk (AC091182.2, AC005229.4) their co-expression ( LIPT2 , GLS ) were verified constitute independent indicator GBM. RT-qPCR analysis confirmed highly expressed cells compared normal astrocytes. By constructing mouse model, found be at higher levels tumor tissues. Furthermore, verify these are associated cuproptosis, cuproptosis models constucted lines astrocyte using Elesclomol CuCl 2 . It was expression decreased upon cuproptosis-induced cells. Interestingly, astrocytes less sensitive than cuproptosis-inducing drugs, effects drugs on opposite conclusion, two identified. Their specific pointing demonstrated through variety experiments. These might serve markers indicators for provide theoretical support future treatment.

Language: Английский

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Integrated multiomic analysis reveals disulfidptosis subtypes in glioblastoma: implications for immunotherapy, targeted therapy, and chemotherapy

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Feb. 26, 2024

Introduction Glioblastoma (GBM) presents significant challenges due to its malignancy and limited treatment options. Precision requires subtyping patients based on prognosis. Disulfidptosis, a novel cell death mechanism, is linked aberrant glucose metabolism disulfide stress, particularly in tumors expressing high levels of SLC7A11. The exploration disulfidptosis may provide new perspective for precise diagnosis glioblastoma. Methods Transcriptome sequencing was conducted samples from GBM treated at Tiantan Hospital (January 2022 - December 2023). Data CGGA TCGA databases were collected. Consensus clustering features categorized into two subtypes (DRGclusters). Tumor immune microenvironment, response immunotherapy, drug sensitivity analyzed. An 8-gene disulfidptosis-based subtype predictor developed using LASSO machine learning algorithm validated dataset. Results Patients DRGcluster A exhibited improved overall survival (OS) compared B. showed differences tumor microenvironment immunotherapy. effectively stratified low-risk groups. Significant IC50 values chemotherapy targeted therapy observed between risk Discussion Disulfidptosis-based classification offers promise as prognostic GBM. It provides insights therapy. aids patient stratification personalized selection, potentially improving outcomes patients.

Language: Английский

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Functional Analysis and Experimental Validation of the Prognostic and Immune Effects of the Oncogenic Protein CDC45 in Breast Cancer

Breast Cancer Targets and Therapy, Journal Year: 2025, Volume and Issue: Volume 17, P. 11 - 25

Published: Jan. 1, 2025

Purpose: Cell division cycle protein 45 (CDC45) plays a crucial role in DNA replication. This study investigates its breast cancer (BC) and impact on tumor progression. Methods: We utilized the GEO database to screen differentially expressed genes (DEGs) conducted enrichment analysis these genes. established Nomogram model based CDC45 other clinical indicators. Additionally, we performed protein-protein interaction (PPI) network construction, drug sensitivity analysis, immune correlation of CDC45. The function was further verified through cell animal experiments. Results: is highly most tumors, including BC. expression level significantly associated with age, sex, race, stage, molecular subtypes (all p < 0.05). incorporated into model, which showed moderate accuracy predicting patient prognosis. also analyzed co-expression CDC45, TOPBP1, GINS2, MCM5, GINS1, GINS4, POLE2, MCM2, MCM6, MCM4, MCM7. Furthermore, closely linked infiltration levels, checkpoint inhibitors, therapeutic response small molecule drugs. Finally, both vitro vivo experiments confirmed cancer-promoting effect Conclusion: prognosis, infiltration, In have that acts as cancer. Keywords: cancer, immunity, experiment

Language: Английский

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Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 13, 2025

Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with poor prognosis and limited treatment options. Despite advances in understanding its molecular mechanisms, effective therapeutic strategies remain elusive due to the tumor's genetic complexity heterogeneity. This study employed comprehensive analysis approach integrating 113 machine learning algorithms Mendelian Randomization (MR) investigate underpinnings of GBM. Five publicly available gene expression datasets were analyzed identify differentially expressed genes (DEGs) associated Weighted Gene Co-expression Network Analysis (WGCNA) was used GBM-related modules. Further, set enrichment variation analyses conducted explore biological pathways involved. The models evaluated using Receiver Operating Characteristic (ROC) curves confusion matrices assess their predictive accuracy, best-performing model validated across external datasets. MR performed establish causal relationships between genetically predicted levels GBM outcomes. identified 286 DEGs adjacent normal tissues five WGCNA highlighted yellow module as most relevant GBM, containing key such KLHL3, FOXO4, MAP1A. Of tested, Ridge regression achieved highest area under curve (AUC) 0.92, demonstrating robust accuracy. Validation confirmed model's reliability, classification accuracy 89.5% training 85.3% validation sets. provided strong evidence relationship risk. demonstrates power combining uncover novel markers for offer promising potential biomarkers diagnosis therapy, providing new avenues personalized strategies.

Language: Английский

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Impact of cuproptosis in gliomas pathogenesis with targeting options

Chemico-Biological Interactions, Journal Year: 2025, Volume and Issue: unknown, P. 111394 - 111394

Published: Jan. 1, 2025

Language: Английский

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Design, Synthesis, and Antitumor Activity of NSDs Inhibitors Targeting Lung Squamous Cell Carcinoma

European Journal of Medicinal Chemistry, Journal Year: 2025, Volume and Issue: 289, P. 117388 - 117388

Published: Feb. 13, 2025

Language: Английский

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Decoding the heterogeneous subpopulations of glioblastoma for prognostic stratification and uncovering the promalignant role of PSMC2

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 17, 2025

Language: Английский

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Regulation of Glycolysis by SMAD5 in Glioma Cells: Implications for Tumor Growth and Apoptosis

Neurochemical Research, Journal Year: 2025, Volume and Issue: 50(2)

Published: Feb. 18, 2025

Language: Английский

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Identification of Prognostic Genes Related to Cell Senescence and Lipid Metabolism in Glioblastoma Based on Transcriptome and Single-Cell RNA-Seq Data

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(5), P. 1875 - 1875

Published: Feb. 21, 2025

Glioblastoma (GBM) is the most aggressive primary brain cancer, with poor prognosis due to its behavior and high heterogeneity. This study aimed identify cellular senescence (CS) lipid metabolism (LM)-related prognostic genes improve GBM treatment. Transcriptome scRNA-seq data, CS-associated (CSAGs), LM-related (LMRGs) were acquired from public databases. Prognostic identified by intersecting CSAGs, LMRGs, differentially expressed (DEGs), followed WGCNA univariate Cox regression. A risk model nomogram constructed. Analyses covered clinicopathological features, immune microenvironment, somatic mutations, drug sensitivity. data key cells gene expression. SOCS1 PHB2 as markers, contributing construction of a robust excellent predictive ability. High-risk group (HRG) patients had poorer survival, higher stromal scores, distinct mutation profiles. Drug sensitivity analysis revealed significant differences in IC50 values. In microglia differentiation, showed dynamic expression patterns. These findings provide new strategies for

Language: Английский

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