In situ targeting nanoparticles-hydrogel hybrid system for combined chemo-immunotherapy of glioma

Wang, Xiaoqi; Lu, Yao; He, Weichong; Teng, Chuanhui; Sun, Shanbo; Lu, Hongdan; Li, Shengnan; Lv, Lingyan; Cao, Xiang; Yin, Haoyuan; Lv, Wei; Xin, Hongliang

doi:10.1016/j.jconrel.2022.03.050

Cited by 44 publications

(30 citation statements)

References 46 publications

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“…As shown in the box plots, individual essential sub-cluster genes were significantly different between the disease and control groups ( Figure 7A ), and the ROC curves showed that all 17 essential sub-cluster genes had excellent robustness for glioblastoma (area under the ROC curve >0.6) ( Figure 7B ). The immune heat map showed that the significant regulatory targets of the critical cluster genes were mainly in the immune pathways of Macrophages_M0, Macrophages_M2, Mast_cells_activated, and T_cells_follicular_helper ( Figures 7C,D ), indicating that these genes are involved in regulating glioma immune function, which is consistent with previous studies ( Wang et al, 2022 ).…”

Section: Resultssupporting

confidence: 91%

Mechanism of action of paclitaxel for treating glioblastoma based on single-cell RNA sequencing data and network pharmacology

Rao

et al. 2022

Front. Pharmacol.

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Paclitaxel is an herbal active ingredient used in clinical practice that shows anti-tumor effects. However, its biological activity, mechanism, and cancer cell-killing effects remain unknown. Information on the chemical gene interactions of paclitaxel was obtained from the Comparative Toxicogenomics Database, SwishTargetPrediction, Binding DB, and TargetNet databases. Gene expression data were obtained from the GSE4290 dataset. Differential gene analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology analyses were performed. Gene set enrichment analysis was performed to evaluate disease pathway activation; weighted gene co-expression network analysis with diff analysis was used to identify disease-associated genes, analyze differential genes, and identify drug targets via protein-protein interactions. The Molecular Complex Detection (MCODE) analysis of critical subgroup networks was conducted to identify essential genes affected by paclitaxel, assess crucial cluster gene expression differences in glioma versus standard samples, and perform receiver operator characteristic mapping. To evaluate the pharmacological targets and signaling pathways of paclitaxel in glioblastoma, the single-cell GSE148196 dataset was acquired from the Gene Expression Omnibus database and preprocessed using Seurat software. Based on the single-cell RNA-sequencing dataset, 24 cell clusters were identified, along with marker genes for the two different cell types in each cluster. Correlation analysis revealed that the mechanism of paclitaxel treatment involves effects on neurons. Paclitaxel may affect glioblastoma by improving glucose metabolism and processes involved in modulating immune function in the body.

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Section: Resultssupporting

confidence: 91%

Mechanism of action of paclitaxel for treating glioblastoma based on single-cell RNA sequencing data and network pharmacology

Rao

et al. 2022

Front. Pharmacol.

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“…Gliomas are resistant to chemotherapy due to their high heterogeneity and increased proliferation rate. [47,48] According to the results of the drug sensitivity analysis, patients in the high-risk group were more sensitive to 6 common chemotherapy drugs. Differences in the TMB and the effects of ICI treatment between the 2 risk groups were further evaluated.…”

Section: Discussionmentioning

confidence: 99%

A lipid metabolism-related risk signature for patients with gliomas constructed with TCGA and CGGA data

Meng

Liu

2022

Medicine

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Lipid metabolism affects cell proliferation, differentiation, membrane homeostasis and drug resistance. An in-depth exploration of lipid metabolism in gliomas might provide a novel direction for gliomas treatment. A lipid metabolism-related risk signature was constructed in our study to assess the prognosis of patients with gliomas. Lipid metabolism-related genes were extracted. Differentially expressed genes (DEGs) were screened, and a risk signature was built. The ability of the risk signature to predict the outcomes of patients with gliomas was assessed using the log-rank test and Cox regression analysis. The relationships between immunological characteristics, drug sensitivity and the risk score were evaluated, and the risk-related mechanisms were also estimated. Twenty lipid metabolism-related DEGs associated with the patient prognosis were included in the risk signature. The survival rate of high-risk patients was worse than that of low-risk patients. The risk score independently predicted the outcomes of patients. Immunological parameters, drug sensitivity, immunotherapy benefits, and numerous molecular mechanisms were significantly associated with the risk score. A lipid metabolism-related risk signature might effectively assess the prognosis of patients with gliomas. The risk score might guide individualized treatment and further clinical decision-making for patients with gliomas.Abbreviations: CGGA = The Chinese Gliomas Genome Atlas, CIBERSORT = cell-type identification by estimating relative subsets of RNA transcripts, DEGs = differentially expressed genes, ESTIMATE = estimation of stromal and immune cells in malignant tumor tissues using expression data, GBM = glioblastoma, GSEA = gene set enrichment analysis, GTEx = genotypetissue expression, ICIs = immune checkpoint inhibitors, KEGG = Kyoto encyclopedia of genes and genomes, LGG = low-grade gliomas, PPI = protein-protein interaction, TCGA = The Cancer Genome Atlas, TIDE = tumor immune dysfunction and exclusion, TMB = tumor mutational burden, TME = tumor immune microenvironment.

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“…When glutathione (GSH)-responsive PTX prodrug nanoparticles were delivered into GBM cells, the PTX induced ICD via the secretion of HMGB1, which abrogated the TME-mediated immunosuppression in GBM. Besides, the mannose-modified immunoadjuvant CpG NPs directly targeted macrophages to increase the number of M1 TAMs and reduce the proportion of Tregs within the TME, while also boosting IL-12, TNF-α, and IFN-γ expression ( 134 ). Zhang et al.…”

Section: Tam-targeting Translational Research In Gbmmentioning

confidence: 99%

Targeting tumor-associated macrophages for the immunotherapy of glioblastoma: Navigating the clinical and translational landscape

et al. 2022

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Tumor-associated macrophages (TAMs) can directly clear tumor cells and enhance the phagocytic ability of immune cells. An abundance of TAMs at the site of the glioblastoma tumor indicates that TAM-targeting immunotherapy could represent a potential form of treatment for this aggressive cancer. Herein, we discuss: i) the dynamic role of TAMs in glioblastoma; ii) describe the formation of the immunosuppressive tumor microenvironment; iii) summarize the latest clinical trial data that reveal how TAM function can be regulated in favor tumor eradication; and lastly, iv) evaluate the implications of existing and novel translational approaches for treating glioblastoma in clinical practice.

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In situ targeting nanoparticles-hydrogel hybrid system for combined chemo-immunotherapy of glioma

Cited by 44 publications

References 46 publications

Mechanism of action of paclitaxel for treating glioblastoma based on single-cell RNA sequencing data and network pharmacology

Mechanism of action of paclitaxel for treating glioblastoma based on single-cell RNA sequencing data and network pharmacology

A lipid metabolism-related risk signature for patients with gliomas constructed with TCGA and CGGA data

Targeting tumor-associated macrophages for the immunotherapy of glioblastoma: Navigating the clinical and translational landscape

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