Network pharmacological analysis of active components of Xiaoliu decoction in the treatment of glioblastoma multiforme

Wu, Ji; Li, Xueyu; Liang, Jing; Fang, Dalang; Yang, Zhao-Jian; Wei, Jie; Chen, Zhijun

doi:10.3389/fgene.2022.940462

Cited by 8 publications

(6 citation statements)

References 63 publications

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“…GBM is an aggressive primary intracranial malignant tumor, accounting for approximately 14.5% and 48.6% of all tumors and malignant tumors, respectively. 10 It is generally associated with a high rate of recurrence, signi cant mortality, and poor clinical prognosis. 11 Existing chemotherapeutic agents, including temozolomide, exhibit limited therapeutic potential and drug resistance is often encountered in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Therapeutic Mechanisms and Prognostic Targets of Biochanin A in Glioblastoma via Network Pharmacology and Bioinformatics Analysis

Ge,

Yuan,

Wang

et al. 2023

Preprint

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Purpose Glioblastoma (GBM) is the most malignant type of brain tumor, characterized by a poor prognosis and high recurrence and mortality rates. Biochanin A (BCA) has demonstrated promising clinical antitumor effects. This study aimed to explore the pharmacological mechanisms by which BCA acts against GBM. Methods Network pharmacology was employed to identify overlapping target genes between BCA and GBM. Differentially expressed genes were extracted from the Gene Expression Profiling Interactive Analysis 2 database and visualized using VolcaNose. The STRING database was used to analyze interactions among these overlapping genes. Protein–protein interaction networks were constructed using Cytoscape 3.8.1 software. Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology enrichment analyses were conducted using DAVID. Survival analyses for these genes were performed using the GEPIA2 database. The Chinese Glioma Genome Atlas database was employed to analyze correlations between key prognostic genes. Molecular docking was verified using the DockThor database and visualized with PyMol software. Results A total of 63 target genes were initially identified as potential targets for BCA in the treatment of GBM. Enrichment analysis results suggested that the pharmacological mechanisms of BCA primarily involved inhibition of the cell cycle, induction of cell apoptosis, and regulation of immunity. Based on these findings, AKT1, EGFR, CASP3, and MMP9 were preliminarily predicted as key prognostic target genes for BCA in treating GBM. Conclusion In this study, target prediction based on network pharmacology and bioinformatics analyses offered a novel research approach for the multi-target treatment of GBM using BCA.

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Section: Discussionmentioning

confidence: 99%

Exploring the Therapeutic Mechanisms and Prognostic Targets of Biochanin A in Glioblastoma via Network Pharmacology and Bioinformatics Analysis

Ge,

Yuan,

Wang

et al. 2023

Preprint

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“…Using the search drug-gene interactions option on the web page, and by entering PD-CR-DEGs into the prediction, results were obtained and downloaded from the webpage by censoring the gene-drugs reported in the literature and visualizing the results of the gene-drug prediction network using Cytoscape 3.8.2. The top 5 gene-drug interaction scores were selected by eliminating chemotherapeutic drugs from the above results and using AutoDock to perform small-molecule drug-large-molecule protein docking, as in our previous studies ( 19 , 20 ). The energy required for docking was heat mapped using the R package pheatmap.…”

Section: Methodsmentioning

confidence: 99%

Machine learning screening for Parkinson’s disease-related cuproptosis-related typing development and validation and exploration of personalized drugs for cuproptosis genes

Wu¹,

Qin²,

Cai³

et al. 2023

Ann Transl Med

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Background: Parkinson's disease (PD) is a common, degenerative disease of the nervous system that is characterized by the death of dopaminergic neurons in the substantia nigra densa (SNpc). There is growing evidence that copper (Cu) is involved in myelin formation and is involved in cell death through modulation of synaptic activity as well as neurotrophic factor-induced excitotoxicity.Methods: This study aimed to explore potential cuproptosis-related genes (CRGs) and immune infiltration patterns in PD and the development of Cu chelators relevant for PD treatment. The PD datasets GSE7621, GSE20141, and GSE49036 were downloaded from the Gene Expression Omnibus (GEO) database. The consensus clustering method was used to classify the specimens of PD. Using weighted gene co-expression network analysis (WGCNA) and random forest (RF) tree model, support vector machine (SVM) learning model, extreme gradient boosting (XGBoost) model, and general linear model (GLM) algorithms to screen disease progression-related models, the column charts were created to verify the accuracy of these CRGs in predicting PD progression. Single sample genomic enrichment analysis (ssGSEA) was used to estimate the correlation between genes associated with copper poisoning and genes associated with immune cells and immune function. Molecular docking was used to verify interactions with copper chelating agents associated with cuproptosis for PD treatment.Results: Through ssGSEA, we identified three copper poisoning related genes ATP7A, NFE2L2 and MTF1, which are related to immune cells in PD. We also verified that LAGASCATRIOL can bind to NFE2L2 through molecular docking. Consistent cluster analysis identified two subtypes, among which C2 subtype was just enriched in PD. And to more accurately diagnose PD progression, patients can benefit from a feature map based on these genes.Conclusions: CRGs such as NFE2L2, MTF1, and ATP7B were identified to be associated with the pathogenesis of PD and provide a possible new direction for the treatment of PD, which needs further indepth study.

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“…RNA-seq data for 529 low-grade gliomas (LGG), 168 High grade gliomas (GBM) and 1152 normal brain samples were downloaded from the UCSC database ( http://xena.ucsc.edu/ )( 10 ) and in the TCGA database ( https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga ) to download RNA-seq data for 693 glioma samples and corresponding patient clinical data, with clinical and RNA-seq data for 693 and 325 glioma samples downloaded from the CGGA database ( http://www.cgga.org.cn/ ). Other glioma data from the GlioVis database ( http://gliovis.bioinfo.cnio.es/ )( 11 ).were used as from the Mitochondrial Associated Gene Dataset ( https://www.broadinstitute.org/mitocarta/mitocarta30-inventory-mammalian-mitochondrial-proteins-and-pathways .)…”

Section: Methodsmentioning

confidence: 99%

Novel prognostic features and personalized treatment strategies for mitochondria-related genes in glioma patients

Zhou

Chai

et al. 2023

Front. Endocrinol.

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BackgroundGliomas are the most common intracranial nervous system tumours that are highly malignant and aggressive, and mitochondria are an important marker of metabolic reprogramming of tumour cells, the prognosis of which cannot be accurately predicted by current histopathology. Therefore, Identify a mitochondrial gene with immune-related features that could be used to predict the prognosis of glioma patients.MethodsGliomas data were downloaded from the TCGA database and mitochondrial-associated genes were obtained from the MITOCARTA 3.0 dataset. The CGGA, kamoun and gravendeel databases were used as external datasets. LASSO(Least absolute shrinkage and selection operator) regression was applied to identify prognostic features, and area and nomograms under the ROC(Receiver Operating Characteristic) curve were used to assess the robustness of the model. Single sample genomic enrichment analysis (ssGSEA) was employed to explore the relationship between model genes and immune infiltration, and drug sensitivity was used to identify targeting drugs. Cellular studies were then performed to demonstrate drug killing against tumours.ResultsCOX assembly mitochondrial protein homolog (CMC1), Cytochrome c oxidase protein 20 homolog (COX20) and Cytochrome b-c1 complex subunit 7 (UQCRB) were identified as prognostic key genes in glioma, with UQCRB, CMC1 progressively increasing and COX20 progressively decreasing with decreasing risk scores. ROC curve analysis of the TCGA training set model yielded AUC (Area Under The Curve) values >0.8 for 1-, 2- and 3-year survival, and the model was associated with both CD8+ T cells and immune checkpoints. Finally, using cellMiner database and molecular docking, it was confirmed that UQCRB binds covalently to Amonafide via lysine at position 78 and threonine at position 82, while cellular assays showed that Amonafide inhibits glioma migration and invasion.ConclusionOur three mitochondrial genomic composition-related features accurately predict Survival in glioma patients, and we also provide glioma chemotherapeutic agents that may be mitochondria-related targets.

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Network pharmacological analysis of active components of Xiaoliu decoction in the treatment of glioblastoma multiforme

Cited by 8 publications

References 63 publications

Exploring the Therapeutic Mechanisms and Prognostic Targets of Biochanin A in Glioblastoma via Network Pharmacology and Bioinformatics Analysis

Exploring the Therapeutic Mechanisms and Prognostic Targets of Biochanin A in Glioblastoma via Network Pharmacology and Bioinformatics Analysis

Machine learning screening for Parkinson’s disease-related cuproptosis-related typing development and validation and exploration of personalized drugs for cuproptosis genes

Novel prognostic features and personalized treatment strategies for mitochondria-related genes in glioma patients

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