Identifying and validating key genes mediating intracranial aneurysm rupture using weighted correlation network analysis and exploration of personalized treatment

Wu, Ji; Chen, Zhijun; Liang, Jing; Lai, Chang-Sheng; Li, Xueyu; Yang, Zhao-Jian

doi:10.21037/atm-22-4068

Cited by 7 publications

(4 citation statements)

References 56 publications

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“…To understand the immune microenvironment in PD, we used a previously established method ( 17 ) to quantify immune infiltration and associated immune functions by ssGSEA, which calculates an enrichment score representing the level of immune cell infiltration and immune-related pathway activity. The “ggplot2” package was used to create a heat map of the distribution and variation of immune cells.…”

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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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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“…The co-expression networks for the ACP disease module were constructed using the “WGCNA” package in R ( 22 ). Genes were aligned using Pearson correlation matrices.…”

Section: Methodsmentioning

confidence: 99%

Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies

Wu¹,

Qin²,

Fang³

et al. 2023

Transl Pediatr

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Background Adamantinoma craniopharyngioma (ACP) is a non-malignant tumour of unknown pathogenesis that frequently occurs in children and has malignant potential. The main treatment options are currently surgical resection and radiotherapy. These treatments can lead to serious complications that greatly affect the overall survival and quality of life of patients. It is therefore important to use bioinformatics to explore the mechanisms of ACP development and progression and to identify new molecules. Methods Sequencing data of ACP was downloaded from the comprehensive gene expression database for differentially expressed gene identification and visualized by Gene Ontology, Kyoto Gene, and gene set enrichment analyses (GSEAs). Weighted correlation network analysis was used to identify the genes most strongly associated with ACP. GSE94349 was used as the training set and five diagnostic markers were screened using machine learning algorithms to assess diagnostic accuracy using receiver operating characteristic (ROC) curves, while GSE68015 was used as the validation set for verification. Results Type I cytoskeletal 15 (KRT15), Follicular dendritic cell secreted peptide (FDCSP), Rho-related GTP-binding protein RhoC (RHOC), Modulates negatively TGFB1 signaling in keratinocytes (CD109), and type II cytoskeletal 6A (KRT6A) (area under their receiver operating characteristic curves is 1 for both the training and validation sets), Nomograms constructed using these five markers can predict progression of ACP patients. Whereas ACP tissues with activated T-cell surface glycoprotein CD4, Gamma delta T cells, eosinophils and regulatory T cells were expressed at higher levels than in normal tissues, which may contribute to the pathogenesis of ACP. According to the analysis of the CellMiner database (Tumor cell and drug related database tools), high CD109 levels showed significant drug sensitivity to Dexrazoxane, which has the potential to be a therapeutic agent for ACP. Conclusions Our findings extend understandings of the molecular immune mechanisms of ACP and suggest possible biomarkers for the targeted and precise treatment of ACP.

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“…To understand the immune microenvironment in AS, we used single-sample gene set enrichment analysis (ssGSEA) (20) technology to analyze the in ltration of immune cells. Immunological in ltration of all samples was evaluated using the GSVA package (21).…”

Section: Evaluating the Immune Cell In Ltrationmentioning

confidence: 99%

Identification and immuno-infiltration analysis of cuproptosis regulators in human atherosclerosis

Ming

Wen

Zeng

et al. 2023

Preprint

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Introduction The development of atherosclerosis (AS) may be aided by cuproptosis. As a result, we examined the cuproptosis regulators in human AS, gauged the degree of immune cell infiltration, and developed a prediction model. Methods We obtained the GSE100927 gene expression dataset associated with AS from the Gene Expression Omnibus (GEO) database and used it to identify cuproptosis-related differentially expressed genes (CuDEGs). This was accomplished by comparing AS samples and control samples. We also examined the relationship between CuDEGs and immune cell infiltration status, and investigated the molecular groupings of both CuDEGs and immune cell infiltration status. To pinpoint cluster-specific differentially expressed genes, we employed weighted gene co-expression network analysis (WGCNA). Furthermore, gene set variation analysis (GSVA) was carried out to annotate the enriched genes. From four different machine-learning models, we selected the model with the best performance. Lastly, we validated the accuracy of our predictions using nomograms and ROC curves. Results Our study confirmed the presence of CuDEGs and activated immune responses among AS and control samples. We identified 12 CuDEGs through the dataset, and we also discovered two clusters in AS. Analysis of immune cell infiltration showed that there is heterogeneity in immunity between these two clusters. Cuproptosis-related molecular Cluster 2 was marked by enhanced expressions of NLRP3, SLC31A1, FDX1, LIPT2 and CDKN2A. And Cluster 1 exhibited a higher proportion of T cells CD4 memory resting、Monocytes、Macrophages M1 and Mast cells resting. And enriched KEGG pathways revealed the pathway of leukocyte transendothelial migration was up-regulated in Cluster 1. We subsequently developed a support vector machine (SVM) model based on five genes, which demonstrated good performance in predicting AS in the external validation dataset (AUC = 0.895). Our results indicate that this combined nomogram is highly accurate in predicting AS. Conclusion Our study sheds light on the relationship between AS and cuproptosis, as well as the association between CRGs and immune-infiltrated cells in AS. Additionally, we have established a good predictive model.

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Identifying and validating key genes mediating intracranial aneurysm rupture using weighted correlation network analysis and exploration of personalized treatment

Cited by 7 publications

References 56 publications

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

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

Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies

Identification and immuno-infiltration analysis of cuproptosis regulators in human atherosclerosis

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