Hub Genes, Diagnostic Model, and Predicted Drugs Related to Iron Metabolism in Alzheimer's Disease

Gu, Xuefeng; Lai, Donglin; Liu, Shuang; Chen, Kai‐Jie; Zhang, Peng; Chen, Bing; Huang, Gang; Cheng, Xiaoqin; Lu, Changlian

doi:10.3389/fnagi.2022.949083

Cited by 23 publications

(13 citation statements)

References 109 publications

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“…8 b, 11 b). We further validated these hub genes with screening thresholds p. adj < 0.01 and abs(logFC) > 0.585 19 , and the results demonstrate the reliability of these hub genes in terms of statistical significance (See Table 6 in the Supplementary Material). Of these, MAPK3 and MAPK1 were the most strongly connected to the rest of the network with connectivity scores of 15, followed by SP1 (10) and CASP3 (10) (See Fig.…”

Section: Resultsmentioning

confidence: 86%

Identification of oxidative stress-related genes differentially expressed in Alzheimer’s disease and construction of a hub gene-based diagnostic model

Zhang

Kiryu

2023

Sci Rep

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Alzheimer’s disease (AD) is the most prevalent dementia disorder globally, and there are still no effective interventions for slowing or stopping the underlying pathogenic mechanisms. There is strong evidence implicating neural oxidative stress (OS) and ensuing neuroinflammation in the progressive neurodegeneration observed in the AD brain both during and prior to symptom emergence. Thus, OS-related biomarkers may be valuable for prognosis and provide clues to therapeutic targets during the early presymptomatic phase. In the current study, we gathered brain RNA-seq data of AD patients and matched controls from the Gene Expression Omnibus (GEO) to identify differentially expressed OS-related genes (OSRGs). These OSRGs were analyzed for cellular functions using the Gene Ontology (GO) database and used to construct a weighted gene co-expression network (WGCN) and protein-protein interaction (PPI) network. Receiver operating characteristic (ROC) curves were then constructed to identify network hub genes. A diagnostic model was established based on these hub genes using Least Absolute Shrinkage and Selection Operator (LASSO) and ROC analyses. Immune-related functions were examined by assessing correlations between hub gene expression and immune cell brain infiltration scores. Further, target drugs were predicted using the Drug-Gene Interaction database, while regulatory miRNAs and transcription factors were predicted using miRNet. In total, 156 candidate genes were identified among 11046 differentially expressed genes, 7098 genes in WGCN modules, and 446 OSRGs, and 5 hub genes (MAPK9, FOXO1, BCL2, ETS1, and SP1) were identified by ROC curve analyses. These hub genes were enriched in GO annotations “Alzheimer’s disease pathway,” “Parkinson’s Disease,” “Ribosome,” and “Chronic myeloid leukemia.” In addition, 78 drugs were predicted to target FOXO1, SP1, MAPK9, and BCL2, including fluorouracil, cyclophosphamide, and epirubicin. A hub gene-miRNA regulatory network with 43 miRNAs and hub gene-transcription factor (TF) network with 36 TFs were also generated. These hub genes may serve as biomarkers for AD diagnosis and provide clues to novel potential treatment targets.

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Section: Resultsmentioning

confidence: 86%

Identification of oxidative stress-related genes differentially expressed in Alzheimer’s disease and construction of a hub gene-based diagnostic model

Zhang

Kiryu

2023

Sci Rep

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“…Data from GSE135511 and GSE117935 ( 25 , 26 ) were also utilized for further validation. The iron-related data was derived from a study on pivotal genes and diagnostic models related to iron metabolism in Alzheimer’s disease, encompassing approximately 520 iron metabolism-related genes ( 27 , 28 ).…”

Section: Methodsmentioning

confidence: 99%

Iron metabolism disorder and multiple sclerosis: a comprehensive analysis

Tang,

Yang,

Zhu

et al. 2024

Front. Immunol.

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BackgroundMultiple sclerosis (MS) is the most common chronic inflammatory, demyelinating and neurodegenerative disease of the central nervous system.Methods and materialsThe study utilized publicly available databases and bioinformatics techniques for gene expression data analysis, including differential expression analysis, weighted correlation network analysis, gene enrichment analysis, and construction of logistic regression models. Subsequently, Mendelian randomization was used to assess the causal relationship between different iron metabolism markers and MS.ResultsThis study identified IREB2, LAMP2, ISCU, ATP6V1G1, ATP13A2, and SKP1 as genes associated with multiple sclerosis (MS) and iron metabolism, establishing their multi-gene diagnostic value for MS with an AUC of 0.83. Additionally, Mendelian randomization analysis revealed a potential causal relationship between transferrin saturation and MS (p=2.22E-02; OR 95%CI=0.86 (0.75, 0.98)), as well as serum transferrin and MS (p=2.18E-04; OR 95%CI=1.22 (1.10, 1.36)).ConclusionThis study comprehensively explored the relationship between metabolism and MS through integrated bioinformatics analysis and Mendelian randomization methods. The findings provide important insights for further research into the role of iron metabolism disorder in the pathogenesis of MS and offer crucial theoretical support for the prevention and treatment of related diseases is changed to treatment of MS.

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“…Hypoxia-related genes were retrieved from the msigdb.org website. Through our search, we identified gene sets M5891, M10508, and M641, comprising 311 hypoxia genes in total ( 18 ). Additionally, we established a validation cohort (GSE53625), which included the RNA sequencing of 358 patients and corresponding survival data from the GEO repository ( https://www.ncbi.nlm.nih.gov/geo/ ) ( 19 ).…”

Section: Methodsmentioning

confidence: 99%

Deciphering the Hypoxia-immune interface in esophageal squamous carcinoma: a prognostic network model

Hu,

Liu,

Feng

et al. 2023

Front. Oncol.

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IntroductionThe rapid progress and poor prognosis of the exercise of esophageal squamous cell carcinoma (ESCA) bring great challenges to the treatment. Hypoxia in the tumor microenvironment has become a key factor in the pathogenesis of tumors. However, due to the lack of clear therapeutic targets, hypoxia targeted therapy of ESCA is still in the exploratory stage.MethodsTo bridge this critical gap, we mined a large number of gene expression profiles and clinical data on ESCA from public databases. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed. We next delved into the relationship between hypoxia and apoptotic cell interactions. Meanwhile, using LASAS-Cox regression, we designed a robust prognostic risk score, which was subsequently validated in the GSE53625 cohort. In addition, we performed a comprehensive analysis of immune cell infiltration and tumor microenvironment using cutting-edge computational tools.ResultsHypoxia-related genes were identified and classified by WGCNA. Functional enrichment analysis further elucidated the mechanism by which hypoxia affected the ESCA landscape. The results of the interaction analysis of hypoxia and apoptotic cells revealed their important roles in driving tumor progression. The validation results of the prognostic risk score model in the GSE53625 cohort obtained a good area under the receiver operating characteristic (ROC) curve, and the risk score was independently verified as a significant predictor of ESCA outcome. The results of immune cell infiltration and tumor microenvironment analysis reveal the profound impact of immune cell dynamics on tumor evolution.ConclusionOverall, our study presents a pioneering hypoxiacentered gene signature for prognostication in ESCA, providing valuable prognostic insights that could potentially revolutionize patient stratification and therapeutic management in clinical practice.

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Hub Genes, Diagnostic Model, and Predicted Drugs Related to Iron Metabolism in Alzheimer's Disease

Cited by 23 publications

References 109 publications

Identification of oxidative stress-related genes differentially expressed in Alzheimer’s disease and construction of a hub gene-based diagnostic model

Identification of oxidative stress-related genes differentially expressed in Alzheimer’s disease and construction of a hub gene-based diagnostic model

Iron metabolism disorder and multiple sclerosis: a comprehensive analysis

Deciphering the Hypoxia-immune interface in esophageal squamous carcinoma: a prognostic network model

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