Identification of KIAA0513 and Other Hub Genes Associated With Alzheimer Disease Using Weighted Gene Coexpression Network Analysis

Zhu, Min; Jia, Longfei; Li, Fangyu; Jia, Jianping

doi:10.3389/fgene.2020.00981

Cited by 31 publications

(20 citation statements)

References 56 publications

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“…[9][10][11] Among them, the weighted gene coexpression network analysis (WGCNA) identifies candidate biomarkers and therapeutic targets by finding gene clusters having a high correlation with the phenotype. [12][13][14][15] In this study, we used multiple independent gene expression datasets from the Gene Expression Omnibus (GEO) database to discover potential therapeutic targets and candidate biomarkers for HCM. First, we merged two independent datasets and performed differential expression analysis.…”

Section: Introductionmentioning

confidence: 99%

Identification of Underlying Hub Genes Associated with Hypertrophic Cardiomyopathy by Integrated Bioinformatics Analysis

Wang

et al. 2021

PGPM

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Background: Considered as one of the major reasons of sudden cardiac death, hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease. However, effective treatment for HCM is still lacking. Identification of hub gene may be a powerful tool for discovering potential therapeutic targets and candidate biomarkers. Methods: We analysed three gene expression datasets for HCM from the Gene Expression Omnibus. Two of them were merged by "sva" package. The merged dataset was used for analysis while the other dataset was used for validation. Following this, a weighted gene coexpression network analysis (WGCNA) was performed, and the key module most related to HCM was identified. Based on the intramodular connectivity, we identified the potential hub genes. Then, a receiver operating characteristic curve analysis was performed to verify the diagnostic values of hub genes. Finally, we validated changes of hub genes, for genetic transcription and protein expression levels, in datasets of HCM patients and myocardium of transverse aortic constriction (TAC) mice. Results: In the merged dataset, a total of 455 differentially expressed genes (DEGs) were identified from normal and hypertrophic myocardium. In WGCNA, the blue module was identified as the key module and the genes in this module showed a high positive correlation with HCM. Functional enrichment analysis of DEGs and key module revealed that the extracellular matrix, fibrosis, and neurohormone pathways played important roles in HCM. FRZB, COL14A1, CRISPLD1, LUM, and sFRP4 were identified as hub genes in the key module. These genes showed a good predictive value for HCM and were significantly up-regulated in HCM patients and TAC mice. We also found protein expression of LUM and sFRP4 increased in myocardium of TAC mice. Conclusion:This study revealed that five hub genes are involved in the occurrence and development of HCM, and they are potentially to be used as therapeutic targets and biomarkers for HCM.

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Section: Introductionmentioning

confidence: 99%

Identification of Underlying Hub Genes Associated with Hypertrophic Cardiomyopathy by Integrated Bioinformatics Analysis

Wang

et al. 2021

PGPM

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“…Thus, some proteins in the blood might be associated with AD pathology. Recent studies have also adopted the strategy to integrate brain and blood datasets to identify potential AD biomarkers ( Yao et al, 2018 ; Zhu et al, 2020 ). Our results shed new light on diagnosis biomarker identification.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Key Genes and Identification of Potential Diagnosis Biomarkers in Alzheimer’s Disease Using Bioinformatics Analysis

Yang

et al. 2021

Front. Aging Neurosci.

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BackgroundAlzheimer’s disease (AD) is one of the major threats of the twenty-first century and lacks available therapy. Identification of novel molecular markers for diagnosis and treatment of AD is urgently demanded, and genetic biomarkers show potential prospects.MethodWe identify and intersected differentially expressed genes (DEGs) from five microarray datasets to detect consensus DEGs. Based on these DEGs, we conducted Gene Ontology (GO), performed the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, constructed a protein—protein interaction (PPI) network, and utilized Cytoscape to identify hub genes. The least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify potential diagnostic biomarkers. Gene set enrichment analysis (GSEA) was performed to investigate the biological functions of the key genes.ResultWe identified 608 consensus DEGs, several dysregulated pathways, and 18 hub genes. Sixteen hub genes dysregulated as AD progressed. The diagnostic model of 35 genes was constructed, which has a high area under the curve (AUC) value in both the validation dataset and combined dataset (AUC = 0.992 and AUC = 0.985, respectively). The model can also differentiate mild cognitive impairment and AD patients from controls in two blood datasets. Brain-derived neurotrophic factor (BDNF) and WW domain-containing transcription regulator protein 1 (WWTR1), which are associated with the Braak stage, Aβ 42 levels, and β-secretase activity, were identified as critical genes of AD.ConclusionOur study identified 16 hub genes correlated to the neuropathological stage and 35 potential biomarkers for the diagnosis of AD. WWTR1 were identified as candidate genes for future studies. This study deepens our understanding of the transcriptomic and functional features and provides new potential diagnostic biomarkers and therapeutic targets for AD.

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“…The results of DEGs were introduced to the heatmap and the volcano map. The location of DEGs on chromosomes was illustrated with the “OmicCircos” package from R software ( Zhu et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of Diagnostic Markers Correlated With HIV+ Immune Non-response Based on Bioinformatics Analysis

Bai

Hou

et al. 2021

Front. Mol. Biosci.

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Background: HIV-infected immunological non-responders (INRs) are characterized by their inability to reconstitute CD4+ T cell pools after antiretroviral therapy. The risk of non-AIDS-related diseases in INRs is increased, and the outcome and prognosis of INRs are inferior to that of immunological responders (IRs). However, few markers can be used to define INRs precisely. In this study, we aim to identify further potential diagnostic markers associated with INRs through bioinformatic analyses of public datasets.Methods: This study retrieved the microarray data sets of GSE106792 and GSE77939 from the Gene Expression Omnibus (GEO) database. After merging two microarray data and adjusting the batch effect, differentially expressed genes (DEGs) were identified. Gene Ontology (GO) resource and Kyoto Encyclopedia of Genes and Genomes (KEGG) resource were conducted to analyze the biological process and functional enrichment. We performed receiver operating characteristic (ROC) curves to filtrate potential diagnostic markers for INRs. Gene Set Enrichment Analysis (GSEA) was conducted to perform the pathway enrichment analysis of individual genes. Single sample GSEA (ssGSEA) was performed to assess scores of immune cells within INRs and IRs. The correlations between the diagnostic markers and differential immune cells were examined by conducting Spearman’s rank correlation analysis. Subsequently, miRNA-mRNA-TF interaction networks in accordance with the potential diagnostic markers were built with Cytoscape. We finally verified the mRNA expression of the diagnostic markers in clinical samples of INRs and IRs by performing RT-qPCR.Results: We identified 52 DEGs in the samples of peripheral blood mononuclear cells (PBMC) between INRs and IRs. A few inflammatory and immune-related pathways, including chronic inflammatory response, T cell receptor signaling pathway, were enriched. FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1 were considered as potential diagnostic markers. ssGSEA results showed that the IRs had significantly higher enrichment scores of seven immune cells compared with IRs. The miRNA-mRNA-TF network was constructed with 97 miRNAs, 6 diagnostic markers, and 26 TFs, which implied a possible regulatory relationship.Conclusion: The six potential crucial genes, FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1, may be associated with clinical diagnosis in INRs. Our study provided new insights into diagnostic and therapeutic targets.

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Identification of KIAA0513 and Other Hub Genes Associated With Alzheimer Disease Using Weighted Gene Coexpression Network Analysis

Cited by 31 publications

References 56 publications

Identification of Underlying Hub Genes Associated with Hypertrophic Cardiomyopathy by Integrated Bioinformatics Analysis

Identification of Underlying Hub Genes Associated with Hypertrophic Cardiomyopathy by Integrated Bioinformatics Analysis

Exploring the Key Genes and Identification of Potential Diagnosis Biomarkers in Alzheimer’s Disease Using Bioinformatics Analysis

Identification of Diagnostic Markers Correlated With HIV+ Immune Non-response Based on Bioinformatics Analysis

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