scholarly journals The application of weighted gene co-expression network analysis in identifying key modules and hub genes associated with disease status in Alzheimer’s disease

2019 ◽  
Vol 7 (24) ◽  
pp. 800-800 ◽  
Author(s):  
Yan Sun ◽  
Jinghan Lin ◽  
Liming Zhang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Disease Status ◽  
Hub Genes

scholarly journals Key Gene Network Related to Primary Ciliary Dyskinesia in Hippocampus of Patients With Alzheimer’s Disease Revealed by Weighted Gene Co-expression Network Analysis

2021 ◽  
Author(s):  
Pengcheng Xia ◽  
Jing Chen ◽  
Xiaohui Bai ◽  
Ming Li ◽  
Le Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Cell Biology ◽  
Primary Ciliary Dyskinesia ◽  
Gene Network ◽  
Pathophysiological Mechanism ◽  
Hub Genes ◽  
Ciliary Movement ◽  
Ciliary Dyskinesia

Abstract Background. Alzheimer's disease (AD) is closely related to aging, showing an increasing incidence rate for years. As one of the main organs involved in AD, hippocampus has been extensively studied due to its association with many human diseases. However, little knowledge is known on its association with primary ciliary dyskinesia (PCD).Material and Methods. The microarray data of hippocampus on AD were retrieved from the Gene Expression Omnibus (GEO) database to construct the co-expression network by weighted gene co-expression network analysis (WGCNA). The gene network modules associated with AD screened with the common genes were further annotated based on Gene Ontology (GO) database and enriched based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The protein-protein interaction (PPI) network was constructed based on STRING database to identify the hub genes in the network.Results. Genes involved in PCD were identified in the hippocampus of AD patients. Functional analysis revealed that these genes were mainly enriched in ciliary tissue, ciliary assembly, axoneme assembly, ciliary movement, microtubule based process, microtubule based movement, organelle assembly, axoneme dynamin complex, cell projection tissue, and microtubule cytoskeleton tissue. A total of 20 central genes, e.g.,DYNLRB2, ZMYND10, DRC1, DNAH5, WDR16, TTC25, and ARMC4 were identified as hub genes related to PCD in hippocampus of AD patients.Conclusion. Our study demonstrated that AD and PCD have shared metabolic pathways. These common pathways provide novel evidence for further investigation of the pathophysiological mechanism and the hub genes suggest new therapeutic targets for the diagnosis and treatment of AD and PCD.Subjects Bioinformatics, Cell Biology, Molecular Biology, Neurology

scholarly journals Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Tingting Zhang ◽  
Nanyang Liu ◽  
Wei Wei ◽  
Zhen Zhang ◽  
Hao Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Roc Curve ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis ◽  
Six Genes

Background. Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease; however, there are no comprehensive therapeutic interventions. Therefore, this study is aimed at identifying novel molecular targets that may improve the diagnosis and treatment of patients with AD. Methods. In our study, GSE5281 microarray dataset from the GEO database was collected and screened for differential expression analysis. Genes with a P value of <0.05 and ∣ log 2 FoldChange ∣ > 0.5 were considered differentially expressed genes (DEGs). We further profiled and identified AD-related coexpression genes using weighted gene coexpression network analysis (WGCNA). Functional enrichment analysis was performed to determine the characteristics and pathways of the key modules. We constructed an AD-related model based on hub genes by logistic regression and least absolute shrinkage and selection operator (LASSO) analyses, which was also verified by the receiver operating characteristic (ROC) curve. Results. In total, 4674 DEGs were identified. Nine distinct coexpression modules were identified via WGCNA; among these modules, the blue module showed the highest positive correlation with AD ( r = 0.64 , P = 3 e − 20 ), and it was visualized by establishing a protein–protein interaction network. Moreover, this module was particularly enriched in “pathways of neurodegeneration—multiple diseases,” “Alzheimer disease,” “oxidative phosphorylation,” and “proteasome.” Sixteen genes were identified as hub genes and further submitted to a LASSO regression model, and six genes (EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF) were identified based on the model index. Additionally, we assessed the accuracy of the LASSO model by plotting an ROC curve ( AUC = 0.940 ). Conclusions. Using the WGCNA and LASSO models, our findings provide a better understanding of the role of biomarkers EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF and provide a basis for further studies on AD progression.

scholarly journals Key Gene Network Related to Primary Ciliary Dyskinesia in Hippocampus of Patients with Alzheimer’S Disease Revealed by Weighted Gene Co-Expression Network Analysis

2021 ◽  
Author(s):  
Pengcheng Xia ◽  
Jing Chen ◽  
Xiaohui Bai ◽  
Ming Li ◽  
Le Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Cell Biology ◽  
Primary Ciliary Dyskinesia ◽  
Gene Network ◽  
Pathophysiological Mechanism ◽  
Hub Genes ◽  
Ciliary Movement ◽  
Ciliary Dyskinesia

Abstract Background. Alzheimer's disease (AD) is closely related to aging, showing an increasing incidence rate for years. As one of the main organs involved in AD, hippocampus has been extensively studied due to its association with many human diseases. However, little knowledge is known on its association with primary ciliary dyskinesia (PCD). Material and Methods. The microarray data of hippocampus on AD were retrieved from the Gene Expression Omnibus (GEO) database to construct the co-expression network by weighted gene co-expression network analysis (WGCNA). The gene network modules associated with AD screened with the common genes were further annotated based on Gene Ontology (GO) database and enriched based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The protein-protein interaction (PPI) network was constructed based on STRING database to identify the hub genes in the network. Results. Genes involved in PCD were identified in the hippocampus of AD patients. Functional analysis revealed that these genes were mainly enriched in ciliary tissue, ciliary assembly, axoneme assembly, ciliary movement, microtubule based process, microtubule based movement, organelle assembly, axoneme dynamin complex, cell projection tissue, and microtubule cytoskeleton tissue. A total of 20 central genes, e.g., DYNLRB2, ZMYND10, DRC1, DNAH5, WDR16, TTC25, and ARMC4 were identified as hub genes related to PCD in hippocampus of AD patients. Conclusion. Our study demonstrated that AD and PCD have shared metabolic pathways. These common pathways provide novel evidence for further investigation of the pathophysiological mechanism and the hub genes suggest new therapeutic targets for the diagnosis and treatment of AD and PCD. Subjects Bioinformatics, Cell Biology, Molecular Biology, Neurology

scholarly journals CSF metabolites associate with CSF tau and improve prediction of Alzheimer's disease status

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Ruocheng Dong ◽  
Burcu F. Darst ◽  
Yuetiva Deming ◽  
Yue Ma ◽  
Qiongshi Lu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Status

scholarly journals Shared Blood Transcriptomic Signatures between Alzheimer’s Disease and Diabetes Mellitus

Biomedicines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 34
Author(s):  
Taesic Lee ◽  
Hyunju Lee
Keyword(s):  
Diabetes Mellitus ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interactions ◽  
Expression Patterns ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Gene Modules ◽  
Gene Regulatory ◽  
The Brain

Alzheimer’s disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases.

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

Co-expression Network Analysis Revealing the Potential Regulatory Roles of lncRNAs in Alzheimer’s Disease

2019 ◽  
Vol 11 (4) ◽  
pp. 645-654 ◽  
Author(s):  
Jiong Wu ◽  
Linhui Chen ◽  
Chaobo Zheng ◽  
Shanhu Xu ◽  
Yuhai Gao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis

Associations Between Obesity and Alzheimer’s Disease: Multiple Bioinformatic Analyses

2021 ◽  
pp. 1-11
Author(s):  
Qi-Shuai Zhuang ◽  
Lei Meng ◽  
Zhe Wang ◽  
Liang Shen ◽  
Hong-Fang Ji
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Drug Targets ◽  
Protective Factor ◽  
Genetic Locus ◽  
Meta Analysis ◽  
Current Evidence ◽  
Causal Association ◽  
Ppi Network

Background: Identifying modifiable risk factors, such as obesity, to lower the prevalence of Alzheimer’s disease (AD) has gained much interest. However, whether the association is causal remains to be evaluated. Objective: The present study was designed: 1) to make a quantitative assessment of the association between obesity and AD; 2) to validate whether there was a causal association between them; and 3) to provide genetic clues for the association through a network-based analysis. Methods: Two-sample Mendelian randomization (2SMR) analysis, meta-analysis, and protein-protein interaction (PPI) network analysis, were employed. Results: Firstly, the meta-analysis based on 9 studies comprising 6,986,436 subjects indicated that midlife obesity had 33%higher AD odds than controls (OR = 1.33, 95%CI = [1.03, 1.62]), while late-life obesity were inversely associated with AD risk (OR = 0.57, 95%CI = [0.47, 0.68]). Secondly, 2SMR analysis indicated that there was no causal association between them. Thirdly, CARTPT was identified to be shared by the anti-obesity drug targets and AD susceptibility genes. Further PPI network analysis found that CARTPT interacted with CD33, a strong genetic locus linked to AD. Finally, 2SMR analysis showed that CNR1 could be a protective factor for AD. Conclusion: Multiple bioinformatic analyses indicated that midlife obesity might increase the risk of AD, while current evidence indicated that there was no causal association between them. Further, CARTPT might be an important factor linking the two disease conditions. It could help to better understand the mechanisms underlying the associations between obesity and AD.

Hippocampal and amygdalar morphological abnormalities in Alzheimer’s disease based on three Chinese MRI datasets

2021 ◽  
Vol 18 ◽  
Author(s):  
Yuanyuan Wei ◽  
Nianwei Huang ◽  
Yong Liu ◽  
Xi Zhang ◽  
Silun Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Shape Analysis ◽  
Early Stage ◽  
Disease Status ◽  
Brain Structures ◽  
Statistical Shape Analysis ◽  
Social Significance ◽  
Statistical Shape ◽  
Shape Characteristics

Background: Early detection of Alzheimer’s disease (AD) and its early stage, the mild cognitive impairment (MCI), has important scientific, clinical and social significance. Magnetic resonance imaging (MRI) based statistical shape analysis provides an opportunity to detect regional structural abnormalities of brain structures caused by AD and MCI. Objective: In this work, we aimed to employ a well-established statistical shape analysis pipeline, in the framework of large deformation diffeomorphic metric mapping, to identify and quantify the regional shape abnormalities of the bilateral hippocampus and amygdala at different prodromal stages of AD, using three Chinese MRI datasets collected from different domestic hospitals. Methods: We analyzed the region-specific shape abnormalities at different stages of the neuropathology of AD by comparing the localized shape characteristics of the bilateral hippocampi and amygdalas between healthy controls and two disease groups (MCI and AD). In addition to group comparison analyses, we also investigated the association between the shape characteristics and the Mini Mental State Examination (MMSE) of each structure of interest in the disease group (MCI and AD combined) as well as the discriminative power of different morphometric biomarkers. Results: We found the strongest disease pathology (regional atrophy) at the subiculum and CA1 subregions of the hippocampus and the basolateral, basomedial as well as centromedial subregions of the amygdala. Furthermore, the shape characteristics of the hippocampal and amygdalar subregions exhibiting the strongest AD related atrophy were found to have the most significant positive associations with the MMSE. Employing the shape deformation marker of the hippocampus or the amygdala for automated MCI or AD detection yielded a significant accuracy boost over the corresponding volume measurement. Conclusion: Our results suggested that the amygdalar and hippocampal morphometrics, especially those of shape morphometrics, can be used as auxiliary indicators for monitoring the disease status of an AD patient.

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