scholarly journals Gene Network Analysis of Alzheimer’s Disease Based on Network and Statistical Methods

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1365
Author(s):  
Chen Zhou ◽  
Haiyan Guo ◽  
Shujuan Cao
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Network ◽  
Functional Module ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Essential Genes ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Core Network

Gene network associated with Alzheimer’s disease (AD) is constructed from multiple data sources by considering gene co-expression and other factors. The AD gene network is divided into modules by Cluster one, Markov Clustering (MCL), Community Clustering (Glay) and Molecular Complex Detection (MCODE). Then these division methods are evaluated by network structure entropy, and optimal division method, MCODE. Through functional enrichment analysis, the functional module is identified. Furthermore, we use network topology properties to predict essential genes. In addition, the logical regression algorithm under Bayesian framework is used to predict essential genes of AD. Based on network pharmacology, four kinds of AD’s herb-active compounds-active compound targets network and AD common core network are visualized, then the better herbs and herb compounds of AD are selected through enrichment analysis.

scholarly journals Identification of the Hub Genes in Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huiwen Gui ◽  
Qi Gong ◽  
Jun Jiang ◽  
Mei Liu ◽  
Huanyin Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Elderly ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Geo Database

Purpose. Alzheimer’s disease (AD) is considered to be the most common neurodegenerative disease and also one of the major fatal diseases affecting the elderly, thus bringing a huge burden to society. Therefore, identifying AD-related hub genes is extremely important for developing novel strategies against AD. Materials and Methods. Here, we extracted the gene expression profile GSE63061 from the National Center for Biotechnology Information (NCBI) GEO database. Once the unverified gene chip was removed, we standardized the microarray data after quality control. We utilized the Limma software package to screen the differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs. Subsequently, we constructed a protein-protein interaction (PPI) network using the STRING database. Result. We screened 2169 DEGs, comprising 1313 DEGs with upregulation and 856 DEGs with downregulation. Functional enrichment analysis showed that the response of immune, the degranulation of neutrophils, lysosome, and the differentiation of osteoclast were greatly enriched in DEGs with upregulation; peptide biosynthetic process, translation, ribosome, and oxidative phosphorylation were dramatically enriched in DEGs with downregulation. 379 nodes and 1149 PPI edges were demonstrated in the PPI network constructed by upregulated DEGs; 202 nodes and 1963 PPI edges were shown in the PPI network constructed by downregulated DEGs. Four hub genes, including GAPDH, RHOA, RPS29, and RPS27A, were identified to be the newly produced candidates involved in AD pathology. Conclusion. GAPDH, RHOA, RPS29, and RPS27A are expected to be key candidates for AD progression. The results of this study can provide comprehensive insight into understanding AD’s pathogenesis and potential new therapeutic targets.

scholarly journals Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics

2021 ◽  
Vol 12 ◽  
Author(s):  
Xingxing Zhao ◽  
Hongmei Yao ◽  
Xinyi Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Temporal Cortex ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Machine Learning Algorithms ◽  
Functional Enrichment ◽  
Control Group ◽  
Hub Genes ◽  
Expression Levels

Alzheimer’s disease (AD) is a neurodegenerative disease with unelucidated molecular pathogenesis. Herein, we aimed to identify potential hub genes governing the pathogenesis of AD. The AD datasets of GSE118553 and GSE131617 were collected from the NCBI GEO database. The weighted gene coexpression network analysis (WGCNA), differential gene expression analysis, and functional enrichment analysis were performed to reveal the hub genes and verify their role in AD. Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). We obtained 33, 42, 42, and 41 hub genes in modules associated with AD in TC, FC, EC, and CE tissues, respectively. Significant differences were recorded in the expression levels of hub genes between AD and the control group in the TC and EC tissues (P < 0.05). The differences in the expressions of FCGRT, SLC1A3, PTN, PTPRZ1, and PON2 in the FC and CE tissues among the AD and control groups were significant (P < 0.05). The expression levels of PLXNB1, GRAMD3, and GJA1 were statistically significant between the Braak NFT stages of AD. Overall, our study uncovered genes that may be involved in AD pathogenesis and revealed their potential for the development of AD biomarkers and appropriate AD therapeutics targets.

scholarly journals Common genetic signatures of Alzheimer’s disease in Down Syndrome

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1299
Author(s):  
Ayati Sharma ◽  
Alisha Chunduri ◽  
Asha Gopu ◽  
Christine Shatrowsky ◽  
Wim E. Crusio ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Down Syndrome ◽  
Early Onset ◽  
Cell Function ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Extra Copy ◽  
Network Analyses

Background: People with Down Syndrome (DS) are born with an extra copy of Chromosome (Chr) 21 and many of these individuals develop Alzheimer’s Disease (AD) when they age. This is due at least in part to the extra copy of the APP gene located on Chr 21. By 40 years, most people with DS have amyloid plaques which disrupt brain cell function and increase their risk for AD. About half of the people with DS develop AD and the associated dementia around 50 to 60 years of age, which is about the age at which the hereditary form of AD, early onset AD, manifests. In the absence of Chr 21 trisomy, duplication of APP alone is a cause of early onset Alzheimer’s disease, making it likely that having three copies of APP is important in the development of AD and in DS. In individuals with both DS and AD, early behavior and cognition-related symptoms may include a reduction in social behavior, decreased enthusiasm, diminished ability to pay attention, sadness, fearfulness or anxiety, irritability, uncooperativeness or aggression, seizures that begin in adulthood, and changes in coordination and walking. Methods: We investigate the relationship between AD and DS through integrative analysis of genesets derived from a MeSH query of AD and DS associated beta amyloid peptides, Chr 21, GWAS identified AD risk factor genes, and differentially expressed genes in DS individuals. Results: Unique and shared aspects of each geneset were evaluated based on functional enrichment analysis, transcription factor profile and network analyses. Genes that may be important to both disorders: ACSM1, APBA2, APLP1, BACE2, BCL2L, COL18A1, DYRK1A, IK, KLK6, METTL2B, MTOR, NFE2L2, NFKB1, PRSS1, QTRT1, RCAN1, RUNX1, SAP18 SOD1, SYNJ1, S100B. Conclusions: Our findings indicate that oxidative stress, apoptosis, and inflammation/immune system processes likely underlie the pathogenesis of AD and DS.

scholarly journals Integrated Analyses Identify Immune-Related Signature Associated with Qingyihuaji Formula for Treatment of Pancreatic Ductal Adenocarcinoma Using Network Pharmacology and Weighted Gene Co-Expression Network

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiang Qian ◽  
Zhuo Chen ◽  
Sha Sha Chen ◽  
Lu Ming Liu ◽  
Ai Qin Zhang
Keyword(s):  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Ductal Adenocarcinoma ◽  
Network Pharmacology ◽  
Active Ingredients ◽  
Kaplan Meier ◽  
Human Protein Atlas

The study aimed to clarify the potential immune-related targets and mechanisms of Qingyihuaji Formula (QYHJ) against pancreatic cancer (PC) through network pharmacology and weighted gene co-expression network analysis (WGCNA). Active ingredients of herbs in QYHJ were identified by the TCMSP database. Then, the putative targets of active ingredients were predicted with SwissTargetPrediction and the STITCH databases. The expression profiles of GSE32676 were downloaded from the GEO database. WGCNA was used to identify the co-expression modules. Besides, the putative targets, immune-related targets, and the critical module genes were mapped with the specific disease to select the overlapped genes (OGEs). Functional enrichment analysis of putative targets and OGEs was conducted. The overall survival (OS) analysis of OGEs was investigated using the Kaplan-Meier plotter. The relative expression and methylation levels of OGEs were detected in UALCAN, human protein atlas (HPA), Oncomine, DiseaseMeth version 2.0 and, MEXPRESS database, respectively. Gene set enrichment analysis (GSEA) was conducted to elucidate the key pathways of highly-expressed OGEs further. OS analyses found that 12 up-regulated OGEs, including CDK1, PLD1, MET, F2RL1, XDH, NEK2, TOP2A, NQO1, CCND1, PTK6, CTSE, and ERBB2 that could be utilized as potential diagnostic indicators for PC. Further, methylation analyses suggested that the abnormal up-regulation of these OGEs probably resulted from hypomethylation, and GSEA revealed the genes markedly related to cell cycle and proliferation of PC. This study identified CDK1, PLD1, MET, F2RL1, XDH, NEK2, TOP2A, NQO1, CCND1, PTK6, CTSE, and ERBB2 might be used as reliable immune-related biomarkers for prognosis of PC, which may be essential immunotherapies targets of QYHJ.

scholarly journals Soluble Amyloid-Beta Isoforms Predict Downstream Alzheimer’s Disease Pathology

2021 ◽  
Author(s):  
Guilherme Povala ◽  
Bruna Bellaver ◽  
Marco Antônio De Bastiani ◽  
Wagner S. Brum ◽  
Pamela C. L. Ferreira ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amyloid Beta ◽  
Area Under The Curve ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Biological Processes ◽  
Proteomics Data ◽  
Proteomics Analysis ◽  
Significant Enrichment

Abstract Background: Changes in soluble amyloid-beta (Aβ) levels in cerebrospinal fluid (CSF) are detectable at early preclinical stages of Alzheimer's disease (AD). However, whether Aβ levels can predict downstream AD pathological features in cognitively unimpaired (CU) individuals remains unclear. With this in mind, we aimed at investigating whether a combination of soluble Aβ isoforms can predict tau pathology (T+) and neurodegeneration (N+) positivity. Methods: We used CSF measurements of three soluble Aβ peptides (Aβ1‑38, Aβ1‑40 and Aβ1‑42) in CU individuals (n = 318) as input features in machine learning (ML) models aiming at predicting T+ and N+. Input data was used for building 2046 tuned predictive ML models with a nested cross-validation technique. Additionally, proteomics data was employed to investigate the functional enrichment of biological processes altered in T+ and N+ individuals. Results: Our findings indicate that Aβ isoforms can predict T+ and N+ with an area under the curve (AUC) of 0.929 and 0.936, respectively. Additionally, proteomics analysis identified 17 differentially expressed proteins (DEPs) in individuals wrongly classified by our ML algorithm. More specifically, enrichment analysis of gene ontology biological processes revealed an upregulation in myelinization and glucose metabolism-related processes in CU individuals wrongly predicted as T+. A significant enrichment of DEPs in pathways including biosynthesis of amino acids, glycolysis/gluconeogenesis, carbon metabolism, cell adhesion molecules and prion disease was also observed. Conclusions: Our results demonstrate that, by applying a refined ML analysis, a combination of Ab isoforms can predict T+ and N+ with a high AUC. CSF proteomics analysis highlighted a promising group of proteins that can be further explored for improving T+ and N+ prediction.

scholarly journals Study on the Mechanism of Acori Graminei Rhizoma in the Treatment of Alzheimer’s Disease Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yi Kuan Du ◽  
Yue Xiao ◽  
Shao Min Zhong ◽  
Yi Xing Huang ◽  
Qian Wen Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
The Elderly ◽  
Estrogen Signaling ◽  
Network Pharmacology ◽  
Active Components

Alzheimer’s disease is a common neurodegenerative disease in the elderly. This study explored the curative effect and possible mechanism of Acori graminei rhizoma on Alzheimer’s disease. In this paper, 8 active components of Acori graminei rhizoma were collected by consulting literature and using the TCMSP database, and 272 targets were screened using the PubChem and Swiss Target Prediction databases. Introduce it into the software of Cytoscape 3.7.2 and establish the graph of “drug-active ingredient-ingredient target.” A total of 276 AD targets were obtained from OMIM, Gene Cards, and DisGeNET databases. Import the intersection targets of drugs and diseases into STRING database for enrichment analysis, and build PPI network in the Cytoscape 3.7.2 software, whose core targets involve APP, AMPK, NOS3, etc. GO analysis and KEGG analysis showed that there were 195 GO items and 30 AD-related pathways, including Alzheimer’s disease pathway, serotonin synapse, estrogen signaling pathway, dopaminergic synapse, and PI3K-Akt signaling pathway. Finally, molecular docking was carried out to verify the binding ability between Acori graminei rhizoma and core genes. Our results predict that Acori graminei rhizoma can treat AD mainly by mediating Alzheimer’s signal pathway, thus reducing the production of Aβ, inhibiting the hyperphosphorylation of tau protein, regulating neurotrophic factors, and regulating the activity of kinase to change the function of the receptor.

scholarly journals Network pharmacology, molecular docking, and experimental study for the mechanisms of Qishen Yiqi Pills against Cardiovascular Diseases

2021 ◽  
Author(s):  
Jie-wen Zhao ◽  
Hai-dong Liu ◽  
Ming-yin Man ◽  
Lv-ya Wang ◽  
Ning Li ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Cardiovascular Diseases ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Literature Mining ◽  
Therapeutic Effects ◽  
Active Components

Abstract Background Qishen Yiqi Pills (QSYQP) is a traditional Chinese compound recipe. However, our understanding of its mechanism has been hindered due to the complexity of its components and targets. In this work, the network pharmacology-based approaches were used to explore QSYQP’s pharmacological mechanism on treating cardiovascular diseases (CVD). Results From ETCM and TCM MESH databases we collected QSYQP’s 333 active components and their 674 putative targets. We constructed the sub-network influence by CVD genes and found that 40% QSYQP targets appeared in 20 modules, in which QSYQP’s targets and CVD genes co-existed as hub nodes in the sub-network. Functional enrichment analysis suggested that the 42 key targets were mainly expressed in platelets, blood vessels, cardiomyocytes, and other tissues. The main signaling pathways regulated and controlled by the key targets were inflammation, immunity, blood coagulation and energy metabolism. Network and pathway analysis identified 7 key targets, which were regulated by 7 compounds of QSYQP. 26 of the 42 important targets, including the 7 key targets were verified by literature mining. Twelve pairs of interactions between key targets and QSYQP’s compounds were validated by molecular docking. Further validation experiments suggested that QSYQP suppressed H/R induced apoptosis and cytoskeleton disruption of cardiomyocytes. Western blotting showed that the expression of cardiovascular diseases-related genes including ACTC1, FoxO1 and DIAPH1 was significantly decreased by establishing the hypoxia-reoxygenation model in vitro, while the protein expression of experimental group was significantly increased by adding QSYQP or its ingredients. Conclusion These results indicated the correlation of QSYQP treatment to the therapeutic effects of CVD. At the molecular level, this study revealed the multicomponent and multitargeting mechanisms of QSYQP in the regulation and treatment of cardiovascular diseases, potentially providing a reference for the further utilization of QSYQP.

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