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 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.

Integrated Bioinformatics Analysis Identifies Hub Genes Associated with Viral Infection and Alzheimer’s Disease

2021 ◽  
pp. 1-9
Author(s):  
Xiaoru Sun ◽  
Hui Zhang ◽  
Dongdong Yao ◽  
Yaru Xu ◽  
Qi Jing ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Viral Infection ◽  
Bioinformatics Analysis ◽  
Expression Patterns ◽  
Temporal Cortex ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Barr Virus

Background: Alzheimer’s disease (AD) is a fatal neurodegenerative disease, the etiology of which is unclear. Previous studies have suggested that some viruses are neurotropic and associated with AD. Objective: By using bioinformatics analysis, we investigated the potential association between viral infection and AD. Methods: A total of 5,066 differentially expressed genes (DEGs) in the temporal cortex between AD and control samples were identified. These DEGs were then examined via weighted gene co-expression network analysis (WGCNA) and clustered into modules of genes with similar expression patterns. Of identified modules, module turquoise had the highest correlation with AD. The module turquoise was further characterized using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. Results: Our results showed that the KEGG pathways of the module turquoise were mainly associated with viral infection signaling, specifically Herpes simplex virus, Human papillomavirus, and Epstein-Barr virus infections. A total of 126 genes were enriched in viral infection signaling pathways. In addition, based on values of module membership and gene significance, a total of 508 genes within the module were selected for further analysis. By intersecting these 508 genes with those 126 genes enriched in viral infection pathways, we identified 4 hub genes that were associated with both viral infection and AD: TLR2, COL1A2, NOTCH3, and ZNF132. Conclusion: Through bioinformatics analysis, we demonstrated a potential link between viral infection and AD. These findings may provide a platform to further our understanding of AD pathogenesis.

Construction of Long Noncoding RNA-Associated ceRNA Networks Reveals Potential Biomarkers in Alzheimer’s Disease

2021 ◽  
pp. 1-15
Author(s):  
Guan-yong Ou ◽  
Wen-wen Lin ◽  
Wei-jiang Zhao
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Noncoding Rna ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Potential Biomarkers

Background: Alzheimer’s disease (AD) is a chronic neurodegenerative disease that seriously impairs both cognitive and memory functions mainly in the elderly, and its incidence increases with age. Recent studies demonstrated that long noncoding RNAs (lncRNAs) play important roles in AD by acting as competing endogenous RNAs (ceRNAs). Objective: In this study, we aimed to construct lncRNA-associated ceRNA regulatory networks composed of potential biomarkers in AD based on the ceRNA hypothesis. Methods: A total of 20 genes (10 upregulated genes and 10 downregulated genes) were identified as the hub differentially expressed genes (DEGs). The functional enrichment analysis showed that the most significant pathways of DEGs involved include retrograde endocannabinoid signaling, synaptic vesicle circle, and AD. The upregulated hub genes were mainly enriched in the cytokine-cytokine receptor interaction pathway, whereas downregulated hub genes were involved in the neuroactive ligand-receptor interaction pathway. After convergent functional genomic (CFG) ranks and expression level analysis in different brain regions of hub genes, we found that CXCR4, GFAP, and GNG3 were significantly correlated with AD. We further identified crucial miRNAs and lncRNAs of targeted genes to construct lncRNA-associated ceRNA regulatory networks. Results: The results showed that two lncRNAs (NEAT1, MIAT), three miRNAs (hsa-miR-551a, hsa-miR-133b and hsa-miR-206), and two mRNA (CXCR4 and GNG3), which are highly related to AD, were preliminarily identified as potential AD biomarkers. Conclusion: Our study provides new insights for understanding the pathogenic mechanism underlying AD, which may potentially contribute to the ceRNA mechanism in AD.

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 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 CCTs as new biomarkers for the prognosis of head and neck squamous cancer

Open Medicine ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 672-688
Author(s):  
Yanbo Dong ◽  
Siyu Lu ◽  
Zhenxiao Wang ◽  
Liangfa Liu
Keyword(s):  
Head And Neck ◽  
Survival Data ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Advanced Tumor Stage ◽  
Expression Levels ◽  
T Complex ◽  
Advanced Tumor ◽  
Squamous Cancer

AbstractThe chaperonin-containing T-complex protein 1 (CCT) subunits participate in diverse diseases. However, little is known about their expression and prognostic values in human head and neck squamous cancer (HNSC). This article aims to evaluate the effects of CCT subunits regarding their prognostic values for HNSC. We mined the transcriptional and survival data of CCTs in HNSC patients from online databases. A protein–protein interaction network was constructed and a functional enrichment analysis of target genes was performed. We observed that the mRNA expression levels of CCT1/2/3/4/5/6/7/8 were higher in HNSC tissues than in normal tissues. Survival analysis revealed that the high mRNA transcriptional levels of CCT3/4/5/6/7/8 were associated with a low overall survival. The expression levels of CCT4/7 were correlated with advanced tumor stage. And the overexpression of CCT4 was associated with higher N stage of patients. Validation of CCTs’ differential expression and prognostic values was achieved by the Human Protein Atlas and GEO datasets. Mechanistic exploration of CCT subunits by the functional enrichment analysis suggests that these genes may influence the HNSC prognosis by regulating PI3K-Akt and other pathways. This study implies that CCT3/4/6/7/8 are promising biomarkers for the prognosis of HNSC.

scholarly journals Identification of Key Genes and Pathways Associated with Differences of Subchondral Bone in Osteoarthritis

2020 ◽  
Author(s):  
Yiyuan Zhang ◽  
Rongguo Yu ◽  
Jiayu Zhang ◽  
Eryou Feng ◽  
Haiyang Wang ◽  
...  
Keyword(s):  
Subchondral Bone ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Common Chronic Disease ◽  
Drug Candidates ◽  
Therapeutic Modalities ◽  
Pathogenic Genes

Abstract BackgroundOsteoarthritis (OA) is a common chronic disease worldwide. Subchondral bone is an important pathological change in OA and responds more rapidly to adverse loading and events compared to cartilage. However, the pathogenic genes and pathways of subchondral bone are largely unclear.ObjectiveThis study aimed to identify signature differences in genes involved in knee lateral tibial (LT) and medial tibial (MT) plateaus of subchondral bone tissue while exploring their potential molecular mechanisms via bioinformatics analysis.MethodsFirst, the gene expression data of GSE51588 was downloaded from the GEO database. Differentially expressed genes (DEGs) between knee LT and MT were identified, and functional enrichment analyses were performed. Then, a protein-protein interactive network was constructed in order to acquire the hub genes, and modules analysis was conducted using STRING and Cytoscape for further analysis. The enriched hub genes were queried in DGIdb database to find suitable drug candidates in OA.ResultsA total of 202 DEGs (112 upregulated genes and 84 downregulated genes) were determined. In the PPI network, ten hub genes were identified. Five significant modules were identified using the MCODE plugin unit. Functional enrichment analysis revealed the most important signaling pathways. Six of the ten hub genes were targetable by a total of 35 drugs, suggesting their possible therapeutic use for OA .ConclusionsThe identified hub genes and functional enrichment pathways were implicated in the development and progression of subchondral bone in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic modalities.

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