scholarly journals Autophagy-Related Genes in Atherosclerosis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuankun Chen ◽  
Ao Zeng ◽  
Shumiao He ◽  
Siqing He ◽  
Chunmei Li ◽  
...  
Keyword(s):  
Expression Profile ◽  
Regulatory Networks ◽  
Gene Expression Omnibus ◽  
Cerebrovascular Diseases ◽  
Vital Role ◽  
Differentially Expressed ◽  
Protein Protein Interaction ◽  
Target Network ◽  
Transcription Factor Target ◽  
The Relationship

Background. Atherosclerosis (AS) is a common chronic vascular inflammatory disease and one of the main causes of cardiovascular/cerebrovascular diseases (CVDs). Autophagy-related genes (ARGs) play a crucial part in pathophysiological processes of AS. However, the expression profile of ARGs has rarely been adopted to explore the relationship between autophagy and AS. Therefore, using the expression profile of ARGs to explore the relationship between autophagy and AS may provide new insights for the treatment of CVDs. Methods. The differentially expressed ARGs of the GSE57691 dataset were obtained from the Human Autophagy Database (HADb) and the Gene Expression Omnibus (GEO) database, and the GSE57691 dataset contains 9 aortic atheroma tissues and 10 normal aortic tissues. The differentially expressed ARGs of the GSE57691 dataset were analyzed by protein-protein interaction (PPI), gene ontology analysis (GO), and Kyoto Encyclopedia of Genes and Genomes analysis (KEGG) and were chosen to explore related miRNAs/transcriptional factors. Results. The GSE57691 dataset had a total of 41 differentially expressed ARGs. The GO analysis results revealed that ARGs were mainly enriched in autophagy, autophagosome, and protein serine/threonine kinase activity. KEGG analysis results showed that ARGs were mainly enriched in autophagy-animal and longevity regulating signaling pathways. Expressions of ATG5, MAP1LC3B, MAPK3, MAPK8, and RB1CC1 were regarded as focus in the PPI regulatory networks. Furthermore, 11 related miRNAs and 6 related transcription factors were obtained by miRNAs/transcription factor target network analysis. Conclusions. Autophagy and ARGs may play a vital role in regulating the pathophysiology of AS.

scholarly journals Identification of Key Genes and miRNAs in Osteosarcoma Patients with Chemoresistance by Bioinformatics Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Binbin Xie ◽  
Yiran Li ◽  
Rongjie Zhao ◽  
Yuzi Xu ◽  
Yuhui Wu ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Regulation Of Transcription ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Search Tool ◽  
Poor Outcomes

Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify Chemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription, DNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool for the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were screened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with a better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs (DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668 DEGs and 5 DEMs were extracted from GSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify potential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper understanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma.

scholarly journals Identification of microRNAs and genes as biomarkers of atrial fibrillation using a bioinformatics approach

2019 ◽  
Vol 47 (8) ◽  
pp. 3580-3589 ◽  
Author(s):  
Yingyuan Li ◽  
Wulin Tan ◽  
Fang Ye ◽  
Faling Xue ◽  
Shaowei Gao ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Control Groups ◽  
Protein Protein Interaction ◽  
Differentially Expressed Mirnas ◽  
Software Modules ◽  
And Control

Objective We aimed to explore potential microRNAs (miRNAs) and target genes related to atrial fibrillation (AF). Methods Data for microarrays GSE70887 and GSE68475, both of which include AF and control groups, were downloaded from the Gene Expression Omnibus database. Differentially expressed miRNAs between AF and control groups were identified within each microarray, and the intersection of these two sets was obtained. These miRNAs were mapped to target genes in the miRNet database. Functional annotation and enrichment analysis of these target genes was performed in the DAVID database. The protein-protein interaction (PPI) network from the STRING database and the miRNA-target-gene network were merged into a PPI-miRNA network using Cytoscape software. Modules of this network containing miRNAs were detected and further analyzed. Results Ten differentially expressed miRNAs and 1520 target genes were identified. Three PPI-miRNA modules were constructed, which contained miR-424, miR-15a, miR-542-3p, and miR-421 as well as their target genes, CDK1, CDK6, and CCND3. Conclusion The identified miRNAs and genes may be related to the pathogenesis of AF. Thus, they may be potential biomarkers for diagnosis and targets for treatment of AF.

Uncovering potential lncRNAs and nearby mRNAs in systemic lupus erythematosus from the Gene Expression Omnibus dataset

Epigenomics ◽  
2019 ◽  
Vol 11 (16) ◽  
pp. 1795-1809 ◽  
Author(s):  
Haiyu Cao ◽  
Dong Li ◽  
Huixiu Lu ◽  
Jing Sun ◽  
Haibin Li
Keyword(s):  
Gene Expression ◽  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Noncoding Rnas ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Systemic Lupus ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Aim: The aim of this study was to find potential differentially expressed long noncoding RNAs (lncRNAs) and mRNAs in systemic lupus erythematosus. Materials & methods: Differentially expressed lncRNAs and mRNAs were obtained in the Gene Expression Omnibus dataset. Functional annotation of differentially expressed mRNAs was performed, followed by protein–protein interaction network analysis. Then, the interaction network of lncRNA-nearby targeted mRNA was built. Results: Several interaction pairs of lncRNA-nearby targeted mRNA including NRIR-RSAD2, RP11-153M7.5-TLR2, RP4-758J18.2-CCNL2, RP11-69E11.4-PABPC4 and RP11-496I9.1-IRF7/ HRAS/ PHRF1 were identified. Measles and MAPK were significantly enriched signaling pathways of differentially expressed mRNAs. Conclusion: Our study identified several differentially expressed lncRNAs and mRNAs. And their interactions may play a crucial role in the process of systemic lupus erythematosus.

Immune-Related Hub Genes and the Competitive Endogenous RNA Network in Alzheimer’s Disease

2020 ◽  
Vol 77 (3) ◽  
pp. 1255-1265
Author(s):  
Hui Xu ◽  
Jianping Jia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Data Set ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Immune Phenomena ◽  
Competitive Endogenous Rna

Background: The pathogenesis of Alzheimer’s disease (AD) involves various immune-related phenomena; however, the mechanisms underlying these immune phenomena and the potential hub genes involved therein are unclear. An understanding of AD-related immune hub genes and regulatory mechanisms would help develop new immunotherapeutic targets. Objective: The aim of this study was to explore the hub genes and the mechanisms underlying the regulation of competitive endogenous RNA (ceRNA) in immune-related phenomena in AD pathogenesis. Methods: We used the GSE48350 data set from the Gene Expression Omnibus database and identified AD immune-related differentially expressed RNAs (DERNAs). We constructed protein–protein interaction (PPI) networks for differentially expressed mRNAs and determined the degree for screening hub genes. By determining Pearson’s correlation coefficient and using StarBase, DIANA-LncBase, and Human MicroRNA Disease Database (HMDD), the AD immune-related ceRNA network was generated. Furthermore, we assessed the upregulated and downregulated ceRNA subnetworks to identify key lncRNAs. Results: In total, 552 AD immune-related DERNAs were obtained. Twenty hub genes, including PIK3R1, B2M, HLA-DPB1, HLA-DQB1, PIK3CA, APP, CDC42, PPBP, C3AR1, HRAS, PTAFR, RAB37, FYN, PSMD1, ACTR10, HLA-E, ARRB2, GGH, ALDOA, and VAMP2 were identified on PPI network analysis. Furthermore, upon microRNAs (miRNAs) inhibition, we identified LINC00836 and DCTN1-AS1 as key lncRNAs regulating the aforementioned hub genes. Conclusion: AD-related immune hub genes include B2M, FYN, PIK3R1, and PIK3CA, and lncRNAs LINC00836 and DCTN1-AS1 potentially contribute to AD immune-related phenomena by regulating AD-related hub genes.

scholarly journals Identification of Prognostic Biomarker Signatures and Candidate Drugs in Colorectal Cancer: Insights from Systems Biology Analysis

Medicina ◽  
2019 ◽  
Vol 55 (1) ◽  
pp. 20 ◽  
Author(s):  
Md. Rezanur Rahman ◽  
Tania Islam ◽  
Esra Gov ◽  
Beste Turanli ◽  
Gizem Gulfidan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Regulatory Networks ◽  
Prognostic Biomarker ◽  
Drug Repositioning ◽  
Gene Expression Omnibus ◽  
Transcriptional Regulatory Networks ◽  
Survival Analyses ◽  
Drug Candidates ◽  
Protein Protein Interaction ◽  
Kaplan Meier

Colorectal cancer (CRC) is the second most common cause of cancer-related death in the world, but early diagnosis ameliorates the survival of CRC. This report aimed to identify molecular biomarker signatures in CRC. We analyzed two microarray datasets (GSE35279 and GSE21815) from the Gene Expression Omnibus (GEO) to identify mutual differentially expressed genes (DEGs). We integrated DEGs with protein–protein interaction and transcriptional/post-transcriptional regulatory networks to identify reporter signaling and regulatory molecules; utilized functional overrepresentation and pathway enrichment analyses to elucidate their roles in biological processes and molecular pathways; performed survival analyses to evaluate their prognostic performance; and applied drug repositioning analyses through Connectivity Map (CMap) and geneXpharma tools to hypothesize possible drug candidates targeting reporter molecules. A total of 727 upregulated and 99 downregulated DEGs were detected. The PI3K/Akt signaling, Wnt signaling, extracellular matrix (ECM) interaction, and cell cycle were identified as significantly enriched pathways. Ten hub proteins (ADNP, CCND1, CD44, CDK4, CEBPB, CENPA, CENPH, CENPN, MYC, and RFC2), 10 transcription factors (ETS1, ESR1, GATA1, GATA2, GATA3, AR, YBX1, FOXP3, E2F4, and PRDM14) and two microRNAs (miRNAs) (miR-193b-3p and miR-615-3p) were detected as reporter molecules. The survival analyses through Kaplan–Meier curves indicated remarkable performance of reporter molecules in the estimation of survival probability in CRC patients. In addition, several drug candidates including anti-neoplastic and immunomodulating agents were repositioned. This study presents biomarker signatures at protein and RNA levels with prognostic capability in CRC. We think that the molecular signatures and candidate drugs presented in this study might be useful in future studies indenting the development of accurate diagnostic and/or prognostic biomarker screens and efficient therapeutic strategies in CRC.

scholarly journals The Screening of Critical Related Genes in Celiac Disease Based on Intraepithelial Lymphocytes Investigation: A Bioinformatics Analysis

2019 ◽  
Vol 8 ◽  
pp. 1407
Author(s):  
Mohammad Rostami-Nejad ◽  
Reza Vafaee ◽  
Mohammad Javad Ehsani-Ardakani ◽  
Nika Aghamohammadi ◽  
Aliasghar Keramatinia ◽  
...  
Keyword(s):  
Celiac Disease ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Intraepithelial Lymphocytes ◽  
Gene Expression Omnibus ◽  
T Cell Differentiation ◽  
Differentially Expressed ◽  
Protein Protein Interaction ◽  
Critical Nodes ◽  
Geo Database

Background: Celiac disease (CD) is an immunological intestinal disorder, which is characterized by response to gluten. In addition to the environmental factors and dysbiosis of the gut microbiota, genetic susceptibility has an important role in the pathogenesis of this multifactorial disorder. Therefore, this study aims to present the crucial involved genes in CD pathogenesis. Materials and Methods: In this bioinformatics analysis study, significant differentially expressed genes of intraepithelial lymphocytes (IELs) samples of celiac patients versus normal patients from Gene Expression Omnibus (GEO) database were screened via the protein-protein interaction (PPI) network. The critical nodes based on degree values, betweenness centrality, and fold changes were determined and enriched by ClueGO to find relative biological terms. Results: According to the network analysis, five central nodes including IL2, PIK3CA, PRDM10, AKT1, and SRC and eight significant differentially expressed genes (DEGs) were determined as the critical genes related to CD. Also, CD4+, CD25+, alpha-beta regulatory T cell differentiation are identified as prominent biological terms in the celiac disease patients. Conclusion: There is a possible biomarker panel related to CD that can be used as a therapeutic or diagnostic tool to manage the disease. [GMJ.2019;8:e1407]

scholarly journals Bioinformatics-Based Identification of a circRNA-miRNA-mRNA Axis in Esophageal Squamous Cell Carcinomas

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhaojun Wang ◽  
Haifeng Li ◽  
Fajun Li ◽  
Xin Su ◽  
Junhang Zhang ◽  
...  
Keyword(s):  
Squamous Cell ◽  
Circular Rna ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Messenger Rnas ◽  
Disease Symptoms ◽  
Protein Protein Interaction ◽  
Esophageal Squamous Cell Carcinomas ◽  
Microarray Datasets ◽  
Important Nodes

Background. Esophageal squamous cell carcinoma (ESCC) has a poor prognosis due to the lack of early disease symptoms. Using bioinformatics tools, this study aimed to discover differentially expressed nonprotein-coding RNAs and genes with potential prognostic relevance in ESCC. Methods. Two microRNAs (miRNAs) and one circular RNA (circRNA) microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression of miRNAs (DEMs) and circRNAs (DECs) was, respectively, identified in ESCC tissue and compared to adjacent healthy tissue. Further analysis was performed using the miRNA microarray datasets, where miRTarBase was used to predict which messenger RNAs (mRNAs) was present. This was followed by protein-protein interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) analyses. Moreover, cytoHubba and UALCAN were used to predict the important nodes and perform patient survival analysis, respectively. The miRNA-associated circRNAs were predicted using the ENCORI website. The interaction between DECs and the predicted circRNAs was also determined. A circRNA-miRNA-mRNA axis was constructed. Results. Associated with RAP1B and circ_0052867, two miRNAs (miR-133b and miR-139-5p) were identified as being differentially expressed and downregulated across the two datasets. Finally, the circ_0052867/miR-139-5p/RAP1B regulatory axis was established. Conclusion. This study provides support for the possible mechanisms of disease progression in ESCC.

scholarly journals Bioinformatics Analysis of Key Genes and circRNA-miRNA-mRNA Regulatory Network in Gastric Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Yiting Tian ◽  
Yang Xing ◽  
Zheng Zhang ◽  
Rui Peng ◽  
Luyu Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Extracellular Matrix ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Future Research ◽  
Circular Rnas ◽  
Ppi Network ◽  
Protein Protein Interaction

Gastric cancer (GC) is one of the most common malignancies in the world, with morbidity and mortality ranking second among all cancers. Accumulating evidences indicate that circular RNAs (circRNAs) are closely correlated with tumorigenesis. However, the mechanisms of circRNAs still remain unclear. This study is aimed at determining hub genes and circRNAs and analyzing their potential biological functions in GC. Expression profiles of mRNAs and circRNAs were downloaded from the Gene Expression Omnibus (GEO) data sets of GC and paracancer tissues. Differentially expressed genes (DEGs) and differentially expressed circRNAs (DE-circRNAs) were identified. The target miRNAs of DE-circRNAs and the bidirectional interaction between target miRNAs and DEGs were predicted. Functional analysis was performed, and the protein-protein interaction (PPI) network and the circRNA-miRNA-mRNA network were established. A total of 456 DEGs and 2 DE-circRNAs were identified with 3 mRNA expression profiles and 2 circRNA expression profiles. GO analysis indicated that DEGs were mainly enriched in extracellular matrix and cell adhesion, and KEGG confirmed that DEGs were mainly associated with focal adhesion, the PI3K-Akt signaling pathway, extracellular matrix- (ECM)- receptor interaction, and gastric acid secretion. 15 hub DEGs (BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, UBE2C, CCNB1, CD44, CXCL8, COL3A1, COL5A2, and THBS1) were identified from the PPI network. Furthermore, the survival analysis indicate that GC patients with a high expression of the following 9 hub DEGs, namely, BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, and UBE2C, had significantly worse overall survival. The circRNA-miRNA-mRNA network was constructed based on 1 circRNA, 15 miRNAs, and 45 DEGs. In addition, the 45 DEGs included 5 hub DEGs. These results suggested that hub DEGs and circRNAs could be implicated in the pathogenesis and development of GC. Our findings provide novel evidence on the circRNA-miRNA-mRNA network and lay the foundation for future research of circRNAs in GC.

scholarly journals Construction of a MicroRNA-mRNA Network Underlying Decidualized Endometriotic Cyst Stromal Cells Using Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Junzui Li ◽  
Bin Zhao ◽  
Cui Yang ◽  
Qionghua Chen
Keyword(s):  
Stromal Cells ◽  
Regulatory Networks ◽  
Target Genes ◽  
Malignant Tumors ◽  
Gene Expression Omnibus ◽  
Overlapping Genes ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Go Enrichment ◽  
Endometriotic Cyst

Background. Decidualization of ectopic endometrium often leads to the extensive proliferation of local tissue and is easily misdiagnosed as malignant tumors. The study is aimed at constructing a microRNA- (miRNA-) mRNA network underlying decidualized endometriotic cyst stromal cells (ECSCs). Methods. All data were collected from the Gene Expression Omnibus (GEO) database. Firstly, the differentially expressed genes (DEGs, adj. P‐Val<0.05, | log FC | ≥1) and miRNAs (DEMs, P‐Val<0.05, ∣log FC∣≥1) were analyzed by the limma package. Secondly, we predicted the target genes (TGs) of these DEMs through the TargetScan, miRDB, and miRTarBase databases. The overlapping genes between DEGs and TGs were screened out. Thirdly, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses of the overlapping genes were performed for integrated discovery, visualization, and annotation. Then, the protein-protein interaction (PPI) network of the overlapping genes was conducted by the STRING database. Finally, we combined the PPI network and the miRNA-mRNA pairs to build a miRNA-mRNA network. Results. There are 29 DEMs and 523 DEGs. Fourteen overlapping genes were screened out, and these genes were significantly enriched in metabolism and immunity. What is more, a miRNA-mRNA network, including 14 mRNAs and 9 miRNAs, was successfully constructed. Conclusions. Taken together, the miRNA-mRNA regulatory networks described in this study may provide new insights in the decidualization of ECSCs, suggesting further investigations in novel pathogenic mechanisms.

Virtual Screening of Potential Anti-fatigue Mechanism of Polygonati Rhizoma Based on Network Pharmacology

2020 ◽  
Vol 22 (9) ◽  
pp. 612-624 ◽  
Author(s):  
Ze-Feng Wang ◽  
Ye-Qing Hu ◽  
Qi-Guo Wu ◽  
Rui Zhang
Keyword(s):  
Virtual Screening ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Vital Role ◽  
Network Pharmacology ◽  
Pathway Network ◽  
Protein Protein Interaction ◽  
Target Network ◽  
Protein Protein Interaction Network ◽  
Fatigue Mechanism

Background and Objective: A large number of people are facing the danger of fatigue due to the fast-paced lifestyle. Fatigue is common in some diseases, such as cancer. The mechanism of fatigue is not definite. Traditional Chinese medicine is often used for fatigue, but the potential mechanism of Polygonati Rhizoma (PR) is still not clear. This study attempts to explore the potential anti-fatigue mechanism of Polygonati Rhizoma through virtual screening based on network pharmacology. Methods: The candidate compounds of PR and the known targets of fatigue are obtained from multiple professional databases. PharmMapper Server is designed to identify potential targets for the candidate compounds. We developed a Herbal medicine-Compound-Disease-Target network and analyzed the interactions. Protein-protein interaction network is developed through the Cytoscape software and analyzed by topological methods. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment are carried out by DAVID Database. Finally, we develop Compound-Target-Pathway network to illustrate the anti-fatigue mechanism of PR. Results: This approach identified 12 active compounds and 156 candidate targets of PR. The top 10 annotation terms for GO and KEGG were obtained by enrichment analysis with 35 key targets. The interaction between E2F1 and PI3K-AKT plays a vital role in the anti-fatigue effect of PR due to this study. Conclusions: This study demonstrates that PR has multi-component, multi-target and multipathway effects.

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