scholarly journals Analysis of the Potential Role of Key Genes in Atrial Fibrillation Based on Bioinformatics Approach

2021 ◽  
Author(s):  
Dan He ◽  
Zhong-bao Ruan ◽  
Gui-xian Song ◽  
Ge-cai Chen ◽  
Li Zhu ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Enrichment Analysis ◽  
Diagnosis And Treatment ◽  
Control Group ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Qrt Pcr ◽  
Key Genes ◽  
Microarray Datasets ◽  
Potential Biomarkers

Abstract Objective: Our study aims to explore the key differentially expressed genes (DEGs) that may serve as potential biomarkers for the diagnosis and treatment of atrial fibrillation (AF) using bioinformatics tools.Methods: Microarray datasets of GSE31821 and GSE79768 were downloaded from Gene Expression Synthesis (GEO) database. DEGs were analyzed after merging all microarray data and adjusting batch effect. The screened DEGs were further used for Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. Protein-protein interaction (PPI) network was constructed using the STRING database，and PPI nodes were counted by R software. Finally, combined with the above important bioinformatics information, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to detect some DEGs in the tissues of patients with AF.Results:114 DEGs (|log2 FC|≥0.5) were identified in the AF group compared with the control group. Combining DEGs, enrichment analysis and PPI results, CXCL10, TLR7, DDX58, CCR2, RSAD2, KIT, LYN, and CXCL11 were identified as potential key genes. The expression of two key genes (RSAD2 and CXCL11) was also verified by qRT-PCR in the tissues of AF patients, illustrating the reliability and biomarker potential of the key genes.Conclusion: 8 potential key genes may play an important role in the development of AF, and they may serve as potential biomarkers for the diagnosis and treatment of AF.

scholarly journals CCDC114, DNAI2 and TOP2A involves in the effects of tibolone treatment on postmenopausal endometrium

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yanhua Lv ◽  
Yanqing Liu ◽  
Yueqiang Wang ◽  
Fanrong Kong ◽  
Qiuxiang Pang ◽  
...  
Keyword(s):  
Postmenopausal Women ◽  
Molecular Mechanisms ◽  
Ion Homeostasis ◽  
Enrichment Analysis ◽  
Control Group ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Target Interaction ◽  
Protein Protein Interaction ◽  
Movement Function

Abstract Background This study aimed to explore the molecular mechanisms of tibolone treatment in postmenopausal women. Methods The gene set enrichment profile, GSE12446, which includes 9 human endometrial samples from postmenopausal women treated with tibolone (tibolone group) and 9 control samples (control group), was downloaded from GEO database for analysis. Differentially expressed genes (DEGs) in tibolone vs. control groups were identified and then used for function and pathway enrichment analysis. Protein–protein interaction (PPI) network and module analyses were also performed. Finally, drug–target interaction was predicted for genes in modules, and then were validated in Pubmed. Results A total of 238 up-regulated DEGs and 72 down-regulated DEGs were identified. These DEGs were mainly enriched in various biological processed and pathways, such as cilium movement (e.g., CCDC114 and DNAI2), calcium ion homeostasis, regulation of hormone levels and complement/coagulation cascades. PPI network contained 368 interactions and 166 genes, of which IGF1, DNALI1, CCDC114, TOP2A, DNAH5 and DNAI2 were the hue genes. A total of 96 drug–gene interactions were obtained, including 94 drugs and eight genes. TOP2A and HTR2B were found to be targets of 28 drugs and 38 drugs, respectively. Among the 94 obtained drugs, only 12 drugs were reported in studies, of which 7 drugs (e.g., epirubicin) were found to target TOP2A. Conclusions CCDC114 and DNAI2 might play important roles in tibolone-treated postmenopausal women via cilium movement function. TOP2A might be a crucial target of tibolone in endometrium of postmenopausal women.

scholarly journals Metabolic profiling of serum for osteoarthritis biomarkers

2021 ◽  
Author(s):  
Ziqian Xiao ◽  
Zhenyang Zhang ◽  
Shanbin Huang ◽  
Jerome Rumdon Lon ◽  
Shuilin Xie
Keyword(s):  
Enrichment Analysis ◽  
Normal Serum ◽  
Rabbit Serum ◽  
Control Group ◽  
Case Group ◽  
Pathway Enrichment Analysis ◽  
Serum Samples ◽  
Major Health Problem ◽  
The World ◽  
Potential Biomarkers

AbstractOsteoarthritis is a prevalent aging disease in the world, and in recent years it has shown a trend toward younger age, which is becoming a major health problem in the world and seriously endangers the health of the elderly. However, the etiology and pathogenesis of osteoarthritis are still unclear, causing great trouble for treatment. To screen out potential biomarkers that could be used as identification of osteoarthritis and explore the pathogenesis of osteoarthritis, we performed untargeted metabolomics analysis of nine New Zealand rabbit serum samples by LC-MS / MS, including three normal serum samples (control group) and six osteoarthritis serum samples (case group). Finally 44 differential metabolites were identified, and the ROC analysis results indicated that a total of 36 differential metabolites could be used as potential biomarkers. Further metabolic pathway enrichment analysis was performed on these differential metabolites, and we found that a total of 17 metabolic pathways were affected, which may provide directions for the study of osteoarthritis mechanisms.

scholarly journals Differentially expressed genes SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma

2020 ◽  
Author(s):  
Sheng Chang ◽  
Yang Cao
Keyword(s):  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Osteogenic Sarcoma ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Potential Biomarkers

Abstract Background: Osteosarcoma (osteogenic sarcoma, OS) is a primary cause of morbidity and mortality and is associated with poor prognosis in the field of orthopedic. Globally, rates of OS are highest among 15 to 25-year-old adolescent. However, the mechanism of gene regulation and signaling pathway is unknown. Material and Methods: GSE9508, including 34 OS samples and 5 non-malignant bone samples, was gained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were picked out by GEO2R online R soft tool. Furthermore, the protein-protein interaction (PPI) network between the DEGs was molded utilizing STRING online software. Afterward, PPI network of DEGs was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out on DAVID online tool and visualized via cytoscape software. Subsequently, module analysis of PPI was performed by using MCODE app. What’s more, prognosis-related genes were screened by using online databases including GEPIA, UALCAN and cBioPortal databases. Results: Totally, 671 DEGs were picked out, including 501 up-regulated genes and 170 down-regulated genes. Moreover, 22 hub genes were identified to be significantly expressed in PPI network (16 up-regulated and 6 down-regulated). We found that spliceosome signaling pathway may provide a potential target in OS. Furthermore, on the basis of common crucial pathway, PRPF38A and SNRPC were closely associated with spliceosome. Conclusion: This study showed that SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma.

scholarly journals Exploratory Analysis of Circulating miRNA Signatures in Atrial Fibrillation Patients Determining Potential Biomarkers to Support Decision-Making in Anticoagulation and Catheter Ablation

2020 ◽  
Vol 21 (7) ◽  
pp. 2444 ◽  
Author(s):  
Naoki Kiyosawa ◽  
Kenji Watanabe ◽  
Yoshiyuki Morishima ◽  
Takeshi Yamashita ◽  
Naoharu Yagi ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Quantitative Information ◽  
Pathway Enrichment Analysis ◽  
Risk Patients ◽  
Novel Biomarkers ◽  
Positive Correlations ◽  
Potential Biomarkers

Novel biomarkers are desired to improve risk management for patients with atrial fibrillation (AF). We measured 179 plasma miRNAs in 83 AF patients using multiplex qRT-PCR. Plasma levels of eight (i.e., hsa-miR-22-3p, hsa-miR-128-3p, hsa-miR-130a-3p, hsa-miR-140-5p, hsa-miR-143-3p, hsa-miR-148b-3p, hsa-miR-497-5p, hsa-miR-652-3p) and three (i.e., hsa-miR-144-5p, hsa-miR-192-5p, hsa-miR-194-5p) miRNAs showed positive and negative correlations with CHA2DS2-VASc scores, respectively, which also showed negative and positive correlations with catheter ablation (CA) procedure, respectively, within the follow-up observation period up to 6-month after enrollment. These 11 miRNAs were functionally associated with TGF-β signaling and androgen signaling based on pathway enrichment analysis. Seven of possible target genes of these miRNAs, namely TGFBR1, PDGFRA, ZEB1, IGFR1, BCL2, MAPK1 and DICER1 were found to be modulated by more than four miRNAs of the eleven. Of them, TGFBR1, PDGFRA, ZEB1 and BCL2 are reported to exert pro-fibrotic functions, suggesting that dysregulations of these eleven miRNAs may reflect pro-fibrotic condition in the high-risk patients. Although highly speculative, these miRNAs may potentially serve as potential biomarkers, providing mechanistic and quantitative information for pathophysiology in daily clinical practice with AF such as possible pro-fibrotic state in left atrium, which would enhance the risk of stroke and reduce the preference for performing CA.

scholarly journals Identification of Biomarkers and Enriched Pathways Involved in Lung Cancer

2020 ◽  
Author(s):  
Nikita Singh ◽  
Mukesh Kumar ◽  
Atanu Bhattacharjee ◽  
Prashant Kumar Sonker ◽  
Agni Saroj
Keyword(s):  
Lung Cancer ◽  
Close Association ◽  
Enrichment Analysis ◽  
Notch Signaling Pathway ◽  
P Value ◽  
Pathway Enrichment Analysis ◽  
Ampk Signaling ◽  
Protein Protein Interaction ◽  
Viral Carcinogenesis ◽  
Key Genes

Objective: The aim of study is to find key genes and enriched pathways associated with lung cancer. Participants and Methods: Differentially expressed genes (DEGs) data of 54674 genes based on stage, tumor and status of lung cancer was taken from 66 patients of African American (AAs) origin. 2392 DEGs were found based on stage, 13502 DEGs were found based on tumor, 2927 DEGs were found based on status having p value (p<0.05). Results: Total 33 common DEGs were found from stage, tumor and status of lung cancer. Gene ontology (GO) and KEGG pathway enrichment analysis was performed and 49 significant pathways were obtained, out of which 10 pathways were found to be exclusively involved in lung cancer development. Protein-protein interaction (PPI) network analysis found 69 nodes and 324 edges and identified 10 hub genes based on their highest degrees. Module analysis of PPI found that ‘Viral carcinogenesis’, ‘pathways in cancer’, ‘notch signaling pathway’, ‘AMPK signaling pathways’ had a close association with lung cancer. Conclusion: These identified DEGs regulate other genes which play important role in growth of lung cancer. The key genes and enriched pathways identified can thus help in better identification and prediction of lung cancer.

scholarly journals Inflammation as a risk factor for stroke in atrial fibrillation: data from a microarray data analysis

2020 ◽  
Vol 48 (5) ◽  
pp. 030006052092167
Author(s):  
Yingyuan Li ◽  
Wulin Tan ◽  
Fang Ye ◽  
Shihong Wen ◽  
Rong Hu ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Microarray Data Analysis ◽  
Protein Protein Interaction ◽  
Inflammatory Processes ◽  
Key Genes

Objective Stroke is a severe complication of atrial fibrillation (AF). We aimed to discover key genes and microRNAs related to stroke risk in patients with AF using bioinformatics analysis. Methods GSE66724 microarray data, including peripheral blood samples from eight patients with AF and stroke and eight patients with AF without stroke, were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AF patients with and without stroke were identified using the GEO2R online tool. Functional enrichment analysis was performed using the DAVID database. A protein–protein interaction (PPI) network was obtained using the STRING database. MicroRNAs (miRs) targeting these DEGs were obtained from the miRNet database. A miR–DEG network was constructed using Cytoscape software. Results We identified 165 DEGs (141 upregulated and 24 downregulated). Enrichment analysis showed enrichment of certain inflammatory processes. The miR–DEG network revealed key genes, including MEF2A, CAND1, PELI1, and PDCD4, and microRNAs, including miR-1, miR-1-3p, miR-21, miR-21-5p, miR-192, miR-192-5p, miR-155, and miR-155-5p. Conclusion Dysregulation of certain genes and microRNAs involved in inflammation may be associated with a higher risk of stroke in patients with AF. Evaluating these biomarkers could improve prediction, prevention, and treatment of stroke in patients with AF.

scholarly journals BPI and KIR6.1 as significant hub genes for vein graft restenosis

2020 ◽  
Vol 48 (11) ◽  
pp. 030006052096933
Author(s):  
Yun-peng Bai ◽  
Bo-chen Yao ◽  
Mei Wang ◽  
Xian-kun Liu ◽  
Xiao-long Zhu ◽  
...  
Keyword(s):  
Vein Graft ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Control Group ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction

Background Vein graft restenosis (VGR), which appears to be caused by dyslipidemia following vascular transplantation, seriously affects the prognosis and long-term quality of life of patients. Methods This study analyzed the genetic data of restenosis (VGR group) and non-stenosis (control group) vessels from patients with coronary heart disease post-vascular transplantation and identified hub genes that might be responsible for its occurrence. GSE110398 was downloaded from the Gene Expression Omnibus database. A repeatability test for the GSE110398 dataset was performed using R language. This included the identification of differentially expressed genes (DEGs), enrichment analysis via Metascape software, pathway enrichment analysis, and construction of a protein–protein interaction network and a hub gene network. Results Twenty-four DEGs were identified between VGR and control groups. The four most important hub genes ( KIR6.1, PCLP1, EDNRB, and BPI) were identified, and Pearson’s correlation coefficient showed that KIR6.1 and BPI were significantly correlated with VGR. KIR6.1 could also sensitively predict VGR (0.9 < area under the curve ≤1). Conclusion BPI and KIR6.1 were differentially expressed in vessels with and without stenosis after vascular transplantation, suggesting that these genes or their encoded proteins may be involved in the occurrence of VGR.

scholarly journals DHX36, BAX, and ARPC1B May Be Critical for the Diagnosis and Treatment of Tuberculosis

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yunli Zhang ◽  
Yanming Li ◽  
Hongling Li ◽  
Qingxia Liu ◽  
Wei Wang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Hierarchical Cluster ◽  
Diagnosis And Treatment ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Peripheral Blood Mononuclear ◽  
Type Group ◽  
Pathway Enrichment ◽  
Key Genes

Background. Tuberculosis (TB) is usually caused by Mycobacterium tuberculosis, which has the highest mortality rate among infectious diseases. This study is designed to identify the key genes affecting the diagnosis and treatment of TB. Methods. GSE54992, which included 39 peripheral blood mononuclear cell (PBMC) samples, was extracted from the Gene Expression Omnibus database. After the samples were classified into type and time groups by limma package, the differentially expressed genes (DEGs) were analyzed using the Analysis of Variance. Using pheatmap package, hierarchical cluster analysis was performed for the DEGs. Then, the key modules correlated with TB were selected using the WGCNA package. Finally, functional and pathway enrichment analyses were carried out using clusterProfiler package. Results. The DEGs in subclusters 3, 6, 7, and 8 were chosen for further analyses. Based on WGCNA analysis, blue and green modules in type group and pink module in time group were selected as key modules. From the key modules, 9 (including BAX and ARPC1B) hub genes in type group and 6 (including DHX36) hub genes in time group were screened. Through pathway enrichment analysis, the TNF signaling pathway was enriched for the green module. Conclusion. DHX36, BAX, and ARPC1B might be key genes acting in the mechanisms of TB. Besides, the TNF signaling pathway might also be critical for the diagnosis and therapy of the disease.

scholarly journals Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

2021 ◽  
Vol 22 (12) ◽  
pp. 6505
Author(s):  
Jishizhan Chen ◽  
Jia Hua ◽  
Wenhui Song
Keyword(s):  
Stem Cells ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Negative Control ◽  
Gene Set Enrichment Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Two Samples ◽  
Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

scholarly journals Study on potential differentially expressed genes in stroke by bioinformatics analysis

2021 ◽  
Author(s):  
Xitong Yang ◽  
Pengyu Wang ◽  
Shanquan Yan ◽  
Guangming Wang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Normal Control ◽  
Enrichment Analysis ◽  
Normal Control Group ◽  
Control Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Key Genes

AbstractStroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine − cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

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