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 Metabolomic and Proteomic Analyses of Persistent Valvular Atrial Fibrillation and Non-Valvular Atrial Fibrillation

2021 ◽  
Vol 12 ◽  
Author(s):  
Bo Hu ◽  
Wen Ge ◽  
Yuliang Wang ◽  
Xiaobin Zhang ◽  
Tao Li ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Enrichment Analysis ◽  
Heart Rhythm ◽  
Tca Cycle ◽  
Inorganic Pyrophosphatase ◽  
Pathway Enrichment Analysis ◽  
Increased Risk ◽  
Novel Biomarkers ◽  
Glycerol 3 Phosphate Dehydrogenase ◽  
Altered Proteins

Atrial fibrillation (AF) is an abnormal heart rhythm related to an increased risk of heart failure, dementia, and stroke. The distinction between valvular and non-valvular AF remains a debate. In this study, proteomics and metabolomics were integrated to describe the dysregulated metabolites and proteins of AF patients relative to sinus rhythm (SR) patients. Totally 47 up-regulated and 41 down-regulated proteins in valvular AF, and 59 up-regulated and 149 down-regulated proteins in non-valvular AF were recognized in comparison to SR patients. Moreover, 58 up-regulated and 49 significantly down-regulated metabolites in valvular AF, and 47 up-regulated and 122 down-regulated metabolites in persistent non-valvular AF patients were identified in comparison to SR patients. Based on analysis of differential levels of metabolites and proteins, 15 up-regulated and 22 down-regulated proteins, and 13 up-regulated and 122 down-regulated metabolites in persistent non-valvular AF were identified relative to valvular AF. KEGG pathway enrichment analysis showed the altered proteins and metabolites were significantly related to multiple metabolic pathways, such as Glycolysis/Gluconeogenesis. Interestingly, the enrichment pathways related to non-valvular AF were obviously different from those in valvular AF. For example, valvular AF was significantly related to Glycolysis/Gluconeogenesis, but non-valvular AF was more related to Citrate cycle (TCA cycle). Correlation analysis between the differentially expressed proteins and metabolites was also performed. Several hub proteins with metabolites were identified in valvular AF and non-valvular AF. For example, Taurine, D-Threitol, L-Rhamnose, and DL-lactate played crucial roles in valvular AF, while Glycerol-3-phosphate dehydrogenase, Inorganic pyrophosphatase 2, Hydroxymethylglutaryl-CoAlyase, and Deoxyuridine 5-triphosphate nucleotidohydrolase were crucial in non-valvular AF. Then two hub networks were recognized as potential biomarkers, which can effectively distinguish valvular AF and non-valvular persistent AF from SR samples, with areas under curve of 0.75 and 0.707, respectively. Hence, these metabolites and proteins can be used as potential clinical molecular markers to discriminate two types of AF from SR samples. In summary, this study provides novel insights to understanding the mechanisms of AF progression and identifying novel biomarkers for prognosis of non-valvular AF and valvular AF by using metabolomics and proteomics analyses.

scholarly journals Genomic Contributors to Rhythm Outcome of Atrial Fibrillation Catheter Ablation – Pathway Enrichment Analysis of GWAS Data

PLoS ONE ◽  
2016 ◽  
Vol 11 (11) ◽  
pp. e0167008 ◽  
Author(s):  
Daniela Husser ◽  
Petra Büttner ◽  
Laura Ueberham ◽  
Borislav Dinov ◽  
Philipp Sommer ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Enrichment Analysis ◽  
Gwas Data ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment

scholarly journals Abnormal expression profile of plasma-derived exosomal microRNAs in patients with treatment-resistant depression

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Lian-Di Li ◽  
Muhammad Naveed ◽  
Zi-Wei Du ◽  
Huachen Ding ◽  
Kai Gu ◽  
...  
Keyword(s):  
Target Genes ◽  
Treatment Effectiveness ◽  
Enrichment Analysis ◽  
Small Sample ◽  
Pathway Enrichment Analysis ◽  
Healthy Controls ◽  
Treatment Resistant Depression ◽  
Treatment Resistant ◽  
Novel Biomarkers ◽  
Resistant Depression

AbstractWhether microRNAs (miRNAs) from plasma exosomes might be dysregulated in patients with depression, especially treatment-resistant depression (TRD), remains unclear, based on study of which novel biomarkers and therapeutic targets could be discovered. To this end, a small sample study was performed by isolation of plasma exosomes from patients with TRD diagnosed by Hamilton scale. In this study, 4 peripheral plasma samples from patients with TRD and 4 healthy controls were collected for extraction of plasma exosomes. Exosomal miRNAs were analyzed by miRNA sequencing, followed by image collection, expression difference analysis, target gene GO enrichment analysis, and KEGG pathway enrichment analysis. Compared with the healthy controls, 2 miRNAs in the plasma exosomes of patients with TRD showed significant differences in expression, among which has-miR-335-5p were significantly upregulated and has-miR-1292-3p were significantly downregulated. Go and KEGG analysis showed that dysregulated miRNAs affect postsynaptic density and axonogenesis as well as the signaling pathway of axon formation and cell growths. The identification of these miRNAs and their target genes may provide novel biomarkers for improving diagnosis accuracy and treatment effectiveness of TRD.

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 Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suthanthiram Backiyarani ◽  
Rajendran Sasikala ◽  
Simeon Sharmiladevi ◽  
Subbaraya Uma
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

scholarly journals Identification of Key miRNAs in the Treatment of Dabrafenib-Resistant Melanoma

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Guangyu Gao ◽  
Zhen Yao ◽  
Jiaofeng Shen ◽  
Yulong Liu
Keyword(s):  
Drug Resistance ◽  
Molecular Mechanism ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Obvious Association

Dabrafenib resistance is a significant problem in melanoma, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism of drug resistance of dabrafenib and to explore the key genes and pathways that mediate drug resistance in melanoma. GSE117666 was downloaded from the Gene Expression Omnibus (GEO) database and 492 melanoma statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Besides, differentially expressed miRNAs (DEMs) were identified by taking advantage of the R software and GEO2R. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and FunRich was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify potential pathways and functional annotations linked with melanoma chemoresistance. 9 DEMs and 872 mRNAs were selected after filtering. Then, target genes were uploaded to Metascape to construct protein-protein interaction (PPI) network. Also, 6 hub mRNAs were screened after performing the PPI network. Furthermore, a total of 4 out of 9 miRNAs had an obvious association with the survival rate ( P < 0.05 ) and showed a good power of risk prediction model of over survival. The present research may provide a deeper understanding of regulatory genes of dabrafenib resistance in melanoma.

scholarly journals Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Yun Tang ◽  
Xiaobo Yang ◽  
Huaqing Shu ◽  
Yuan Yu ◽  
Shangwen Pan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Acute Kidney Injury ◽  
Molecular Mechanisms ◽  
Therapeutic Targets ◽  
Kidney Injury ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Blood Samples ◽  
Potential Biomarkers

Abstract Background Sepsis and septic shock are life-threatening diseases with high mortality rate in intensive care unit (ICU). Acute kidney injury (AKI) is a common complication of sepsis, and its occurrence is a poor prognostic sign to septic patients. We analyzed co-differentially expressed genes (co-DEGs) to explore relationships between septic shock and AKI and reveal potential biomarkers and therapeutic targets of septic-shock-associated AKI (SSAKI). Methods Two gene expression datasets (GSE30718 and GSE57065) were downloaded from the Gene Expression Omnibus (GEO). The GSE57065 dataset included 28 septic shock patients and 25 healthy volunteers and blood samples were collected within 0.5, 24 and 48 h after shock. Specimens of GSE30718 were collected from 26 patients with AKI and 11 control patents. AKI-DEGs and septic-shock-DEGs were identified using the two datasets. Subsequently, Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs. We also evaluated co-DEGs and corresponding predicted miRNAs involved in septic shock and AKI. Results We identified 62 DEGs in AKI specimens and 888, 870, and 717 DEGs in septic shock blood samples within 0.5, 24 and 48 h, respectively. The hub genes of EGF and OLFM4 may be involved in AKI and QPCT, CKAP4, PRKCQ, PLAC8, PRC1, BCL9L, ATP11B, KLHL2, LDLRAP1, NDUFAF1, IFIT2, CSF1R, HGF, NRN1, GZMB, and STAT4 may be associated with septic shock. Besides, co-DEGs of VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 coupled with corresponding predicted miRNAs, especially miR-29b-3p, miR-152-3p, and miR-223-3p may be regarded as promising targets for the diagnosis and treatment of SSAKI in the future. Conclusions Septic shock and AKI are related and VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 genes are significantly associated with novel biomarkers involved in the occurrence and development of SSAKI.

scholarly journals Network Pharmacology and Molecular Docking Suggest the Mechanism for Biological Activity of Rosmarinic Acid

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Minglong Guan ◽  
Lan Guo ◽  
Hengli Ma ◽  
Huimei Wu ◽  
Xiaoyun Fan
Keyword(s):  
Biological Activity ◽  
Molecular Docking ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Rosmarinic Acid ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis

Rosmarinic acid (RosA) is a natural phenolic acid compound, which is mainly extracted from Labiatae and Arnebia. At present, there is no systematic analysis of its mechanism. Therefore, we used the method of network pharmacology to analyze the mechanism of RosA. In our study, PubChem database was used to search for the chemical formula and the Chemical Abstracts Service (CAS) number of RosA. Then, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to evaluate the pharmacodynamics of RosA, and the Comparative Toxicogenomics Database (CTD) was used to identify the potential target genes of RosA. In addition, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of target genes were carried out by using the web-based gene set analysis toolkit (WebGestalt). At the same time, we uploaded the targets to the STRING database to obtain the protein interaction network. Then, we carried out a molecular docking about targets and RosA. Finally, we used Cytoscape to establish a visual protein-protein interaction network and drug-target-pathway network and analyze these networks. Our data showed that RosA has good biological activity and drug utilization. There are 55 target genes that have been identified. Then, the bioinformatics analysis and network analysis found that these target genes are closely related to inflammatory response, tumor occurrence and development, and other biological processes. These results demonstrated that RosA can act on a variety of proteins and pathways to form a systematic pharmacological network, which has good value in drug development and utilization.

scholarly journals In Silico Integration Approach Reveals Key MicroRNAs and Their Target Genes in Follicular Thyroid Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Shengqing Hu ◽  
Yunfei Liao ◽  
Juan Zheng ◽  
Luoning Gou ◽  
Anita Regmi ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
Target Genes ◽  
Follicular Thyroid Carcinoma ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Biological Targets ◽  
Protein Protein Interaction ◽  
Bioinformatic Tools ◽  
The Common

To better understand the molecular mechanism for the pathogenesis of follicular thyroid carcinoma (FTC), this study aimed at identifying key miRNAs and their target genes associated with FTC, as well as analyzing their interactions. Based on the gene microarray data GSE82208 and microRNA dataset GSE62054, the differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained using R and SAM software. The common DEMs from R and SAM were fed to three different bioinformatic tools, TargetScan, miRDB, and miRTarBase, respectively, to predict their biological targets. With DEGs intersected with target genes of DEMs, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed through the DAVID database. Then a protein-protein interaction (PPI) network was constructed by STRING. Finally, the module analysis for PPI network was performed by MCODE and BiNGO. A total of nine DEMs were identified, and their function might work through regulating hub genes in the PPI network especially KIT and EGFR. KEGG analysis showed that intersection genes were enriched in the PI3K-Akt signaling pathway and microRNAs in cancer. In conclusion, the study of miRNA-mRNA network would offer molecular support for differential diagnosis between malignant FTC and benign FTA, providing new insights into the potential targets for follicular thyroid carcinoma diagnosis and treatment.

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.

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