scholarly journals Importance of Mitochondrial-Related Genes in Dilated Cardiomyopathy Based on Bioinformatics Analysis

2020 ◽  
Vol 5 (2) ◽  
pp. 117-129
Author(s):  
Yukuan Chen ◽  
Xiaohui Wu ◽  
Danchun Hu ◽  
Wei Wang
Keyword(s):  
Dilated Cardiomyopathy ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Biological Activities ◽  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Functional Enrichment ◽  
Cellular Respiration ◽  
Wide Range ◽  
Key Genes

We designed this study to identify potential key protein interaction networks, genes, and correlated pathways in dilated cardiomyopathy (DCM) via bioinformatics methods. We selected the GSE3586 microarray dataset, consisting of 15 dilated cardiomyopathic heart biopsy samples and 13 nonfailing heart biopsy samples. Initially, the GSE3586 dataset was downloaded and was analyzed with the limma package to identify differentially expressed genes (DEGs). A total of 172 DEGs consisting of 162 upregulated genes and ten downregulated genes in DCM were selected by the criterion of adjusted Pvalues less than 0.01 and the log2-fold change of 0.6 or greater. Gene Ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to view the biological processes, cellular components, molecular function, and KEGG pathways of the DEGs. Next, protein-protein interactions were constructed, and the hub protein modules were identified. Then we selected the key genes DLD, UQCRC2, DLAT, SUCLA2, ATP5A1, PRDX3, FH, SDHD, and NDUFV1, which are involved in a wide range of biological activities, such as the citrate cycle, oxidation-reduction processes and cellular respiration, and energy derivation by oxidation of organic compounds in mitochondria. Finally, we found that currently there are no related gene-targeting drugs after exploring the predicted interactions between key genes and drugs, and transcription factors. In conclusion, our study provides greater understanding of the pathogenesis and underlying molecular mechanisms in DCM. This contributes to the exploration of potential gene therapy targets.

scholarly journals Biodata Mining-based Elucidation of Mechanisms of Hesperetin as a Candidate for Atherosclerosis

2020 ◽  
Author(s):  
Lianzhou Huang ◽  
Zexiu Huang ◽  
Yuanqiu Chen ◽  
Xin Jin ◽  
Ji Xiao ◽  
...  
Keyword(s):  
Active Ingredient ◽  
Molecular Mechanisms ◽  
Fluid Shear Stress ◽  
Biological Activities ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Citrus Aurantium ◽  
Wide Range ◽  
Anti Inflammatory

Abstract BackgroundHesperetin, an active ingredient derived from Citrus × aurantium L., possesses a wide range of biological activities, including anti-inflammatory, anti-oxidation, and anti-cancer activity. Notably, hesperetin has been proposed as a candidate for atherosclerosis owing to the lipid-regulating and anti-inflammatory effect, while the underlying mechanisms remains obscure.ResultsIn our present study, the pharmacological and molecular properties of hesperetin were first evaluated to determine the druggability of hesperetin. Subsequently, 53 hesperetin-atherosclerosis crossover targets were collected to establish the protein-protein interaction network. The result of Gene Ontology enrichment analysis indicated that the crossover targets were involved in the regulation of lipid metabolism and inflammatory response. Moreover, the Kyoto Encyclopedia of Genes and Genomes pathway analyses demonstrated that the crossover targets were highly correlated with the pathogenesis of atherosclerosis, such as fluid shear stress and atherosclerosis pathway and the TNF signaling pathway. Finally, an entire hesperetin-target-pathway network was constructed to provide a systematic overview of the pharmacological mechanisms of action of hesperetin against atherosclerosis.ConclusionsThe pharmacological mechanisms of actions of hesperetin against atherosclerosis was unveiled based on biodata mining from the public database and the bioinformatics data analysis-based strategy in this study, contributing to a deeper understanding of the molecular mechanisms of hesperetin in the treatment of atherosclerosis. Based on the results of network pharmacology analysis, we can conclude that hesperetin is surely an excellent candidate for atherosclerosis. We believe our work would be beneficial for further research and development of hesperetin as a natural active ingredient derived from Citrus × aurantium L. for the treatment of atherosclerosis.

scholarly journals Identification of Potential Key Genes and Pathways in Early-Onset Colorectal Cancer Through Bioinformatics Analysis

2019 ◽  
Vol 26 (1) ◽  
pp. 107327481983126 ◽  
Author(s):  
Bin Zhao ◽  
Zulqarnain Baloch ◽  
Yunhan Ma ◽  
Zheng Wan ◽  
Yani Huo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Muscle Contraction ◽  
Protein Interactions ◽  
Early Onset ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Smooth Muscle Contraction ◽  
Functional Enrichment ◽  
Early Onset Colorectal Cancer

This study was designed to identify the potential key protein interaction networks, genes, and correlated pathways in early-onset colorectal cancer (CRC) via bioinformatics methods. We selected microarray data GSE4107 consisting 12 patient’s colonic mucosa and 10 healthy control mucosa; initially, the GSE4107 were downloaded and analyzed using limma package to identify differentially expressed genes (DEGs). A total of 131 DEGs consisting of 108 upregulated genes and 23 downregulated genes of patients in early-onset CRC were selected by the criteria of adjusted P values <.01 and |log2 fold change (FC)| ≥ 2. The gene ontology functional enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were accomplished to view the biological process, cellular components, molecular function, and the KEGG pathways of DEGs. Finally, protein–protein interactions (PPIs) were constructed, and the hub protein module was identified. Genes such as ACTA2, ACTG2, MYH11, CALD1, MYL9, TPM2, and LMOD1 were strongly implicated in CRC. In summary, in this study, we indicated that molecular mechanisms were involved in muscle contraction and vascular smooth muscle contraction signaling pathway, which improve our understanding of CRC and could be used as new therapeutic targets for CRC.

scholarly journals An integrated analysis of mRNAs, lncRNAs, and miRNAs based on weighted gene co-expression network analysis involved in bovine endometritis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Negin Sheybani ◽  
Mohammad Reza Bakhtiarizadeh ◽  
Abdolreza Salehi
Keyword(s):  
Network Analysis ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Biological Pathways ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Rna Seq

AbstractIn dairy cattle, endometritis is a severe infectious disease that occurs following parturition. It is clear that genetic factors are involved in the etiology of endometritis, however, the molecular pathogenesis of endometritis is not entirely understood. In this study, a system biology approach was used to better understand the molecular mechanisms underlying the development of endometritis. Forty transcriptomic datasets comprising of 20 RNA-Seq (GSE66825) and 20 miRNA-Seq (GSE66826) were obtained from the GEO database. Next, the co-expressed modules were constructed based on RNA-Seq (Rb-modules) and miRNA-Seq (mb-modules) data, separately, using a weighted gene co-expression network analysis (WGCNA) approach. Preservation analysis was used to find the non-preserved Rb-modules in endometritis samples. Afterward, the non-preserved Rb-modules were assigned to the mb-modules to construct the integrated regulatory networks. Just highly connected genes (hubs) in the networks were considered and functional enrichment analysis was used to identify the biological pathways associated with the development of the disease. Furthermore, additional bioinformatic analysis including protein–protein interactions network and miRNA target prediction were applied to enhance the reliability of the results. Thirty-five Rb-modules and 10 mb-modules were identified and 19 and 10 modules were non-preserved, respectively, which were enriched in biological pathways related to endometritis like inflammation and ciliogenesis. Two non-preserved Rb-modules were significantly assigned to three mb-modules and three and two important sub-networks in the Rb-modules were identified, respectively, including important mRNAs, lncRNAs and miRNAs genes like IRAK1, CASP3, CCDC40, CCDC39, ZMYND10, FOXJ1, TLR4, IL10, STAT3, FN1, AKT1, CD68, ENSBTAG00000049936, ENSBTAG00000050527, ENSBTAG00000051242, ENSBTAG00000049287, bta-miR-449, bta-miR-484, bta-miR-149, bta-miR-30b and bta-miR-423. The potential roles of these genes have been previously demonstrated in endometritis or related pathways, which reinforced putative functions of the suggested integrated regulatory networks in the endometritis pathogenesis. These findings may help further elucidate the underlying mechanisms of bovine endometritis.

scholarly journals Identification of potential biomarkers and candidate small molecule drugs in glioblastoma

2020 ◽  
Author(s):  
Wei-cheng Lu ◽  
Hui Xie ◽  
Ce Yuan ◽  
Jin-jiang Li ◽  
Zhao-yang Li ◽  
...  
Keyword(s):  
Protein Expression ◽  
Small Molecule ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Expression Levels ◽  
Small Molecule Drugs ◽  
Key Genes

Abstract Background and aioms: Glioblastoma (GBM) is a common and aggressive primary brain tumor, and the prognosis for GBM patients remains poor. This study aimed to identify the key genes associated with GBM and to further analyze the prognostic factors and small molecular drugs. Methods: Three microarray datasets (GSE111260, GSE103227, and GSE104267) were selected to identify the differential expressed genes (DEGs) between GBM and normal tissues. Then, prognosis related DEGs were screened by survival analysis, followed by functional enrichment analysis was performed. The protein-protein interaction (PPI) network was constructed to explore the hub genes associated with GBM. The prognostic of these hub genes in GBM patients were analyzed using The Cancer Genome Atlas (TCGA) database, and the protein expression level of hub genes were validated using the Human Protein Atlas (HPA) databases. Subsequently, the small molecule drugs of GBM were predicted by Connectivity Map (CMAP) database. Results: A total of 78 DEGs related to GBM prognosis were identified, and 10 hub genes with high degree were obtained. The mRNA expression and protein expression levels of CETN2, MKI67, ARL13B, and SETDB1 were overexpressed in GBM tissues, while the expression levels of CALN1, ELAVL3, ADCY3, SYN2, SLC12A5, and SOD1 were down-regulated in GBM tissues. Additionally, these genes were significantly associated with the prognosis of GBM. A total of 98 small molecular drugs were predicted, among these, adiphenine and podophyllotoxin were considered as the potential drugs to treat GBM.Conclusions: Our study provided 10 key genes for diagnosis, prognosis, and therapeutic target for GBM. These findings might contribute to a better comprehension of molecular mechanisms of GBM development, and provide new perspective for further GBM research. However, specific regulatory mechanism of these genes needed further elaboration.

Systems Biology Approaches Reveal a Multi-stress Responsive WRKY Transcription Factor and Stress Associated Gene Co-expression Networks in Chickpea

2019 ◽  
Vol 14 (7) ◽  
pp. 591-601 ◽  
Author(s):  
Aravind K. Konda ◽  
Parasappa R. Sabale ◽  
Khela R. Soren ◽  
Shanmugavadivel P. Subramaniam ◽  
Pallavi Singh ◽  
...  
Keyword(s):  
Expression Pattern ◽  
Gene Networks ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Wrky Transcription Factor ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Callose Deposition ◽  
Significant Probability

Background: Chickpea is a nutritional rich premier pulse crop but its production encounters setbacks due to various stresses and understanding of molecular mechanisms can be ascribed foremost importance. Objective: The investigation was carried out to identify the differentially expressed WRKY TFs in chickpea in response to herbicide stress and decipher their interacting partners. Methods: For this purpose, transcriptome wide identification of WRKY TFs in chickpea was done. Behavior of the differentially expressed TFs was compared between other stress conditions. Orthology based cofunctional gene networks were derived from Arabidopsis. Gene ontology and functional enrichment analysis was performed using Blast2GO and STRING software. Gene Coexpression Network (GCN) was constructed in chickpea using publicly available transcriptome data. Expression pattern of the identified gene network was studied in chickpea-Fusarium interactions. Results: A unique WRKY TF (Ca_08086) was found to be significantly (q value = 0.02) upregulated not only under herbicide stress but also in other stresses. Co-functional network of 14 genes, namely Ca_08086, Ca_19657, Ca_01317, Ca_20172, Ca_12226, Ca_15326, Ca_04218, Ca_07256, Ca_14620, Ca_12474, Ca_11595, Ca_15291, Ca_11762 and Ca_03543 were identified. GCN revealed 95 hub genes based on the significant probability scores. Functional annotation indicated role in callose deposition and response to chitin. Interestingly, contrasting expression pattern of the 14 network genes was observed in wilt resistant and susceptible chickpea genotypes, infected with Fusarium. Conclusion: This is the first report of identification of a multi-stress responsive WRKY TF and its associated GCN in chickpea.

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 HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhixin Wu ◽  
Yinxian Wen ◽  
Guanlan Fan ◽  
Hangyuan He ◽  
Siqi Zhou ◽  
...  
Keyword(s):  
Femoral Head ◽  
Roc Curve ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Overlapping Genes ◽  
Hub Genes ◽  
Diagnostic Biomarkers ◽  
Key Genes

Abstract Background Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH. Methods The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve. Results Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14. Conclusions Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

scholarly journals A Systematic Analysis of Candidate Genes Associated with Nicotine Addiction

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Meng Liu ◽  
Xia Li ◽  
Rui Fan ◽  
Xinhua Liu ◽  
Ju Wang
Keyword(s):  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Nicotine Addiction ◽  
Clustering Coefficient ◽  
Functional Enrichment ◽  
Moderate Degree ◽  
Cancer Genes ◽  
Systematic Analysis ◽  
The Central Nervous System

Nicotine, as the major psychoactive component of tobacco, has broad physiological effects within the central nervous system, but our understanding of the molecular mechanism underlying its neuronal effects remains incomplete. In this study, we performed a systematic analysis on a set of nicotine addiction-related genes to explore their characteristics at network levels. We found that NAGenes tended to have a more moderate degree and weaker clustering coefficient and to be less central in the network compared to alcohol addiction-related genes or cancer genes. Further, clustering of these genes resulted in six clusters with themes in synaptic transmission, signal transduction, metabolic process, and apoptosis, which provided an intuitional view on the major molecular functions of the genes. Moreover, functional enrichment analysis revealed that neurodevelopment, neurotransmission activity, and metabolism related biological processes were involved in nicotine addiction. In summary, by analyzing the overall characteristics of the nicotine addiction related genes, this study provided valuable information for understanding the molecular mechanisms underlying nicotine addiction.

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.

scholarly journals Integrative analyses of biomarkers and pathways for heart failure

2021 ◽  
Author(s):  
Shaowei Fan ◽  
Yuanhui Hu
Keyword(s):  
Heart Failure ◽  
Extracellular Matrix ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Signal Pathway ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Ppi Networks ◽  
Key Genes

Abstract Background: Heart failure (HF) is the most common potential cause of death, causing a huge health and economic burden all over the world. So far, some impressive progress has been made in the study of pathogenesis. However, the underlying molecular mechanisms leading to this disease remain to be fully elucidated. Methods: The microarray data sets of GSE76701, GSE21610 and GSE8331 were retrieved from the gene expression comprehensive database (GEO). After merging all microarray data and adjusting batch effects, differentially expressed genes (DEG) were determined. Functional enrichment analysis was performed based on Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, gene set enrichment analysis (GSEA), response pathway database and Disease Ontology (DO). Protein protein interaction (PPI) network was constructed using string database. Combined with the above important bioinformatics information, the potential key genes were selected. The comparative toxicological genomics database (CTD) is used to explore the interaction between potential key genes and HF. Results: We identified 38 patients with heart failure and 16 normal controls. There were 315 DEGs among HF samples, including 278 up-regulated genes and 37 down-regulated genes. Pathway enrichment analysis showed that most DEGs were significantly enriched in BMP signal pathway, transmembrane receptor protein serine / threonine kinase signal pathway, extracellular matrix, basement membrane, glycosaminoglycan binding, sulfur compound binding and so on. Similarly, GSEA enrichment analysis showed that DEGs were mainly enriched in extracellular matrix and extracellular matrix related proteins. BBS9, CHRD, BMP4, MYH6, NPPA and CCL5 are central genes in PPI networks and modules. Conclusions: the enrichment pathway of DEGs and go ontology may reveal the molecular mechanism of HF. Among them, target genes EIF1AY, RPS4Y1, USP9Y, KDM5D, DDX3Y, NPPA, HBB, TSIX, LOC28556 and XIST are expected to become new targets for heart failure. Our findings provide potential biomarkers or therapeutic targets for the further study of heart failure and contribute to the development of advanced prediction, diagnosis and treatment strategies.

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