scholarly journals Identification of miRNA Regulatory Networks and Candidate Markers for Fracture Healing in Mice

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xianglu Li ◽  
Zhaohua Zhong ◽  
Enguang Ma ◽  
Xiaowei Wu
Keyword(s):  
Network Analysis ◽  
Fracture Healing ◽  
Collagen Fibril ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Healing Process ◽  
Differentially Expressed ◽  
Differential Analysis ◽  
Ppi Network ◽  
Hub Genes

Background. It is important to improve the understanding of the fracture healing process at the molecular levels, then to discover potential miRNA regulatory mechanisms and candidate markers. Methods. Expression profiles of mRNA and miRNA were obtained from the Gene Expression Omnibus database. We performed differential analysis, enrichment analysis, protein-protein interaction (PPI) network analysis. The miRNA-mRNA network analysis was also performed. Results. We identified 499 differentially expressed mRNAs (DEmRs) that were upregulated and 534 downregulated DEmRs during fracture healing. They were mainly enriched in collagen fibril organization and immune response. Using the PPI network, we screened 10 hub genes that were upregulated and 10 hub genes downregulated with the largest connectivity. We further constructed the miRNA regulatory network for hub genes and identified 13 differentially expressed miRNAs (DEmiRs) regulators. Cd19 and Col6a1 were identified as key candidate mRNAs with the largest fold change, and their DEmiR regulators were key candidate regulators. Conclusion. Cd19 and Col6a1 might serve as candidate markers for fracture healing in subsequent studies. Their expression is regulated by miRNAs and is involved in collagen fibril organization and immune responses.

scholarly journals Identification of key genes and pathways in endometriosis by integrated expression profiles analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10171
Author(s):  
Ding Cui ◽  
Yang Liu ◽  
Junyan Ma ◽  
Kaiqing Lin ◽  
Kaihong Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Western Blotting ◽  
Expression Profiles ◽  
Hippo Pathway ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Endometrium

The purpose of this study was to integrate the existing expression profile data on endometriosis (EM)-related tissues in order to identify the differentially expressed genes. In this study, three series of raw expression data were downloaded from GEO database. Differentially expressed genes (DEGs) in three tissue types were screened. GO, KEGG pathway enrichment analysis, core differential genes (CDGs) protein–protein interaction (PPI) network and weighted gene co-expression network analysis (WGCNA) were performed, finally, the dysregulation of Hippo pathway in ectopic endometrium (EC) was detected by Western blotting. A total of 1,811 DEGs between eutopic (EU) and normal endometrium (NE), 5,947 DEGs between EC and EU, and 3,192 DEGs between EC and NE datasets were identified. After screening, 394 CDGs were obtained, and 5 hub genes identified in the PPI network. CDGs enrichment and WGCNA network analysis revealed cell proliferation, differentiation, migration and other biological processes, Hippo and Wnt signaling pathways, and a variety of tumor-related pathways. Western blotting results showed that YAP/TAZ was upregulated, and MOB1, pMOB1, SAV1, LATS1 and LATS2 were downregulated in EC. Moreover, CDGs, especially the hub genes, are potential biomarkers and therapeutic targets. Finally, the Hippo pathway might play a key role in the development of endometriosis.

scholarly journals Insight into Molecular Profile Changes after Skeletal Muscle Contusion Using Microarray and Bioinformatics Analyses

2020 ◽  
Author(s):  
Na Li ◽  
Ru-feng Bai ◽  
Chun Li ◽  
Li-hong Dang ◽  
Qiu-xiang Du ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Healing Process ◽  
Differentially Expressed ◽  
Molecular Profile ◽  
Wound Age ◽  
Functional Modules

Abstract Background: Muscle trauma frequently occurs in daily life. However, the molecular mechanisms of muscle healing, which partly depend on the extent of the damage, are not well understood. This study aimed to investigate gene expression profiles following mild and severe muscle contusion, and to provide more information about the molecular mechanisms underlying the repair process.Methods: A total of 33 rats were divided randomly into control (n = 3), mild contusion (n = 15), and severe contusion (n = 15) groups; the contusion groups were further divided into five subgroups (1, 3, 24, 48, and 168 h post-injury; n = 3 per subgroup). Then full genome microarray of RNA isolated from muscle tissue was performed to access the gene expression changes during healing process.Results: A total of 2,844 and 2,298 differentially expressed genes were identified in the mild and severe contusion groups, respectively. The analysis of the overlapping differentially expressed genes showed that there are common mechanisms of transcriptomic repair of mild and severe contusion within 48 h post-contusion. This was supported by the results of principal component analysis, hierarchical clustering, and weighted gene co‐expression network analysis of the 1,620 coexpressed genes in mildly and severely contused muscle. From these analyses, we discovered that the gene profiles in functional modules and temporal clusters were similar between the mild and severe contusion groups; moreover, the genes showed time-dependent patterns of expression, which allowed us to identify useful markers of wound age. We then performed an analysis of the functions of genes (including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway annotation, and protein–protein interaction network analysis) in the functional modules and temporal clusters, and the hub genes in each module–cluster pair were identified. Interestingly, we found that genes downregulated within 24−48 h of the healing process were largely associated with metabolic processes, especially oxidative phosphorylation of reduced nicotinamide adenine dinucleotide phosphate, which has been rarely reported. Conclusions: These results improve our understanding of the molecular mechanisms underlying muscle repair, and provide a basis for further studies of wound age estimation.

scholarly journals Comprehensive analysis of DNA methylation and gene expression profiles in rectal cancer

2020 ◽  
Author(s):  
Cheng Zhang ◽  
Di Meng ◽  
Songjie Chao ◽  
Chunlin Ge
Keyword(s):  
Gene Expression ◽  
Rectal Cancer ◽  
Differentially Expressed Genes ◽  
Cox Regression ◽  
Expression Profiles ◽  
Methylation Status ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Differentially Methylated Genes

Abstract BackgroundAbnormal hypomethylation of oncogenes and hypermethylation of tumor suppressor genes play important roles in human tumorigenesis and cancer progression, including those of rectal cancer (RC). However, conjoint analysis of RC involving both gene expression and methylation profiling datasets remains rare. This study aimed to identify methylation-regulated differentially expressed genes (MeDEGs) and to evaluate their prognostic value in RC through bioinformatics analysis.MethodsGene expression (GSE20842 and GSE68204) and gene methylation (GSE75546) profiling datasets were obtained from the Gene Expression Omnibus database. GEO2R was adopted to identify differentially expressed genes (DEGs) and differentially methylated genes (DMGs). MeDEGs were obtained by overlapping the DEGs and DMGs and then subjected to protein–protein interaction (PPI) network analysis using STRING. Modules and hub genes within the network were identified using MCODE and CytoHubba, respectively. Prognostic MeDEGs were selected by univariate Cox regression. Finally, our findings were validated based on The Cancer Genome Atlas (TCGA) database.ResultsIn total, 243 upregulated-hypomethylated and 51 downregulated-hypermethylated genes were identified as MeDEGs. A PPI network of MeDEGs was constructed with 290 nodes and 578 edges. Three modules and three hub genes—COL3A1, FPR1, and PLK1—within the network were identified. Three MeDEGs—NFE2, COMP, and LAMA1—were found to be survival-related. Furthermore, the expression and methylation status of two hub genes (excluding FPR1) and the three prognostic MeDEGs were also significantly altered in TCGA and were consistent with our findings.ConclusionsWe identified novel MeDEGs and explored their relationship with survival in RC. Our methodology may provide an effective bioinformatics basis for further understanding of the methylation-mediated regulatory mechanisms in RC.

LncRNA-mRNA Expression Profiles and Functional Networks Associated with Cognitive Impairment in Folate-deficient Mice

2021 ◽  
Vol 24 ◽  
Author(s):  
Xiaojin Feng ◽  
Fenfang Zhan ◽  
Jialing Hu ◽  
Fuzhou Hua ◽  
Guohai Xu
Keyword(s):  
Gene Expression ◽  
Cognitive Impairment ◽  
Mrna Expression ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Functional Networks ◽  
Ppi Network ◽  
Hub Genes ◽  
Cerna Network ◽  
Deficient Mice

Background: Cognitive impairment is a common neurocognitive disorder that affects millions of worldwide people’s health,related tofolate deficiency. Objective: The present study aimed to investigate the lncRNA-mRNA functional networks associated with cognitive impairment in folate-deficient mice and elucidate their possible molecular mechanisms. Methods: We downloaded the gene expression profile (GSE148126) of lncRNAs and mRNAs from NCBI Gene Expression Omnibus (GEO) database. Four groups of mouse hippocampi were analyzed, including 4 months (4mo) and 18 months (18mo) of folic acid (FA) deficiency/supplementation. The differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified using gplots and heatmap packages. The functions of the DEmRNAs were evaluated using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The hub genes wereidentified by CytoHubba plugins of Cytoscape, and protein-protein interaction (PPI) network of deregulated mRNAs was performed using STRING database. Finally, lncRNA-mRNA co-expression and competitive endogenous RNA (ceRNA) network analyses were constructed. Results: In total, we screened 67 lncRNAs with 211 mRNAs, and 89 lncRNAs with 229 mRNAs were differentially expressed in 4mo_FAand 18mo_FA deficient mice, respectively. GO analyses indicated that DEmRNAs were highly related to terms involved in binding and biological regulation. KEGG pathway analyses demonstrated that these genes were significantly enriched for Renin secretion, Pancreatic secretion and AMPK signaling pathways in 18mo_FA deficiency group. Subsequently, the top 5 hub genes werescreened from the PPI network, which may be key genes with the progression of folate deficiency. Upon the lncRNA-mRNA co-expression network analysis, we identified the top 10 lncRNAs having the maximum number of connections with related mRNAs. Finally, a ceRNA network was constructed for DE lncRNAs and DEmRNAs, and several pivotal miRNAs were predicted. Conclusions: This study identified the lncRNA-mRNA expression profiles and functional networks associated with cognitive impairment in folate-deficient mice, which provided support for the possible mechanisms and therapy for this disease.

scholarly journals Dysfunctional network and mutation genes of hypertrophic cardiomyopathy

2020 ◽  
Author(s):  
Yunwen Cui ◽  
Cheng Liu ◽  
Jian Luo ◽  
Jie Liang
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Differential Analysis ◽  
Ppi Network ◽  
Roc Curve Analysis ◽  
Study Gene Expression

Abstract Background Hypertrophic cardiomyopathy (HCM) is a group of heterogeneous diseases that affect the myocardium. It is also a common familial disease. The symptoms are not common and easy to find. Methods In this study, gene expression profiles of 37 samples (GSE130036) were downloaded from GEO database. Differential analysis was used to identify the related dysregulated genes in patients with HCM. Enrichment analysis identified the biological function and signal pathway of these differentially expressed genes. Then, we build PPI network and verify it in GSE36961 dataset. Finally, the gene of single nuclear variants (SNVs) in HCM samples was screened by means of maftools. Results Herein, we obtained 920 differentially expressed genes, and found that these genes are mainly related to metabolic related signaling pathways. 187 interacting genes were identified by PPI network analysis, and the expression trends of C1QB, F13A1, CD163, FCN3, PLA2G2A and CHRDL2 were verified by another dataset. ROC curve analysis showed that they had certain clinical diagnostic ability, and they were the potential key dysfunctional genes of HCM. In addition, we found that PRMT5 mutation was the most frequent in HCM samples, which may affect the pathogenesis of HCM. Conclusions Therefore, the key genes and enrichment results identified by our analysis may provide a reference for the occurrence and development mechanism of HCM. In addition, mutations in PRMT5 may be a useful therapeutic and diagnostic target for HCM.

scholarly journals Integrated bioinformatics analysis of aberrantly-methylated differentially-expressed genes and pathways in age-related macular degeneration

2020 ◽  
Author(s):  
Yinchen Shen ◽  
Mo Li ◽  
Kun Liu ◽  
Xiaoyin Xu ◽  
Shaopin Zhu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Macular Degeneration ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Age Related Macular Degeneration ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Age Related ◽  
Low Expression

Abstract Background: Age-related macular degeneration (AMD) represents the leading cause of visual impairment in the aging population. The goal of this study was to identify aberrantly-methylated, differentially-expressed genes (MDEGs) in AMD and explore the involved pathways via integrated bioinformatics analysis.Methods: Data from expression profile GSE29801 and methylation profile GSE102952 were obtained from the Gene Expression Omnibus database. We analyzed differentially-methylated genes and differentially-expressed genes using R software. Functional enrichment and protein–protein interaction (PPI) network analysis were performed using the R package and Search Tool for the Retrieval of Interacting Genes online database. Hub genes were identified using Cytoscape. Results: In total, 827 and 592 genes showed high and low expression, respectively, in GSE29801; 4117 hyper-methylated genes and 511 hypo-methylated genes were detected in GSE102952. Based on overlap, we categorized 153 genes as hyper-methylated, low-expression genes (Hyper-LGs) and 24 genes as hypo-methylated, high-expression genes (Hypo-HGs). Four Hyper-LGs (CKB, PPP3CA, TGFB2, SOCS2) overlapped with AMD risk genes in the Public Health Genomics and Precision Health Knowledge Base. KEGG pathway enrichment analysis indicated that Hypo-HGs were enriched in the calcium signaling pathway, whereas Hyper-LGs were enriched in sphingolipid metabolism. In GO analysis, Hypo-HGs were enriched in fibroblast migration, membrane raft, and coenzyme binding, among others. Hyper-LGs were enriched in mRNA transport, nuclear speck, and DNA binding, among others. In PPI network analysis, 23 nodes and two edges were established from Hypo-HGs, and 151 nodes and 73 edges were established from Hyper-LGs. Hub genes (DHX9, MAPT, PAX6) showed the greatest overlap. Conclusion: This study revealed potentially aberrantly MDEGs and pathways in AMD, which might improve the understanding of this disease.

scholarly journals Integrated bioinformatics analysis of aberrantly-methylated differentially-expressed genes and pathways in age-related macular degeneration

2020 ◽  
Author(s):  
Yinchen Shen ◽  
Mo Li ◽  
Kun Liu ◽  
Xiaoyin Xu ◽  
Shaopin Zhu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Macular Degeneration ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Age Related Macular Degeneration ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Age Related ◽  
Low Expression

Abstract Background: Age-related macular degeneration (AMD) represents the leading cause of visual impairment in the aging population. The goal of this study was to identify aberrantly-methylated, differentially-expressed genes (MDEGs) in AMD and explore the involved pathways via integrated bioinformatics analysis. Methods: Data from expression profile GSE29801 and methylation profile GSE102952 were obtained from the Gene Expression Omnibus database. We analyzed differentially-methylated genes and differentially-expressed genes using R software. Functional enrichment and protein–protein interaction (PPI) network analysis were performed using the R package and Search Tool for the Retrieval of Interacting Genes online database. Hub genes were identified using Cytoscape. Results: In total, 827 and 592 genes showed high and low expression, respectively, in GSE29801; 4117 hyper-methylated genes and 511 hypo-methylated genes were detected in GSE102952. Based on overlap, we categorized 153 genes as hyper-methylated, low-expression genes (Hyper-LGs) and 24 genes as hypo-methylated, high-expression genes (Hypo-HGs). Four Hyper-LGs ( CKB , PPP3CA , TGFB2 , SOCS2 ) overlapped with AMD risk genes in the Public Health Genomics and Precision Health Knowledge Base. KEGG pathway enrichment analysis indicated that Hypo-HGs were enriched in the calcium signaling pathway, whereas Hyper-LGs were enriched in sphingolipid metabolism. In GO analysis, Hypo-HGs were enriched in fibroblast migration, membrane raft, and coenzyme binding, among others. Hyper-LGs were enriched in mRNA transport, nuclear speck, and DNA binding, among others. In PPI network analysis, 23 nodes and two edges were established from Hypo-HGs, and 151 nodes and 73 edges were established from Hyper-LGs. Hub genes ( DHX9 , MAPT , PAX6 ) showed the greatest overlap. Conclusion: This study revealed potentially aberrantly MDEGs and pathways in AMD, which might improve the understanding of this disease.

Genome-Wide Identification of differently expressed lncRNAs, mRNAs, and circRNAs in patients with osteoarthritis

2020 ◽  
Vol 15 ◽  
Author(s):  
Yeqing Sun ◽  
Lei Chen ◽  
Yingqi Zhang ◽  
Jincheng Zhang ◽  
Shashi Ranjan Tiwari
Keyword(s):  
Regulatory Networks ◽  
Expression Profiles ◽  
Interleukin 1 ◽  
Interaction Network ◽  
Circular Rna ◽  
Negative Regulation ◽  
Differentially Expressed ◽  
Positive Regulation ◽  
Proteinprotein Interaction ◽  
Differently Expressed Genes

Background: Osteoarthritis (OA), one of the most important causes leading to joint disability, was considered as an untreatable disease. A series of genes were reported to regulate the pathogenesis of OA, including microRNAs, Long non-coding RNAs and Circular RNA. So far, the expression profiles and functions of lncRNAs, mRNAs, and circRNAs in OA are not fully understood. Objective: The present study aimed to identify differently expressed genes in OA. Methods: The present study conducted RNA-seq to identify differently expressed genes in OA. Ontology (GO) analysis was used to analysis the Molecular Function and Biological Process. KEGG pathway analysis was used to perform the differentially expressed lncRNAs in biological pathways. Results: Hierarchical clustering revealed a total of 943 mRNAs, 518 lncRNAs, and 300 circRNAs were dysregulated in OA compared to normal samples. Furthermore, we constructed differentially expressed mRNAs mediated proteinprotein interaction network, differentially expressed lncRNAs mediated trans regulatory networks, and competitive endogenous RNA (ceRNA) to reveal the interaction among these genes in OA. Bioinformatics analysis revealed these dysregulated genes were involved in regulating multiple biological processes, such as wound healing, negative regulation of ossification, sister chromatid cohesion, positive regulation of interleukin-1 alpha production, sodium ion transmembrane transport, positive regulation of cell migration, and negative regulation of inflammatory response. To the best of our knowledge, this study for the first time revealed the expression pattern of mRNAs, lncRNAs and circRNAs in OA. Conclusion: This study provided novel information to validate these differentially expressed RNAs may be as possible biomarkers and targets in OA.

scholarly journals Identification of key genes in calcific aortic valve disease via weighted gene co-expression network analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jin-Yu Sun ◽  
Yang Hua ◽  
Hui Shen ◽  
Qiang Qu ◽  
Jun-Yan Kan ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Network Analysis ◽  
Aortic Valve ◽  
Aortic Valve Disease ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Valve Disease ◽  
Hub Genes ◽  
Calcific Aortic Valve Disease ◽  
Underlying Mechanisms

Abstract Background Calcific aortic valve disease (CAVD) is the most common subclass of valve heart disease in the elderly population and a primary cause of aortic valve stenosis. However, the underlying mechanisms remain unclear. Methods The gene expression profiles of GSE83453, GSE51472, and GSE12644 were analyzed by ‘limma’ and ‘weighted gene co-expression network analysis (WGCNA)’ package in R to identify differentially expressed genes (DEGs) and key modules associated with CAVD, respectively. Then, enrichment analysis was performed based on Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, DisGeNET, and TRRUST database. Protein–protein interaction network was constructed using the overlapped genes of DEGs and key modules, and we identified the top 5 hub genes by mixed character calculation. Results We identified the blue and yellow modules as the key modules. Enrichment analysis showed that leukocyte migration, extracellular matrix, and extracellular matrix structural constituent were significantly enriched. SPP1, TNC, SCG2, FAM20A, and CD52 were identified as hub genes, and their expression levels in calcified or normal aortic valve samples were illustrated, respectively. Conclusions This study suggested that SPP1, TNC, SCG2, FAM20A, and CD52 might be hub genes associated with CAVD. Further studies are required to elucidate the underlying mechanisms and provide potential therapeutic targets.

scholarly journals Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Baojie Wu ◽  
Shuyi Xi
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Targets ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

Sign in / Sign up

Export Citation Format

Share Document