MicroRNA-9 might be a novel protective factor for osteoarthritis patients.
ABSTRACT: BACKGROUND:The study aimed to identify the targeting genes and miRNAs using the microarray expression profile dataset for Osteoarthritis (OA) patients. Differentially expressed genes (DEGs) between OA and control samples were identified using Bayes method of limma package. Subsequently, a protein-protein interaction (PPI) network was constructed. miRNAs and transcription factor (TFs) based on DEGs in PPI network were identified using Webgestalt and ENCODE, respectively. Finally, MCODE, Gene Ontology (GO) function, and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed. The expressions of several DEGs and predicted miRNAs in OA rats were detected by RT-PCR. RESULTS:A total of 594 DEGs were identified. In PPI network, there were 313 upregulated DEGs and 22 downregulated DEGs. Besides, the regulatory relationships included 467 upregulated interactions and 85 downregulated interactions (miR-124A???QKI and MAP 1B) between miRNA and DEGs in PPI network. The module from downregulated DEGs-TFs-miRNA networks was mainly enriched to low-density lipoprotein particle clearance, response to linoleic acid, and small molecule metabolic process BP terms. Moreover, QKI, MAP 1B mRNA and miR-9 expressions were significantly reduced in OA rats. CONCLUSION:miR-9 might be a protective factor for OA patients via inhibiting proliferation and differentiation of cartilage progenitor cells. miR-124A might play an important role in progression of OA through targeting QKI and MAP 1B.
Project description:<b>Background:</b> Osteoarthritis (OA) is one of the main causes of disability in the elderly population, accompanied by a series of underlying pathologic changes, such as cartilage degradation, synovitis, subchondral bone sclerosis, and meniscus injury. The present study aimed to identify key genes, signaling pathways, and miRNAs in knee OA associated with the entire joint components, and to explain the potential mechanisms using computational analysis. <b>Methods:</b> The differentially expressed genes (DEGs) in cartilage, synovium, subchondral bone, and meniscus were identified using the Gene Expression Omnibus 2R (GEO2R) analysis based on dataset from GSE43923, GSE12021, GSE98918, and GSE51588, respectively and visualized in Volcano Plot. Venn diagram analyses were performed to identify the overlapping DEGs (overlapping DEGs) that expressed in at least two types of tissues mentioned above. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, protein-protein interaction (PPI) analysis, and module analysis were conducted. Furthermore, qRT-PCR was performed to validate above results using our clinical specimens. <b>Results:</b> As a result, a total of 236 overlapping DEGs were identified, of which 160 were upregulated and 76 were downregulated. Through enrichment analysis and constructing the PPI network and miRNA-mRNA network, knee OA-related key genes, such as <i>HEY1</i>, <i>AHR</i>, <i>VEGFA</i>, <i>MYC</i>, and <i>CXCL12</i> were identified. Clinical validation by qRT-PCR experiments further supported above computational results. In addition, knee OA-related key miRNAs such as miR-101, miR-181a, miR-29, miR-9, and miR-221, and pathways such as Wnt signaling, HIF-1 signaling, PI3K-Akt signaling, and axon guidance pathways were also identified. Among above identified knee OA-related key genes, pathways and miRNAs, genes such as <i>AHR</i>, <i>HEY1</i>, <i>MYC</i>, <i>GAP43</i>, and <i>PTN</i>, pathways like axon guidance, and miRNAs such as miR-17, miR-21, miR-155, miR-185, and miR-1 are lack of research and worthy for future investigation. <b>Conclusion:</b> The present informatic study for the first time provides insight to the potential therapeutic targets of knee OA by comprehensively analyzing the overlapping genes differentially expressed in multiple joint components and their relevant signaling pathways and interactive miRNAs.
Project description:This study aimed to identify the underlying therapeutic targets of angiotensin II (AngII)-induced hypertension, and screen the related drugs.The gene expression profiles of GSE93579 and GSE75815 were used to identify differentially expressed genes (DEGs) between AngII-induced hypertension and control samples based on meta-analysis. These DEGs were analyzed using Gene-Ontology (GO) function and pathway enrichment methods. Subsequently, the weighed gene coexpression network analysis (WGCNA)-based meta-analysis was applied to determine transcriptional signature with DEGs. Additionally, the functions of the modules were analyzed based on the network, and miRNAs were identified. Finally, small molecule drugs correlation with DEGs was identified.In total, 346 upregulated DEGs (e.g., Rgs7?bp) and 360 downregulated DEGs (e.g., Ebf3) were identified between AngII and control samples. In addition, a total of 150 DEGs in the brown, red, and yellow modules with higher correlation coefficient according to WGCNA, were used to construct the coexpression network, including Rgs7?bp and Ebf3, etc. in brown modules. Besides, 3 modules were obtained after the functions of the modules analysis. Moreover, 5 miRNAs were integrated in modules, including miR-124A, miR-524, miR-493, miR-323, and miR-203. Finally, anisomycin was the highest correlation with DEGs.MiR-124a might be involved in the pathogenesis of hypertension via targeting Ebf3 and Rgs7?bp, which possibly represent a novel and effective strategy for treatment of hypertension. Anisomycin might be performed to reduce blood pressure by blocking MAPK signaling pathway.
Project description:Gene expression data were analysed using bioinformatic tools to demonstrate molecular mechanisms underlying the glioma CpG island methylator phenotype (CIMP). A gene expression data set (accession no. GSE30336) was downloaded from Gene Expression Omnibus, including 36 CIMP+ and 16 CIMP- glioma samples. Differential analysis was performed for CIMP+ vs. CIMP? samples using the limma package in R. Functional enrichment analysis was subsequently conducted for differentially expressed genes (DEGs) using Database for Annotation, Visualization and Integration Discovery. Protein?protein interaction (PPI) networks were constructed for upregulated and downregulated genes with information from STRING. MicroRNAs (miRNAs) targeting DEGs were also predicted using WebGestalt. A total of 439 DEGs were identified, including 214 upregulated and 198 downregulated genes. The upregulated genes were involved in extracellular matrix organisation, defence and immune response, collagen fibril organisation and regulation of cell motion and the downregulated genes in cell adhesion, sensory organ development, regulation of system process, neuron differentiation and membrane organisation. A PPI network containing 134 nodes and 314 edges was constructed from the upregulated genes, whereas a PPI network consisting of 85 nodes and 80 edges was obtained from the downregulated genes. miRNAs regulating upregulated and downregulated genes were predicted, including miRNA?124a and miRNA?34a. Numerous key genes associated with glioma CIMP were identified in the present study. These findings may advance the understanding of glioma and facilitate the development of appropriate therapies.
Project description:This study aimed to explore the molecular mechanism of osteoarthritis (OA) and provide information about new genes as potential targets for OA treatment. Gene expression profile of GSE105027, including 12 OA serum samples (OA group) and 12 healthy serum samples (ctrl group), was downloaded. The differentially expressed miRNAs (DEMs) as well as miRNA-mRNAs interactions were investigated, followed by function and pathway investigation. Then the protein-protein interaction (PPI) network was performed. Furthermore, the long non-coding RNA (lncRNA)-miRNA-mRNA interactions (competing endogenous RNAs, ceRNAs) were investigated. A total of 17 downregulated miRNAs were revealed between OA and ctrl groups. These DEMs such as has-miR-1202 were mainly enriched in GO functions like histone acetyltransferase binding and KEGG pathways like cellular senescence. The integrated PPI network analysis showed that has-miR-1202, has-miR-33b-3p, has-miR-940, has-miR-4284, and has-miR-4281 were 5 downregulated miRNAs in this network. Furthermore, the lncRNA-miRNA-mRNA interactions such as KCNQ1OT1-has-miR-1202-ETS1 were revealed in the present ceRNA network. Key DEMs such as miR-33b-3p, miR-940, and miR-1202 may be involved in OA. miR-1202 may regulate OA development via histone acetyltransferase pathway binding function and cellular senescence pathway. Furthermore, KCNQ1OT1-has-miR-1202-ETS1 might be vital for the process of OA.
Project description:The present study aimed to identify genes and microRNAs (miRNAs or miRs) that were abnormally expressed in the vastus lateralis muscle of patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). The gene expression profile of GSE10828 was downloaded from the Gene Expression Omnibus database, and this dataset was comprised of 4 samples from patients with AECOPD and 5 samples from patients with stable COPD. Differentially expressed genes (DEGs) were screened using the Limma package in R. A protein?protein interaction (PPI) network of DEGs was built based on the STRING database. Module analysis of the PPI network was performed using the ClusterONE plugin and functional analysis of DEGs was conducted using DAVID. Additionally, key miRNAs were enriched using gene set enrichment analysis (GSEA) software and a miR-gene regulatory network was constructed using Cytoscape software. In total, 166 up- and 129 downregulated DEGs associated with muscle weakness in AECOPD were screened. Among them, NCL, GOT1, TMOD1, TSPO, SOD2, NCL and PA2G4 were observed in the modules consisting of upregulated or downregulated genes. The upregulated DEGs in modules (including KLF6 and XRCC5) were enriched in GO terms associated with immune system development, whereas the downregulated DEGs were enriched in GO terms associated with cell death and muscle contraction. Additionally, 39 key AECOPD?related miRNAs were also predicted, including miR-1, miR-9 and miR-23a, miR-16 and miR-15a. In conclusion, DEGs (NCL, GOT1, SOD2, KLF6, XRCC5, TSPO and TMOD1) and miRNAs (such as miR-1, miR-9 and miR-23a) may be associated with the loss of muscle force in patients during an acute exacerbation of COPD which also may act as therapeutic targets in the treatment of AECOPD.
Project description:BACKGROUND: Diagnosis at an early stage of chronic pancreatitis (CP) is challenging. It has been reported that microRNAs (miRNAs) are increasingly found and applied as targets for the diagnosis and treatment of various cancers. However, to the best of our knowledge, few published papers have described the role of miRNAs in the diagnosis of CP. METHOD: We downloaded gene expression profile data from the Gene Expression Omnibus and identified differentially expressed genes (DEGs) between CP and normal samples of Harlan mice and Jackson Laboratory mice. Common DEGs were filtered out, and the semantic similarities of gene classes were calculated using the GOSemSim software package. The gene class with the highest functional consistency was selected, and then the Lists2Networks web-based system was used to analyse regulatory relationships between miRNAs and gene classes. The functional enrichment of the gene classes was assessed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway annotation terms. RESULTS: A total of 405 common upregulated DEGs and 7 common downregulated DEGs were extracted from the two kinds of mice. Gene cluster D was selected from the common upregulated DEGs because it had the highest semantic similarity. miRNA 124a (miR-124a) was found to have a significant regulatory relationship with cluster D, and DEGs such as CHSY1 and ABCC4 were found to be regulated by miR-124a. The GO term of response to DNA damage stimulus and the pathway of Escherichia coli infection were significantly enriched in cluster D. CONCLUSION: DNA damage and E. coli infection might play important roles in CP pathogenesis. In addition, miR-124a might be a potential target for the diagnosis and treatment of CP.
Project description:Osteosarcoma (OS) is the most frequently occurring primary bone malignancy with a rapid progression and poor survival. In the present study, in order to examine the molecular mechanisms of OS, we analyzed the microarray of GSE28425. GSE28425 was downloaded from Gene Expression Omnibus, which also included the miRNA expression profile, GSE28423, and the mRNA expression profile, GSE28424. Each of the expression profiles included 19 OS cell lines and 4 normal bones. The differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) were screened using the limma package in Bioconductor. The DEGs associated with tumors were screened and annotated. Subsequently, the potential functions of the DEGs were analyzed by Gene Ontology (GO) and pathway enrichment analyses. Furthermore, the protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. Furthermore, modules of the PPI network were screened using the ClusterOne plugin in Cytoscape. Additionally, the transcription factor (TF)-DEG regulatory network, DE-miRNA-DEG regulatory network and miRNA-function collaborative network were separately constructed to obtain key DEGs and DE-miRNAs. In total, 1,609 DEGs and 149 DE-miRNAs were screened. Upregulated FOS-like antigen 1 (FOSL1) also had the function of an oncogene. MAD2 mitotic arrest deficient-like 1 (MAD2L1; degree, 65) and aurora kinase A (AURKA; degree, 64) had higher degrees in the PPI network of the DEGs. In the TF-DEG regulatory network, the TF, signal transducer and activator of transcription 3 (STAT3) targeted the most DEGs. Moreover, in the DE-miRNA-DEG regulatory network, downregulated miR?1 targeted many DEGs and estrogen receptor 1 (ESR1) was targeted by several highly expressed miRNAs. Moreover, in the miRNA-function collaborative networks of upregulated miRNAs, miR?128 targeted myeloid dendritic associated functions. On the whole, our data indicate that MAD2L1, AURKA, STAT3, ESR1, FOSL1, miR?1 and miR?128 may play a role in the development and/or progressio of OS.
Project description:BACKGROUND:This study aimed to identify and evaluate potential molecular targets associated with the development of proliferative diabetic retinopathy (DR). METHODS:The microarray dataset "GSE60436" generated from fibrovascular membranes (FVMs) associated with proliferative DR was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) from the active FVMs and control or inactive FVMs and control were evaluated and co-DEGs were identified using VEEN analysis. Functional enrichment analysis, and protein-protein interactions (PPI) network and module analyses were performed on the upregulated and downregulated coDEGs. Finally, several predictions regarding microRNAs (miRNAs) and transcription factors (TFs) were made to construct a putative TF-miRNA-target network. RESULTS:A total of 1475 co-DEGs were screened in active/inactive FVM samples, including 461 upregulated and 1014 downregulated genes, which were enriched for angiogenesis [Hypoxia Inducible Factor 1 Subunit Alpha (HIF1A) and Placental Growth Factor (PGF)] and visual perception, respectively. In the case of the upregulated co-DEGs, Kinesin Family Member 11 (KIF11), and BUB1 Mitotic Checkpoint Serine/Threonine Kinase (BUB1) exhibited the highest values in both the PPI network and module analyses, as well as the genes related to mitosis. In the case of downregulated co-DEGs, several G protein subunits, including G Protein Subunit Beta 3 (GNB3), exhibited the highest values in both the PPI network and module analyses. The genes identified in the module analysis were found to be from the signal transduction-related pathways. In addition, we were able to identify four miRNAs and five TFs, including miR-136 and miR-374. CONCLUSIONS:In brief, HIF1A, PGF, KIF11, G protein subunits, and miR-136, miR-374 may all be involved in angiogenesis, retinal endothelial cell proliferation, and visual signal transduction in proliferative DR. This study provides a number of novel insights that may aid the development of future studies dedicated to discovering novel therapeutic targets in proliferative DR.
Project description:Sepsis is a type of systemic inflammatory response caused by infection. The present study aimed to identify novel targets for the treatment of sepsis. We conducted bioinformatic analysis of the microarray Gene Expression Omnibus dataset GSE12624, which includes data on 34 patients with sepsis and 36 healthy individuals without sepsis. Differentially expressed genes (DEGs) in sepsis patients were identified using Bayesian methods included in the limma package in R. Correlations among the expression values of DEGs were analyzed using the weighted gene co?expression network analysis (WGCNA) to construct a co?expression network. Subsequently, the generated co?expression network was visualized using Cytoscape 3.3 software. Additionally, a protein?protein interaction (PPI) network was constructed based on all the DEGs using STRING. Finally, the integrated regulatory network was constructed based on DEGs, microRNAs (miRNAs) and transcription factors (TFs). A total of 407 DEGs were identified in the sepsis samples, including 227 upregulated DEGs and 180 downregulated DEGs. WGCNA grouped the DEGs into 13 co?expressed modules. Additionally, MAP3K8 and RPS6KA5 in the MEyellow module were enriched in the MAPK and TNF signaling pathways. In addition, the PPI network comprised 48 nodes and 112 edges, which included the pairs MAP3K8?RPS6KA5, MAP3K8?IL10, RPS6KA5?EXOSC4 and EXOSC4?EXOSC5. Lastly, the TF?miRNA?target DEG regulatory network was constructed based on eight TFs (NF??B), seven miRNAs (miR152, miR?148A/B), and 52 TF?miRNA?target gene triplets (17 upregulated genes, including MAP3K8, and 10 downregulated genes, including RPS6KA5). Our analysis showed that the members of the miR?148 family (miR?148A/B and miR?152) are candidate biomarkers for sepsis.
Project description:This study was aimed to explore the crucial genes and microRNAs (miRNAs) associated with the carotid atherosclerosis (CA).Two public datasets GSE28829 and GSE43292 were obtained from Gene Expression Omnibus databases to analyze the differentially expressed genes (DEGs) between primary and advanced atherosclerotic plaque tissues. The Gene Ontology (GO) terms, pathways, and protein-protein interactions (PPIs) of these DEGs were analyzed. miRNAs and transcription factor (TF) were predicted.A total of 112 upregulated and 179 downregulated intersection DEGs were identified between 2 datasets. In the PPI network, HSP90AB1 (degree?=?19), RAP1A (degree?=?14), and integrin subunit beta 1 (ITGB1) had higher degrees. A total of 23 miRNAs were predicted, such as miR-126, miR-155, miR-19A, and miR-19B. Four TFs were associated with upregulated DEGs, while 10 TFs were identified to be associated with downregulated genes.Our study suggests the important roles of HSP90AB1, RAP1A, and integrins proteins of ITGB1, ITGA11, ITGA9, and ITGB2 in the progression of CA plaque. Additionally, miR-126, miR-155, miR-19B, and miR-19A may be considered as biomarkers of CA.