Integrated network analysis to explore the key genes regulated by parathyroid hormone receptor 1 in osteosarcoma.
ABSTRACT: As an invasive malignant tumor, osteosarcoma (OS) has high mortality. Parathyroid hormone receptor 1 (PTHR1) contributes to maintaining proliferation and undifferentiated state of OS. This study is designed to reveal the action mechanisms of PTHR1 in OS.Microarray dataset GSE46861, which included six PTHR1 knockdown OS samples and six control OS samples, was obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified and then performed with enrichment analysis separately using the limma package and DAVID online tool. Then, protein-protein interaction (PPI) network and module analyses were conducted using Cytoscape software. Using the WebGestalt tool, microRNAs (miRNAs) were predicted for the DEGs involved in the PPI network. Following this, transcription factors (TFs) were predicted and an integrated network was constructed by Cytoscape software.There were 871 DEGs in the PTHR1 knockdown OS samples compared with the control OS samples. Besides, upregulated ZFPM2 was involved in the miRNA-DEG regulatory network. Moreover, TF LEF1 was predicted for the miRNA-DEG regulatory network of the downregulated genes. In addition, LEF1, NR4A2, HAS2, and RHOC had higher degrees in the integrated network.ZFPM2, LEF1, NR4A2, HAS2, and RHOC might be potential targets of PTHR1 in OS.
Project description:Increasing evidence has indicated parathyroid hormone type 1 receptor (PTHR1) plays important roles for the development and progression of osteosarcoma (OS). However, its function mechanisms remain unclear. The goal of this study was to further illuminate the roles of PTHR1 in OS using microarray data.Microarray data were available from the Gene Expression Omnibus database under the accession number GSE46861, including six tumors from mice with PTHR1 knockdown (PTHR1.358) and six tumors from mice with control knockdown (Ren.1309). Differentially expressed genes (DEGs) between PTHR1.358 and Ren.1309 were identified using the LIMMA method, and then, protein-protein interaction (PPI) network was constructed using data from STRING database to screen crucial genes associated with PTHR1. KEGG pathway enrichment analysis was performed to investigate the underlying functions of DEGs using DAVID tool.A total of 1163 genes were identified as DEGs, including 617 downregulated (Lef1, lymphoid enhancer-binding factor 1) and 546 upregulated genes (Dkk1, Dickkopf-related protein 1). KEGG enrichment analysis indicated upregulated DEGs were involved in Renin-angiotensin system (e.g., Agt, angiotensinogen) and Wnt signaling pathway (e.g., Dkk1), while downregulated DEGs participated in Basal cell carcinoma (e.g., Lef1). A PPI network (534 nodes and 2830 edges) was constructed, in which Agt gene was demonstrated to be the hub gene and its interactive genes (e.g., CCR3, CC chemokine receptor 3; and CCL9, chemokine CC chemokine ligand 9) were inflammation related.Our present study preliminarily reveals the pro-malignant effects of PTHR1 in OS cells may be mediated by activating Wnt, angiogenesis, and inflammation pathways via changing the expressions of the crucial enriched genes (Dkk1, Lef1, Agt-CCR3, and Agt-CCL9).
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:AIM:To reveal the mechanisms of heat-shock transcription factor 4 (HSF4) mutation-induced cataract. METHODS:GSE22362, including 3 HSF4-null lens and 3 wild-type lens, was obtained from Gene Expression Omnibus database. After data preprocessing, the differentially expressed genes (DEGs) were identified using the limma package. Based on Database for Annotation, Visualization and Integrated Discovery (DAVID) tool, functional and pathway enrichment analyses were performed for the DEGs. Followed by protein-protein interaction (PPI) network was constructed using STRING database and Cytoscape software. Furthermore, the validated microRNA (miRNA)-DEG pairs were obtained from miRWalk2.0 database, and then miRNA-DEG regulatory network was visualized by Cytoscape software. RESULTS:A total of 176 DEGs were identified in HSF4-null lens compared with wild-type lens. In the PPI network, FBJ osteosarcoma oncogene (FOS), early growth response 1 (EGR1) and heme oxygenase (decycling) 1 (HMOX1) had higher degrees and could interact with each other. Besides, mmu-miR-15a-5p and mmu-miR-26a-5p were among the top 10 miRNAs in the miRNA-DEG regulatory network. Additionally, mmu-miR-26a-5p could target EGR1 in the regulatory network. CONCLUSION:FOS, EGR1, HMOX1, mmu-miR-26a-5p and mmu-miR-15a-5p might function in the pathogenesis of HSF4 mutation-induced cataract.
Project description:Background. The pressure-induced axonal injury of the vulnerable ONH has led many researchers to view glaucoma from the perspective of the genetic basis of the angle of the ONH. However, genetic studies on POAG from this perspective are limited. Methods. Microarray dataset GSE45570 of the ONH of healthy individuals and POAG patients were downloaded from the Gene Expression Omnibus. After screening for the DEGs using the limma package, enrichment analysis was performed using DAVID. The DEG interaction network was constructed using cancer spider at BioProfiling.de. Thereafter, DEG-related TFs were predicted using TRANSFAC, and TF-DEG regulatory networks were visualized using Cytoscape. Results. Thirty-one DEGs were identified including 11 upregulated and 20 downregulated DEGs. Thereafter, gene ontology terms of nucleosome assembly, sensory perception and cognition, and pathway of signaling by GPCR were found to be enriched among the DEGs. Furthermore, DEG interaction and TF-DEG networks were constructed. NEUROD1 was present in both the DEG network and the TF-DEG network as the node with the highest degree and was predicted as a marker gene in the ONH of patients with POAG. Conclusion. NEUROD1 may contribute greatly to the ONH of patients with POAG and was found to be involved in eye development and diseases.
Project description:BACKGROUND:Neonatal sepsis is an inflammatory systemic syndrome, which is a major cause of morbidity and mortality in premature infants. We analyzed the expression profile data of E-MTAB-4785 to reveal the pathogenesis of the disease. METHODS:The expression profile dataset E-MTAB-4785, which contained 17 sepsis samples and 19 normal samples, was obtained from the ArrayExpress database. The differentially expressed genes (DEGs) were analyzed by the Bayesian testing method in limma package. Based on the DAVID online tool, enrichment analysis was conducted for the DEGs. Using STRING database and Cytoscape software, protein-protein interaction (PPI) network and module analyses were performed. Besides, transcription factor (TF)-DEG regulatory network was also constructed by Cytoscape software. Additionally, miRNA-DEG pairs were searched using miR2Disease and miRWalk 2.0 databases, followed by miRNA-DEG regulatory network was visualized by Cytoscape software. RESULTS:A total of 275 DEGs were identified from the sepsis samples in comparison to normal samples. TSPO, MAPK14, and ZAP70 were the hub nodes in the PPI network. Pathway enrichment analysis indicated that CEBPB and MAPK14 were enriched in TNF signaling pathway. Moreover, CEBPB and has-miR-150 might function in neonatal sepsis separately through targeting MAPK14 and BCL11B in the regulatory networks. These genes and miRNA might be novel targets for the clinical treatment of neonatal sepsis. CONCLUSION:TSPO, ZAP70, CEBPB targeting MAPK14, has-miR-150 targeting BCL11B might affect the pathogenesis of neonatal sepsis. However, their roles in neonatal sepsis still needed to be confirmed by further experimental researches.
Project description:Pancreatic ductal adenocarcinoma (PDAC) is a class of the commonest malignant carcinomas. The present study aimed to elucidate the potential biomarker and prognostic targets in PDAC. The array data of GSE41368, GSE43795, GSE55643, and GSE41369 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and differentially expressed microRNAs (DEmiRNAs) in PDAC were obtained by using GEO2R, and overlapped DEGs were acquired with Venn Diagrams. Functional enrichment analysis of overlapped DEGs and DEmiRNAs was conducted with Metascape and FunRich, respectively. The protein-protein interaction (PPI) network of overlapped DEGs was constructed by STRING and visualized with Cytoscape. Overall survival (OS) of DEmiRNAs and hub genes were investigated by Kaplan-Meier (KM) plotter (KM plotter). Transcriptional data and correlation analyses among hub genes were verified through GEPIA and Human Protein Atlas (HPA). Additionally, miRNA targets were searched using miRTarBase, then miRNA-DEG regulatory network was visualized with Cytoscape. A total of 32 DEmiRNAs and 150 overlapped DEGs were identified, and Metascape showed that DEGs were significantly enriched in cellular chemical homeostasis and pathways in cancer, while DEmiRNAs were mainly enriched in signal transduction and Glypican pathway. Moreover, seven hub genes with a high degree, namely, V-myc avian myelocytomatosis viral oncogene homolog (MYC), solute carrier family 2 member 1 (SLC2A1), PKM, plasminogen activator, urokinase (PLAU), peroxisome proliferator activated receptor ? (PPARG), MET proto-oncogene, receptor tyrosine kinase (MET), and integrin subunit ? 3 (ITGA3), were identified and found to be up-regulated between PDAC and normal tissues. miR-135b, miR-221, miR-21, miR-27a, miR-199b-5p, miR-143, miR-196a, miR-655, miR-455-3p, miR-744 and hub genes predicted poor OS of PDAC. An integrative bioinformatics analysis identified several hub genes that may serve as potential biomarkers or targets for early diagnosis and precision target treatment of PDAC.
Project description:BACKGROUND:This study sought to investigate crucial genes correlated with diabetic nephropathy (DN), and their potential functions, which might contribute to a better understanding of DN pathogenesis. METHODS:The microarray dataset GSE1009 was downloaded from Gene Expression Omnibus, including 3 diabetic glomeruli samples and 3 healthy glomeruli samples. The differentially expressed genes (DEGs) were identified by LIMMA package. Their potential functions were then analyzed by the GO and KEGG pathway enrichment analyses using the DAVID database. Furthermore, miRNAs and transcription factors (TFs) regulating DEGs were predicted by the GeneCoDis tool, and miRNA-DEG-TF regulatory network was visualized by Cytoscape. Additionally, the expression of DEGs was validated using another microarray dataset GSE30528. RESULTS:Totally, 14 up-regulated DEGs and 430 down-regulated ones were identified. Some DEGs (e.g. MTSS1, CALD1 and ACTN4) were markedly relative to cytoskeleton organization. Besides, some other ones were correlated with arrhythmogenic right ventricular cardiomyopathy (e.g. ACTN4, CTNNA1 and ITGB5), as well as complement and coagulation cascades (e.g. C1R and C1S). Furthermore, a series of miRNAs and TFs modulating DEGs were identified. The transcription factor LEF1 regulated the majority of DEGs, such as ITGB5, CALD1 and C1S. Hsa-miR-33a modulated 28 genes, such as C1S. Additionally, 143 DEGs (one upregulated gene and 142 downregulated genes) were also differentially expressed in another dataset GSE30528. CONCLUSIONS:The genes involved in cytoskeleton organization, cardiomyopathy, as well as complement and coagulation cascades may be closely implicated in the progression of DN, via the regulation of miRNAs and TFs.
Project description:Objective:Lung cancer has high incidence and mortality rate, and non-small cell lung cancer (NSCLC) takes up approximately 85% of lung cancer cases. This study is aimed to reveal miRNAs and genes involved in the mechanisms of NSCLC. Materials and Methods:In this retrospective study, GSE21933 (21 NSCLC samples and 21 normal samples), GSE27262 (25 NSCLC samples and 25 normal samples), GSE43458 (40 NSCLC samples and 30 normal samples) and GSE74706 (18 NSCLC samples and 18 normal samples) were searched from gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) were screened from the four microarray datasets using MetaDE package, and then conducted with functional annotation using DAVID tool. Afterwards, protein-protein interaction (PPI) network and module analyses were carried out using Cytoscape software. Based on miR2Disease and Mirwalk2 databases, microRNAs (miRNAs)-DEG pairs were selected. Finally, Cytoscape software was applied to construct miRNA-DEG regulatory network. Results:Totally, 727 DEGs (382 up-regulated and 345 down-regulated) had the same expression trends in all of the four microarray datasets. In the PPI network, TP53 and FOS could interact with each other and they were among the top 10 nodes. Besides, five network modules were found. After construction of the miRNA-gene network, top 10 miRNAs (such as hsa-miR-16-5p, hsa-let-7b-5p, hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-let-7a-5p and hsa-miR-34a- 5p) and genes (such as HMGA1, BTG2, SOD2 and TP53) were selected. Conclusion:These miRNAs and genes might contribute to the pathogenesis of NSCLC.
Project description:The purpose of the current study was to explore the carboplatin?induced sequential changes in gene expression and screen out key genes, which were associated with effects of carboplatin on epithelial ovarian cancer (EOC). The microarray dataset GSE13525 was downloaded from the Gene Expression Omnibus database, including 6 EOC cell samples separately treated with carboplatin at 24, 30 and 36 h (case group), and 6 samples treated with phosphate?buffered saline at the same time points (control group). A total of 3 sets of differentially expressed genes (DEGs) were respectively identified in case samples at 24, 30 and 36 h compared with the control group via the Limma package, and separately recorded as DEG?24, DEG?30 and DEG?36. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the overlapped DEGs were performed via the Database for Annotation, Visualization and Integrated Discovery. The protein?protein interaction (PPI) network was constructed and analyzed by Cytoscape software. In addition, the survival curves were drawn to illustrate the association between the expression levels of certain critical genes and the prognosis of EOC. A total of 170, 605 and 1043 DEGs were separately obtained in DEG?24 DEG?30 and DEG?36, and 110 overlaps were identified. The overlaps were enriched in 77 GO terms and 3 KEGG pathways. A total of 152 pairs were involved in the PPI network, and the abnormal expression levels (high or low) of c?Jun and cyclin B1 (CCNB1) would reduce the survival time of patients with EOC. The study indicated that c?Jun and CCNB1 may be the prognostic biomarkers of EOC treated with carboplatin, and certain pathways (such as p53 signaling pathway, cell cycle and mitogen?activation protein kinase signaling pathway) may be involved in carbo-platin?resistant EOC.
Project description: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.