Dataset Information

Identification of a potential gene target for osteoarthritis based on bioinformatics analyses.

ABSTRACT: BACKGROUND:Osteoarthritis (OA) is the most common chronic joint disease worldwide. It is characterized by pain and limited mobility in the affected joints and may even cause disability. Effective clinical options for its prevention and treatment are still unavailable. This study aimed to identify differences in gene signatures between tissue samples from OA and normal knee joints and to explore potential gene targets for OA. METHODS:Five gene datasets, namely GSE55457, GSE55235, GSE12021, GSE10575, and GSE1919, were selected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the R programming software. The functions of these DEGs were analyzed, and a protein-protein interaction (PPI) network was constructed. Subsequently, the most relevant biomarker genes were screened using a receiver operating characteristic (ROC) curve analysis. Finally, the expression of the protein encoded by the core gene PTHLH was evaluated in clinical samples. RESULTS:Eleven upregulated and 9 downregulated DEGs were shared between the five gene expression datasets. Based on the PPI network and the ROC curves of upregulated genes, PTHLH was identified as the most relevant gene for OA and was selected for further validation. Immunohistochemistry confirmed significantly higher PTHLH expression in OA tissues than in normal tissues. Moreover, similar PTHLH levels were detected in the plasma and knee synovial fluid of OA patients. CONCLUSION:The bioinformatics analysis and preliminary experimental verification performed in this study identified PTHLH as a potential target for the treatment of OA.

SUBMITTER: Duan ZX

PROVIDER: S-EPMC7310002 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of a potential gene target for osteoarthritis based on bioinformatics analyses.

Duan Zhi-Xi ZX Li Yu-Sheng YS Tu Chao C Xie Peng P Li Yi-Han YH Qi Lin L Li Zhi-Hong ZH

Journal of orthopaedic surgery and research 20200622 1

<h4>Background</h4>Osteoarthritis (OA) is the most common chronic joint disease worldwide. It is characterized by pain and limited mobility in the affected joints and may even cause disability. Effective clinical options for its prevention and treatment are still unavailable. This study aimed to identify differences in gene signatures between tissue samples from OA and normal knee joints and to explore potential gene targets for OA.<h4>Methods</h4>Five gene datasets, namely GSE55457, GSE55235, G ...[more]

PMID: 32571421

Similar Datasets

Project description:Preeclampsia is a complication of pregnancy characterized by new-onset hypertension and proteinuria of gestation, with serious consequences for mother and infant. Although a vast amount of research has been performed on the pathogenesis of preeclampsia, the underlying mechanisms of this multisystemic disease have remained to be fully elucidated. Data were retrieved from Gene Expression Omnibus database GSE40182 dataset. After data preprocessing, differentially expressed genes of placental cells cultured in vitro from preeclampsia and normal pregnancy were determined and subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify the associated pathways. Furthermore, functional principal component analysis (FPCA) was used to calculate the corresponding F-value of each gene. In order to further study the key signaling pathways of preeclampsia, an elastic-net regression model and the Mann-Whitney U (MWU) test were used to estimate the weight of the signaling pathways. Finally, a co-expression network was generated and hub genes were identified based on the topological features. A total of 134 pathways with a role in preeclampsia were identified. The gene expression data of placenta cells cultured in vitro for different durations were determined and F-values of genes were estimated using the FPCA model. The top 1,000 genes were identified as the differentially expressed genes and subjected to further analysis by elastic-net regression and MWU test. Two key signaling pathways were different between the preeclampsia and control groups, namely hsa05142 Chagas disease and hsa05204 Chemical carcinogenesis. Among the genes involved in these two key pathways, 13 hub genes were identified from the co-expression network. Clustering analysis demonstrated that depending on these hub genes, it was possible to divide the sample into four distinct groups based on different incubation time. The top 3 candidates were Toll-like receptor 2 (TLR2), glutathione S-transferase omega 1 (GSTO1) and mitogen-activated protein kinase 13 (MAPK13). TLR2 and associated pathways are known to be closely associated with preeclampsia, indirectly demonstrating the applicability of the analytic process applied. However, the role of GSTO1 and MAPK13 in preeclampsia has remained poorly investigated, and elucidation thereof may be a worthwhile endeavor. The present study may provide a basis for exploring potential novel genes and pathways as therapeutic targets for preeclampsia.

Project description:BackgroundLiver cirrhosis is one of the leading causes of death worldwide. MicroRNAs (miRNAs) can regulate liver fibrosis, but the underlying mechanisms are not fully understood, and the interactions between miRNAs and mRNAs are not clearly elucidated.MethodsmiRNA and mRNA expression arrays of cirrhotic samples and control samples were acquired from the Gene Expression Omnibus database. miRNA-mRNA integrated analysis, functional enrichment analysis and protein-protein interaction (PPI) network construction were performed to identify differentially expressed miRNAs (DEMs) and mRNAs (DEGs), miRNA-mRNA interaction networks, enriched pathways and hub genes. Finally, the results were validated with in vitro cell models.ResultsBy bioinformatics analysis, we identified 13 DEMs between cirrhotic samples and control samples. Among these DEMs, six upregulated (hsa-miR-146b-5p, hsa-miR-150-5p, hsa-miR-224-3p, hsa-miR-3135b, hsa-miR-3195, and hsa-miR-4725-3p) and seven downregulated (hsa-miR-1234-3p, hsa-miR-30b-3p, hsa-miR-3162-3p, hsa-miR-548aj-3p, hsa-miR-548x-3p, hsa-miR-548z, and hsa-miR-890) miRNAs were further validated in activated LX2 cells. miRNA-mRNA interaction networks revealed a total of 361 miRNA-mRNA pairs between 13 miRNAs and 245 corresponding target genes. Moreover, PPI network analysis revealed the top 20 hub genes, including COL1A1, FBN1 and TIMP3, which were involved in extracellular matrix (ECM) organization; CCL5, CXCL9, CXCL12, LCK and CD24, which participated in the immune response; and CDH1, PECAM1, SELL and CAV1, which regulated cell adhesion. Functional enrichment analysis of all DEGs as well as hub genes showed similar results, as ECM-associated pathways, cell surface interaction and adhesion, and immune response were significantly enriched in both analyses.ConclusionsWe identified 13 differentially expressed miRNAs as potential biomarkers of liver cirrhosis. Moreover, we identified 361 regulatory pairs of miRNA-mRNA and 20 hub genes in liver cirrhosis, most of which were involved in collagen and ECM components, immune response, and cell adhesion. These results would provide novel mechanistic insights into the pathogenesis of liver cirrhosis and identify candidate targets for its treatment.

Project description:Pancreatic ductal adenocarcinoma (PDAC) is one of the most complicated and fatally pathogenic human malignancies. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of PDAC. The aim of the present study was to identify the key genes and signaling pathways associated with PDAC using bioinformatics analysis. Four transcriptome microarray datasets (GSE15471, GSE55643, GSE62165 and GSE91035) were acquired from Gene Expression Omnibus datasets, which included 226 PDAC samples and 65 normal pancreatic tissue samples. We screened differentially expressed genes (DEGs) with GEO2R and investigated their biological function by Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. The overall survival data was obtained from UALCAN, which calculated the data shared with The Cancer Genome Atlas. In addition, a protein-protein interaction (PPI) network of the DEGs was constructed by STRING and Cytoscape software. The four sets of DEGs exhibited an intersection consisting of 205 genes (142 up-regulated and 63 down-regulated), which may be associated with PDAC. GO analysis showed that the 205 DEGs were significantly enriched in the plasma membrane, cell adhesion molecule activity and the Energy pathways, and glycine, serine, threonine metabolism were the most enriched pathways according to KEGG pathway analysis. Kaplan-Meier survival analysis revealed that 22 of 205 common genes were significantly associated with the overall survival of pancreatic cancer patients. In the PPI network and sub-network, DKK1 and HMGA2 were considered as hub genes with high connectivity degrees. DKK1 and HMGA2 are strongly associated with WNT3A and TP53 separately, which indicates that they may play an important role in the Wnt and P53 signaling pathways. Using integrated bioinformatics analysis, we identified DKK1 and HMGA2 as candidate genes in PDAC, which may improve our understanding of the mechanisms of the pathogenesis and integration; the two genes may be therapeutic targets and prognostic markers for PDAC.

Project description:Background:Cholangiocarcinoma (CCA) is an invasive malignancy arising from biliary epithelial cells; it is the most common primary tumour of the bile tract and has a poor prognosis. The aim of this study was to screen prognostic biomarkers for CCA by integrated multiomics analysis. Methods:The GSE32225 dataset was derived from the Gene Expression Omnibus (GEO) database and comprehensively analysed by using R software and The Cancer Genome Atlas (TCGA) database to obtain the differentially expressed RNAs (DERNAs) associated with CCA prognosis. Quantitative isobaric tags for relative and absolute quantification (iTRAQ) proteomics was used to screen differentially expressed proteins (DEPs) between CCA and nontumour tissues. Through integrated analysis of DERNA and DEP data, we obtained candidate proteins APOF, ITGAV and CASK, and immunohistochemistry was used to detect the expression of these proteins in CCA. The relationship between CASK expression and CCA prognosis was further analysed. Results:Through bioinformatics analysis, 875 DERNAs were identified, of which 10 were associated with the prognosis of the CCA patients. A total of 487 DEPs were obtained by using the iTRAQ technique. Comprehensive analysis of multiomics data showed that CASK, ITGAV and APOF expression at both the mRNA and protein levels were different in CCA compared with nontumour tissues. CASK was found to be expressed in the cytoplasm and nucleus of CCA cells in 38 (45%) of 84 patients with CCA. Our results suggested that patients with positive CASK expression had significantly better overall survival (OS) and recurrence-free survival (RFS) than those with negative CASK expression. Univariate and multivariate analyses demonstrated that negative expression of CASK was a significantly independent risk factor for OS and RFS in CCA patients. Conclusions:CASK may be a tumour suppressor; its low expression is an independent risk factor for a poor prognosis in CCA patients, and so it could be used as a clinically valuable prognostic marker.

Dataset Information

Identification of a potential gene target for osteoarthritis based on bioinformatics analyses.

Publications

Identification of a potential gene target for osteoarthritis based on bioinformatics analyses.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets