Dataset Information

Identification of potential diagnostic gene biomarkers in patients with osteoarthritis.

ABSTRACT: The current study was aimed to identify diagnostic gene signature for osteoarthritis (OA). The differentially expressed genes (DEGs) in synovial membrane samples and blood samples were respectively identified from the GEO dataset. The intersection DEGs between synovial membrane and blood were further screened out, followed by the functional annotation of these common DEGs. The optimal intersection gene biomarkers for OA diagnostics were determined. The GSE51588 dataset of articular cartilage was used for expression validation and further diagnostic analysis validation of identified gene biomarkers for OA diagnostics. There were 379 intersection DEGs were obtained between the synovial membrane and blood samples of OA. 22 DEGs had a diagnostic value for OA. After further screening, a total of 9 DEGs including TLR7, RTP4, CRIP1, ZNF688, TOP1, EIF1AY, RAB2A, ZNF281 and UIMC1 were identified for OA diagnostic. The identified DEGs could be considered as potential diagnostic biomarkers for OA.

SUBMITTER: Wang X

PROVIDER: S-EPMC7424510 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of potential diagnostic gene biomarkers in patients with osteoarthritis.

Wang Xinling X Yu Yang Y Huang Yong Y Zhu Mingshuang M Chen Rigao R Liao Zhanghui Z Yang Shipeng S

Scientific reports 20200812 1

The current study was aimed to identify diagnostic gene signature for osteoarthritis (OA). The differentially expressed genes (DEGs) in synovial membrane samples and blood samples were respectively identified from the GEO dataset. The intersection DEGs between synovial membrane and blood were further screened out, followed by the functional annotation of these common DEGs. The optimal intersection gene biomarkers for OA diagnostics were determined. The GSE51588 dataset of articular cartilage was ...[more]

PMID: 32788627

Similar Datasets

Project description:The definition of biomarkers correlating with disease progression could have impact on the rational design of novel diagnostic approaches for bovine tuberculosis in cattle. We have used the advantages of the murine model to identify promising candidates in the host transcriptome post-infection. RNA from BALB/c splenocytes and lung cells after aerogenic Mycobacterium bovis infection were with high-density microarrays to defining biomarkers of disease progression. In antigen-stimulated splenocytes we found statically significant modulation of 1109 genes early after infection and 1134 at later time-point post-infection. 618 of these genes were modulated at both time points. In the lung 282 genes were significantly modulated post-infection. Amongst the most strongly up-regulated genes were granzyme A in spleen, and cxcl9, granzyme B, interleukin-22, interluekin-17A receptor, and ccr6 in lungs. The expression of 20 of the most up-regulated genes identified in the murine studies were evaluated using PBMC from uninfected and naturally infected cattle. We could show that the expression of cxcl9, granzyme A and interleukin-22 following in vitro stimulation with PPD was significantly increased in infected cows compared to naïve animals. Thus, we have demonstrated that murine transcriptome analysis can be used to predict responses in cattle. In addition, we have prioritised CXCL9, GnzA and IL-22 as potential additional readout systems for the ante-mortem diagnosis of bovine tuberculosis. Two groups of 5 BALB/c mice each were infecetd with approx 600 CFU M. bovis via the intranasal route. At days 3 and 14 post challenge five mice per group were euthanized and spleens and lungs harvested. One group of 5 mice were used as a control. Their splenocytes and lung cells were stimulated in vitro for 3 days with M7 protein pool. Antigen cell culture stimulations in mice were performed using an equal pool of seven secreted, immunogenic recombinant mycobacterial proteins (Rv1886c, Rv3019c, Rv3763, Rv3804c, Rv0251, Rv0287 and Rv0288) common to M. bovis and BCG, referred here as M7 protein cocktail. Each protein was used at final concentration of 2μg/ml in 3-day culture.

Project description:The present study aimed to identify potential novel biomarkers in synovial tissue obtained from patients with Rheumatoid Arthritis (RA) and Osteoarthritis (OA) for differential diagnosis. The genome?wide expression profiling datasets of synovial tissues from RA and OA cohorts, including GSE55235, GSE55457 and GSE55584 datasets, were retrieved and used to identify differentially expressed genes (DEGs; P<0.05; false discovery rate <0.05 and Fold Change >2) between RA and OA using R software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of DEGs were performed to determine molecular and biochemical pathways associated with the identified DEGs, and a protein?protein interaction (PPI) network of the DEGs was constructed using Cytoscape software. Significant modules in the PPI network and candidate driver genes were screened using the Molecular Complex Detection Algorithm. Potential biomarkers were evaluated by receiver operating characteristic and logistic regression analyses. Large numbers of DEGs were detected, including 273, 205 and 179 DEGs in the GSE55235, GSE55457 and GSE55584 datasets, respectively. Among them, 80 DEGs exhibited identical expression trends in all the three datasets, including 49 upregulated and 31 downregulated genes in patients with RA. DEGs in patients suffering from RA compared with patients suffering from OA were predominantly associated with the primary immunodeficiency pathway, including interleukin 7 receptor (IL7R) and signal transducer activator of transcription 1 (STAT1). The sensitivity of IL7R + STAT1 to differentiate RA from OA was 93.94% with a specificity of 80.77%. The results generated from analyses of the GSE36700 dataset were closely associated with results generated from analyses of GSE55235, GSE55457 and GSE55584 datasets, which further verified the reliability of the aforementioned results. The results of the present study suggested that increased expression of IL7R and STAT1 in synovial tissue as well as in the primary immunodeficiency may be associated with RA occurrence. These identified novel biomarkers may be used to predict disease occurrence and clinically differentiate RA from OA.

Dataset Information

Identification of potential diagnostic gene biomarkers in patients with osteoarthritis.

Publications

Identification of potential diagnostic gene biomarkers in patients with osteoarthritis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets