Screening and identification of osteoarthritis related differential genes and construction of a risk prognosis model based on bioinformatics analysis

You, Ran; Liu, Siyi; Tan, Jun

doi:10.21037/atm-22-1135

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…Therefore, many efforts have been devoted to seeking reliable diagnosis biomarkers in recent years, and some novel molecules as OA diagnosis markers have been reported, such as ATF3 [ 22 ], Apolipoprotein D [ 23 ], and CXCL13 [ 24 ]. The rapid development of genomic sequencing technology and big-data analysis methods represented by machine learning generates new opportunities to disclose novel biomarkers and to develop novel diagnostic tools, and many achievements have been obtained in OA [ 25 – 27 ]. Nevertheless, the reports about OA diagnosis models based on machine algorithms are relatively rare at the moment.…”

Section: Discussionmentioning

confidence: 99%

A two-gene random forest model to diagnose osteoarthritis based on RNA-binding protein-related genes in knee cartilage tissue

Yin

Lei

Yang

et al. 2023

Aging

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Osteoarthritis (OA) is one of the most common diseases in the orthopedic clinic, characterized by progressive cartilage degradation. RNA-binding proteins (RBPs) are capable of binding to RNAs at transcription and translation levels, playing an important role in the pathogenesis of OA. This study aims to investigate the diagnosis values of RBP-related genes in OA. The RBPs were collected from previous studies, and the GSE114007 dataset (control = 18, OA = 20) was downloaded from the Gene Expression Omnibus (GEO) as the training cohort. Through various bioinformatical and machine learning methods, including genomic difference detection, protein-protein interaction network analyses, Lasso regression, univariate logistic regression, Boruta algorithm, and SVM-RFE, RNMT and RBM24 were identified and then included into the random forest (RF) diagnosis model. GSE117999 dataset (control = 10, OA = 10) and clinical samples collected from local hospital (control = 10, OA = 11) were used for external validation. The RF model was a promising tool to diagnose OA in the training dataset (area under curve [AUC] = 1.000, 95% confidence interval [CI] = 1.000-1.000), the GSE117999 cohort (AUC = 0.900, 95% CI = 0.769–1.000), and local samples (AUC = 0.759, 95% CI = 0.568–0.951). Besides, qPCR and Western Blotting experiments showed that RNMT ( P < 0.05) and RBM24 ( P < 0.01) were both down-regulated in CHON-001 cells with IL-1β treatment. In all, an RF model to diagnose OA based on RNMT and RBM24 in cartilage tissue was constructed, providing a promising clinical tool and possible cut-in points in molecular mechanism clarification.

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Section: Discussionmentioning

confidence: 99%

A two-gene random forest model to diagnose osteoarthritis based on RNA-binding protein-related genes in knee cartilage tissue

Yin

Lei

Yang

et al. 2023

Aging

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“…The GSE55457 gene expression profiles were downloaded from the GEO database [https://www.ncbi. nlm.nih.gov/geo/query/acc.cgi?acc=GSE55457; GPL96 platform, Affymetrix Human Genome U133A Array] (27). The GSE55457 dataset contains data from 79 samples, including 20 healthy control individuals, 33 patients with RA and 26 patients with osteoarthritis.…”

Section: Methodsmentioning

confidence: 99%

“…To identify differentially expressed genes (DEGs) in RA, the GEO was used to assess RA data. The GSE55457 gene expression profiles were downloaded from the GEO database [ ; GPL96 platform, Affymetrix Human Genome U133A Array] ( 27 ). The GSE55457 dataset contains data from 79 samples, including 20 healthy control individuals, 33 patients with RA and 26 patients with osteoarthritis.…”

Section: Methodsmentioning

confidence: 99%

Ubiquitin D promotes the progression of rheumatoid arthritis via activation of the p38 MAPK pathway

et al. 2023

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Ubiquitin D (UBD), a member of the ubiquitin-like modifier family, has been reported to be highly expressed in various types of cancer and its overexpression is positively associated with tumor progression. However, the role and mechanism of UBD in rheumatoid arthritis (RA) remain elusive. In the present study, the gene expression profiles of GSE55457 were downloaded from the Gene Expression Omnibus database to assess differentially expressed genes and perform functional enrichment analyses. UBD was overexpressed by lentivirus transfection. The protein level of UBD, p-p38 and p38 in RA-fibroblast-like synoviocytes (FLSs) were examined by western blotting. Cell Counting Kit-8 and flow cytometry assays were used to detect the functional changes of RA-FLSs transfected with UBD and MAPK inhibitor SB202190. The concentrations of inflammatory factors (IL-2, IL-6, IL-10 and TNF-α) were evaluated using ELISA kits. The results revealed that UBD was overexpressed in RA tissues compared with in the healthy control tissues. Functionally, UBD significantly accelerated the viability and proliferation of RA-FLSs, whereas it inhibited their apoptosis. Furthermore, UBD significantly promoted the secretion of inflammatory factors (IL-2, IL-6, IL-10 and TNF-α). Mechanistically, elevated UBD activated phospohorylated-p38 in RA-FLSs. By contrast, UBD overexpression and treatment with the p38 MAPK inhibitor SB202190 not only partially relieved the UBD-dependent effects on cell viability and proliferation, but also reversed its inhibitory effects on cell apoptosis. Furthermore, SB202190 partially inhibited the effects of UBD overexpression on the enhanced secretion of inflammatory factors. The present study indicated that UBD may mediate the activation of p38 MAPK, thereby facilitating the proliferation of RA-FLSs and ultimately promoting the progression of RA. Therefore, UBD may be considered a potential therapeutic target and a promising prognostic biomarker for RA.

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“…The hub genes ( 33 ) in key positions in the PPI network were screened with a variety of algorithms through the cytoHubba plug-in of Cytoscape ( 34 ). The obtained hub genes were reintroduced into the STRING website to determine the PPI interaction ( 35 ).…”

Section: Methodsmentioning

confidence: 99%

Prediction and analysis of osteoarthritis hub genes with bioinformatics

Zhong¹,

Ding²

2023

Ann Transl Med

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Background Osteoarthritis (OA) is the most common type of arthritis. OA can cause joint pain, stiffness, and loss of function. The pathogenesis of OA is not completely clear. Moreover, there is no effective treatment, and clinical management is limited to symptomatic relief or joint surgery. This study utilized bioinformatics to analyze normal and OA articular cartilage samples to find biomarkers and therapeutic targets for OA. Methods The GSE169077 gene chip dataset was downloaded from the public gene chip data platform of the National Biotechnology Information Center. The dataset included 6 samples of OA tissues and 5 samples of healthy cartilage tissues. Differentially expressed genes (DEGs) were screened using the R language “limma” function package under the threshold of log 2 [fold change (FC)] ≥2 and a P value <0.05. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathways of the target genes were enriched and analyzed using the database for annotation, visualization, and integrated discovery (DAVID), and a protein-protein interaction (PPI) network was further constructed using the search tool for the retrieval of interacting genes/proteins (STRING) database. The coexpression relationship of the genes in the module was visualized and screened with Cytoscape. Results A total of 27 DEGs were identified, including 9 downregulated genes and 18 upregulated genes. GO signal pathway enrichment analysis showed involvement in hypoxic response, fibrous collagen trimer, and extracellular matrix structural components. KEGG analysis demonstrated associations with protein digestion and absorption, extracellular matrix receptor interaction, and the peroxisome proliferator-activated receptor signal pathway, among several other pathways. A PPI network was obtained through STRING analysis, and the results were imported into Cytoscape software. The 27 DEGs were sequenced by the cytoHubba plug-in by various calculation methods, and 5 hub genes ( COL1A1, COL1A2, POSTN, BMP1 , and MMP13 ) were finally selected. These genes were analyzed by PPI again and annotated with GO and KEGG in different colors. Conclusions Bioinformatics technology effectively identified differential genes in the knee cartilage tissue of healthy controls and patients with OA, providing opportunities to further explore the mechanism and treatment of OA on a transcriptional level.

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Screening and identification of osteoarthritis related differential genes and construction of a risk prognosis model based on bioinformatics analysis

Cited by 6 publications

References 36 publications

A two-gene random forest model to diagnose osteoarthritis based on RNA-binding protein-related genes in knee cartilage tissue

A two-gene random forest model to diagnose osteoarthritis based on RNA-binding protein-related genes in knee cartilage tissue

Ubiquitin D promotes the progression of rheumatoid arthritis via activation of the p38 MAPK pathway

Prediction and analysis of osteoarthritis hub genes with bioinformatics

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