Identification of CIRBP and TRPV4 as Immune-Related Diagnostic Biomarkers in Osteoarthritis

Tang, Chengyang; Liu, Qiang; Zhang, Yaxuan; Gui-hu, Liu; Shen, Genhai

doi:10.2147/ijgm.s342286

Cited by 9 publications

(3 citation statements)

References 46 publications

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“…As a whole, these hub genes are valuable for being recognized as diagnostic biomarkers of OA. It has also been observed that several studies reported some diagnostic biomarkers in osteoarthritis, which were different from the hub genes found or derived from the same gene family [ 38 - 40 ]. The possible reasons might be the differences in the resources of the datasets analyzed.…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis

Yan

Zhao

Sun

et al. 2023

CCHTS

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Background: Osteoarthritis (OA) is a worldwide chronic disease of the articulating joints. An increasing body of data demonstrates the immune system's involvement in osteoarthritis. The molecular mechanisms of OA are still unclear. This study aimed to search for OA immunerelated hub genes and determine appropriate diagnostic markers to help the detection and treatment of the disease. Methods: Gene expression data were downloaded from the GEO database. Firstly, we analyzed and identified the differentially expressed genes（DEGs）using R packages. Meanwhile, ssGSEA was used to determine the activation degree of immune-related genes (IRGs), and WGCNA analysis was applied to search for co-expressed gene modules associated with immune cells. Then, critical networks and hub genes were found in the PPI network. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway enrichment analyzed the biological functions of genes. The ability of the hub genes to differentiate OA from controls was assessed by the area under the ROC curve. A miRNA and transcription factor (TF) regulatory network was constructed according to their relationship with hub genes. Finally, the validation of hub genes was carried out by qPCR. Results: In total, 353 DEGs were identified in OA patients compared with controls, including 222 upregulated and 131 downregulated genes. WGCNA successfully identified 34 main functional modules involved in the pathogenesis of OA. The most crucial functional module involved in OA included 89 genes. 19 immune-related genes were obtained by overlapping DEGs with the darkgrey module. The String database was constructed using the protein-protein interaction (PPI) network of 19 target genes, and 7 hub genes were identified by MCODE. ROC curve showed that 7 hub genes were potential biomarkers of OA. The expression levels of hub genes were validated by qPCR, and the results were consistent with those from bioinformatic analyses. Conclusions: Immune-related hub genes, including TYROBP, ITGAM, ITGB2, C1QC, MARCO, C1QB, and TLR8, may play critical roles in OA development. ITGAM had the highest correction on immune cells.

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Section: Discussionmentioning

confidence: 99%

Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis

Yan

Zhao

Sun

et al. 2023

CCHTS

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“…Osteoarthritis (OA) is the most common arthritis leading to ache, joint destruction, and eventually handicap; women and the aged are disproportionately affected by this disease (1)(2)(3). The combined effects of an aging global population, the increasing incidence of obesity and numbers of joint trauma have led to this disease becoming much more prevalent, with a minimum of 500 million people estimated to be affected by OA worldwide (1,2).…”

Section: Introductionmentioning

confidence: 99%

Expression patterns of eight RNA-modified regulators correlating with immune infiltrates during the progression of osteoarthritis

Chen

Wang²,

Hua³

2023

Front. Immunol.

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BackgroundRNA modifications in eukaryotic cells have emerged as an exciting but under-explored area in recent years and are considered to be associated with many human diseases. While several studies have been published relating to m6A in osteoarthritis (OA), we only have limited knowledge of other kinds of RNA modifications. Our study investigated eight RNA modifiers’ specific roles in OA including A-to-I, APA, m5C, m6A, m7G, mcm5s2U, Nm and Ψ together with their relationship with immune infiltration.MethodsRNA modification patterns in OA samples were identified based on eight-type RNA modifiers and their correlation with the degree of immune infiltration was also methodically investigated. Receiver operating characteristic curves (ROC) and qRT-PCR was performed to confirm the abnormal expression of hub genes. The RNA modification score (Rmscore) was generated by the applications of principal component analysis (PCA) algorithm in order to quantify RNA modification modes in individual OA patients.ResultsWe identified 21 differentially-expressed RNA modification related genes between OA and healthy samples. For example, CFI, CBLL1 and ALKBH8 were expressed at high levels in OA (P<0.001), while RPUSD4, PUS1, NUDT21, FBL and WDR4 were expressed at low levels (P<0.001). Two candidate RNA modification regulators (WDR4 and CFI) were screened out utilizing a random forest machine learning model. We then identified two distinctive RNA modification modes in OA which were found to display distinctive biological features. High Rmscore, characterized by increased immune cell infiltration, indicated an inflamed phenotype.ConclusionsOur study was the first to systematically reveal the crosstalk and dysregulations eight-type of RNA modifications in OA. Assessing individuals’ RNA modification patterns will be conductive to enhance our understanding of the properties of immune infiltration, provide novel diagnostic and prognostic biomarkers, and guide more effective immunotherapy strategies in the future.

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“…Least absolute shrinkage and selection operator (LASSO) regression analysis can shrink the regression coefficients of some variables to zero by imposing constraints on the model parameters, so it retains the advantages of subset shrinkage and minimizes the prediction error [ 23 ]. It has been successfully applied in bioinformatics analysis and clinical research related to KOA [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis

Zhao

Kuang

et al. 2022

Computational and Mathematical Methods in Medicine

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Background. Knee osteoarthritis (KOA) is a common degenerative joint disease. In this study, we aimed to identify new biomarkers of KOA to improve the accuracy of diagnosis and treatment. Methods. GSE98918 and GSE51588 were downloaded from the Gene Expression Omnibus database as training sets, with a total of 74 samples. Gene differences were analyzed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and Disease Ontology enrichment analyses for the differentially expressed genes (DEGs), and GSEA enrichment analysis was carried out for the training gene set. Through least absolute shrinkage and selection operator regression analysis, the support vector machine recursive feature elimination algorithm, and gene expression screening, the range of DEGs was further reduced. Immune infiltration analysis was carried out, and the prediction results of the combined biomarker logistic regression model were verified with GSE55457. Results. In total, 84 DEGs were identified through differential gene expression analysis. The five biomarkers that were screened further showed significant differences in cartilage, subchondral bone, and synovial tissue. The diagnostic accuracy of the model synthesized using five biomarkers through logistic regression was better than that of a single biomarker and significantly better than that of a single clinical trait. Conclusions. CX3CR1, SLC7A5, ARL4C, TLR7, and MTHFD2 might be used as novel biomarkers to improve the accuracy of KOA disease diagnosis, monitor disease progression, and improve the efficacy of clinical treatment.

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Identification of CIRBP and TRPV4 as Immune-Related Diagnostic Biomarkers in Osteoarthritis

Cited by 9 publications

References 46 publications

Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis

Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis

Expression patterns of eight RNA-modified regulators correlating with immune infiltrates during the progression of osteoarthritis

Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis

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