A novel serological biomarker are associated with disease severity in patients with osteoarthritis

Li, Bin; Zhan, Huixian; Luo, Jian; Wang, Xuewen; Cao, Ting; Wei, Biaofang

doi:10.1007/s00774-022-01364-0

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…Previous independent studies have revealed the low expression of the differential genes APOD and FASN in single diseases (either OA or RA). For instance, Li et al (2022) observed significantly lower levels of ApoD in the serum of patients with knee osteoarthritis (KOA) compared to healthy controls [ 20 ]. Collins-Racie et al (2009) found significantly decreased expression levels of LXR target genes ABCG1, as well as apolipoproteins D and E, in OA cartilage [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

Analysis of common differential gene expression in synovial cells of osteoarthritis and rheumatoid arthritis

Liao,

He,

et al. 2024

PLoS ONE

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Objective To elucidate potential molecular mechanisms differentiating osteoarthritis (OA) and rheumatoid arthritis (RA) through a bioinformatics analysis of differentially expressed genes (DEGs) in patient synovial cells, aiming to provide new insights for clinical treatment strategies. Materials and methods Gene expression datasets GSE1919, GSE82107, and GSE77298 were downloaded from the Gene Expression Omnibus (GEO) database to serve as the training groups, with GSE55235 being used as the validation dataset. The OA and RA data from the GSE1919 dataset were merged with the standardized data from GSE82107 and GSE77298, followed by batch effect removal to obtain the merged datasets of differential expressed genes (DEGs) for OA and RA. Intersection analysis was conducted on the DEGs between the two conditions to identify commonly upregulated and downregulated DEGs. Enrichment analysis was then performed on these common co-expressed DEGs, and a protein-protein interaction (PPI) network was constructed to identify hub genes. These hub genes were further analyzed using the GENEMANIA online platform and subjected to enrichment analysis. Subsequent validation analysis was conducted using the GSE55235 dataset. Results The analysis of differentially expressed genes in the synovial cells from patients with Osteoarthritis (OA) and Rheumatoid Arthritis (RA), compared to a control group (individuals without OA or RA), revealed significant changes in gene expression patterns. Specifically, the genes APOD, FASN, and SCD were observed to have lower expression levels in the synovial cells of both OA and RA patients, indicating downregulation within the pathological context of these diseases. In contrast, the SDC1 gene was found to be upregulated, displaying higher expression levels in the synovial cells of OA and RA patients compared to normal controls.Additionally, a noteworthy observation was the downregulation of the transcription factor PPARG in the synovial cells of patients with OA and RA. The decrease in expression levels of PPARG further validates the alteration in lipid metabolism and inflammatory processes associated with the pathogenesis of OA and RA. These findings underscore the significance of these genes and the transcription factor not only as biomarkers for differential diagnosis between OA and RA but also as potential targets for therapeutic interventions aimed at modulating their expression to counteract disease progression. Conclusion The outcomes of this investigation reveal the existence of potentially shared molecular mechanisms within Osteoarthritis (OA) and Rheumatoid Arthritis (RA). The identification of APOD, FASN, SDC1, TNFSF11 as key target genes, along with their downstream transcription factor PPARG, highlights common potential factors implicated in both diseases. A deeper examination and exploration of these findings could pave the way for new candidate targets and directions in therapeutic research aimed at treating both OA and RA. This study underscores the significance of leveraging bioinformatics approaches to unravel complex disease mechanisms, offering a promising avenue for the development of more effective and targeted treatments.

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Section: Discussionmentioning

confidence: 99%

Analysis of common differential gene expression in synovial cells of osteoarthritis and rheumatoid arthritis

Liao,

He,

et al. 2024

PLoS ONE

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“…[23,24] Studies have shown that radiographic grading with the Kellgren-Lawrence scale revealed a significant relationship to the joint space width and joint space, the joint space width decreased with increasing Kellgren-Lawrence grade, [25] the range of joint motion was significantly reduced with the increase in KL grading. [26] The severity of KL grade was associated with a variety of cytokines and telomere size, [27][28][29] these cytokines are signaling molecules involved in inflammation and immune responses, telomere size decreases with the increase in KL grading, shorter telomeres indicate premature aging, which aggravates the aging of chondrocytes and degenerative changes in joints. [30] Our study included 392 patients with osteoarthritis of the knee aged 50 to 75 years.…”

Section: Discussionmentioning

confidence: 99%

Therapeutic analysis of laser moxibustion for different KL graded knee osteoarthritis

Yan,

Lin,

Cheng

et al. 2024

Medicine

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Background: Our previous studies showed that laser moxibustion may be effective in alleviating the symptoms of knee osteoarthritis. However, the therapeutic effect in patients with different Kellgren-Lawrence (KL) grades is still unclear. We aimed to compare the efficacy of laser moxibustion in the treatment of knee osteoarthritis with different KL grades. Methods: A total of 392 symptomatic KOA patients with different KL grades were randomly assigned to the laser treatment or sham laser control group (1:1). The patients received laser moxibustion treatment or sham treatment 3 times a week for 4 weeks. Outcomes were measured using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores and Visual Analog Scale (VAS) scores, and the primary outcome measurement was the change in WOMAC pain scores from baseline to week 4. Results: Among 392 randomized participants, 364 (92.86%) completed the trial. Participants with KL grades 2, 3, and 4 had significantly higher pain, functional, and total WOMAC scores than those with KL grade 1. Spearman correlation test results showed a positive correlation between KL grade and WOMAC pain, function, stiffness scores, and WOMAC total scores. That is, the higher the KL grade, the higher the WOMAC pain, function, stiffness, and WOMAC total scores. After 4 weeks of treatment, patients with KL grades 2 and 3 had significantly higher improvement scores in pain, function, and total scores than those with KL grade 1, whereas those with KL grade 2 had significantly higher improvement scores in stiffness than those with KL grade 1. Patients with KL grade 4 showed no significant effects after laser moxibustion treatment. Conclusion: Laser moxibustion is effective for pain reduction and functional improvement in the treatment of KOA with KL grades 2 and 3.

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“…Currently, the diagnosis of OA mainly depends on the clinical presentations and imaging examination, often leading to delayed diagnosis and missing the optimal time to intervene [21]. 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][26][27].…”

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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A novel serological biomarker are associated with disease severity in patients with osteoarthritis

Cited by 6 publications

References 27 publications

Analysis of common differential gene expression in synovial cells of osteoarthritis and rheumatoid arthritis

Analysis of common differential gene expression in synovial cells of osteoarthritis and rheumatoid arthritis

Therapeutic analysis of laser moxibustion for different KL graded knee osteoarthritis

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

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