Revisiting prostaglandin E2: A promising therapeutic target for osteoarthritis

Yang, Dinglong; Xu, Ke; Xu, Xin; Xu, Peng

doi:10.1016/j.clim.2024.109904

Cited by 5 publications

(1 citation statement)

References 184 publications

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“…These cytokines promote the conversion of arachidonic acid into in ammatory prostaglandins, resulting in an in ammatory response 8 . During this process, the synovial membrane, cartilage, and subchondral bone interact with each other, jointly promoting disease development [9][10][11] . Although the etiology of OA is multi-factorial, the exact cause remains unknown.…”

Section: Introductionmentioning

confidence: 99%

Identification and validation of novel characteristic genes based on multi-tissue osteoarthritis

Zheng,

Ouyang,

Chen

et al. 2024

Preprint

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Background Osteoarthritis (OA) is characterized by synovial inflammation, articular cartilage degradation, and subchondral bone changes. Currently, there are no reliable biomarkers for the diagnosis and treatment of OA. Therefore, exploring OA biomarkers is crucial for its prevention, diagnosis, and treatment.Materials and Methods The GSE51588, GSE12021, GSE55457, GSE56409, GSE114007, GSE168505, GSE169077, GSE55235, GSE129147, and GSE48556 datasets of patients with OA and normal control samples were obtained from the GEO database. Differentially expressed genes (DEGs) in OA and normal controls were identified using R language. Protein-protein interaction (PPI) network and module analysis were performed to screen and filter key genes. Enrichment analyses were conducted to determine the biological functions and pathways of key DEGs and predict potential transcription factors. Machine learning models (XGBoost, LASSO regression, and SVM) were used to identify the best characteristic genes, and the intersection of hub genes was used as the final diagnostic genes. ROC analysis and nomogram were used to evaluate the diagnostic value of candidate genes. The expression levels of characteristic genes were validated in external GEO datasets containing cartilage, synovial membrane, and blood samples from patients. The expression levels of the key gene IRS2 in chondrocytes were further confirmed through in vitro experiments.Results Fifteen OA characteristic genes (IRS2, ADM, SIK1, PTN, CX3CR1, WNT5A, IL21R, APOD, CRLF1, FKBP5, PNMAL1, NPR3, RARRES1, ASPN, POSTN) were identified using three machine learning algorithms. Enrichment analysis indicated that abnormal expression of DEGs and hub genes may be mediated by extracellular matrix organization, extracellular structure organization, Relaxin signaling pathway, IL-17 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and PI3K-Akt signaling pathway, which are involved in OA occurrence. Four diagnostic genes (IRS2, WNT5A, PTN, POSTN) were highly correlated with OA. Validation data set analysis showed that IRS2 was down-regulated, while WNT5A, PTN, and POSTN were up-regulated in the experimental group compared to the normal group. qRT-PCR and WB results verified that the expression level of diagnostic gene IRS2 was consistent with bioinformatics analysis results.Conclusion This study integrates bioinformatics analysis and machine learning algorithms to identify and validate four promising biomarkers: IRS2, WNT5A, PTN, and POSTN. POSTN can be used as a biomarker for OA cartilage, and early diagnosis of PTN in OA deserves attention. WNT5A and IRS2 offer new diagnostic perspectives for OA.

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Section: Introductionmentioning

confidence: 99%

Identification and validation of novel characteristic genes based on multi-tissue osteoarthritis

Zheng,

Ouyang,

Chen

et al. 2024

Preprint

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Controlled Release of Ceria and Ferric Oxide Nanoparticles via Collagen Hydrogel for Enhanced Osteoarthritis Therapy

Chen,

Wang,

Zhang

et al. 2024

Adv Healthcare Materials

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Osteoarthritis (OA), characterized by chronic inflammation and cartilage degeneration, significantly affects over 500 million people globally. Nanoparticles have emerged as promising treatments for OA; however, current strategies often employ a single type of nanoparticle targeting specific disease stages, limiting sustained therapeutic efficacy. In this study, a novel collagen hydrogel is introduced, thiol crosslinked collagen‐cerium oxide‐poly(D,L‐lactic‐co‐glycolic acid) microspheres encapsulating nanoparticles (CSH‐CeO2‐pFe2O3), designed for the controlled release of cerium oxide (CeO2) and ferric oxide (Fe2O3) nanoparticles for comprehensive OA management. The sulfhydryl cross‐linked collagen matrix embeds CeO2 nanoparticles and poly(D,L‐lactic‐co‐glycolic acid) (PLGA) microspheres encapsulating Fe2O3 nanoparticles. The CSH‐CeO2‐pFe2O3 hydrogel exhibits enhanced mechanical strength and remarkable injectability, along with a significant promotion of cell adhesion, proliferation, and chondrogenic differentiation. Notably, the hydrogel demonstrates intelligent responsiveness to high levels of reactive oxygen species, initiating the rapid release of CeO2 nanoparticles to address the intense inflammatory responses of early‐stage OA, followed by the sustained release of Fe2O3 nanoparticles to facilitate cartilage regeneration during the proliferative phase. In a rat model with cartilage defects, the hydrogel significantly alleviates inflammation and enhances cartilage regeneration, holding substantial potential for effectively managing the pathologically complex OA.

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Zerumbone exhibits anti-inflammatory effects by suppressing eicosanoid signaling: Evidence from LPS-induced peripheral blood leukocytes

Uppin,

Zarei,

Acharya

et al. 2024

Prostaglandins & Other Lipid Mediators

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Revisiting prostaglandin E2: A promising therapeutic target for osteoarthritis

Cited by 5 publications

References 184 publications

Identification and validation of novel characteristic genes based on multi-tissue osteoarthritis

Identification and validation of novel characteristic genes based on multi-tissue osteoarthritis

Controlled Release of Ceria and Ferric Oxide Nanoparticles via Collagen Hydrogel for Enhanced Osteoarthritis Therapy

Zerumbone exhibits anti-inflammatory effects by suppressing eicosanoid signaling: Evidence from LPS-induced peripheral blood leukocytes

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