Rheumatoid arthritis fibroblast-like synovial cells (RA-FLS) show resistance to methotrexate (MTX) treatment. To better understand the mechanisms of this resistance, RA-FLS and osteoarthritis fibroblast-like synovial cells (OA-FLS) were isolated and exposed to MTX. We analyzed the autophagy induced by MTX in vitro and its relationship to apoptosis. Cell viability was evaluated using a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, and apoptosis was detected by flow cytometry and Western blot analysis. Autophagy was determined by transmission electron microscopy as well as Western blot analysis. The expression levels of Beclin-1, LC3, Akt, p-Akt, mammalian target of rapamycin (mTOR), p-mTOR, high mobility group box chromosomal protein 1 (HMGB1), and an 85 kDa caspase cleaved fragment of poly(ADP-ribose) polymerase were measured by Western blotting. MTX-induced apoptosis was increased in OA-FLS compared with RA-FLS. However, MTX stimulated the autophagy response in RA-FLS by inducing autophagosome formation, but not in OA-FLS. In RA-FLS, transfection with Beclin-1 small interfering RNA inhibited autophagy and increased susceptibility to MTX, which induces cell death. MTX upregulated autophagy through its ability to enhance the expression of HMGB1 and Beclin-1 rather than through the Akt/mTOR pathway. Autophagy induction contributes to resistance to MTX treatment in fibroblasts from patients with rheumatoid arthritis.

We previously found that high-mobility group box protein 1 (HMGB1) promoted cell proliferation, migration, invasion, and autophagy in rheumatoid arthritis fibroblast-like synoviocytes (RA-FLS), but little is known about its regulatory mechanism. The aim of this study was to investigate the regulatory mechanism of HMGB1 at the posttranscription level. Real-time qPCR, CCK-8 cell proliferation assay, transwell cell migration assay, enzyme-linked immunosorbent assay (ELISA), and western blotting were used in this study. The targeting relationship between miRNA and mRNA was presented by the luciferase reporter assay. MiR-449a was downregulated in RA synovial tissue and inhibited RA-FLS proliferation, migration, and IL-6 production. MiR-449a directly targeted HMGB1 and inhibited its expression. Yin Yang 1(YY1) negatively regulated miR-449a expression and formed a mutual inhibition loop in RA-FLS. MiR-449a inhibited TNFα-mediated HMGB1 and YY1 overexpression and IL-6 production. Our results reveal the regulatory mechanism of HMGB1 in RA and demonstrate that miR-449a is a crucial molecule in RA pathogenesis and a suitable candidate for miRNA replacement therapies in RA.

Delirium is a common postoperative complication in elderly hip fracture patients that seriously affects patients’ lives and health, and early delirium risk prediction, and targeted measures can significantly reduce the incidence of delirium. The purpose of this study was to develop and validate a nomogram for the prediction of postoperative delirium (POD) in elderly hip fracture patients. A total of 328 elderly patients with hip fractures enrolled retrospectively in department 1 of our hospital were randomly divided into the training set (n = 230) and the internal validation set (n = 98). The least absolute shrinkage and selection operator (LASSO) regression analysis was used for feature variable selection, and multivariate logistic regression with a backward stepwise method was used to construct a nomogram in the training set. The discrimination efficacy and calibration efficacy of the nomogram were evaluated through the receiver operating characteristic (ROC) curve and calibration curve, respectively. The clinical usefulness was estimated through decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Another validation set from department 2 of our hospital, containing 76 elderly patients with hip fractures, was used for external validation of the nomogram. A total of 43 (13.1%) and 12 (15.8%) patients had POD in department 1 and department 2, respectively. The nomogram was constructed by three predictors, including dementia, chronic obstructive pulmonary disease (COPD), and albumin level. The nomogram showed good discrimination efficacy and calibration efficacy, with the AUC of 0.791 (95% CI, 0.708–0.873), 0.820 (95% CI, 0.676–0.964), and 0.841 (95% CI, 0.717–0.966) in the training set, the internal validation set, and the external validation set, respectively. Both DCA and CIC demonstrated that this nomogram has good clinical usefulness. The nomogram constructed by dementia, COPD, and albumin level can be conveniently used to predict POD in patients with elderly hip fractures.

Objective. Osteosarcoma (OS) represents a prevalent primary bone neoplasm predominantly affecting the pediatric and adolescent populations, presenting a considerable challenge to human health. The objective of this investigation is to develop a prognostic model centered on anoikis‐related genes (ARGs), with the aim of accurately forecasting the survival outcomes of individuals diagnosed with OS and offering insights into modulating the immune microenvironment. Methods. The study’s training cohort comprised 86 OS patients sourced from The Cancer Genome Atlas database, while the validation cohort consisted of 53 OS patients extracted from the Gene Expression Omnibus database. Differential analysis utilized the GSE33382 dataset, encompassing three normal samples and 84 OS samples. Subsequently, the study executed gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses. Identification of differentially expressed ARGs associated with OS prognosis was carried out through univariate COX regression analysis, followed by LASSO regression analysis to mitigate overfitting risks and construct a robust prognostic model. Model accuracy was assessed via risk curves, survival curves, receiver operating characteristic curves, independent prognostic analysis, principal component analysis, and t‐distributed stochastic neighbor embedding (t‐SNE) analysis. Additionally, a nomogram model was devised, exhibiting promising potential in predicting OS patient prognosis. Further investigations incorporated gene set enrichment analysis to delineate active pathways in high‐ and low‐risk groups. Furthermore, the impact of the risk prognostic model on the immune microenvironment of OS was evaluated through tumor microenvironment analysis, single‐sample gene set enrichment analysis (ssGSEA), and immune infiltration cell correlation analysis. Drug sensitivity analysis was conducted to identify potentially effective drugs for OS treatment. Ultimately, the verification of the implicated ARGs in the model construction was conducted through the utilization of real‐time quantitative polymerase chain reaction (RT‐qPCR). Results. The ARGs risk prognostic model was developed, comprising seven high‐risk ARGs (CBS, MYC, MMP3, CD36, SCD, COL13A1, and HSP90B1) and four low‐risk ARGs (VASH1, TNFRSF1A, PIP5K1C, and CTNNBIP1). This prognostic model demonstrates a robust capability in predicting overall survival among patients. Analysis of immune correlations revealed that the high‐risk group exhibited lower immune scores compared to the low‐risk group within our prognostic model. Specifically, CD8+ T cells, neutrophils, and tumor‐infiltrating lymphocytes were notably downregulated in the high‐risk group, alongside significant downregulation of checkpoint and T cell coinhibition mechanisms. Additionally, three immune checkpoint‐related genes (CD200R1, HAVCR2, and LAIR1) displayed significant differences between the high‐ and low‐risk groups. The utilization of a nomogram model demonstrated significant efficacy in prognosticating the outcomes of OS patients. Furthermore, tumor metastasis emerged as an independent prognostic factor, suggesting a potential association between ARGs and OS metastasis. Notably, our study identified eight drugs—Bortezomib, Midostaurin, CHIR.99021, JNK.Inhibitor.VIII, Lenalidomide, Sunitinib, GDC0941, and GW.441756—as exhibiting sensitivity toward OS. The RT‐qPCR findings indicate diminished expression levels of CBS, MYC, MMP3, and PIP5K1C within the context of OS. Conversely, elevated expression levels were observed for CD36, SCD, COL13A1, HSP90B1, VASH1, and CTNNBIP1 in OS. Conclusion. The outcomes of this investigation present an opportunity to predict the survival outcomes among individuals diagnosed with OS. Furthermore, these findings hold promise for progressing research endeavors focused on prognostic evaluation and therapeutic interventions pertaining to this particular ailment.

Disulfidptosis is a newly recognized form of regulated cell death characterized by actin cytoskeleton collapse under disulfide stress. Recent studies suggest it may significantly contribute to osteoarthritis (OA), though its exact role in OA development remains unclear. This study aims to analyze the expression of disulfidptosis-related genes in OA, identifying potential biomarkers and candidate therapeutic leads. Transcriptomic data from six datasets related to OA and single-cell RNA sequencing data were sourced from the GEO database. To identify differentially expressed genes (DEGs) associated with disulfidptosis, we employed robust rank aggregation. We then utilized functional enrichment analysis, constructed a protein-protein interaction network, and applied machine learning models to prioritize key genes of interest. A single-cell analysis was conducted to evaluate gene expression within specific subsets of chondrocytes. Additionally, we predicted drug-target binding affinity using the GraphDTA model and performed molecular docking studies to assess drug-to-target binding affinity. Finally, immunohistochemistry, RT-qPCR, and Western blotting were performed for experimental validation. We identified several DEGs related to disulfidptosis, including SLC2A3, PDLIM1, and SLC3A2, which were found to be downregulated in OA tissues, particularly in pre-fibrocartilage chondrocyte subtypes. Our functional analysis indicated that these genes were enriched in pathways associated with cytoskeleton organization and the oxidative stress response. Furthermore, machine learning models demonstrated that these genes have significant diagnostic potential across various datasets. Using GraphDTA for drug prediction and molecular docking, we discovered that talaroflavone exhibits a strong binding affinity to SLC2A3, with highest predicted affinity (13.98) and a binding energy of -8.8 kcal/mol. Experimental validation confirmed the downregulation of SLC2A3 in OA-affected cartilage. Functional assays indicated that modulating SLC2A3 could alleviate cartilage matrix degradation and oxidative stress, suggesting a potential functional association between SLC2A3 and OA progression. This study providing functional evidence for a potential association between disulfidptosis-related genes and OA, while highlighting SLC2A3 as a potential functional regulator for further investigation. Additionally, was identified talaroflavone as a computationally predicted lead compound that warrants future experimental validation. These findings not only enhance our understanding of the molecular pathways underlying OA but also lay the groundwork for potential development of targeted therapeutic strategies.