Osteoarthritis (OA) represents the foremost degenerative joint disease observed in a clinical context. The escalating issue of population aging significantly exacerbates the prevalence of OA, thereby imposing an immense annual economic burden on societies worldwide. The current therapeutic landscape falls short in offering reliable pharmaceutical interventions and efficient treatment methodologies to tackle this growing problem. However, the scientific community continues to dedicate significant efforts towards advancing OA treatment research. Contemporary studies have discovered that the progression of OA may be slowed through the strategic influence on peroxisome proliferator-activated receptors (PPARs). PPARs are ligand-activated receptors within the nuclear hormone receptor family. The three distinctive subtypes—PPARα, PPARβ/δ, and PPARγ—find expression across a broad range of cellular terminals, thus managing a multitude of intracellular metabolic operations. The activation of PPARγ and PPARα has been shown to efficaciously modulate the NF-κB signaling pathway, AP-1, and other oxidative stress-responsive signaling conduits, leading to the inhibition of inflammatory responses. Furthermore, the activation of PPARγ and PPARα may confer protection to chondrocytes by exerting control over its autophagic behavior. In summation, both PPARγ and PPARα have emerged as promising potential targets for the development of effective OA treatments.

Disulfidptosis, a newly discovered type of programmed cell death, could be a mechanism of cell death controlled by SLC7A11. This could be closely associated with tumor development and advancement. Nevertheless, the biological mechanism behind disulfidptosis-related genes (DRGs) in sarcoma (SARC) is uncertain. This study identified three valuable genes (SLC7A11, RPN1, GYS1) associated with disulfidptosis in sarcoma (SARC) and developed a prognostic model. The multiple databases and RT-qPCR data confirmed the upregulated expression of prognostic DRGs in SARC. The TCGA internal and ICGC external validation cohorts were utilized to validate the predictive model capacity. Our analysis of DRG riskscores revealed that the low-risk group exhibited a more favorable prognosis than the high-risk group. Furthermore, we observed a significant association between DRG riskscores and different clinical features, immune cell infiltration, immune therapeutic sensitivity, drug sensitivity, and RNA modification regulators. In addition, two external independent immunetherapy datasets and clinical tissue samples were collected, validating the value of the DRGs risk model in predicting immunotherapy response. Finally, the SLC7A11/hsa-miR-29c-3p/LINC00511, and RPN1/hsa-miR-143-3p/LINC00511 regulatory axes were constructed. This study provided DRG riskscore signatures to predict prognosis and response to immunotherapy in SARC, guiding personalized treatment decisions.

Background: Glioma is a highly aggressive brain tumor with limited therapeutic options and poor prognosis. While immune checkpoint inhibitors and molecular therapies have emerged, effective biomarkers for patient stratification remain scarce. Long non-coding RNAs (lncRNAs) associated with lysine crotonylation (LCRlncRNAs) have been implicated in cancer progression, but their role in glioma remains largely unexplored.Methods: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) glioma cohort were analyzed to identify prognostic LCRlncRNAs. A multigene risk score model was constructed using univariate Cox, LASSO, and multivariate Cox regression analyses. Functional enrichment analyses (GO, KEGG, GSEA) and immune landscape profiling (CIBERSORT, ssGSEA, ESTIMATE) were performed to explore potential mechanisms. Associations with immune checkpoint expression, tumor mutational burden (TMB), and microsatellite instability (MSI) were also assessed. In addition, RT-qPCR, EdU, Transwell, and xenograft experiments, as well as qPCR, Western blot, serum ELISA, and immunohistochemistry (IHC) analyses, were conducted to validate the functional and mechanistic roles of the representative LCRlncRNA POLR2J4.Results: Six LCRlncRNAs were identified as independent prognostic factors, and the risk score model stratified patients into high- and low-risk groups with distinct survival outcomes. The high-risk group exhibited enriched immunosuppressive features, including increased regulatory T cells, M2 macrophages, and elevated expression of immune checkpoints (e.g., PD-L1, CTLA4). TIDE analysis indicated poor immunotherapy response in high-risk patients. Drug sensitivity analysis revealed that high-risk patients were more sensitive to DNA-damaging agents such as cisplatin. Functional assays confirmed that POLR2J4 promotes glioma proliferation, migration, and cisplatin resistance. Mechanistically, POLR2J4 knockdown reduced the expression of drug resistance genes (ABCB1, ABCC1, BCL2), decreased serum levels of IL-6 and TGF-β1, and downregulated TGF-β1 and PD-L1 in tumor tissues, highlighting its role in establishing an immunosuppressive, drug-resistant microenvironment.Conclusion: Our study demonstrates that LCRlncRNAs are closely linked to glioma prognosis, immune microenvironment remodeling, and therapeutic response. The LCRlncRNA-based risk model provides a promising tool for prognostic evaluation and personalized therapy design in glioma.

Background: N7-methylguanosine (m7G) methylation is an RNA modification associated with cancer progression, but its specific role in head and neck squamous cell carcinoma (HNSCC) remains unclear.Methods: This study analyzed the differential expression of m7G-related genes (m7GRGs) in HNSCC using the TCGA-HNSCC dataset, identifying key pathways associated with the cell cycle, DNA replication, and focal adhesion. A LASSO-Cox regression model was constructed based on four m7GRGs (EIF3D, EIF1, LARP1, and METTL1) and validated with GEO datasets and clinical samples. Further validation of gene upregulation in HNSCC tissues was conducted using RT-qPCR and immunohistochemistry, while the role of LARP1 in HNSCC cells was assessed via knockout experiments.Results: The constructed model demonstrated strong predictive performance, with the risk score significantly correlating with prognosis, immune infiltration, and drug sensitivity. An external dataset and clinical specimens further confirmed the model’s predictive accuracy for immunotherapy response. Additionally, two regulatory axes—LINC00707/hsa-miR-30b-5p/LARP1 and SNHG16/hsa-miR-30b-5p/LARP1—were identified. LARP1 knockout experiments revealed that suppressing LARP1 markedly inhibited HNSCC cell proliferation, migration, and invasion.Conclusion: The m7GRG-based prognostic model developed in this study holds strong clinical potential for predicting prognosis and therapeutic responses in HNSCC. The identification of LARP1 and its related regulatory pathways offers new avenues for targeted therapy in HNSCC.