Background: Osteosarcoma (OS) stands as the prevailing form of primary bone cancer in clinical practice. Lack of effective treatment options and an overall poor prognosis are caused by the disease’s exceptionally rare occurrence and unclear rationale. Objective: This study’s goal is to determine diagnostic marker genes involved in the progression of OS and investigate related pathways and mechanisms with the purpose of offering effective methods for OS diagnostics and therapy. Methods: The Gene Expression Omnibus database provided the gene microarray data. Core genes were identified through differential expression analysis and WGCNA. Three techniques for machine learning, random forest, least absolute shrinkage and selection operator regression, and support vector machine recursive feature elimination, were used to further screen the core genes and obtain diagnostic marker genes for OS. The specificity and sensitivity of the diagnostic marker genes for OS diagnosis were evaluated using receiver operating characteristic curves. Western blotting analysis was used for preliminary validation of the diagnostic marker genes and their related pathways. Results: Two diagnostic marker genes were identified through screening, including CEP55 and VWF. Receiver operating characteristic curves have been utilized to assess the diagnostic and therapeutic effects of CEP55 and VWF on OS. Western blotting analysis preliminarily validated the overexpression of CEP55 in OS and its capacity to control MMP2 and MMP9 levels by activating the JAK2/STAT3 signaling pathway. Conclusion: At the first time, this research shows that CEP55 and VWF are more powerful diagnostic and predictive indicators for OS. CEP55 holds the capacity to activate the JAK2/STAT3 signaling pathway and modulate MMP2 and MMP9 levels, thereby positioning it as a promising target in OS treatment.

Background: Osteoarthritis (OA) is the most prevalent and commonly chronic joint disease that frequently develops among the elderly population. It is not just a single tissue that is affected, but rather a pathology involving the entire joint. Among them, synovitis is a key pathological change in OA. Ferroptosis is a newly discovered form of cell death that results from the buildup of lipid peroxidation. However, the role and impact of it in OA are yet to be explored. Objective: The key to this work is to uncover the mechanisms of ferroptosis-related OA pathogenesis and develop more novel diagnostic biomarkers to facilitate the diagnostic and therapeutic of OA. Materials and methods: Download ferroptosis-related genes and OA synovial chip datasets separately from the FerrDB and Gene Expression Omnibus databases. Identify ferroptosis differentially expressed genes using R software, obtain the intersection genes through two machine learning algorithms, and obtain diagnostic biomarkers after logistic regression analysis. Verify the diagnostic and therapeutic efficacy of specific genes for OA through the construction of clinical risk prognostic models using ROC curves and nomogram. Simultaneously, correlations between specific genes and OA immune cell infiltration co-expression were constructed. Finally, verify the differential presentation of specific genes in OA and health control synovium. Results: Obtain 38 ferroptosis differentially expressed genes through screening. Based on machine learning algorithms and logistic regression analysis, select AGPS, BRD4, RBMS1, and EGR1 as diagnostic biomarker genes. The diagnostic and therapeutic efficacy of the four specific genes for OA has been validated by ROC curves and nomogram of clinical risk prognostic models. The analysis of immune cell infiltration and correlation suggests a close association between specific genes and OA immune cell infiltration. Further revealing the diagnostic value of specific genes for OA by the differential presentation analysis of their differential presentation in synovial tissue from OA and health control. Conclusion: This study identified four diagnostic biomarkers for OA that are associated with iron death. The establishment of a risk-prognostic model is conducive to the premature diagnosis of OA, evaluating functional recovery during rehabilitation, and guidance for subsequent treatment.