Calcified Adrenal Metastasis of High-Grade Osteosarcoma on FDG PET/CT

Nanoparticles and bone microenvironment: a comprehensive review for malignant bone tumor diagnosis and treatment

Malignant bone tumors, which are difficult to treat with current clinical strategies, originate from bone tissues and can be classified into primary and secondary types. Due to the specificity of the bone microenvironment, the results of traditional means of treating bone tumors are often unsatisfactory, so there is an urgent need to develop new treatments for malignant bone tumors. Recently, nanoparticle-based approaches have shown great potential in diagnosis and treatment. Nanoparticles (NPs) have gained significant attention due to their versatility, making them highly suitable for applications in bone tissue engineering, advanced imaging techniques, and targeted drug delivery. For diagnosis, NPs enhance imaging contrast and sensitivity by integrating targeting ligands, which significantly improve the specific recognition and localization of tumor cells for early detection. For treatment, NPs enable targeted drug delivery, increasing drug accumulation at tumor sites while reducing systemic toxicity. In conclusion, understanding bone microenvironment and using the unique properties of NPs holds great promise in improving disease management, enhancing treatment outcomes, and ultimately improving the quality of life for patients with malignant bone tumors. Further research and development will undoubtedly contribute to the advancement of personalized medicine in the field of bone oncology.

Construction and Validation of an Autophagy-Related Prognostic Model for Osteosarcoma Patients

While the prognostic value of autophagy-related genes (ARGs) in OS patients remains scarcely known, increasing evidence is indicating that autophagy is closely associated with the development and progression of osteosarcoma (OS). Therefore, we explored the prognostic value of ARGs in OS patients and illuminate associated mechanisms in this study. When the OS patients in the training/validation cohort were stratified into high- and low-risk groups according to the risk model established using least absolute shrinkage and selection operator (LASSO) regression analysis, we observed that patients in the low-risk group possessed better prognosis (P < 0.0001). Univariate/Multivariate COX regression and subgroup analysis demonstrated that the ARGs-based risk model was an independent survival indicator for OS patients. The nomogram incorporating the risk model and clinical features exhibited excellent prognostic accuracy. GO, KEGG, and GSVA analyses collectively indicated that bone development-associated pathway mediated the contribution of ARGs to the malignance of OS. Immune infiltration analysis suggested the potential pivotal role of macrophage in OS. In summary, the risk model based on 12 ARGs possessed potent capacity in predicting the prognosis of OS patients. Our work may assist clinicians to map out more reasonable treatment strategies and facilitate individual-targeted therapy in osteosarcoma.