Characterizing the skeletal muscle immune microenvironment for sarcopenia: insights from transcriptome analysis and histological validation.

Background: Sarcopenia is a condition characterized by the age-related loss of skeletal muscle mass and function. The pathogenesis of the disease is influenced by chronic low-grade inflammation. However, the specific changes in the immune landscape changes of sarcopenic muscle are not yet fully understood. Methods: To gain insights into the immune cell composition and interactions, we combined single-nucleus RNA sequencing data, bulk RNA sequencing dataset, and comprehensive bioinformatic analyses on the skeletal muscle samples from young, aged, and sarcopenic individuals. Histological staining was then performed on skeletal muscles to validate the distribution of immune cells in clinical samples. Results: We analyzed the transcriptomes of 101,862 single nuclei, revealing a total of 10 major cell types and 6 subclusters of immune cell types within the human skeletal muscle tissues. Notable variations were identified in the immune microenvironment between young and aged skeletal muscle. Among the immune cells from skeletal muscle microenvironment, macrophages constituted the largest fraction. A specific marker gene LYVE1 for skeletal muscle resident macrophages was further identified. Cellular subclasses included four distinct groups of resident macrophages, which play different roles in physiological or non-physiological conditions. Utilizing bulk RNA sequencing data, we observed a significant enrichment of macrophage-rich inflammation in sarcopenia. Conclusions: Our findings demonstrate age-related changes in the composition and cross-talk of immune cells in human skeletal muscle microenvironment, which contribute to chronic inflammation in aged or sarcopenia muscle. Furthermore, macrophages emerge as a potential therapeutic target, thus advancing our understanding of the pathogenesis of sarcopenia.

CT-derived Radiomics Predicts the Efficacy of Tyrosine Kinase Inhibitors in Osteosarcoma Patients with Pulmonary Metastasis.

BACKGROUND: To construct and validate the CT-based radiomics model for predicting the tyrosine kinase inhibitors (TKIs) effects in osteosarcoma (OS) patients with pulmonary metastasis. METHODS: OS patients with pulmonary metastasis treated with TKIs were randomly separated into training and testing cohorts (2:1 ratio). Radiomic features were extracted from the baseline unenhanced chest CT images. The random survival forest (RSF) and Kaplan-Meier survival analyses were performed to construct and evaluate radiomics signatures (R-model-derived). The univariant and multivariant Cox regression analyses were conducted to establish clinical (C-model) and combined models (RC-model). The discrimination abilities, goodness of fit and clinical benefits of the three models were assessed and validated in both training and testing cohorts. RESULTS: A total of 90 patients, 57 men and 33 women, with a mean age of 18 years and median progression-free survival (PFS) of 7.2 months, were enrolled. The R-model was developed with nine radiomic features and demonstrated significant predictive and prognostic values. In both training and testing cohorts, the time-dependent area under the receiver operating characteristic curves (AUC) of the R-model and RC-model exhibited obvious superiority over C-model. The calibration and decision curve analysis (DCA) curves indicated that the accuracy of the R-model was comparable to RC-model, which exhibited significantly better performance than C-model. CONCLUSIONS: The R-model showed promising potential as a predictor for TKI responses in OS patients with pulmonary metastasis. It can potentially identify pulmonary metastatic OS patients most likely to benefit from TKIs treatment and help guide optimized clinical decisions.