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Sarcoma, the second common type of solid tumor in children and adolescents, has a wide variety of subtypes that are often not properly diagnosed at an early stage, leading to late metastases and causing serious loss of life and property to patients and families. It exhibits a high degree of heterogeneity at the cellular, molecular, and epigenetic levels, where DNA methylation has been proposed to play a role in the diagnosis of sarcoma subtypes. Thus, this study is aimed at finding potential biomarkers at the DNA methylation level to distinguish different sarcoma subtypes. A machine learning process was designed to analyse sarcoma samples, each of which was represented by lots of methylation sites. Irrelevant sites were removed using the Boruta method, and remaining sites related to the target variables were kept for further analyses. Afterward, three feature ranking methods (LASSO, LightGBM, and MCFS) were adopted to rank these features, and six classification models were constructed by combining incremental feature selection and two classification algorithms (decision tree and random forest). Among these models, the performance of RF model was higher than that of DT model under all three ranking conditions. The specific expression of genes obtained from the annotation of highly correlated methylation site features, such as PRKAR1B, INPP5A, and GLI3, was proven to be associated with sarcoma by publications. Moreover, the quantitative rules obtained by decision tree algorithm helped us to understand the essential differences between various sarcoma types and classify sarcoma subtypes, providing a new means of clinical identification and determining new therapeutic targets.

This study aimed to investigate the correlation between magnetic resonance imaging (MRI) findings of articular cartilage and clinical symptoms in patients with osteoarthritis (OA). Eighty patients with OA were selected as the study subjects (OA group) and 80 healthy subjects during the same period were also selected as the control group. All subjects underwent knee sagittal PDW-SPAIR, sagittal T1WI-aTSE, sagittal T2WI-TSE, coronal PDW-SPAIR, sagittal 3D-WATSc, and sagittal T2 mapping scans. Thereafter, all subjects underwent clinical assessment. The whole-organ MRI score (WORMS) was adopted for MRI examination and semiquantitative analysis, and the T2 value was calculated. The correlation among T2 value, WORMS, and Western Ontario and Mc Master University OA Index (WOMAC) was then compared and analyzed. The correlation coefficients between T2 values and WORMS in each sub-region of patients with OA were 0.8, 0.55, −0.038, 0.811, and 0.743; the correlation coefficients between WORMS and WOAMC were 0.66, 0.71, 0.46, and 0.88; and the correlation coefficients between T2 values and WOAMC were 0.483, 0.33, 0.282, and 0.636, respectively. There was a significant positive correlation between the results of MRI semiquantitative analysis and clinical symptoms as well as disease severity in patients with OA.