osteosarcoma

Background The osteosarcoma has unique molecular features and clinical heterogeneity. This study aimed to identify the characteristics of immune cell infiltration (ICI) subtypes for evaluating prognosis and therapeutic benefits. Methods The independent gene datasets, and corresponding clinical information were collected from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. The ICI contents were evaluated by ‘ESTIMATE’ and ‘CIBERSORT’. We performed two computational algorithms to identify the ICI landscape related to prognosis and found the unique infiltration characteristics. Next, principal component analysis (PCA) was conducted to construct ICI score based on three ICI patterns. We stratified patients into prognostic-related high- and low- ICI score groups (HSG and LSG, respectively). The key genes were preliminary screened by weighted gene co-expression network analysis (WGCNA) based on ICI scores. And they were further identified by construction of prognosis predicting model. The predictive ability of ICI score for metastasis and HUVOS grade were also verified in cohorts. Results The ICI patients with a better prognosis are characterized by the presence of CD8+ T cells, monocytes, M1 macrophages, M2 macrophages, activated dendritic cells, resting mast cells, and activated mast cells. The genes identified by ICI scores and various levels included WAS, ARHGAP30, and PARVG. Conclusion The identification of ICI subtypes and ICI scores will help gain insights into the heterogeneity in osteosarcoma, and identify patients probably benefiting from treatments. ICI scores and the key genes could serve as an effective biomarker to predict prognosis and the sensitivity of immunotherapy.