Development of Personalized Signature Based on the Immune Landscape to Predict the Prognosis of Osteosarcoma and the Response to Immunotherapy and Targeted Therapy

• DOI: 10.3389/fmolb.2021.783915
• Publication Date: 2022-01-20T07:15:33Z
• Authors: Xiaofei Feng; Zhenrui Zhao; Yuhao Zhao; Zhengdong Song; Yao Ma; Wenji Wang
• Keywords: Targeted Therapy; Gene signature

As a heterogeneous and aggressive disease, osteosarcoma (OS) faces great challenges to prognosis individualized treatment. Hence, we explore the role of immune-related genes in predicting responsiveness immunotherapy targeted therapies patients with OS based on immunological landscape osteosarcoma. Based database Therapeutical Applicable Research Generate Effective Treatments (TARGET), single-sample gene set enrichment analysis (ssGSEA) was used obtain scores 29 immune characteristics. A series bioinformatics methods were performed construct prognostic signature (IRPS). Gene variation biological functions IRPS. We also analyzed relationship between IRPS tumor microenvironment. Lastly, reactivity checkpoint therapy drugs explored. The ssGSEA algorithm define two subtypes, namely Immunity_High Immunity_Low. associated good an independent factor OS. containing 7 constructed by least absolute shrinkage selection operator Cox regression. can divide into low- high-risk patients. Compared patients, low-risk had better positively correlated cell infiltration function. Low-risk benefited more from immunotherapy, sensitivity high- groups determined. be predict provide therapeutic therapy.