Exploring osteosarcoma based on the tumor microenvironment

Area of Science:

• Oncology
• Immunology
• Bioinformatics

Background:

• Osteosarcoma, a bone cancer from mesenchymal cells, presents challenges with metastasis, drug resistance, disability, and mortality.
• The tumor microenvironment's (TME) immunological characteristics are crucial for osteosarcoma prognosis and treatment, necessitating sensitive prognostic signatures.

Purpose of the Study:

• To identify a sensitive prognostic signature for osteosarcoma by analyzing immune characteristics within the TME.
• To develop and validate a risk assessment model based on immune-related differentially expressed genes (IR-DEGs) for predicting osteosarcoma patient outcomes.

Main Methods:

• Analysis of 84 osteosarcoma samples from the UCSC Xena database for immune infiltration and classification.
• Identification of differentially expressed genes (DEGs) and immune-related DEGs (IR-DEGs) using TIMER database and intersection analysis.
• Construction of a risk model using univariate COX regression and LASSO analysis, followed by survival analysis and immune checkpoint evaluation.

Main Results:

• The majority of DEGs identified were enriched in the immune domain, highlighting the role of immunity in osteosarcoma.
• The developed risk assessment model showed significant prognostic distinctions between high-risk and low-risk scoring groups.
• The model's findings were consistent with previous research and yielded meaningful results in analyzing immune checkpoints.

Conclusions:

• The developed risk assessment model is precise and dependable for forecasting osteosarcoma outcomes.
• The model aids in analyzing the immunological characteristics of osteosarcoma, offering potential for improved patient management.