Comprehensive Analysis of Inflammation Response Related Genes and Immune Infiltrates in Osteosarcoma

Abstract

Background:Osteosarcoma is one of the most common bone malignant tumors in children and young adults. Inflammatory response in the microenvironment which acts as active cross-talk signals between host and tumor may play a vital role in osteosarcoma. In present study, bioinformatics algorithms were applied to establish inflammatory response-related genes (IRG) signature to improve prognosis prediction in osteosarcoma.Methods:Clinical and mRNA expression profiles data of osteosarcoma patients were collected via Gene Expression Omnibus (GEO) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases. Prognostic values of IRG were evaluated via univariate and LASSO Cox regression analysis and combined to construct risk siganture. Relationship between immune cell infiltration and signature was investigated in present study. Single-sample gene set enrichment analysis was implemented to calculate immune related pathway activity and immune cell infiltration score. Results:We found 17 IRG were correlated with overall survival (OS). From LASSO Cox regression analyses, 11 IRG were identified as candidate genes to combine into risk score formulas. Patients were divided into high and low risk subgroups. patients in low-risk subgroup had a significantly better OS than patients in high-risk according to Kaplan-Meier curve result. In addition, gene set variation analysis of risk stratification may explain the different survival. Immune infiltration result demonstrated that high risk subgroups had lower levels of key antitumor infiltrating immune cells and antitumor immunity.Conclusion:The present study established IRG signature to act as a robust predictor of prognosis and a novel therapeutic target for treatment in osteosarcoma.