A five ferroptosis-related genes risk score for prognostic prediction of osteosarcoma

Abstract

Background: Osteosarcoma (OS) is the most common bone cancer in adolescents, and has a high propensity to metastasize. Ferroptosis is a unique modality of cell death, driving the metastasis of cancer cells. Identifying ferroptosis-related genes (FRGs) as prognostic factors will be critical to predict the outcomes of OS. This study aimed to explore the prognostic value of FRGs in OS and build a prognostic model to indirectly improve OS patients’ outcomes. Methods: OS data were downloaded from the TARGET database and 2 Gene Expression Omnibus datasets. Univariate Cox regression was conducted to assess FRGs. A risk score model basing on 5 FRGs was constructed via LASSO-Cox regression. Multivariate Cox regression analysis was used to determine the independent prognostic factors. The Nomogram model was built using independent prognostic factors. The relationship between the risk score and the immune cell infiltration was estimated by CIBERSORT, and the correlation between the risk score and immune checkpoints was also analyzed. Results: Based on the prognosis-related FRGs, we built a regression model: Risk score = (−0.01382853 × ACSL4) − (0.05371778 × HMOX1) − (0.02434655 × GPX4) − (0.16432810 × PRNP) − (0.15567120 × ATG7). OS patients with high risk score tended to suffer from poor prognosis, validated in 2 Gene Expression Omnibus datasets. The Nomogram model showed the combination of the risk score and the tumour-node-metastasis stage improved predictive effectiveness. The risk score was also related to immune cell infiltration and immune checkpoint expression. Conclusion: The risk score model based on 5 FRGs was a reliable prognostic predictive indicator for OS patients.