A novel defined ferroptosis-related gene signature for predicting the prognosis of Osteosarcoma

Abstract

Abstract Background Osteosarcoma is a malignant tumor that is harmful to adolescents and has a poor prognosis. There is no effective treatment at now. This study aimed to construct a prognosis based on ferroptosis-related genes to provide guidance for the treatment of osteosarcoma. Methods In this study, we identified three ferroptosis-regulating genes that were distinctly expressed in osteosarcoma and normal tissues. Osteosarcoma patients from three datasets were classified using consensus clustering of these three ferroptosis-regulated genes, and gene differences among the three subtypes were analyzed in one step, and these differentially expressed genes were analyzed by univariate regression (DEGs), the ssGSEA method was used to assess the tumor microenvironment (TME), and the principal component analysis algorithm was used to quantitatively construct a predictive model. Finally, we use qRT-PCR to detect gene expression to validate the model. Results We identified three distinct ferroptosis genotypes, and the differences between the three subtypes were used to build a prognostic model, which we termed the "Ferroptosis-Score". The score can predict the prognosis and overall survival time of patients with osteosarcoma. The survival of patients with high scores is higher than that of patients with low scores (P < 0.05). At the same time, it is observed that immune cell infiltration in patients with high scores is significantly higher than that in patients with low scores. Conclusion We constructed a Ferroptosis-Score scoring system, the higher the score, the better the prognosis of the patient. And found that cluster C may activates ferroptosis through autophagy and JAK-STAT signaling pathway, so the patients of cluster C have better prognosis.