Ferroptosis‐related gene signature associates with immunity and predicts prognosis accurately in patients with osteosarcoma

• Published in: Cancer science Vol. 112; no. 11; pp. 4785 - 4798
• Main Authors: Lei, Ting, Qian, Hu, Lei, Pengfei, Hu, Yihe
• Format: Journal Article
• Language: English
• Published: Tokyo John Wiley & Sons, Inc 01.11.2021; John Wiley and Sons Inc
• Subjects: Algorithms; Apoptosis; Bone cancer; Bone tumors; Cancer therapies; Chemotherapy; Dendritic cells; Ferroptosis; Gene expression; Gene set enrichment analysis; Genetic engineering; Genomes; Immune status; immunity; Lung cancer; Lymphocytes; Medical prognosis; Metabolism; Metastasis; Microenvironments; Molecular modelling; Ontology; Original; Osteosarcoma; Patients; Prognosis; Regression analysis; Software; Survival; Survival analysis; tumor microenvironment; Variance analysis
• DOI: 10.1111/cas.15131
• ISSN: 1347-9032; 1349-7006

Osteosarcoma has been the most common malignant bone tumor in children and adolescents, while the 5‐y survival of osteosarcoma patients gained no significant improvement over the past decades. This study aimed to explore the role of ferroptosis‐related genes (FRGs) in the development and prognosis of osteosarcoma. The datasets of osteosarcoma patients including RNA sequencing data and clinical information were acquired from the TRGET and Gene Expression Omnibus (GEO) databases. The identification of molecular subgroups with different FRG expression patterns was achieved through nonnegative matrix factorization (NMF) clustering. The prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis. The ESTIMATE algorithm was applied for determining the stromal score, immune score, ESTIMA score, and tumor purity of osteosarcoma patients. Functional analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were conducted to explore the underlying mechanisms in the development and prognosis of osteosarcoma. Two molecular subgroups with different FRGs expression patterns were identified. The molecular subgroups with higher immune score and more active immune status showed better prognostic survival. On the basis of FRGs, a prognostic model and a nomogram integrating clinical characteristics were constructed and their prediction efficiency for osteosarcoma prognosis were well validated. Gene functional enrichment analysis showed that these differentially expressed FRGs were mainly enriched in immunity‐related signaling pathways, indicating that FRGs may affect the development and prognosis of osteosarcoma by regulating the immune microenvironment. The expression profiles of FRGs were closely related to the immunity status and prognostic survival of osteosarcoma patients. The interaction between ferroptosis and immunity in the development of osteosarcoma could provide a new insight into the exploration of molecular mechanisms and targeted therapies of osteosarcoma patients. Ferroptosis‐related gene signature associates with immunity and predicts prognosis of osteosarcoma.