AUTHOR=Hong-bin Shao , Wan-jun Yang , Chen-hui Dong , Xiao-jie Yang , Shen-song Li , Peng Zhou
TITLE=Identification of an Iron Metabolism-Related lncRNA Signature for Predicting Osteosarcoma Survival and Immune Landscape
JOURNAL=Frontiers in Genetics
VOLUME=13
YEAR=2022
URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.816460
DOI=10.3389/fgene.2022.816460
ISSN=1664-8021
ABSTRACT=Background: Long noncoding RNAs (lncRNAs) act as epigenetic regulators in the process of ferroptosis and iron metabolism. This study aimed to identify an iron metabolism-related lncRNA signature to predict osteosarcoma (OS) survival and the immune landscape.
Methods: RNA-sequencing data and clinical information were obtained from the TARGET dataset. Univariate Cox regression and LASSO Cox analysis were used to develop an iron metabolism-related lncRNA signature. Consensus clustering analysis was applied to identify subtype-based prognosis-related lncRNAs. CIBERSORT was used to analyze the difference in immune infiltration and the immune microenvironment in the two clusters.
Results: We identified 302 iron metabolism-related lncRNAs based on 515 iron metabolism-related genes. The results of consensus clustering showed the differences in immune infiltration and the immune microenvironment in the two clusters. Through univariate Cox regression and LASSO Cox regression analysis, we constructed an iron metabolism-related lncRNA signature that included seven iron metabolism-related lncRNAs. The signature was verified to have good performance in predicting the overall survival, immune-related functions, and immunotherapy response of OS patients between the high- and low-risk groups.
Conclusion: We identified an iron metabolism-related lncRNA signature that had good performance in predicting survival outcomes and showing the immune landscape for OS patients. Furthermore, our study will provide valuable information to further develop immunotherapies of OS.