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=Volume 13 - 2022

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=long non-coding RNA (lncRNA)acts as an epigenetic regulator to promote the process of ferroptosis and involve in iron metabolism. This study aimed to identify iron metabolism-related lncRNA signature to predicting osteosarcoma (OS) survival and immune landscape.
Methods: RNA-sequence data and clinical information were from the Target. univariate Cox regression and LASSO−Cox analysis were used to develop iron metabolism-related lncRNA signature. Consensus clustering analysis was applied to identify subtypes based prognosis-related lncRNAs. CIBERSORT was used to analyze the difference of immune infiltration and immune microenvironment in two clusters.
Results: We identified 302 iron metabolism -related lncRNAs based 515 iron metabolism-related genes. Results of consensus clustering showed the differences of immune infiltration and immune microenvironment in two clusters. Through univariate cox regression and Lasso cox regression analysis, we constructed an iron metabolism-related lncRNA signature including seven iron metabolism-related lncRNAs. The signature was verified that had good performance in predicting overall survival, immune-related functions, and immunotherapy response of OS patients between high- and low-risk groups.
Conclusion: We identified an iron metabolism-related lncRNA signature that had good performance in predicting the survival outcomes and showing the immune landscape for OS patients. Furthermore, our study will provide the valuable information for further developing the immunotherapy.