Ferroptosis-related lncRNAs guiding osteosarcoma prognosis and immune microenvironment

Abstract

Abstract Objective: To study the ferroptosis-related LncRNAs (FRLncs) that can guide the prognosis and immune microenvironment of osteosarcoma (OS), and provide a basis for clinical decision-making of OS. Methods: 86 OS transcriptome data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database, GSE19276 data set was downloaded from Gene Expression Omnibus (GEO) database, and ferroptosis-related genes (FRGs) list were obtained from FerrDb database. Differential FRGs related to OS were obtained by combined analysis of 86 OS transcriptome data and GSE19276 data set, and FRLncs related to OS were obtained by co-expression analysis. Univariate COX regression analysis and Lasso Cox regression analysis were used to construct the OS risk prognostic model of FRLncs. Further risk curve analysis, survival analysis, (Receiver operating characteristic curve, ROC) curve analysis and independent prognosis analysis were performed. Model validation of clinical groupings was performed to observe whether risk-prognostic models were applicable to patients in different clinical groups. Single sample Gene Set Enrichment Analysis (ssGSEA) explored differential immune cells and immune functions in risk prognostic models. Immune checkpoint differential analysis obtained immune checkpoint-related genes associated with OS prognosis. Results: Finally, 13 FRLncs that could guide OS prognosis and immune microenvironment were obtained, including 5 high-risk FRLncs (AP000757.1, AL035530.2, AC006160.1, PRR34-AS1 and LINC01719) and 8 low-risk FRLncs (AC090559.1, AC100847.1, MIS18A-AS1, ITCH-IT1, AL031722.1, AC027575.2, AC104561.1 and NBR2). 8 types of immune cells (B cells, macrophages, neutrophils, natural killer (NK) cells, plasmacytoid dendritic cells (pDCs), helper T cells, tumor infiltrating lymphocytes (TIL) and regulatory T cells (Treg)) and 4 immune functions (APC-co-stimulation, check-point, cytolytic-activity and T cell- co-inhibition) were down-regulated in the high-risk group. In addition, we also obtained 17 immune checkpoint-related genes associated with OS prognosis, of which LGALS9, BTLA and TNFSF15 had extremely high statistical significance(P＜0.001). Conclusion: The FRLncs that can guide OS prognosis and immune microenvironment and the immune checkpoint-related genes associated with OS prognosis found in this study provide a theoretical basis for OS survival research and clinical decision-making.