Sarcomas are seen as mixed-up nature with genetic and transcriptional heterogeneity and poor prognosis. Although the genes involved in ferroptosis are still unclear, iron loss is considered to be the core of glioblastoma, tumor progression, and tumor microenvironment. Here, we developed and tested the prognosis of SARC, which is a genetic marker associated with iron residues. The ferroptosis-related gene expression, one-way Cox analysis, and least-selection absolute regression algorithm (LASSO) are used to track prognostic-related genes and create risk assessment models. Finally, immune system infiltration and immune control point analysis are used to study the characteristics of the tumor microenvironment related to risk assessment. Moreover, LncRNA-miRNA-mRNA network was contributed in our studies. We determined the biomarker characteristics associated with iron degradation in gene 32 and developed a risk assessment model. ROC analysis showed that its model was accurately predicted, with 1, 2, 3, 4, and 5 years of overall survival in TCGA cohort of SARC patients. A comparative analysis of settings found that overall survival (OS) was lower in the high-risk than that in the low-risk group. The nomogram survival prediction model also helped to predict the OS of SARC patients. The nomogram survival prediction model has strong predictive power for the overall survival of SARC patients in TCGA dataset. GSEA analysis shows that high-risk groups are rich in inflammation, cancer-related symptoms, and pathological processes. High risk is related to immune cell infiltration and immune checkpoint. Our prediction model is based on SARC ferritin-related genes, which may support SARC prediction and provide potential attack points.
Keywords: bioinformatics; ferroptosis; prognosis; sarcoma; tumor microenvironment.
Copyright © 2022 Guan, Liu, Luo, Wu, Lu, Guan and Tao.