Identification of Iron Metabolism-Related Gene Signatures for Predicting the Prognosis of Patients With Sarcomas.

Jianyi Li,Chuan Hu,Huiqiang Hu,Yukun Du,Yongming Xi,Cheng Shao,Zheng Zhao,Yingyi Sheng,Jianwei Guo,Xiaojie Tang,Tongshuai Xu

doi:10.3389/fonc.2020.599816

Abstract

Iron is one of the essential trace elements in the human body. An increasing amount of evidence indicates that the imbalance of iron metabolism is related to the occurrence and development of cancer. Here, we obtained the gene expression and clinical data of sarcoma patients from TCGA and the GEO database. The prognostic value of iron metabolism-related genes (IMRGs) in patients with sarcoma and the relationship between these genes and the immune microenvironment were studied by comprehensive bioinformatics analyses. Two signatures based on IMRGs were generated for the overall survival (OS) and disease-free survival (DFS) of sarcoma patients. At 3, 5, and 7 years, the areas under the curve (AUCs) of the OS signature were 0.708, 0.713, and 0.688, respectively. The AUCs of the DFS signature at 3, 5, and 7 years were 0.717, 0.689, and 0.702, respectively. Kaplan–Meier survival analysis indicated that the prognosis of high-risk patients was worse than that of low-risk patients. In addition, immunological analysis showed that there were different patterns of immune cell infiltration among patients in different clusters. Finally, we constructed two nomograms that can be used to predict the OS and DFS of sarcoma patients. The C-index was 0.766 (95% CI: 0.697–0.835) and 0.763 (95% CI: 0.706–0.820) for the OS and DFS nomograms, respectively. Both the ROC curves and the calibration plots showed that the two nomograms have good predictive performance. In summary, we constructed two IMRG-based prognostic models that can effectively predict the OS and DFS of sarcoma patients.

Highlights

Sarcomas are extremely rare malignancies of mesenchymal origin with high heterogeneity, and they account for approximately 1% of adult malignancies [1]
To explore whether there was a correlation between the clustering result and clinical outcome, we compared the disease-free survival (DFS) among the three clusters of patients via the Kaplan–Meier analysis
The results showed that patients in the cluster 3 subgroup had shorter DFS (p = 0.044) than the other two clusters (Figure 1F)

Summary

Introduction

Sarcomas are extremely rare malignancies of mesenchymal origin with high heterogeneity, and they account for approximately 1% of adult malignancies [1]. It is estimated that the total incidence of sarcomas in EU countries is 5.6 per 100,000 [2]. More than 70 histological subtypes of sarcoma have been identified, and they can occur in different anatomical locations. Sarcomas can be IMRGs in Sarcoma Prognosis divided into two categories: soft tissue sarcoma (STS), which accounts for 80% of sarcomas, and osteosarcoma [3]. Due to the characteristics of aggressive growth and a high risk of metastasis, the prognosis of sarcoma patients is unsatisfactory [4]. It is vital to develop new biomarkers for accurately predicting the prognosis of sarcoma patients

Methods

Results

Conclusion