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Updated: Sep 11, 2025
Three-Dimensional Bone Extracellular Matrix Model for Osteosarcoma
Published on: April 12, 2019
Constructing a ferroptosis-related prognostic model for osteosarcoma based on scRNA-seq dataset and WGCNA analysis
Hong-Chi Yi1, Qing-Zhong Wei2, Ji-Ming Gan2
1Department of Orthopaedics, First Clinical Medical College, Yunnan University of Chinese Medicine Kunming 650021, Yunnan, China.
None:
Osteosarcoma (OS) is a primary malignant bone tumor. Ferroptosis is closely related to the progression of osteosarcoma. The aim of this study is to explore the mechanism of ferroptosis-related genes in the progression of osteosarcoma.
Methods:
Utilizing the scRNA-seq dataset of osteosarcoma, differentially expressed genes (scRNA-DEGs) were identified between the osteosarcoma group and the control group, and ferroptosis-related genes in the TARGET-OS dataset were identified through WGCNA. By intersecting these two sets of ferroptosis-related genes, key candidate genes related to ferroptosis were obtained. The prognostic genes were selected from key candidate genes through univariate Cox and LASSO regression analysis. A prognostic model based on these genes was then constructed to investigate the relationship between ferroptosis and the prognosis of osteosarcoma patients. The correlation between the prognostic genes and immune cells, as well as immune checkpoint genes, was investigated through immune infiltration analysis. The drugs binding to prognostic genes were predicted.
Results:
We identified 48 ferroptosis-related genes in the scRNA-seq dataset, and 3859 ferroptosis-related genes were identified in the TARGET-OS dataset. After intersecting the two sets, 12 key ferroptosis-related genes were obtained, among which four genes (IFNG, HMOX1, CDKN1A, LGMN) were related to the prognosis of osteosarcoma patients. The prognostic model could accurately predict patient survival with good stability. Immune infiltration analysis revealed that the prognostic genes were significantly correlated with multiple immune cell types, and the expression of some immune checkpoint genes showed significant differences between control and osteosarcoma groups. Furthermore, we found that the prognostic genes were highly expressed in osteoblasts and macrophages.
Conclusions:
Four genes (IFNG, HMOX1, CDKN1A, LGMN) were closely related to the prognosis of osteosarcoma patients. These genes may serve as potential therapeutic targets for the treatment of osteosarcoma.
