Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis

Xiangwen Shi; Linmeng Tang; Haonan Ni; Mingjun Li; Yipeng Wu; Yongqing Xu

doi:10.2147/JIR.S406562

Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis

J Inflamm Res. 2023 Apr 26:16:1805-1823. doi: 10.2147/JIR.S406562. eCollection 2023.

Authors

Xiangwen Shi^#^{1

2}, Linmeng Tang^#³, Haonan Ni^#¹, Mingjun Li¹, Yipeng Wu^{1

2}, Yongqing Xu²

Affiliations

¹ Kunming Medical University, Kunming, People's Republic of China.
² Laboratory of Yunnan Traumatology and Orthopedics Clinical Medical Center, Yunnan Orthopedics and Sports Rehabilitation Clinical Medical Research Center, Department of Orthopedic Surgery, 920th Hospital of Joint Logistics Support Force of PLA, Kunming, People's Republic of China.
³ Bone and Joint Imaging Center, Department of Medical Imaging, the First Affiliated Hospital of Hebei North University, Zhangjiakou, People's Republic of China.

^# Contributed equally.

Abstract

Objective: Staphylococcus aureus (SA)-induced osteomyelitis (OM) is one of the most common refractory diseases in orthopedics. Early diagnosis is beneficial to improve the prognosis of patients. Ferroptosis plays a key role in inflammation and immune response, while the mechanism of ferroptosis-related genes (FRGs) in SA-induced OM is still unclear. The purpose of this study was to determine the role of ferroptosis-related genes in the diagnosis, molecular classification and immune infiltration of SA-induced OM by bioinformatics.

Methods: Datasets related to SA-induced OM and ferroptosis were collected from the Gene Expression Omnibus (GEO) and ferroptosis databases, respectively. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were combined to screen out differentially expressed-FRGs (DE-FRGs) with diagnostic characteristics, and gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to explore specific biological functions and pathways. Based on these key DE-FRGs, a diagnostic model was established, and molecular subtypes were divided to explore the changes in the immune microenvironment between molecular subtypes.

Results: A total of 41 DE-FRGs were identified. After screening and intersecting with LASSO and SVM-RFE algorithms, 8 key DE-FRGs with diagnostic characteristics were obtained, which may regulate the pathogenesis of OM through the immune response and amino acid metabolism. The ROC curve indicated that the 8 DE-FRGs had excellent diagnostic ability for SA-induced OM (AUC=0.993). Two different molecular subtypes (subtype 1 and subtype 2) were identified by unsupervised cluster analysis. The CIBERSORT analysis revealed that the subtype 1 OM had higher immune cell infiltration rates, mainly in T cells CD4 memory resting, macrophages M0, macrophages M2, dendritic cells resting, and dendritic cells activated.

Conclusion: We developed a diagnostic model related to ferroptosis and molecular subtypes significantly related to immune infiltration, which may provide a novel insight for exploring the pathogenesis and immunotherapy of SA-induced OM.

Keywords: biomarker; ferroptosis; immune infiltration; molecular subtype; osteomyelitis.

Grants and funding

This study was funded by National Natural Science Foundation of China (Grant No.81772367, 82072392); the Yunnan Traumatology and Orthopedics Clinical Medical Center (Grant No. ZX20191001); the Grants from Yunnan Orthopedics and Sports Rehabilitation Clinical Medicine Research Center (Grant No. 202102AA310068).