Genetic Analysis of Potential Biomarkers and Therapeutic Targets in Ferroptosis from Steroid-Induced Osteonecrosis of the Femoral Head Based on Machine Learning.

Abstract

Objective: To locate the candidate therapeutic target genes involved in ferroptosis in steroid-induced osteonecrosis of the femoral head (SONFH).

Study design: Bioinformatics analysis study. Place and Duration of the Study: Department of Orthopaedic Surgery, Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine, Guangdong, China, from March to July 2023.

Methodology: After processing the gene expression omnibus (GEO) data with the R programming language, differentially expressed ferroptosis-related genes in SONFH were identified. To pinpoint the genes most strongly linked to SONFH in association with ferroptosis, least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) were employed. Subsequently, the screened essential genes were analysed to investigate immune cell infiltration, and competing endogenous RNA (ceRNA) networks involving these marker genes were constructed.

Results: The machine learning algorithms identified three genes i.e., SOCS1 (suppressor of cytokine signalling1), MYCN (N-myc proto-oncogene protein), and KLF2 (Kruppel-like factor 2) as diagnostic feature biomarkers associated with ferroptosis. Additionally, CIBERSORT analysis revealed that alterations in the immune microenvironment, such as Macrophages M1, Monocytes, and T cells CD4 naive, could be linked to SOCS1, MYCN, and KLF2. Moreover, the competing endogenous RNA (ceRNA) network exposed a complex regulatory relationship based on marker genes.

Conclusion: SOCS1, MYCN, and KLF2 are potential biomarkers associated with ferroptosis in SONFH, pending confirmation in future studies.

Key words: Steroid-induced osteonecrosis of the femoral head, Ferroptosis, Machine learning, Genetic analysis.