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

Jun Zhao, Junjie Guan, Xingshi Zhang, Jizhao Jiang, Kun Dou
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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.

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基于机器学习的类固醇诱导的股骨头骨坏死潜在生物标志物和治疗靶点的遗传分析
研究目的研究设计:研究设计:生物信息学分析研究。研究地点和时间研究地点和时间:中国广东省珠海市中西医结合医院骨科,2023年3月至7月:用R语言处理基因表达总库(GEO)数据后,确定SONFH中与铁突变相关的差异表达基因。为了找出与 SONFH 铁病关联最密切的基因,采用了最小绝对收缩和选择算子(LASSO)回归和支持向量机-递归特征消除(SVM-RFE)。随后,对筛选出的重要基因进行了分析,以研究免疫细胞浸润,并构建了涉及这些标记基因的竞争内源性 RNA(ceRNA)网络:结果:机器学习算法确定了三个基因,即 SOCS1(细胞因子信号抑制因子 1)、MYCN(N-myc 原癌基因蛋白)和 KLF2(Kruppel 样因子 2),作为与铁蛋白沉着症相关的诊断特征生物标志物。此外,CIBERSORT分析显示,免疫微环境的改变,如巨噬细胞M1、单核细胞和CD4幼稚T细胞,可能与SOCS1、MYCN和KLF2有关。此外,竞争性内源性 RNA(ceRNA)网络揭示了基于标记基因的复杂调控关系:结论:SOCS1、MYCN 和 KLF2 是与 SONFH 中铁细胞凋亡相关的潜在生物标志物,有待未来研究证实:类固醇诱导的股骨头坏死 铁沉积 机器学习 遗传分析
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