Abstract：
OBJECTIVE: To locate the candidate therapeutic target genes involved in ferroptosis in steroid-induced osteonecrosis of the femoral head (SONFH).
METHODS: 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.
METHODS: 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.
CONCLUSIONS: SOCS1, MYCN, and KLF2 are potential biomarkers associated with ferroptosis in SONFH, pending confirmation in future studies.
BACKGROUND: Steroid-induced osteonecrosis of the femoral head, Ferroptosis, Machine learning, Genetic analysis.
METHODS: 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.
METHODS: 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.
CONCLUSIONS: SOCS1, MYCN, and KLF2 are potential biomarkers associated with ferroptosis in SONFH, pending confirmation in future studies.
BACKGROUND: Steroid-induced osteonecrosis of the femoral head, Ferroptosis, Machine learning, Genetic analysis.
摘要：
目的：寻找激素性股骨头坏死（SONFH）中与铁凋亡有关的候选治疗靶基因。
方法：生物信息学分析研究。研究的地点和持续时间：骨科，珠海市中西医结合医院,广东,中国,2023年3月至7月。
方法：用R编程语言处理基因表达综合(GEO)数据后，鉴定了SONFH中差异表达的铁凋亡相关基因。为了确定与铁凋亡相关的SONFH最密切相关的基因，采用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，对筛选的必需基因进行分析，以研究免疫细胞浸润，并构建了涉及这些标记基因的竞争性内源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）作为与铁性凋亡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T细胞CD4幼稚，可以链接到SOCS1，MYCN，KLF2此外，竞争性内源性RNA（ceRNA）网络暴露了基于标记基因的复杂调控关系。
结论：SOCS1，MYCN，和KLF2是SONFH中与铁凋亡相关的潜在生物标志物，有待在未来的研究中确认。
背景：激素性股骨头坏死，Ferroptosis,机器学习,遗传分析。
方法：生物信息学分析研究。研究的地点和持续时间：骨科，珠海市中西医结合医院,广东,中国,2023年3月至7月。
方法：用R编程语言处理基因表达综合(GEO)数据后，鉴定了SONFH中差异表达的铁凋亡相关基因。为了确定与铁凋亡相关的SONFH最密切相关的基因，采用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，对筛选的必需基因进行分析，以研究免疫细胞浸润，并构建了涉及这些标记基因的竞争性内源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）作为与铁性凋亡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T细胞CD4幼稚，可以链接到SOCS1，MYCN，KLF2此外，竞争性内源性RNA（ceRNA）网络暴露了基于标记基因的复杂调控关系。
结论：SOCS1，MYCN，和KLF2是SONFH中与铁凋亡相关的潜在生物标志物，有待在未来的研究中确认。
背景：激素性股骨头坏死，Ferroptosis,机器学习,遗传分析。
