机器学习识别激素性股骨头坏死中线粒体自噬诊断标志物及免疫浸润分析

Machine learning identification of mitochondrial autophagy diagnostic biomarkers and immune infiltration analysis in steroid-induced osteonecrosis of the femoral head

背景:线粒体自噬和激素性股骨头坏死的发生、发展关系密切,但具体生物标志物及调控机制尚未明确。目的:通过机器学习算法识别激素性股骨头坏死中线粒体自噬的关键标志物及免疫浸润分析。方法:从GEO数据库下载股骨头坏死数据集GSE123568和GSE74089,分别作为训练集和验证集,在激素性股骨头坏死组和对照组之间选择差异表达基因,进行加权共表达分析。从MitoCarta 3.0数据库下载线粒体自噬相关基因,然后与差异基因和模块基因取交集。利用两种机器学习算法鉴定激素性股骨头坏死线粒体自噬关键基因,利用外部验证集进行验证。采用CIBERSORT和免疫浸润分析免疫细胞占比,单样本基因集富集分析线粒体自噬基因与免疫细胞的相关性分析。结果与结论:①差异分析共获得1163个差异基因,其中有663个上调基因和500个下调基因;加权共表达分析鉴定出4个相关模块,共1412个模块基因;②最终与线粒体自噬基因取交集初步筛选出39个交叉基因可能是疾病相关线粒体自噬基因;GO富集分析结果显示,生物过程主要涉及血红素代谢、线粒体运输、核苷酸双磷酸代谢和硫酯代谢过程,细胞组分主要涉及线粒体基质、线粒体外膜、细胞器外膜和线粒体内膜,分子功能主要涉及脂肪酸连接酶活性、铁-硫簇结合和辅酶A连接酶活性;KEGG富集分析结果共映射出6条通路,主要涉及脂肪酸降解、线粒体自噬、丁酸代谢、脂肪酸生物合成和辅因子生物合成;③经LASSO回归和随机森林算法分析最终得到4个核心基因(ALDH5A1、FBXL4、MCL1和STOM),4个核心基因和诊断列线图外部验证集的受试者工作特征曲线均大于0.9;④激素性股骨头坏死发生发展与活化的树突状细胞、骨髓来源的抑制性细胞、调节性T细胞和中心记忆CD8T细胞等免疫细胞有关;⑤结果显示,4个关键的线粒体自噬基因ALDH5A1、FBXL4、MCL1和STOM通过破骨细胞分化和免疫机制在激素性股骨头坏死进展中发挥关键作用,均具有较好的疾病预测效果,可能作为激素性股骨头坏死诊断和治疗的生物标志物。

BACKGROUND:Mitochondrial autophagy is closely related to the occurrence and development of steroid-induced osteonecrosis of the femoral head(SONFH),but specific biomarkers and regulatory mechanisms remain unclear.OBJECTIVE:To identify the key biomarkers of mitochondrial autophagy in steroid-induced osteonecrosis of the femoral head using machine learning algorithms and to conduct an immune infiltration analysis.METHODS:The SONFH datasets GSE123568 and GSE74089 were downloaded from the GEO database,serving as the training and validation sets,respectively.Differentially expressed genes between SONFH and control groups were selected,and weighted gene co-expression network analysis was performed.Mitochondrial autophagy-related genes were obtained from MitoCarta3.0 and intersected with differentially expressed genes and module genes.Two machine learning algorithms were utilized to identify key genes of SONFH mitochondrial autophagy,and validated using an external validation set.CIBERSORT and immune infiltration analysis were employed to assess the proportion of immune cells,and ssGSEA was used to analyze the correlation between mitochondrial autophagy genes and immune cells.RESULTS AND CONCLUSION:Differential analysis identified a total of 1163 differentially expressed genes,including 663 upregulated genes and 500 downregulated genes.Weighted gene co-expression network analysis identified 4 key modules,comprising 1412 module genes.Intersection with mitochondrial autophagy genes yielded 39 intersecting genes as disease-related mitochondrial autophagy genes.Gene ontology enrichment analysis showed that the biological processes were mainly related to heme metabolism,mitochondrial transport,nucleotide bisphosphate metabolism and thioester metabolism,and the cellular components were mainly related to mitochondrial matrix,mitochondrial outer membrane,organelle outer membrane and mitochondrial inner membrane,and the molecular functions were mainly related to fatty acid ligase activity,iron-sulfur cluster binding,and cofactor A ligase activity.Kyoto Encyclopedia of Genes and Genomes enrichment analysis mapped out a total of six pathways,which were mainly related to fatty acid degradation,mitochondrial autophagy,butyric acid metabolism,fatty acid biosynthesis and cofactor biosynthesis.Through LASSO regression and RFE-SVM algorithm analysis,four intersecting genes(ALDH5A1,FBXL4,MCL1,and STOM)were identified.The receiver operating characteristic curves of the four core genes and the diagnostic column chart validation set were all greater than 0.9.The occurrence and development of SONFH were related to immune cells such as dendritic cells,bone marrow-derived suppressor cells,regulatory T cells,and central memory CD8 T cells.To conclude,the four key mitochondrial autophagy genes ALDH5A1,FBXL4,MCL1,and STOM play a crucial role in the progression of SONFH through osteoclast differentiation and immune mechanisms.Additionally,all four genes have good disease prediction efficacy and can serve as biomarkers for the diagnosis and treatment of SONFH.