Identification of key mitophagy-related genes in osteomyelitis: Insights from differential gene expression and machine learning models

Osteomyelitis, a severe skeletal infection, involves complex pathogenic mechanisms. Mitophagy (mitophagy) is crucial for cellular homeostasis and has been linked to various diseases, including osteomyelitis. This study explores the genetic basis of mitophagy in osteomyelitis, identifying differentially expressed genes related to mitophagy and their potential as biomarkers and therapeutic targets. Using the GSE30119 dataset from GEO, mRNA expression profiles from 49 osteomyelitis patients and 44 healthy individuals were analyzed. Differential gene expression analysis identified 2876 differentially expressed genes with a threshold of |log FC| > 0.5 and P < .05. Mitophagy-related genes were sourced from GeneCards, with 25 overlapping genes identified. Functional enrichment analyses, including gene ontology and Kyoto encyclopedia of genes and genomes, were conducted. Four machine learning models (generalized linear model, random forest, support vector machine, and extreme gradient boosting) identified 9 key genes for a diagnostic model. Gene set enrichment analysis and immune cell infiltration analysis using CIBERSORT were performed. Furthermore, RT-qPCR was performed to experimentally validate the expression patterns of the identified key genes in an inflammatory cellular model. Twenty-five intersecting genes were identified, with gene ontology and Kyoto encyclopedia of genes and genomes analyses highlighting their roles in autophagy and mitochondrial functions. The random forest model identified 9 key genes: BMP2KL, ALPK1, PGAM5, PINK1, TP53, GPC1, ITPKC, PEX3, and P2RX5, validated with an area under the receiver operating characteristic curve of 0.905. Gene set enrichment analysis indicated their involvement in metabolic and signaling pathways. Immune infiltration analysis revealed significant differences between patients and healthy individuals. RT-qPCR validation confirmed significant expression changes of the 9 key genes under inflammatory conditions, consistent with bioinformatics predictions. This study identifies key mitophagy genes and targets in osteomyelitis, providing a basis for future research and therapies.