Screening of ferroptosis-inflammation associated peripheral blood biomarkers in spinal cord injury based on bioinformatics analysis and machine learning

English Keywords:Spinal cord injury  Bioinformatics analysis  Machine learning  Ferroptosis  Inflammation  Biomarkers

English Abstract:

  【Abstract】 Objectives: To screen peripheral blood biomarkers associated with the synergistic effects of ferroptosis and inflammation, and to provide novel clues and potential targets for subsequent in-depth elucidation of their underlying molecular crosstalk. Methods: The training dataset GSE151371[38 cases of spinal cord injury(SCI) vs 10 healthy controls] and validation dataset GSE45006(4 cases of SCI vs 4 healthy controls) were downloaded from the Gene Expression Omnibus(GEO) database. The ferroptosis-related gene set(471 genes) from the FerrDB database and the inflammation-related gene set(2,389 genes) from the GeneCards database were integrated, and differentially expressed genes(DEGs) were screened using the DESeq2 software. Subsequently, Gene ontology(GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis and gene set enrichment analysis(GSEA) were performed on ferroptosis- and inflammation-related DEGs. Then, a protein-protein interaction(PPI) network was constructed to screen hub genes, which were further refined to identify core biomarkers using three machine learning algorithms. Finally, the diagnostic efficacy was evaluated by area under the curve(AUC) of receiver operating characteristic(ROC). Results: A total of 59 ferroptosis-inflammation-related DEGs were identified, including 40 upregulated genes and 19 downregulated genes, which were significantly enriched in the ferroptosis pathway and the toll-like receptor(TLR) signaling pathway. A total of 8 hub genes(HIF1A, JUN, TP53, STAT3, IL1B, PTEN, MAPK3, IFNG) were preliminarily identified by PPI network analysis. Further screening and validation by the three machine learning algorithms of least absolute shrinkage and selection operator(LASSO) regression, support vector machine(SVM), and random forest(RF), confirmed tumor protein p53(TP53), interferon-gamma(IFNG), and interleukin 1 beta(IL1B) as the core biomarkers. AUC analysis demonstrated their significant diagnostic efficacy(TP53, AUC=0.945; IFNG, AUC=0.883; IL1B, AUC=0.821). Conclusions: In this study, we screened three key genes, TP53, IFNG and IL1B, which are closely associated with the crosstalk between ferroptosis and inflammation in the peripheral blood of patients with SCI. This finding provides key molecular targets and new research directions for the in-depth analysis of the pathological mechanisms of SCI.