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Abstract

Objective: To identify a transcriptomic signature associated with EV-A71 infection using peripheral blood mononuclear cells (PBMC) transcriptome profiles from the public dataset GSE135964, which compares samples from subjects with Enterovirus A71 (EV-A71) infection against healthy controls based on bioinformatics and machine learning algorithms, for exploratory biomarker discovery. Methods: The publicly available dataset GSE135964, accessed through the Gene Expression Omnibus (GEO) database WAS analyzed. Differential expression analysis was conducted with a |log2FC| ≥ 0.5 and an adj.P.Val < 0.05 cutoff. The resulting gene set was then visualized through box plots, PCA, volcano plots, and heatmaps. Subsequently, we utilized the random forest algorithm, which quantified gene importance by integrating the mean decrease in both accuracy and the Gini index. This process culminated in the selection of the highest-ranking 10% of genes for subsequent candidacy. Functional characterization of the candidate genes involved Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, alongside the establishment of a protein-protein interaction (PPI) network leveraging the STRING database. Results: Our analysis revealed 438 differentially expressed genes (DEGs), with a marked imbalance towards upregulation (415 genes) compared to downregulation (23 genes). Random forest screening identified 44 candidate genes. PPI analysis identified 23 key genes, and LASSO regression finally pinpointed three feature genes: IFIT5, OASL, and PLSCR1. ROC analysis showed AUC values of 0.964, 0.921, and 0.894 for OASL, PLSCR1, and IFIT5, respectively, indicating a strong association with EV-A71 infection. Enrichment analysis implicated these feature genes in antiviral biological processes, suggesting a core mechanism involving antiviral defense (GO:0051607) to viral RNA binding (GO:0003727). Conclusions: The identified three-gene signature, enriched in antiviral response pathways, provides a theoretical basis for future research into host transcriptomic responses to EV-A71 infection and the RIG-I signaling axis. This transcriptomic signature analysis offers an exploratory resource for understanding EV-A71–associated HFMD.

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