Exploring Infantile Epileptic Spasm Syndrome: A Proteomic Analysis of Plasma Using the Data-Independent Acquisition Approach

Abstract

This study aimed to identify characteristic proteins in infantile epileptic spasm syndrome (IESS) patients' plasma, offering insights into potential early diagnostic biomarkers and its underlying causes. Plasma samples were gathered from 60 patients with IESS and 40 healthy controls. Data-independent acquisition proteomic analysis was utilized to identify differentially expressed proteins (DEPs). These DEPs underwent functional annotation through Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Gene set enrichment analysis (GSEA) was employed for both GO (GSEA-GO) and KEGG (GSEA-KEGG) analyses to examine the gene expression profiles. Receiver operating characteristic (ROC) curves assessed biomarkers' discriminatory capacity. A total of 124 DEPs were identified in IESS patients' plasma, mainly linked to pathways, encompassing chemokines, cytokines, and oxidative detoxification. GSEA-GO and GSEA-KEGG analyses indicated significant enrichment of genes associated with cell migration, focal adhesion, and phagosome pathways. ROC curve analysis demonstrated that the combination of PRSS1 and ACTB, PRSS3, ACTB, and PRSS1 alone exhibited AUC values exceeding 0.7. This study elucidated the significant contribution of cytokines, chemokines, oxidative detoxification, and phagosomes to the IESS pathogenesis. The combination of PRSS1 and ACTB holds promise as biomarkers for the early diagnosis of IESS.

Keywords: biomarker; data-independent acquisition; infantile epileptic spasm syndrome; plasma.

Figure 1

DEPs in plasma of patients with IESS and healthy controls. (A) Volcano plot illustrating DEPs. (B) Heat map illustrating the cluster analysis of DEPs. Red indicates significant upregulation, while blue indicates significant downregulation.

Figure 2

GO and KEGG pathway analyses of the most significantly enriched DEPs in IESS patients compared with healthy controls. (A) GO enrichment analysis included BP, CC, and MF categories. (B) KEGG pathway analysis. Each pathway was ranked by GeneRatio.

Figure 3

GSEA-GO and GSEA-KEGG analysis. (A) Most significantly enriched GSEA-GO pathways. (B) Most significantly enriched GSEA-KEEG pathways. Each pathway was ranked by normalized enrichment scores (NES). (C,D) GSEA-based KEGG-enrichment plots illustrating representative gene sets, including phagosome and the PI3K-Akt signaling pathway. The red line is indicative of enrichment profile.

Figure 4

Schematic representation of the PPI network of the DEPs. The line width of the interactions corresponds to the combined interaction score of each interaction, ranging from 0.4 to 1. A wider line indicates a higher score, while a narrower line suggests a lower score. A score of 0.4 represents a medium score, 0.7 indicates a high level, and 0.9 indicates a very high level of interaction confidence. Additionally, different colors were used to denote individual proteins based on their fold change (|log2FC|). The confidence score of interaction is 0.4. In the network, ACTB, PF4V1, THBS1, HP, PF4, and PPBP serve as hub proteins.

Figure 5

Validation and assessment of potential biomarkers. (A) Receiver operating characteristic (ROC) curve analysis of IESS samples versus healthy control samples to validate candidate biomarkers. The models included eight candidate biomarkers and one integrating ACTB + PRSS1. (B) Quantification of plasma levels of PRSS3, ACTB, PRSS1, PPBP, HP, PF4V1, THBS1, and RPL11 in healthy controls and IESS patients. P < 0.05 indicates a significant difference in quantification between the two groups.