CELL INTELLIGENCE · 肿瘤细胞治疗研究

Hepatoblastoma (HB) is a rare and aggressive pediatric liver tumor with complex etiology. Although necroptosis has been implicated in various cancers, its role in HB remains unclear. This study aimed to investigate the involvement of necroptosis-related genes and immune landscape in HB using integrative bioinformatics and machine learning approaches. Gene expression data from two independent HB datasets were integrated and analyzed. Differentially expressed genes (DEGs) and necroptosis-related DEGs (NR-DEGs) were identified, followed by functional enrichment analysis. Machine learning algorithms were employed to identify hub NR-DEGs. The immune landscape and hub NR-DEGs were investigated using single-sample gene set enrichment analysis (ssGSEA). A total of 1330 upregulated and 1061 downregulated common DEGs were identified. Five upregulated and fourteen downregulated NR-DEGs were identified, which were mainly enriched in immune-related pathways. Four hub NR-DEGs (SLC25A6, HSP90AB1, USP21, and CAMK2B) were identified as potential diagnostic biomarkers for HB. Immune infiltration analysis revealed elevated proportions of CD56bright natural killer cells and gamma delta T cells in HB patients, which significantly correlated with hub NR-DEG expression. ssGSEA indicated that hub NR-DEGs regulate various cellular processes, including cell cycle progression, RNA metabolism, protein synthesis, and viral infection response in HB. This study reveals the involvement of necroptosis-related genes and altered immune infiltration in HB pathogenesis, providing novel insights and potential therapeutic targets.