METTL1 驱动的核苷酸代谢重编程肝细胞癌免疫微环境：一种用于预后生物标志物发现的多组学方法

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide, partly due to an incomplete understanding of the metabolic and immune dysregulation driving its progression. Here, we uncover a novel role of METTL1 in driving nucleotide metabolism reprogramming, which significantly modulates the tumor immune microenvironment.

Utilizing an integrated multi-omics approach, we analyzed nucleotide metabolism-related genes derived from TCGA, GEO, and ICGC datasets. Non-negative matrix factorization (NMF) clustering stratified HCC patients into distinct subgroups with varied clinical features. Weighted Gene Co-expression Network Analysis (WGCNA) identified hub genes that were subsequently used to construct robust prognostic models via multiple machine learning algorithms. These computational findings were validated through in vitro experiments, immune infiltration assessments, and single-cell RNA sequencing analysis.

Our analyses demonstrate that METTL1 is markedly upregulated in HCC, driving a reprogramming of nucleotide metabolism that modulates the expression of key immune checkpoints, including PD-L1 and CTLA-4. This regulation is associated with an immunosuppressive tumor microenvironment, reduced infiltration of activated T cells, and poorer clinical outcomes. Moreover, the prognostic model integrating METTL1 expression and immune checkpoint profiles shows strong predictive performance across independent cohorts, highlighting its potential clinical utility.

This study highlights the innovative role of METTL1-driven nucleotide metabolism reprogramming in reshaping the immune microenvironment of HCC. The findings provide novel insights into HCC pathogenesis and pave the way for developing personalized therapeutic strategies based on targeting METTL1 and its associated metabolic pathways.