Single-cell transcriptome analysis identifies neutrophil-related prognostic signatures in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with neutrophils playing complex dual roles in tumor progression and immune regulation. The heterogeneous nature of the tumor immune microenvironment necessitates precise molecular characterization for improved prognostic stratification and therapeutic targeting. This study aimed to develop and validate a neutrophil-related prognostic signature for HCC by integrating single-cell and bulk transcriptomic data, in order to improve prognostic stratification and identify potential therapeutic targets. Methods: We performed comprehensive single-cell RNA sequencing (scRNA-seq) analysis of 52,841 high-quality cells from HCC patients and controls using GSE282701 dataset. Neutrophil-specific differentially expressed genes were identified through integrated bioinformatics approaches. A prognostic signature was constructed using least absolute shrinkage and selection operator (LASSO) regression and validated across The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) (n=424) and GSE14520 (n=221) cohorts. Immune microenvironment characterization was performed using CIBERSORT deconvolution, while drug sensitivity predictions utilized the Genomics of Drug Sensitivity in Cancer (GDSC) database. Results: Single-cell analysis identified 304 neutrophil-specific genes significantly associated with HCC pathogenesis. Cox regression and LASSO analysis selected 14 genes for the final prognostic model, effectively stratifying patients into high-risk and low-risk groups with significantly different overall survival across all cohorts (P<0.001). The model demonstrated robust predictive performance with area under the curve (AUC) values exceeding 0.70 in training cohorts and 0.60 in external validation. High-risk patients exhibited distinct immune infiltration patterns characterized by increased immunosuppressive cell populations and reduced anti-tumor immunity. Pathway enrichment analysis revealed significant activation of PI3K/AKT/mTOR signaling, DNA repair mechanisms, and inflammatory pathways in high-risk patients. Drug sensitivity analysis identified six therapeutic compounds with differential responses between risk groups, providing preliminary evidence for potential personalized treatment selection. Conclusions: This neutrophil-related prognostic signature provides novel insights into HCC immune microenvironment heterogeneity and represents a promising candidate tool for risk stratification and treatment guidance that warrants further prospective validation. The identified molecular markers may serve as potential therapeutic targets for precision medicine approaches in HCC management.