Qian Wang

Liver hepatocellular carcinoma (LIHC) is a common malignancy, yet the core genes driving its progression and potential therapeutic targets remain insufficiently explored. Ribosome biogenesis (RB) is a critical biological process linked to various cancers; however, its systematic role in LIHC remains unclear. This study integrated LIHC single-cell RNA-Seq, bulk RNA-Seq, and spatial transcriptomic data with ribosome biogenesis-related gene sets to construct a single-cell atlas of LIHC. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to characterize myeloid cell subsets. Furthermore, an LIHC prognostic risk model based on RB-related genes was developed using 117 machine-learning algorithm combinations. Key findings were subsequently corroborated through experimental validation and clinical sample analysis. We identified a distinct macrophage subpopulation with high ribosome biogenesis activity, termed ribosome biogenesis-active macrophages (RAMs). These cells exhibited strong communication with inflammatory macrophages, potentially mediated by MIF-related receptor-ligand interactions. We further constructed an 8-gene prognostic model (PA2G4, GNL2, PWP1, DDX49, NOC4L, GDI2, CST7, and RCL1), which showed good predictive performance. Drug sensitivity analysis suggested that the high-risk group may be more responsive to several agents, including docetaxel. Among these genes, GNL2 was selected for further investigation. Elevated GNL2 expression was associated with increased stemness features in myeloid cells. Molecular docking analysis identified several candidate compounds with potential binding affinity to GNL2. Functionally, GNL2 knockdown in macrophages reduced TGF-β and TNF-α expression and was associated with decreased proliferation, migration, and invasion of LIHC cells. We identified a highly active ribosome biogenesis-macrophage subpopulation (RAM), and constructed a robust risk model to aid in the diagnosis, prognosis, and treatment of LIHC. GNL2 is associated with increased expression of TGF-β and TNF-α and may contribute to LIHC progression.

Background: Liver hepatocellular carcinoma (LIHC), a prevalent gastrointestinal malignancy, continues to demonstrate poor prognosis despite therapeutic advances improving clinical outcomes. Histone acetylation, a key epigenetic modification, regulates critical processes including chromatin remodeling, gene expression and drives tumor progression in multiple cancers (e.g., lung, gastric) yet its systemic role in LIHC remains unclear. Methods: This study integrated LIHC single-cell/RNA-seq data and histone acetylation-related gene sets to construct a LIHC risk prediction model based on histone acetylation-related genes using 101 machine learning combinatorial algorithms. The model’s comprehensive value was evaluated through prognostic analysis, pathway enrichment analysis, immune landscape analysis, chemosensitivity analysis, mutation analysis, ferroptosis, and m6A methylation analysis. NEU1’s functional role was investigated via cell communication networks and molecular docking. Experimental validation included in vitro assays (Cell Counting Kit-8, migration, invasion), and clinical sample verification (quantitative real-time PCR (qRT-PCR) and Western Blot (WB)); these were performed to validate the key findings. Results: Using 101 machine learning combinations, we constructed an 11-gene LIHC risk model (HLA-B, HEXB, CDK4, ACAT1, NAA10, B2M, HSPD1, NPM1, PON1, NEU1, CFB) demonstrating robust prognostic accuracy across training/validation cohorts and 10 LIHC subtypes. Immune landscape analysis revealed that the high-risk group exhibited higher tumor purity and lower immune infiltration, with better responses to PD-L1 and PD-L2 treatment. Chemosensitivity analysis showed that the high-risk group had increased sensitivity to four drugs, including Axitinib, but decreased sensitivity to 21 drugs, including Cisplatin. The risk model score significantly correlated with the expression levels of ferroptosis-related genes such as GPX4 and m6A methylation-related genes such as METTL3. NEU1 was identified as a key risk factor in this model, with the NEU1 high-expression group showing of intercellular communication in endothelial cells and other cell types. Pseudotime analysis suggested that NEU1 may promote LIHC progression by blocking normal differentiation of endothelial cells. Molecular docking revealed that five compounds, including Oseltamivir, could bind directly to NEU1. Knockdown of NEU1 significantly reduced proliferation, migration, and invasion of LIHC cells, and slowed LIHC tumor growth. Conclusions: We constructed a histone acetylation-based risk model for LIHC diagnosis, prognosis, and therapy, identifying NEU1 as a key biomarker and potential therapeutic target.