Nanbin Liu

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.

DHX37, a member of the DEAD/H-box RNA helicase family, has been implicated in various diseases, including tumors. However, the biological characteristics and prognostic significance of DHX37 in HCC remain unclear. In this study, we use R software 3.6.3 and multiple bioinformatics analysis tools, such as GDSC, HPA, STRING, TISCH, and TIMER2, to analyze the characterization and function of DHX37 in HCC. In addition, Western blot (WB) and immunohistochemistry (IHC) based on clinical samples validated some of the findings. DHX37 was more highly expressed in HCC samples compared to adjacent non-tumor tissues. Higher DHX37 expression is correlated with various clinicopathological characteristics in HCC, including AFP, adjacent hepatic tissue inflammation, histologic grade, T stage, and pathologic stage. Survival analysis revealed that the high DHX37 group had significantly shorter overall survival (OS), progress-free interval (PFI), and disease-specific survival (DSS) compared to the low DHX37 group. By analyzing the correlation between DHX37 and the IC50 of chemotherapeutic drugs, the results showed that DHX37 expression level was negatively correlated with the IC50 of 11 chemotherapeutic drugs. Further analysis indicated that DHX37 and its co-expressed genes may play important roles in activating the cell cycle, DNA repair, chemokine signaling pathways, and regulating the immune response, which leads to a poor prognosis in HCC. High expression of DHX37 is an independent risk factor for poor prognosis in HCC, and DHX37 is expected to be a potential target to inhibit tumor progression. Targeting DHX37 may enhance chemotherapeutic drug sensitivity and immunotherapeutic efficacy in HCC.