ArticleBiological procedures online2025
Tumor Prognostic Risk Model Related to Monocytes/Macrophages in Hepatocellular Carcinoma Based on Machine Learning and Multi-Omics.
Article in Biological procedures online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Decoding the spatial logic of immune evasion in hepatocellular carcinoma: from single-cell profiling to spatial omics.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Tumor-associated macrophages (TAMs) are crucial in hepatocellular carcinoma (HCC) development and invasion. This study explores monocyte/ macrophage-associated gene expression profiles in HCC, constructs a prognostic model based on these genes, and examines its relationship with drug resistance and immune therapy responses. Single-cell RNA sequencing(scRNA-seq) data from 10 HCC tissue biopsy samples, totaling 24,597 cells, were obtained from the GEO database to identify monocyte/macrophage-associated genes. A prognostic model was constructed and validated using external datasets and Western blot. Relationships between the model, clinical correlates, drug sensitivity, and immune therapy responses were investigated. From scRNA-seq data, 2,799 monocyte/macrophage marker genes were identified. Using the TCGA dataset, a prognostic model based on the single-gene UQCRH was constructed, stratifying patients into high-risk and low-risk groups based on overall survival rates. High-risk group patients showed reduced survival rates and higher UQCRH expression in tumor tissues. Western blot analysis further confirmed the elevated expression of UQCRH in HCC cell lines. Spatial transcriptomics analysis revealed that high UQCRH expression co-localized with malignant cells in the tumor tissue. Drug sensitivity analysis revealed that the high-risk group had lower sensitivity to sorafenib and axitinib. Immune therapy response analysis indicated poorer outcomes in the high-risk group, with more pronounced APC inhibition and a weaker IFN-II response. Clinical indicator analysis showed a positive correlation between high UQCRH expression and tumor invasion. Enrichment analysis of UQCRH and associated molecules indicated involvement in oxidative phosphorylation and mitochondrial electron transport. This study introduces a prognostic model for HCC patients based on monocyte/macrophage marker genes. The single-gene model predicts HCC patient survival and treatment outcomes, identifying high-risk individuals with varying drug sensitivities and immune suppression states.
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Registered trials
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