ArticleFrontiers in immunology2025
Machine learning-based tumor associated macrophages polarity signature predicts prognosis and treatment response in hepatocellular carcinoma.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Tumor-associated macrophages (TAMs) shape the tumor microenvironment and drive hepatocellular carcinoma (HCC) progression. However, the prognostic significance of TAM polarity-related genes, particularly based on the CXCL9:SPP1 signature, remains unclear. Methods: We identified 372 TAM polarity-related genes in the TCGA-LIHC dataset. Prognostic candidates were selected using univariate Cox regression, bootstrap resampling, and the Boruta algorithm. Seven machine learning models were compared, and XGBoost was selected to construct a TAM polarity-related signature (TPS) consisting of 17 genes. TPS was validated in two external cohorts. Associations with clinical features, biological pathways, immune status, and drug sensitivity were explored. scRNA-seq and qRT-PCR were performed to investigate cellular expression and functional relevance. Results: TPS markedly different patients into high- and low-risk groups with significantly different survival outcomes (TCGA 1-, 3-, 5-year AUCs: 0.91, 0.89, 0.88). High-risk patients showed enrichment in glycan metabolism, DNA repair, and oncogenic pathways, whereas low-risk patients displayed elevated lipid and amino acid metabolism. Immune profiling revealed greater infiltration of immunosuppressive cells and higher expression of immune checkpoints in high-risk patients. Drug sensitivity analysis identified potential therapeutic targets and candidate compounds, including CDK1, PLK1, and statins. scRNA-seq analysis highlighted disrupted macrophage-immune interactions and identified SPP1 as a key signaling mediator. Silencing of TTC1 and G6PD suppressed HCC cell proliferation. Conclusion: We developed and validated a robust TAM polarity-related signature that effectively stratifies HCC patients by prognosis. TPS provides insights into tumor immunity, metabolism, and drug response, and may serve as a valuable tool for precision medicine in HCC.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.