Key Takeaways & Executive Findings
- •• • The ligand-receptor-based LASSO model achieved an AUC that, while lower than autophagy- or cuproptosis-focused signatures, provides a broader network view; this trade-off may limit immediate clinical adoption but offers a foundation for multi-pathway integration. • • TREM1 knockdown significantly reduced HCC cell proliferation and migration and increased apoptosis in vitro, with concomitant decreases in IL-1β, TNF-α, and MCP-1, indicating TREM1 as a actionable node for combination immunotherapy. • • In xenograft models, TREM1 downregulation shifted the tumor microenvironment by decreasing M1 macrophages and increasing Tregs, suggesting that TREM1 inhibition could reverse immunosuppression but may require careful timing to avoid compensatory Treg expansion. • • The Nrf2/Keap1 oxidative stress pathway was identified as a key downstream effector of TREM1, with its suppression upon TREM1 knockdown; this links TREM1 to redox homeostasis and presents a potential biomarker for patient stratification in oxidative-stress-targeted trials.
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Abstract
Hepatocellular carcinoma (HCC) remains a highly aggressive malignancy with a five-year survival rate of 12–15%, and unresectable cases exhibit unsatisfactory responses to approved multikinase inhibitors and immune checkpoint blockade. This study integrated single-cell and bulk RNA sequencing to construct a 13-cell-type atlas of the HCC ecosystem and derived a ligand-receptor-based LASSO prognostic model. The model stratified patients into low- and high-risk groups with divergent immune microenvironments, primarily involving macrophages, CD4+ T cells, M1 macrophages, and regulatory T cells. Experimental validation demonstrated that TREM1 promotes HCC cell proliferation, migration, and suppresses apoptosis, while positively modulating IL-1β, TNF-α, and MCP-1. TREM1 knockdown altered M1 macrophage and Treg proportions in a xenograft model and downregulated the Nrf2/Keap1 oxidative stress pathway. Despite a relatively low AUC compared to specialized models, this general network approach offers a novel signature and identifies TREM1 as a potential therapeutic target, though real-world validation and deeper mechanistic studies are warranted.
1. Introduction
Hepatocellular carcinoma (HCC) presents a formidable clinical challenge, with a five-year overall survival rate stagnating at 12–15% despite the approval of antiangiogenic multikinase inhibitors such as sorafenib and lenvatinib as first-line therapies, and immune checkpoint inhibitors against PD-1/PD-L1 as second-line options. Unresectable HCC patients continue to face dismal outcomes, underscoring the urgent need for novel prognostic tools and therapeutic targets that address the underlying tumor heterogeneity and immune evasion mechanisms.
Existing prognostic models for HCC often rely on narrowly defined gene sets—such as those related to autophagy, cuproptosis, or T-cell depletion—which, while achieving high AUC values, fail to capture the complex intercellular communication within the tumor microenvironment. This study bridges that gap by integrating single-cell and bulk transcriptome data to construct a ligand-receptor-based signature, thereby providing a more holistic view of the HCC ecosystem. Through this approach, we identify TREM1 as a critical regulator of malignant behaviors and immune modulation, offering a potential target that warrants further clinical validation.
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ZHANG Jiemin, HUANG Qian, ZHENG Yingying, TANG Jianqing, KANG Naling, LIU Yurui, ZENG Dawu (2025). Single-cell and bulk transcriptome analysis unveils a ligand-receptor-based signature for prognostication and reveals that TREM1 controls the malignant behaviors of hepatocellular carcinoma. Acta Biochimica et Biophysica Sinica. https://doi.org/10.3724/abbs.2025059
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Frequently Asked Questions
What is the quantitative performance of the ligand-receptor-based LASSO model in terms of AUC, and how does it compare to existing HCC prognostic signatures?
The model's AUC is relatively unsatisfactory compared to signatures focused on autophagy, cuproptosis, or T-cell depletion, which often report higher values. However, the study emphasizes that its strength lies in the general network approach, capturing broader ligand-receptor interactions rather than a single biological process. No exact AUC value is provided in the available text, but the authors acknowledge the trade-off between breadth and discriminative power.
What specific in vitro and in vivo evidence supports TREM1 as a driver of HCC malignancy, and what are the effect sizes?
In vitro, TREM1 knockdown significantly inhibited proliferation and migration and induced apoptosis, as measured by CCK-8, scratch assay, and flow cytometry. It also reduced proinflammatory cytokines IL-1β, TNF-α, and MCP-1. In vivo, TREM1 downregulation in a xenograft model altered the proportions of M1 macrophages and Tregs in tumor tissue, indicating a role in shaping the immune microenvironment. These findings are qualitative but consistent across assays.
How does TREM1 modulation affect the tumor immune microenvironment, and what are the implications for immunotherapy?
TREM1 downregulation decreased M1 macrophage proportions and increased Tregs in xenograft tumors, suggesting a shift toward an immunosuppressive state. This paradoxical effect implies that TREM1 inhibition alone may not be therapeutically beneficial without concurrent strategies to block Treg expansion. The data highlight the need for combination approaches targeting both TREM1 and compensatory immune regulatory pathways.
What is the mechanistic link between TREM1 and the Nrf2/Keap1 pathway, and what is the potential for therapeutic targeting?
The Nrf2/Keap1 oxidative stress pathway was identified as a key downstream effector of TREM1, with its downregulation upon TREM1 knockdown. This suggests that TREM1 promotes HCC malignancy partly through modulating redox homeostasis. Targeting TREM1 could therefore sensitize tumors to oxidative stress-induced cell death, but further studies are needed to establish causality and to assess whether Nrf2/Keap1 status can serve as a predictive biomarker.
What are the major limitations of this study regarding clinical translation and model validation?
The authors acknowledge that the prognostic model's AUC is lower than that of specialized signatures, and real-world validation of TREM1's prognostic value is lacking. Additionally, deeper mechanistic experiments are required to fully establish TREM1's role in HCC pathogenesis. The study calls for future testing of both prognostic and treatment value in clinical cohorts, indicating that the current findings are preliminary and require prospective validation.
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