J Hepatocell Carcinoma. 2026 Aug 26;13:615781. doi: 10.2147/JHC.S615781. eCollection 2026.

ABSTRACT

OBJECTIVE: Hepatocellular carcinoma (HCC) has poor prognosis and variable immunotherapy response. Resveratrol exhibits anti-HCC activity, but its direct targets and association with immunotherapy response are unclear. This study identifies core resveratrol targets in HCC and evaluates their prognostic and predictive value.

METHODS: Resveratrol targets were intersected with TCGA-LIHC differentially expressed genes. A prognostic risk model was built using LASSO-Cox regression. Drug-target binding was assessed by molecular dynamics simulations and qRT-PCR. Single-cell and spatial transcriptomics, cell-cell communication, and a pan-immunotherapy cohort were used to investigate KIF11. An HCC mouse model validated immunomodulatory effects via flow cytometry.

RESULTS: Thirty-four resveratrol-associated targets were identified, enriched in metabolism pathways. A nine-gene risk model showed robust prognostic performance. Resveratrol stably binds to KIF11's ATP-binding pocket. KIF11 is overexpressed in malignant hepatocytes and proliferating T cells; KIF11⁺ cells orchestrate VEGF-mediated microenvironment remodeling. High KIF11 expression correlated with poor prognosis but predicted superior survival in the immunotherapy cohort, a phenomenon attributed to the observation that KIF11-high tumors exhibit both enhanced immunogenicity and active immunosuppression. In vivo, resveratrol enhanced CD8⁺ T cell infiltration, proliferation, effector function, and central memory T cells, while reducing Tregs.

CONCLUSION: KIF11 drives HCC progression and predicts immunotherapy response. It is a candidate direct resveratrol target and a potential biomarker for patient stratification in immune checkpoint therapy, although further experimental validation is warranted.

PMID:42670538 | PMC:PMC13526380 | DOI:10.2147/JHC.S615781

J Ethnopharmacol. 2026 Mar 26;365:121591. doi: 10.1016/j.jep.2026.121591. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Acute lung injury (ALI) lacks effective therapies. HIF-1α-driven glycolysis can promote histone lactylation and sustain pro-inflammatory (M1) macrophage responses. Daxiefei Decoction (DXFD), a classic traditional Chinese medicine formula, is used for pulmonary inflammatory diseases, but its immunometabolic mechanism remains unclear.

AIM OF THE STUDY: To evaluate the protective efficacy of DXFD against lipopolysaccharide (LPS)-induced ALI and to determine whether it acts through the HIF-1α/glycolysis/histone H3K18 lactylation (H3K18la) axis to regulate macrophage polarization.

MATERIALS & METHODS: DXFD constituents were characterized by UPLC-LTQ-Orbitrap-MS/MS, followed by network pharmacology, molecular docking, and molecular dynamics (MD) simulations. Lung transcriptomics and metabolomics were performed in ALI mice. Efficacy and mechanisms were assessed in LPS-challenged mice and RAW264.7 macrophages using histopathology, ELISA, qRT-PCR, Western blotting, and immunofluorescence. HIF-1α overexpression was used for validation.

RESULTS: DXFD dose-dependently alleviated lung injury and reduced pro-inflammatory cytokines in vivo, and suppressed M1 polarization in vivo and in LPS-stimulated macrophages. Multi-omics indicated activation of HIF-1α-associated inflammatory and glycolytic programs in ALI, which were normalized by DXFD. DXFD decreased glycolytic enzyme expression and reduced histone H3K18 lactylation (H3K18la); these effects were partially reversed by HIF-1α overexpression. Molecular docking and dynamics suggested stable binding of baicalin to HIF-1α.

CONCLUSIONS: DXFD mitigates ALI by dampening HIF-1α-dependent glycolysis and H3K18la, thereby restraining M1-driven inflammatory amplification.

PMID:41903585 | DOI:10.1016/j.jep.2026.121591

J Transl Med. 2026 Mar 20. doi: 10.1186/s12967-026-08022-3. Online ahead of print.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is a predominant contributor to cancer‑related mortality globally. Lung‑associated fibroblasts (LAFs) are intricately linked to tumorigenesis and the tumor microenvironment (TME), but their heterogeneity and prognostic relevance in LUAD remain incompletely understood. This study aimed to systematically characterize LAF subsets across the spectrum of pulmonary disease, identify LAF subpopulations associated with LUAD prognosis, and construct a robust LAF‑based prognostic signature.

METHODS: We employed a multi-omics approach, leveraging bulk RNA data of 2719 patients from 19 LUAD cohorts, single-cell RNA (scRNA) sequencing data of 368,904 cells from 93 samples, and spatial transcriptomics data of 15,673 spots from 6 samples to characterize the landscape of LAFs across various stages of pulmonary disease. We employed multiple advanced machine learning algorithms to construct and validate a robust nuclear division LAFs (nLAFs) risk score (nLRS) prediction model.

RESULTS: We observed a dynamic and gradual increase in the proportion of LAFs during the progression of LUAD. Throughout this process, we identified nine LAFs subtypes and found nLAFs are significantly associated with the prognosis of LUAD. Utilizing 100 machine learning algorithm combinations and integrating nLAFs marker genes, we developed a five gene based nLRS model, which demonstrated superior performance than other 49 published models in predicting clinical outcomes for LUAD. Additionally, we observed distinct biological functions and immune cell infiltration in the TME between high and low nLRS groups. Exploratory analysis of pan-cancer immunotherapy cohorts suggested that patients with high nLRS scores may exhibit resistance to immunotherapy in some cancer types, but prospective validation in LUAD-specific cohorts is required. Conversely, high nLRS patients displayed increased sensitivity to chemotherapeutic and targeted therapies in preclinical models.

CONCLUSION: Our study introduces a candidate five-gene signature derived from nLAFs that may serve as a robust prognostic biomarker pending prospective validation, offering insights into personalized therapeutic strategies for LUAD patients.

PMID:41862916 | DOI:10.1186/s12967-026-08022-3