Signal Transduct Target Ther. 2026 Sep 28;11(1):418. doi: 10.1038/s41392-026-02875-2.

ABSTRACT

Hepatocellular carcinoma (HCC) exhibits a suboptimal response to immune checkpoint blockade (ICB) therapy; to overcome this resistance, we aimed to delineate key immune resistance factors via multi-omics analysis, develop strategies to block their immunosuppressive axes, and engineer a targeted nanosystem to enhance immunotherapy efficacy against PD-1 resistance in HCC. Using transcriptomic and proteomic data from anti-PD-1-treated HCC patients, along with functional validation in murine models and mechanistic molecular and cell biology studies, we identified transketolase-like 1 (TKTL1) as a dual-nature biomarker where overexpression predicted poor baseline prognosis yet enhanced response to ICB. Mechanistically, TKTL1 diverts glucose flux into glycolysis rather than pentose phosphate pathway (PPP), recruiting USP9X to deubiquitinate and stabilize HIF-1α, which upregulates HK2 to amplify glycolytic output and lactate accumulation. This metabolic rewiring orchestrates dual immunosuppressive circuits through HIF-1α-driven CCL4 secretion recruiting PD-L1^high dendritic cells (DCs), coupled with lactate-induced TRIM28^K408 lactylation that stabilizes PD-L1 by blocking ubiquitin-mediated degradation. We engineered a hepatoma-membrane-coated MnO₂ nanosystem (CQLH) co-delivering a TKTL1 inhibitor and lactate oxidase, which disrupted the TKTL1-HIF-1α-HK2 axis, depleted lactate, and reprogrammed the tumor microenvironment, thereby enhanced anti-PD-1 therapy to suppress tumor growth, especially in TKTL1^high tumors. These findings define a critical "TKTL1-glycolysis-lactate-DC" axis driving anti-PD-1 sensitivity in HCC, position TKTL1 as both a potential biomarker for ICB response and a tractable therapeutic target, and demonstrate that the targeted CQLH nanosystem overcomes resistance and enhances anti-PD-1 efficacy, offering a precision immunotherapeutic strategy for TKTL1^high HCC.

PMID:42802226 | PMC:PMC13616917 | DOI:10.1038/s41392-026-02875-2

Int Immunopharmacol. 2026 Sep 13;189:117355. doi: 10.1016/j.intimp.2026.117355. Online ahead of print.

ABSTRACT

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8⁺ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8⁺ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A₂-arachidonate 5-lipoxygenase (cPLA₂-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8⁺ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

PMID:42732672 | DOI:10.1016/j.intimp.2026.117355

Int Immunopharmacol. 2026 Sep 13;189:117355. doi: 10.1016/j.intimp.2026.117355. Online ahead of print.

ABSTRACT

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8⁺ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8⁺ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A₂-arachidonate 5-lipoxygenase (cPLA₂-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8⁺ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

PMID:42732672 | DOI:10.1016/j.intimp.2026.117355

Front Mol Biosci. 2026 Aug 12;13:1752442. doi: 10.3389/fmolb.2026.1752442. eCollection 2026.

ABSTRACT

BACKGROUND: While MUC family genes have been established as prognostic biomarkers in gastric cancer, and GWAS studies link EMCN mutations to chemotherapy-induced myelosuppression in NSCLC, the systematic characterization of EMCN in lung adenocarcinoma (LUAD) remains elusive.

METHODS: This multi-omics strategy combining bulk and single-cell transcriptomics study integrated differential expression analysis, WGCNA, and machine learning algorithms (LASSO/SVM-RFE/Random Forest) to identify EMCN as a diagnostic hub gene, followed by experimental validation using immunohistochemistry Western blot and qRT-PCR.

RESULTS: EMCN (Endomucin) is a sialomucin-like glycoprotein predominantly expressed in vascular endothelial cells. Using bulk transcriptomic datasets and single-cell RNA-seq analysis, we found that EMCN expression was reduced in lung adenocarcinoma (LUAD) compared with non-tumor controls and was primarily localized to the endothelial compartment. Survival analysis using the median expression cutoff showed that high EMCN expression was associated with improved overall survival (Cox HR_high vs. low = 0.73, p = 0.04), indicating that low EMCN expression correlates with poorer prognosis. Machine learning-based feature selection (LASSO, Random Forest, and SVM) further prioritized EMCN among consensus candidate genes, supporting its potential relevance to the vascular-associated tumor microenvironment in LUAD. EMCN expression levels also showed a significant positive correlation with the degree of immune cell infiltration. Gene set enrichment analysis (GSEA) revealed that high EMCN expression in tumor tissues activates negative regulatory pathways associated with angiogenesis. Receiver operating characteristic (ROC) curve analysis highlights EMCN's excellent diagnostic potential for LUAD, with an area under the curve (AUC) of 0.963. In vitro experiments confirm the downregulation of EMCN at both protein and mRNA levels, consistent with our bioinformatics predictions.

CONCLUSION: This first comprehensive study establishes EMCN as a dual-functional regulator of vascular-immune crosstalk in LUAD, providing both a molecular diagnostic tool and therapeutic target for precision oncology.

PMID:42656419 | PMC:PMC13506425 | DOI:10.3389/fmolb.2026.1752442

Cell Death Dis. 2026 Apr 14. doi: 10.1038/s41419-026-08712-2. Online ahead of print.

ABSTRACT

Lung adenocarcinoma (LUAD), a leading cause of cancer mortality, involves incompletely understood epigenetic-metabolic crosstalk. We identified ANP32E as a key regulator through multi-omics (TCGA, scRNA-seq) and clinical analyses, finding its overexpression correlates with poor prognosis. Functionally, ANP32E knockdown suppressed proliferation, migration, and glycolysis in LUAD cells (A549/H1975) and attenuated xenograft growth, while overexpression promoted tumorigenesis. Mechanistically, ANP32E transcriptionally upregulates histone demethylase KDM3B, reducing repressive H3K9me2 marks at the EGFR promoter to enhance EGFR transcription. This activates PI3K/AKT signaling, inducing inhibitory GSK3β phosphorylation. Combined with ANP32E-mediated GSK3β suppression, this dual inactivation liberates oncogenic glycolysis. Crucially, KDM3B silencing or EGFR inhibition (Cetuximab) abrogated ANP32E-driven phenotypes. High-throughput screening identified Penta-O-galloyl-β-D-glucose (PGG) as an ANP32E-targeting compound, with molecular dynamics confirming binding. PGG dose-dependently inhibited the ANP32E/KDM3B/EGFR axis in vitro and suppressed tumor growth in vivo. Thus, ANP32E drives LUAD progression via KDM3B/EGFR-mediated GSK3β inactivation, representing a prognostic biomarker and therapeutic target validated by PGG.

PMID:41980942 | DOI:10.1038/s41419-026-08712-2

Int J Gen Med. 2026 Mar 24;19:590621. doi: 10.2147/IJGM.S590621. eCollection 2026.

ABSTRACT

Inflammatory bowel disease (IBD) involves chronic intestinal inflammation driven by gut-brain axis imbalance, fostering complications through an "inflammation-neuro-coagulation" triad. Current staging systems inadequately capture the dynamics of this multidimensional network. Therefore, integrated multi-omics analyses-including metagenomics, metabolomics, and single-cell transcriptomics-are essential to construct dynamic models that monitor coagulation, microbiome, and metabolism for precise assessment of disease activity and thrombotic or bleeding risks. Interventions targeting gut-brain axis nodes, such as eliminating tissue factor-positive (TF⁺) T cells or modulating vagal activity, show potential to disrupt the inflammation-coagulation cycle, although rigorous randomized trials are still needed. Artificial intelligence (AI)-assisted systems that integrate real-time biomarker monitoring with multi-omics predictions represent a novel paradigm for managing IBD-related coagulation dysfunction. Key challenges include elucidating gut-brain-liver axis regulation of coagulation and characterizing platelet functional heterogeneity. Future efforts must prioritize ethically compliant multi-omics platforms and racially stratified risk models to advance personalized coagulation management in IBD.

PMID:41913906 | PMC:PMC13033200 | DOI:10.2147/IJGM.S590621

J Int Med Res. 2026 Mar;54(3):3000605261430163. doi: 10.1177/03000605261430163. Epub 2026 Mar 16.

ABSTRACT

ObjectiveTo explore the potential involvement of PANoptosis-related genes in gastric cancer susceptibility through multiomics analyses.MethodsSummary-data-based Mendelian randomization was performed by integrating blood-derived methylation, gene expression, and protein quantitative trait loci data with genome-wide association study results. The findings were further evaluated in The Cancer Genome Atlas cohort, followed by protein-protein interaction analysis, drug prediction, and molecular docking.ResultsSummary-data-based Mendelian randomization and colocalization analyses identified several traits suggestively associated with gastric cancer risk. Genetically predicted higher expression of apoptosis and caspase activation inhibitor (AVEN) and hepatocyte growth factor (HGF) as well as higher HGF protein levels were associated with increased risk, whereas higher levels of protein phosphatase 2 regulatory subunit B beta (PPP2R2B) appeared to be protective. Multiomics integration suggested epigenetic regulation of HGF and PPP2R2B. The Cancer Genome Atlas analysis corroborated the dysregulation of these candidates, with high AVEN expression associated with poorer survival. Protein-protein interaction and drug prediction analyses highlighted functional networks and potential therapeutics, supported by molecular docking demonstrating strong HGF-binding affinities. However, these associations did not reach statistical significance in the independent validation cohort, possibly due to limited statistical power.ConclusionsThis study identified AVEN, HGF, and PPP2R2B as potential candidate genes for gastric cancer. These findings require further validation in larger cohorts.

PMID:41840829 | DOI:10.1177/03000605261430163