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A clinically derived lipid-endothelial signature links serum multi-omics to immune exclusion and clinical stratification in hepatocellular carcinoma

2 September 2026 at 18:00

Ther Adv Med Oncol. 2026 Aug 31;18:17588359261481797. doi: 10.1177/17588359261481797. eCollection 2026.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is driven by extensive metabolic reprogramming, vascular remodeling, and immune microenvironmental dysfunction. Although numerous stratification signatures have been proposed, few are grounded in clinically derived serum multi-omics and biologically linked to endothelial remodeling, endothelial regulation, and immune exclusion.

OBJECTIVES: This study aimed to identify a serum-derived lipid-endothelial program associated with immune exclusion and clinical stratification in HCC.

DESIGN: A translational multi-omics study integrating clinically collected serum samples, public transcriptomic cohorts, single-cell RNA sequencing, and experimental validation.

METHODS: Proteomic and metabolomic sequencing was performed on serum samples from patients with HCC and normal controls. Dysregulated pathways were integrated with transcriptomic data from TCGA-LIHC and ICGC-LIRI-JP cohorts to identify genes jointly associated with lipid metabolism and leukocyte transendothelial migration. A risk score was calculated using the expression of PON1, TXNRD1, CLDN4, CLDN6, CYP2C9, and CTSA. Higher expression of TXNRD1, CLDN4, CLDN6, and CTSA contributed to a higher risk score, whereas PON1 and CYP2C9 contributed protective coefficients. Immune contexture, tumor mutation burden, and exploratory therapeutic sensitivity patterns were further evaluated using transcriptome-based drug sensitivity prediction, followed by single-cell RNA sequencing and experimental expression validation.

RESULTS: Serum multi-omics analysis revealed prominent dysregulation of lipid metabolic pathways and leukocyte transendothelial migration-related processes in HCC. Integrative analysis identified a six-gene lipid-endothelial signature (PON1, TXNRD1, CLDN4, CLDN6, CYP2C9, and CTSA) that stratified patients into high- and low-risk groups. In the TCGA-LIHC cohort, high-risk patients had significantly poorer overall survival than low-risk patients (log-rank P < 0.0001), and this survival-stratifying association was externally supported in the ICGC-LIRI-JP cohort (log-rank P = 0.016). The high-risk phenotype was associated with immune-excluded features, distinct somatic mutation patterns, and altered predicted sensitivity to several selected anticancer agents. Single-cell analysis and experimental assays further supported the association between the six-gene program, malignant epithelial states, endothelial-related remodeling, and immune microenvironmental heterogeneity.

CONCLUSION: This study defines a clinically derived lipid-endothelial program associated with immune exclusion, adverse prognosis, and potential differences in therapeutic vulnerability in HCC. The proposed signature provides a biologically informed framework for prognostic assessment and may support future evaluation of targeted interventions in HCC.

PMID:42682961 | PMC:PMC13530516 | DOI:10.1177/17588359261481797

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  • Contractual Skills: A GovernSpec Design Framework for Enterprise AI Agents Ting Liu
    arXiv:2605.22634v2 Announce Type: replace-cross Abstract: Skills have become a practical packaging mechanism for agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, a skill often needs to express more than task guidance: goals, input boundaries, permissions, human approval points, evidence requirements, output contracts, quality criteria, verification steps, and handoff rules. This paper proposes contractual skills, a GovernSpec-inspired design fram
     

Contractual Skills: A GovernSpec Design Framework for Enterprise AI Agents

arXiv:2605.22634v2 Announce Type: replace-cross Abstract: Skills have become a practical packaging mechanism for agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, a skill often needs to express more than task guidance: goals, input boundaries, permissions, human approval points, evidence requirements, output contracts, quality criteria, verification steps, and handoff rules. This paper proposes contractual skills, a GovernSpec-inspired design framework for organizing SKILL.md files as readable task contracts while preserving lightweight skill discovery and progressive loading. The framework clarifies the boundary between contractual skills, GovernSpec YAML contracts, Model Context Protocol (MCP) surfaces, tool adapters, runtime guardrails, tracing, and evaluation systems. We evaluate the framework with three offline empirical studies. The first text-generation experiment covers three enterprise skills, fifteen synthetic tasks, four instruction conditions, and eight generation models, producing 960 outputs and 1680 cross-judge score records. The second study is a public-skill A/B expansion: eight public skills are compared with contractual rewrites across forty-eight synthetic tasks, six generation models, two repeats, 1152 outputs, and two complete judge files. In this setting, contractual skills raise mean quality from 4.692 to 4.914 and reduce critical-error rate from 0.083 to 0.013. The third study is an offline tool-calling challenge with eight models and 192 simulated tool-call records. The results suggest that contractual skills are best understood as a governance layer that makes task intent, boundaries, and acceptance criteria explicit, not as a standalone safety mechanism.

Pan-neurodegeneration proteomics reveals disease subtypes and molecular signatures

A pan-neurodegeneration atlas built from multilayer, deep proteomics of 2,279 brain samples across 6 major diseases integrates whole proteome, detergent-insoluble proteome, and posttranslational modifications to enable intra- and inter-disease comparisons to reveal disease-specific subtypes and dysregulated pathways, while identifying shared changes such as GPNMB upregulation and NPTX2 downregulation.

WNT7A correlates with immunosuppression and predicts adverse prognosis in lung adenocarcinoma: Potential implication of the NF-kappaB/CCL2 Axis

Cytokine. 2026 Apr 6;202:157144. doi: 10.1016/j.cyto.2026.157144. Online ahead of print.

ABSTRACT

BACKGROUND: The remodeling of the tumor immune microenvironment (TME) is a pivotal determinant of therapeutic efficacy and clinical outcome in Lung Adenocarcinoma (LUAD). While WNT signaling is a known oncogenic driver, the specific immunomodulatory role of WNT7A and its potential crosstalk with inflammatory pathways in LUAD remain to be fully elucidated. We sought to define the prognostic value of WNT7A and explore the molecular mechanisms by which it may foster an immunosuppressive TME.

METHODS: We performed a multi-omics analysis utilizing the TCGA-LUAD cohort (N = 508) and validated findings in an independent external cohort (GSE30219, N = 293). The prognostic significance of WNT7A was evaluated using Kaplan-Meier and multivariate Cox regression analyses. TME composition was dissected via ssGSEA, focusing on myeloid-derived suppressor cell (MDSC) infiltration. Mechanistic pathways were identified using Gene Set Enrichment Analysis (GSEA) and gene co-expression networks.

RESULTS: High WNT7A expression was identified as a significant predictor of poor Overall Survival (OS) in the TCGA cohort (P < 0.05) and validated in the external cohort (P < 0.05). Multivariate analysis confirmed WNT7A as an independent prognostic risk factor (HR = 1.085, P = 0.036). Immunologically, WNT7A expression was positively correlated with MDSC infiltration (R = 0.43, P < 0.001), suggesting a shift towards an immune-tolerant phenotype. Mechanistically, GSEA revealed a robust activation of inflammatory signaling in the high-WNT7A group. Specifically, the TNFA Signaling via NF-κB pathway was significantly enriched(NES = 2.52, P < 0.001). Consistent with this pathway activation, WNT7A showed a statistically significant positive correlation with CCL2 (P < 0.001), a critical chemokine for MDSC recruitment, implicating the NF-κB/CCL2 axis in this process.

CONCLUSION: WNT7A serves as a prognostic biomarker linked to immune evasion in LUAD, potentially by modulating the NF-κB/CCL2/MDSC axis. This study identifies WNT7A as a potential therapeutic target to remodel the immune microenvironment, providing a rationale for future investigations into WNT-targeted strategies to improve immunotherapy efficacy.

PMID:41946008 | DOI:10.1016/j.cyto.2026.157144

WNT7A correlates with immunosuppression and predicts adverse prognosis in lung adenocarcinoma: Potential implication of the NF-kappaB/CCL2 Axis

Cytokine. 2026 Apr 6;202:157144. doi: 10.1016/j.cyto.2026.157144. Online ahead of print.

ABSTRACT

BACKGROUND: The remodeling of the tumor immune microenvironment (TME) is a pivotal determinant of therapeutic efficacy and clinical outcome in Lung Adenocarcinoma (LUAD). While WNT signaling is a known oncogenic driver, the specific immunomodulatory role of WNT7A and its potential crosstalk with inflammatory pathways in LUAD remain to be fully elucidated. We sought to define the prognostic value of WNT7A and explore the molecular mechanisms by which it may foster an immunosuppressive TME.

METHODS: We performed a multi-omics analysis utilizing the TCGA-LUAD cohort (N = 508) and validated findings in an independent external cohort (GSE30219, N = 293). The prognostic significance of WNT7A was evaluated using Kaplan-Meier and multivariate Cox regression analyses. TME composition was dissected via ssGSEA, focusing on myeloid-derived suppressor cell (MDSC) infiltration. Mechanistic pathways were identified using Gene Set Enrichment Analysis (GSEA) and gene co-expression networks.

RESULTS: High WNT7A expression was identified as a significant predictor of poor Overall Survival (OS) in the TCGA cohort (P < 0.05) and validated in the external cohort (P < 0.05). Multivariate analysis confirmed WNT7A as an independent prognostic risk factor (HR = 1.085, P = 0.036). Immunologically, WNT7A expression was positively correlated with MDSC infiltration (R = 0.43, P < 0.001), suggesting a shift towards an immune-tolerant phenotype. Mechanistically, GSEA revealed a robust activation of inflammatory signaling in the high-WNT7A group. Specifically, the TNFA Signaling via NF-κB pathway was significantly enriched(NES = 2.52, P < 0.001). Consistent with this pathway activation, WNT7A showed a statistically significant positive correlation with CCL2 (P < 0.001), a critical chemokine for MDSC recruitment, implicating the NF-κB/CCL2 axis in this process.

CONCLUSION: WNT7A serves as a prognostic biomarker linked to immune evasion in LUAD, potentially by modulating the NF-κB/CCL2/MDSC axis. This study identifies WNT7A as a potential therapeutic target to remodel the immune microenvironment, providing a rationale for future investigations into WNT-targeted strategies to improve immunotherapy efficacy.

PMID:41946008 | DOI:10.1016/j.cyto.2026.157144

Human-specific features of the cerebellum and ZP2-regulated synapse development

Human-specific transcriptomic and regulatory features are present in the cerebellum, with ZP2 playing a key role in synapse regulation. ZP2 expression is induced by pontine mossy fibers, leading to decreased synaptic proteins and neuronal activity, which provides insights into the evolutionary development of the human cerebellum.

Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering

arXiv:2602.19569v1 Announce Type: cross Abstract: Question Answering over Temporal Knowledge Graphs (TKGQA) has attracted growing interest for handling time-sensitive queries. However, existing methods still struggle with: 1) weak incorporation of temporal constraints in question representation, causing biased reasoning; 2) limited ability to perform explicit multi-hop reasoning; and 3) suboptimal fusion of language and graph representations. We propose a novel framework with temporal-aware question encoding, multi-hop graph reasoning, and multi-view heterogeneous information fusion. Specifically, our approach introduces: 1) a constraint-aware question representation that combines semantic cues from language models with temporal entity dynamics; 2) a temporal-aware graph neural network for explicit multi-hop reasoning via time-aware message passing; and 3) a multi-view attention mechanism for more effective fusion of question context and temporal graph knowledge. Experiments on multiple TKGQA benchmarks demonstrate consistent improvements over multiple baselines.
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