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Integration of multi-omics and machine learning to identify core genes in PANoptosisof lung adenocarcinoma and their mechanisms in the tumor microenvironment and therapeutic potential

Naunyn Schmiedebergs Arch Pharmacol. 2026 Apr 5. doi: 10.1007/s00210-026-05251-7. Online ahead of print.

ABSTRACT

Lung adenocarcinoma (LUAD) is one of the leading causes of cancer-related deaths worldwide, and its complex tumor microenvironment (TME) is a key barrier to treatment. PANoptosis is a novel programmed cell death mechanism that integrates features of pyroptosis, apoptosis, and necroptosis. However, its core regulatory network and cell specific role in LUAD are still unclear. This study integrated three LUAD transcriptome datasets, screened differentially expressed genes through bioinformatics analysis, and intersected with PANoptosis-related genes to construct a protein interaction network, using a combination of 113 machine learning algorithms to screen and validate core genes and using CIBERSORT and single-cell transcriptome data to analyze the spatial expression characteristics of immune cell infiltration and core genes. Finally, the intervention mechanism of core targets and ginsenosides was validated through molecular docking, immunohistochemistry, and cell experiments (CCK-8, Western Blot). Six core genes of LUAD PANoptosis, including IRF1, NLRP3, CASP1, TIMP1, S100A8, and TLR4, were identified in the study. Single-cell analysis revealed that these genes were significantly enriched in M2 macrophages. Functional enrichment indicates that they jointly regulate death- and inflammation-related pathways such as NF-ΞΊB signaling and NOD-like receptor signaling. In vitro experiments have confirmed that ginsenosides can induce PANoptosis, promote tumor cell death, or inhibit LUAD cell proliferation by upregulating the ZBP1/AIM2/RIPK3/CASP1 death complex and inhibiting the TLR4/NLRP3 survival signaling axis. This study systematically revealed a PANoptosis core gene network centered on M2 macrophages in LUAD, elucidating a new mechanism by which ginsenosides induce integrated cell death by regulating this network. This provides new potential targets and theoretical basis for the immunotherapy of LUAD and the development of traditional Chinese medicine monomers.

PMID:41935997 | DOI:10.1007/s00210-026-05251-7

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Integration of multi-omics and machine learning to identify core genes in PANoptosisof lung adenocarcinoma and their mechanisms in the tumor microenvironment and therapeutic potential

Naunyn Schmiedebergs Arch Pharmacol. 2026 Apr 5. doi: 10.1007/s00210-026-05251-7. Online ahead of print.

ABSTRACT

Lung adenocarcinoma (LUAD) is one of the leading causes of cancer-related deaths worldwide, and its complex tumor microenvironment (TME) is a key barrier to treatment. PANoptosis is a novel programmed cell death mechanism that integrates features of pyroptosis, apoptosis, and necroptosis. However, its core regulatory network and cell specific role in LUAD are still unclear. This study integrated three LUAD transcriptome datasets, screened differentially expressed genes through bioinformatics analysis, and intersected with PANoptosis-related genes to construct a protein interaction network, using a combination of 113 machine learning algorithms to screen and validate core genes and using CIBERSORT and single-cell transcriptome data to analyze the spatial expression characteristics of immune cell infiltration and core genes. Finally, the intervention mechanism of core targets and ginsenosides was validated through molecular docking, immunohistochemistry, and cell experiments (CCK-8, Western Blot). Six core genes of LUAD PANoptosis, including IRF1, NLRP3, CASP1, TIMP1, S100A8, and TLR4, were identified in the study. Single-cell analysis revealed that these genes were significantly enriched in M2 macrophages. Functional enrichment indicates that they jointly regulate death- and inflammation-related pathways such as NF-ΞΊB signaling and NOD-like receptor signaling. In vitro experiments have confirmed that ginsenosides can induce PANoptosis, promote tumor cell death, or inhibit LUAD cell proliferation by upregulating the ZBP1/AIM2/RIPK3/CASP1 death complex and inhibiting the TLR4/NLRP3 survival signaling axis. This study systematically revealed a PANoptosis core gene network centered on M2 macrophages in LUAD, elucidating a new mechanism by which ginsenosides induce integrated cell death by regulating this network. This provides new potential targets and theoretical basis for the immunotherapy of LUAD and the development of traditional Chinese medicine monomers.

PMID:41935997 | DOI:10.1007/s00210-026-05251-7

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Strand-asymmetric G-runs and G4s downstream of TSS modulate tumor suppressor gene transcription

Oncogene, Published online: 02 April 2026; doi:10.1038/s41388-026-03761-5

Strand-asymmetric G-runs and G4s downstream of TSS modulate tumor suppressor gene transcription
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Pyruvate is a natural suppressor of interferon signaling by inducing STAT1 protein pyruvylation

Yibo et al. identify protein pyruvylation as a post-translational modification that can modulate immune signaling and host antiviral response.
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SecureVibeBench: Evaluating Secure Coding Capabilities of Code Agents with Realistic Vulnerability Scenarios

arXiv:2509.22097v2 Announce Type: replace-cross Abstract: Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benchmarks have provided valuable insights, but they fail to capture scenarios in which vulnerabilities are actually introduced by human developers, making fair comparisons between humans and agents infeasible. We therefore introduce SecureVibeBench, a benchmark of 105 C/C++ secure coding tasks sourced from 41 projects in OSS-Fuzz for code agents. SecureVibeBench has the following features: (i) realistic task settings that require multi-file edits in large repositories, (ii)~aligned contexts based on real-world open-source vulnerabilities with precisely identified vulnerability introduction points, and (iii) comprehensive evaluation that combines functionality testing and security checking with both static and dynamic oracles. We evaluate 5 popular code agents like OpenHands, supported by 5 LLMs (e.g., Claude sonnet 4.5) on SecureVibeBench. Results show that current agents struggle to produce both correct and secure code, as even the best-performing one, produces merely 23.8\% correct and secure solutions on SecureVibeBench.
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AI Agents and Epidemic Intelligence on Respiratory Infectious Diseases: Toward a Conceptual Framework Integrating Decision Support

Traditional epidemic intelligence relies heavily on human epidemiologists for data interpretation and reporting, which makes it resource intensive, slow to respond, and vulnerable to variability in professional expertise. To overcome these limitations, we propose an expanded conceptual epidemic intelligence quadripartite framework that extends the classical trinity of (1) surveillance, (2) risk evaluation, and (3) early warning with a fourth pillar, (4) decision support and intervention optimization through AI agents. Acting as 24/7 digital epidemiologists, multiagent systems can integrate heterogeneous signals from multisource surveillance systems, conduct contextual risk evaluation and adaptive forecasting, generate tailored early warnings, and provide actionable recommendations for targeted controlβ€”closing the loop between detection and response. Embedding interpretability and mandatory human-in-the-loop oversight enhances trust and accountability. Nonetheless, real-world deployment requires addressing context-specific challenges of data quality, interoperability, robustness, governance, circular reporting, and equity. If designed with transparency, inclusiveness, and resilience, AI agents have the potential to transform epidemic intelligence into a continuously adaptive and globally connected system.
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