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-κB 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

Phys Med Biol. 2026 Mar 13. doi: 10.1088/1361-6560/ae5209. Online ahead of print.

ABSTRACT

ObjectiveThis study aims to develop a functional-based multi-omics model for early prediction of radiation pneumonitis (RP) by extracting radiomic and dosiomic features from functionally defined lung regions, using generated perfusion (Q) and ventilation (V) from pre-radiotherapy planning computed tomography (CT).
ApproachWe retrospectively analyzed data from 121 patients with locally advanced non-small cell lung cancer (NSCLC) treated with curative-intent IMRT between 2015 and 2019, including pre-treatment CT and dose maps. Q and V maps were generated from CT with deep learning-based and supervoxel-based approaches, respectively. Regions of interest (ROIs) combined the planning target volume (PTV) with each of three functional lung regions-high functional lung (HFL), low functional lung (LFL), and whole lung (WL)-defined by thresholds on Q and V maps. Radiomic and dosiomic features were extracted from CT and dose distributions within each ROI. For each ROI, For each ROI, three methods-radiomics (R), dosiomics (D), and dual-omics (RD)-were constructed. 13 machine learning algorithms were trained and evaluated using 10-fold cross-validation, and model performance was assessed by the average area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. RP was defined as CTCAE grade ≥ 2.
Main resultsOf the 35 selected features, 20 were from HFL. In dual-omics models, using HFL features improved predictive performance for RP (AUC 0.879±0.105) compared to WL (AUC 0.778 ± 0.100). In HFL, the RD method outperformed both R (AUC 0.786± 0.076) and D (AUC 0.791 ± 0.107) methods. Decision curve analysis showed the dual-omics model based on HFL provided the highest net benefit across threshold probabilities.
SignificanceThis study is the first to systematically demonstrate that features extracted from CT-derived HFL capture important functional differences and provide strong predictive value for RP. Compared to conventional methods, integrating radiomics, dosiomics, and CT-based functional information further improves predictive performance.&#xD.

PMID:41825133 | DOI:10.1088/1361-6560/ae5209