The persistence of an immunosuppressive microenvironment remains a formidable challenge for cancer immunotherapy, particularly in tumors with immune-excluded or immune-desert phenotypes. Increasing evidence indicates that chronic inflammation and tumor progression are intrinsically linked through shared signaling hubs, including NF-κB and PI3K/Akt. Osthole, a natural coumarin compound, has been reported to exhibit both potent anti-inflammatory and antitumor activities; however, whether these effects reflect a coordinated regulation of the inflammation–cancer axis remains unclear. In this study, we deployed an integrative framework founded on network pharmacology, molecular docking, and rigorous molecular dynamics simulations, complemented by literature-based evidence synthesis, to computationally explore the potential mechanisms underlying Osthole’s dual activities. Our analysis revealed that Osthole’s predicted targets are significantly enriched in signaling pathways bridging inflammatory and oncogenic processes, most notably the PI3K/Akt, NF-κB, and TGF-β/Smad pathways. Crucially, MD simulations provided supportive computational evidence, suggesting that Osthole forms stable, energetically favorable complexes with core protein hubs (AKT1, RELA, and TGFB1) under the simulated conditions. Evidence from representative inflammatory and tumor models supports the biological plausibility of these predictions, including suppression of pro-inflammatory signaling, mitigation of maladaptive tissue remodeling, and induction of apoptosis. Furthermore, in hepatocellular carcinoma models, Osthole-mediated apoptosis appeared linked to HMGB1-related inflammatory signaling, highlighting its potential to modulate the local immune niche. Collectively, this convergence of systems-level predictions and dynamic structural evidence identifies Osthole as a promising multi-target candidate for the coordinated regulation of inflammation-associated tumor progression, providing a robust rationale for further experimental validation.

Background: Dry eye disease (DED) is a multifactorial ocular surface disorder characterized by tear film instability, inflammation, and neurosensory abnormalities. Current therapies remain limited by slow onset and suboptimal efficacy. Teriflunomide, an immunomodulatory agent approved for multiple sclerosis, has shown therapeutic potential in DED, but its multi-target mechanisms remain unclear. Methods: We employed an integrated computational and transcriptomic framework combining ADMET profiling, multi-dataset transcriptomic integration, and single-cell RNA sequencing (scRNA-seq) to identify disease-relevant targets. Candidate genes were further refined through molecular docking and 50 ns molecular dynamics (MD) simulations. The AetherCell virtual cell model was applied to evaluate both the concordance between target perturbation and drug-induced responses and the potential mechanistic roles of candidate targets. Results: Transcriptomic integration identified 16 consensus genes across heterogeneous DED models, which were further localized to disease-relevant epithelial and immune cell populations by scRNA-seq. Molecular simulations prioritized three core targets—CTSS, STAT1, and PTGS1—based on binding stability and affinity. AetherCell simulations demonstrated that perturbation of these targets not only recapitulated teriflunomide-induced transcriptional and pathway changes but also revealed their distinct mechanistic contributions, including epithelial barrier regulation (CTSS), microvascular and lipid homeostasis (PTGS1), and inflammation suppression coupled with tissue repair (STAT1). Conclusions: Teriflunomide exerts therapeutic effects in DED through coordinated multi-target regulation involving inflammation control, barrier restoration, and tissue repair. This study provides a rationale for novel therapeutic targets in dry eye disease, establishes a paradigm for applying virtual cell modeling to elucidate drug mechanisms, and offers a bioinformatics framework for validating drug repositioning outcomes.

Background/Objectives: Non-small-cell lung cancer (NSCLC) is one of the most common malignancies in Vietnam, yet its molecular mechanisms remain incompletely understood. This study aimed to identify prognostic genes in Vietnamese NSCLC patients using integrative transcriptomic and bioinformatics analyses. Methods: RNA-seq data from 30 Vietnamese NSCLC patients treated at Military Hospital 103 (January 2023–April 2024) were analyzed and cross-validated with the Gene Expression Omnibus (GEO) dataset GSE140343 to identify shared differentially expressed genes (DEGs). Subsequent analyses included functional enrichment (GO and KEGG), protein–protein interaction (PPI) network construction via STRING, and module/centrality analyses to pinpoint hub genes. Finally, prognostic significance was evaluated using overall survival data from The Cancer Genome Atlas (TCGA) via the GEPIA platform. Results: A total of 1900 shared DEGs were identified, most of which were enriched in cancer-related pathways. The resulting PPI network (comprising 1528 nodes and 8185 edges) yielded eight significant modules containing 64 high-centrality candidate genes. Survival analyses demonstrated that high expression of CCNA2 and S100A12, and low expression of ADRB2, ARRB1, PTGS2, and SMAD7 were significantly associated with poor overall survival in NSCLC patients. Conclusions: These findings highlight potential biomarkers for prognosis and may inform future therapeutic strategies in Vietnamese NSCLC patients.

Indigenous sheep breeds represent valuable reservoirs of genetic diversity shaped by long-term adaptation to local environments and management systems. Greek autochthonous sheep breeds remain underrepresented in genomic and functional studies. The objective of this study was to characterize and compare coding sequence variation in three indigenous Greek sheep breeds—Lesvos (LES), Serres (SER), and Thrace (THR)—and to identify shared and breed-associated functional patterns. The study was designed using a two-stage approach, comprising a discovery (exploratory) phase and a validation phase. In the discovery phase, whole genome sequencing data (one animal per breed; total n = 3; mean sequencing depth ~36.9×) were analyzed to identify protein-altering exonic variants, focusing on missense single-nucleotide polymorphisms (SNPs) and exonic insertions/deletions (indels). Variants were examined at breed-specific and comparative levels, followed by functional enrichment analyses using Gene Ontology (GO) and KEGG pathways. Normalized variant density metrics identified genes with elevated polymorphism levels. In the validation phase, a subset of prioritized missense SNPs was genotyped in an independent cohort of 54 animals (18 per breed) using MassARRAY genotyping. Genes harboring prioritized missense SNPs showed a conserved enrichment profile across breeds, dominated by genome maintenance, DNA repair, cytoskeletal organization, and core regulatory functions. Distinct breed-associated patterns were also observed. LES showed enrichment in metabolic, biosynthetic, and sensory-related processes, SER in regulatory and signaling functions, and THR in cytoskeletal, extracellular matrix, and organelle-associated pathways. Polymorphism density analyses highlighted highly variable genes across breeds, including olfactory receptor (OR) gene families, keratin-associated protein genes (KRTAPs), and loci involved in immune and regulatory functions (e.g., PRKDC, CDH15). The validation phase confirmed the expected allele frequency patterns for most prioritized SNPs, supporting the robustness of the approach. This study identifies functionally relevant coding variation across Greek indigenous sheep breeds, revealing conserved genomic patterns and breed-associated signatures linked to metabolic, structural, and regulatory processes.

The research investigates synthetic hydroxytyrosol (HT) antiviral properties against enveloped and non-enveloped viruses using in silico and in vitro methods. Molecular docking and ADMET analyses suggested favorable interactions of HT with ceramide and sphingomyelin (binding energies of −6.0 and −5.9 kcal/mol, respectively). Favorable predicted pharmacokinetics and safety profiles were also observed. In vitro tests provided preliminary evidence of the dose- and time-dependent virucidal effect of HT against several enveloped viruses, including HSV-1, West Nile virus, SARS-CoV-2 and various influenza A subtypes, which resulted in substantial viral load decreases at 1000 µg/mL. The viral titer of the measles virus decreased by 4.62 log10 units during the 2 h of exposure. No virucidal activity was observed against the non-enveloped bovine rotavirus. Overall, these findings suggest that hydroxytyrosol may represent a promising candidate for further investigation as a virucidal agent, particularly against enveloped viruses.

Lymphocyte activation gene-3 (LAG-3) is a pivotal immune checkpoint receptor that exerts a negative regulatory effect on T-cell function. Although LAG-3-blocking antibodies have shown promising clinical potential, the inherent limitations of conventional monoclonal antibodies necessitate the development of novel antibody formats with enhanced biological and pharmacological properties. In this study, a panel of single-domain antibodies (sdAbs) targeting human LAG-3 was generated via phage display technology. Among these candidates, 2H-G7 was identified as a high-affinity sdAb that binds to LAG-3 with an equilibrium dissociation constant (KD) in the nanomolar range. Notably, 2H-G7 potently blocks the interactions of LAG-3 with both of its key ligands, fibrinogen-like protein 1 (FGL1) and major histocompatibility complex class II (MHC-II). Its capacity to restore impaired T-cell function was validated by quantifying interleukin-2 (IL-2) secretion and CD69 expression in stimulated primary human peripheral blood mononuclear cells (PBMCs). Epitope mapping studies localized the binding site of 2H-G7 to the D1D2 extracellular domains of LAG-3, distinct from relatlimab, a clinically approved LAG-3-blocking antibody serving as the benchmark. In a xenogeneic mouse model of non-small-cell lung cancer (NSCLC), 2H-G7-Fc exhibited superior tumor growth inhibition efficacy compared with relatlimab. These findings demonstrate that 2H-G7 is a promising lead candidate for the development of next-generation LAG-3-targeted tumor immunotherapies.

The tumor microenvironment [TME] is a dynamic ecosystem where spatial organization and metabolic reprogramming play a crucial role in immune response, tumor progression, and therapeutic response. Recent breakthroughs in spatial transcriptomics, metabolomics, and multiplexed imaging studies have shown that complex immunometabolic niches are involved in therapeutic resistance, including conventional and immunotherapeutic approaches. Artificial intelligence [AI] technology has been recognized as a revolutionary concept that allows the integration of complex data, thereby facilitating the scalable extraction of spatial, molecular, and cellular features from routine histopathology and multi-omics platforms. This review of the current evidence on AI-based spatial immunometabolic studies of the tumor microenvironment aims to provide a comprehensive overview of the current evidence, including AI-based spatial immunometabolic studies of the tumor mi-croenvironment, with special reference to digital pathology, spatial transcriptomics, and multimodal data fusion. The current challenges, including data heterogeneity, model interpretability, generalizability, and biological validation, will be discussed. The emerging trends in AI-based spatial immunometabolism, including multimodal foundation models, federated learning, and spatially resolved target discovery, will be discussed. AI-based spatial immunometabolism will be a cornerstone in precision oncology, with the potential to improve patient stratification, therapeutic approaches, and clinical translation.

In recent years, plant stress biology has moved beyond single-pathway descriptions toward an integrated framework in which stress perception, hormonal control, and gene regulation are tightly interconnected. Early events such as membrane-associated sensing, calcium influx, reactive oxygen species (ROS) generation, and kinase activation converge with phytohormonal networks to shape context-dependent responses. Within this framework, abscisic acid, salicylic acid, jasmonates, ethylene, auxin, cytokinins, gibberellins, brassinosteroids, and strigolactones function not as isolated regulators but as components of a dynamic signaling matrix that balances survival, defense, growth restraint, and recovery. These hormonal signals are ultimately translated into adaptive outcomes through extensive transcriptional and post-transcriptional reprogramming mediated by transcription factors, RNA-based regulators, chromatin remodeling, and stress memory mechanisms. This review synthesizes current understanding of how plants integrate stress perception, phytohormonal crosstalk, and transcriptional regulation to establish stress tolerance. We first examine the molecular basis of stress sensing and early signaling. We then discuss the central functions of major phytohormones and the logic of hormone–hormone interaction networks in coordinating stress adaptation. Next, we analyze transcriptional, post-transcriptional, and epigenetic mechanisms that determine response specificity, intensity, and persistence. We further highlight points of convergence between abiotic and biotic stress responses and discuss how combined stresses challenge traditional single-stress models. Finally, we consider the roles of omics, systems biology, and translational technologies in decoding and engineering stress-resilient phenotypes. By integrating these perspectives, this review presents plant stress tolerance as a multilevel systems property and outlines key priorities for future research aimed at developing climate-resilient crops.

Chronic obstructive pulmonary disease (COPD) lacks reliable molecular biomarkers for early diagnosis and risk stratification beyond conventional spirometry-based assessment. Synthetic lethality (SL)-related gene prioritization provides a biologically informed framework for identifying disease-associated candidate biomarkers in COPD. In this study, we integrated public transcriptomic datasets, SL-related gene sets, and machine learning approaches to identify a diagnostic signature for COPD. Using GSE47460 as the training cohort (220 COPD and 108 controls) and GSE57148 as the external validation cohort (98 COPD and 91 controls), we identified 74 SL-related differentially expressed genes enriched in inflammatory signaling and extracellular matrix organization. LASSO regression and random forest analysis yielded a five-gene diagnostic signature consisting of CYP1B1, VEGFA, RET, FGG, and S100A9. The integrated nomogram showed good diagnostic performance in the validation cohort, with an AUC of 0.8311 (95% CI: 0.7839–0.8783), outperforming individual genes and supporting its potential use as an adjunctive molecular tool for COPD diagnosis and risk assessment. Single-cell RNA sequencing, immune infiltration analysis, and preliminary in vitro experiments further supported the biological relevance of the identified genes. Overall, this study supports SL-related gene prioritization combined with multi-omic integration as a useful strategy for COPD biomarker discovery while generating testable hypotheses regarding disease-associated vulnerability pathways.

The inflammatory response possesses a central role in human pathophysiology, regulating the tissue microenvironment and cell signaling. Inflammation occurs either as a symptom of homeostasis disturbance or as a driver for determining cell fate. In this context, cells recruit secreted cytokines, chemokines and intracellular mediators, in cooperation with their surrounding cellular components, to integrate inflammatory stimuli. The extracellular matrix (ECM) acts as a scaffold for shaping tissue structure and simultaneously undergoes continuous remodeling to provide a dynamic network for intercellular communication. Serglycin (SRGN) is the only known intracellular and extracellular proteoglycan, implicated in the formation of secretory vesicles and ECM reorganization. The regulatory roles of SRGN in the bioavailability of secreted factors, as well as SRGN pleiotropic interactions within the ECM, as well as with cell surface receptors, have emerged to beessential for inflammatory diseases and tumor progression. Its overexpression and excessive secretion, alongside its contribution to cell signaling, highlight the potential diagnostic and therapeutic aspects of SRGN in human diseases.