Drug Des Devel Ther. 2026 May 18;20:603491. doi: 10.2147/DDDT.S603491. eCollection 2026.

ABSTRACT

This review systematically summarizes the chemical composition, mechanisms of action, and recent progress in research and clinical applications of Yupingfeng San (YPFS) in various diseases. Research indicates that YPFS contains abundant active constituents-such as flavonoids, coumarins, and terpenoids, and demonstrates diverse biological activities,including immune modulation, anti-inflammatory, antiviral, antibacterial, and antitumor activities. Recent pharmacological and network pharmacology studies have elucidated that YPFS regulates key signaling pathways - such as PI3K-Akt, NF-κB, TLR4/MyD88, and JAK-STAT - through multi-target and multi-pathway mechanisms. These effects contribute to its therapeutic role in allergic rhinitis (AR), asthma, atopic dermatitis (AD), liver cancer, lung cancer, and other conditions. Although limited clinical trials and meta-analyses suggest that YPFS, when combined with conventional therapies, can improve treatment efficacy, relieve symptoms, and demonstrate good safety and tolerability, current research is constrained by low evidence quality and the absence of standardized quality control measures. Future research should prioritize large-scale, multi-center clinical trials and integrate multi-omics approaches to identify the main active components and mechanisms of action.

PMID:42182585 | PMC:PMC13196821 | DOI:10.2147/DDDT.S603491

Drug Des Devel Ther. 2026 May 18;20:603491. doi: 10.2147/DDDT.S603491. eCollection 2026.

ABSTRACT

This review systematically summarizes the chemical composition, mechanisms of action, and recent progress in research and clinical applications of Yupingfeng San (YPFS) in various diseases. Research indicates that YPFS contains abundant active constituents-such as flavonoids, coumarins, and terpenoids, and demonstrates diverse biological activities,including immune modulation, anti-inflammatory, antiviral, antibacterial, and antitumor activities. Recent pharmacological and network pharmacology studies have elucidated that YPFS regulates key signaling pathways - such as PI3K-Akt, NF-κB, TLR4/MyD88, and JAK-STAT - through multi-target and multi-pathway mechanisms. These effects contribute to its therapeutic role in allergic rhinitis (AR), asthma, atopic dermatitis (AD), liver cancer, lung cancer, and other conditions. Although limited clinical trials and meta-analyses suggest that YPFS, when combined with conventional therapies, can improve treatment efficacy, relieve symptoms, and demonstrate good safety and tolerability, current research is constrained by low evidence quality and the absence of standardized quality control measures. Future research should prioritize large-scale, multi-center clinical trials and integrate multi-omics approaches to identify the main active components and mechanisms of action.

PMID:42182585 | PMC:PMC13196821 | DOI:10.2147/DDDT.S603491

Gland Surg. 2026 Apr 30;15(4):91. doi: 10.21037/gs-2025-396. Epub 2026 Mar 27.

ABSTRACT

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies due to a lack of reliable tools for individualized risk stratification. A comprehensive understanding of the multi-omics landscape may uncover clinically applicable biomarkers and inform precision prognostic assessment. This study aims to establish a prognostic model directly from the complete omics landscape and extract biomarkers.

METHODS: We developed prognostic models using multi-omics data from a PDAC proteogenomic cohort comprising 75 deceased tumor samples. An independent cohort of 63 deceased PDAC cases from The Cancer Genome Atlas (TCGA)-pancreatic adenocarcinoma (PAAD) was used for external validation. Logistic regression models with least absolute shrinkage and selection operator (LASSO) regularization were constructed, and SHapley Additive exPlanations (SHAP) were applied to evaluate feature importance and identify signature genes. Model selection was based on the average area under the receiver operating characteristic curve (AUROC) across cross-validation folds. Functional validation was performed in PANC-1 cells by knockdown (KD) or overexpression (OE) of representative microRNA-, RNA-, and proteomics-derived signature genes, followed by Cell Counting Kit-8 (CCK-8) proliferation and Transwell migration assays.

RESULTS: Systematic evaluation of 120 multi-omics combinations identified a top-performing prognostic model integrating RNA, microRNA, proteomics, and mutation features. This model achieved a mean AUROC of 0.92±0.11 and accuracy of 0.87±0.01 on internal validation, and 0.99±0.00 and 0.98±0.01 on the TCGA test set. The sensitivity, specificity, precision, recall and F1 scores on the TCGA test set were 0.98±0.01, 0.97±0.02, 0.98±0.02, 0.98±0.01, 0.98±0.01, respectively. SHAP analysis revealed interpretable and clinically relevant prognostic biomarkers, many of which are implicated in immune signaling, metabolic regulation, and cell cycle control. Importantly, modulation of representative signature genes in PANC-1 cells significantly altered proliferation and migration in directions consistent with model-predicted risk associations.

CONCLUSIONS: Our findings demonstrate that explainable multi-omics machine learning frameworks can identify robust prognostic biomarkers and achieve highly accurate survival prediction in PDAC. Functional validation further supports the biological relevance of these signatures, underscoring their translational potential for personalized risk assessment.

PMID:42164702 | PMC:PMC13184197 | DOI:10.21037/gs-2025-396

Front Cell Infect Microbiol. 2026 Feb 23;16:1732958. doi: 10.3389/fcimb.2026.1732958. eCollection 2026.

ABSTRACT

INTRODUCTION: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, is a form of malignant pulmonary nodule that requires clinical differentiation from benign pulmonary nodules (BPN). The mechanisms underlying the development of LUAD are complex, and effective non-invasive methods for differentiating BPN from LUAD are lacking. This study aimed not only to distinguish BPN from LUAD using gut fungi and serum metabolites, but also to establish an integrated network of gut fungi-metabolite-cytokine interactions.

METHODS: Fecal and serum samples from individuals with BPN and patients with LUAD were subjected to internal transcribed spacer sequencing, ultra-performance liquid chromatography-tandem mass spectrometry, and multiplex Luminex assays to quantify gut fungi, metabolites, and cytokines, respectively.

RESULTS: A significant difference in gut fungal communities was observed between the BPN and LUAD groups. Multiple genera and species were more abundant in LUAD than in BPN. Docosapentaenoic acid n-6 (DPAn-6), indole-3-propionic acid (IPA), and interferon-γ-induced protein 10 (IP-10) were significantly elevated in the LUAD group. The integrated model established using a combination of gut fungi and metabolites demonstrated excellent performance in distinguishing BPN from LUAD. A network of interactions was established among differentially abundant gut fungi, serum metabolites, and cytokines.

CONCLUSION: Our study identifies a novel panel of fungal and metabolite biomarkers for differentiating between BPN and LUAD, and constructs a multi-omics network that provides new insights into investigating the mechanistic role of gut mycobiota dysbiosis in LUAD.

PMID:41809995 | PMC:PMC12968269 | DOI:10.3389/fcimb.2026.1732958