Vet Sci. 2026 Aug 18;13(8):823. doi: 10.3390/vetsci13080823.

ABSTRACT

The equine gastrointestinal microbiome plays an essential role in digestion, energy metabolism, immune regulation, and maintenance of intestinal homeostasis. Increasing evidence suggests that microbial-derived metabolites provide a functional link between diet, the microbiome, and host physiology. This narrative review summarizes current knowledge on how dietary factors, including structural carbohydrates, non-structural carbohydrates, protein, and lipids, influence microbial function and metabolite production in horses. Relevant publications available up to June 2026 were identified through searches of PubMed, Web of Science, Scopus, and Google Scholar and were narratively synthesized according to dietary factors, microbial metabolites, associated diseases, and nutritional interventions. Emphasis is placed on major microbial metabolites, including short-chain fatty acids, endotoxins, bile acid derivatives, tryptophan metabolites, and nitrogenous fermentation products, and their potential roles in health and disease. Current evidence supports a central role for the nutrition-microbiota-metabolite axis in gastrointestinal disorders such as colic, colitis, equine gastric ulcer syndrome, and carbohydrate-associated laminitis, whereas its involvement in equine metabolic syndrome and the gut-lung axis remains less well defined. The review also discusses microbiome-targeted nutritional interventions and emerging multi-omics approaches that are improving our understanding of microbial function. Collectively, these findings highlight the potential of microbiome-informed nutritional strategies to support equine health, welfare, disease prevention, and athletic performance.

PMID:42655843 | PMC:PMC13517798 | DOI:10.3390/vetsci13080823

Discov Oncol. 2026 Mar 23. doi: 10.1007/s12672-026-04893-6. Online ahead of print.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide. This study aimed to develop prognostic prediction models for lung squamous cell carcinoma (LUSC) through multi-omics integration using Mendelian randomization analysis.This study addresses a critical gap in lung cancer research through two complementary approaches in major lung cancer subtypes: (1) hypothesis-generating multi-omics analysis in LUSC to identify prognostic biomarkers and characterize the metabolic-immune landscape. This integrated framework provides both predictive tools for personalized medicine and mechanistic insights into metabolic causality.

METHODS: Multi-omics analysis was performed using TCGA data, including RNA-seq, DNA methylation, and whole-exome sequencing. Machine learning models incorporating 15 algorithms were developed and externally validated in two independent GEO cohorts. Mendelian randomization analysis assessed causal relationships between 32 lipid metabolites and SCLC risk. RT-qPCR experiments validated key prognostic genes in lung squamous cell carcinoma (LUSC) cell lines.

RESULTS: The optimal machine learning model (StepCox [forward] + Random Survival Forest) demonstrated superior performance with C-index of 0.73 in internal testing and 0.71 and 0.68 in external validation cohorts. High CD8 + T cell and M1 macrophage infiltration was associated with favorable prognosis. Most lipid metabolites showed no significant causal associations with SCLC risk after multiple testing correction, though two phosphatidylcholine metabolites demonstrated potential protective effects. RT-qPCR validation confirmed significant upregulation of all four key genes in LUSC cell lines.

CONCLUSIONS: This study successfully developed robust machine learning-based prognostic models for LUSC with clinical utility for risk stratification and provided evidence that lipid alterations in lung cancer are likely downstream consequences rather than causal drivers of tumorigenesis.

PMID:41870745 | DOI:10.1007/s12672-026-04893-6

Discov Oncol. 2026 Mar 23. doi: 10.1007/s12672-026-04893-6. Online ahead of print.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide. This study aimed to develop prognostic prediction models for lung squamous cell carcinoma (LUSC) through multi-omics integration using Mendelian randomization analysis.This study addresses a critical gap in lung cancer research through two complementary approaches in major lung cancer subtypes: (1) hypothesis-generating multi-omics analysis in LUSC to identify prognostic biomarkers and characterize the metabolic-immune landscape. This integrated framework provides both predictive tools for personalized medicine and mechanistic insights into metabolic causality.

METHODS: Multi-omics analysis was performed using TCGA data, including RNA-seq, DNA methylation, and whole-exome sequencing. Machine learning models incorporating 15 algorithms were developed and externally validated in two independent GEO cohorts. Mendelian randomization analysis assessed causal relationships between 32 lipid metabolites and SCLC risk. RT-qPCR experiments validated key prognostic genes in lung squamous cell carcinoma (LUSC) cell lines.

RESULTS: The optimal machine learning model (StepCox [forward] + Random Survival Forest) demonstrated superior performance with C-index of 0.73 in internal testing and 0.71 and 0.68 in external validation cohorts. High CD8 + T cell and M1 macrophage infiltration was associated with favorable prognosis. Most lipid metabolites showed no significant causal associations with SCLC risk after multiple testing correction, though two phosphatidylcholine metabolites demonstrated potential protective effects. RT-qPCR validation confirmed significant upregulation of all four key genes in LUSC cell lines.

CONCLUSIONS: This study successfully developed robust machine learning-based prognostic models for LUSC with clinical utility for risk stratification and provided evidence that lipid alterations in lung cancer are likely downstream consequences rather than causal drivers of tumorigenesis.

PMID:41870745 | DOI:10.1007/s12672-026-04893-6

Nutrients. 2026 Mar 4;18(5):834. doi: 10.3390/nu18050834.

ABSTRACT

Background: Gut microbiota dysbiosis is critically implicated in the pathogenesis of allergic airway inflammation (AAI) via the gut-lung axis. While Latilactobacillus curvatus is a promising probiotic candidate, its specific immunomodulatory mechanisms in respiratory diseases remain poorly understood. Objective: In this study, we investigated the protective effects and underlying mechanisms of L. curvatus IM01 in an ovalbumin (OVA)-induced murine AAI model using an integrated multi-omics approach. Results: Our results demonstrated that oral administration of L. curvatus IM01 significantly attenuated airway inflammation, suppressed Th2-type immune responses, and reduced serum IgE levels. Crucially, our multi-omics integration revealed a coherent gut-lung axis narrative driven by microbial and metabolic crosstalk. Specifically, 16S rRNA sequencing indicated that L. curvatus IM01 was closely linked to structural shifts in the gut microbial community, notably characterized by an enrichment trend for beneficial genera such as Odoribacter and Lactobacillus. This microbial restructuring was closely associated with a modulated cecal metabolic profile, as untargeted metabolomics exhibited a clear trend toward the restoration of key systemically active immunoregulatory metabolites, including indolelactic acid (ILA) and choline, which have been previously linked to the alleviation of AAI symptoms. Further linking this metabolic shift to respiratory immune tolerance, lung transcriptomic analysis showed that the treatment is strongly associated with the promotion of the differentiation of CD4⁺ T cells into Foxp3⁺ regulatory T cells (Tregs). Conclusions: Collectively, these findings suggest a novel potential pathway by which L. curvatus IM01 modulates the gut-lung axis through coordinated microbial and metabolic interventions, highlighting its potential as a therapeutic functional food ingredient for AAI.

PMID:41830004 | PMC:PMC12987261 | DOI:10.3390/nu18050834

Nutrients. 2026 Mar 4;18(5):834. doi: 10.3390/nu18050834.

ABSTRACT

Background: Gut microbiota dysbiosis is critically implicated in the pathogenesis of allergic airway inflammation (AAI) via the gut-lung axis. While Latilactobacillus curvatus is a promising probiotic candidate, its specific immunomodulatory mechanisms in respiratory diseases remain poorly understood. Objective: In this study, we investigated the protective effects and underlying mechanisms of L. curvatus IM01 in an ovalbumin (OVA)-induced murine AAI model using an integrated multi-omics approach. Results: Our results demonstrated that oral administration of L. curvatus IM01 significantly attenuated airway inflammation, suppressed Th2-type immune responses, and reduced serum IgE levels. Crucially, our multi-omics integration revealed a coherent gut-lung axis narrative driven by microbial and metabolic crosstalk. Specifically, 16S rRNA sequencing indicated that L. curvatus IM01 was closely linked to structural shifts in the gut microbial community, notably characterized by an enrichment trend for beneficial genera such as Odoribacter and Lactobacillus. This microbial restructuring was closely associated with a modulated cecal metabolic profile, as untargeted metabolomics exhibited a clear trend toward the restoration of key systemically active immunoregulatory metabolites, including indolelactic acid (ILA) and choline, which have been previously linked to the alleviation of AAI symptoms. Further linking this metabolic shift to respiratory immune tolerance, lung transcriptomic analysis showed that the treatment is strongly associated with the promotion of the differentiation of CD4⁺ T cells into Foxp3⁺ regulatory T cells (Tregs). Conclusions: Collectively, these findings suggest a novel potential pathway by which L. curvatus IM01 modulates the gut-lung axis through coordinated microbial and metabolic interventions, highlighting its potential as a therapeutic functional food ingredient for AAI.

PMID:41830004 | PMC:PMC12987261 | DOI:10.3390/nu18050834

World J Oncol. 2026 Mar 5;17(2):247-255. doi: 10.14740/wjon2689. eCollection 2026 Apr.

ABSTRACT

BACKGROUND: We aimed to evaluate the potential association between genetically predicted vanin-2 (VNN2) expression and lung adenocarcinoma (LUAD) risk, and to explore the immune cell subtype that may underlie this relationship.

METHODS: We integrated whole-blood expression quantitative trait loci (eQTL) data from eQTLGen, plasma protein quantitative trait loci (pQTL) data from deCODE, and LUAD genome-wide association study (GWAS) data from European-ancestry cohorts, together with differential expression analysis using GEPIA2, to identify candidate genes for subsequent single-cell eQTL (sc-eQTL) Mendelian randomization (MR) analysis. For the sc-eQTL analysis, VNN2-associated eQTLs from 14 immune cell types profiled in the OneK1K single-cell eQTL resource were tested for associations with LUAD risk.

RESULTS: Bulk-level MR analysis showed that genetically predicted increases in VNN2 expression and protein levels were significantly associated with a reduced risk of LUAD (eQTL-MR: odds ratio (OR) = 0.964, 95% confidence interval (95% CI), 0.934-0.995; P = 0.024; pQTL-MR: OR = 0.946, 95% CI, 0.921-0.970; P = 2.87 × 10^-5). Transcriptomic analyses confirmed significant downregulation of VNN2 in LUAD tumors compared with normal lung tissues. sc-eQTL MR identified the strongest association in resting natural killer (rNK) cells (OR = 0.896, 95% CI, 0.829-0.967; P = 0.005).

CONCLUSIONS: Multi-omics and sc-eQTL MR analyses indicated that genetically predicted increases in VNN2 expression were associated with a reduced risk of LUAD, with the most pronounced effect observed in rNK cells. These findings suggest a potential cell type-specific role of VNN2 in LUAD susceptibility and warrant further studies to validate its biological relevance and clinical implications.

PMID:41822323 | PMC:PMC12978397 | DOI:10.14740/wjon2689