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Integrated multi-omics profiling reveals phenotype- and tissue-specific host-microbiota interactions in paired tumor and peritumoral tissues of advanced gastric cancer patients from Northwest China

Front Cell Infect Microbiol. 2026 Apr 20;16:1763765. doi: 10.3389/fcimb.2026.1763765. eCollection 2026.

ABSTRACT

BACKGROUND: Advanced gastric cancer (AGC) exhibits a high incidence in Northwest China, largely attributed to region-specific dietary patterns and environmental exposures. Its pathogenesis involves complex host-microbiota crosstalk, which has not yet been comprehensively elucidated through integrated multi-omics approaches. Herein, we employed trasncriptomic and shotgun metagenomic sequencing on paired tumoral and peritumoal mucosal tissues from 88 AGC patients in Northwest China. Our aim was to systematically characterize host gene expression profiles, the composition and functional potential of the gastric mucosal microbiota, and their intricate interrelationships.

RESULTS: Transcriptomic profiling clearly distinguished tumoral from peritumoral regions (PERMANOVA, R2 = 0.24, P = 0.0001), with 8870 differentially expressed genes (DEGs) identified between the two tissue types. Tumor tissues harbored 8377 up-regulated DEG, which were enriched in extracellular matrix (ECM) organization, cell cycle regulation, signaling transduction, and inflammatory pathways (e.g., PI3K-Akt, IL-17 signaling). In contrast, peritumoral tissues showed 493 up-regulated DEGs primarily associated with metabolic processes. Host gene expression was significantly modulated by Lauren classification in tumoral mucosa (P = 0.025) and by Helicobacter pylori (Hp) infection in peritumoral tissues (P = 0.0424). Hp-infected tissues exhibited 65 up-regulated DEGs linked to transcriptional misregulation in cancer, inflammation, immune activation and mitochondrial pathways. Lauren subtypes displayed distinct transcriptomic signatures: intestinal-type AGC was enriched in metabolic processes, diffuse-type in immune and signal transduction pathways, and mixed-type in Ras/MAPK/ErbB and NF-κB signaling pathways. Correlation analysis between the 8870 DEGs and seven differentially abundant bacterial species (e.g., Serratia surfactantfaciens, Pseudomonas protegens, Prevotella jejuni, and Streptococcus infantis) revealed 13199 significant correlations. Among these, S. surfactantfaciens and P. protegens exhibited the strongest connectivity with host genes. Functionally, the correlated DEGs were involved in ECM structure, cell cycle progression, immune and inflammatory responses, cellular proliferation and differentiation, and metabolic processes.

CONCLUSIONS: Our findings demonstrated phenotype- and tissue-specific regulation of host gene expression in AGC and revealed extensive host-microbe interactions. This work fills a critical gap in multi-omics research on AGC in the Northwest Chinese population and suggests potential diagnostic and therapeutic targets for AGC.

PMID:42088021 | PMC:PMC13136179 | DOI:10.3389/fcimb.2026.1763765

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Multi-omics integration and machine learning reveal gut-immune signatures in idiopathic pulmonary fibrosis: insights from bulk RNA-seq, single-cell profiles, spatial transcriptomics, and experimental validation

Front Immunol. 2026 Mar 19;17:1730289. doi: 10.3389/fimmu.2026.1730289. eCollection 2026.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal lung disease with limited treatment options and a poor prognosis. Recent studies suggest a critical role for the gut-immune-lung axis in IPF, yet the underlying molecular mechanisms remain unclear.

METHODS: The current study performed in silico multi-omics integration of publicly available datasets, including bulk RNA-seq, single-cell and spatial transcriptomics, as well as peripheral blood multi-omics data to uncover key molecular signatures in IPF. Furthermore, machine learning techniques were utilized to identify core genes, whereas functional analyses and Mendelian randomization were conducted to evaluate the causal relationships among gut microbiota, immune cells, and IPF. Additionally, experimental validation using qPCR and ELISA assays was conducted in vitro, in vivo, and in patient plasma to confirm the expression patterns of key genes.

RESULTS: Across integrated public bulk, single-cell, spatial, and blood multi-omics, CXCL13, IL33, TLR4, and IGF1 were identified as core IPF genes consistently linked to immune infiltration and fibrotic remodeling. Deconvolution, scRNA-seq, and spatial mapping localized their dysregulation to fibroblasts and immune compartments (notably B-cell, macrophage, and mast-cell axes), highlighting fibroblast-immune crosstalk in fibrotic foci. A four-gene model robustly distinguished IPF from controls across cohorts. Mendelian randomization supported a gut-immune-lung axis, indicating causal effects of specific gut taxa on IPF risk via immune phenotypes. qPCR/ELISA in TGF-β1-stimulated fibroblasts, bleomycin mouse lungs, and patient plasma corroborated upregulation of IL33, CXCL13, IGF1 and downregulation of TLR4. Drug-signature reversal nominated cucurbitacin I and temsirolimus; molecular docking was performed as a preliminary in silico, computer-simulation-based assessment of potential ligand-protein interactions between these compounds and the four core targets.

CONCLUSION: This study provides new insights into the importance of gut-immune-lung axis in IPF and identifies CXCL13, IL33, TLR4, and IGF1 as diagnostic signatures and therapeutic targets. By integrating public multi-omics resources with experimental validation, our findings offer a foundation for future diagnostic and treatment strategies aimed at modulating the gut microbiota and immune system in IPF.

PMID:41939867 | PMC:PMC13043422 | DOI:10.3389/fimmu.2026.1730289

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Multi-omics integration and machine learning reveal gut-immune signatures in idiopathic pulmonary fibrosis: insights from bulk RNA-seq, single-cell profiles, spatial transcriptomics, and experimental validation

Front Immunol. 2026 Mar 19;17:1730289. doi: 10.3389/fimmu.2026.1730289. eCollection 2026.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal lung disease with limited treatment options and a poor prognosis. Recent studies suggest a critical role for the gut-immune-lung axis in IPF, yet the underlying molecular mechanisms remain unclear.

METHODS: The current study performed in silico multi-omics integration of publicly available datasets, including bulk RNA-seq, single-cell and spatial transcriptomics, as well as peripheral blood multi-omics data to uncover key molecular signatures in IPF. Furthermore, machine learning techniques were utilized to identify core genes, whereas functional analyses and Mendelian randomization were conducted to evaluate the causal relationships among gut microbiota, immune cells, and IPF. Additionally, experimental validation using qPCR and ELISA assays was conducted in vitro, in vivo, and in patient plasma to confirm the expression patterns of key genes.

RESULTS: Across integrated public bulk, single-cell, spatial, and blood multi-omics, CXCL13, IL33, TLR4, and IGF1 were identified as core IPF genes consistently linked to immune infiltration and fibrotic remodeling. Deconvolution, scRNA-seq, and spatial mapping localized their dysregulation to fibroblasts and immune compartments (notably B-cell, macrophage, and mast-cell axes), highlighting fibroblast-immune crosstalk in fibrotic foci. A four-gene model robustly distinguished IPF from controls across cohorts. Mendelian randomization supported a gut-immune-lung axis, indicating causal effects of specific gut taxa on IPF risk via immune phenotypes. qPCR/ELISA in TGF-β1-stimulated fibroblasts, bleomycin mouse lungs, and patient plasma corroborated upregulation of IL33, CXCL13, IGF1 and downregulation of TLR4. Drug-signature reversal nominated cucurbitacin I and temsirolimus; molecular docking was performed as a preliminary in silico, computer-simulation-based assessment of potential ligand-protein interactions between these compounds and the four core targets.

CONCLUSION: This study provides new insights into the importance of gut-immune-lung axis in IPF and identifies CXCL13, IL33, TLR4, and IGF1 as diagnostic signatures and therapeutic targets. By integrating public multi-omics resources with experimental validation, our findings offer a foundation for future diagnostic and treatment strategies aimed at modulating the gut microbiota and immune system in IPF.

PMID:41939867 | PMC:PMC13043422 | DOI:10.3389/fimmu.2026.1730289

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Tuning the sensitivity of mechanosensory receptors through histidine scanning

Histidine scanning represents a broadly applicable technique for the identification of critical interaction sites within TCRs and other mechanosensory receptors to enhance receptor signaling strength and augment therapeutic efficacy via the catch bond mechanism.
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