Hum Genomics. 2026 May 9. doi: 10.1186/s40246-026-00979-y. Online ahead of print.

ABSTRACT

Cellular senescence plays a context-dependent role in gastric cancer (GC), functioning both through tumor-suppressive arrest and the tumor-promoting senescence-associated secretory phenotype. However, its systematic integration into prognostic models remains limited. Here, we develop a novel interpretable framework to identify and validate a robust senescence-related gene signature for GC prognosis. We first introduce a dual-model interpretable feature selection strategy that integrates a biologically informed Kolmogorov-Arnold Network with a tabular foundation model to identify cancer-associated senescence genes. From the initial candidates, an ensemble of ten machine learning algorithms distills a core 4-gene signature to construct a Senescence Risk Score (SRS). The SRS proves to be a powerful and independent prognostic indicator, effectively stratifies patients into high- and low-risk groups with distinct overall survival across multiple cohorts. High-risk patients exhibit an "immune-hot" but potentially dysfunctional tumor microenvironment, characterized by enriched immune cell infiltration, elevated checkpoint expression, and distinct metabolic reprogramming favoring pathways such as angiogenesis and epithelial-mesenchymal transition (EMT). Furthermore, the SRS correlates with differential somatic mutation profiles and suggests potential sensitivity to specific chemotherapeutic agents. In vitro functional assays confirmed the oncogenic role of SERPINE1, a top-ranked core gene, in promoting GC cell proliferation. Regulatory network analysis revealed potential upstream transcription factors and miRNAs governing the signature. Collectively, we present a validated senescence-related prognostic signature that enables effective risk stratification of patients with gastric cancer.

PMID:42106891 | DOI:10.1186/s40246-026-00979-y

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

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

Nat Commun. 2026 Mar 20;17(1):2655. doi: 10.1038/s41467-026-70193-w.

ABSTRACT

Pulmonary fibrosis represents a progressive interstitial lung disease marked by excessive extracellular matrix deposition and architectural distortion. Vascular endothelial cells critically contribute to fibrogenesis through paracrine secretion of pro-fibrotic mediators, yet their mechanobiological regulation remains elusive. Using integrated single-cell multi-omics profiling of human pulmonary fibrosis specimens and experimental fibrosis models induced by bleomycin or silica, we identify mechanosensitive Piezo1 upregulation in Endothelial cells as a hallmark of fibrotic progression. Endothelial-specific Piezo1 knockout significantly attenuates Bleomycin-induced fibrotic remodeling in male mice, establishing its pathogenic necessity. Mechanistically, PIEZO1 activation promotes pulmonary fibrosis development via CAPN2-mediated STAT3 phosphorylation, which may regulate the secretion of the pro-fibrotic molecule interleukin-33. These findings suggest that the endothelial PIEZO1-CAPN2-STAT3-IL33 axis is a potential therapeutic target for PF intervention.

PMID:41862476 | PMC:PMC13004862 | DOI:10.1038/s41467-026-70193-w