Nat Commun. 2026 Aug 7;17(1):9491. doi: 10.1038/s41467-026-76495-3.

ABSTRACT

Cancer-associated fibroblasts comprise diverse functionally distinct cellular subsets, with certain subpopulations exerting pivotal influence in shaping the pancreatic cancer immune microenvironment. Here we show that Lin28b⁺ cancer-associated fibroblasts contribute to establishing an immunologically cold tumor microenvironment in pancreatic ductal adenocarcinoma. Mechanistically, Lin28b directly binds to STING mRNA and promotes its degradation, thereby suppressing STING expression and downstream type I interferon signaling. Loss of Lin28b in cancer-associated fibroblasts activates the cGAS-STING-interferon signaling cascade, enhancing dendritic cell antigen presentation and CD8⁺ T cell cytotoxic function. Importantly, genetic inhibition of Lin28b in cancer-associated fibroblasts enhances sensitivity to anti-PD-L1 immune checkpoint blockade therapy. These findings reveal that targeting the Lin28b-STING axis represents a promising therapeutic strategy for overcoming the intrinsic resistance of pancreatic ductal adenocarcinoma to immunotherapy.

PMID:42693143 | PMC:PMC13542369 | DOI:10.1038/s41467-026-76495-3

Pharmacol Res. 2026 May 17:108253. doi: 10.1016/j.phrs.2026.108253. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) exemplifies early systemic dissemination, with circulating tumor cells (CTCs) at its core. We advance a unified conceptual framework that positions CTCs as the systemic execution hub of PDAC metastasis, dynamic entity that coordinates the metastatic cascade via four cardinal functions: Seeding, Adapting, Engineering, and Signaling. Integrating eco-evolutionary dynamics, this hub actively drives phenotypic selection, niche remodeling, and immune evasion, while providing real-time biologic intelligence through liquid biopsy. Robust clinical correlation has not yet translated into routine practice because of technical variability, biological complexity, and a lack of interventional evidence. We therefore propose an evidence-driven, phased roadmap: grounded in prospective clinical cohort data, progressing from immediate multi-center technical standardization and pragmatic trials, such as minimal residual disease (MRD)-triggered salvage therapy, to mid-term biomarker-driven adjuvant trials and long-term integration into multimodal liquid biopsy ecosystems, aimed at intercepting this execution hub. By reframing CTCs from correlative indicators to actionable therapeutic targets and dynamic sentinels, this framework charts a path toward transforming the management of this recalcitrant systemic disease.

PMID:42150733 | DOI:10.1016/j.phrs.2026.108253

Commun Biol. 2026 Apr 11. doi: 10.1038/s42003-026-10033-1. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a complex disease without clear etiology or effective therapy. While DNA methylation has been implicated in IPF pathogenesis, the tissue-specific causal effects of the epigenetic factors on IPF remain undetermined. Here, we perform epigenome-wide Mendelian randomization using blood-based methylation quantitative trait loci of 420,509 CpG sites and genome-wide association study for IPF to elucidate the causal effects of the CpG sites on IPF. Totally, 452 CpG sites has shown putative causal effects on IPF risk after Bonferroni correction. Among them, 13 CpG sites have shown strong colocalization evidence with genetic factors associated with IPF. Specifically, DNA methylation at CpG sites within MAN2A2 and TRIM27 shows significant differences between IPF lungs and controls, correlating with altered mRNA expressions of these genes in lung tissues. The CpG site in MAN2A2 is a binding site of ZNF384 according to transcription factor databases. RNA sequencing in the TGFβ1-induced alveolar epithelia confirms significantly reduced expression of MAN2A2 and ZNF384 comparing to the controls. Collectively, our study suggests a putative causal link between DNA methylation within MAN2A2 and IPF risk, wherein lung-specific DNA methylation in MAN2A2 may perturb the interaction between ZNF384 and MAN2A2, revealing novel roles for these genes in IPF pathogenesis.

PMID:41965819 | DOI:10.1038/s42003-026-10033-1

Commun Biol. 2026 Apr 11. doi: 10.1038/s42003-026-10033-1. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a complex disease without clear etiology or effective therapy. While DNA methylation has been implicated in IPF pathogenesis, the tissue-specific causal effects of the epigenetic factors on IPF remain undetermined. Here, we perform epigenome-wide Mendelian randomization using blood-based methylation quantitative trait loci of 420,509 CpG sites and genome-wide association study for IPF to elucidate the causal effects of the CpG sites on IPF. Totally, 452 CpG sites has shown putative causal effects on IPF risk after Bonferroni correction. Among them, 13 CpG sites have shown strong colocalization evidence with genetic factors associated with IPF. Specifically, DNA methylation at CpG sites within MAN2A2 and TRIM27 shows significant differences between IPF lungs and controls, correlating with altered mRNA expressions of these genes in lung tissues. The CpG site in MAN2A2 is a binding site of ZNF384 according to transcription factor databases. RNA sequencing in the TGFβ1-induced alveolar epithelia confirms significantly reduced expression of MAN2A2 and ZNF384 comparing to the controls. Collectively, our study suggests a putative causal link between DNA methylation within MAN2A2 and IPF risk, wherein lung-specific DNA methylation in MAN2A2 may perturb the interaction between ZNF384 and MAN2A2, revealing novel roles for these genes in IPF pathogenesis.

PMID:41965819 | DOI:10.1038/s42003-026-10033-1

Oncol Res. 2026 Mar 23;34(4):27. doi: 10.32604/or.2026.070208. eCollection 2026.

ABSTRACT

BACKGROUND: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression; however, their contribution to the functional classification and personalized treatment of gastric cancer remains poorly defined. This study aimed to identify effective therapeutic targets to facilitate individualized treatment strategies for patients with gastric cancer.

METHODS: Single-cell and bulk transcriptomic analyses were integrated to characterize gastric cancer fibroblasts. "Seurat", "Slingshot", and "CellChat" were used for dimensionality reduction, trajectory inference, and cell-cell communication analyses, respectively. Key metastasis-associated fibroblast modules were identified using High-dimensional weighted gene co-expression network analysis (hdWGCNA) to construct a prognostic model, which was further evaluated for immune infiltration, therapeutic response, and mutational features. The expression and function of the core gene tripeptidyl peptidase 1 (TPP1) were validated through immunoblotting, PCR, and functional assays.

RESULTS: Eight fibroblast subpopulations associated with gastric cancer metastasis exhibited distinct differentiation trajectories and transcriptional heterogeneity. Prognostic analysis indicated that metastasis-associated fibroblasts correlated with poor clinical outcomes. The high-risk subgroup showed marked immunosuppression, resistance to immunotherapy, and reduced mutational burden, with tumor progression-related pathways significantly enriched in this group. In vitro experiments further confirmed that TPP1 knockdown suppressed gastric cancer cell metastasis, invasion, and clonogenic capacity while inducing apoptosis.

CONCLUSION: This study characterized the heterogeneity of gastric cancer-associated fibroblasts using single-cell transcriptomic analysis and established a prognostic model based on metastasis-related fibroblast markers. The model demonstrated strong predictive performance for patient prognosis, immune landscape, and immunotherapy response. Furthermore, the findings highlighted the pivotal role of TPP1 in gastric cancer progression and its potential as a therapeutic target.

PMID:41930144 | PMC:PMC13040347 | DOI:10.32604/or.2026.070208

Cancer Immunol Immunother. 2026 Apr 2;75(4):131. doi: 10.1007/s00262-026-04374-3.

ABSTRACT

Immunotherapy has revolutionized cancer treatment, yet characterizing the spatial complexity of the tumor immune microenvironment remains a challenge. In this study, we established a comprehensive computational framework integrating multi-omics profiling across 27 cancer types to decode immune-related non-coding RNA regulatory networks. Moving beyond traditional bulk analysis, we utilized spatial transcriptomics to dissect the spatial localization of these regulators. We identified the SNHG6-BIRC5 axis as a critical driver of the "immune-cold" phenotype in lung adenocarcinoma. We provide visual evidence that this axis localizes to tumor nests and negatively correlates with T- cell infiltration, elucidating a mechanism of spatial immune exclusion. Validating the clinical relevance of these findings, genome-scale CRISPR-Cas9 screening data confirmed the functional essentiality of these targets for cancer cell survival. Furthermore, pharmacogenomic analysis revealed that high expression of this axis correlates with sensitivity to chemotherapy agents like Vinblastine, suggesting a potential stratification strategy for patients with immune-excluded tumors. To expand the clinical utility to immunotherapy prediction, we developed a pan-cancer XGBoost machine learning model incorporating 14 high-performance regulatory features. This model achieved robust performance in distinguishing immunotherapy responders from non-responders with an AUC of 0.771, outperforming traditional markers such as PD-L1. Collectively, this study highlights spatial determinants of immune exclusion and chemotherapy sensitivity- and presents a generalized machine- learning tool for precision immunotherapy stratification. The developed online resource is freely available to facilitate community-wide biomarker discovery.

PMID:41925746 | DOI:10.1007/s00262-026-04374-3

Cancer Immunol Immunother. 2026 Apr 2;75(4):131. doi: 10.1007/s00262-026-04374-3.

ABSTRACT

Immunotherapy has revolutionized cancer treatment, yet characterizing the spatial complexity of the tumor immune microenvironment remains a challenge. In this study, we established a comprehensive computational framework integrating multi-omics profiling across 27 cancer types to decode immune-related non-coding RNA regulatory networks. Moving beyond traditional bulk analysis, we utilized spatial transcriptomics to dissect the spatial localization of these regulators. We identified the SNHG6-BIRC5 axis as a critical driver of the "immune-cold" phenotype in lung adenocarcinoma. We provide visual evidence that this axis localizes to tumor nests and negatively correlates with T- cell infiltration, elucidating a mechanism of spatial immune exclusion. Validating the clinical relevance of these findings, genome-scale CRISPR-Cas9 screening data confirmed the functional essentiality of these targets for cancer cell survival. Furthermore, pharmacogenomic analysis revealed that high expression of this axis correlates with sensitivity to chemotherapy agents like Vinblastine, suggesting a potential stratification strategy for patients with immune-excluded tumors. To expand the clinical utility to immunotherapy prediction, we developed a pan-cancer XGBoost machine learning model incorporating 14 high-performance regulatory features. This model achieved robust performance in distinguishing immunotherapy responders from non-responders with an AUC of 0.771, outperforming traditional markers such as PD-L1. Collectively, this study highlights spatial determinants of immune exclusion and chemotherapy sensitivity- and presents a generalized machine- learning tool for precision immunotherapy stratification. The developed online resource is freely available to facilitate community-wide biomarker discovery.

PMID:41925746 | PMC:PMC13046951 | DOI:10.1007/s00262-026-04374-3