Front Immunol. 2026 Mar 19;17:1686658. doi: 10.3389/fimmu.2026.1686658. eCollection 2026.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) remains an intractable cancer marked by delayed diagnosis, rapid progression, and significant resistance to current treatments. Conventional biomarkers, such as CA19-9, have insufficient sensitivity and specificity. Meanwhile, the practical use of newer markers such as the tumor mutational burden and microsatellite instability is limited by the absence of standardized testing protocols and definitive threshold values. Circulating tumor DNA and exosomal miRNA hold promise for continuously tracking tumor dynamics and effectiveness of immunotherapy, but additional validation is necessary before their routine clinical application. Recent advancements in multiomics, nanotechnology, and artificial intelligence have opened new possibilities for more accurate and comprehensive biomarkers. For instance, Shah et al. developed shortwave-infrared-emitting nanoprobes to specifically target CD8⁺ cytotoxic T cells, permitting high-sensitivity in vivo imaging in breast cancer models. Batool et al. utilized nanoplasmonic sensors to detect changes in serum programmed death-ligand 1 and cytokine levels within 1-2 weeks post-treatment, achieving picomolar sensitivity. Chang et al. combined fluorescence and photoacoustic imaging in the NanoTrackThera platform, facilitating the real-time monitoring of immunotherapy efficacy. This review highlights the evolution of PDAC biomarkers from traditional markers to multimodal integration and dynamic monitoring. The limitations of current markers and potential of emerging technologies, including metabolic reprogramming markers, epigenetic regulators, and AI-driven predictive models, are discussed. Future directions include multicenter prospective trials to validate multimodal models, standardize detection methods, and increase interdisciplinary collaboration. By integrating genomic, epigenetic, metabolic, and microbiome data, these models can better capture the complexity of PDAC, thereby improving patient outcomes through precision immunotherapy.

PMID:41939911 | PMC:PMC13044031 | DOI:10.3389/fimmu.2026.1686658

Transl Oncol. 2026 Apr 3;67:102748. doi: 10.1016/j.tranon.2026.102748. Online ahead of print.

ABSTRACT

BACKGROUND: Epithelial ovarian cancer (EOC) encompasses five major histological subtypes with marked genetic, immunological, and clinical heterogeneity. While genome-wide association studies (GWAS) have identified subtype-specific risk loci, a critical gap remains in understanding how plasma proteins influence immune-cell traits and contribute to EOC pathogenesis.

METHODS: We integrated subtype-stratified GWAS data from two EOC cohorts with plasma proteomics and immune-cell traits to construct protein-immune-EOC regulatory landscapes using a three-stage Mendelian randomization framework. Single-cell RNA-seq and multiplex immunofluorescence were employed to delineate the cellular distribution and spatial context of causal proteins. Subsequent analyses characterized immune infiltration, macrophage polarization, and clinicopathological associations. Drug-gene correlations were used to identify potential therapeutic targets, and transcriptomic analyses were applied to delineate the underlying transcriptional landscape.

RESULTS: We identified 20 subtype-specific protein-immune-EOC regulatory axes, with FCGR2B emerging as a causal plasma protein in immune regulation and high-grade serous ovarian cancer (HGSOC) progression. FCGR2B was highly expressed in tumor-associated macrophages and was associated with an M2-like polarization phenotype. Functional characterization revealed that FCGR2B was associated with shorter progression-free survival and an immunosuppressive tumor microenvironment. Transcriptomic analyses revealed altered NF-κB signaling upon FCGR2B knockdown, and drug-response data suggested a potential association between high FCGR2B expression and sensitivity to NF-κB inhibitors.

CONCLUSIONS: These findings delineate subtype-specific genetically informed protein-immune regulatory landscapes in EOC and identify FCGR2B as a key immunoregulatory and prognostic biomarker in HGSOC, suggesting FCGR2B as a potential therapeutic vulnerability that warrants further investigation.

PMID:41934917 | DOI:10.1016/j.tranon.2026.102748

Adv Sci (Weinh). 2026 Apr 3:e17640. doi: 10.1002/advs.202517640. Online ahead of print.

ABSTRACT

The embryonic coelomic mesothelium acts as a critical progenitor hub during mammalian organogenesis, undergoing epithelial-to-mesenchymal transition (EMT) to drive vascular growth and parenchymal development in visceral organs. A prominent example is the epicardium, which plays an essential role during heart development. The principles of gene regulation in the coelomic mesothelium remain poorly defined. Specifically, it is unclear how cis-regulatory elements, including enhancers, orchestrate the spatiotemporal patterns of gene expression required for mesothelial identity and function. Here, a multi-omic approach was used to identify trans- and cis-regulatory elements that regulate mesothelial gene expression in three organs: heart, lung, and pancreas. This analysis uncovers a cardiac-specific regulatory circuit in which the transcription factor (TF) TBX20 selectively activates epicardial enhancers to orchestrate essential developmental programs. In contrast, TF MAF orchestrates pan-mesothelial gene expression via conserved CREs, which are absent in non-mesothelial lineages. Our integrated genomic analysis reveals MAF as a central custodian of mesothelial identity, a role underscored by its negative correlation with EMT, evolutionary conservation, and dynamic regulatory activity throughout development. Our work establishes a foundational blueprint of the gene regulatory landscape governing the coelomic mesothelium, defining both conserved principles and organ-specific mechanisms of spatiotemporal gene expression during early mammalian development.

PMID:41933934 | DOI:10.1002/advs.202517640

Eur J Cancer. 2026 Mar 24;239:116699. doi: 10.1016/j.ejca.2026.116699. Online ahead of print.

ABSTRACT

INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection.

METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated.

RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity.

CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.

PMID:41930854 | DOI:10.1016/j.ejca.2026.116699

Front Immunol. 2026 Mar 18;17:1791707. doi: 10.3389/fimmu.2026.1791707. eCollection 2026.

ABSTRACT

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a complex tumor ecosystem that contributes to its progression. Deubiquitinases (DUBs) are vital regulators in cancer. However, the overall activity of DUBs and their role in driving PDAC progression within immune microenvironment remain largely unknown.

METHODS: We employed an integrative multi-omics strategy combining machine learning (ML) on bulk transcriptomic data, single-cell RNA sequencing and spatial transcriptomic profiling. We applied Coxnet and Fuzzy SVM for prognostic modeling, inferCNV for malignant cell identification, SCENIC for transcription factor regulon analysis, LIANA⁺ for inferring inter-cellular communication networks and cell2location for spatial deconvolution. USP54 expression was detected by real-time quantitative PCR, western blotting and immunohistochemistry. USP54 function was validated through in vitro and in vivo assays.

RESULTS: ML-based pathway analysis revealed post-translational modification as a major prognostic category, within which elevated DUBs activity emerged as an independent adverse prognostic factor. At the single-cell level, USP54 was upregulated along the trajectory of malignant ductal cells and correlated with an inflamed tumor microenvironment. Cell-cell communication analysis predicted signaling from monocytes/macrophages to tumor cells via the THBS1-integrin ligand-receptor pair. This immune-derived signaling potentially converged on KLF5-positive tumor cells, with KLF5 identified as a putative transcriptional activator of USP54. Spatial transcriptomics validated the co-localization of USP54 expression, elevated DUB activity, and KRAS signaling within specific tumor niches adjacent to THBS1-enriched immune regions. High USP54 expression was frequently observed in PDAC tissues and associated with poor patient survival. More importantly, in both BxPC-3 and PANC-1 cell lines, USP54 knockdown suppressed cell proliferation and metastasis, whereas its overexpression enhanced these malignant phenotypes. Subcutaneous xenograft growth and tail vein injection experiments validated these findings in vivo.

CONCLUSIONS: Our comprehensive multi-omics analysis and experimental validation identify the deubiquitinase USP54 as a novel promoter of PDAC progression within a spatially organized tumor-immune microenvironment. These findings suggest USP54 as both a candidate prognostic biomarker and a potential therapeutic target for this lethal malignancy.

PMID:41929495 | PMC:PMC13038871 | DOI:10.3389/fimmu.2026.1791707

Cell Rep Med. 2026 Mar 30:102708. doi: 10.1016/j.xcrm.2026.102708. Online ahead of print.

ABSTRACT

Intratumor heterogeneity (ITH) undermines transcriptome-based stratification in intrahepatic cholangiocarcinoma (iCCA). Here, we integrate multi-omics data from multi-region, single-region, and single-cell RNA sequencing cohorts to systematically characterize gene expression ITH. We uncover that immune and stromal heterogeneity are primary drivers of ITH, leading to misclassification of a median 27.8% of tumors by existing subtyping systems. To overcome this, we identify a low-intratumor-heterogeneity/high-intertumor-variability (LIHV) gene set and develop an ITH-insensitive classification system defining five subgroups: inflammatory (SI), metabolic (SII), atypical (SIII-1), immune-silent (SIII-2), and neurodegenerative (SIII-3). These subgroups exhibit distinct clinical outcomes, molecular features, immune landscapes, and therapeutic vulnerabilities. GPRC5A and VTCN1 serve as robust immunohistochemical biomarkers for SI and SIII tumors, while serum CEA and CA19-9 identify inflammatory iCCA. Therapeutically, HSP90 inhibition synergizes with anti-PD1 in inflammatory iCCA, whereas combined anti-PD1 and anti-TIM3 suppresses neurodegenerative iCCA. Collectively, our study provides a robust molecular framework and actionable therapeutic strategies for iCCA.

PMID:41916296 | DOI:10.1016/j.xcrm.2026.102708

Int J Gen Med. 2026 Mar 24;19:590621. doi: 10.2147/IJGM.S590621. eCollection 2026.

ABSTRACT

Inflammatory bowel disease (IBD) involves chronic intestinal inflammation driven by gut-brain axis imbalance, fostering complications through an "inflammation-neuro-coagulation" triad. Current staging systems inadequately capture the dynamics of this multidimensional network. Therefore, integrated multi-omics analyses-including metagenomics, metabolomics, and single-cell transcriptomics-are essential to construct dynamic models that monitor coagulation, microbiome, and metabolism for precise assessment of disease activity and thrombotic or bleeding risks. Interventions targeting gut-brain axis nodes, such as eliminating tissue factor-positive (TF⁺) T cells or modulating vagal activity, show potential to disrupt the inflammation-coagulation cycle, although rigorous randomized trials are still needed. Artificial intelligence (AI)-assisted systems that integrate real-time biomarker monitoring with multi-omics predictions represent a novel paradigm for managing IBD-related coagulation dysfunction. Key challenges include elucidating gut-brain-liver axis regulation of coagulation and characterizing platelet functional heterogeneity. Future efforts must prioritize ethically compliant multi-omics platforms and racially stratified risk models to advance personalized coagulation management in IBD.

PMID:41913906 | PMC:PMC13033200 | DOI:10.2147/IJGM.S590621