J Transl Med. 2026 May 25. doi: 10.1186/s12967-026-08301-z. Online ahead of print.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) remains a major global health burden and a leading cause of cancer-related mortality. Advanced disease is characterized by a profoundly immunosuppressive tumor microenvironment (TME) and limited durable responses to therapy. However, the upstream genetic determinants that drive tumor-associated macrophage (TAM) dysfunction in HCC remain poorly defined. Using an integrative genetic and multi-omics framework, we investigated complement receptor 1 (CR1) as a candidate regulator of this immunosuppressive niche.

METHODS: We combined Mendelian randomization (MR) and metabolite mediation analyses with bulk, single-cell, and spatial transcriptomics to define the role of CR1 in HCC. Public datasets included the TCGA-HCC cohort, a single-cell RNA-sequencing dataset comprising 53,474 high-quality cells from 21 samples, and two spatially profiled HCC sections. Clinical validation was performed in 30 paired HCC and adjacent liver tissues. Functional assays were conducted in THP-1-derived macrophages using CR1 gain- and loss-of-function approaches, phagocytosis assays, and macrophage-CD8⁺ T-cell co-culture experiments.

RESULTS: MR analyses implicated CR1 in HCC susceptibility at both the protein and transcript levels. pQTL analysis linked genetically predicted circulating CR1 levels to HCC risk (IVW OR = 1.403, p = 0.017), and mediation analysis identified specific metabolites as candidate intermediates. Integrative multi-omics analyses showed that CR1 was preferentially enriched in TAMs, spatially co-localized with the M2 marker CD206, and associated with reduced CD8⁺ T-cell infiltration, enhanced T-cell exhaustion signatures, advanced clinicopathological features, and poorer survival. In 30 paired clinical samples, CR1-high tumors exhibited increased M2-like macrophage accumulation and reduced CD8⁺ T-cell infiltration. Functionally, CR1 overexpression drove macrophages toward an M2-like phenotype, enhanced phagocytic activity, increased PD-L1 expression, and suppressed CD8⁺ T-cell proliferation as well as IFN-gamma and granzyme B production, whereas CR1 knockdown produced the opposite phenotype.

CONCLUSIONS: Our study provides the first integrated genetic, spatial, and functional evidence that CR1⁺ TAMs constitute a clinically relevant immunoregulatory axis in HCC. These findings extend current understanding of complement-associated immunosuppression beyond canonical complement cascade activity and support CR1 as a candidate biomarker and therapeutic target for macrophage reprogramming, with potential translational relevance for combination strategies involving immune checkpoint blockade.

PMID:42185899 | DOI:10.1186/s12967-026-08301-z

Phytomedicine. 2026 May 15;157:158305. doi: 10.1016/j.phymed.2026.158305. Online ahead of print.

ABSTRACT

BACKGROUND: Severe acute pancreatitis (SAP) lacks targeted therapies, and massive loss of functional pancreatic acinar cells (PAC) drives mortality. Kaempferol (KA) possesses well-established anti-inflammatory and cytoprotective activities and is derived from herbal medicinal plants, but its direct molecular targets and mechanism of action in SAP remain undefined.

PURPOSE: To evaluate the protective effects of KA against SAP and to elucidate its molecular mechanism of specific action, with a focus on identifying the direct cellular target through which KA exerts its cytoprotective effects.

STUDY DESIGN: Gain‑/loss‑of‑function in vitro and PAC‑specific CD47 SAP mouse models, combined with multi‑omics screening and biophysical assays.

METHODS: CD47 manipulation (siRNA/overexpression) was performed in primary PACs and cell lines, combined with WT/CD47^-/-/Mist1‑CD47‑iOE (PAC‑specific) mouse models. Network pharmacology, transcriptomics and proteomics were integrated to screen and validate KA's protective effects. Computational‑experimental approaches (molecular docking/dynamics, CETSA, SPR, co‑IP, pharmacological epistasis) characterized KA's allosteric modulation of CD47 signaling.

RESULTS: CD47 was upregulated in SAP; its knockout reduced PAC death via KEAP1/PGAM5/AIFM1-driven oxeiptosis. KA reduced PAC death across genotypes, afforded no extra benefit in CD47-KO, and was not overridden by CD47‑OE. Mechanistically, KA allosterically binds CD47 ectodomain, stabilizes the CD47‑ UBQLN1 complex, and redirects signaling from Gαi‑mediated death to Gβγ/ ERK/NRF2‑mediated survival. ERK inhibition attenuated KA's protection. KA's action was CD47‑dependent.

CONCLUSION: This study identifies anti-oxeiptosis as a novel pharmacological activity of KA in SAP. This is achieved through allosteric modulation of CD47, redirecting its signaling from death‑promoting to a protective axis via activating Gβγ/ERK/NRF2 to suppress oxeiptosis. These findings reveal the CD47‑oxeiptosis axis as a therapeutic target and position KA as a promising candidate for SAP therapy, adding a new mechanistic dimension to KA's known pharmacological profile.

PMID:42184499 | DOI:10.1016/j.phymed.2026.158305

Front Pharmacol. 2026 May 8;17:1804673. doi: 10.3389/fphar.2026.1804673. eCollection 2026.

ABSTRACT

Advanced pancreatic ductal adenocarcinoma (PDAC) remains among the most formidable challenges in oncology, driven by a profoundly immunosuppressive tumor microenvironment (TME) and pervasive resistance to systemic and local therapies. Although immune checkpoint inhibitors (ICIs) can synergize with radiotherapy (RT) in several malignancies, the clinical benefit of immunoradiotherapy (iRT) in PDAC has been modest, highlighting the limitations of population-averaged paradigms that fail to capture extensive inter- and intratumoral heterogeneity. Here, we synthesize an artificial intelligence (AI)-enabled framework to refine both the biological rationale and clinical implementation of iRT for advanced PDAC through integrative analysis of multimodal data (clinical variables, imaging, RT dose distributions, and multi-omics). We highlight advances in three domains. First, AI-based deconvolution of TME heterogeneity can delineate clinically relevant molecular subtypes and spatial immune architectures that may be therapeutically tractable. Second, AI-driven modeling can optimize spatiotemporal RT-immunotherapy interactions, informing individualized dose, fractionation, and biologically guided target definition. Third, AI-supported predictive modeling and adaptive feedback can enable response-guided treatment adjustment beyond static planning. We also discuss unresolved clinical questions and key translational barriers, including data scarcity, lack of standardization, and limited interpretability. Finally, we outline priorities for translation-prospective digital biobanks, hybrid mechanistic-data-driven modeling, and adaptive trial designs-to enable rigorous validation and clinical deployment. Collectively, these developments position AI as a catalyst to move iRT for PDAC from empiricism toward real-time, individualized precision medicine.

PMID:42181887 | PMC:PMC13194001 | DOI:10.3389/fphar.2026.1804673

Int J Clin Oncol. 2026 May 23. doi: 10.1007/s10147-026-03049-4. Online ahead of print.

ABSTRACT

BACKGROUND: Hematologic malignancies exhibit marked biological heterogeneity that is often insufficiently characterized by genomic profiling alone. Integrated multi-omics approaches are required to enable more accurate prognostic stratification, elucidate resistance mechanisms, and identify therapeutic vulnerabilities across lymphoma, leukemia, and plasma cell neoplasms.

METHODS: SCRUM-Japan MONSTAR3 is a nationwide, prospective, integrated multi-omics platform. The hematology cohort aims to enroll 400 patients with newly diagnosed or relapsed/refractory hematologic malignancies. Tumor specimens-including bone marrow aspirates/biopsies or lymph node tissues-are collected at diagnosis and at relapse. The multi-omics workflow encompasses whole-exome sequencing, whole-transcriptome sequencing, spatial transcriptomics, plasma proteomics, metabolomics, microbiome analysis, and tumor-informed measurable residual disease (MRD) monitoring. MRD is assessed using next-generation sequencing-based immunoglobulin heavy (IgH) and T-cell receptor (TCR) rearrangement analysis for lymphoid malignancies and whole-genome sequencing-based variant tracking for myeloid malignancies.

RESULTS: Patient enrollment began in December 2024, followed by nationwide multicenter activation in November 2025. Multi-omics analyses have been implemented in a stepwise manner. Early operational indicators, including biospecimen acquisition, data quality control, and initiation of molecular assays, demonstrate the feasibility of coordinated nationwide deployment of this complex platform.

CONCLUSION: The MONSTAR3 hematology cohort represents the first nationwide integrated multi-omics initiative dedicated to hematologic malignancies. Its large scale, standardized biospecimen framework, and capacity to incorporate emerging technologies provide a robust infrastructure for molecular stratification, longitudinal disease monitoring, and hypothesis-driven interventional research, thereby advancing clinically actionable precision hematology.

PMID:42177352 | DOI:10.1007/s10147-026-03049-4

Probiotics Antimicrob Proteins. 2026 May 23. doi: 10.1007/s12602-026-11062-2. Online ahead of print.

ABSTRACT

Autoimmune hepatitis (AIH) is a chronic progressive inflammatory liver disease with a rising global incidence. The treatment of AIH remains challenging because first-line drugs show limited efficacy and systemic side effects. Gut microbiota plays a crucial role in the pathogenesis of AIH, leading to growing interest in developing probiotic-based therapies. In this study, we used multi-omics analysis to investigate the therapeutic effects of a quadruple probiotic mixture (Probiotic-quad) consisting of Bifidobacterium infantis, Lactobacillus acidophilus, Enterococcus faecalis, and Bacillus cereus in a well-established chronic AIH murine model. Our results showed that Probiotic-quad treatment significantly alleviated AIH progression, as evidenced by lower serum liver enzyme levels, ameliorated hepatic inflammatory infiltration and histopathological damage. Metagenomic sequencing results showed that gut dysbiosis in AIH mice was partially reversed after Probiotic-quad administration. Additionally, the integrity of the intestinal epithelial barrier was restored, accompanied by a reduction in serum lipopolysaccharide levels. Untargeted metabolomic and transcriptomic analysis revealed that Probiotic-quad treatment was linked to alterations in hepatic metabolism, including the citrate cycle and tryptophan metabolism, and was associated with reduced activation of the NF-κB and NOD-like receptor signaling pathways. These findings suggest that Probiotic-quad treatment ameliorates AIH severity and is potentially associated with changes in hepatic immune responses, metabolism, gut microbiota, and intestinal barrier function, highlighting its potential as an adjuvant therapy for AIH.

PMID:42176246 | DOI:10.1007/s12602-026-11062-2

Curr Oncol Rep. 2026 May 22;28(1):58. doi: 10.1007/s11912-026-01793-4.

ABSTRACT

PURPOSE OF REVIEW: To provide an updated overview of the role of the human microbiome in the initiation, progression, and therapeutic response of gastrointestinal tumors, emphasizing molecular, immunological, and metabolic mechanisms, as well as its potential as a target for novel therapeutic strategies.

RECENT FINDINGS: Emerging evidence demonstrates that microbiome dysbiosis contributes to carcinogenesis across gastrointestinal malignancies, including colorectal, gastric, hepatic, and pancreatic cancers. Microbial-derived metabolites, such as short-chain fatty acids and secondary bile acids, modulate key signaling pathways involved in cell proliferation, apoptosis, and genomic stability. In addition, the microbiome influences the tumor microenvironment and immune responses, shaping variability in treatment outcomes. Both preclinical and clinical studies have shown that microbiome composition affects the efficacy and toxicity of chemotherapy and immunotherapy. Notably, specific microbial signatures are being explored as non-invasive biomarkers for early detection and prognostic stratification, while microbiome modulation strategies, such as diet, probiotics, antibiotics, and fecal microbiota transplantation, have demonstrated potential to enhance therapeutic response. The bidirectional interaction between the microbiome and the host plays a central role in gastrointestinal tumorigenesis and treatment response. Although this field holds significant promise for precision oncology, its clinical translation remains limited by interindividual variability, methodological heterogeneity, and insufficient longitudinal evidence. Future efforts should focus on standardization, validation of microbiome-based biomarkers, and integration of multi-omics and artificial intelligence approaches to enable clinically actionable applications.

PMID:42171841 | DOI:10.1007/s11912-026-01793-4

Gland Surg. 2026 Apr 30;15(4):91. doi: 10.21037/gs-2025-396. Epub 2026 Mar 27.

ABSTRACT

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies due to a lack of reliable tools for individualized risk stratification. A comprehensive understanding of the multi-omics landscape may uncover clinically applicable biomarkers and inform precision prognostic assessment. This study aims to establish a prognostic model directly from the complete omics landscape and extract biomarkers.

METHODS: We developed prognostic models using multi-omics data from a PDAC proteogenomic cohort comprising 75 deceased tumor samples. An independent cohort of 63 deceased PDAC cases from The Cancer Genome Atlas (TCGA)-pancreatic adenocarcinoma (PAAD) was used for external validation. Logistic regression models with least absolute shrinkage and selection operator (LASSO) regularization were constructed, and SHapley Additive exPlanations (SHAP) were applied to evaluate feature importance and identify signature genes. Model selection was based on the average area under the receiver operating characteristic curve (AUROC) across cross-validation folds. Functional validation was performed in PANC-1 cells by knockdown (KD) or overexpression (OE) of representative microRNA-, RNA-, and proteomics-derived signature genes, followed by Cell Counting Kit-8 (CCK-8) proliferation and Transwell migration assays.

RESULTS: Systematic evaluation of 120 multi-omics combinations identified a top-performing prognostic model integrating RNA, microRNA, proteomics, and mutation features. This model achieved a mean AUROC of 0.92±0.11 and accuracy of 0.87±0.01 on internal validation, and 0.99±0.00 and 0.98±0.01 on the TCGA test set. The sensitivity, specificity, precision, recall and F1 scores on the TCGA test set were 0.98±0.01, 0.97±0.02, 0.98±0.02, 0.98±0.01, 0.98±0.01, respectively. SHAP analysis revealed interpretable and clinically relevant prognostic biomarkers, many of which are implicated in immune signaling, metabolic regulation, and cell cycle control. Importantly, modulation of representative signature genes in PANC-1 cells significantly altered proliferation and migration in directions consistent with model-predicted risk associations.

CONCLUSIONS: Our findings demonstrate that explainable multi-omics machine learning frameworks can identify robust prognostic biomarkers and achieve highly accurate survival prediction in PDAC. Functional validation further supports the biological relevance of these signatures, underscoring their translational potential for personalized risk assessment.

PMID:42164702 | PMC:PMC13184197 | DOI:10.21037/gs-2025-396

Curr Top Med Chem. 2026 May 18. doi: 10.2174/0115680266436608260406113212. Online ahead of print.

ABSTRACT

INTRODUCTION: Epidemiological studies indicate a potential link between mental disorders and cancer; however, the role of neurotransmitter-related genes (NRGs) in carcinogenesis remains unclear. In this study, we employed Mendelian randomization utilizing multi-omics data to investigate the causal effects and mechanisms of NRGs in cancer.

METHODS: We assessed the causal relationships between ten mental disorders and fourteen cancer types. NRGs were sourced from GeneCards, and transcriptome data for breast cancer (BC) were obtained from the Gene Expression Omnibus (GEO). Summary-data-based Mendelian Randomization (SMR) integrated genome-wide association study (GWAS) data with expression quantitative trait loci (eQTLs), DNA methylation QTLs (mQTLs), intestinal eQTLs, and fecal microbiota QTLs (mbQTLs). Colocalization analyses were conducted to explore the relationships between host genes and gut microbiota, with sensitivity assessments performed using two additional Mendelian randomization methods.

RESULTS: Mendelian randomization confirmed a causal association between mental disorders and BC. A meta-analysis of five BC datasets identified 821 differentially expressed genes (DEGs) among 829 non-redundant genes. SMR highlighted KRTCAP2 as a potential causal gene in blood, and cg24674445 as a significant methylation site. The expression of KRTCAP2 was found to be inversely correlated with BC, while methylation at cg24674445 downregulated KRTCAP2, suggesting that cg24674445 may promote BC progression.

DISCUSSION: This study advances beyond established epidemiological correlations by providing genetically validated evidence for a causal link between mental disorders and breast cancer. Its primary significance lies in delineating a plausible biological pathway-epigenetic regulation of neurotransmitter-related genes-that may mechanistically elucidate this connection. By integrating multi-omics data, we transition from mere association to a testable model of disease etiology, where genetic predispositions to mental illness and cancer converge upon shared regulatory mechanisms within the genome.

CONCLUSION: Multi-omics Mendelian randomization demonstrates that DNA methylation modulates the association between neurotransmitter-related genes and breast cancer.

PMID:42163732 | DOI:10.2174/0115680266436608260406113212

Dev Cell. 2026 May 19:S1534-5807(26)00159-0. doi: 10.1016/j.devcel.2026.04.012. Online ahead of print.

ABSTRACT

The lack of accurate human models that recapitulate pancreatic ductal adenocarcinoma (PDAC) initiation has hindered therapeutic development. Using pluripotent stem cell-derived pancreatic progenitor organoids, we established a human PDAC model that faithfully reproduces the genetic, epigenetic, and transcriptomic trajectories of tumor initiation and progression, validated against clinical datasets and tumor histopathology. We demonstrate that CDKN2A loss, which is nearly universal in patients but dispensable in mouse models, is essential for neoplastic transformation when combined with KRAS and TP53 mutations, whereas SMAD4 loss promotes tumor progression. Multi-omics profiling reveals epigenetic repression of the pancreatic lineage program during PDAC initiation, alongside AP-1-driven chromatin remodeling. We identify TET1 suppression as a mechanistic link between oncogenic ERK signaling and hypermethylation of essential pancreatic transcription factors. This model captures genetic and epigenetic determinants of human PDAC, reveals antagonism between oncogenic and lineage restriction programs, and supports TET-based lineage restoration as a potential early intervention strategy.

PMID:42161274 | PMC:PMC13196429 | DOI:10.1016/j.devcel.2026.04.012

Chin Med J (Engl). 2026 May 19. doi: 10.1097/CM9.0000000000004098. Online ahead of print.

ABSTRACT

High-salt diet (HSD) has emerged as a prevalent environmental factor that exacerbates chronic inflammation and insulin resistance in obesity-associated type 2 diabetes (T2D) by modulating macrophage polarization, metabolic reprogramming, and epigenetic imprinting. Current evidence demonstrates that HSD activates p38/mitogen-activated protein kinase (MAPK), nuclear factor kappa-B (NF-κB), and NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome signaling pathways, by which it drives macrophage polarization toward a proinflammatory M1 phenotype while inducing a glycolysis-dominant metabolic shift, thereby establishing a persistent "metabolic memory". Moreover, HSD orchestrates metabolic memory in macrophages through coordinated epigenetic machinery, including histone modifications (Trimethylation of histone H3 at lysine 4 [H3K4me3] and Acetylation of histone H3 at lysine 27 [H3K27ac]), DNA methylation, and noncoding RNAs (e.g., long non-coding RNA MALAT1 and miR-155), leading to sustained inflammatory phenotypes. In multiple metabolic organs (e.g., adipose tissue, liver, pancreas, and gut), the HSD-macrophage axis aggravates systemic insulin resistance through shared proinflammatory signaling and other tissue-specific mechanisms. Most importantly, therapeutic strategies targeting the NLRP3 inflammasome, metabolic pathways, and epigenetic alterations offer novel approaches for managing metabolic inflammation. Future investigations are encouraged to leverage lineage tracing, single-cell sequencing, and spatial multi-omics technologies to advance the development of precision medicine for macrophage-associated metabolic disorders.

PMID:42156155 | DOI:10.1097/CM9.0000000000004098

Diabetologia. 2026 May 18. doi: 10.1007/s00125-026-06750-1. Online ahead of print.

ABSTRACT

AIMS/HYPOTHESIS: Anti-CD3 monoclonal antibody (aCD3) delays progression to stage 3 type 1 diabetes in high-risk individuals by modulating autoimmune activity. Nevertheless, responses remain variable and transient, with therapy providing only indirect beta cell protection. We investigated whether glucagon-like peptide-1-17ß-oestradiol conjugate (GLP1-E2), a beta cell-targeted fusion compound that enhances beta cell survival and function, could potentiate a short low-dose aCD3 course in preventing autoimmune diabetes in NOD mice. We hypothesised that co-targeting immune dysregulation and beta cell fragility would provide complementary and potentially synergistic benefits, resulting in more durable protection than either monotherapy.

METHODS: Female late-stage prediabetic NOD mice were randomised into four groups: untreated controls, aCD3 monotherapy, GLP1-E2 monotherapy and combination therapy. aCD3 was administered intravenously at 2.5 µg/day for 5 consecutive days, while GLP1-E2 was given subcutaneously at 100 nmol kg^-1 day^-1 for 18 weeks. Mice were monitored longitudinally for diabetes onset. The pancreas was analysed by spatial transcriptomics and immunostaining to assess immune infiltration, beta cell integrity and molecular pathway alterations.

RESULTS: At 30 weeks of age, diabetes incidence was 77% in untreated controls, 66% in mono aCD3-treated mice and 61% in mono GLP1-E2-treated mice. Combination therapy significantly reduced diabetes incidence to 38% (p≤0.001) and delayed disease onset by 6 weeks, with sustained protection persisting for 5 weeks after treatment cessation. GLP1-E2 monotherapy reduced islet immune cell infiltration to a similar extent as aCD3 mono- and combination therapy, without affecting peripheral lymphocyte counts. Spatial transcriptomics showed increased gene responses linked to beta cell stress (Hspa5, Eif2ak3, Xbp1, Ddit3), dedifferentiation (Cd81), 'disallowed' genes (Oat, Igfbp4), antigen presentation (H2-K1, H2-Q6, H2-Ab1, H2-Eb1) and inflammation (Cxcl10, Cxcl9, Ccl5) during disease progression. These processes were attenuated by mono- and combination therapy, with aCD3 mostly restoring beta cell identity and GLP1-E2 reducing beta cell stress and immunogenicity. Staining for CD81 and TUNEL in 17-week-old treated mice revealed levels comparable to 12-week-old normoglycaemic NOD mice, while being increased in 17-week-old untreated mice. This reduced beta cell dedifferentiation and death was associated with improved beta cell protection and better preservation of beta cell mass at 26.5 weeks compared with new-onset (diabetic) mice.

CONCLUSIONS/INTERPRETATION: Low-dose aCD3 or GLP1-E2 monotherapy delayed diabetes onset and preserved beta cell mass in female NOD mice, while the combination provided substantially superior protection. Simultaneously targeting immune dysregulation and beta cell vulnerability highlights the potential of combination therapy to enhance and prolong immunotherapeutic efficacy in type 1 diabetes.

PMID:42149241 | DOI:10.1007/s00125-026-06750-1

bioRxiv [Preprint]. 2026 May 5:2026.04.30.721768. doi: 10.64898/2026.04.30.721768.

ABSTRACT

In biomedical scientific discovery, synthesizing prior knowledge from the literature is an essential component of interpreting numerical omics data analyses for disease target identification and drug discovery. Large language models (LLMs) alone can rapidly retrieve disease mechanisms from biomedical text, but text-only outputs are general and unreliable for target and drug prioritization without cohort-specific quantitative evidence. Herein, we propose a provenance-aware Text-to-Target framework that couples schema-constrained multi-model LLM retrieval with numeric omics data analysis. The key design is a modality-aware fusion step: candidates are partitioned into overlap-supported anchors, retrieval-only hidden hubs, and network-emergent novelty nodes, then propagated into staged hypothesis and strategy generation under topology constraints. We evaluate the model in Alzheimer's disease (AD) and pancreatic ductal adenocarcinoma (PDAC). In PDAC, the workflow produced a balanced 75-gene candidate universe and a 23-strategy portfolio, with significant DepMap support at both target level and strategy level. In AD, stricter candidate controls yielded a compact 34-gene universe and 14 strategies; under an expanded CRISPRbrain registry, both target-level axes were significant, with strong strategy-level enrichment. Across both diseases, final strategies preserved full provenance closure to the candidate pool, enabling end-to-end auditability from retrieval artifacts to validation outputs. These results support a transferable discovery architecture in which omics evidence constrains biological activity, LLM retrieval expands mechanistic search space, and network-aware fusion preserves interpretability. The framework provides a reproducible basis for dual-disease target prioritization and motivates continuous literature-mechanism concordance with agentic evidence-refresh loops.

PMID:42146439 | PMC:PMC13174328 | DOI:10.64898/2026.04.30.721768

Clin Transl Med. 2026 May;16(5):e70700. doi: 10.1002/ctm2.70700.

ABSTRACT

BACKGROUND: Type 1 diabetes (T1D) is a lifelong autoimmune disease characterised by progressive immune-mediated destruction of insulin-producing beta (β)T1D-cells, leading to permanent insulin dependence and increased risk of microvascular and macrovascular complications. Despite advances in autoantibody screening and immunotherapies, major clinical challenges persist in early detection, accurate disease staging, prediction of progression and monitoring of therapeutic response. Current biomarkers provide limited insight into real-time β-cell stress and immune activity, restricting opportunities for timely and personalised intervention.

RATIONALE: Extracellular vesicles (EVs) are nano-sized membrane-bound particles released by virtually all cell types and carry proteins, lipids and nucleic acids reflective of their cellular origin and physiological state. Advances in EV isolation, multi-omics profiling and bioinformatics now enable detailed characterisation of EV cargo from accessible biofluids such as blood and urine. These developments position EVs as a minimally invasive platform to interrogate β-cell health, immune activation and systemic complications in T1D, while also offering a novel class of cell-free immunomodulatory therapeutics.

CONTENT: This review synthesises current evidence on the role of EVs in T1D pathogenesis and clinical translation. We discuss how β-cell- and immune cell-derived EVs participate in antigen presentation, immune activation and inflammatory amplification, and how EV cargo signatures (proteins, miRNAs and other RNAs) reflect disease stage, progression and heterogeneity. We summarise emerging data on maternal, neonatal and urinary EVs as early-life and complication-associated biomarkers, and critically evaluate ongoing EV-based clinical studies in T1D. Finally, we examine the therapeutic potential of stem cell-derived and engineered EVs to modulate autoimmunity and preserve residual β-cell function.

CONCLUSION: EVs introduce a potentially clinically actionable layer of information linking cellular stress, immune dysregulation and tissue damage to measurable biomarkers and therapeutic opportunities in T1D. However, the majority of EV applications currently remain at the preclinical or early pilot‑study stage, with limited validation in large, longitudinal patient cohorts. Key challenges include biological heterogeneity, assay reproducibility and the need for standardised isolation, characterisation and regulatory frameworks. While rapid advances in EV technologies and early proof‑of‑concept clinical studies support their long‑term potential, substantial work is required before routine clinical implementation is feasible. For feasible clinical translation of EV-based applications, alignment with regulatory frameworks must be considered early to ensure analytical validity, standardisation and compliance with clinical and diagnostic approval pathways, as well as to address safety, efficacy and manufacturing requirements for EV-based therapeutics.

KEY POINTS: EVs provide a minimally invasive window into β-cell stress, immune activation and disease progression in T1D. EV-associated proteins and RNAs reflect disease stage, heterogeneity and response to immunotherapy beyond traditional biomarkers. Circulating and urinary EVs show promise for early detection of T1D complications before clinical manifestation. Stem cell-derived and engineered EVs represent emerging cell-free immunomodulatory and regenerative therapies. Standardisation, longitudinal validation, EV-focused clinical trial design and early alignment with regulatory frameworks are essential for clinical translation, ensuring robust validation for diagnostics and addressing safety, efficacy and manufacturing requirements for EV-based therapeutics.

PMID:42145066 | PMC:PMC13181332 | DOI:10.1002/ctm2.70700

Cancer Lett. 2026 May 16;653:218585. doi: 10.1016/j.canlet.2026.218585. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma is characterized by a fibrotic and metabolically active tumor microenvironment where cancer-associated fibroblasts (CAFs) mediate metabolic crosstalk, extracellular matrix (ECM) remodeling, and immune regulation. However, the metabolic and spatial heterogeneity of CAFs remains incompletely understood. We integrated spatial transcriptomics and spatial metabolomics data from PDAC tissues and performed SpatialGlue-based multimodal clustering to define CAF subtypes. To characterize metabolic communication, we developed an optimal transport (OT)-based metabolic inference framework to quantitatively model metabolite association between CAFs and tumor cells. Subtype-specific features were independently validated using an independent spatial metabolomics cohort and multiplex immunofluorescence (mIHC) staining. Furthermore, these features were correlated with clinical outcomes via TCGA-PAAD deconvolution. Spatial multi-omics integration identified three robust CAF subtypes with distinct signatures. OT analysis revealed differential metabolic interactions: CAF_C0 mediated amino acid/peptide transfer, CAF_C1 was the primary source of lipids, while CAF_C2 exhibited limited metabolic association but stronger immune and ECM signaling activity. Deconvolution confirmed that CAF composition was strongly associated with prognosis; CAF_C2 enrichment predicted poorer survival and gemcitabine resistance, whereas a higher CAF_C0/CAF_C1 balance correlated with improved outcomes. By combining spatial multi-omics with OT-based modeling, this study delineates metabolically and spatially distinct CAF states with clinical relevance. Our findings suggest CAFs act as both metabolic donors and immune-ECM regulators, providing new insights into stromal reprogramming and potential subtype-specific therapeutic targets in PDAC.

PMID:42144098 | DOI:10.1016/j.canlet.2026.218585

Biochem Biophys Res Commun. 2026 Jul 23;823:153920. doi: 10.1016/j.bbrc.2026.153920. Epub 2026 May 10.

ABSTRACT

Organometallic iridium (III) complexes have garnered significant interest in anticancer research due to their potent efficacy against a wide range of cancers. This study investigates an Ir(III) complex as a targeted DNA-binding agent for advanced cancer therapy using biophysical, cellular, and in silico approaches. Intercalative binding with calf thymus DNA was confirmed through multiple techniques: UV-visible spectroscopy showed a 73.8% hyperchromic shift at 260 nm; the absence Ir(III/IV) oxidation peak at 1.45 V in DNA-Ir-complex vs. free Ir-complex in cyclic voltammetry studies, indicating bulky Ir-DNA adduct formation, viscosity of DNA increased by 28%, competitive fluorescence quenching, circular dichroism perturbations at 245 and 275 nm, and 65% dye displacement validated classical intercalation via minor-groove access and base-stacking interactions. Raman spectroscopy shifts (e.g., 494 → 483 cm^-1 for PO₂^- backbone vibrations, indicating conformational alterations in the phosphodiester framework, 1703 → 1696 cm^-1 corresponding to base carbonyl stretching, suggesting perturbation of hydrogen bonding and base stacking interactions) revealed a ligand-induced structural modification of DNA, possibly reflecting a transition toward an A-like conformation. Molecular docking predicted a binding free energy (ΔG) of -11.17 kJ mol^-1, with preferential interaction at GC-rich regions. Furthermore, the complex induced photoactivated plasmid DNA strand breakage and exhibited potent cytotoxicity (IC₅₀ = 8.46 μM) in MIA PaCa-2 pancreatic carcinoma cells, with comet assay confirming significant DNA damage. Network pharmacology analysis identified 114 high-confidence protein targets involved in key cancer-related pathways. These integrated findings highlight the promising anticancer potential of the Ir(III) complex and paving the way for rational metallodrug design.

PMID:42143443 | DOI:10.1016/j.bbrc.2026.153920

Mol Divers. 2026 Apr 12. doi: 10.1007/s11030-026-11545-y. Online ahead of print.

NO ABSTRACT

PMID:41966666 | DOI:10.1007/s11030-026-11545-y

Metabolism. 2026 Apr 8:156607. doi: 10.1016/j.metabol.2026.156607. Online ahead of print.

ABSTRACT

Endoplasmic reticulum (ER) stress is a critical driver of pancreatic β-cell dysfunction and apoptosis. Although metformin, a drug used to treat type 2 diabetes, primarily decreases blood glucose levels by improving insulin sensitivity, its direct effects on β-cell survival remain unclear. Here, we investigated the effect of metformin on β-cell stress responses under ER stress conditions. Thapsigargin (Tg)-induced ER stress increased β-cell apoptosis in mouse islets, which was prevented by metformin in a dose-dependent manner. Treatment with metformin for 24 h suppressed the Tg-induced upregulation of unfolded protein response (UPR)-related genes, as confirmed by transcriptomic and pathway analyses. Quantitative proteomics revealed that Tg inhibited eIF2 signaling and protein translation, both of which were partially restored by metformin. Enrichment analysis further indicated the attenuation of apoptotic pathways in metformin-treated islets. Polysome profiling and puromycin incorporation assays demonstrated that metformin reduced protein translation independently of ER stress. Metformin promoted the dephosphorylation of 4E-BP1, a key initiator of cap-dependent protein translation that is activated by phosphorylation, and the antiapoptotic effect of metformin was abolished by 4E-BP1 knockdown in MIN6 cells. Phosphoproteomic analysis indicated that the activation of mTOR signaling, a kinase of 4E-BP1, in Tg-treated islets was mitigated by metformin. Taken together, these findings reveal a cytoprotective mechanism of metformin in β-cells, in which metformin suppresses ER stress-induced apoptosis through 4E-BP1-mediated inhibition of mRNA translation and modulation of mTOR signaling. This study highlights a β-cell-intrinsic action of metformin that may contribute to its long-term therapeutic benefits in diabetes management.

PMID:41962652 | DOI:10.1016/j.metabol.2026.156607

bioRxiv [Preprint]. 2026 Mar 11:2026.03.09.710586. doi: 10.64898/2026.03.09.710586.

ABSTRACT

The lack of accurate, human-based models recapitulating early-stage pancreatic ductal adenocarcinoma (PDAC) has hindered therapeutic development. Using pluripotent stem cell-derived pancreatic progenitor organoids, we established a human PDAC model that faithfully reproduces the genetic, epigenetic, and transcriptomic trajectory of tumor initiation and progression in vitro , validated against clinical datasets and histopathology. We demonstrate that CDKN2A loss, nearly universal in patients but dispensable in mouse models, is essential for neoplastic transformation when combined with KRAS and TP53 mutations, while SMAD4 loss promotes tumor progression. Multi-omics profiling reveals epigenetic repression of pancreatic lineage program during PDAC initiation, alongside oncogenic AP-1-driven chromatin remodeling. Notably, we identify TET1 suppression as a mechanistic link between oncogenic ERK signaling and the hypermethylation and silencing of essential pancreatic transcription factors. This model captures the genetic and epigenetic determinants of human PDAC, reveals antagonism between oncogenic and lineage restriction programs, and supports TET-based lineage restoration as a promising early intervention strategy for high-risk individuals.

PMID:41959451 | PMC:PMC13060829 | DOI:10.64898/2026.03.09.710586

bioRxiv [Preprint]. 2026 Mar 12:2026.03.10.710185. doi: 10.64898/2026.03.10.710185.

ABSTRACT

KRAS is mutated in over 90% of pancreatic ductal adenocarcinomas (PDAC), where hotspot alterations in codons 12, 13, and 61 drive tumor initiation and progression. Although distinct biochemical properties have been described for individual KRAS mutants, whether they generate unique allele-specific signaling programs in PDAC cells remains unresolved. Here, we systematically interrogated the molecular consequences of seven common KRAS mutant variants in reconstituted isogenic, KRAS-deficient PDAC cell lines by integrated transcriptomic, proteomic, and phosphoproteomic profiling. We found that baseline cellular state, rather than allele identity, was the predominant driver of molecular variation. Comparisons with established KRAS reference signatures revealed significant but moderate overlap at the mRNA level and less so at the proteome level. Pathway analyses highlighted interferon response and mitochondrial translation as recurrently altered across alleles, while phosphoproteomic data confirmed robust ERK1/2 activity and suppression of DYRK kinase substrates by mutant KRAS expression. Importantly, no robust allele-specific molecular programs were identified. Together, our study establishes a comprehensive multi-omics resource for KRAS signaling in PDAC and demonstrates that cellular context exerts a stronger influence than allele identity in shaping molecular profiles, with implications for interpreting putative allele-specific signaling dependencies and therapeutic vulnerabilities.

PMID:41959224 | PMC:PMC13060958 | DOI:10.64898/2026.03.10.710185

BioData Min. 2026 Apr 9. doi: 10.1186/s13040-026-00546-0. Online ahead of print.

NO ABSTRACT

PMID:41957754 | DOI:10.1186/s13040-026-00546-0