Dig Liver Dis. 2026 Sep 12:S1590-8658(26)00920-5. doi: 10.1016/j.dld.2026.08.021. Online ahead of print.

ABSTRACT

The quality of biological samples is a major determinant of analytical reliability and translational relevance in patients with pancreatic ductal adenocarcinoma (PDAC). However, variability in specimen procurement, handling, transport, processing, and storage can substantially affect tissue integrity and the robustness of downstream analyses. This paper, promoted by the Pathology and Basic Science Task Force of the Italian Association for the Study of the Pancreas (AISP), brings together experts in pathology, molecular biology, translational research, medical oncology, and gastroenterology to provide practical recommendations for the collection, handling, and pre-analytical management of biological samples. Draft recommendations were discussed during dedicated working group meetings and approved by consensus among all authors, supported by key literature. The document identifies the biological specimen as the critical link between patient care, pathology, and research, and provides guidance for clinicians and professionals involved in sample procurement and processing. By addressing the requirements of different analytical platforms, including genomics, organoid generation, immunophenotyping, pharmacogenomics, and multiplex/spatial analyses, this paper aims to reduce pre-analytical variability, improve diagnostic accuracy, and enhance the clinical and translational value of molecular investigations in pancreatic cancer. Standardised procedures across centres may facilitate comparable data collection, support multicentre studies, and strengthen collaboration between clinicians, pathologists, and research laboratories.

PMID:42731958 | DOI:10.1016/j.dld.2026.08.021

FASEB J. 2026 Sep 30;40(18):e72296. doi: 10.1096/fj.202603069R.

ABSTRACT

Pancreatic cancer (PC) presents a significant global health challenge because of its high mortality rate, highlighting the urgent requirement for effective early diagnostic and therapeutic strategies. This study examined the function of phenylalanine metabolism in PC and developed a high-accuracy diagnostic model by integrating metabolomics, Mendelian randomization (MR), and machine learning (ML) algorithms. Initially, MR analysis was conducted on 55 plasma metabolites, revealing a significant causal link between phenylalanine and PC. Utilizing GeneCards and public transcriptomic databases, we determined eight differentially expressed genes (DEGs) in PC associated with phenylalanine. Based on these genes, we utilized 12 ML algorithms, totaling 113 combinations, to select the optimal diagnostic model. We applied Shapley Additive exPlanations (SHAP) for feature interpretation and constructed a prognostic nomogram with strong predictive performance by incorporating clinical variables. Furthermore, immune infiltration analysis demonstrated strong connections between these key genes and specific immune cell populations. Based on the SHAP value, we conducted single-cell RNA sequencing (scRNA-seq) data and simulated gene knockout analyses using SLC6A14 as the key gene. Drug target prediction-guided molecular docking and molecular dynamics simulations, focusing on the core gene SLC6A14, confirmed the high binding stability of candidate compounds. Finally, in vitro cell experiments quantitative real-time PCR (RT-qPCR) verified the expression trends of the key genes in PC cell lines. In conclusion, this study successfully developed an ML diagnostic model with high biological interpretability. This analysis aims to identify biomarkers related to phenylalanine metabolism and potential therapeutic drugs for PC, offering new strategies for personalized targeted therapy of PC.

PMID:42730913 | PMC:PMC13570651 | DOI:10.1096/fj.202603069R

Ann Surg Oncol. 2026 Sep 12. doi: 10.1245/s10434-026-20366-0. Online ahead of print.

ABSTRACT

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy with limited long-term survival despite advances in surgery and systemic therapy.

PATIENTS: The population of interest comprises patients with PDAC characterized by targetable molecular alterations and biologically distinct transcriptomic subtypes.

METHODS: We performed a narrative review of landmark and contemporary clinical trials, translational studies, and emerging molecular-classification platforms relevant to precision oncology in PDAC.

RESULTS: Growing understanding of PDAC molecular biology has identified putative genetic mutations, including homologous recombination repair deficiency, mismatch repair deficiency, and mutated KRAS, enabling the development of targeted therapies and precision treatment strategies. Concurrently, transcriptomic profiling has revealed biologically distinct molecular subtypes associated with differences in prognosis and therapeutic response. Emerging tools such as molecular classifiers, deep learning models, and multiomic platforms may further refine patient selection and treatment personalization.

CONCLUSIONS: This review highlights contemporary efforts of novel targeted therapies, ongoing advances in molecular subtyping, and the evolving role of precision oncology in improving outcomes for patients with PDAC.

PMID:42732021 | DOI:10.1245/s10434-026-20366-0

Front Endocrinol (Lausanne). 2026 Aug 27;17:1938241. doi: 10.3389/fendo.2026.1938241. eCollection 2026.

ABSTRACT

Type 1 diabetes (T1D), which commonly presents in childhood or adolescence, is an autoimmune disease in which immune-mediated destruction of pancreatic β-cells leads to absolute or near-absolute insulin deficiency and lifelong dependence on insulin administration. During adolescence, pubertal changes and increased insulin requirements can worsen glycemic instability and raise the risk of vascular complications, such as cardiovascular disease. Although hyperglycemia promotes vascular injury, different early vascular phenotypes in adolescents with similar hemoglobin A₁c (HbA₁c) levels suggest that additional mechanisms may influence vascular risk. Insulin resistance (IR) may be an important contributor because insulin sensitivity declines during puberty, and this decline is associated with oxidative stress, altered endothelial signaling, inflammation, and adiposity. However, establishing a causal relationship between IR and early vascular injury in T1D remains challenging. This review examines the link between IR and early vascular injury in adolescents with T1D and emphasizes endothelial dysfunction, arterial stiffness, and biomarkers that may connect metabolic stress to vascular damage. Evidence from metabolic, endothelial, inflammatory, omics-based, and imaging studies supports an association between IR, inflammatory pathways, endothelial injury, and impaired vascular repair. Overall, IR, inflammatory, endothelial, and vascular imaging measures may complement HbA₁c when studying vascular risk in adolescents with T1D. Whether they provide additional diagnostic or prognostic value beyond HbA₁c is still unknown.

PMID:42725052 | PMC:PMC13559969 | DOI:10.3389/fendo.2026.1938241

Front Endocrinol (Lausanne). 2026 Aug 27;17:1782791. doi: 10.3389/fendo.2026.1782791. eCollection 2026.

ABSTRACT

Neuroendocrine neoplasms (NENs) are biologically heterogeneous tumors in which differentiation/grade and hormonal functionality are intersecting but non-equivalent axes. This review focuses on functional and non-functional well-differentiated neuroendocrine tumors (NETs), principally gastroenteropancreatic and pancreatic NETs, and critically evaluates how site, lineage, stage, tumor burden, genomic and epigenetic alterations, immune-stromal remodeling, metabolic adaptation, microbiome-associated signals, and treatment pressure converge on metastasis and recurrence. Apparent outcome differences by functionality are inconsistent after clinicopathological adjustment: non-functional presentation is often enriched for delayed diagnosis and adverse features, whereas functional subtypes range from typically indolent insulinomas to clinically aggressive hormone-producing tumors. We reconcile these observations through a layered model in which lineage-defining alterations and chromatin/telomere programs establish cellular state; signaling and metabolic plasticity enable stress adaptation; and hypoxia, angiogenesis, immune cells, fibroblasts, extracellular matrix, and therapy create selective niches for dissemination and relapse. We also define computational strategies for heterogeneous multi-omics integration and a staged biomarker-validation pathway. Evidence remains dominated by pancreatic NETs, and causal support is weakest for microbiome-functionality relationships and several proposed cross-omic links. A spectrum-based framework is therefore most useful when it generates testable, site- and grade-specific hypotheses rather than treating functionality as an isolated prognostic variable.

PMID:42724134 | PMC:PMC13559159 | DOI:10.3389/fendo.2026.1782791

PLoS Comput Biol. 2026 Sep 10;22(9):e1014735. doi: 10.1371/journal.pcbi.1014735. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC), with a 12% 5-year survival rate, is the most aggressive type of cancer. Early diagnosis for this pathology is rare, and conventional treatments such as surgery, radio- or chemotherapy, have little to no effect on reducing mortality. Machine learning (ML) approaches could be used to identify biomarkers that help clinicians stratify patients and improve treatment outcomes. However, most ML techniques perform poorly with incomplete data, which is usually the case in real-world settings, often forcing researchers to discard valuable information. In this study, unsupervised ML algorithms capable of dealing with missing modalities were applied to incomplete multi-omics data from PDAC patients to identify clinically meaningful patient subgroups. Through a large-scale clustering benchmark including six omics layers, we discovered two novel subgroups with statistically significant differences in survival and recurrence after surgery, particularly within the first two years, when most patient deaths occur, as well as distinct tumor mutational burden. Comprehensive multi-omics analyses revealed substantial molecular differences between patients in both groups, identified three methylation biomarkers to stratify patients, and highlighted dysregulation in key oncogenic pathways. Importantly, the identified groups are different from previous PDAC classifications, both in their patient composition, prognosis, and in the oncogenic gene pathway profiles exhibited. Using an independent cohort, we further demonstrated that both the prognostic value of these subtypes and their underlying biological characteristics are reproducible. These results could lead to better stratified treatment regimens to improve the prognosis of PDAC patients.

PMID:42721202 | DOI:10.1371/journal.pcbi.1014735

Mol Omics. 2026 Sep 10:aaiag022. doi: 10.1093/molecular-omics/aaiag022. Online ahead of print.

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-related proteins as recurrently altered across mutant alleles, while phosphoproteomic data confirmed robust ERK1/2 activity and suppression of DYRK kinase substrates by mutant KRAS expression. Importantly, no robust mutant allele-specific molecular programs were identified in our KRAS-reconstituted cell lines. 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.

PMID:42720273 | DOI:10.1093/molecular-omics/aaiag022

Biofactors. 2026 Sep-Oct;52(5):e70150. doi: 10.1002/biof.70150.

ABSTRACT

Gastrointestinal malignancies, including gastric cancer, colorectal cancer, hepatocellular carcinoma, and pancreatic ductal adenocarcinoma, remain major causes of cancer-related morbidity and mortality worldwide. Although chimeric antigen receptor T-cell (CAR-T) therapy has revolutionized the treatment of hematologic malignancies, its efficacy in gastrointestinal solid tumors remains limited by antigen heterogeneity, insufficient trafficking and infiltration, immunosuppressive tumor microenvironments, on-target off-tumor toxicity, and adaptive resistance. In this review, we summarize the current landscape of CAR-T therapy in gastric cancer, colorectal cancer, hepatocellular carcinoma, and pancreatic cancer, with a focus on representative target antigens and emerging biomarker strategies. We further discuss two major categories of biomarkers: target antigen-related biomarkers and conventional dynamic biomarkers, including serum tumor markers, cytokine changes, CAR-T expansion kinetics, and antigen-loss monitoring. In addition, we highlight how single-cell ribonucleic acid sequencing and spatial transcriptomics provide complementary insights into cellular states, immune exhaustion, stromal barriers, and spatially restricted immune exclusion. By integrating these multi-omics approaches with biomarker-guided patient stratification and next-generation CAR-T engineering, gastrointestinal solid tumor CAR-T therapy may evolve from empirical optimization toward mechanism-driven and precision-guided clinical translation.

PMID:42717494 | PMC:PMC13558850 | DOI:10.1002/biof.70150