Pancreatic cancer (PC) is a frequent and aggressive digestive system cancer with a very poor prognosis. The best chance for recovery lies in early surgical removal of the tumor. Unfortunately, because PC often develops without noticeable symptoms, diagnosis is frequently delayed. Limited treatment options, the metastasis potential of pancreatic cancer cells, and its generally poor prognosis mean that patients are often diagnosed late, significantly reducing the effectiveness of treatment. Consequently, there's a critical need for new biomarkers and technologies to improve early detection through screening. Recently, the liquid biopsy has developed as a powerful means for detecting and monitoring cancer at the molecular level. Its advantages include the ease and non-invasive nature of sample collection and its ability to reflect the dynamic changes within a tumor. Platelets, the second most numerous type of blood cell, offer a particularly promising source for liquid biopsy. It is known that cancer affects various aspects of platelets, including their number, size, activation state, and the proteins and RNA they contain. However, the full implications of these changes for cancer detection have not yet been fully integrated into routine clinical practice. Platelets have a unique ability to captivate nucleic acids and proteins from their surroundings, and they alter their transcriptome in response to external signals. This leads to the development of tumor-educated platelets (TEPs). Liquid biopsies that utilize TEP biomarkers hold considerable potential for screening, early detection, prognosis, guiding personalized treatment strategies, ongoing monitoring of the disease, and predicting recurrence. Encouraging results from preclinical studies have highlighted the potential of platelets as a novel liquid biopsy source for a wide range of cancers. This review will explore the potential of using platelets as a liquid biopsy method, specifically for pancreatic cancer.

The aim of this study was to observe the sensitivity of the resistant strains to gemcitabine by interfering with the LCN2.An AsPC-1 gemcitabine-resistant cell line (GEM-R) was generated. Based on GEM-R, a lentivirus-infected shRNA-transfected LCN2 cell line was established. The proliferation of LCN2-regulated GEM-R cells was evaluated using the CCK-8 test and the mRNA expression of Ki-67. The apoptosis level of each drug-resistant strain was detected by flow cytometry. The expression of Bax, Bcl-2, Akt, E-cadherin and Vimentin were detected by western blotting.A gemcitabine-resistant strain of AsPC-1 was successfully induced and constructed as an shRNA LCN2 strain based on GEM-R. The interference of LCN2 expression enhanced the tumour inhibition and pro-apoptotic level of gemcitabine, increased the Bax/Bcl-2 value, and decreased p-Akt/Akt value. Meanwhile, the expression of E-cadherin was enhanced while the expression of Vimentin was decreased.This study confirmed that LCN2 affects gemcitabine sensitivity by participating in apoptosis and EMT processes, which may provide potential for overcoming gemcitabine resistance.

Background: Assessing cancer survival trends is crucial for monitoring progress in cancer management and prevention. As part of the broader HUN-CANCER EPI study, this analysis examined overall survival (OS) in the Hungarian cancer population between 2011 and 2019. Methods: Using data extracted from the Hungarian National Health Insurance Fund (NHIF) database, short- and long-term OS were estimated for various cancer types according to age, sex, and diagnostic period using Kaplan-Meier analysis. The study also identified cancer types with significant early mortality following diagnosis. Results: From 2011 to 2019, a total of 528,808 patients were diagnosed with cancer. During the 2015-2019 diagnostic period, the lowest 5-year OS rates were observed for esophageal (7.0%), pancreatic (10.7%), liver (12.5%), gallbladder (13.9%), and lung cancer (18.4%). Conversely, tumor types with better OS included testicular cancer (91.6%), thyroid cancer (89.0%), Hodgkin's lymphoma (84.0%), melanoma (78.6%), and breast cancer (74.1%). A notable proportion of deaths occurred within 2 months of diagnosis for liver (33.2%), pancreatic (27.9%), and gallbladder cancer (29.0%). Significant early mortality within 6 months post-diagnosis was also noted for esophageal (51.3%), stomach (42.9%), and lung cancer (41.7%). Conclusions: The HUN-CANCER EPI study conducted between 2011 and 2019 provides valuable insights into cancer survival patterns in Hungary, emphasizing the importance of early detection and targeted interventions to improve patient outcomes.

Breast cancer (BC) is a heterogeneous disease with high metastasis potential, especially in the bones, liver, and lungs. Circulating tumor cells (CTCs), which emerge from active tumors, represent an early step toward metastasis and are associated with poor prognosis. CTCs of carcinoma origin are believed to express EpCAM and cytokeratins (CKs), common epithelial markers that are frequently used to identify them. However, in practice, the most aggressive CTCs lose the expression of those markers, leading to the partial loss of important information. Thus, finding some novel markers that identify CTCs regardless of their heterogeneity is crucial. A specific bioinformatics workflow integrating primary tumor and diverse BC cell lines transcriptomic expression analysis was developed and compared with single CTC transcriptomic analyses. We have identified a set of genes that are overexpressed in primary BC cells and are commonly upregulated among BC cell lines. Fifty of them were also found to be expressed in BC CTCs by single-cell transcriptomic analysis. Further in silico sorting narrowed this list to 12 genes. Using ScreenCell technology to isolate cancer cells spiked into normal blood, we tested the protein expression of all corresponding genes in vitro using the double immunocytochemistry method and validated MARCKSL1, SLC9A3R1, and RHOD as the most expressed markers. We then isolated the CTCs of 40 LN-invaded BC patients and 18 healthy donors using ScreenCell technology and showed that the combination of these three markers resulted in significantly better recognition of CTCs compared to EpCAM and CK conventional markers. Employing these novel markers, we found a clear distinction between blood samples from patients and healthy donors. In conclusion, through a specific bioinformatics workflow, in addition to in vitro and further clinical validations, we found three novel markers to precisely identify CTCs. These markers, when used together, enable a significantly more efficient identification of CTCs compared to conventional epithelial markers.

Medical ultrasound is a diagnostic imaging modality used for visualizing internal organs; the frequencies typically used are 2-10 MHz. Scanning acoustic microscopy (SAM) is a form of ultrasound where frequencies typically exceed 50 MHz. Increasing the acoustic frequency increases the specimen's spatial resolution but reduces the imaging depth. The advantages of using SAM over conventional light and electron microscopy include the ability to image cells and tissues without any preparation that could kill or alter them, providing a more accurate representation of the specimen. After scanning the specimen, acoustic signals are merged into an image on the basis of changes in the impedance mismatch between the immersion fluid and the specimens. The acoustic parameters determining the image quality are absorption and scattering. Surface scans can assess surface characteristics of the specimen. SAM is also capable of elastography, that is, studying elastic properties to discern differences between healthy and affected tissues. SAM has significant potential for detection/analysis in research and clinical studies.

Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) has become one of the most important preoperative diagnostic methods for pancreatic tumors, but it often faces challenges of redundant sampling from patients and complex tissue processing that hinders timely diagnosis. Intraoperative rapid on-site evaluation is an auxiliary diagnostic technique that helps assess sample quality in real time, but it heavily depends on pathologists and involves subjectivity and complex procedures. Here, we developed a rapid and label-free approach for intraoperative histology on EUS-FNA specimen via deep learning-based stimulated Raman scattering microscopy, aimed at replacing rapid on-site evaluation and providing a more efficient and objective diagnostic approach. Fresh pancreatic EUS-FNA tissues were imaged with stimulated Raman scattering and compared with hematoxylin and eosin staining to identify key histologic features. Using images from 76 patients, a convolutional neural network model was established to identify benign, malignant, and nondiagnostic areas, achieving a validation accuracy >96% on an external test set of 33 cases. Furthermore, gradient-weighted class activation mapping was able to highlight histologic profiles within individual biopsy. Our approach has potential application in efficient intraoperative assessment of pancreatic biopsy through EUS-FNA.

Pancreatic cancer is characterised by its aggressive progression, late-stage diagnosis and poor prognosis. This case report illustrates the intricate interplay between pancreatic adenocarcinoma, disseminated intravascular coagulation with diffuse alveolar haemorrhage and bone metastasis in an elderly woman. Presenting with asthenia, significant hip pain and haemoptysis, her work-up revealed multiple supradiaphragmatic adenopathies and infradiaphragmatic adenopathies, pancreatic parenchymal heterogeneity and lytic bone lesions. Despite extensive diagnostic efforts, a diagnosis of adenocarcinoma of probable pancreatobiliary origin was established only at an advanced stage, leading to a fatal outcome within a week. The patient experienced rapid clinical deterioration, characterised by refractory respiratory failure, haematological dysfunction and circulatory shock, highlighting the aggressive course of pancreatic cancer and its complications. This case underscores the diagnostic challenges and the critical need for early recognition and tailored strategies in managing complex presentations of pancreatic cancer, especially with rare and severe systemic manifestations.

Pancreatic cancer is a deadly disease with a low survival rate, particularly in its advanced stages. Advanced pancreatic cancer remains a major clinical challenge due to limited treatment options. Surgical resection may not always be feasible, and traditional chemotherapy often shows restricted effectiveness. As a result, researchers are exploring a multifaceted therapeutic approach targeting the genetic and molecular drivers of the disease. A combination of molecular profiling and targeted therapies are being investigated to improve outcomes and address the shortcomings of traditional treatments. The focus of this review is to provide a summary of current and completed clinical trials for the treatment of advanced pancreatic cancer. This includes adagrasib (a KRAS inhibitor), olaparib (a PARP inhibitor for BRCA mutations), APG-1387 (an IAP antagonist), minnelide (an anti-stromal agent), arimastat (an MMP inhibitor), MK-0646 (an IGF1R inhibitor), sirolimus (an mTOR inhibitor), and metabolic inhibitors. These agents are being evaluated both as standalone treatments and in combination with standard therapy. Furthermore, we have summarized novel approaches such as cancer vaccines and ablation techniques as emerging strategies in the treatment of advanced pancreatic cancer. We have also examined the challenges in treating advanced pancreatic cancer and the factors contributing to therapeutic failure, which may offer valuable insights for developing more effective treatment strategies and innovative drug designs.

Cancer remains one of the leading causes of mortality worldwide, driving the need for innovative approaches in research and treatment. Artificial intelligence (AI) has emerged as a powerful tool in oncology, with the potential to revolutionize cancer diagnosis, treatment, and management. This paper reviews recent advancements in AI applications within cancer research, focusing on early detection through computer-aided diagnosis, personalized treatment strategies, and drug discovery. We survey AI-enhanced diagnostic applications and explore AI techniques such as deep learning, as well as the integration of AI with nanomedicine and immunotherapy for cancer care. Comparative analyses of AI-based models versus traditional diagnostic methods are presented, highlighting AI's superior potential. Additionally, we discuss the importance of integrating social determinants of health to optimize cancer care. Despite these advancements, challenges such as data quality, algorithmic biases, and clinical validation remain, limiting widespread adoption. The review concludes with a discussion of the future directions of AI in oncology, emphasizing its potential to reshape cancer care by enhancing diagnosis, personalizing treatments and targeted therapies, and ultimately improving patient outcomes.

Exosomes, nanoscale extracellular vesicles secreted by diverse cell types, have emerged as promising biomarkers for non-invasive tumor diagnostics, offering significant advantages over traditional methods. These vesicles, typically ranging from 30 to 150 nanometers in size, carry a diverse cargo of proteins, lipids, RNA, and microRNAs, which reflect the molecular alterations occurring within their parent cells. Notably, exosomes can be isolated from easily accessible biofluids such as blood, urine, and saliva, making them ideal candidates for liquid biopsy applications. This review explores the transformative potential of exosome-based biomarkers in the early detection and monitoring of cancers across diverse organ systems, including respiratory, digestive, hematological, neurological, endocrine malignancies and so on. Special emphasis is placed on their application in clinical trials, where exosome-based diagnostics have demonstrated promising results in detecting tumors at early stages and monitoring treatment responses, offering a less invasive and more accessible alternative to traditional biopsies. While recent advancements in exosome isolation and characterization technologies have significantly improved the sensitivity and specificity of these diagnostics, challenges such as biological heterogeneity, lack of standardization, and regulatory hurdles remain. Nevertheless, exosome-based diagnostics hold the promise of providing real-time, dynamic insights into tumor progression, enhancing personalized medicine. The integration of exosomes into clinical practice could revolutionize cancer diagnostics and therapy, improving patient outcomes. Further research and large-scale clinical validation are essential to fully realize the clinical potential of exosome-based biomarker applications in routine clinical settings.

Pancreatic cancer has a poor prognosis; however, the implementation of neoadjuvant treatment enables borderline resectable cases to undergo curative resection and improves the overall survival rate. Attempts have been made to expand the eligibility criteria for neoadjuvant treatment, even in resectable cases. Some studies have suggested a correlation between vein abutment and poor prognosis or that the abutment angle may affect prognosis. This study investigated the anatomical factors affecting the vessel abutment angle and its prognostic value in pancreatic cancer.

Patients with pancreatic ductal adenocarcinoma who underwent surgery between 2012 and 2017 were included in this study. Patients who underwent neoadjuvant treatment were excluded. Data from only the intent-to-treat pancreaticoduodenectomy group were included in the analysis. Clinicopathological characteristics; preoperative factors such as CA 19-9, preoperative biliary drainage, American Society of Anesthesiologists physical status classification, portal vein/superior mesenteric vein contact angle measured via CT scan; and intraoperative factors were collected for analysis.

A total of 365 patients were included in this study, and the abutment group included 92 patients (25.2%). The abutment and no-contact groups did not show any significant differences in terms of the overall survival or disease-free survival rate. Among the abutment groups, patients with less than 90° and 90°-180° did not show any significant differences. In the multivariate analysis, the only preoperative factor that had a prognostic effect was CA 19-9, a biological factor.

When there is no vessel invasion in the abutment group, upfront surgery should be considered because the angle does not affect the overall prognosis.

Pancreatic cancer is characterized by its high mortality rate and short survival periods, and novel therapeutic targets and tailor personalized strategies are urgently needed. In this study, we aim to investigate the molecular mechanisms underlying pancreatic ductal adenocarcinoma (PDAC) progression and chemoresistance, with a focus on identifying novel therapeutic targets.

Multiomics approaches were integrated to identify novel actionable targets for PDAC. Public datasets such as TCGA and GEO were utilized to investigate the relationship between gene expression and clinical outcomes. Functional enrichment, cell-cell communication, and metabolic pathway analyses were performed to reveal PDAC heterogeneity and therapeutic resistance mechanisms.

BHLHE40 was identified as a hub gene linked to high-CNV PDAC cells, Gemcitabine resistance, and poor prognosis in PDAC. High BHLHE40 expression is significantly correlated with immunosuppressive tumor microenvironment (TME) features such as reduced CD8+ T infiltration, TCR richness, and lower tumor mutational burden (TMB). ChIP-seq data analysis confirmed BHLHE40 could directly bind to the SAT1 promoter, establishing a transcriptional axis promoting chemoresistance. Single-cell RNA-seq analysis further revealed that the BHLHE40+/SAT1+ subpopulation cells are resistant to Gemcitabine in PDAC.

BHLHE40 is significantly correlated with PDAC malignancy and chemoresistance via SAT1 regulation and immune evasion. Targeting BHLHE40 may sensitize PDACs to Gemcitabine and facilitate personalized treatment for BHLHE40+ PDAC patients.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with increasing incidence and mortality rates, highlighting the urgent need for early diagnosis and treatment. However, early diagnosis of PDAC is extremely challenging due to the atypical early symptoms or the absence of noticeable symptoms. As a result, many patients are diagnosed with local metastasis, and even patients who are eligible for surgical resection have a high postoperative recurrence rate. Consequently, chemotherapy remains the primary treatment for PDAC. However, the unique biological characteristics of PDAC not only promote tumor progression and metastasis but also often lead to chemoresistance, a significant barrier to successful treatment. Recently, nanomaterials have garnered significant attention as promising materials for diagnosing and treating PDAC, showing great potential in cancer therapy, imaging, and drug delivery. Novel targeted nanomedicines, which encapsulate chemotherapy drugs and gene therapy products, offer significant advantages in overcoming resistance. These nanomedicines not only provide innovative solutions to the limitations of conventional chemotherapy but also improve the selectivity for cancer cells to enhance therapeutic outcomes. Current research is focused on the development of advanced nanomedicines, such as liposomes, nanotubes, and polymer-lipid hybrid systems, aimed at making chemotherapy more effective and longer lasting. This review provides a detailed overview of various nanomedicines utilized in the diagnosis and treatment of PDAC and outlines future directions for their development and key breakthroughs.

Pancreatic cancer is highly lethal and has a poor prognosis. Research has highlighted the role of circular RNAs and m6A methylation in cancer progression. METTL3, a key m6A methyltransferase, is linked to various cancers, but its interaction with circular RNAs in pancreatic cancer is unclear. This study examined the role of circCEACAM5 in pancreatic cancer, particularly its regulation by METTL3-mediated m6A methylation and interaction with effectors such as DKC1.

circCEACAM5 expression in pancreatic cancer tissues and cell lines was evaluated via RT‒qPCR. Its characteristics were validated through Sanger sequencing, stability assays, and FISH. Functional assays (CCK-8, EdU, Transwell, and flow cytometry) were conducted in AsPC-1 cells, and in vivo tumor models were established. m6A modification was analyzed via bioinformatics tools and m6A-specific immunoprecipitation, while RNA pull-down assays were used to examine the interaction of circCEACAM5 with METTL3 and DKC1.

circCEACAM5 was significantly upregulated in pancreatic cancer and correlated with poor clinical outcomes. CircCEACAM5 promoted cell proliferation, invasion, and migration while inhibiting apoptosis both in vitro and in vivo. METTL3-mediated m6A methylation of circCEACAM5 was confirmed, and METTL3 knockdown reversed the effects of circCEACAM5 silencing on the malignant behavior of pancreatic cancer cells. circCEACAM5 interacted with DKC1, and DKC1 overexpression reversed the effects of circCEACAM5 knockdown on the malignant behavior of pancreatic cancer cells.

METTL3-mediated m6A methylation of circCEACAM5 drives pancreatic cancer progression by increasing DKC1 expression, suggesting potential new therapeutic targets for this aggressive malignancy.

Pancreatic cancer is distinguished by its high likelihood of metastasis and drug resistance, while the fundamental mechanisms are inadequately elucidated. This study aimed to identify pivotal hub genes associated with pancreatic cancer and assess their potential utility in predicting its onset and progression.

Weighted gene co-expression network analysis (WGCNA) combined with differential expression analysis identified novel susceptibility modules and hub genes for pancreatic cancer. Kyoto Encyclopedia of Genes and Genomes and gene ontology analyses were utilized to explore the potential roles of these hub genes. Receiver operator characteristic curves and nomogram models were developed to evaluate diagnostic efficacy. Mendelian randomization, flow cytometry, Transwell, and RNA sequencing were conducted to explore the association between C-X-C motif chemokine ligand 10 (CXCL10) and immune infiltration.

WGCNA analysis was performed to build gene co-expression networks, and ten key genes were found. CXCL10 was the central gene, and its expression was significantly linked to the survival of patients with pancreatic cancer and their response to immune checkpoint inhibitors. CXCL10 demonstrated the ability to stimulate the differentiation of macrophages toward the M2 phenotype. CXCL10 could facilitate the metastasis of pancreatic cancer cells by modulating macrophage polarization. CXCL10 affects macrophage polarization by regulating the expression of vascular endothelial growth factor A.

CXCL10 demonstrates potential as a therapeutic target for managing pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is difficult to detect early and highly aggressive, often leading to poor patient prognosis. Existing serum biomarkers like CA19-9 are limited in early diagnosis, failing to meet clinical needs. Machine learning (ML)/deep learning (DL) technologies have shown great potential in biomedicine. This study aims to establish PDAC differential diagnosis and prognosis assessment models using ML combined with serum biomarkers for early diagnosis, risk stratification, and personalized treatment recommendations, improving early diagnosis rates and patient survival.

The study included serum biomarker data and prognosis information from 117 PDAC patients. ML models (Random Forest (RF), Neural Network (NNET), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM)) were used for differential diagnosis, evaluated by accuracy, Kappa test, ROC curve, sensitivity, and specificity. COX proportional hazards model and DeepSurv DL model predicted survival risk, compared by C-index and Log-rank test. Based on DeepSurv's risk predictions, personalized treatment recommendations were made and their effectiveness assessed.

Effective PDAC diagnosis and prognosis models were built using ML. The validation set data shows that the accuracy of the RF, NNET, SVM, and GBM models are 84.21%, 84.21%, 76.97%, and 83.55%; the sensitivity are 91.26%, 90.29%, 89.32%, and 88.35%; and the specificity are 69.39%, 71.43%, 51.02%, and 73.47%. The Kappa values are 0.6266, 0.6307, 0.4336, and 0.6215; and the AUC are 0.889, 0.8488, 0.8488, and 0.8704, respectively. BCAT1, AMY, and CA12-5 were selected as modeling parameters for the prognosis model using COX regression. DeepSurv outperformed the COX model on both training and validation sets, with C-indexes of 0.738 and 0.724, respectively. The Kaplan-Meier survival curves indicate that personalized treatment recommendations based on DeepSurv can help patients achieve survival benefits.

This study built efficient PDAC diagnosis and prognosis models using ML, improving early diagnosis rates and prognosis accuracy. The DeepSurv model excelled in prognosis prediction and successfully guided personalized treatment recommendations and supporting PDAC clinical management.

Pancreatic cancer (PC) is a common gastrointestinal cancer with high invasiveness and high mortality. Curcumin is a natural polyphenol with anti-tumor activity against different cancers, including PC. Curcumin has been verified to mediate the expression of circular RNAs (circRNAs) to inhibit tumor development. This study aimed to explore the function and regulatory mechanism of curcumin on circ_0079440 in PC. PC cells were treated with different concentrations of curcumin (0, 5, 10 or 15 μM) for 24 h. Gene expression in PC cells and tissues was detected using RT-qPCR. Cell malignant phenotypes were determined by functional assays. The levels of EMT-related proteins were tested using western blot. RNA interaction was determined using RNA pulldown assay, luciferase reporter assay and RIP assay. The results showed that curcumin suppressed cell proliferative, migratory, and invasive capabilities, and weakened epithelial-mesenchymal transition (EMT) in a concentration-dependent way. Circ_0079440 was expressed at a high level in PC and its level was reduced via curcumin administration in PC cells. Rescue assays showed that circ_0079440 overexpression reversed the suppressive effects of curcumin on PC cell malignant phenotypes. Furthermore, in the xenograft mouse models, curcumin treatment inhibited tumor growth and metastasis, and circ_0079440 upregulation reversed the function of curcumin. Additionally, circ_0079440 was revealed to bind to miR-522-3p to upregulate eukaryotic initiation factor 4A1 (EIF4A1) expression in PC cells. EIF4A1 expression was also downregulated by curcumin, and EIF4A1 overexpression abolished the suppressive functions of curcumin. Moreover, EIF4A overexpression or miR-522-3p inhibition counteracted the anti-tumor effects of circ_0079440 depletion on PC development. To sum up, curcumin suppresses PC development by targeting the circ_0079440/miR-522-3p/EIF4A1 pathway, which might provide novel therapeutic targets for treatment of PC.

Frozen sections are essential in the surgical management of patients, especially those with pancreatic masses, because frozen sections can provide answers intraoperatively and aid in treatment decisions. Pancreas frozen sections are challenging because of the small tissue size, processing artifacts, neoadjuvant treatment effects, and concurrent pancreatitis-like obstructive changes. The authors present a review of intraoperative evaluation of pancreatic specimens.

To provide an approach to the diagnosis of pancreatic adenocarcinoma on frozen sections and to discuss commonly encountered pitfalls. Indications for pancreas frozen sections and specific margin evaluation will be discussed. We will also review frozen section diagnosis of subcapsular liver lesions and tumors other than metastases of pancreatic ductal adenocarcinoma.

Data sources included a literature review and the personal experiences of the authors.

The features for diagnosis of pancreatic adenocarcinoma include disordered architecture, glands at abnormal locations, and atypical cytology. It is important to be aware of the pitfalls and clues on frozen section. The evaluation of resection margins can be challenging, and in the setting of the resection of cystic tumors, the key is the diagnosis of high-grade dysplasia or cancer. Finally, it is vital to remember the differential diagnosis for subcapsular liver lesions because not all lesions will be metastases of adenocarcinomas or bile duct adenomas. Frozen sections remain a useful tool for the intraoperative management of patients with pancreatic tumors.

Pancreatic cancer has one of the worst prognoses of any malignant tumor. The value of MIDN, midnolin-related genes and midnolin-related immune infiltrating cells (MICs) in the prognosis of pancreatic cancer remains unknown.

Single-cell analysis were used to identify midnolin-related genes. Immune cell infiltration was obtained using CIBERSORT. The prognostic midnolin-related genes were identified through the utilization of Cox regression and the least absolute selection operator (LASSO) approach. The combined prognostic model was created using multifactorial Cox regression analysis. Survival analyses, immune microenvironment assessments, drug sensitivity checks were performed to evaluate the combined model performance. Finally, cellular experiments were carried out to confirm MIDN significance in pancreatic cancer.

The combined model was constructed based on MIDN expression, prognostic model of 10 midnolin-related genes and M1 cell infiltration. Most immune checkpoint-related genes were expressed at greater levels in the low-risk group, suggesting a greater chance of immunotherapy's benefits. The most significant model gene, MIDN, was shown to have a function by cellular tests. In pancreatic cancer, MIDN knockdown drastically decreased pancreatic cancer cell lines' activity, proliferation, and invasive potential.

The combined model helped assess the prognosis of pancreatic cancer and offered fresh perspectives on immunotherapy in particular.

To explore the potential of serum exosomal miRNAs as novel biomarkers for pancreatic ductal adenocarcinoma (PDAC).

Serum exosomal miRNAs were screened and verified by microarray analysis and quantitative real-time PCR (qRT-PCR) in patients with PDAC and healthy controls. The correlation between the clinical characteristics of PDAC and candidate exosomal miRNAs was analyzed, and the diagnostic performance of the candidate biomarkers was evaluated.

Serum exosomal miR-7977 and miR-451a were significantly upregulated in PDAC patients compared with healthy controls, and the levels of miR-7977 and miR-451a in serum exosomes were closely associated with the clinical stage and metastasis of PDAC patients. The area under curve (AUC) values of serum exosomal miR-7977 and miR-451a for PDAC were 0.825 and 0.804 in the training set and 0.796 and 0.830 in the validation set, respectively. A biomarker panel consisting of these two miRNAs resulted in a diagnostic power with an AUC of 0.901 in the training set and 0.918 in the validation set.

Serum exosomal miR-7977 and miR-451a might be diagnostic biomarkers for PDAC. These two miRNAs, when combined, exhibit optimal diagnostic performance.

The pancreas, an organ with dual functions, regulates blood glucose levels through the endocrine system by secreting hormones such as insulin and glucagon. It also aids digestion through the exocrine system by secreting digestive enzymes. Complex interactions and signaling mechanisms between the endocrine and exocrine functions of the pancreas play a crucial role in maintaining metabolic homeostasis and overall health. Compelling evidence indicates direct and indirect crosstalk between the endocrine and exocrine parts, influencing the development of diseases affecting both. From a developmental perspective, the exocrine and endocrine parts share the same origin-the "tip-trunk" domain. In certain circumstances, pancreatic exocrine cells may transdifferentiate into endocrine-like cells, such as insulin-secreting cells. Additionally, several pancreatic diseases, including pancreatic cancer, pancreatitis, and diabetes, exhibit potential relevance to both endocrine and exocrine functions. Endocrine cells may communicate with exocrine cells directly through cytokines or indirectly by regulating the immune microenvironment. This crosstalk affects the onset and progression of these diseases. This review summarizes the history and milestones of findings related to the exocrine and endocrine pancreas, their embryonic development, phenotypic transformations, signaling roles in health and disease, the endocrine-exocrine crosstalk from the perspective of diseases, and potential therapeutic targets. Elucidating the regulatory mechanisms of pancreatic endocrine and exocrine signaling and provide novel insights for the understanding and treatment of diseases.

The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late-stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation. Artificial intelligence, particularly machine learning and deep learning, has emerged as a revolutionary force in healthcare, enhancing diagnostic precision and personalizing treatment. This narrative review explores Artificial intelligence's role in pancreatic imaging, its technological advancements, clinical applications, and associated challenges. Following the PRISMA-DTA guidelines, a comprehensive search of databases including PubMed, Scopus, and Cochrane Library was conducted, focusing on Artificial intelligence, machine learning, deep learning, and radiomics in pancreatic imaging. Articles involving human subjects, written in English, and published up to March 31, 2024, were included. The review process involved title and abstract screening, followed by full-text review and refinement based on relevance and novelty. Recent Artificial intelligence advancements have shown promise in detecting and diagnosing pancreatic diseases. Deep learning techniques, particularly convolutional neural networks (CNNs), have been effective in detecting and segmenting pancreatic tissues as well as differentiating between benign and malignant lesions. Deep learning algorithms have also been used to predict survival time, recurrence risk, and therapy response in pancreatic cancer patients. Radiomics approaches, extracting quantitative features from imaging modalities such as CT, MRI, and endoscopic ultrasound, have enhanced the accuracy of these deep learning models. Despite the potential of Artificial intelligence in pancreatic imaging, challenges such as legal and ethical considerations, algorithm transparency, and data security remain. This review underscores the transformative potential of Artificial intelligence in enhancing the diagnosis and treatment of pancreatic diseases, ultimately aiming to improve patient outcomes and survival rates.

This review provides a comprehensive overview of the current landscape in pancreatic cancer (PC) screening, diagnosis, and early detection. This emphasizes the need for targeted screening in high-risk groups, particularly those with familial predispositions and genetic mutations, such as BRCA1, BRCA2, and PALB2. This review highlights the sporadic nature of most PC cases and significant risk factors, including smoking, alcohol consumption, obesity, and diabetes. Advanced imaging techniques, such as Endoscopic Ultrasound (EUS) and Contrast-Enhanced Harmonic Imaging (CEH-EUS), have been discussed for their superior sensitivity in early detection. This review also explores the potential of novel biomarkers, including those found in body fluids, such as serum, plasma, urine, and bile, as well as the emerging role of liquid biopsy technologies in analyzing circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosomes. AI-driven approaches, such as those employed in Project Felix and CancerSEEK, have been highlighted for their potential to enhance early detection through deep learning and biomarker discovery. This review underscores the importance of universal genetic testing and the integration of AI with traditional diagnostic methods to improve outcomes in high-risk individuals. Additionally, this review points to future directions in PC diagnostics, including next-generation imaging, molecular biomarkers, and personalized medicine, aiming to overcome current diagnostic challenges and improve survival rates. Ultimately, the review advocates the adoption of informatics and AI-driven strategies to enhance early detection, reduce morbidity, and save lives in the fight against pancreatic cancer.

Pancreatic cancer is a highly fatal malignancy due to poor early detection rate and resistance to conventional therapies. This review examines the potential for liquid biopsy as a transformative technology to identify diagnostic and therapeutic targets in pancreatic cancer. Specifically, we explore emerging biomarkers such as exosomal non-coding RNAs (ncRNAs), circulating tumor DNA (ctDNA), and circulating tumor cells (CTCs). Tumor-derived exosomes contain nucleic acid and protein that reflect the unique molecular landscape of the malignancy and can serve as an alternative diagnostic approach vs traditional biomarkers like CA19-9. Herein we highlight exosomal miRNAs, lncRNAs, and other ncRNAs alongside ctDNA and CTC-based strategies, evaluating their combined ability to improve early detection, disease monitoring and treatment response. Furthermore, the therapeutic implications of ncRNAs such as lncRNA UCA1 and miR-3960 in chemoresistance and progression are also discussed via suppression of EZH2 and PTEN/AKT pathways. Emerging therapeutic strategies that target the immune response, epithelial-mesenchymal transition (EMT) and drug resistance are explored. This review demonstrates a paradigm shift in pancreatic cancer management toward personalized, less invasive and more effective approaches.

This study analyzed the burden of pancreatic cancer by investigating its incidence, mortality and disability-adjusted life years (DALYs), as well as the proportion of pancreatic cancer deaths attributable to behavioural and metabolic risk factors in Latin America and the Caribbean (LAC) countries.

This study focuses on pancreatic cancer using the Global Burden of Disease 2019 study database. Results were described for 23 LAC countries for 1990-2019, evaluating their age-standardised incidence rates, mortality rates, DALYs, average annual percent change and the fraction of deaths attributable to behavioural and metabolic risk factors.

We observed that in LAC, pancreatic cancer incidence rates ranged from 1.2 in Haiti to 15.8/100,000 in Uruguay among men. The highest increase in incidence rate was observed in Trinidad and Tobago: 7.7% per year. The mortality rate was higher in Uruguay and lower in Haiti, for both sexes. The highest rise in the numbers of DALYs in 2019 was observed in Brazil and Mexico. The proportion of pancreatic cancer deaths attributable to smoking was reduced between 1990 and 2019 for both sexes in LAC countries; however, it increased for metabolic risk factors.

The increasing trend in pancreatic cancer observed in LAC may be associated with a rise in risk factors such as high fasting plasma glucose and high body mass index in both sexes. This trend will likely have a substantial impact on the healthcare system in the coming decades.

Pancreatic cancer remains one of the most lethal malignancies worldwide, with a poor prognosis often attributed to late diagnosis. Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.

To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.

We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution. Pathological types were determined by histopathological examination of the surgical specimens or biopsy samples. The imaging features were assessed using computed tomography, magnetic resonance imaging, and endoscopic ultrasound. Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.

There were 320 (64%) cases of pancreatic ductal adenocarcinoma, 75 (15%) of intraductal papillary mucinous neoplasms, 50 (10%) of neuroendocrine tumors, and 55 (11%) of other rare types. Distinct imaging features were identified in each pathological type. Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography, whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules. Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging. Statistical analysis revealed significant correlations between specific imaging features and pathological types (P < 0.001).

This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features. These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.

Current diagnostic imaging modalities have limited ability to differentiate between malignant and benign pancreaticobiliary disease, and lack accuracy in detecting lymph node metastases. 18F-Prostate-Specific Membrane Antigen (PSMA) PET/CT is an imaging modality used for staging of prostate cancer, but has incidentally also identified PSMA-avid pancreatic lesions, histologically characterized as pancreatic ductal adenocarcinoma (PDAC). This phase I/II study aimed to assess the feasibility of 18F-PSMA PET/CT to detect PDAC.

Seventeen patients with clinically resectable PDAC underwent 18F-PSMA PET/CT prior to surgical resection. Images were analyzed both visually and (semi)quantitatively by deriving the maximum standardized uptake value (SUVmax) and tumor-to-background ratio (TBR). TBR was defined as the ratio between SUVmax of the primary tumor divided by SUVmax of the aortic blood pool. Finally, tracer uptake on PET was correlated to tissue expression of PSMA in surgical specimens.

Out of 17 PSMA PET/CT scans, 13 scans demonstrated positive PSMA tracer uptake, with a mean SUVmax of 5.0 ± 1.3. The suspected primary tumor was detectable (TBR ≥ 2) with a mean TBR of 3.3 ± 1.3. For histologically confirmed PDAC, mean SUVmax and mean TBR were 4.9 ± 1.2 and 3.3 ± 1.5, respectively. Although eight patients had histologically confirmed regional lymph node metastases and two patients had distant metastases, none of these metastases demonstrated 18F-PSMA uptake. There was no correlation between 18F-PSMA PET/CT SUVmax and tissue expression of PSMA in surgical specimens.

18F-PSMA PET/CT was able to detect several pancreaticobiliary cancers, including PDAC. However, uptake was generally low, not specific to PDAC and no tracer uptake was observed in lymph node or distant metastases. The added value of PSMA PET in this setting appears to be limited.

The trial is registered as PANSCAN-2 in the European Clinical Trials Database (EudraCT number: 2020-002185-14).

A significant contributor to cancer-related death, pancreatic cancer (PC) has a terrible prognosis in general that has not altered over many years. Currently, it is extremely difficult to prevent disease or discover it early enough to initiate treatment. PC is a challenging malignancy to treat, and several major impediments significantly impact the effectiveness of its treatment. These obstacles primarily include chemoresistance, drug toxicity, and limited drug bioavailability. Phytochemicals can be used as an alternative to chemotherapeutic drugs, or they can augment the anticancer properties of the chemotherapeutic agents. Nimbolide (NL) is a prominent limonoid compound found in Azadirachta indica, and has garnered substantial attention as a phytochemical with anticancer potential. It has powerful antiproliferative effects on a variety of cancer cell lines and is effective as a chemotherapeutic in preclinical studies. The primary modes of action of NL include suppression of metastasis and angiogenesis, activation of apoptosis, anti-proliferation, and control of enzymes that metabolize carcinogens. Despite numerous pharmacodynamic (PD) investigations, NL is still in the early stages of the drug development process because no comprehensive pharmacokinetic studies or long-term toxicity studies. Preclinical and toxicological assessments should be conducted to establish an appropriate dosage range, ensuring the safety of NL for its application in initial human clinical trials. This review endeavors to provide a comprehensive summary of the current developmental stage of NL along with nanoparticles as a principal candidate for therapeutic purposes in PC.

Controlled photooxidation-mediated disruption of collagens in the tumor microenvironment can reduce desmoplasia and enhance immune responsiveness. However, achieving effective light delivery to solid tumors, particularly those with dynamic volumetric changes like pancreatic ductal adenocarcinoma (PDAC), remains challenging and limits the repeated and sustained photoactivation of drugs. Here, 3D, shape-morphing, implantable photonic devices (IPDs) are introduced that enable tumor-specific and continuous light irradiation for effective metronomic photodynamic therapy (mPDT). This IPD adheres seamlessly to the surface of orthotopic PDAC tumors, mitigating issues related to mechanical mismatch, delamination, and internal lesions. In freely moving mouse models, mPDT using the IPD with close adhesion significantly reduces desmoplastic tumor volume without causing cytotoxic effects in healthy tissues. These promising in vivo results underscore the potential of an adaptable and unidirectional IPD design in precisely targeting cancerous organs, suggesting a meaningful advance in light-based therapeutic technologies.

Gastrointestinal cancers comprise nearly one-third of global mortality from cancer, yet the comprehensive global burden of these cancers remains uninvestigated.

We aimed to assess the global, regional and national burden of gastrointestinal cancers.

Data on oesophagus, gastric, colorectal, liver, pancreas and biliary tract cancers were extracted from the Global Burden of Disease 2021 database. Age-standardised incidence rate (ASIR) and age-standardised death rate (ASDR) were calculated by sex, region and Sociodemographic Index (SDI).

In 2021, there were 5.26 million incidences and 3.70 million deaths from gastrointestinal cancer. The greatest burden is from colorectal, followed by gastric, oesophageal, pancreatic, liver and biliary tract cancer. We noted geographical and socioeconomic differences in ASIR and ASDR across all types of cancers. From 2000 to 2021, ASIR increased for colorectal cancer (annual percent change (APC): 0.10%, 95% CI 0.05% to 0.14%), pancreatic cancer (APC: 0.27%, 95% CI 0.14% to 0.41%), and liver cancer from metabolic dysfunction-associated steatotic liver disease (APC: 0.62%, 95% CI 0.58% to 0.67%) and alcohol-related liver disease (APC: 0.26%, 95% CI 0.22% to 0.30%). ASDR increased for pancreatic cancer (APC: 0.18%, 95% CI 0.02% to 0.34%). Higher SDI countries had higher incidence rates for most types of gastrointestinal cancer.

Although the ASIR of oesophageal, gastric and biliary tract cancer has decreased, the ASIR still increased in colorectal, pancreatic and liver cancer from steatotic liver disease. Public policies are important for controlling gastrointestinal cancers-most importantly, reducing alcohol consumption, hepatitis B immunisation and tackling the burden of metabolic diseases.

Tumoral heterogeneity poses a challenge for personalized cancer treatments. Especially in metastasized cancer, it remains a major limitation for successful targeted therapy, often leading to drug resistance due to tumoral escape mechanisms. This work explores a non-invasive radiomics-based approach to capture textural heterogeneity in liver lesions and compare it between colorectal cancer (CRC) and pancreatic cancer (PDAC).

In this retrospective single-center study 73 subjects (42 CRC, 31 PDAC) with 1291 liver metastases (430 CRC, 861 PDAC) were segmented fully automated on contrast-enhanced CT images by a UNet for medical images. Radiomics features were extracted using the Python package Pyradiomics. The mean coefficient of variation (CV) was calculated patient-wise for each feature to quantify the heterogeneity. An unpaired t-test identified features with significant differences in feature variability between CRC and PDAC metastases.

In both colorectal and pancreatic liver metastases, interlesional heterogeneity in imaging can be observed using quantitative imaging features. 75 second-order features were extracted to compare the varying textural characteristics. In total, 18 radiomics features showed a significant difference (p < 0.05) in their expression between the two malignancies. Out of these, 16 features showed higher levels of variability within the cohort of pancreatic metastases, which, as illustrated in a radar plot, suggests greater textural heterogeneity for this entity.

Radiomics has the potential to identify the interlesional heterogeneity of CT texture among individual liver metastases. In this proof-of-concept study for the quantification and comparison of imaging-related heterogeneity in liver metastases a variation in the extent of heterogeneity levels in CRC and PDAC liver metastases was shown.

The aggressiveness of pancreatic ductal adenocarcinoma is primarily due to lack of effective early detection biomarkers. Circulating non-coding RNAs serve as diagnostic or prognostic biomarkers in multiple types of cancer. Comparison of their expression between diseased tissue and relevant body fluids such as saliva, urine, bile, pancreatic juice, blood etc. may reveal mechanistic involvement of common non-coding RNAs. piwi-interacting RNAs (piRNAs) are a class of non-coding RNAs. The aim of the present study was to investigate plasma and tumour tissue piRNA changes in patients with pancreatic cancer (PC) and explore the possible role in tumorigenesis and pancreatic inflammation. Sequencing of circulating plasma small RNAs from patients with PC and chronic pancreatitis (CP) was performed and differentially expressed piRNAs were compared with those in tissues. Subsequent search for target genes for those piRNAs was performed followed by pathway and cluster analysis. A total of 36 piRNAs were shown to be deregulated in pancreatic tumour tissue and alteration of 11 piRNAs was detected in plasma of patients with PC. piRNAs hsa-piR-23246, hsa-piR-32858 and hsa-piR-9137 may serve a key role in PC development as their expression was correlated in both plasma and tumour tissue. Key piRNA-target interactions interfering with key biological pathways were also characterized. A total of 19 deregulated piRNAs in plasma samples of patients with CP was identified; these targeted genes responsible for chronic inflammation. Therefore, the present study provides a comprehensive description of piRNA alteration in pancreatic malignancy and inflammation; these may be explored for biomarker potential in future.

The pancreaticobiliary duodenal junction (PBDJ) is the connecting area of the pancreatic duct, bile duct and duodenum. In a broad sense, it refers to a region formed by the head of the pancreas, the pancreatic segment of the common bile duct and the intraduodenal segment, the descending and the horizontal part of the duodenum, and the soft tissue around the pancreatic head. In a narrow sense, it refers to the anatomical Vater ampulla. Due to its complex and variable anatomical features, and the diversity of pathological changes, it is challenging to make an early diagnosis of malignancy at the PBDJ and define the histological type. The unique anatomical structure of this area may be the basis for the occurrence of malignant tumors. Therefore, understanding and subclassifying the anatomical configuration of the PBDJ is of great significance for the prevention and treatment of malignant tumors at their source. The present review comprehensively discusses commonly used imaging techniques and other new technologies for diagnosing malignancy at the PBDJ, offering evidence for physicians and patients to select appropriate examination methods.

Pancreatic Ductal Adenocarcinoma (PDAC) is an exceptionally deadly form of pancreatic cancer with an extremely low survival rate. From diagnosis to treatment, PDAC is highly challenging to manage. Studies have demonstrated that PDAC tumors in distinct regions of the pancreas exhibit unique characteristics, influencing symptoms, treatment responses, and survival rates. Gaining insight into the heterogeneity of PDAC tumors based on their location in the pancreas can significantly enhance overall management of PDAC. Previous studies have explored PDAC tumor heterogeneity across pancreatic subregions based on their genetic and molecular profiles through biopsy-based histologic assessment. However, biopsy examinations are highly invasive and impractical for large populations. Abdominal imaging, such as Computed Tomography (CT) offers a completely non-invasive means to evaluate PDAC tumor heterogeneity across pancreatic subregions and an opportunity to correlate image feature of tumors with treatment outcome and monitoring. In this study, we explored the inter-tumor heterogeneity in PDAC tumors across three primary pancreatic subregions: the head, body, and tail. Utilizing contrast-enhanced abdominal CT scans and a thorough radiomic analysis of PDAC tumors, several morphological and textural tumor features were identified to be notably different between tumors in the head and those in the body and tail regions. To validate the significance of the identified features, a machine learning ML model was trained to automatically classify PDAC tumors into their respective regions i.e. head or body/tail subregion using their CT features. The study involved 200 CT abdominal scans, with 100 used for radiomic analysis and model training, and the remaining 100 for model testing. The ML model achieved an average classification accuracy, sensitivity, and specificity of 87%, 86%, and 88% on the testing scans respectively. Evaluating the heterogeneity of PDAC tumors across pancreatic subregions provides valuable insights into tumor composition and has the potential to enhance diagnosis and personalize treatment based on tumor characteristics and location.

Surgical resection for pancreatic ductal adenocarcinoma (PDAC) entails the excision of the primary tumour and regional lymphadenectomy. This traditional strategy is challenged by the high rate of early recurrence, suggesting inadequate disease staging. Novel methods of intra-operative staging are needed to allow surgical resection to be tailored to the disease's biology.

A search of published articles on the PubMed and Embase databases was performed using the terms 'pancreas' OR 'pancreatic' AND 'intra-operative staging/detection' OR 'guided surgery'. Articles published between January 2000 and June 2023 were included. Technologies that offered intra-operative staging and tailored treatment were curated and summarised in the following integrative review.

lymph node (LN) mapping and radioimmunoguided surgery have shown promising results but lacked practicality to facilitate real-time intra-operative staging for PDAC. Fluorescence-guided surgery (FGS) offers high contrast and sensitivity, enabling the identification of cancerous tissue and positive LNs with improved precision following intravenous administration of a fluorescent agent. The unique properties of optical coherence tomography and ultrasound elastography lend themselves to be platforms for virtual biopsy intra-operatively.

Accurate intra-operative staging of PDAC, localisation of metastatic LNs, and identification of extra-pancreatic disease remain clinically unmet needs under current detection methods and staging standards. Tumour-specific FGS combined with other diagnostic and therapeutic modalities could improve tumour detection and staging in patients with PDAC.

Pancreatic cancer is one of the most lethal malignancies, characterized by late diagnosis, rapid progression, and limited treatment options. This literature review comprehensively examines the epidemiology, risk factors, diagnostic challenges, treatment modalities, and prognosis of pancreatic cancer. It highlights the global disparities in incidence and outcomes, exploring the influence of socioeconomic, environmental, and genetic factors on disease progression. In addition, this review discusses recent advancements in diagnostic tools and treatment strategies, identifying gaps in current research and clinical practices. The synthesis aims to inform future research directions and policy-making efforts to reduce the global burden of pancreatic cancer and improve patient outcomes.

A distinct feature of pancreatic ductal adenocarcinoma (PDAC) is a prominent tumor microenvironment (TME) with remarkable cellular and spatial heterogeneity that meaningfully impacts disease biology and treatment resistance. The dynamic crosstalk between cancer cells and the dense stromal compartment leads to spatially and temporally heterogeneous metabolic alterations, such as acidic pH that contributes to drug resistance in PDAC. Thus, monitoring the extracellular pH metabolic fluctuations within the TME is crucial to predict and to quantify anticancer drug efficacy. Here, a simple and reliable alginate-based 3D PDAC model embedding ratiometric optical pH sensors and cocultures of tumor (AsPC-1) and stromal cells for simultaneously monitoring metabolic pH variations and quantify drug response is presented. By means of time-lapse confocal laser scanning microscopy (CLSM) coupled with a fully automated computational analysis, the extracellular pH metabolic variations are monitored and quantified over time during drug testing with gemcitabine, folfirinox, and paclitaxel, commonly used in PDAC therapy. In particular, the extracellular acidification is more pronounced after drugs treatment, resulting in increased antitumor effect correlated with apoptotic cell death. These findings highlight the importance of studying the influence of cellular metabolic mechanisms on tumor response to therapy in 3D tumor models, this being crucial for the development of personalized medicine approaches.