UNASSIGNED: Due to a lack of early diagnosis methods and effective drugs, pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. DNA methylation, transcriptome expression and gene copy number variation (CNV) have critical relationships with development and progression of various diseases. The purpose of the study was to screen reliable early diagnostic biomarkers and potential drugs based on integrative multiomics analysis.
UNASSIGNED: We used methylation, transcriptome and CNV profiles to build a diagnostic model for PDAC. The protein expression of three model-related genes were externally validated using PDAC samples. Then, potential therapeutic drugs for PDAC were identified by interaction information related to existing drugs and genes.
UNASSIGNED: Four significant differentially methylated regions (DMRs) were selected from 589 common DMRs to build a high-performance diagnostic model for PDAC. Then, four hub genes, PHF12, FXYD3, PRKCB and ZNF582, were obtained. The external validation results showed that PHF12, FXYD3 and PRKCB protein expression levels were all upregulated in tumor tissues compared with adjacent normal tissues (P<0.05). Promising candidate drugs with activity against PDAC were screened and repurposed through gene expression analysis of online datasets. The five drugs, including topotecan, PD-0325901, panobinostat, paclitaxel and 17-AAG, with the highest activity among 27 PDAC cell lines were filtered.
UNASSIGNED: Overall, the diagnostic model built based on four significant DMRs could accurately distinguish tumor and normal tissues. The five drug candidates might be repurposed as promising therapeutics for particular PDAC patients.

UNASSIGNED: Pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancer (PC), is a highly aggressive malignancy with a dismal prognosis. Age is shown to be an independent factor affecting survival outcomes in patients with PDAC. Our study aimed to identify prognostic factors and construct a nomogram to predict survival in PDAC patients aged ≥60 years.
UNASSIGNED: Data of PDAC patients aged ≥60 years were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate Cox regression analysis was used to determined prognostic factors of overall survival (OS) and cancer-specific survival (CSS), and two nomograms were constructed and validated by calibration plots, concordance index (C-index) and decision curve analysis (DCA). Additionally, 432 patients from the First Affiliated Hospital of Wenzhou Medical University were included as an external cohort. Kaplan-Meier curves were applied to further verify the clinical validity of the nomograms.
UNASSIGNED: Ten independent prognostic factors were identified to establish the nomograms. The C-indexes of the training and validation groups based on the OS nomogram were 0.759 and 0.760, higher than those of the tumor-node-metastasis (TNM) staging system (0.638 and 0.636, respectively). Calibration curves showed high consistency between predictions and observations. Better area under the receiver operator characteristic (ROC) curve (AUC) values and DCA were also obtained compared to the TNM system. The risk stratification based on the nomogram could distinguish patients with different survival risks.
UNASSIGNED: We constructed and externally validated a population-based survival-predicting nomogram for PDAC patients aged ≥60 years. The new model could help clinicians personalize survival prediction and risk assessment.

This review comprehensively investigates the intricate interplay between small non-coding RNAs (sncRNAs) and pancreatic ductal adenocarcinoma (PDAC), a devastating malignancy with limited therapeutic options. Our analysis reveals the pivotal roles of sncRNAs in various facets of PDAC biology, spanning diagnosis, pathogenesis, drug resistance, and therapeutic strategies. sncRNAs have emerged as promising biomarkers for PDAC, demonstrating distinct expression profiles in diseased tissues. sncRNA differential expression patterns, often detectable in bodily fluids, hold potential for early and minimally invasive diagnostic approaches. Furthermore, sncRNAs exhibit intricate involvement in PDAC pathogenesis, regulating critical cellular processes such as proliferation, apoptosis, and metastasis. Additionally, mechanistic insights into sncRNA-mediated pathogenic pathways illuminate novel therapeutic targets and interventions. A significant focus of this review is dedicated to unraveling sncRNA mechanisms underlying drug resistance in PDAC. Understanding these mechanisms at the molecular level is imperative for devising strategies to overcome drug resistance. Exploring the therapeutic landscape, we discuss the potential of sncRNAs as therapeutic agents themselves as their ability to modulate gene expression with high specificity renders them attractive candidates for targeted therapy. In summary, this review integrates current knowledge on sncRNAs in PDAC, offering a holistic perspective on their diagnostic, pathogenic, and therapeutic relevance. By elucidating the roles of sncRNAs in PDAC biology, this review provides valuable insights for the development of novel diagnostic tools and targeted therapeutic approaches, crucial for improving the prognosis of PDAC patients.

UNASSIGNED: Advancements in surgical techniques have improved outcomes in patients undergoing pancreatic surgery. To date there have been no meta-analyses comparing robotic and laparoscopic approaches for distal pancreatectomies (DP) in patients with pancreatic adenocarcinoma (PDAC). This systematic review and network meta-analysis aims to explore the oncological outcomes of laparoscopic distal pancreatectomy (LDP), robotic distal pancreatectomy (RDP) and open distal pancreatectomy (ODP).
UNASSIGNED: A systematic search was conducted for studies reporting laparoscopic, robotic or open surgery for DP. Frequentist network meta-analysis of oncological outcomes (overall survival, resection margins, tumor recurrence, examined lymph nodes, administration of adjuvant therapy) were performed.
UNASSIGNED: Fifteen studies totalling 9,301 patients were included in the network meta-analysis. 1,946, 605 and 6,750 patients underwent LDP, RDP and ODP respectively. LDP (HR: 0.761, 95% CI: 0.642-0.901, p = 0.002) and RDP (HR: 0.757, 95% CI: 0.617-0.928, p = 0.008) were associated with overall survival (OS) benefit when compared to ODP. LDP (HR: 1.00, 95% CI: 0.793-1.27, p = 0.968) was not associated with OS benefit when compared to RDP. There were no significant differences between LDP, RDP and ODP for resection margins, tumor recurrence, examined lymph nodes and administration of adjuvant therapy.
UNASSIGNED: This study highlights the longer OS in both LDP and RDP when compared to ODP for patients with PDAC.
UNASSIGNED: https://www.crd.york.ac.uk/, PROSPERO (CRD42022336417).

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is renowned for its formidable and lethal nature, earning it a notorious reputation among malignant tumors. Due to its challenging early diagnosis, high malignancy, and resistance to chemotherapy drugs, the treatment of pancreatic cancer has long been exceedingly difficult in the realm of oncology. γ-Glutamyl cyclotransferase (GGCT), a vital enzyme in glutathione metabolism, has been implicated in the proliferation and progression of several tumor types, while the biological function of GGCT in pancreatic ductal adenocarcinoma remains unknown.
METHODS: The expression profile of GGCT was validated through western blotting, immunohistochemistry, and RT-qPCR in both pancreatic cancer tissue samples and cell lines. Functional enrichment analyses including GSVA, ssGSEA, GO, and KEGG were conducted to explore the biological role of GGCT. Additionally, CCK8, Edu, colony formation, migration, and invasion assays were employed to evaluate the impact of GGCT on the proliferation and migration abilities of pancreatic cancer cells. Furthermore, the LASSO machine learning algorithm was utilized to develop a prognostic model associated with GGCT.
RESULTS: Our study revealed heightened expression of GGCT in pancreatic cancer tissues and cells, suggesting an association with poorer patient prognosis. Additionally, we explored the immunomodulatory effects of GGCT in both pan-cancer and pancreatic cancer contexts, found that GGCT may be associated with immunosuppressive regulation in various types of tumors. Specifically, in patients with high expression of GGCT in pancreatic cancer, there is a reduction in the infiltration of various immune cells, leading to poorer responsiveness to immunotherapy and worse survival rates. In vivo and in vitro assays indicate that downregulation of GGCT markedly suppresses the proliferation and metastasis of pancreatic cancer cells. Moreover, this inhibitory effect appears to be linked to the regulation of GGCT on c-Myc. A prognostic model was constructed based on genes derived from GGCT, demonstrating robust predictive ability for favorable survival prognosis and response to immunotherapy.

Macropinocytosis is a cellular process that enables cells to engulf extracellular material, such as nutrients, growth factors, and even whole cells. It is involved in several physiological functions as well as pathological conditions. In cancer cells, macropinocytosis plays a crucial role in promoting tumor growth and survival under nutrient-limited conditions. In particular KRAS mutations have been identified as main drivers of macropinocytosis in pancreatic, breast, and non-small cell lung cancers. We performed a high-content screening to identify inhibitors of macropinocytosis in pancreatic ductal adenocarcinoma (PDAC)-derived cells, aiming to prevent nutrient scavenging of PDAC tumors. The screening campaign was conducted in a well-known pancreatic KRAS-mutated cell line (MIAPaCa-2) cultured under nutrient deprivation and using FITC-dextran to precisely quantify macropinocytosis. We assembled a collection of 3584 small molecules, including drugs approved by the Food and Drug Administration (FDA), drug-like molecules against molecular targets, kinase-targeted compounds, and molecules designed to hamper protein-protein interactions. We identified 28 molecules that inhibited macropinocytosis, with potency ranging from 0.4 to 29.9 μM (EC50). A few of them interfered with other endocytic pathways, while 11 compounds did not and were therefore considered specific \"bona fide\" macropinocytosis inhibitors and further characterized. Four compounds (Ivermectin, Tyrphostin A9, LY2090314, and Pyrvinium Pamoate) selectively hampered nutrient scavenging in KRAS-mutated cancer cells. Their ability to impair albumin-dependent proliferation was replicated both in different 2D cell culture systems and 3D organotypic models. These findings provide a new set of compounds specifically targeting macropinocytosis, which could have therapeutic applications in cancer and infectious diseases.

Pancreatic ductal adenocarcinoma (PDAC) manifests diverse molecular subtypes, including the classical/progenitor and basal-like/squamous subtypes, with the latter known for its aggressiveness. We employed integrative transcriptome and metabolome analyses to identify potential genes contributing to the molecular subtype differentiation and its metabolic features. Transcriptome analysis in PDAC patient cohorts revealed downregulation of adrenoceptor alpha 2A (ADRA2A) in the basal-like/squamous subtype, suggesting its potential role as a candidate suppressor of this subtype. Reduced ADRA2A expression was significantly associated with a high frequency of lymph node metastasis, higher pathological grade, advanced disease stage, and decreased survival among PDAC patients. In vitro experiments demonstrated that ADRA2A transgene expression and ADRA2A agonist inhibited PDAC cell invasion. Additionally, ADRA2A-high condition downregulated the basal-like/squamous gene expression signature, while upregulating the classical/progenitor gene expression signature in our PDAC patient cohort and PDAC cell lines. Metabolome analysis conducted on the PDAC cohort and cell lines revealed that elevated ADRA2A levels were associated with suppressed amino acid and carnitine/acylcarnitine metabolism, which are characteristic metabolic profiles of the classical/progenitor subtype. Collectively, our findings suggest that heightened ADRA2A expression induces transcriptome and metabolome characteristics indicative of classical/progenitor subtype with decreased disease aggressiveness in PDAC patients. These observations introduce ADRA2A as a candidate for diagnostic and therapeutic targeting in PDAC.

In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma (PDAC) patients. Building upon this insight, we developed sex-specific 3-year survival predictive models along with a single comprehensive model. These sex-specific models outperformed the single general model despite the smaller sample sizes with accuracies of 88.47% for males and 88.94% for females, respectively. We further refined our models by using the most important features extracted from these initial models. The refined sex-specific predictive models achieved improved accuracies of 92.62% for males and 91.96% for females, respectively, versus an accuracy of 87.84% from the refined comprehensive model, further highlighting the value of sex-specific analysis. Based on these findings, we created Gap-App, a web application that enables the use of individual gene expression profiles combined with sex information for personalized survival predictions. Gap-App, the first online tool aiming to bridge the gap between complex genomic data and clinical application and facilitating more precise and individualized cancer care, marks a significant advancement in personalized prognosis. The study not only underscores the importance of acknowledging sex differences in personalized prognosis, but also sets the stage for the shift from traditional one-size-fits-all to more personalized and targeted medicine. The GAP-App service is freely available at www.gap-app.org .

Pancreatic ductal adenocarcinoma (PDAC) accounts for more than 90% of all pancreatic cancers and is the most fatal of all cancers. The treatment response from combination chemotherapies is far from satisfactory and surgery remains the mainstay of curative strategies. These challenges warrant identifying effective treatments for combating this deadly cancer. PDAC tumor progression is associated with the robust activation of the coagulation system. Notably, cancer-associated thrombosis (CAT) is a significant risk factor in PDAC. CAT is a concept whereby cancer cells promote thromboembolism, primarily venous thromboembolism (VTE). Of all cancer types, PDAC is associated with the highest risk of developing VTE. Hypoxia in a PDAC tumor microenvironment also elevates thrombotic risk. Direct oral anticoagulants (DOACs) or low-molecular-weight heparin (LMWH) are used only as thromboprophylaxis in PDAC. However, a precision medicine approach is recommended to determine the precise dose and duration of thromboprophylaxis in clinical setting.

BACKGROUND: Complete surgical removal of pancreatic ductal adenocarcinoma (PDAC) is central to all curative treatment approaches for this aggressive disease, yet this is only possible in patients technically amenable to resection. Hence, an accurate assessment of whether patients are suitable for surgery is of paramount importance. The SCANPatient trial aims to test whether implementing a structured synoptic radiological report results in increased institutional accuracy in defining surgical resectability of non-metastatic PDAC.
METHODS: SCANPatient is a batched, stepped wedge, comparative effectiveness, cluster randomised clinical trial. The trial will be conducted at 33 Australian hospitals all of which hold regular multi-disciplinary team meetings (MDMs) to discuss newly diagnosed patients with PDAC. Each site is required to manage a minimum of 20 patients per year (across all stages). Hospitals will be randomised to begin synoptic reporting within a batched, stepped wedge design. Initially all hospitals will continue to use their current reporting method; within each batch, after each 6-month period, a randomly selected group of hospitals will commence using the synoptic reports, until all hospitals are using synoptic reporting. Each hospital will provide data from patients who (i) are aged 18 or older; (ii) have suspected PDAC and have an abdominal CT scan, and (iii) are presented at a participating MDM. Non-metastatic patients will be documented as one of the following categories: (1) locally advanced and surgically unresectable; (2) borderline resectable; or (3) anatomically clearly resectable (Note: Metastatic disease is treated as a separate category). Data collection will last for 36 months in each batch, and a total of 2400 patients will be included.
CONCLUSIONS: Better classifying patients with non-metastatic PDAC as having tumours that are either clearly resectable, borderline or locally advanced and unresectable may improve patient outcomes by optimising care and treatment planning. The borderline resectable group are a small but important cohort in whom surgery with curative intent may be considered; however, inconsistencies with definitions and an understanding of resectability status means these patients are often incorrectly classified and hence overlooked for curative options.
BACKGROUND: The SCANPatient trial was registered on 17th May 2023 in the Australian New Zealand Clinical Trials Registry (ANZCTR) (ACTRN12623000508673).