UNASSIGNED: Assessing the perioperative outcomes of pancreaticoduodenectomy (PD) based solely on individual complications is not comprehensive, and the association between perioperative outcomes and the long-term prognosis of individuals diagnosed with pancreatic ductal adenocarcinoma (PDAC) remains uncertain. Our study is designed to evaluate the impact of a novel composite indicator, textbook outcomes (TO), on the long-term prognosis of patients undergoing PD for PDAC.
UNASSIGNED: This study conducted a retrospective analysis of 139 patients who underwent PD for pathologically confirmed PDAC at our hospital between January 2018 and December 2021. After applying exclusion criteria, a total of 111 patients were included in the subsequent analysis. These patients were categorized into two groups: the non-TO group (n=42) and the TO group (n=69). The Kaplan-Meier survival curve was employed to describe the relationship between TO and disease-free survival (DFS) and overall survival (OS). Cox regression was employed to assess the impact of achieving TO on long-term survival. Logistic regression was employed to investigate the risk factors affecting the achievement of TO.
UNASSIGNED: Out of the 111 PDAC patients, 69 (62.2%) achieved TO following PD. The achievement of TO significantly improved the OS of PDAC patients [P=0.03; hazard ratio (HR) =0.60; 95% confidence interval (CI): 0.37-0.83]. Cox regression analysis indicated that achieving TO was a protective factor for OS (P=0.04; HR =4.08; 95% CI: 1.07-15.61). Logistic regression analysis indicated that high amylase in drainage fluid on the third day after surgery (>1,300 U/L) was detrimental to achieve TO [odds ratio (OR) =0.10; 95% CI: 0.02-0.58; P=0.01], longer surgery durations (≥6.25 hours) was detrimental to achieve TO (OR =0.19; 95% CI: 0.06-0.54; P=0.002), and soft pancreatic texture was detrimental to achieve TO (OR =0.31; 95% CI: 0.10-0.93, P=0.04).
UNASSIGNED: Achievement of TO significantly improves the OS of PDAC patients and has the potential to serve as a robust prognostic indicator. Looking ahead, it is highly necessary for TO to become a standard surgical quality control measure in hospitals.

UNASSIGNED: There is no unified scope for regional lymph node (LN) dissection in patients with pancreatic ductal adenocarcinoma (PDAC). Incomplete regional LN dissection can lead to postoperative recurrence, while blind expansion of the scope of regional LN dissection significantly increases the perioperative risk without significantly prolonging overall survival. We aimed to establish a noninvasive visualization tool based on dual-layer detector spectral computed tomography (DLCT) to predict the probability of regional LN metastasis in patients with PDAC.
UNASSIGNED: A total of 163 regional LNs were reviewed and divided into a metastatic cohort (n=58 LNs) and nonmetastatic cohort (n=105 LNs). The DLCT quantitative parameters and the nodal ratio of the longest axis to the shortest axis (L/S) of the regional LNs were compared between the two cohorts. The DLCT quantitative parameters included the iodine concentration in the arterial phase (APIC), normalized iodine concentration in the arterial phase (APNIC), effective atomic number in the arterial phase (APZeff), normalized effective atomic number in the arterial phase (APNZeff), slope of the spectral attenuation curves in the arterial phase (APλHU), iodine concentration in the portal venous phase (PVPIC), normalized iodine concentration in the portal venous phase (PVPNIC), effective atomic number in the portal venous phase (PVPZeff), normalized effective atomic number in the portal venous phase (PVPNZeff), and slope of the spectral attenuation curves in the portal venous phase (PVPλHU). Logistic regression analysis based on area under the curve (AUC) was used to analyze the diagnostic performance of significant DLCT quantitative parameters, L/S, and the models combining significant DLCT quantitative parameters and L/S. A nomogram based on the models with highest diagnostic performance was developed as a predictor. The goodness of fit and clinical applicability of the nomogram were assessed through calibration curve and decision curve analysis (DCA).
UNASSIGNED: The combined model of APNIC + L/S (APNIC + L/S) had the highest diagnostic performance among all models, yielding an AUC, sensitivity, and specificity of 0.878 [95% confidence interval (CI): 0.825-0.931], 0.707, and 0.886, respectively. The calibration curve indicated that the APNIC-L/S nomogram had good agreement between the predicted probability and the actual probability. Meanwhile, the decision curve indicated that the APNIC-L/S nomogram could produce a greater net benefit than could the all- or-no-intervention strategy, with threshold probabilities ranging from 0.0 to 0.75.
UNASSIGNED: As a valid and visual noninvasive prediction tool, the APNIC-L/S nomogram demonstrated favorable predictive efficacy for identifying metastatic LNs in patients with PDAC.

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.

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.

UNASSIGNED: Solute carrier family 16 member 1 (SLC16A1) serves as a biomarker in numerous types of cancer. Tumor immune infiltration has drawn increasing attention in cancer progression and treatment. The objective of our study was to explore the association between SLC16A1 and the tumor immune microenvironment in pancreatic ductal adenocarcinoma (PDAC).
UNASSIGNED: Data were obtained from The Cancer Genome Atlas. The xCell web tool was used to calculate the proportion of immune cells according to SLC16A1 expression. To further explore the mechanism of SLC16A1, immunity-related genes were screened from differentially expressed genes through weighted gene coexpression network analysis, examined via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, and filtrated using univariate Cox regression and least absolute shrinkage and selection operator regression model combined correlation analysis (P<0.05). Next, CIBERSORT was used to analyze the correlation between immune cells and five important genes. SLC16A1 expression and its clinical role in pancreatic cancer was clarified via immunohistochemical staining experiments. Finally, the effects of SLC16A1 on the results of cancer immunity were evaluated by in vitro experiments.
UNASSIGNED: SLC16A1 was overexpressed in PDAC tissues and could be an independent prognostic factor. SLC16A1 was significantly negatively correlated with overall survival and suppressed the tumor immunity of PDAC. In clinic, SLC16A1 expression was significantly positively correlated with tumor progression and poor prognosis. We also found that SLC16A1 could suppress the antitumor ability of CD8+ T cells.
UNASSIGNED: SLC16A1 is a biomarker for the prognosis of PDAC and can influence the immune environment of PDAC. These findings provide new insights into the treatment of PDAC.

暂无摘要。

Pancreatic cancer, particularly pancreatic ductal adenocarcinoma (PDAC), stands as the fourth leading cause of cancer-related deaths in the United States, marked by challenging treatment and dismal prognoses. As immunotherapy emerges as a promising avenue for mitigating PDAC\'s malignant progression, a comprehensive understanding of the tumor\'s immunosuppressive characteristics becomes imperative. This paper systematically delves into the intricate immunosuppressive network within PDAC, spotlighting the significant crosstalk between immunosuppressive cells and factors in the hypoxic acidic pancreatic tumor microenvironment. By elucidating these mechanisms, we aim to provide insights into potential immunotherapy strategies and treatment targets, laying the groundwork for future studies on PDAC immunosuppression. Recognizing the profound impact of immunosuppression on PDAC invasion and metastasis, this discussion aims to catalyze the development of more effective and targeted immunotherapies for PDAC patients.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor response to all therapeutic modalities and dismal prognosis. The presence of tertiary lymphoid structures (TLSs) in various solid cancers is of crucial prognostic significance, highlighting the intricate interplay between the tumor microenvironment and immune cells aggregation. However, the extent to which TLS and immune status affect PDAC prognosis remains incompletely understood. Here, we sought to unveil the unique properties of TLS in PDAC by leveraging both single-cell and bulk transcriptomics, and culminating in a risk model that predicts clinical outcomes. We used TLS score based on 12 genes (CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11 and CXCL13) and 9 genes (PTGDS, RBP5, EIF1AY, CETP, SKAP1, LAT, CCR6, CD1D and CD79B) signature, respectively, and examined their distribution in cell clusters of single-cell data from PDAC samples. The markers involved in these clusters were selected to develop a prognostic model using The Cancer Genome Atlas Program (TCGA) database as the training cohort and Gene Expression Omnibus (GEO) database as the validation cohort. Further we compared the immune infiltration, drug sensitivity, enriched and differentially expressed genes between the high-risk and low-risk groups in our model. Therefore, we established a risk model that has significant implications for the prognostic assessment of PADC patients with remarkable differences in immune infiltration and chemo-sensitivity between the low-risk and high-risk groups. And this paradigm established by TLS-related cell marker genes provides a prognostic prediction and a panel of novel therapeutic targets for exploring potential immunotherapy.

This study aimed to compare the effectiveness of chemotherapy in different histological types of pancreatic cancer using data collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients who were diagnosed with pancreatic cancer between 2004 and 2015 were selected from the SEER database. Propensity score matching (PSM) was employed to minimize the selection bias. The Kaplan-Meier survival curves and the log-rank test were utilized to compare the overall survival (OS) and cancer-specific survival (CSS) among different groups. Of the 7,653 pancreatic cancer patients, both OS and CSS were higher in the chemotherapy group than those in the non-chemotherapy group (p < 0.001). After PSM, 2381 pairs were generated. The Kaplan-Meier survival curved indicated that both OS and CSS for pancreatic ductal adenocarcinoma (PDAC), pancreatic adenosquamous carcinoma (PASC), and pancreatic mucin-producing adenocarcinoma (PMPAC) (p < 0.001) in the chemotherapy group were superior to those in the non-chemotherapy group, while there was no significant difference in pancreatic mucinous adenocarcinoma (PMAC) (p = 0.2586). Compared with PASC and PMPAC, PDAC exhibited longer OS and CSS. The results of statistical analysis showed that PASC tumors were mainly poorly differentiated, and the majority of patients with PMPAC had distant metastasis. Chemotherapy could prolong pancreatic cancer patients\' survival, especially for patients with advanced disease. PMPAC patients had a higher rate of metastasis, accompanying with the worse survival.