Pancreatic cancer is a devastating disease with poor prognosis. The association between vitamin A, retinol and carotenoid intake and the risk of pancreatic cancer occurrence remains controversial, and therefore it is necessary to make a meta-analysis to clarify the association between vitamin A, retinol and carotenoid intake and pancreatic cancer risk. In the present study, PubMed and EMBASE databases were used to identify qualified studies. The association between dietary vitamin A, retinol and carotenoids was estimated by pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs). It was found that there was an inverse correlation between vitamin A, beta-carotene and lycopene intake and the risk of pancreatic cancer (for vitamin A, pooled OR = 0.85, 95%CI = 0.74–0.97, P = 0.015; for beta-carotene, pooled OR = 0.78, 95%CI = 0.66–0.92, P = 0.003; for lycopene, pooled OR = 0.84, 95%CI = 0.73–0.97, P = 0.020), which was more prominent in case-control study subgroup. In conclusion, dietary vitamin A, beta-carotene and lycopene might inversely correlate with pancreatic cancer.

Purpose: To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC). Materials and methods: In this retrospective study, 225 patients with surgically resected, pathologically confirmed PDAC underwent multislice computed tomography (MSCT) between January 2014 and January 2017. Radiomics features were extracted from arterial CT scans. The least absolute shrinkage and selection operator method was used to select the features. Multivariable logistic regression analysis was used to develop the predictive model, and a radiomics nomogram was built and internally validated in 45 consecutive patients with PDAC between February 2017 and December 2017. The performance of the nomogram was assessed in the training and validation cohort. Finally, the clinical usefulness of the nomogram was estimated using decision curve analysis (DCA). Results: The radiomics signature, which consisted of 13 selected features of the arterial phase, was significantly associated with LN status (p < 0.05) in both the training and validation cohorts. The multivariable logistic regression model included the radiomics signature and CT-reported LN status. The individualized prediction nomogram showed good discrimination in the training cohort [area under the curve (AUC), 0.75; 95% confidence interval (CI), 0.68–0.82] and in the validation cohort (AUC, 0.81; 95% CI, 0.69–0.94) and good calibration. DCA demonstrated that the radiomics nomogram was clinically useful. Conclusions: The presented radiomics nomogram that incorporates the radiomics signature and CT-reported LN status is a noninvasive, preoperative prediction tool with favorable predictive accuracy for LN metastasis in patients with PDAC.

Background/Aims: MicroRNAs (miRNAs) are a group of non-coding RNAs that play diverse roles in pancreatic carcinogenesis. In pancreatic ductal adenocarcinoma (PDAC), NF-kB is constitutively activated in most patients and is linked to a mutation in KRAS via IkB kinase complex 1 (IKK1, also known as IKKa). We investigated the link between PDAC aggressiveness and miR-1290. Methods: We used miRCURY^TM LNA Array and in situ hybridization to investigate candidate miRNAs and validated the findings with PCR. The malignant behavior of cell lines was assessed with Cell Counting Kit-8, colony formation, and Transwell assays. A dual-luciferase reporter assay was used to evaluate the interaction between miR-1290 and IKK1. Protein expression was observed by western blotting. Results: In this study, 36 miRNAs were dysregulated in high-grade pancreatic intraepithelial neoplasia (PanIN) and PDAC tissues compared with low-grade PanIN tissues. The area under the curve values of miR-1290 and miR-31-5p were 0.829 and 0.848, respectively (95% confidence interval, 0.722–0.936 and 0.749–0.948, both P < 0.001). There was a significant correlation between miR-1290 and histological differentiation (P = 0.029), pT stage (P = 0.006), and lymph node metastasis (P = 0.001). In addition, the in vitro work showed that miR-1290 promoted PDAC cell proliferation, invasion, and migration. Western blotting and the dual-luciferase reporter assay showed that miR-1290 promoted cancer aggressiveness by directly targeting IKK1. The synergist effect of miR-1290 on the proliferation and metastasis of PDAC cells was attenuated and enhanced by IKK1 overexpression and knockdown, respectively. Consistent with the in vitro results, a subcutaneous tumor mouse model showed that miR-1290 functioned as a potent promoter of PDAC in vivo. Conclusion: MiR-1290 may act as an oncogene by directly targeting the 3’-untranslated region of IKK1, and the miR-1290/IKK1 pathway may prove to be a novel diagnostic and therapeutic target for PDAC.

Chronic pancreatitis (CP) is a fibroinflammatory disorder of the pancreas. Our understanding of CP pathogenesis is partly limited by the incomplete characterization of pancreatic cell types. Here, we performed single-cell RNA sequencing on 3825 cells from the pancreas of one control mouse and mice with caerulein-induced CP. An analysis of the single-cell transcriptomes revealed 16 unique clusters and cell type-specific gene expression patterns in the mouse pancreas. Sub-clustering of the pancreatic mesenchymal cells from the control mouse revealed four clusters of cells with specific gene expression profiles (combinatorial expressions of Smoc2, Cxcl14, Tnfaip6, and Fn1). We observed that immune cells in the pancreas of the CP mice were abundant and diverse in cellular type. Compared to the control, 547 upregulated genes (including Mmp7, Ttr, Rgs5, Adh1, and Cldn2) and 257 downregulated genes were identified in ductal cells from the CP group. The elevated expression levels of MMP7 and TTR were further verified in the pancreatic ducts of CP patients. This study provides a preliminary description of the single-cell transcriptome profiles of mouse pancreata and accurately demonstrates the characteristics of pancreatic ductal cells in CP. The findings provide insight into novel disease-specific biomarkers and potential therapeutic targets of CP.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating malignancy with poor prognostic outcomes. Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) play an important role in the development and progression of carcinogenesis. Nevertheless, little is known about the role of lncRNAs in PDAC. The aim of the current study was to find differentially expressed lncRNAs and related mRNAs in human PDAC tissues and adjacent normal tissues by microarray analysis, and investigate the relationship between lncRNA RP11-263F15.1 levels and the clinicaopathological features of PDAC patients. It was found that 4364 lncRNAs and 4862 related mRNAs were significantly dysregulated in PDAC tissues as compared with adjacent normal tissues with a fold change ≥2.0 (P<0.05). GO and pathway analyses showed that the up-regulated gene profiles were related to several pathways associated with carcinogenesis, while the down-regulated gene profiles were closely correlated with nutrient metabolism. RP11-263F15.1 levels were associated with histologic differentiation (P=0.001). Besides, Kaplan-Meier analysis showed that high expression of RP11-263F15.1 was associated with poor outcomes, but multivariate analysis suggested that RP11-263F15.1 was not an independent factor for predicting prognosis of PDAC. In conclusion, these data indicate that differentially expressed lncRNAs and mRNAs were involved in the carcinogenesis of PDAC, and RP11-263F15.1 may prove to be a potential biomarker for the diagnosis and prognostic prediction of PDAC.

Background and Objectives: Accurate survival prediction for pancreatic ductal adenocarcinoma (PDAC) is crucial for personalized treatment strategies. This study aims to construct a novel pathomics indicator using hematoxylin and eosin–stained whole slide images and deep learning to enhance PDAC prognosis prediction. Methods: A retrospective, 2-center study analyzed 864 PDAC patients diagnosed between January 2015 and March 2022. Using weakly supervised and multiple instance learning, pathologic features predicting 2-year survival were extracted. Pathomics features, including probability histograms and TF-IDF, were selected through random survival forests. Survival analysis was conducted using Kaplan-Meier curves, log-rank tests, and Cox regression, with AUROC and C-index used to assess model discrimination. Results: The study cohort comprised 489 patients for training, 211 for validation, and 164 in the neoadjuvant therapy (NAT) group. A pathomics score was developed using 7 features, dividing patients into high-risk and low-risk groups based on the median score of 131.11. Significant survival differences were observed between groups (P<0.0001). The pathomics score was a robust independent prognostic factor [Training: hazard ratio (HR)=3.90; Validation: HR=3.49; NAT: HR=4.82; all P<0.001]. Subgroup analyses revealed higher survival rates for early-stage low-risk patients and NAT responders compared to high-risk counterparts (both P<0.05 and P<0.0001). The pathomics model surpassed clinical models in predicting 1-, 2-, and 3-year survival. Conclusions: The pathomics score serves as a cost-effective and precise prognostic tool, functioning as an independent prognostic indicator that enables precise stratification and enhances the prediction of prognosis when combined with traditional pathologic features. This advancement has the potential to significantly impact PDAC treatment planning and improve patient outcomes.