The machine learning–aided serum lipidomics approach could be used to help early diagnosis of patients with PDAC.

Background: A growing number of studies have focused on investigating circRNAs as crucial regulators in the progression of multiple cancer types. Nevertheless, the biological effects and underlying mechanisms of circRNAs in pancreatic ductal adenocarcinoma (PDAC) remain unclear. Methods: Differentially expressed circRNAs between cancerous tissue and adjacent normal tissues were identified by RNA sequencing in PDAC. Subsequently, in vitro and in vivo functional experiments were performed to investigate the functional roles of circNEIL3 in PDAC tumour growth and metastasis. Furthermore, RNA pull-down, dual-luciferase reporter assays, RNA immunoprecipitation (RIP) assays, fluorescent in situ hybridization (FISH) and Sanger sequencing assays were performed to examine the circular interaction among circNEIL3, miR-432-5p and adenosine deaminases acting on RNA 1 (ADAR1). Results: CircNEIL3 was upregulated in PDAC and promoted the progression of PDAC cells both in vitro and in vivo. Mechanistically, circNEIL3 was shown to regulate the expression of ADAR1 by sponging miR-432-5p to induce RNA editing of glioma-associated oncogene 1 (GLI1), ultimately influencing cell cycle progression and promoting epithelial-to-mesenchymal transition (EMT) in PDAC cells. Moreover, we discovered that the circNEIL3/miR-432-5p/ADAR1 axis was correlated with the PDAC clinical stage and overall survival of PDAC patients, while ADAR1 may reduce the biogenesis of circNEIL3. Conclusions: Our findings reveal that circNEIL3 facilitates the proliferation and metastasis of PDAC through the circNEIL3/miR-432-5p/ADAR1/GLI1/cell cycle and EMT axis and that its expression is regulated by ADAR1 through a negative feedback loop. Therefore, circNEIL3 may serve as a prognostic marker and a therapeutic target for PDAC.

Natural killer (NK) cells play a key role in non-specific immune response in different cancers, including pancreatic cancer. However the anti-tumor effect of NK cells decreases during pancreatic cancer progression. The regulatory pathways by which NK cells facilitate tumor immune escape are unclear, therefore our purpose was to investigate the roles of the contributory factors.

Background: Circular RNAs (circRNAs) are becoming a unique member of non-coding RNAs (ncRNAs) with emerging evidence of their regulatory roles in various cancers. However, with regards to pancreatic ductal adenocarcinoma (PDAC), circRNAs biological functions remain largely unknown and worth investigation for potential therapeutic innovation. Methods: In our previous study, next-generation sequencing was used to identify differentially expressed circRNAs in 3 pairs of PDAC and adjacent normal tissues. Further validation of circRHOBTB3 expression in PDAC tissues and cell lines and gain-and-loss function experiments verified the oncogenic role of circRHOBTB3. The mechanism of circRHOBTB3 regulatory role was validated by pull-down assays, RIP, luciferase reporter assays. The autophagy response of PANC-1 and MiaPaca-2 cells were detected by mCherry-GFP-LC3B labeling and confocal microscopy, transmission electron microscopy and protein levels of LC3B or p62 via Western blot. Results: circRHOBTB3 is highly expressed in PDAC cell lines and tissues, which also promotes PDAC autophagy and then progression in vitro and in vivo. Mechanistically, circRHOBTB3 directly binds to miR-600 and subsequently acts as a miRNA-sponge to maintain the expression level of miR-600-targeted gene NACC1, which facilitates the autophagy response of PDAC cells for adaptation of proliferation via Akt/mTOR pathway. Moreover, the RNA-binding protein FUS (FUS) directly binds to pre-RHOBTB3 mRNA to mediate the biogenesis of circRHOBTB3. Clinically, circRHOBTB3, miR-600 and NACC1 expression levels are correlated with the prognosis of PDAC patients and serve as independent risk factors for PDAC patients. Conclusions: FUS-mediated circRHOBTB3 functions as a tumor activator to promote PDAC cell proliferation by modulating miR-600/NACC1/Akt/mTOR axis regulated autophagy.

INTRODUCTION: Hypertriglyceridemia-associated acute pancreatitis (HTG-AP) is one of the most common etiologies of acute pancreatitis (AP) worldwide. Compared with other etiologies, patients with HTG-AP may develop more severe AP, but previous studies yielded controversial conclusion due to the lack of adequate adjustment for the confounders. Therefore, the aim of this study was to examine the possibility and risk factors of developing severe AP in HTG-AP. METHODS: Data from patients with an established diagnosis of AP were collected from January 2013 to December 2023 using a predesigned data collection form and were gathered from 5 tertiary cross-regional centers of China. HTG-AP was defined as serum triglyceride levels >500 mg/dL and excluded other etiologies. The possibility and risk factors of severe AP were assessed by multivariable logistic regressions after adjusting potential confounders. A prediction model was established and validated. RESULTS: Between 2013 and 2023, we identified a total of 6,996 patients with AP, of whom 4,378 were included in the final analysis. Compared with other etiologies, patients with HTG-AP had a higher risk of developing severe AP (odds ratio: 1.897; 95% confidence interval: 1.380–2.608; P < 0.001) and organ failure. HTG-AP patients showed higher possibility for developing respiratory and circulation failure but renal failure compared with other etiologies. In HTG-AP patients, risk factors of severe AP included age, fasting blood glucose, white blood cell counts, and presence of pleural effusion. TG level was found not significantly associated with severity in HTG-AP patients. A prediction model incorporating these risk factors demonstrated an area under the curve (AUC) of 0.837 in the training and 0.883 in the testing set, with adequate calibration. DISCUSSION: Using a multicenter cross-regional cohort, we demonstrated that HTG-AP had a higher risk of developing severe AP and organ failure. A risk prediction model for predicting severe AP was developed and effectively stratified patients.