Pancreatic ductal adenocarcinoma is an invasive tumor with similar incidence and mortality rates. Pancreaticoduodenectomy has morbidity and mortality rates of up to 60% and 5%, respectively. The purpose of our study was to assess preoperative features contributing to unfavorable 1-year survival prognosis. Study Design: Retrospective, single-center study evaluating the impact of preoperative features on short-term survival outcomes in head PDAC patients. Forty-four prior features of 172 patients were tested using different supervised machine learning models. Patient records were randomly divided into training and validation sets (80–20%, respectively), and model performance was assessed by area under curve (AUC) and classification accuracy (CA). Additionally, 33 patients were included as an independent revalidation or holdout dataset group. Results: Eleven relevant features were identified: age, sex, Ca-19-9, jaundice, ERCP with biliary stent, neutrophils, lymphocytes, lymphocyte/neutrophil ratio, neoadjuvant treatment, imaging tumor size, and ASA. Tree regression (tree model) and logistic regression (LR) performed better than the other tested models. The tree model had an AUC = 0.92 and CA = 0.85. LR had an AUC = 0.74 and CA = 0.78, allowing the development of a nomogram based on absolute feature significance. The best performance model was the tree model which allows us to have a decision tree to help clinical decisions. Discussion and conclusions: Based only on preoperative data, it was possible to predict 1-year survival (91.5% vs. 78.1% alive and 70.9% vs. 76.6% deceased for the tree model and LR, respectively). These results contribute to informed decision-making in the selection of which patients with PDAC can benefit from pancreatoduodenectomy. A machine learning algorithm was developed for the recognition of unfavorable 1-year survival prognosis in patients with pancreatic ductal adenocarcinoma. This will contribute to the identification of patients who would benefit from pancreatoduodenectomy. In our cohort, the tree regression model had an AUC = 0.92 and CA = 0.85, whereas the logistic regression had an AUC = 0.74 and CA = 0.78. To further inform decision-making, a decision tree based on tree regression was developed.

Cancer metabolism is a hallmark of cancer. However, the impact of systemic metabolism and diet on tumour evolution is less understood. This study delves into the role of glucagon, as a component of the pancreatic microenvironment, in regulating features of pancreatic neuroendocrine tumour (pNET) cells and the metabolic remodelling occurring in the presence and absence of glucose. pNET cell lines (BON-1 and QGP-1) and the non-malignant pancreatic α-TC1 cell line were used as models. Results showed that pNET cells responded differently to glucose deprivation than α-TC1 cells. Specifically, pNET cells upregulated the GCGR in the absence of glucose, while α-TC1 cells did so in high-glucose conditions, allowing the glucagon-related pERK1/2 activation under these conditions in pNET cells. Glucagon enhanced cancerous features in pNET BON-1 cells under glucose-deprived and hyperglucagonemia-compatible concentrations. In the α-TC1 cell line, glucagon modulated cell features under high-glucose and physiological glucagon levels. NMR exometabolome analysis revealed differences in metabolic processes based on glucose availability and glucagon stimulation across cell lines, highlighting amino acid metabolism, glycolysis, and gluconeogenesis. The expression of metabolic genes was consistent with these findings. Interestingly, QGP-1 and α-TC1 cells produced glucose in no-glucose conditions, and glucagon upregulated glucose production in α-TC1 cells. This suggests that gluconeogenesis may be beneficial for some pNET subsets, pointing out novel metabolism-based strategies to manage pNETs, as well as a step forward in endocrinology and systemic metabolism. The association between GCGR expression and malignancy and a negative correlation between glucagon receptor (GCGR) and glucagon-like peptide-1 receptor (GLP-1R) expression was observed, indicating a biological role of glucagon in pNETs that deserves to be explored.