Background: Pancreatic cancer is the most common pancreatic solid malignancy with an aggressive clinical course and low survival rate. There are a limited number of reliable prognostic biomarkers and a need to understand the pathogenesis of pancreatic tumors; neuroendocrine (PNET) and pancreatic ductal adenocarcinomas (PDAC) encouraged us to analyze the serum metabolome of pancreatic tumors and disturbances in the metabolism of PDAC and PNET. Methods: Using the AbsoluteIDQ^® p180 kit (Biocrates Life Sciences AG, Innsbruck, Austria) with liquid chromatography–mass spectrometry (LC-MS), we identified changes in metabolite profiles and disrupted metabolic pathways serum of NET and PDAC patients. Results: The concentration of six metabolites showed statistically significant differences between the control group and PDAC patients (p.adj < 0.05). Glutamine (Gln), acetylcarnitine (C2), and citrulline (Cit) presented a lower concentration in the serum of PDAC patients, while phosphatidylcholine aa C32:0 (PC aa C32:0), sphingomyelin C26:1 (SM C26:1), and glutamic acid (Glu) achieved higher concentrations compared to serum samples from healthy individuals. Five of the tested metabolites: C2 (FC = 8.67), and serotonin (FC = 2.68) reached higher concentration values in the PNET serum samples compared to PDAC, while phosphatidylcholine aa C34:1 (PC aa C34:1) (FC = −1.46 (0.68)) had a higher concentration in the PDAC samples. The area under the curves (AUC) of the receiver operating characteristic (ROC) curves presented diagnostic power to discriminate pancreatic tumor patients, which were highest for acylcarnitines: C2 with AUC = 0.93, serotonin with AUC = 0.85, and PC aa C34:1 with AUC = 0.86. Conclusions: The observations presented provide better insight into the metabolism of pancreatic tumors, and improve the diagnosis and classification of tumors. Serum-circulating metabolites can be easily monitored without invasive procedures and show the present clinical patients’ condition, helping with pharmacological treatment or dietary strategies.

The data demonstrating a correlation between sonographic markers of malignancy of thyroid cancer (TC) and its genetic status are scarce. This study aimed to assess whether the addition of genetic analysis at the preoperative step of TC patients’ stratification could aid their clinical management. The material consisted of formalin-fixed paraffin-embedded tumor fragments of 49 patients who underwent thyroidectomy during the early stages of papillary TC (PTC). Tumor DNA and RNA were subjected to next-generation sequencing (NGS) on Ion Proton using the Oncomine™ Comprehensive Assay panel. We observed a significant correlation between BRAF V600E and a higher EU-TIRADS score (p-value = 0.02) with a correlation between hypoechogenicity and taller-than-wide tumor shape in analysed patients. There were no other significant associations between the identified genetic variants and other clinicopathological features. For TC patient’s stratification, a strong suspicion of BRAF V600E negativity in preoperative management of TC patients could limit the over-treatment of asymptomatic, very low-risk, indolent disease and leave room for active surveillance.

Background: Type 1 diabetes mellitus (T1DM) is an increasingly common chronic disease caused by autoimmune damage to pancreatic beta cells. Metabolomic profiling may provide insights into metabolic disturbances associated with T1DM and identify candidate biomarkers. Methods: The study included 29 children with T1DM and 11 controls. Targeted serum metabolomic profiling was performed using the AbsoluteIDQ^® p180 kit based on liquid chromatography–mass spectrometry. Differential metabolites were identified using statistical analysis with false discovery rate correction. Receiver operating characteristic (ROC) analysis was applied to evaluate the discriminatory performance of selected metabolites. Results: Significant metabolic alterations were observed in children with T1DM, particularly in glycerophospholipids, acylcarnitines, and selected amino acids. Among glycerophospholipids, 23 metabolites were significantly decreased, whereas one metabolite was increased in the T1DM group. Four acylcarnitines differed significantly between groups, with higher concentrations of octadecenoylcarnitine (C18:1), acetylcarnitine, and malonylcarnitine and/or hydroxybutyrylcarnitine [C3-DC (C4-OH)], and reduced free carnitine. In addition, three amino acids (alanine, arginine, and proline) were significantly decreased, while taurine was increased in T1DM patients. ROC analysis showed high apparent discriminatory performance for C18:1 (AUC = 0.925) and diacyl phosphatidylcholine (PC aa) C42:5 (AUC = 0.922) in this exploratory dataset. These AUC values were obtained from the same dataset used for metabolite discovery and therefore represent apparent in-sample discriminatory performance rather than an externally validated diagnostic performance. Correlation analysis showed that higher HbA1c levels were associated with lower concentrations of several amino acids and lipids; BMI z-score and disease duration were associated mainly with phosphatidylcholines and amino acids. However, none of these correlation-based associations remained significant after FDR correction and should therefore be considered hypothesis-generating. Exploratory multivariable analyses suggested associations of HbA1c, BMI z-score, and disease duration with selected metabolites; however, given the limited sample size and multiple model testing, these findings should be considered hypothesis-generating. Conclusions: Targeted metabolomic profiling revealed significant disturbances in lipid and amino acid metabolism in children with T1DM. In particular, increased C18:1 and decreased PC aa C42:5 may represent candidate metabolic features for distinguishing T1DM patients from non-diabetic individuals. Due to the exploratory study and limited sample size, our results should be interpreted with caution, and further studies in larger, independent cohorts are required to validate these findings.