Pancreatic cancer is one of the most lethal malignancies, with a mortality rate nearing 95 %. In a significant proportion of cases, tumors arise in the pancreatic ducts, classifying the disease as pancreatic ductal adenocarcinoma (PDAC). Due to the asymptomatic nature of early-stage PDAC and the lack of effective screening methods, diagnosis often occurs at advanced stages, limiting treatment options and contributing to poor patient outcomes. A deeper understanding of the metabolic alterations associated with pancreatic cancer is crucial for elucidating disease progression. In this study, we utilized RNA transcriptomic data from PDAC patients to construct context-specific genome-scale metabolic models (GEMs) using the fast task-driven integrative network inference for tissues (ftINIT) algorithm. We systematically applied ftINIT to multiple input combinations to identify the optimal parameters for generating biologically relevant metabolic models. Furthermore, we investigated the metabolic impact of melanoma associated antigen A3 (MAGE-A3) overexpression in pancreatic cancer cells. MAGE-A3 is known to contribute to tumorigenesis and is associated with poor clinical outcomes; however, its role in cancer cell metabolism remains largely unexplored. By characterizing the metabolic consequences of MAGE-A3 upregulation, we aimed to provide novel insights into its potential functional impact on pancreatic cancer metabolism, paving the way for further research into its role in disease progression.
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