Identification of carcinogenesis and tumor progression processes in pancreatic ductal adenocarcinoma using high-throughput proteomics

Pancreatic adenocarcinoma (PDAC) is an aggressive disease with an overall 5 year-survival rate of just 5%. A better understanding of both the carcinogenesis processes and the mechanisms of progression of PDAC disease is mandatory. For this, proteomics data from FFPE samples of 173 primary tumor, normal tissue, preneoplastic lesions (PanIN), or lymph node metastases were analyzed from a Systems Biology perspective. Protein expression data was analyzed using probabilistic graphical models, allowing functional characterization. Functional node activities were calculated as the mean of expression of those proteins related to the main function of each node. Comparisons between groups were done using linear mixed models.