  Pancreatic cancer (PC) is the fourth leading cause of cancer death in the
United States due to its five-year survival rate of 10%. Late diagnosis,
affiliated with the asymptomatic nature in early stages and the location of the
cancer with respect to the pancreas, makes current widely-accepted screening
methods unavailable. Prior studies have achieved low (70-75%) diagnostic
accuracy, possibly because 80% of PC cases are associated with diabetes,
leading to misdiagnosis. To address the problems of frequent late diagnosis and
misdiagnosis, we developed an accessible, accurate and affordable diagnostic
tool for PC, by analyzing the expression of nineteen genes in PC and diabetes.
First, machine learning algorithms were trained on four groups of subjects,
depending on the occurrence of PC and Diabetes. The models were analyzed with
400 PC subjects at varying stages to ensure validity. Naive Bayes, Neural
Network and K-Nearest Neighbors models achieved the highest testing accuracy of
around 92.6%. Second, the biological implication of the nineteen genes was
investigated using bioinformatics tools. It was found that these genes were
significantly involved in regulating the cytoplasm, cytoskeleton and nuclear
receptor activity in the pancreas, specifically in acinar and ductal cells. Our
novel tool is the first in the literature that achieves a PC diagnostic
accuracy of above 90%, having the potential to significantly improve the
detection of PC in the background of diabetes and increase the five-year
survival rate.
