Deep Learning Techniques to Characterize the Pseudogene and the - Gene as Drug Potential Targets in Pancreatic Cancer Patients.

The molecular explanation about why some pancreatic cancer (PaCa) patients die early and others die later is poorly understood. This study aimed to discover potential novel markers and drug targets that could be useful to stratify and extend expected survival in prospective early-death patients. We deployed a deep learning algorithm and analyzed the gene copy number, gene expression, and protein expression data of death versus alive PaCa patients from the GDC cohort. The genes with higher relative amplification (copy number >4 times in the dead compared with the alive group) were , , , , , and . The most highly up-regulated genes (>8.5-fold change) in the death group were , , , , _, , , -, , and . None of their corresponding proteins were up or down-regulated in the death group. The mRNA of the pseudogene could act as ceRNA sponging the miRNA that was originally directed to the parental gene . We propose mRNA as the most druggable target that can be modulated with small molecules or the RNA technology approach. These markers could be added as criteria to patient stratification in future PaCa drug trials, but further validation in the target populations is encouraged.