# Deep Learning Techniques to Characterize the RPS28P7 Pseudogene and the Metazoa-SRP Gene as Drug Potential Targets in Pancreatic Cancer Patients

## TL;DR

This study uses deep learning to identify genes and pseudogenes that may help predict survival in pancreatic cancer patients and could serve as potential drug targets.

## Contribution

The study introduces RPS28P7 pseudogene as a novel druggable target in pancreatic cancer through deep learning analysis.

## Key findings

- Genes like EWSR1, FLT3, and GPC3 show higher amplification in deceased pancreatic cancer patients.
- RPS28P7 pseudogene mRNA is up-regulated and may act as a ceRNA sponge for miRNA in dead patients.
- Proposed RPS28P7 as a potential drug target modulatable via small molecules or RNA technology.

## Abstract

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 EWSR1, FLT3, GPC3, HIF1A, HLF, and MEN1. The most highly up-regulated genes (>8.5-fold change) in the death group were RPL30, RPL37, RPS28P7, RPS11, Metazoa_SRP, CAPNS1, FN1, H3−3B, LCN2, and OAZ1. None of their corresponding proteins were up or down-regulated in the death group. The mRNA of the RPS28P7 pseudogene could act as ceRNA sponging the miRNA that was originally directed to the parental gene RPS28. We propose RPS28P7 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.