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ORIGINAL RESEARCH ARTICLE

Systemic drug repurposing for pancreatic cancer based on genetic and epigenetic network analysis using a systems biology approach and deep neural learning of drug-target interactions

Yi-Hsin Tsai1 ,  Bor-Sen Chen1*
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1 Laboratory of Automatic Control, Signal Processing and Systems Biology, Department of Electrical Engineering, Institute of Electronic Engineering, National Tsing Hua University, Hsinchu, Taiwan, China
Tumor Discovery, 4709 https://doi.org/10.36922/td.4709
Submitted: 30 August 2024 | Accepted: 24 October 2024 | Published: 20 November 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Pancreatic cancer is a malignant tumor associated with a high mortality rate. This research presents a systems biology approach to explore the mechanisms of pancreatic ductal adenocarcinoma (PDAC), aiming to identify significant biomarkers that can serve as drug targets. We propose a systematic drug repurposing strategy that incorporates a deep neural network (DNN)-based drug-target interaction (DTI) model along with drug design specifications to develop a potential multi-molecule drug for PDAC treatment. We first established candidate protein-protein interaction networks and gene regulatory networks using big data mining techniques. Real PDAC and non-PDAC genome-wide genetic and epigenetic networks (GWGENs) were systematically identified using their corresponding microarray data through system identification and system order detection methods. The top 6,000 core GWGENs of PDAC and non-PDAC were extracted using the Principal Network Projection method. Subsequently, we annotated the core GWGENs using the Kyoto Encyclopedia of Genes and Genomes pathways to construct their respective core signaling pathways. By comparing upstream microenvironmental factors, core signaling pathways, and downstream aberrant cellular functions between PDAC and non-PDAC, we investigated the carcinogenic mechanisms of PDAC. Notably, c-MYC, forkhead box O3, and tumor suppressor p53 were identified as significant biomarkers for potential drug targets. Furthermore, the DNN-based DTI model predicted the interaction probabilities between candidate molecular drugs and these biomarkers. Based on drug design specifications such as regulatory ability, sensitivity, and toxicity, suitable multi-molecular potential drugs were selected. Ultimately, gemcitabine and MK-2206 were identified as a promising multi-molecular drug combination for PDAC treatment.

Keywords
Pancreatic cancer mechanisms
Systems biology
Big data mining
Genome-wide genetic and epigenetic networks
Kyoto Encyclopedia of Genes and Genomes pathways
Deep neural network-based drug-target interaction model
Drug design specifications
Principal network projection
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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