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
Year: 2024
Type: article
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.
Source: Tumor Discovery
DOI: 10.36922/td.4709
Authors: Yi-Hsin Tsai, Bor-Sen Chen
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404957023