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Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and

Po-Wei Su1, Bor-Sen Chen1

  • 1Laboratory of Automatic Control, Signal Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.

International Journal of Molecular Sciences
|November 26, 2022
PubMed
Summary
This summary is machine-generated.

This study reveals bladder cancer

Keywords:
advanced bladder cancer (ABC)deep neural network (DNN)-based drug-target interaction (DTI) modeldrug combinationdrug design specificationsdrug targetsmuscle-invasive bladder cancer (MIBC)

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Area of Science:

  • Oncology
  • Systems Biology
  • Bioinformatics

Background:

  • Bladder cancer is a significant global health concern, ranking as the 10th most common cancer.
  • Current treatment limitations and a poor understanding of oncogenic mechanisms in muscle-invasive bladder cancer (MIBC) and advanced bladder cancer (ABC) necessitate novel therapeutic strategies.

Purpose of the Study:

  • To elucidate the oncogenic mechanisms driving MIBC and ABC using systems biology approaches.
  • To identify novel drug targets and design effective molecular drug combinations for MIBC and ABC.

Main Methods:

  • Genome-wide microarray data analysis to construct and refine genome-wide genetic and epigenetic networks (GWGENs).
  • Principal Network Projection (PNP) method to extract core GWGENs and identify significant proteins, genes, and epigenetic factors.
  • Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to investigate carcinogenic mechanisms.
  • Deep Neural Network (DNN)-based drug-target interaction (DTI) model for predicting candidate drugs and designing drug combinations based on regulation ability, sensitivity, and toxicity.

Main Results:

  • Identified key oncogenic mechanisms and significant drug targets for MIBC (NFKB1, LEF1, MYC) and ABC (LEF1, MYC, NOTCH1, FOXO1).
  • Predicted potential drug combinations for MIBC (Embelin and Obatoclax) and ABC (Obatoclax, Entinostat, and Imiquimod) using a DNN-based DTI model and specific drug design criteria.

Conclusions:

  • The study provides a comprehensive investigation into the molecular underpinnings of MIBC and ABC.
  • Novel therapeutic strategies and drug combinations are proposed, offering promising avenues for treating advanced bladder cancer.