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. 2022 Nov 10;23(22):13869.
doi: 10.3390/ijms232213869.

Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications

Affiliations

Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications

Po-Wei Su et al. Int J Mol Sci. .

Abstract

Bladder cancer is the 10th most common cancer worldwide. Due to the lack of understanding of the oncogenic mechanisms between muscle-invasive bladder cancer (MIBC) and advanced bladder cancer (ABC) and the limitations of current treatments, novel therapeutic approaches are urgently needed. In this study, we utilized the systems biology method via genome-wide microarray data to explore the oncogenic mechanisms of MIBC and ABC to identify their respective drug targets for systems drug discovery. First, we constructed the candidate genome-wide genetic and epigenetic networks (GWGEN) through big data mining. Second, we applied the system identification and system order detection method to delete false positives in candidate GWGENs to obtain the real GWGENs of MIBC and ABC from their genome-wide microarray data. Third, we extracted the core GWGENs from the real GWGENs by selecting the significant proteins, genes and epigenetics via the principal network projection (PNP) method. Finally, we obtained the core signaling pathways from the corresponding core GWGEN through the annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to investigate the carcinogenic mechanisms of MIBC and ABC. Based on the carcinogenic mechanisms, we selected the significant drug targets NFKB1, LEF1 and MYC for MIBC, and LEF1, MYC, NOTCH1 and FOXO1 for ABC. To design molecular drug combinations for MIBC and ABC, we employed a deep neural network (DNN)-based drug-target interaction (DTI) model with drug specifications. The DNN-based DTI model was trained by drug-target interaction databases to predict the candidate drugs for MIBC and ABC, respectively. Subsequently, the drug design specifications based on regulation ability, sensitivity and toxicity were employed as filter criteria for screening the potential drug combinations of Embelin and Obatoclax for MIBC, and Obatoclax, Entinostat and Imiquimod for ABC from their candidate drugs. In conclusion, we not only investigated the oncogenic mechanisms of MIBC and ABC, but also provided promising therapeutic options for MIBC and ABC, respectively.

Keywords: advanced bladder cancer (ABC); deep neural network (DNN)-based drug-target interaction (DTI) model; drug combination; drug design specifications; drug targets; muscle-invasive bladder cancer (MIBC).

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The flowchart of the systems biology method and systematic drug discovery design. The construction of candidate GWGEN, real GWGEN, core GWGEN and core signaling pathways for investigating carcinogenic mechanisms to identify the biomarkers as drug targets of MIBC and ABC, and systematic drug discovery and design of potential drug combinations as multiple-molecule drugs to target the corresponding multiple drug targets for the treatment of MIBC and ABC.
Figure 2
Figure 2
The common and specific core signaling pathways and their downstream cellular dysfunctions between MIBC and ABC. The figure shows the genetic and epigenetic carcinogenic mechanisms of MIBC and ABC. The orange background contains the specific core signaling pathways of MIBC. The green background contains the overlapping core signaling pathways between MIBC and ABC (i.e., common core signaling pathways). The blue background contains the specific core signaling pathways of ABC. The gene symbols in red or green font denote the selected significant biomarkers as drug targets.
Figure 3
Figure 3
The flowchart of systematic drug design and discovery of MIBC and ABC. The drug-target interaction databases contain drug-target interaction data to construct the drug-target feature vector. After data preprocessing, the data is divided into training data and testing data to train the DNN-based DTI model. The feature vectors of biomarkers and drugs from drug-target interaction databases are used for the well-trained DNN-based DTI model to predict candidate drugs for the identified biomarkers (drug targets) of MIBC and ABC. The candidate drugs are then filtered by the drug design specifications to obtain potential drug combinations as multiple-molecule drugs for the treatment of MIBC and ABC.

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