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. 2022 Sep 8;23(18):10409.
doi: 10.3390/ijms231810409.

Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications

Yi-Chung Lin  1 , Bor-Sen Chen  1
Affiliations

Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications

Yi-Chung Lin et al. Int J Mol Sci. .

Abstract

In this study, we provide a systems biology method to investigate the carcinogenic mechanism of oral squamous cell carcinoma (OSCC) in order to identify some important biomarkers as drug targets. Further, a systematic drug discovery method with a deep neural network (DNN)-based drug-target interaction (DTI) model and drug design specifications is proposed to design a potential multiple-molecule drug for the medical treatment of OSCC before clinical trials. First, we use big database mining to construct the candidate genome-wide genetic and epigenetic network (GWGEN) including a protein-protein interaction network (PPIN) and a gene regulatory network (GRN) for OSCC and non-OSCC. In the next step, real GWGENs are identified for OSCC and non-OSCC by system identification and system order detection methods based on the OSCC and non-OSCC microarray data, respectively. Then, the principal network projection (PNP) method was used to extract core GWGENs of OSCC and non-OSCC from real GWGENs of OSCC and non-OSCC, respectively. Afterward, core signaling pathways were constructed through the annotation of KEGG pathways, and then the carcinogenic mechanism of OSCC was investigated by comparing the core signal pathways and their downstream abnormal cellular functions of OSCC and non-OSCC. Consequently, HES1, TCF, NF-κB and SP1 are identified as significant biomarkers of OSCC. In order to discover multiple molecular drugs for these significant biomarkers (drug targets) of the carcinogenic mechanism of OSCC, we trained a DNN-based drug-target interaction (DTI) model by DTI databases to predict candidate drugs for these significant biomarkers. Finally, drug design specifications such as adequate drug regulation ability, low toxicity and high sensitivity are employed to filter out the appropriate molecular drugs metformin, gefitinib and gallic-acid to combine as a potential multiple-molecule drug for the therapeutic treatment of OSCC.

Keywords: deep neural network-based drug–target interaction (DNN-based DTI) model; drug design specifications; genome-wide genetic and epigenetic network (GWGEN); oral squamous cell carcinoma (OSCC); significant biomarkers.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of the systems biology method and the outline of the systematic drug discovery design. The candidate GWGEN consists of a gene regulation network (GRN) and protein–protein interaction network (PPIN), where the candidate GRN was constructed through integrating gene regulation databases, and candidate PPI was constructed via protein–protein interaction databases. The candidate GWGEN was identified to obtain real GWGEN by OSCC microarray data from GSE30784 and GSE17913 through system identification and system order detection, and core GWGEN was extracted from real GWGEN by the PNP method. The core signaling pathways of non-OSCC and OSCC are obtained by core GWGENs of non-OSCC and OSCC via the denotation of KEGG pathways, respectively. The carcinogenic biomarkers were identified by comparing the core signaling pathways and their down streaming abnormal cellular functions of non-OSCC and OSCC. The DNN-based DTI model can be employed to predict candidate molecular drugs for these carcinogenic biomarkers, and drug design specifications are used to select a multiple-molecule drug for OSCC.
Figure 5
Figure 5
The flowchart of the systematic drug discovery and design procedure for OSCC. The drug–target binding datasets were obtained from the binding database BindingDB, which integrates the information of drugs and targets from several databases. Then, the drug and target features were preprocessed respectively, including descriptor transformation, standardization and PCA dimension reduction. Afterwards, the processed data were split into the training data for DNN-based DTI model training and the testing data for DTI model performance validation in Figure 5 and Figure 6. We updated the model parameters through the model error between the true binding label and the predicted binding label of drug–target pairs. The well-trained DNN-based DTI model was used to predict the binding probability between drugs and targets (biomarkers). Therefore, candidate drugs were predicted for each biomarker in Table 5 by the well-trained DNN-based DTI model from the drug databases and further filtered as potential drugs by the drug design specifications of suitable regulation ability, low toxicity and high sensitivity, which are combined as a multiple-molecule drug for OSCC in Table 6.
Figure 6
Figure 6
Training and validation loss of the DNN-based DTI model (five-fold cross validation).
Figure 2
Figure 2
(A) The real GWGEN of non-OSCC. (B) The real GWGEN of OSCC. The numbers indicate the node numbers of proteins, TFs, Receptors, LncRNAs and miRNAs. The purple lines indicate the protein–protein interactions, and the orange lines denote the gene regulations.
Figure 2
Figure 2
(A) The real GWGEN of non-OSCC. (B) The real GWGEN of OSCC. The numbers indicate the node numbers of proteins, TFs, Receptors, LncRNAs and miRNAs. The purple lines indicate the protein–protein interactions, and the orange lines denote the gene regulations.
Figure 3
Figure 3
(A) The core GWGEN of non-OSCC. (B) The core GWGEN of OSCC. The core GWGENs were extracted by the PNP method from the real GWGENs to simplify the annotation of KEGG pathways for the carcinogenic mechanism analysis of OSCC. The numbers denote the node numbers of proteins, TFs, Receptors, LncRNAs and miRNAs, respectively. The purple lines indicate the protein–protein interactions, and the orange lines denote the gene regulations.
Figure 3
Figure 3
(A) The core GWGEN of non-OSCC. (B) The core GWGEN of OSCC. The core GWGENs were extracted by the PNP method from the real GWGENs to simplify the annotation of KEGG pathways for the carcinogenic mechanism analysis of OSCC. The numbers denote the node numbers of proteins, TFs, Receptors, LncRNAs and miRNAs, respectively. The purple lines indicate the protein–protein interactions, and the orange lines denote the gene regulations.
Figure 4
Figure 4
The core signaling pathways in three blocks represent specific OSCC, common non-OSCC and specific non-OSCC core signaling pathways from left to right, respectively. The core signaling pathways of non-OSCC and OSCC are based on the annotation of core GWGENs of non-OSCC and OSCC in Figure 3, respectively. For investigating the genetic and epigenetic carcinogenic mechanism of OSCC, the core signaling pathways and the downstream abnormal cellular functions of non-OSCC and OSCC are compared. The genes and proteins in the core signaling pathways were chosen from core GWGENs of the non-OSCC and OSCC by the annotation of KEGG pathways. The gene regulations and protein interactions were constructed based on the edges in core GWGENs of non-OSCC and OSCC. The low and high expression arrow-head marks are relative to non-OSCC.
Figure 7
Figure 7
Training and validation accuracy of the DNN-based DTI model (five-fold cross validation).
Figure 8
Figure 8
The receiver operating characteristic (ROC) curve measure of the probability of the prediction accuracy of the DNN-based DTI model, with the area under the curve of ROC (AUC-ROC) score of 0.981.
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