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Published on: December 26, 2016
Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray
1Laboratory of Automatic Control, Signaling Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.
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.
Insights
This study identifies key biomarkers for oral squamous cell carcinoma (OSCC) and uses a deep neural network to discover a potential multi-molecule drug for its treatment.
Area of Science:
- Systems biology
- Genomics
- Pharmacology
Background:
- Oral squamous cell carcinoma (OSCC) presents a significant health challenge.
- Understanding the complex carcinogenic mechanisms of OSCC is crucial for effective treatment development.
Purpose of the Study:
- To investigate the carcinogenic mechanism of OSCC using a systems biology approach.
- To identify novel biomarkers for OSCC and develop a potential multi-molecule drug for its treatment.
Main Methods:
- Constructed genome-wide genetic and epigenetic networks (GWGENs) from big database mining.
- Identified real GWGENs and extracted core networks using system identification and principal network projection (PNP).
- Utilized a deep neural network (DNN)-based drug-target interaction (DTI) model for drug discovery.
Main Results:
- Identified HES1, TCF, NF-κB, and SP1 as significant OSCC biomarkers.
- The DNN-DTI model predicted metformin, gefitinib, and gallic-acid as potential therapeutic agents.
- These drugs were filtered based on design specifications for a potential multi-molecule drug.
Conclusions:
- The study provides a novel systems biology framework for investigating OSCC.
- Identified key biomarkers and a potential multi-molecule drug combination for OSCC treatment.
- This approach may accelerate the development of targeted therapies for OSCC.
