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Tsinghua Science and Technology 2023, 28(4): 649-664 https://doi.org/10.26599/TST.2022.9010035
Open Access | Issue | Published: 06 January 2023

Identification of Key Genes as Potential Drug Targets for Gastric Cancer

Show Author's Information Md. Tofazzal Hossain1,2,3,†,  Md. Selim Reza1,2,†,  Yin Peng4,  Shengzhong Feng1,  Yanjie Wei1( )
Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, Shenzhen University, Shenzhen 518060, China

Md. Tofazzal Hossain and Md. Selim Reza contribute equally to this paper.

Keywords:
molecular docking, gastric cancer, hub genes, candidate genes, candidate drugs
Cite this article:
Hossain MT, Reza MS, Peng Y, et al. Identification of Key Genes as Potential Drug Targets for Gastric Cancer. Tsinghua Science and Technology, 2023, 28(4): 649-664. https://doi.org/10.26599/TST.2022.9010035
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Abstract Full text Electronic supplementary material About this article

Gastric cancer (GC) is one of the most common cancers and ranks the third in cancer mortality all over the world. The goal of this study was to identify potential hub-genes, highlighting their functions, signaling pathways, and candidate drugs for the treatment of GC patients. We used publicly available next generation sequencing (NGS) data to identify differentially expressed (DE) genes. The top DE genes were mapped to STRING database to construct the protein-protein interaction (PPI) network and top hub genes were selected for further analysis. We found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in GC. We selected the top 400 (200 upregulated and 200 downregulated) genes to construct a PPI network and extracted the top 15 hub genes. The gene ontology (GO) term and kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC patients. The survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2, COL4A1, and COL5A2 were associated with better survival of GC patients. These three genes might be the candidate biomarkers for the diagnosis and treatment of GC. Then, we considered 3 key proteins (genomic biomarkers) (COL4A1, CDH2, and CO5A2) as the drug target proteins (receptors), performed their docking analysis with the 102 meta-drug agents, and found Everolimus, Docetaxel, Lanreotide, Venetoclax, Temsirolimus, and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC patients. Therefore, the proposed drugs might play vital role for the treatment against GC patients.


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Abstract
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Electronic supplementary material
About this article

Identification of Key Genes as Potential Drug Targets for Gastric Cancer

Show Author's information Md. Tofazzal Hossain1,2,3,†, Md. Selim Reza1,2,†, Yin Peng4, Shengzhong Feng1, Yanjie Wei1( )
Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, Shenzhen University, Shenzhen 518060, China

Md. Tofazzal Hossain and Md. Selim Reza contribute equally to this paper.

Abstract

Gastric cancer (GC) is one of the most common cancers and ranks the third in cancer mortality all over the world. The goal of this study was to identify potential hub-genes, highlighting their functions, signaling pathways, and candidate drugs for the treatment of GC patients. We used publicly available next generation sequencing (NGS) data to identify differentially expressed (DE) genes. The top DE genes were mapped to STRING database to construct the protein-protein interaction (PPI) network and top hub genes were selected for further analysis. We found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in GC. We selected the top 400 (200 upregulated and 200 downregulated) genes to construct a PPI network and extracted the top 15 hub genes. The gene ontology (GO) term and kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC patients. The survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2, COL4A1, and COL5A2 were associated with better survival of GC patients. These three genes might be the candidate biomarkers for the diagnosis and treatment of GC. Then, we considered 3 key proteins (genomic biomarkers) (COL4A1, CDH2, and CO5A2) as the drug target proteins (receptors), performed their docking analysis with the 102 meta-drug agents, and found Everolimus, Docetaxel, Lanreotide, Venetoclax, Temsirolimus, and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC patients. Therefore, the proposed drugs might play vital role for the treatment against GC patients.

Keywords: molecular docking, gastric cancer, hub genes, candidate genes, candidate drugs

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Received: 18 February 2022
Revised: 31 May 2022
Accepted: 29 August 2022
Published: 06 January 2023
Issue date: August 2023

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© The author(s) 2023.

Acknowledgements

This work was partly supported by the National Key Research and Development Program of China (No. 2018YFB0204403), Key Research and Development Project of Guangdong Province (No. 2021B0101310002), Strategic Priority CAS Project (No. XDB38050100), National Science Foundation of China (No. U1813203), the Shenzhen Basic Research Fund (Nos. RCYX2020071411473419, KQTD20200820113106007, and JSGG20201102163800001), CAS Key Lab (No. 2011DP173015), and the Youth Innovation Promotion Association (No. Y2021101).

We would also like to thank all the members of Computational Biology and Bioinformatics Lab, Center for High Performance Computing, SIAT, CAS for their valuable suggestions and feed-backs.

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