Identifying key genes and screening therapeutic agents associated with diabetes mellitus and HCV-related hepatocellular carcinoma by bioinformatics analysis

Muhammad Sufyan; Usman Ali Ashfaq; Sajjad Ahmad; Fatima Noor; Muhammad Hamzah Saleem; Muhammad Farhan Aslam; Hamed A El-Serehy; Sidra Aslam

doi:10.1016/j.sjbs.2021.07.068

Identifying key genes and screening therapeutic agents associated with diabetes mellitus and HCV-related hepatocellular carcinoma by bioinformatics analysis

Saudi J Biol Sci. 2021 Oct;28(10):5518-5525. doi: 10.1016/j.sjbs.2021.07.068. Epub 2021 Jul 30.

Authors

Muhammad Sufyan¹, Usman Ali Ashfaq¹, Sajjad Ahmad², Fatima Noor¹, Muhammad Hamzah Saleem³, Muhammad Farhan Aslam⁴, Hamed A El-Serehy⁵, Sidra Aslam¹

Affiliations

¹ Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Allama Iqbal Road, Faisalabad-38000, Pakistan.
² Department of Health and Biological Sciences, Abasyn University, Peshawar, Pakistan.
³ MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
⁴ School of Biological Sciences, University of Edinburgh, United Kingdom.
⁵ Department of Zoology, King Saud University, Riyadh 11451, Saudi Arabia.

Abstract

Objective: Incidence of both Type 2 diabetes mellitus (T2DM) and hepatocellular carcinoma (HCC) are rapidly increasing worldwide. One of the leading causes of HCC is hepatitis C virus (HCV), which is a resource of blood-borne viral infection. HCV increases the risk for HCC probably by promoting fibrosis and cirrhosis. Association among T2DM and HCV related HCC remains significant, indicating that such association is clinically reliable and robust. Lawson was the first who uncovered HCC in person suffered from T2DM. Until now, genetic association between HCV related HCC and T2DM is poorly known. Current work was designed to figure out the molecular mechanisms of both diseases by identifying the hub genes and therapeutic drugs using integrated bioinformatics analysis.

Methods: Four microarray datasets were downloaded from GEO database and analyzed using R in order to obtain different expressed genes (DEGs). Protein-protein interaction (PPI) networks was constructed using STRING tool and visualized by Cytoscape. Moreover, hub genes were identified on the basis of their degree of connectivity. Finally, Networkanalyst and DGIdb were used for the identification of transcription factors (TFs) and selection of candidate drugs, respectively.

Results: A total of 53 DEGs were identified, of which 41 were upregulated genes and 12 were downregulated genes. PPI network obtained from STRING were subjected to Cytoscape plugin cytoHubba, and top 10 genes (AURKA, JUN, AR, MELK, NCOA2, CENPF, NCAPG, PCK1, RAD51AP1, and GTSE1) were chosen as the target hub genes based on the highest degree of connectivity. Furthermore, 47 drugs of AURKA, JUN, AR, MELK, and NCOA2 were found having therapeutic potential to treat HCV-HCC in patients with T2DM.

Conclusion: This study updates the information and yield a new perspective in context of understanding the pathogenesis and development of HCV related HCC in affected persons with T2DM. In vivo and in vitro investigation of hub genes and pathway interaction is essential to delineate the specific roles of the novel hub genes, which may help to reveal the genetic association between HCV-HCC and T2DM. In future, hub genes along with their candidate drugs might be capable of improving the personalized detection and therapies for both diseases.

Keywords: Bioinformatics analysis; Candidate drugs; DEGs, Differential Expressed Genes; GO, Gene Ontology; Gene expression profiling; HCC, Hepatocellular Carcinoma; Hepatocellular carcinoma; Hub genes; MCOD, Molecular Complex Detection; PPI, Protein-Protein Interaction network; T2DM, Type2 Diabetes Mellitus; TFs, Transcription factors; Type 2 diabetes mellitus.