Identifying key genes and screening therapeutic agents associated with diabetes mellitus and HCV-related hepatocellular carcinoma by bioinformatics analysis
| Autor: | Sidra Aslam, Usman Ali Ashfaq, Hamed A. El-Serehy, Sajjad Ahmad, Muhammad Farhan Aslam, Muhammad Sufyan, Fatima Noor, Muhammad Hamzah Saleem |
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| Rok vydání: | 2021 |
| Předmět: |
MCOD
Molecular Complex Detection Microarray Hepatocellular carcinoma Hepatitis C virus TFs Transcription factors Context (language use) Computational biology medicine.disease_cause Bioinformatics analysis Type 2 diabetes mellitus GO Gene Ontology medicine PPI Protein-Protein Interaction network DEGs Differential Expressed Genes Gene ComputingMethodologies_COMPUTERGRAPHICS Candidate drugs Genetic association biology CENPF medicine.disease Gene expression profiling digestive system diseases biology.protein T2DM Type2 Diabetes Mellitus Original Article HCC Hepatocellular Carcinoma General Agricultural and Biological Sciences Hub genes |
| Zdroj: | Saudi Journal of Biological Sciences |
| ISSN: | 1319-562X |
| DOI: | 10.1016/j.sjbs.2021.07.068 |
| Popis: | Graphical 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. |
| Databáze: | OpenAIRE |
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