Bioinformatics Analysis of Differentially Expressed Genes in Liver Cancer for Identification of Key Genes and Pathways
- Author:
Aisya Fathiya Che Rosli
1
,
2
,
3
Author Information
1. Cluster for Oncological &
2. Radiological Sciences, Advanced Medical &
3. Dental Institute, Universiti Sains Malaysia, Bertam, 13200 Kepala Batas, Pulau Pinang
- Collective Name:Aisya Fathiya Che Rosli; Siti Razila Abdul Razak; Nurulisa Zulkifle
- Publication Type:Journal Article
- Keywords:
Bioinformatics;
Liver cancer;
Differentially expressed genes;
Enrichment analysis;
Protein–protein interaction
- MeSH:
Liver cancer
- From:Malaysian Journal of Medicine and Health Sciences
2019;15(SP2):18-24
- CountryMalaysia
- Language:English
-
Abstract:
Introduction: Liver cancer is among the main leading cause of mortality in Malaysia and the world. Therefore, there is an urgent need to understand the complex mechanisms and pathways involved in liver cancer. Methods: Microarray datasets GSE84402, GSE60502, GSE29721 and GSE19665 were downloaded from GEO database. The datasets were normalised and differentially expressed genes (DEGs) were calculated using GeneSpring software. GO and KEGG pathway enrichment analyses were then performed using DAVID. Finally, Cytoscape stringApp plugin was utilised to construct a protein-protein interaction (PPI) network. Results: A total of 1382, 714, 1038 and 1828 DEGs satisfying p value cut-off 0.01 and fold change cut-off 2.0 are identified from each dataset. 412 DEGs appeared in at least three datasets, consisting of 167 up-regulated and 245 down-regulated genes. These genes are most significantly enriched in terms related to cell division and mitotic nuclear division. Construction of PPI network produced a network with 275 nodes and 2157 edges with confidence score 0.7. Topological analysis identified CDK1, TOP2A and NDC80 as key genes. MCODE plugin extracted five modules from the network with mitotic cell cycle process being the most enriched term in module 1. Meanwhile, platelet degranulation, epoxygenase P450 pathway, cellular response to zinc ion and complement and coagulation cascade are the terms enriched in module 2, 3, 4 and 5. Conclusion: The key genes and pathways identified from this study provide information on the molecular mechanism underlying liver cancer to increase our understanding regarding liver cancer development and progression at molecular level
- Full text:11.2019my0425.pdf
