Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach.
Abhijeet R PatilMing-Ying LeungSourav RoyPublished in: International journal of environmental research and public health (2021)
Colorectal cancer (CRC) is the third most common cancer that contributes to cancer-related morbidity. However, the differential expression of genes in different phases of CRC is largely unknown. Moreover, very little is known about the role of stress-survival pathways in CRC. We sought to discover the hub genes and identify their roles in several key pathways, including oxidative stress and apoptosis in the different stages of CRC. To identify the hub genes that may be involved in the different stages of CRC, gene expression datasets were obtained from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) common among the different datasets for each group were obtained using the robust rank aggregation method. Then, gene enrichment analysis was carried out with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Finally, the protein-protein interaction networks were constructed using the Cytoscape software. We identified 40 hub genes and performed enrichment analysis for each group. We also used the Oncomine database to identify the DEGs related to stress-survival and apoptosis pathways involved in different stages of CRC. In conclusion, the hub genes were found to be enriched in several key pathways, including the cell cycle and p53 signaling pathway. Some of the hub genes were also reported in the stress-survival and apoptosis pathways. The hub DEGs revealed from our study may be used as biomarkers and may explain CRC development and progression mechanisms.
Keyphrases
- bioinformatics analysis
- genome wide
- genome wide identification
- gene expression
- oxidative stress
- cell cycle
- network analysis
- squamous cell carcinoma
- small molecule
- protein protein
- cell proliferation
- transcription factor
- cell cycle arrest
- young adults
- machine learning
- copy number
- wastewater treatment
- epithelial mesenchymal transition
- drug induced
- squamous cell