Gene prioritization of colorectal cancer using computational approach - Pankaj Kumar Tripathi; Chakresh Kumar Jain (International Journal of Bioinformatics and Computational Biology, 2024)
Colorectal cancer (CRC) continues to be a major worldwide health issue, underscoring the need to pinpoint crucial genetic elements influencing its initiation and advancement. In this investigation, we utilize sophisticated computational methods to prioritize potential genes linked to CRC development. Through the utilization of various bioinformatics tools and comprehensive methodologies, we systematically examine extensive microarray datasets to identify potential genetic contributors. Initially, a vast amount of CRC microarray data (approximately more than 175 GSE datasets) was collected for gene expression analysis and applied filters such as unavailability of sample vs control, non-significant genes, untreated, non-published data, etc. After applying filters, we selected 25 datasets for further analysis. From these datasets, we find the common upregulated and downregulated genes and perform the network analysis to prioritize the genes based on their topological analysis, functional relevance, and biological interactions in CRC tissues. By integrating information from various sources, and applying different filters, we aim to identify key molecular players who may serve as biomarkers for CRC treatments. After applying our gene prioritizing method, we filter out the top ten genes (CDK1, CCNA2, BUB1B, CCNB1, KIF20A, BUB1, KIF11, CENPF, TOP2A, PLK1) have been identified, which are crucial for the development and proliferation of tumor cells, either directly or indirectly. Although CENPF gene role in CRC is unclear, this study shows it's significantly involved in cell division, spindle assembly checkpoint, chromosome segregation, microtubule binding, chromatin binding, and protein binding. Therefore, our finding proposed CENPF as a novel biomarker for CRC progression. This extensive computational gene prioritization study enhances the current comprehension of the molecular landscape of CRC, offering valuable insights for subsequent experimental validation and potential clinical applications.
Keywords: Bioinformatics; Gene Identification; Microarray data; Network analysis; Pathway analysis; colorectal cancer