Identifying Effective Pathways and Genes in Colorectal Cancer by Analyzing Microarray and RNA Sequencing Data

Introduction Colorectal cancer (CRC) is the third most common cancer globally and the second leading cause of cancer-related deaths. The analysis of microarray and RNA-sequencing data available in databases such as GEO and TCGA has significantly contributed to the identification of genes involved in the development, progression, and recurrence of this cancer. The availability of online bioinformatics tools for identifying biological pathways and the interactions between proteins encoded by these genes, as well as for constructing modules of genes with the highest correlation, has helped us find clues to better understand this complex disease. In this study, three microarray datasets were downloaded from the GEO database, and after data normalization, differentially expressed genes (DEGs) were identified. The DEGs that were common in these three datasets were identified. Gene ontology and KEGG pathway analysis were performed using the DAVID database. A protein-protein interaction network related to these genes was drawn, and the best modules were identified based on the strength of their connections. The gene ontology analysis for the genes in the first module was assessed. The genes in the first module were then compared with the final data obtained from RNA-seq data analysis to validate the result. Material and method Three datasets (GSE۲۱۵۱۰, GSE۲۱۸۱۵, GSE۳۵۲۷۹) were retrieved from the GEO database. The data was normalized and differentially expressed genes (DEGs) were identified using the Limma package in R. Additionally, RNA sequencing data from the TCGA project focusing on colorectal adenocarcinoma (COAD) was obtained and normalized utilizing the DESeq۲ package. Gene ontology and KEGG pathway analysis were performed using the DAVID database. Protein-protein interaction (PPI) networks were created through the STRING database and visualized with Cytoscape software, while the MCODE plugin was applied to discern modules present within the PPI networks. Result Differentially expressed genes (DEGs) were identified in each dataset. A total of ۱۰۷۳ DEGs were common across the three datasets, exhibiting significant differential expression between tumor and normal samples (|logFC| > ۱.۵). Gene ontology analysis of these genes revealed enrichment in biological processes such as cell division, chromosome segregation, and DNA repair. Molecular function and cellular component analyses, as well as KEGG pathway analysis, further supported these findings. A large-scale protein-protein interaction network was constructed for these genes. The MCODE plugin was used to identify modules of highly interconnected genes. The first module with the highest score, comprising ۸۶ genes, all showing upregulation, was selected for further gene ontology analysis. This analysis revealed enrichment in cell division, chromosome segregation, and mitotic spindle organization pathways. To validate these ۸۶ genes, RNA-seq data from the COAD project was utilized. The DEGs identified from this dataset also showed significant upregulation for these ۸۶ genes. Conclusion The existence of various studies related to the role of these genes in CRC confirms the key role of these genes and their associated pathways in the carcinogenesis process. By elucidating the interactions between these genes and others, we can further piece together this complex puzzle, which may ultimately contribute to strategies for the prevention or treatment of CRC.

کلیدواژه ها:

Colorectal cancer ، Microarray data analysis ، RNA-sequencing data analysis ، Gene ontology ، Protein-protein interaction network