Comprehensive RNA-sequencing analysis of colorectal cancer in a Korean cohort

Mol Cells. 2024 Mar;47(3):100033. doi: 10.1016/j.mocell.2024.100033. Epub 2024 Feb 23.


Considering the recent increase in the number of colorectal cancer (CRC) cases in South Korea, we aimed to clarify the molecular characteristics of CRC unique to the Korean population. To gain insights into the complexities of CRC and promote the exchange of critical data, RNA-sequencing analysis was performed to reveal the molecular mechanisms that drive the development and progression of CRC; this analysis is critical for developing effective treatment strategies. We performed RNA-sequencing analysis of CRC and adjacent normal tissue samples from 214 Korean participants (comprising a total of 381 including 169 normal and 212 tumor samples) to investigate differential gene expression between the groups. We identified 19,575 genes expressed in CRC and normal tissues, with 3,830 differentially expressed genes (DEGs) between the groups. Functional annotation analysis revealed that the upregulated DEGs were significantly enriched in pathways related to the cell cycle, DNA replication, and IL-17, whereas the downregulated DEGs were enriched in metabolic pathways. We also analyzed the relationship between clinical information and subtypes using the Consensus Molecular Subtype (CMS) classification. Furthermore, we compared groups clustered within our dataset to CMS groups and performed additional analysis of the methylation data between DEGs and CMS groups to provide comprehensive biological insights from various perspectives. Our study provides valuable insights into the molecular mechanisms underlying CRC in Korean patients and serves as a platform for identifying potential target genes for this disease. The raw data and processed results have been deposited in a public repository for further analysis and exploration.

Keywords: Cell cycle; Colorectal neoplasm; DNA replication; RNA; RNA-sequencing.

MeSH terms

  • Colorectal Neoplasms* / metabolism
  • Computational Biology / methods
  • Gene Expression Profiling* / methods
  • Gene Expression Regulation, Neoplastic
  • Humans
  • RNA


  • RNA