Stage-dependent gene expression profiling in colorectal cancer

Man Sun Kim, Dongsan Kim, Jeong Rae Kim

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Temporal gene expression profiles have been widely considered to uncover the mechanism of cancer development and progression. Gene expression patterns, however, have been analyzed for limited stages with small samples, without proper data pre-processing, in many cases. With those approaches, it is difficult to unveil the mechanism of cancer development over time. In this study, we analyzed gene expression profiles of two independent colorectal cancer sample datasets, each of which contained 556 and 566 samples, respectively. To find specific gene expression changes according to cancer stage, we applied the linear mixed-effect regression model LMER that controls other clinical variables. Based on this methodology, we found two types of gene expression patterns: continuously increasing and decreasing genes as cancer develops. We found that continuously increasing genes are related to the nervous and developmental system, whereas the others are related to the cell cycle and metabolic processes. We further analyzed connected sub-networks related to the two types of genes. From these results, we suggest that the gene expression profile analysis can be used to understand underlying the mechanisms of cancer development such as cancer growth and metastasis. Furthermore, our approach can provide a good guideline for advancing our understanding of cancer developmental processes.

Original languageEnglish
Article number3370685
Pages (from-to)1685-1692
Number of pages8
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number5
StatePublished - Sep 2019


  • Protein-protein interaction network
  • TCGA data
  • cancer developmental process
  • disease free survival
  • gene expression profile
  • linear mixed-effect regression model


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