Paper Info
Title
Stage-Dependent Gene Expression Profiling in Colorectal Cancer.
Abstract
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 contains 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.
Year
DOI
Venue
2019
10.1109/TCBB.2018.2814043
IEEE/ACM transactions on computational biology and bioinformatics
Keywords
Field
DocType
Cancer,Gene expression,Proteins,Databases,Correlation,Ontologies
Metastasis,Gene,Computer science,Regression analysis,Gene expression,Computational biology,Cell cycle,Bioinformatics,Colorectal cancer,Cancer,Gene expression profiling
Journal
Volume
Issue
ISSN
16
5
1557-9964
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Man-Sun Kim1121.77
Dong San Kim281.97
Jeong-Rae Kim311.03