TY - JOUR
T1 - Stage-dependent gene expression profiling in colorectal cancer
AU - Kim, Man Sun
AU - Kim, Dongsan
AU - Kim, Jeong Rae
N1 - Publisher Copyright:
© 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
KW - Protein-protein interaction network
KW - TCGA data
KW - cancer developmental process
KW - disease free survival
KW - gene expression profile
KW - linear mixed-effect regression model
UR - http://www.scopus.com/inward/record.url?scp=85043447903&partnerID=8YFLogxK
U2 - 10.1109/TCBB.2018.2814043
DO - 10.1109/TCBB.2018.2814043
M3 - Article
C2 - 29994071
AN - SCOPUS:85043447903
SN - 1545-5963
VL - 16
SP - 1685
EP - 1692
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 5
M1 - 3370685
ER -