TY - GEN
T1 - Identifying functional evolution processes according to the pathological stages of colorectal cancer
AU - Chen, Bolin
AU - Yang, Manting
AU - Gao, Li
AU - Shang, Xuequn
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Colorectal cancer (CRC) is one of the malignant tumors with high morbidity and mortality. A prevalent method for studying colorectal cancer is to identify differentially expressed genes (DEGs) between control and patient samples, followed by the pathway enrichment analyses. However, many of those studies ignore the fact that different pathological stages of the cancer are often highly different from each other. The mixture of those heterogeneous samples may lack the efficiency of identifying the real DEGs, and loss the opportunity to analyze the dynamic evolution process of cancer. In this study, we develop a feasible framework to identify function evolution processes of cancers according to their pathological stages. Firstly, the limma package was used to identify DEG sets between control and CRC stage I, II, III, and IV samples, separately. Secondly, a pathway interaction network was constructed by taking a comprehensive analysis of pathways at individual stages, and a functional module interaction network was also generated independently by clustering genes into modules in a PPI network. The relationship between pathways and modules in adjacent stages was analyzed for all stages of CRC. A total of 479, 313, 349, and 383 DEGs were identified and they were enriched in 17, 16, 20, and 24 pathways, respectively. A functional evolution network was constructed by using those modules, and two significant evolution processes a2-b1-c2-d1 (Mod1) and a1-b2-c1-d2 (Mod2) were identified which may play critical roles in the development of CRC. The framework proposed in this study can be used to explore molecular mechanisms and evolution processes of CRC.
AB - Colorectal cancer (CRC) is one of the malignant tumors with high morbidity and mortality. A prevalent method for studying colorectal cancer is to identify differentially expressed genes (DEGs) between control and patient samples, followed by the pathway enrichment analyses. However, many of those studies ignore the fact that different pathological stages of the cancer are often highly different from each other. The mixture of those heterogeneous samples may lack the efficiency of identifying the real DEGs, and loss the opportunity to analyze the dynamic evolution process of cancer. In this study, we develop a feasible framework to identify function evolution processes of cancers according to their pathological stages. Firstly, the limma package was used to identify DEG sets between control and CRC stage I, II, III, and IV samples, separately. Secondly, a pathway interaction network was constructed by taking a comprehensive analysis of pathways at individual stages, and a functional module interaction network was also generated independently by clustering genes into modules in a PPI network. The relationship between pathways and modules in adjacent stages was analyzed for all stages of CRC. A total of 479, 313, 349, and 383 DEGs were identified and they were enriched in 17, 16, 20, and 24 pathways, respectively. A functional evolution network was constructed by using those modules, and two significant evolution processes a2-b1-c2-d1 (Mod1) and a1-b2-c1-d2 (Mod2) were identified which may play critical roles in the development of CRC. The framework proposed in this study can be used to explore molecular mechanisms and evolution processes of CRC.
KW - colorectal cancer
KW - differentially expressed gene
KW - functional evolution process
KW - module network
UR - http://www.scopus.com/inward/record.url?scp=85084332612&partnerID=8YFLogxK
U2 - 10.1109/BIBM47256.2019.8983256
DO - 10.1109/BIBM47256.2019.8983256
M3 - 会议稿件
AN - SCOPUS:85084332612
T3 - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
SP - 193
EP - 196
BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
A2 - Yoo, Illhoi
A2 - Bi, Jinbo
A2 - Hu, Xiaohua Tony
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Y2 - 18 November 2019 through 21 November 2019
ER -