1
|
You T, Song K, Guo W, Fu Y, Wang K, Zheng H, Yang J, Jin L, Qi L, Guo Z, Zhao W. A Qualitative Transcriptional Signature for Predicting CpG Island Methylator Phenotype Status of the Right-Sided Colon Cancer. Front Genet 2020; 11:971. [PMID: 33193579 PMCID: PMC7658404 DOI: 10.3389/fgene.2020.00971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 07/31/2020] [Indexed: 12/24/2022] Open
Abstract
A part of colorectal cancer which is characterized by simultaneous numerous hypermethylation CpG islands sites is defined as CpG island methylator phenotype (CIMP) status. Stage II and III CIMP−positive (CIMP+) right-sided colon cancer (RCC) patients have a better prognosis than CIMP−negative (CIMP−) RCC treated with surgery alone. However, there is no gold standard available in defining CIMP status. In this work, we selected the gene pairs whose relative expression orderings (REOs) were associated with the CIMP status, to develop a qualitative transcriptional signature to individually predict CIMP status for stage II and III RCC. Based on the REOs of gene pairs, a signature composed of 19 gene pairs was developed to predict the CIMP status of RCC through a feature selection process. A sample is predicted as CIMP+ when the gene expression orderings of at least 12 gene pairs vote for CIMP+; otherwise the CIMP−. The difference of prognosis between the predicted CIMP+ and CIMP− groups was more significantly different than the original CIMP status groups. There were more differential methylation and expression characteristics between the two predicted groups. The hierarchical clustering analysis showed that the signature could perform better for predicting CIMP status of RCC than current methods. In conclusion, the qualitative transcriptional signature for classifying CIMP status at the individualized level can predict outcome and guide therapy for RCC patients.
Collapse
Affiliation(s)
- Tianyi You
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Kai Song
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenbing Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yelin Fu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Kai Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hailong Zheng
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jing Yang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Liangliang Jin
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Lishuang Qi
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zheng Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Fujian Provincial Key Laboratory on Hematology, Fujian Medical University, Fuzhou, China
| | - Wenyuan Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| |
Collapse
|