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Using bioinformatics approaches to investigate driver genes and identify BCL7A as a prognostic gene in colorectal cancer

Authors :
Jeffrey Yung-chuan Chao
Hsin-Chuan Chang
Jeng-Kai Jiang
Chih-Yung Yang
Fang-Hsin Chen
Yo-Liang Lai
Wen-Jen Lin
Chia-Yang Li
Shu-Chi Wang
Muh-Hwa Yang
Yu-Feng Lin
Wei-Chung Cheng
Source :
Computational and Structural Biotechnology Journal, Vol 19, Iss , Pp 3922-3929 (2021)
Publication Year :
2021
Publisher :
Elsevier, 2021.

Abstract

Colorectal cancer (CRC) results from the uncontrolled growth of cells in the colon, rectum, or appendix. The 5-year relative survival rate for patients with CRC is 65% and is correlated with the stage at diagnosis (being 91% for stage I at diagnosis versus 12% for stage IV). This study aimed to identify CRC driver genes to assist in the design of a cancer panel to detect gene mutations during clinical early-stage screening and identify genes for use in prognostic assessments and the evaluation of appropriate treatment options. First, we utilized bioinformatics approaches to analyze 354 paired sequencing profiles from The Cancer Genome Atlas (TCGA) to identify CRC driver genes and analyzed the sequencing profiles of 38 patients with >5 years of follow-up data to search for prognostic genes. The results revealed eight driver genes and ten prognostic genes. Next, the presence of the identified gene mutations was verified using tissue and blood samples from Taiwanese CRC patients. The results showed that the set identified gene mutations provide high coverage for driver gene screening, and APC, TP53, PIK3CA, and FAT4 could be detected in blood as ctDNA test targets. We further found that BCL7A gene mutation was correlated with prognosis in CRC (log-rank p-value = 0.02), and that mutations of BCL7A could be identified in ctDNA samples. These findings may be of value in clinical early cancer detection, disease monitoring, drug development, and treatment efforts in the future.

Details

Language :
English
ISSN :
20010370
Volume :
19
Issue :
3922-3929
Database :
Directory of Open Access Journals
Journal :
Computational and Structural Biotechnology Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.418360957aca483988173b82ae0d0b88
Document Type :
article
Full Text :
https://doi.org/10.1016/j.csbj.2021.06.044