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Using Bioinformatics Approaches to Investigate Driver Genes and Identify BCL7A As a Prognostic Gene in Colorectal Cancer

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Specialty Biotechnology
Date 2021 Jul 26
PMID 34306573
Citations 3
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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 , and could be detected in blood as ctDNA test targets. We further found that gene mutation was correlated with prognosis in CRC (log-rank p-value = 0.02), and that mutations of 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.

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References
1.
Lawrence M, Stojanov P, Polak P, Kryukov G, Cibulskis K, Sivachenko A . Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013; 499(7457):214-218. PMC: 3919509. DOI: 10.1038/nature12213. View

2.
Vogelstein B, Papadopoulos N, Velculescu V, Zhou S, Diaz Jr L, Kinzler K . Cancer genome landscapes. Science. 2013; 339(6127):1546-58. PMC: 3749880. DOI: 10.1126/science.1235122. View

3.
Hindorff L, Sethupathy P, Junkins H, Ramos E, Mehta J, Collins F . Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A. 2009; 106(23):9362-7. PMC: 2687147. DOI: 10.1073/pnas.0903103106. View

4.
Lee J, Liles E, Bent S, Levin T, Corley D . Accuracy of fecal immunochemical tests for colorectal cancer: systematic review and meta-analysis. Ann Intern Med. 2014; 160(3):171. PMC: 4189821. DOI: 10.7326/M13-1484. View

5.
Bamford S, Dawson E, Forbes S, Clements J, Pettett R, Dogan A . The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br J Cancer. 2004; 91(2):355-8. PMC: 2409828. DOI: 10.1038/sj.bjc.6601894. View