The Genomic Landscape of Colorectal Cancer in the Saudi Arabian Population Using a Comprehensive Genomic Panel
Year: 2023
Type: article
Study design: Observational Study
Abstract: Purpose: Next-generation sequencing (NGS) technology detects specific mutations that can provide treatment opportunities for colorectal cancer (CRC) patients. Patients and Methods: We analyzed the mutation frequencies of common actionable genes and their association with clinicopathological characteristics and oncologic outcomes using targeted NGS in 107 Saudi Arabian patients without a family history of CRC. Results: Approximately 98% of patients had genetic alterations. Frequent mutations were observed in BRCA2 (79%), CHEK1 (78%), ATM (76%), PMS2 (76%), ATR (74%), and MYCL (73%). The APC gene was not included in the panel. Statistical analysis using the Cox proportional hazards model revealed an unusual positive association between poorly differentiated tumors and survival rates (p = 0.025). Although no significant univariate associations between specific mutations or overall mutation rate and overall survival were found, our preliminary analysis of the molecular markers for CRC in a predominantly Arab population can provide insights into the molecular pathways that play a significant role in the underlying disease progression. Conclusions: These results may help optimize personalized therapy when drugs specific to a patient’s mutation profile have already been developed.
Source: Diagnostics
Language: English
Subfield: Pathology and Forensic Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,224
Awards: CAREER: Developing efficient and scalable bioinformatics methods and databases to analyze the adaptive immune repertoires of vertebrate species, G198621255, G2792600807, EAGER: Developing a framework to identify and mitigate perceptual and technical barriers in code sharing to facilitate reproducible and transparent research, G2923281071, G4012457088, G4241624374, G4625835944, G4768766693, RCN-UBE: Sustainable, nationwide network to promote reproducible big-data analysis in biology programs within community colleges and minority-serving institutions, G720296508, G7352689120, G8546719858, G9000160949
OpenAlex ID: W4386849413
