Early detection and stratification of colorectal cancer using plasma cell-free DNA fragmentomic profiling

Timely accurate and cost-efficient detection of colorectal cancer (CRC) is of great clinical importance. This study aims to establish prediction models for detecting CRC using plasma cell-free DNA (cfDNA) fragmentomic features. Whole-genome sequencing (WGS) was performed on cfDNA from 620 participan...

Full description

Saved in:

Bibliographic Details
Published in:	Genomics (San Diego, Calif.) Vol. 116; no. 4; p. 110876
Main Authors:	Zhou, Jiyuan, Pan, Yuanke, Wang, Shubing, Wang, Guoqiang, Gu, Chengxin, Zhu, Jinxin, Tan, Zhenlin, Wu, Qixian, He, Weihuang, Lin, Xiaohui, Xu, Shu, Yuan, Kehua, Zheng, Ziwen, Gong, Xiaoqing, JiangHe, Chenhao, Han, Zhoujian, Huang, Bingding, Ruan, Ruyun, Feng, Mingji, Cui, Pin, Yang, Hui
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01-07-2024
Subjects:	Adult Aged Biomarkers, Tumor - blood Biomarkers, Tumor - genetics Cell-free DNA Cell-Free Nucleic Acids - blood Cell-Free Nucleic Acids - genetics Colorectal cancer Colorectal Neoplasms - blood Colorectal Neoplasms - diagnosis Colorectal Neoplasms - genetics DNA Fragmentation Early Detection of Cancer - methods Female Fragmentation profile Humans Machine Learning Male Middle Aged Whole Genome Sequencing - methods Cell-free DNA Fragmentation profile Colorectal cancer Machine learning
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Timely accurate and cost-efficient detection of colorectal cancer (CRC) is of great clinical importance. This study aims to establish prediction models for detecting CRC using plasma cell-free DNA (cfDNA) fragmentomic features. Whole-genome sequencing (WGS) was performed on cfDNA from 620 participants, including healthy individuals, patients with benign colorectal diseases and CRC patients. Using WGS data, three machine learning methods were compared to build prediction models for the stratification of CRC patients. The optimal model to discriminate CRC patients of all stages from healthy individuals achieved a sensitivity of 92.31% and a specificity of 91.14%, while the model to separate early-stage CRC patients (stage 0-II) from healthy individuals achieved a sensitivity of 88.8% and a specificity of 96.2%. Additionally, the cfDNA fragmentation profiles reflected disease-specific genomic alterations in CRC. Overall, this study suggests that cfDNA fragmentation profiles may potentially become a noninvasive approach for the detection and stratification of CRC. •This study has identified clearly different profiles of cfDNA fragmentation profile and end motifs of the CRC patients compared to the healthy individuals or patients with colorectal benign disease.•cfDNA fragmentation profiles exhibit a better diagnostic value for the detection of early-stage CRC.•The genome-wide cfDNA fragmentation profiles reflected disease-specific genomic alterations in CRC.•cfDNA fragmentation profiles may potentially become a noninvasive assay for the detection and stratification of CRC.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0888-7543 1089-8646 1089-8646
DOI:	10.1016/j.ygeno.2024.110876