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

IF 4.3 3区材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

ACS Applied Electronic Materials Pub Date : 2024-06-05 DOI:10.1016/j.ygeno.2024.110876

Jiyuan Zhou , Yuanke Pan , Shubing Wang , Guoqiang Wang , Chengxin Gu , Jinxin Zhu , Zhenlin Tan , Qixian Wu , Weihuang He , Xiaohui Lin , Shu Xu , Kehua Yuan , Ziwen Zheng , Xiaoqing Gong , Chenhao JiangHe , Zhoujian Han , Bingding Huang , Ruyun Ruan , Mingji Feng , Pin Cui , Hui Yang

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引用次数: 0

Abstract

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.

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利用无血浆细胞 DNA 片段组图谱对结直肠癌进行早期检测和分层。

及时准确、经济高效地检测出结直肠癌（CRC）具有重要的临床意义。本研究旨在利用血浆无细胞 DNA（cfDNA）片段组特征建立检测 CRC 的预测模型。研究人员对620名参与者的cfDNA进行了全基因组测序（WGS），其中包括健康人、良性结直肠疾病患者和CRC患者。利用 WGS 数据，比较了三种机器学习方法，以建立对 CRC 患者进行分层的预测模型。区分各期 CRC 患者与健康人的最佳模型灵敏度为 92.31%，特异度为 91.14%；区分早期 CRC 患者（0-II 期）与健康人的模型灵敏度为 88.8%，特异度为 96.2%。此外，cfDNA 片段图谱还反映了 CRC 中特定疾病的基因组改变。总之，这项研究表明，cfDNA 片段图谱有可能成为检测和分层 CRC 的一种无创方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Electronic Materials Multiple-

CiteScore

7.20

自引率

4.30%

发文量

567