한빛사
>
한빛사논문
한빛사논문
- 조회272
J. Extracell. Vesicles, Jun 2025, 14 (6) e70088 | https://doi.org/10.1002/jev2.70088
Enhanced Early Detection of Colorectal Cancer via Blood Biomarker Combinations Identified Through Extracellular Vesicle Isolation and Artificial Intelligence Analysis
Bonhan Koo1, Young Il Kim2, Minju Lee1, Seok-Byung Lim2, Yong Shin1
1Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seodaemun-gu, Republic of Korea
2Division of Colon and Rectal Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, Republic of Korea
Bonhan Koo and Young Il Kim contributed equally to this study.
Correspondence: Seok-Byung Lim, Yong Shin
1Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seodaemun-gu, Republic of Korea
2Division of Colon and Rectal Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, Republic of Korea
Bonhan Koo and Young Il Kim contributed equally to this study.
Correspondence: Seok-Byung Lim, Yong Shin
Abstract
Colorectal cancer (CRC) remains a major cause of cancer-related deaths worldwide, with early detection being crucial for improving survival rates. Despite the potential of extracellular vesicles (EVs) as blood biomarkers for CRC diagnosis, their clinical utility is limited due to complex and time-consuming isolation methods, unverified biomarkers and low diagnostic performance. Here, we introduce the ZAHV-AI system, which combines the zeolite-amine and homobifunctional hydrazide-based extracellular vesicle isolation (ZAHVIS) platform with AI-driven analysis for enhanced CRC diagnosis. The ZAHVIS platform enables simple, rapid and cost-effective EV isolation and one-step extraction of EV-derived proteins and nucleic acids (NAs), providing a streamlined approach. Using blood plasma samples from 80 CRC patients across all stages and 20 healthy individuals, we identified four EV-derived miRNA blood biomarkers (miR-23a-3p, miR-92a-3p, miR-125a-3p and miR-150-5p) by confirming statistical significance with relative quantification (RQ) values from real-time PCR and integrated these with carcinoembryonic antigen (CEA) levels into an AI-driven diagnostic model. Among 31 combinations used to identify optimal sets, optimal combination (miR-23a-3p, miR-92a-3p, miR-150-5p and CEA) for overall CRC achieved an area under the curve (AUC) of 0.9861, outperforming individual markers and conventional CEA tests. Notably, the system achieved perfect performance in detecting stages 0-1 (AUC: 1.0) and demonstrated high accuracy for stage 2 (AUC: 0.9722) and early-stage CRC (AUC: 0.9861), using stage-specific optimal combinations. Therefore, the ZAHV-AI system offers a reliable and clinically relevant tool for CRC diagnostics, significantly enhancing early detection and monitoring capabilities.
논문정보
- Research article
- 2025년 06월 (BRIC 등록일 2025-06-16)
- 국내 연구진
- 바이오·의료융합 > 의료기기
관련 보도자료
AI 기반 대장암 조기진단 기술 ‘ZAHV-AI’ 개발
연세대학교 생명공학과 신용 교수 연구팀이 서울아산병원 대장항문외과 임석병·김영일 교수와 함께, 대장암을 비침습적으로 조기에 진단할 수 있는 인공지능 기반 기술 ‘ZAHV-AI’를 개발했다. 이 기술은 혈액 내 세포외 소포체에서 유래한 마이크로RNA 등 바이오마커를 기반으로, 인공지능(AI)을 활용해 대장암을 정밀하게 판별한다. 특히 0~1기 조기 병기 환자에서도 완벽한 진단 정확도(AUC 1.0)를 보여, 향후 내시경 검사에 대한 부담을 줄일 수 있는 대안으로 주목된다.
- Bio통신원
- 2025. 06. 18
댓글 작성 로그인
팝업 닫기관련 링크
연구자 키워드
관련분야 연구자보기
관련분야 논문보기
