인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Early detection of gastrointestinal (GI) cancers-including colorectal cancer (CRC), gastric cancer (GC), and esophagogastric junction cancer (EGJC)-is essential for improving patient outcomes. However, current diagnostic methods such as endoscopy and colonoscopy are invasive, costly, and not widely accessible. Proteases are elevated in many cancers and are detectable in peripheral blood, making them promising candidates for noninvasive diagnostic strategies. We employed a six-probe charge-changing peptide (CCP) panel to profile cancer-associated protease activity in human plasma. Each CCP undergoes a charge shift upon cleavage by a specific protease, enabling detection via gel electrophoresis. Plasma samples from GI cancer patients (CRC, GC, EGJC; N = 68) and healthy controls (HC; N = 31) were analyzed. Protease activity profiles were analyzed using statistical tests, principal component analysis, and binary logistic regression (LR) models trained on the most informative probes. Model performance was evaluated through repeated cross-validation. Distinct protease activity profiles were observed among CRC, upper GI cancers (UGIC; GC + EGJC), and HC groups. Probe designed to be cleaved by cathepsin B showed the strongest discrimination between cancer and control samples, while probes designed to be cleaved by ubiquitin-specific peptidase 15 and plasmin were identified as the most informative subtype-specific markers for UGIC and CRC, respectively. LR models built on these single probes demonstrated excellent diagnostic performance, with AUCs exceeding 0.95, and both sensitivity and specificity greater than 90%. Our findings highlight CCP-based protease profiling as a minimally invasive, accurate, and scalable method for GI cancer detection and classification. This platform holds strong potential for clinical application in cancer screening, pending further validation in larger, independent cohorts.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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