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
Adjuvant chemotherapy (ACT) is usually used to reduce the risk of disease relapse and improve survival for stage II/III colorectal cancer (CRC). However, only a subset of patients could benefit from ACT. Thus, there is an urgent need to identify improved biomarkers to predict survival and stratify patients to refine the selection of ACT. We used high-throughput proteomics to analyze tumor and adjacent normal tissues of stage II/III CRC patients with /without relapse to identify potential markers for predicting prognosis and benefit from ACT. The machine learning approach was applied to identify relapse-specific markers. Then the artificial intelligence (AI)-assisted multiplex IHC was performed to validate the prognostic value of the relapse-specific markers and construct a proteomic-derived classifier for stage II/III CRC using 3 markers, including FHL3, GGA1, TGFBI. The proteomics profiling-derived signature for stage II/III CRC (PS) not only shows good accuracy to classify patients into high and low risk of relapse and mortality in all three cohorts, but also works independently of clinicopathologic features. ACT was associated with improved disease-free survival (DFS) and overall survival (OS) in stage II (pN0) patients with high PS and pN2 patients with high PS. This study demonstrated the clinical significance of proteomic features, which serve as a valuable source for potential biomarkers. The PS classifier provides prognostic value for identifying patients at high risk of relapse and mortality and optimizes individualized treatment strategy by detecting patients who may benefit from ACT for survival.
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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