Li J, Jiang T, Ren ZC, Wang ZL, Zhang PJ, Xiang GA. Early detection of colorectal cancer based on circular DNA and common clinical detection indicators. World J Gastrointest Surg 2022; 14(8): 833-848 [PMID: 36157359 DOI: 10.4240/wjgs.v14.i8.833]
Corresponding Author of This Article
Guo-An Xiang, MD, Doctor, The Second School of Clinical Medicine, Southern Medical University, No. 253 Gongye Avenue Haizhu, Guangzhou 510515, Guangdong Province, China. guoanxiang_66@126.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Observational Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Jian Li, Guo-An Xiang, The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, Guangdong Province, China
Jian Li, Guo-An Xiang, Department of General Surgery, Guangdong Second Provincial General Hospital, Guangzhou 510317, Guangdong Province, China
Jian Li, Zeng-Ci Ren, Zhen-Lei Wang, Department of General Surgery, Henan Tumor Hospital, Affiliated Tumor Hospital of Zhengzhou University, Zhengzhou 450000, Henan Province, China
Tao Jiang, Medicine Innovation Research Division of Chinese PLA General Hospital, Beijing 100853, China
Peng-Jun Zhang, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Interventional Therapy Department, Peking University Cancer Hospital and Institute, Beijing 100142, China
Author contributions: Li J and Xian GA designed the study; Li J, Ren ZC and Jiang T performed the research; Li J, Wang ZL and Jiang T analyzed the date; Li J wrote the paper; Xiang GA and Zhang PJ revised the manuscript for final submission; Li J and Jiang T contributed equally to this study; Zhang PJ and Xiang GA the co-corresponding author.
Supported byNational Natural Science Foundation of China, No. 81972010; National Key Research and Development Program of China, No. 2020YFC2002700; National Key Research and Development Program of China, No. 2020YFC2004604.
Institutional review board statement: The study was reviewed and approved by the Chinese PLA General Hospital Review Board.
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: We declare that we have no financial or personal relationships with other individuals or organizations that can inappropriately influence our work and that there is no professional or other personal interest of any nature in any product, service and/or company that could be construed as influencing the position presented in or the review of the manuscript.
Data sharing statement: No data was to share.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Guo-An Xiang, MD, Doctor, The Second School of Clinical Medicine, Southern Medical University, No. 253 Gongye Avenue Haizhu, Guangzhou 510515, Guangdong Province, China. guoanxiang_66@126.com
Received: April 12, 2022 Peer-review started: April 12, 2022 First decision: May 11, 2022 Revised: May 14, 2022 Accepted: August 6, 2022 Article in press: August 6, 2022 Published online: August 27, 2022 Processing time: 134 Days and 1.5 Hours
Abstract
BACKGROUND
Colorectal cancer (CRC) is the third most common cancer worldwide, and it is the second leading cause of death from cancer in the world, accounting for approximately 9% of all cancer deaths. Early detection of CRC is urgently needed in clinical practice.
AIM
To build a multi-parameter diagnostic model for early detection of CRC.
METHODS
Total 59 colorectal polyps (CRP) groups, and 101 CRC patients (38 early-stage CRC and 63 advanced CRC) for model establishment. In addition, 30 CRP groups, and 62 CRC patients (30 early-stage CRC and 32 advanced CRC) were separately included to validate the model. 51 commonly used clinical detection indicators and the 4 extrachromosomal circular DNA markers NDUFB7, CAMK1D, PIK3CD and PSEN2 that we screened earlier. Four multi-parameter joint analysis methods: binary logistic regression analysis, discriminant analysis, classification tree and neural network to establish a multi-parameter joint diagnosis model.
RESULTS
Neural network included carcinoembryonic antigen (CEA), ischemia-modified albumin (IMA), sialic acid (SA), PIK3CD and lipoprotein a (LPa) was chosen as the optimal multi-parameter combined auxiliary diagnosis model to distinguish CRP and CRC group, when it differentiated 59 CRP and 101 CRC, its overall accuracy was 90.8%, its area under the curve (AUC) was 0.959 (0.934, 0.985), and the sensitivity and specificity were 91.5% and 82.2%, respectively. After validation, when distinguishing based on 30 CRP and 62 CRC patients, the AUC was 0.965 (0.930-1.000), and its sensitivity and specificity were 66.1% and 70.0%. When distinguishing based on 30 CRP and 32 early-stage CRC patients, the AUC was 0.960 (0.916-1.000), with a sensitivity and specificity of 87.5% and 90.0%, distinguishing based on 30 CRP and 30 advanced CRC patients, the AUC was 0.970 (0.936-1.000), with a sensitivity and specificity of 96.7% and 86.7%.
CONCLUSION
We built a multi-parameter neural network diagnostic model included CEA, IMA, SA, PIK3CD and LPa for early detection of CRC, compared to the conventional CEA, it showed significant improvement.
Core Tip: Most patients with colorectal cancer (CRC) are diagnosed at an advanced stage. The high morbidity and mortality of advanced CRC indicates an urgent need for clinical improvements in early CRC detection and individualized management. Compared with free linear DNA, extrachromosomal circular DNA is not easily degraded by nucleases, and its structure is more stable. In this study, we aimed to build a multi-parameter diagnostic model for early detection of CRC.
