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Early detection of colorectal cancer based on circular DNA and common clinical detection indicators
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 d...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9453338/ https://www.ncbi.nlm.nih.gov/pubmed/36157359 http://dx.doi.org/10.4240/wjgs.v14.i8.833 |
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author | Li, Jian Jiang, Tao Ren, Zeng-Ci Wang, Zhen-Lei Zhang, Peng-Jun Xiang, Guo-An |
author_facet | Li, Jian Jiang, Tao Ren, Zeng-Ci Wang, Zhen-Lei Zhang, Peng-Jun Xiang, Guo-An |
author_sort | Li, Jian |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9453338 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-94533382022-09-23 Early detection of colorectal cancer based on circular DNA and common clinical detection indicators Li, Jian Jiang, Tao Ren, Zeng-Ci Wang, Zhen-Lei Zhang, Peng-Jun Xiang, Guo-An World J Gastrointest Surg Observational Study 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. Baishideng Publishing Group Inc 2022-08-27 2022-08-27 /pmc/articles/PMC9453338/ /pubmed/36157359 http://dx.doi.org/10.4240/wjgs.v14.i8.833 Text en ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved. https://creativecommons.org/licenses/by-nc/4.0/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/ |
spellingShingle | Observational Study Li, Jian Jiang, Tao Ren, Zeng-Ci Wang, Zhen-Lei Zhang, Peng-Jun Xiang, Guo-An Early detection of colorectal cancer based on circular DNA and common clinical detection indicators |
title | Early detection of colorectal cancer based on circular DNA and common clinical detection indicators |
title_full | Early detection of colorectal cancer based on circular DNA and common clinical detection indicators |
title_fullStr | Early detection of colorectal cancer based on circular DNA and common clinical detection indicators |
title_full_unstemmed | Early detection of colorectal cancer based on circular DNA and common clinical detection indicators |
title_short | Early detection of colorectal cancer based on circular DNA and common clinical detection indicators |
title_sort | early detection of colorectal cancer based on circular dna and common clinical detection indicators |
topic | Observational Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9453338/ https://www.ncbi.nlm.nih.gov/pubmed/36157359 http://dx.doi.org/10.4240/wjgs.v14.i8.833 |
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