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Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram
Accurate prediction of peritoneal metastasis for gastric cancer (GC) with serosal invasion is crucial in clinic. The presence of collagen in the tumour microenvironment affects the metastasis of cancer cells. Herein, we propose a collagen signature, which is composed of multiple collagen features in...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794254/ https://www.ncbi.nlm.nih.gov/pubmed/33420057 http://dx.doi.org/10.1038/s41467-020-20429-0 |
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author | Chen, Dexin Liu, Zhangyuanzhu Liu, Wenju Fu, Meiting Jiang, Wei Xu, Shuoyu Wang, Guangxing Chen, Feng Lu, Jianping Chen, Hao Dong, Xiaoyu Li, Guoxin Chen, Gang Zhuo, Shuangmu Yan, Jun |
author_facet | Chen, Dexin Liu, Zhangyuanzhu Liu, Wenju Fu, Meiting Jiang, Wei Xu, Shuoyu Wang, Guangxing Chen, Feng Lu, Jianping Chen, Hao Dong, Xiaoyu Li, Guoxin Chen, Gang Zhuo, Shuangmu Yan, Jun |
author_sort | Chen, Dexin |
collection | PubMed |
description | Accurate prediction of peritoneal metastasis for gastric cancer (GC) with serosal invasion is crucial in clinic. The presence of collagen in the tumour microenvironment affects the metastasis of cancer cells. Herein, we propose a collagen signature, which is composed of multiple collagen features in the tumour microenvironment of the serosa derived from multiphoton imaging, to describe the extent of collagen alterations. We find that a high collagen signature is significantly associated with a high risk of peritoneal metastasis (P < 0.001). A competing-risk nomogram including the collagen signature, tumour size, tumour differentiation status and lymph node metastasis is constructed. The nomogram demonstrates satisfactory discrimination and calibration. Thus, the collagen signature in the tumour microenvironment of the gastric serosa is associated with peritoneal metastasis in GC with serosal invasion, and the nomogram can be conveniently used to individually predict the risk of peritoneal metastasis in GC with serosal invasion after radical surgery. |
format | Online Article Text |
id | pubmed-7794254 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77942542021-01-15 Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram Chen, Dexin Liu, Zhangyuanzhu Liu, Wenju Fu, Meiting Jiang, Wei Xu, Shuoyu Wang, Guangxing Chen, Feng Lu, Jianping Chen, Hao Dong, Xiaoyu Li, Guoxin Chen, Gang Zhuo, Shuangmu Yan, Jun Nat Commun Article Accurate prediction of peritoneal metastasis for gastric cancer (GC) with serosal invasion is crucial in clinic. The presence of collagen in the tumour microenvironment affects the metastasis of cancer cells. Herein, we propose a collagen signature, which is composed of multiple collagen features in the tumour microenvironment of the serosa derived from multiphoton imaging, to describe the extent of collagen alterations. We find that a high collagen signature is significantly associated with a high risk of peritoneal metastasis (P < 0.001). A competing-risk nomogram including the collagen signature, tumour size, tumour differentiation status and lymph node metastasis is constructed. The nomogram demonstrates satisfactory discrimination and calibration. Thus, the collagen signature in the tumour microenvironment of the gastric serosa is associated with peritoneal metastasis in GC with serosal invasion, and the nomogram can be conveniently used to individually predict the risk of peritoneal metastasis in GC with serosal invasion after radical surgery. Nature Publishing Group UK 2021-01-08 /pmc/articles/PMC7794254/ /pubmed/33420057 http://dx.doi.org/10.1038/s41467-020-20429-0 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Chen, Dexin Liu, Zhangyuanzhu Liu, Wenju Fu, Meiting Jiang, Wei Xu, Shuoyu Wang, Guangxing Chen, Feng Lu, Jianping Chen, Hao Dong, Xiaoyu Li, Guoxin Chen, Gang Zhuo, Shuangmu Yan, Jun Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram |
title | Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram |
title_full | Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram |
title_fullStr | Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram |
title_full_unstemmed | Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram |
title_short | Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram |
title_sort | predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794254/ https://www.ncbi.nlm.nih.gov/pubmed/33420057 http://dx.doi.org/10.1038/s41467-020-20429-0 |
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