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A qualitative transcriptional signature for the early diagnosis of colorectal cancer

Currently, using biopsy specimens for the early diagnosis of colorectal cancer (CRC) is not entirely reliable due to insufficient sampling amount and inaccurate sampling location. Thus, it is necessary to develop a signature that can accurately identify patients with CRC under these clinical scenari...

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Autores principales: Guan, Qingzhou, Zeng, Qiuhong, Yan, Haidan, Xie, Jiajing, Cheng, Jun, Ao, Lu, He, Jun, Zhao, Wenyuan, Chen, Kui, Guo, You, Guan, Guoxian, Guo, Zheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6778657/
https://www.ncbi.nlm.nih.gov/pubmed/31335996
http://dx.doi.org/10.1111/cas.14137
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author Guan, Qingzhou
Zeng, Qiuhong
Yan, Haidan
Xie, Jiajing
Cheng, Jun
Ao, Lu
He, Jun
Zhao, Wenyuan
Chen, Kui
Guo, You
Guan, Guoxian
Guo, Zheng
author_facet Guan, Qingzhou
Zeng, Qiuhong
Yan, Haidan
Xie, Jiajing
Cheng, Jun
Ao, Lu
He, Jun
Zhao, Wenyuan
Chen, Kui
Guo, You
Guan, Guoxian
Guo, Zheng
author_sort Guan, Qingzhou
collection PubMed
description Currently, using biopsy specimens for the early diagnosis of colorectal cancer (CRC) is not entirely reliable due to insufficient sampling amount and inaccurate sampling location. Thus, it is necessary to develop a signature that can accurately identify patients with CRC under these clinical scenarios. Based on the relative expression orderings of genes within individual samples, we developed a qualitative transcriptional signature to discriminate CRC tissues, including CRC adjacent normal tissues from non‐CRC individuals. The signature was validated using multiple microarray and RNA sequencing data from different sources. In the training data, a signature consisting of 7 gene pairs was identified. It was well validated in both biopsy and surgical resection specimens from multiple datasets measured by different platforms. For biopsy specimens, 97.6% of 42 CRC tissues and 94.5% of 163 non‐CRC (normal or inflammatory bowel disease) tissues were correctly classified. For surgically resected specimens, 99.5% of 854 CRC tissues and 96.3% of 81 CRC adjacent normal tissues were correctly identified as CRC. Notably, we additionally measured 33 CRC biopsy specimens by the Affymetrix platform and 13 CRC surgical resection specimens, with different proportions of tumor epithelial cells, ranging from 40% to 100%, by the RNA sequencing platform, and all these samples were correctly identified as CRC. The signature can be used for the early diagnosis of CRC, which is also suitable for minimum biopsy specimens and inaccurately sampled specimens, and thus has potential value for clinical application.
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spelling pubmed-67786572019-10-11 A qualitative transcriptional signature for the early diagnosis of colorectal cancer Guan, Qingzhou Zeng, Qiuhong Yan, Haidan Xie, Jiajing Cheng, Jun Ao, Lu He, Jun Zhao, Wenyuan Chen, Kui Guo, You Guan, Guoxian Guo, Zheng Cancer Sci Original Articles Currently, using biopsy specimens for the early diagnosis of colorectal cancer (CRC) is not entirely reliable due to insufficient sampling amount and inaccurate sampling location. Thus, it is necessary to develop a signature that can accurately identify patients with CRC under these clinical scenarios. Based on the relative expression orderings of genes within individual samples, we developed a qualitative transcriptional signature to discriminate CRC tissues, including CRC adjacent normal tissues from non‐CRC individuals. The signature was validated using multiple microarray and RNA sequencing data from different sources. In the training data, a signature consisting of 7 gene pairs was identified. It was well validated in both biopsy and surgical resection specimens from multiple datasets measured by different platforms. For biopsy specimens, 97.6% of 42 CRC tissues and 94.5% of 163 non‐CRC (normal or inflammatory bowel disease) tissues were correctly classified. For surgically resected specimens, 99.5% of 854 CRC tissues and 96.3% of 81 CRC adjacent normal tissues were correctly identified as CRC. Notably, we additionally measured 33 CRC biopsy specimens by the Affymetrix platform and 13 CRC surgical resection specimens, with different proportions of tumor epithelial cells, ranging from 40% to 100%, by the RNA sequencing platform, and all these samples were correctly identified as CRC. The signature can be used for the early diagnosis of CRC, which is also suitable for minimum biopsy specimens and inaccurately sampled specimens, and thus has potential value for clinical application. John Wiley and Sons Inc. 2019-09-03 2019-10 /pmc/articles/PMC6778657/ /pubmed/31335996 http://dx.doi.org/10.1111/cas.14137 Text en © 2019 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Articles
Guan, Qingzhou
Zeng, Qiuhong
Yan, Haidan
Xie, Jiajing
Cheng, Jun
Ao, Lu
He, Jun
Zhao, Wenyuan
Chen, Kui
Guo, You
Guan, Guoxian
Guo, Zheng
A qualitative transcriptional signature for the early diagnosis of colorectal cancer
title A qualitative transcriptional signature for the early diagnosis of colorectal cancer
title_full A qualitative transcriptional signature for the early diagnosis of colorectal cancer
title_fullStr A qualitative transcriptional signature for the early diagnosis of colorectal cancer
title_full_unstemmed A qualitative transcriptional signature for the early diagnosis of colorectal cancer
title_short A qualitative transcriptional signature for the early diagnosis of colorectal cancer
title_sort qualitative transcriptional signature for the early diagnosis of colorectal cancer
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6778657/
https://www.ncbi.nlm.nih.gov/pubmed/31335996
http://dx.doi.org/10.1111/cas.14137
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