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A network‐based predictive gene expression signature for recurrence risks in stage II colorectal cancer

The current criteria for defining the recurrence risks of stage II colorectal cancer (CRC) are not robust; therefore, we aimed to explore novel gene signatures to predict recurrence risks and to reveal the underlying mechanisms of stage II CRC. First, the gene expression profiles of 124 patients wit...

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Autores principales: Yang, Wen‐Jing, Wang, Hai‐Bo, Wang, Wen‐Da, Bai, Peng‐Yu, Lu, Hong‐Xia, Sun, Chang‐He, Liu, Zi‐Shen, Guan, Ding‐Kun, Yang, Guo‐Wang, Zhang, Gan‐Lin
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/PMC6943157/
https://www.ncbi.nlm.nih.gov/pubmed/31724326
http://dx.doi.org/10.1002/cam4.2642
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author Yang, Wen‐Jing
Wang, Hai‐Bo
Wang, Wen‐Da
Bai, Peng‐Yu
Lu, Hong‐Xia
Sun, Chang‐He
Liu, Zi‐Shen
Guan, Ding‐Kun
Yang, Guo‐Wang
Zhang, Gan‐Lin
author_facet Yang, Wen‐Jing
Wang, Hai‐Bo
Wang, Wen‐Da
Bai, Peng‐Yu
Lu, Hong‐Xia
Sun, Chang‐He
Liu, Zi‐Shen
Guan, Ding‐Kun
Yang, Guo‐Wang
Zhang, Gan‐Lin
author_sort Yang, Wen‐Jing
collection PubMed
description The current criteria for defining the recurrence risks of stage II colorectal cancer (CRC) are not robust; therefore, we aimed to explore novel gene signatures to predict recurrence risks and to reveal the underlying mechanisms of stage II CRC. First, the gene expression profiles of 124 patients with stage II CRC from The Cancer Genome Atlas (TCGA) database were obtained to screen differentially expressed genes (DEGs). A total of 202 DEGs, including 128 upregulated and 74 downregulated, were identified in the recurrence group (n = 24) compared to the nonrecurrence group (n = 100). Furthermore, the top 5 DEGs (ZNF561, WFS1, SLC2A1, MFI2, and PTGR1) were identified by random forest variable hunting, and four (ZNF561, WFS1, SLC2A1, and PTGR1) were selected to create a four‐gene recurrent model (GRM), with an area under the curve (AUC) of 0.882 according to the receiver operating characteristic curve, and the robust diagnostic effectiveness of the GRM was further validated with another gene expression profiling dataset (GSE12032), with an AUC of 0.943. The diagnostic effectiveness of the GRM regarding recurrence was associated with poor disease‐free survival in all stages of CRC. In addition, gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed 18 enriched functions and 6 enriched pathways. Four genes, ABCG2, CACNA1F, CYP19A1, and TF, were identified as hub genes by the protein‐protein interaction network, which further validated that these genes were correlated with a poor pathologic stage and overall survival in all stages of CRC. In conclusion, the GRM can effectively classify stage II CRC into groups of high and low risks of recurrence, thereby making up for the prognostic value of the traditional clinicopathological risk factors defined by the National Comprehensive Cancer Network guidelines. The hub genes may be useful therapeutic targets for recurrence. Thus, the GRM and hub genes could offer clinical value in directing individualized and precision therapeutic regimens for stage II CRC patients.
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spelling pubmed-69431572020-01-07 A network‐based predictive gene expression signature for recurrence risks in stage II colorectal cancer Yang, Wen‐Jing Wang, Hai‐Bo Wang, Wen‐Da Bai, Peng‐Yu Lu, Hong‐Xia Sun, Chang‐He Liu, Zi‐Shen Guan, Ding‐Kun Yang, Guo‐Wang Zhang, Gan‐Lin Cancer Med Clinical Cancer Research The current criteria for defining the recurrence risks of stage II colorectal cancer (CRC) are not robust; therefore, we aimed to explore novel gene signatures to predict recurrence risks and to reveal the underlying mechanisms of stage II CRC. First, the gene expression profiles of 124 patients with stage II CRC from The Cancer Genome Atlas (TCGA) database were obtained to screen differentially expressed genes (DEGs). A total of 202 DEGs, including 128 upregulated and 74 downregulated, were identified in the recurrence group (n = 24) compared to the nonrecurrence group (n = 100). Furthermore, the top 5 DEGs (ZNF561, WFS1, SLC2A1, MFI2, and PTGR1) were identified by random forest variable hunting, and four (ZNF561, WFS1, SLC2A1, and PTGR1) were selected to create a four‐gene recurrent model (GRM), with an area under the curve (AUC) of 0.882 according to the receiver operating characteristic curve, and the robust diagnostic effectiveness of the GRM was further validated with another gene expression profiling dataset (GSE12032), with an AUC of 0.943. The diagnostic effectiveness of the GRM regarding recurrence was associated with poor disease‐free survival in all stages of CRC. In addition, gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed 18 enriched functions and 6 enriched pathways. Four genes, ABCG2, CACNA1F, CYP19A1, and TF, were identified as hub genes by the protein‐protein interaction network, which further validated that these genes were correlated with a poor pathologic stage and overall survival in all stages of CRC. In conclusion, the GRM can effectively classify stage II CRC into groups of high and low risks of recurrence, thereby making up for the prognostic value of the traditional clinicopathological risk factors defined by the National Comprehensive Cancer Network guidelines. The hub genes may be useful therapeutic targets for recurrence. Thus, the GRM and hub genes could offer clinical value in directing individualized and precision therapeutic regimens for stage II CRC patients. John Wiley and Sons Inc. 2019-11-14 /pmc/articles/PMC6943157/ /pubmed/31724326 http://dx.doi.org/10.1002/cam4.2642 Text en © 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Cancer Research
Yang, Wen‐Jing
Wang, Hai‐Bo
Wang, Wen‐Da
Bai, Peng‐Yu
Lu, Hong‐Xia
Sun, Chang‐He
Liu, Zi‐Shen
Guan, Ding‐Kun
Yang, Guo‐Wang
Zhang, Gan‐Lin
A network‐based predictive gene expression signature for recurrence risks in stage II colorectal cancer
title A network‐based predictive gene expression signature for recurrence risks in stage II colorectal cancer
title_full A network‐based predictive gene expression signature for recurrence risks in stage II colorectal cancer
title_fullStr A network‐based predictive gene expression signature for recurrence risks in stage II colorectal cancer
title_full_unstemmed A network‐based predictive gene expression signature for recurrence risks in stage II colorectal cancer
title_short A network‐based predictive gene expression signature for recurrence risks in stage II colorectal cancer
title_sort network‐based predictive gene expression signature for recurrence risks in stage ii colorectal cancer
topic Clinical Cancer Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6943157/
https://www.ncbi.nlm.nih.gov/pubmed/31724326
http://dx.doi.org/10.1002/cam4.2642
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