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Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis

BACKGROUND: Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer. METHODS: The gene expression profile data of colon cance...

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Autores principales: Fang, Zhengyu, Xu, Sumei, Xie, Yiwen, Yan, Wenxi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7807455/
https://www.ncbi.nlm.nih.gov/pubmed/33441161
http://dx.doi.org/10.1186/s12957-020-02116-y
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author Fang, Zhengyu
Xu, Sumei
Xie, Yiwen
Yan, Wenxi
author_facet Fang, Zhengyu
Xu, Sumei
Xie, Yiwen
Yan, Wenxi
author_sort Fang, Zhengyu
collection PubMed
description BACKGROUND: Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer. METHODS: The gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram. RESULTS: Five colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses identified there were 14 prognosis-associated DEGs, of which 12 DEGs were included in the optimized prognostic gene signature. This prognostic model presented a high forecast ability for the prognosis of colon cancer both in the TCGA training dataset and the validation datasets (GSE38832 and GSE17538; AUC > 0.8). In addition, patients’ age, T classification, recurrence status, and prognostic risk score were associated with the prognosis of TCGA patients with colon cancer. The nomogram was constructed using the above factors, and the predictive 3- and 5-year survival probabilities had high compliance with the actual survival proportions. CONCLUSIONS: The 12-gene signature prognostic model had a high predictive ability for the prognosis of colon cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12957-020-02116-y.
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spelling pubmed-78074552021-01-14 Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis Fang, Zhengyu Xu, Sumei Xie, Yiwen Yan, Wenxi World J Surg Oncol Research BACKGROUND: Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer. METHODS: The gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram. RESULTS: Five colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses identified there were 14 prognosis-associated DEGs, of which 12 DEGs were included in the optimized prognostic gene signature. This prognostic model presented a high forecast ability for the prognosis of colon cancer both in the TCGA training dataset and the validation datasets (GSE38832 and GSE17538; AUC > 0.8). In addition, patients’ age, T classification, recurrence status, and prognostic risk score were associated with the prognosis of TCGA patients with colon cancer. The nomogram was constructed using the above factors, and the predictive 3- and 5-year survival probabilities had high compliance with the actual survival proportions. CONCLUSIONS: The 12-gene signature prognostic model had a high predictive ability for the prognosis of colon cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12957-020-02116-y. BioMed Central 2021-01-13 /pmc/articles/PMC7807455/ /pubmed/33441161 http://dx.doi.org/10.1186/s12957-020-02116-y Text en © The Author(s) 2021 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Fang, Zhengyu
Xu, Sumei
Xie, Yiwen
Yan, Wenxi
Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis
title Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis
title_full Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis
title_fullStr Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis
title_full_unstemmed Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis
title_short Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis
title_sort identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7807455/
https://www.ncbi.nlm.nih.gov/pubmed/33441161
http://dx.doi.org/10.1186/s12957-020-02116-y
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