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A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer
BACKGROUND: Gene mutations play critical roles in tumorigenesis and cancer development. Our study aimed to screen survival-related mutations and explore a novel gene signature to predict the overall survival in pancreatic cancer. METHODS: Somatic mutation data from three cohorts were used to identif...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011162/ https://www.ncbi.nlm.nih.gov/pubmed/33789615 http://dx.doi.org/10.1186/s12885-021-08066-2 |
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author | Liu, Xun Chen, Bobo Chen, Jiahui Sun, Shaolong |
author_facet | Liu, Xun Chen, Bobo Chen, Jiahui Sun, Shaolong |
author_sort | Liu, Xun |
collection | PubMed |
description | BACKGROUND: Gene mutations play critical roles in tumorigenesis and cancer development. Our study aimed to screen survival-related mutations and explore a novel gene signature to predict the overall survival in pancreatic cancer. METHODS: Somatic mutation data from three cohorts were used to identify the common survival-related gene mutation with Kaplan-Meier curves. RNA-sequencing data were used to explore the signature for survival prediction. First, Weighted Gene Co-expression Network Analysis was conducted to identify candidate genes. Then, the ICGC-PACA-CA cohort was applied as the training set and the TCGA-PAAD cohort was used as the external validation set. A TP53-associated signature calculating the risk score of every patient was developed with univariate Cox, least absolute shrinkage and selection operator, and stepwise regression analysis. Kaplan-Meier and receiver operating characteristic curves were plotted to verify the accuracy. The independence of the signature was confirmed by the multivariate Cox regression analysis. Finally, a prognostic nomogram including 359 patients was constructed based on the combined expression data and the risk scores. RESULTS: TP53 mutation was screened to be the robust and survival-related mutation type, and was associated with immune cell infiltration. Two thousand, four hundred fifty-five genes included in the six modules generated in the WGCNA were screened as candidate survival related TP53-associated genes. A seven-gene signature was constructed: Risk score = (0.1254 × ERRFI1) - (0.1365 × IL6R) - (0.4400 × PPP1R10) - (0.3397 × PTOV1-AS2) + (0.1544 × SCEL) - (0.4412 × SSX2IP) – (0.2231 × TXNL4A). Area Under Curves of 1-, 3-, and 5-year ROC curves were 0.731, 0.808, and 0.873 in the training set and 0.703, 0.677, and 0.737 in the validation set. A prognostic nomogram including 359 patients was constructed and well-calibrated, with the Area Under Curves of 1-, 3-, and 5-year ROC curves as 0.713, 0.753, and 0.823. CONCLUSIONS: The TP53-associated signature exhibited good prognostic efficacy in predicting the overall survival of PC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08066-2. |
format | Online Article Text |
id | pubmed-8011162 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80111622021-03-31 A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer Liu, Xun Chen, Bobo Chen, Jiahui Sun, Shaolong BMC Cancer Research Article BACKGROUND: Gene mutations play critical roles in tumorigenesis and cancer development. Our study aimed to screen survival-related mutations and explore a novel gene signature to predict the overall survival in pancreatic cancer. METHODS: Somatic mutation data from three cohorts were used to identify the common survival-related gene mutation with Kaplan-Meier curves. RNA-sequencing data were used to explore the signature for survival prediction. First, Weighted Gene Co-expression Network Analysis was conducted to identify candidate genes. Then, the ICGC-PACA-CA cohort was applied as the training set and the TCGA-PAAD cohort was used as the external validation set. A TP53-associated signature calculating the risk score of every patient was developed with univariate Cox, least absolute shrinkage and selection operator, and stepwise regression analysis. Kaplan-Meier and receiver operating characteristic curves were plotted to verify the accuracy. The independence of the signature was confirmed by the multivariate Cox regression analysis. Finally, a prognostic nomogram including 359 patients was constructed based on the combined expression data and the risk scores. RESULTS: TP53 mutation was screened to be the robust and survival-related mutation type, and was associated with immune cell infiltration. Two thousand, four hundred fifty-five genes included in the six modules generated in the WGCNA were screened as candidate survival related TP53-associated genes. A seven-gene signature was constructed: Risk score = (0.1254 × ERRFI1) - (0.1365 × IL6R) - (0.4400 × PPP1R10) - (0.3397 × PTOV1-AS2) + (0.1544 × SCEL) - (0.4412 × SSX2IP) – (0.2231 × TXNL4A). Area Under Curves of 1-, 3-, and 5-year ROC curves were 0.731, 0.808, and 0.873 in the training set and 0.703, 0.677, and 0.737 in the validation set. A prognostic nomogram including 359 patients was constructed and well-calibrated, with the Area Under Curves of 1-, 3-, and 5-year ROC curves as 0.713, 0.753, and 0.823. CONCLUSIONS: The TP53-associated signature exhibited good prognostic efficacy in predicting the overall survival of PC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08066-2. BioMed Central 2021-03-31 /pmc/articles/PMC8011162/ /pubmed/33789615 http://dx.doi.org/10.1186/s12885-021-08066-2 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 Article Liu, Xun Chen, Bobo Chen, Jiahui Sun, Shaolong A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer |
title | A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer |
title_full | A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer |
title_fullStr | A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer |
title_full_unstemmed | A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer |
title_short | A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer |
title_sort | novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011162/ https://www.ncbi.nlm.nih.gov/pubmed/33789615 http://dx.doi.org/10.1186/s12885-021-08066-2 |
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