Cargando…
An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma
Long non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6877726/ https://www.ncbi.nlm.nih.gov/pubmed/31767907 http://dx.doi.org/10.1038/s41598-019-53811-0 |
_version_ | 1783473394486870016 |
---|---|
author | Cui, Jie Wen, Qingquan Tan, Xiaojun Piao, Jinsong Zhang, Qiong Wang, Qian He, Lizhen Wang, Yan Chen, Zhen Liu, Genglong |
author_facet | Cui, Jie Wen, Qingquan Tan, Xiaojun Piao, Jinsong Zhang, Qiong Wang, Qian He, Lizhen Wang, Yan Chen, Zhen Liu, Genglong |
author_sort | Cui, Jie |
collection | PubMed |
description | Long non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to help to improve the accuracy of recurrence prediction for head and neck squamous cell carcinoma (HNSCC) patients. The HNSCC lncRNAs profiling data and the corresponding clinicopathologic information were downloaded from TANRIC database and cBioPortal. Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier related to recurrence. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate clinical value of our nomogram. Consequently, fifteen recurrence-free survival (RFS) -related lncRNAs were identified, and the classifier consisting of the established 15 lncRNAs could effectively divide patients into high-risk and low-risk subgroup. The prediction ability of the 15-lncRNAs-based classifier for predicting 3- year and 5-year RFS were 0.833 and 0.771. Independent factors derived from multivariable analysis to predict recurrence were number of positive LNs, margin status, mutation count and lncRNAs classifier, which were all embedded into the nomogram. The calibration curve for the recurrence probability showed that the predictions based on the nomogram were in good coincide with practical observations. The C-index of the nomogram was 0.76 (0.72–0.79), and the area under curve (AUC) of nomogram in predicting RFS was 0.809, which were significantly higher than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. The results were confirmed externally. In summary, a visually inclusive nomogram for patients with HNSCC, comprising genomic and clinicopathologic variables, generates more accurate prediction of the recurrence probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice. |
format | Online Article Text |
id | pubmed-6877726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68777262019-12-05 An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma Cui, Jie Wen, Qingquan Tan, Xiaojun Piao, Jinsong Zhang, Qiong Wang, Qian He, Lizhen Wang, Yan Chen, Zhen Liu, Genglong Sci Rep Article Long non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to help to improve the accuracy of recurrence prediction for head and neck squamous cell carcinoma (HNSCC) patients. The HNSCC lncRNAs profiling data and the corresponding clinicopathologic information were downloaded from TANRIC database and cBioPortal. Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier related to recurrence. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate clinical value of our nomogram. Consequently, fifteen recurrence-free survival (RFS) -related lncRNAs were identified, and the classifier consisting of the established 15 lncRNAs could effectively divide patients into high-risk and low-risk subgroup. The prediction ability of the 15-lncRNAs-based classifier for predicting 3- year and 5-year RFS were 0.833 and 0.771. Independent factors derived from multivariable analysis to predict recurrence were number of positive LNs, margin status, mutation count and lncRNAs classifier, which were all embedded into the nomogram. The calibration curve for the recurrence probability showed that the predictions based on the nomogram were in good coincide with practical observations. The C-index of the nomogram was 0.76 (0.72–0.79), and the area under curve (AUC) of nomogram in predicting RFS was 0.809, which were significantly higher than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. The results were confirmed externally. In summary, a visually inclusive nomogram for patients with HNSCC, comprising genomic and clinicopathologic variables, generates more accurate prediction of the recurrence probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice. Nature Publishing Group UK 2019-11-25 /pmc/articles/PMC6877726/ /pubmed/31767907 http://dx.doi.org/10.1038/s41598-019-53811-0 Text en © The Author(s) 2019 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 Cui, Jie Wen, Qingquan Tan, Xiaojun Piao, Jinsong Zhang, Qiong Wang, Qian He, Lizhen Wang, Yan Chen, Zhen Liu, Genglong An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma |
title | An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma |
title_full | An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma |
title_fullStr | An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma |
title_full_unstemmed | An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma |
title_short | An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma |
title_sort | integrated nomogram combining lncrnas classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6877726/ https://www.ncbi.nlm.nih.gov/pubmed/31767907 http://dx.doi.org/10.1038/s41598-019-53811-0 |
work_keys_str_mv | AT cuijie anintegratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT wenqingquan anintegratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT tanxiaojun anintegratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT piaojinsong anintegratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT zhangqiong anintegratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT wangqian anintegratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT helizhen anintegratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT wangyan anintegratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT chenzhen anintegratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT liugenglong anintegratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT cuijie integratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT wenqingquan integratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT tanxiaojun integratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT piaojinsong integratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT zhangqiong integratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT wangqian integratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT helizhen integratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT wangyan integratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT chenzhen integratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma AT liugenglong integratednomogramcombininglncrnasclassifierandclinicopathologicfactorstopredicttherecurrenceofheadandnecksquamouscellcarcinoma |