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A genomic-clinicopathologic Nomogram for the preoperative prediction of lymph node metastasis in gastric cancer
BACKGROUND: Preoperative evaluation of lymph node (LN) state is of pivotal significance for informing therapeutic decisions in gastric cancer (GC) patients. However, there are no non-invasive methods that can be used to preoperatively identify such status. We aimed at developing a genomic biosignatu...
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/PMC8066490/ https://www.ncbi.nlm.nih.gov/pubmed/33892676 http://dx.doi.org/10.1186/s12885-021-08203-x |
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author | Zhong, Xin Xuan, Feichao Qian, Yun Pan, Junhai Wang, Suihan Chen, Wenchao Lin, Tianyu Zhu, Hepan Wang, Xianfa Wang, Guanyu |
author_facet | Zhong, Xin Xuan, Feichao Qian, Yun Pan, Junhai Wang, Suihan Chen, Wenchao Lin, Tianyu Zhu, Hepan Wang, Xianfa Wang, Guanyu |
author_sort | Zhong, Xin |
collection | PubMed |
description | BACKGROUND: Preoperative evaluation of lymph node (LN) state is of pivotal significance for informing therapeutic decisions in gastric cancer (GC) patients. However, there are no non-invasive methods that can be used to preoperatively identify such status. We aimed at developing a genomic biosignature based model to predict the possibility of LN metastasis in GC patients. METHODS: We used the RNA profile retrieving strategy and performed RNA expression profiling in a large GC cohort (GSE62254, n = 300) from Gene Expression Ominus (GEO). In the exploratory stage, 300 GC patients from GSE62254 were involved and the differentially expressed RNAs (DERs) for LN-status were determined using the R software. GC samples in GSE62254 were randomly allocated into a learning set (n = 210) and a verification set (n = 90). By using the Least absolute shrinkage and selection operator (LASSO) regression approach, a set of 23-RNA signatures were established and the signature based nomogram was subsequently built for distinguishing LN condition. The diagnostic efficiency, as well as the clinical performance of this model were assessed using the decision curve analysis (DCA). Metascape was used for bioinformatic analysis of the DERs. RESULTS: Based on the genomic signature, we established a nomogram that robustly distinguished LN status in the learning (AUC = 0.916, 95% CI 0.833–0.999) and verification sets (AUC = 0.775, 95% CI 0.647–0.903). DCA demonstrated the clinical value of this nomogram. Functional enrichment analysis of the DERs was performed using bioinformatics methods which revealed that these DERs were involved in several lymphangiogenesis-correlated cascades. CONCLUSIONS: In this study, we present a genomic signature based nomogram that integrates the 23-RNA biosignature based scores and Lauren classification. This model can be utilized to estimate the probability of LN metastasis with good performance in GC. The functional analysis of the DERs reveals the prospective biogenesis of LN metastasis in GC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08203-x. |
format | Online Article Text |
id | pubmed-8066490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80664902021-04-26 A genomic-clinicopathologic Nomogram for the preoperative prediction of lymph node metastasis in gastric cancer Zhong, Xin Xuan, Feichao Qian, Yun Pan, Junhai Wang, Suihan Chen, Wenchao Lin, Tianyu Zhu, Hepan Wang, Xianfa Wang, Guanyu BMC Cancer Research Article BACKGROUND: Preoperative evaluation of lymph node (LN) state is of pivotal significance for informing therapeutic decisions in gastric cancer (GC) patients. However, there are no non-invasive methods that can be used to preoperatively identify such status. We aimed at developing a genomic biosignature based model to predict the possibility of LN metastasis in GC patients. METHODS: We used the RNA profile retrieving strategy and performed RNA expression profiling in a large GC cohort (GSE62254, n = 300) from Gene Expression Ominus (GEO). In the exploratory stage, 300 GC patients from GSE62254 were involved and the differentially expressed RNAs (DERs) for LN-status were determined using the R software. GC samples in GSE62254 were randomly allocated into a learning set (n = 210) and a verification set (n = 90). By using the Least absolute shrinkage and selection operator (LASSO) regression approach, a set of 23-RNA signatures were established and the signature based nomogram was subsequently built for distinguishing LN condition. The diagnostic efficiency, as well as the clinical performance of this model were assessed using the decision curve analysis (DCA). Metascape was used for bioinformatic analysis of the DERs. RESULTS: Based on the genomic signature, we established a nomogram that robustly distinguished LN status in the learning (AUC = 0.916, 95% CI 0.833–0.999) and verification sets (AUC = 0.775, 95% CI 0.647–0.903). DCA demonstrated the clinical value of this nomogram. Functional enrichment analysis of the DERs was performed using bioinformatics methods which revealed that these DERs were involved in several lymphangiogenesis-correlated cascades. CONCLUSIONS: In this study, we present a genomic signature based nomogram that integrates the 23-RNA biosignature based scores and Lauren classification. This model can be utilized to estimate the probability of LN metastasis with good performance in GC. The functional analysis of the DERs reveals the prospective biogenesis of LN metastasis in GC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08203-x. BioMed Central 2021-04-23 /pmc/articles/PMC8066490/ /pubmed/33892676 http://dx.doi.org/10.1186/s12885-021-08203-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Zhong, Xin Xuan, Feichao Qian, Yun Pan, Junhai Wang, Suihan Chen, Wenchao Lin, Tianyu Zhu, Hepan Wang, Xianfa Wang, Guanyu A genomic-clinicopathologic Nomogram for the preoperative prediction of lymph node metastasis in gastric cancer |
title | A genomic-clinicopathologic Nomogram for the preoperative prediction of lymph node metastasis in gastric cancer |
title_full | A genomic-clinicopathologic Nomogram for the preoperative prediction of lymph node metastasis in gastric cancer |
title_fullStr | A genomic-clinicopathologic Nomogram for the preoperative prediction of lymph node metastasis in gastric cancer |
title_full_unstemmed | A genomic-clinicopathologic Nomogram for the preoperative prediction of lymph node metastasis in gastric cancer |
title_short | A genomic-clinicopathologic Nomogram for the preoperative prediction of lymph node metastasis in gastric cancer |
title_sort | genomic-clinicopathologic nomogram for the preoperative prediction of lymph node metastasis in gastric cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066490/ https://www.ncbi.nlm.nih.gov/pubmed/33892676 http://dx.doi.org/10.1186/s12885-021-08203-x |
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