Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhong, Xin, Xuan, Feichao, Qian, Yun, Pan, Junhai, Wang, Suihan, Chen, Wenchao, Lin, Tianyu, Zhu, Hepan, Wang, Xianfa, Wang, Guanyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
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
_version_ 1783682582132555776
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
work_keys_str_mv AT zhongxin agenomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT xuanfeichao agenomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT qianyun agenomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT panjunhai agenomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT wangsuihan agenomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT chenwenchao agenomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT lintianyu agenomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT zhuhepan agenomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT wangxianfa agenomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT wangguanyu agenomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT zhongxin genomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT xuanfeichao genomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT qianyun genomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT panjunhai genomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT wangsuihan genomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT chenwenchao genomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT lintianyu genomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT zhuhepan genomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT wangxianfa genomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer
AT wangguanyu genomicclinicopathologicnomogramforthepreoperativepredictionoflymphnodemetastasisingastriccancer