Cargando…
Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer
Accumulating evidence has shown that lymph node metastasis (LNM) is not only an important prognostic factor but also an indicator of the need for postoperative chemoradiotherapy. Therefore, identifying risk factors or molecular markers related to LNM is critical for predicting the prognosis and guid...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859285/ https://www.ncbi.nlm.nih.gov/pubmed/33553172 http://dx.doi.org/10.3389/fcell.2020.631491 |
_version_ | 1783646697690234880 |
---|---|
author | Zhang, Yu Sun, Di Song, Jiayu Yang, Nan Zhang, Yunyan |
author_facet | Zhang, Yu Sun, Di Song, Jiayu Yang, Nan Zhang, Yunyan |
author_sort | Zhang, Yu |
collection | PubMed |
description | Accumulating evidence has shown that lymph node metastasis (LNM) is not only an important prognostic factor but also an indicator of the need for postoperative chemoradiotherapy. Therefore, identifying risk factors or molecular markers related to LNM is critical for predicting the prognosis and guiding individualized treatment of patients with cervical cancer. In this study, we used the machine learning-based feature selection approach to identify eight optimal biomarkers from the list of 250 differentially expressed protein-coding genes and long non-coding RNAs (lncRNAs) in the TCGA cohort. Then a coding-non-coding signature (named CNC8SIG) was developed using the elastic-net logistic regression approach based on the expression levels of eight optimal biomarkers, which is useful in discriminating patients with LNM from those without LNM in the discovery cohort. The predictive performance of the CNC8SIG was further validated in two independent patient cohorts. Moreover, the CNC8SIG was significantly associated with patient’s survival in different patient cohorts. In silico functional analysis suggested that the CNC8SIG-associated mRNAs are enriched in known cancer-related biological pathways such as the Wnt signaling pathway, the Ras signaling pathway, Rap1 signaling pathway, and PI3K-Akt signaling pathway. |
format | Online Article Text |
id | pubmed-7859285 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78592852021-02-05 Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer Zhang, Yu Sun, Di Song, Jiayu Yang, Nan Zhang, Yunyan Front Cell Dev Biol Cell and Developmental Biology Accumulating evidence has shown that lymph node metastasis (LNM) is not only an important prognostic factor but also an indicator of the need for postoperative chemoradiotherapy. Therefore, identifying risk factors or molecular markers related to LNM is critical for predicting the prognosis and guiding individualized treatment of patients with cervical cancer. In this study, we used the machine learning-based feature selection approach to identify eight optimal biomarkers from the list of 250 differentially expressed protein-coding genes and long non-coding RNAs (lncRNAs) in the TCGA cohort. Then a coding-non-coding signature (named CNC8SIG) was developed using the elastic-net logistic regression approach based on the expression levels of eight optimal biomarkers, which is useful in discriminating patients with LNM from those without LNM in the discovery cohort. The predictive performance of the CNC8SIG was further validated in two independent patient cohorts. Moreover, the CNC8SIG was significantly associated with patient’s survival in different patient cohorts. In silico functional analysis suggested that the CNC8SIG-associated mRNAs are enriched in known cancer-related biological pathways such as the Wnt signaling pathway, the Ras signaling pathway, Rap1 signaling pathway, and PI3K-Akt signaling pathway. Frontiers Media S.A. 2021-01-21 /pmc/articles/PMC7859285/ /pubmed/33553172 http://dx.doi.org/10.3389/fcell.2020.631491 Text en Copyright © 2021 Zhang, Sun, Song, Yang and Zhang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cell and Developmental Biology Zhang, Yu Sun, Di Song, Jiayu Yang, Nan Zhang, Yunyan Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer |
title | Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer |
title_full | Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer |
title_fullStr | Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer |
title_full_unstemmed | Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer |
title_short | Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer |
title_sort | integrated profiles analysis identified a coding-non-coding signature for predicting lymph node metastasis and prognosis in cervical cancer |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859285/ https://www.ncbi.nlm.nih.gov/pubmed/33553172 http://dx.doi.org/10.3389/fcell.2020.631491 |
work_keys_str_mv | AT zhangyu integratedprofilesanalysisidentifiedacodingnoncodingsignatureforpredictinglymphnodemetastasisandprognosisincervicalcancer AT sundi integratedprofilesanalysisidentifiedacodingnoncodingsignatureforpredictinglymphnodemetastasisandprognosisincervicalcancer AT songjiayu integratedprofilesanalysisidentifiedacodingnoncodingsignatureforpredictinglymphnodemetastasisandprognosisincervicalcancer AT yangnan integratedprofilesanalysisidentifiedacodingnoncodingsignatureforpredictinglymphnodemetastasisandprognosisincervicalcancer AT zhangyunyan integratedprofilesanalysisidentifiedacodingnoncodingsignatureforpredictinglymphnodemetastasisandprognosisincervicalcancer |