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Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis
BACKGROUND: Previous studies have found that the microenvironment of cervical cancer (CESC) affects the progression and treatment of this disease. Thus, we constructed a multigene model to assess the survival of patients with cervical cancer. METHODS: We scored 307 CESC samples from The Cancer Genom...
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/PMC7992856/ https://www.ncbi.nlm.nih.gov/pubmed/33766042 http://dx.doi.org/10.1186/s12935-021-01867-2 |
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author | Chen, Qian Qiu, Bingqing Zeng, Xiaoyun Hu, Lang Huang, Dongping Chen, Kaihua Qiu, Xiaoqiang |
author_facet | Chen, Qian Qiu, Bingqing Zeng, Xiaoyun Hu, Lang Huang, Dongping Chen, Kaihua Qiu, Xiaoqiang |
author_sort | Chen, Qian |
collection | PubMed |
description | BACKGROUND: Previous studies have found that the microenvironment of cervical cancer (CESC) affects the progression and treatment of this disease. Thus, we constructed a multigene model to assess the survival of patients with cervical cancer. METHODS: We scored 307 CESC samples from The Cancer Genome Atlas (TCGA) and divided them into high and low matrix and immune scores using the ESTIMATE algorithm for differential gene analysis. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The multigene signature prognostic model was constructed by Cox analyses. Multivariate Cox analysis was applied to evaluate the significance of the multigene signature for cervical cancer prognosis. Prognosis was assessed by Kaplan–Meier curves comparing the different groups, and the accuracy of the prognostic model was analyzed by receiver operating characteristic-area under the curve (ROC-AUC) analysis and calibration curve. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the relationship between the multigene signature and immune cell infiltration. RESULTS: We obtained 420 differentially expressed genes in the tumor microenvironment from 307 patients with cervical cancer. A three-gene signature (SLAMF1, CD27, SELL) model related to the tumor microenvironment was constructed to assess patient survival. Kaplan–Meier analysis showed that patients with high risk scores had a poor prognosis. The ROC-AUC value indicated that the model was an accurate predictor of cervical cancer prognosis. Multivariate cox analysis showed the three-gene signature to be an independent risk factor for the prognosis of cervical cancer. A nomogram combining the three-gene signature and clinical features was constructed, and calibration plots showed that the nomogram resulted in an accurate prognosis for patients. The three-gene signature was associated with T stage, M stage and degree of immune infiltration in patients with cervical cancer. CONCLUSIONS: This research suggests that the developed three-gene signature may be applied as a biomarker to predict the prognosis of and personalized therapy for CESC. |
format | Online Article Text |
id | pubmed-7992856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-79928562021-03-25 Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis Chen, Qian Qiu, Bingqing Zeng, Xiaoyun Hu, Lang Huang, Dongping Chen, Kaihua Qiu, Xiaoqiang Cancer Cell Int Primary Research BACKGROUND: Previous studies have found that the microenvironment of cervical cancer (CESC) affects the progression and treatment of this disease. Thus, we constructed a multigene model to assess the survival of patients with cervical cancer. METHODS: We scored 307 CESC samples from The Cancer Genome Atlas (TCGA) and divided them into high and low matrix and immune scores using the ESTIMATE algorithm for differential gene analysis. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The multigene signature prognostic model was constructed by Cox analyses. Multivariate Cox analysis was applied to evaluate the significance of the multigene signature for cervical cancer prognosis. Prognosis was assessed by Kaplan–Meier curves comparing the different groups, and the accuracy of the prognostic model was analyzed by receiver operating characteristic-area under the curve (ROC-AUC) analysis and calibration curve. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the relationship between the multigene signature and immune cell infiltration. RESULTS: We obtained 420 differentially expressed genes in the tumor microenvironment from 307 patients with cervical cancer. A three-gene signature (SLAMF1, CD27, SELL) model related to the tumor microenvironment was constructed to assess patient survival. Kaplan–Meier analysis showed that patients with high risk scores had a poor prognosis. The ROC-AUC value indicated that the model was an accurate predictor of cervical cancer prognosis. Multivariate cox analysis showed the three-gene signature to be an independent risk factor for the prognosis of cervical cancer. A nomogram combining the three-gene signature and clinical features was constructed, and calibration plots showed that the nomogram resulted in an accurate prognosis for patients. The three-gene signature was associated with T stage, M stage and degree of immune infiltration in patients with cervical cancer. CONCLUSIONS: This research suggests that the developed three-gene signature may be applied as a biomarker to predict the prognosis of and personalized therapy for CESC. BioMed Central 2021-03-25 /pmc/articles/PMC7992856/ /pubmed/33766042 http://dx.doi.org/10.1186/s12935-021-01867-2 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 | Primary Research Chen, Qian Qiu, Bingqing Zeng, Xiaoyun Hu, Lang Huang, Dongping Chen, Kaihua Qiu, Xiaoqiang Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis |
title | Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis |
title_full | Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis |
title_fullStr | Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis |
title_full_unstemmed | Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis |
title_short | Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis |
title_sort | identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis |
topic | Primary Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7992856/ https://www.ncbi.nlm.nih.gov/pubmed/33766042 http://dx.doi.org/10.1186/s12935-021-01867-2 |
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