Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Qian, Qiu, Bingqing, Zeng, Xiaoyun, Hu, Lang, Huang, Dongping, Chen, Kaihua, Qiu, Xiaoqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
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
_version_ 1783669466362544128
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
work_keys_str_mv AT chenqian identificationofatumormicroenvironmentrelatedgenesignaturetoimprovethepredictionofcervicalcancerprognosis
AT qiubingqing identificationofatumormicroenvironmentrelatedgenesignaturetoimprovethepredictionofcervicalcancerprognosis
AT zengxiaoyun identificationofatumormicroenvironmentrelatedgenesignaturetoimprovethepredictionofcervicalcancerprognosis
AT hulang identificationofatumormicroenvironmentrelatedgenesignaturetoimprovethepredictionofcervicalcancerprognosis
AT huangdongping identificationofatumormicroenvironmentrelatedgenesignaturetoimprovethepredictionofcervicalcancerprognosis
AT chenkaihua identificationofatumormicroenvironmentrelatedgenesignaturetoimprovethepredictionofcervicalcancerprognosis
AT qiuxiaoqiang identificationofatumormicroenvironmentrelatedgenesignaturetoimprovethepredictionofcervicalcancerprognosis