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Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients

BACKGROUND: Although the incidence of cervical cancer has decreased in recent decades with the development of human papillomavirus vaccines and cancer screening, cervical cancer remains one of the leading causes of cancer-related death worldwide. Identifying potential biomarkers for cervical cancer...

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Autores principales: Huo, Xiao, Zhou, Xiaoshuang, Peng, Peng, Yu, Mei, Zhang, Ying, Yang, Jiaxin, Cao, Dongyan, Sun, Hengzi, Shen, Keng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873033/
https://www.ncbi.nlm.nih.gov/pubmed/33574675
http://dx.doi.org/10.2147/OTT.S276553
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author Huo, Xiao
Zhou, Xiaoshuang
Peng, Peng
Yu, Mei
Zhang, Ying
Yang, Jiaxin
Cao, Dongyan
Sun, Hengzi
Shen, Keng
author_facet Huo, Xiao
Zhou, Xiaoshuang
Peng, Peng
Yu, Mei
Zhang, Ying
Yang, Jiaxin
Cao, Dongyan
Sun, Hengzi
Shen, Keng
author_sort Huo, Xiao
collection PubMed
description BACKGROUND: Although the incidence of cervical cancer has decreased in recent decades with the development of human papillomavirus vaccines and cancer screening, cervical cancer remains one of the leading causes of cancer-related death worldwide. Identifying potential biomarkers for cervical cancer treatment and prognosis prediction is necessary. METHODS: Samples with mRNA sequencing, copy number variant, single nucleotide polymorphism and clinical follow-up data were downloaded from The Cancer Genome Atlas database and randomly divided into a training dataset (N=146) and a test dataset (N=147). We selected and identified a prognostic gene set and mutated gene set and then integrated the two gene sets with the random survival forest algorithm and constructed a prognostic signature. External validation and immunohistochemical staining were also performed. RESULTS: We obtained 1416 differentially expressed prognosis-related genes, 624 genes with copy number amplification, 1038 genes with copy number deletion, and 163 significantly mutated genes. A total of 75 candidate genes were obtained after overlapping the differentially expressed genes and the genes with genomic variations. Subsequently, we obtained six characteristic genes through the random survival forest algorithm. The results showed that high expression of SLC19A3, FURIN, SLC22A3, and DPAGT1 and low expression of CCL17 and DES were associated with a poor prognosis in cervical cancer patients. We constructed a six-gene signature that can separate cervical cancer patients according to their different overall survival rates, and it showed robust performance for predicting survival (training set: p ˂ 0.001, AUC = 0.82; testing set: p ˂ 0.01, AUC = 0.59). CONCLUSION: Our study identified a novel six-gene signature and nomogram for predicting the overall survival of cervical cancer patients, which may be beneficial for clinical decision-making for individualized treatment.
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spelling pubmed-78730332021-02-10 Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients Huo, Xiao Zhou, Xiaoshuang Peng, Peng Yu, Mei Zhang, Ying Yang, Jiaxin Cao, Dongyan Sun, Hengzi Shen, Keng Onco Targets Ther Original Research BACKGROUND: Although the incidence of cervical cancer has decreased in recent decades with the development of human papillomavirus vaccines and cancer screening, cervical cancer remains one of the leading causes of cancer-related death worldwide. Identifying potential biomarkers for cervical cancer treatment and prognosis prediction is necessary. METHODS: Samples with mRNA sequencing, copy number variant, single nucleotide polymorphism and clinical follow-up data were downloaded from The Cancer Genome Atlas database and randomly divided into a training dataset (N=146) and a test dataset (N=147). We selected and identified a prognostic gene set and mutated gene set and then integrated the two gene sets with the random survival forest algorithm and constructed a prognostic signature. External validation and immunohistochemical staining were also performed. RESULTS: We obtained 1416 differentially expressed prognosis-related genes, 624 genes with copy number amplification, 1038 genes with copy number deletion, and 163 significantly mutated genes. A total of 75 candidate genes were obtained after overlapping the differentially expressed genes and the genes with genomic variations. Subsequently, we obtained six characteristic genes through the random survival forest algorithm. The results showed that high expression of SLC19A3, FURIN, SLC22A3, and DPAGT1 and low expression of CCL17 and DES were associated with a poor prognosis in cervical cancer patients. We constructed a six-gene signature that can separate cervical cancer patients according to their different overall survival rates, and it showed robust performance for predicting survival (training set: p ˂ 0.001, AUC = 0.82; testing set: p ˂ 0.01, AUC = 0.59). CONCLUSION: Our study identified a novel six-gene signature and nomogram for predicting the overall survival of cervical cancer patients, which may be beneficial for clinical decision-making for individualized treatment. Dove 2021-02-05 /pmc/articles/PMC7873033/ /pubmed/33574675 http://dx.doi.org/10.2147/OTT.S276553 Text en © 2021 Huo et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Huo, Xiao
Zhou, Xiaoshuang
Peng, Peng
Yu, Mei
Zhang, Ying
Yang, Jiaxin
Cao, Dongyan
Sun, Hengzi
Shen, Keng
Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients
title Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients
title_full Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients
title_fullStr Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients
title_full_unstemmed Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients
title_short Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients
title_sort identification of a six-gene signature for predicting the overall survival of cervical cancer patients
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873033/
https://www.ncbi.nlm.nih.gov/pubmed/33574675
http://dx.doi.org/10.2147/OTT.S276553
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