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A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma

BACKGROUND: In this study, we aimed to mine immune-related RNAs expressed in early cervical squamous cell carcinoma to construct prognostic prediction models. METHODS: The RNA sequencing data of 309 cervical squamous cell carcinoma (CSCC) cases, including data of individuals with available clinical...

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Autores principales: Qin, Rui, Cao, Lu, Ye, Cong, Wang, Junrong, Sun, Ziqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7885601/
https://www.ncbi.nlm.nih.gov/pubmed/33588862
http://dx.doi.org/10.1186/s12920-021-00885-3
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author Qin, Rui
Cao, Lu
Ye, Cong
Wang, Junrong
Sun, Ziqian
author_facet Qin, Rui
Cao, Lu
Ye, Cong
Wang, Junrong
Sun, Ziqian
author_sort Qin, Rui
collection PubMed
description BACKGROUND: In this study, we aimed to mine immune-related RNAs expressed in early cervical squamous cell carcinoma to construct prognostic prediction models. METHODS: The RNA sequencing data of 309 cervical squamous cell carcinoma (CSCC) cases, including data of individuals with available clinical information, were obtained from The Cancer Genome Atlas (TCGA) database. We included 181 early-stage CSCC tumor samples with clinical survival and prognosis information (training dataset). Then, we downloaded the GSE44001 gene expression profile data from the National Center for Biotechnology Information Gene Expression Omnibus (validation dataset). Gene ontology annotation and the Kyoto Encyclopedia of Genes and Genomes pathway analyses were used to analyze the biological functions of differentially expressed immune-related genes (DEIRGs). We established protein–protein interactions and competing endogenous RNA networks using Cytoscape. Using the Kaplan–Meier method, we evaluated the association between the high- and low-risk groups and the actual survival and prognosis information. Our univariate and multivariate Cox regression analyses screened for independent prognostic factors. RESULTS: We identified seven prognosis-related signature genes (RBAKDN, CXCL2, ZAP70, CLEC2D, CD27, KLRB1, VCAM1), the expression of which was markedly associated with overall survival (OS) in CSCC patients. Also, the risk score of the seven-gene signature discripted superior ability to categorize CSCC patients into high-risk and low-risk groups, with a observablydifferent OS in the training and validation datasets. We screened two independent prognostic factors (Pathologic N and prognostic score model status) that correlated significantly by univariate and multivariate Cox regression analyses in the TCGA dataset. To further explore the potential mechanism of immune-related genes, we observed associated essential high-risk genes with a cytokine–cytokine receptor interaction. CONCLUSIONS: This study established an immune-related RNA signature, which provided a reliable prognostic tool and may be of great significance for determining immune-related biomarkers in CSCC.
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spelling pubmed-78856012021-02-22 A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma Qin, Rui Cao, Lu Ye, Cong Wang, Junrong Sun, Ziqian BMC Med Genomics Research Article BACKGROUND: In this study, we aimed to mine immune-related RNAs expressed in early cervical squamous cell carcinoma to construct prognostic prediction models. METHODS: The RNA sequencing data of 309 cervical squamous cell carcinoma (CSCC) cases, including data of individuals with available clinical information, were obtained from The Cancer Genome Atlas (TCGA) database. We included 181 early-stage CSCC tumor samples with clinical survival and prognosis information (training dataset). Then, we downloaded the GSE44001 gene expression profile data from the National Center for Biotechnology Information Gene Expression Omnibus (validation dataset). Gene ontology annotation and the Kyoto Encyclopedia of Genes and Genomes pathway analyses were used to analyze the biological functions of differentially expressed immune-related genes (DEIRGs). We established protein–protein interactions and competing endogenous RNA networks using Cytoscape. Using the Kaplan–Meier method, we evaluated the association between the high- and low-risk groups and the actual survival and prognosis information. Our univariate and multivariate Cox regression analyses screened for independent prognostic factors. RESULTS: We identified seven prognosis-related signature genes (RBAKDN, CXCL2, ZAP70, CLEC2D, CD27, KLRB1, VCAM1), the expression of which was markedly associated with overall survival (OS) in CSCC patients. Also, the risk score of the seven-gene signature discripted superior ability to categorize CSCC patients into high-risk and low-risk groups, with a observablydifferent OS in the training and validation datasets. We screened two independent prognostic factors (Pathologic N and prognostic score model status) that correlated significantly by univariate and multivariate Cox regression analyses in the TCGA dataset. To further explore the potential mechanism of immune-related genes, we observed associated essential high-risk genes with a cytokine–cytokine receptor interaction. CONCLUSIONS: This study established an immune-related RNA signature, which provided a reliable prognostic tool and may be of great significance for determining immune-related biomarkers in CSCC. BioMed Central 2021-02-15 /pmc/articles/PMC7885601/ /pubmed/33588862 http://dx.doi.org/10.1186/s12920-021-00885-3 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 Research Article
Qin, Rui
Cao, Lu
Ye, Cong
Wang, Junrong
Sun, Ziqian
A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma
title A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma
title_full A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma
title_fullStr A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma
title_full_unstemmed A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma
title_short A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma
title_sort novel prognostic prediction model based on seven immune-related rnas for predicting overall survival of patients in early cervical squamous cell carcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7885601/
https://www.ncbi.nlm.nih.gov/pubmed/33588862
http://dx.doi.org/10.1186/s12920-021-00885-3
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