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A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer
Objectives: The study is performed to analyze the relationship between immune-related long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We constructed a prognostic model and explored the immune characteristics of different risk groups. Methods: We downloaded the gene expr...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734341/ https://www.ncbi.nlm.nih.gov/pubmed/33328990 http://dx.doi.org/10.3389/fphar.2020.585255 |
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author | Chen, Peijie Gao, Yuting Ouyang, Si Wei, Li Zhou, Min You, Hua Wang, Yao |
author_facet | Chen, Peijie Gao, Yuting Ouyang, Si Wei, Li Zhou, Min You, Hua Wang, Yao |
author_sort | Chen, Peijie |
collection | PubMed |
description | Objectives: The study is performed to analyze the relationship between immune-related long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We constructed a prognostic model and explored the immune characteristics of different risk groups. Methods: We downloaded the gene expression profiles and clinical data of 227 patients from The Cancer Genome Atlas database and extracted immune-related lncRNAs. Cox regression analysis was used to pick out the predictive lncRNAs. The risk score of each patient was calculated based on the expression level of lncRNAs and regression coefficient (β), and a prognostic model was constructed. The overall survival (OS) of different risk groups was analyzed and compared by the Kaplan–Meier method. To analyze the distribution of immune-related genes in each group, principal component analysis and Gene set enrichment analysis were carried out. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data was performed to explore the immune microenvironment. Results: Patients were divided into training set and validation set. Five immune-related lncRNAs (H1FX-AS1, AL441992.1, USP30-AS1, AP001527.2, and AL031123.2) were selected for the construction of the prognostic model. Patients in the training set were divided into high-risk group with shorter OS and low-risk group with longer OS (p = 0.004); meanwhile, similar result were found in validation set (p = 0.013), combination set (p < 0.001) and patients with different tumor stages. This model was further confirmed in 56 cervical cancer tissues by Q-PCR. The distribution of immune-related genes was significantly different in each group. In addition, the immune score and the programmed death-ligand 1 expression of the low-risk group was higher. Conclusions: The prognostic model based on immune-related lncRNAs could predict the prognosis and immune status of cervical cancer patients which is conducive to clinical prognosis judgment and individual treatment. |
format | Online Article Text |
id | pubmed-7734341 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77343412020-12-15 A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer Chen, Peijie Gao, Yuting Ouyang, Si Wei, Li Zhou, Min You, Hua Wang, Yao Front Pharmacol Pharmacology Objectives: The study is performed to analyze the relationship between immune-related long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We constructed a prognostic model and explored the immune characteristics of different risk groups. Methods: We downloaded the gene expression profiles and clinical data of 227 patients from The Cancer Genome Atlas database and extracted immune-related lncRNAs. Cox regression analysis was used to pick out the predictive lncRNAs. The risk score of each patient was calculated based on the expression level of lncRNAs and regression coefficient (β), and a prognostic model was constructed. The overall survival (OS) of different risk groups was analyzed and compared by the Kaplan–Meier method. To analyze the distribution of immune-related genes in each group, principal component analysis and Gene set enrichment analysis were carried out. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data was performed to explore the immune microenvironment. Results: Patients were divided into training set and validation set. Five immune-related lncRNAs (H1FX-AS1, AL441992.1, USP30-AS1, AP001527.2, and AL031123.2) were selected for the construction of the prognostic model. Patients in the training set were divided into high-risk group with shorter OS and low-risk group with longer OS (p = 0.004); meanwhile, similar result were found in validation set (p = 0.013), combination set (p < 0.001) and patients with different tumor stages. This model was further confirmed in 56 cervical cancer tissues by Q-PCR. The distribution of immune-related genes was significantly different in each group. In addition, the immune score and the programmed death-ligand 1 expression of the low-risk group was higher. Conclusions: The prognostic model based on immune-related lncRNAs could predict the prognosis and immune status of cervical cancer patients which is conducive to clinical prognosis judgment and individual treatment. Frontiers Media S.A. 2020-11-30 /pmc/articles/PMC7734341/ /pubmed/33328990 http://dx.doi.org/10.3389/fphar.2020.585255 Text en Copyright © 2020 Wang, Chen, Gao, Ouyang, Wei, Zhou and You. https://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 | Pharmacology Chen, Peijie Gao, Yuting Ouyang, Si Wei, Li Zhou, Min You, Hua Wang, Yao A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer |
title | A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer |
title_full | A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer |
title_fullStr | A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer |
title_full_unstemmed | A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer |
title_short | A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer |
title_sort | prognostic model based on immune-related long non-coding rnas for patients with cervical cancer |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734341/ https://www.ncbi.nlm.nih.gov/pubmed/33328990 http://dx.doi.org/10.3389/fphar.2020.585255 |
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