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Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer

Purpose: This study aimed to construct a novel signature to predict the survival of patients with colon cancer and the associated immune landscape, based on immune-related long noncoding ribonucleic acids (irlncRNAs). Methods: Expression profiles of irlncRNAs in 457 patients with colon cancer were r...

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Autores principales: Wang, Xu, Chen, Ke, Wang, Zhenglin, Xu, Yuanmin, Dai, Longfei, Bai, Tao, Chen, Bo, Yang, Wenqi, Chen, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514752/
https://www.ncbi.nlm.nih.gov/pubmed/34660608
http://dx.doi.org/10.3389/fcell.2021.750709
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author Wang, Xu
Chen, Ke
Wang, Zhenglin
Xu, Yuanmin
Dai, Longfei
Bai, Tao
Chen, Bo
Yang, Wenqi
Chen, Wei
author_facet Wang, Xu
Chen, Ke
Wang, Zhenglin
Xu, Yuanmin
Dai, Longfei
Bai, Tao
Chen, Bo
Yang, Wenqi
Chen, Wei
author_sort Wang, Xu
collection PubMed
description Purpose: This study aimed to construct a novel signature to predict the survival of patients with colon cancer and the associated immune landscape, based on immune-related long noncoding ribonucleic acids (irlncRNAs). Methods: Expression profiles of irlncRNAs in 457 patients with colon cancer were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed (DE) irlncRNAs were identified and irlncRNA pairs were recognized using Lasso regression and Cox regression analyses. Akaike information criterion (AIC) values of receiver operating characteristic (ROC) curve were calculated to identify the ideal cut-off point for dividing patients into two groups and constructing the prognosis signature. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the expression of LINC02195 and SCARNA9 in colon cancer. Results: We identified 22 irlncRNA pairs and patients were divided into high-risk and low-risk groups based on the calculated risk score using these 22 irlncRNA pairs. The irlncRNA pairs were significantly related to patient survival. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). The area under the curve of the signature to predict 5-year survival was 0.951. The risk score correlated with tumor stage, infiltration depth, lymph node metastasis, and distant metastasis. The risk score remained significant after univariate and multivariate Cox regression analyses. A nomogram model to predict patient survival was developed based on the results of Cox regression analysis. Immune cell infiltration status, expression of some immune checkpoint genes, and sensitivity to chemotherapeutics were also related to the risk score. The results of qRT-PCR revealed that LINC02195 and SCARNA9 were significantly upregulated in colon cancer tissues. Conclusion: The constructed prognosis signature showed remarkable efficiency in predicting patient survival, immune cell infiltration status, expression of immune checkpoint genes, and sensitivity to chemotherapeutics.
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spelling pubmed-85147522021-10-15 Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer Wang, Xu Chen, Ke Wang, Zhenglin Xu, Yuanmin Dai, Longfei Bai, Tao Chen, Bo Yang, Wenqi Chen, Wei Front Cell Dev Biol Cell and Developmental Biology Purpose: This study aimed to construct a novel signature to predict the survival of patients with colon cancer and the associated immune landscape, based on immune-related long noncoding ribonucleic acids (irlncRNAs). Methods: Expression profiles of irlncRNAs in 457 patients with colon cancer were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed (DE) irlncRNAs were identified and irlncRNA pairs were recognized using Lasso regression and Cox regression analyses. Akaike information criterion (AIC) values of receiver operating characteristic (ROC) curve were calculated to identify the ideal cut-off point for dividing patients into two groups and constructing the prognosis signature. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the expression of LINC02195 and SCARNA9 in colon cancer. Results: We identified 22 irlncRNA pairs and patients were divided into high-risk and low-risk groups based on the calculated risk score using these 22 irlncRNA pairs. The irlncRNA pairs were significantly related to patient survival. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). The area under the curve of the signature to predict 5-year survival was 0.951. The risk score correlated with tumor stage, infiltration depth, lymph node metastasis, and distant metastasis. The risk score remained significant after univariate and multivariate Cox regression analyses. A nomogram model to predict patient survival was developed based on the results of Cox regression analysis. Immune cell infiltration status, expression of some immune checkpoint genes, and sensitivity to chemotherapeutics were also related to the risk score. The results of qRT-PCR revealed that LINC02195 and SCARNA9 were significantly upregulated in colon cancer tissues. Conclusion: The constructed prognosis signature showed remarkable efficiency in predicting patient survival, immune cell infiltration status, expression of immune checkpoint genes, and sensitivity to chemotherapeutics. Frontiers Media S.A. 2021-09-30 /pmc/articles/PMC8514752/ /pubmed/34660608 http://dx.doi.org/10.3389/fcell.2021.750709 Text en Copyright © 2021 Wang, Chen, Wang, Xu, Dai, Bai, Chen, Yang and Chen. 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 Cell and Developmental Biology
Wang, Xu
Chen, Ke
Wang, Zhenglin
Xu, Yuanmin
Dai, Longfei
Bai, Tao
Chen, Bo
Yang, Wenqi
Chen, Wei
Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer
title Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer
title_full Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer
title_fullStr Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer
title_full_unstemmed Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer
title_short Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer
title_sort using immune-related long non-coding ribonucleic acids to develop a novel prognosis signature and predict the immune landscape of colon cancer
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514752/
https://www.ncbi.nlm.nih.gov/pubmed/34660608
http://dx.doi.org/10.3389/fcell.2021.750709
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