Cargando…

Identification of a Six-Immune-Related Long Non-coding RNA Signature for Predicting Survival and Immune Infiltrating Status in Breast Cancer

Long non-coding RNAs (lncRNAs) play critical roles in tumor immunity; however, the functional roles of immune-related lncRNAs in breast cancer (BC) remain elusive. To further explore the immune−related lncRNAs in BC, whole genomic expression data and corresponding clinical information were obtained...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Zheng, Li, Yaming, Wang, Xiaolong, Yang, Qifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7358358/
https://www.ncbi.nlm.nih.gov/pubmed/32733537
http://dx.doi.org/10.3389/fgene.2020.00680
_version_ 1783558836452327424
author Li, Zheng
Li, Yaming
Wang, Xiaolong
Yang, Qifeng
author_facet Li, Zheng
Li, Yaming
Wang, Xiaolong
Yang, Qifeng
author_sort Li, Zheng
collection PubMed
description Long non-coding RNAs (lncRNAs) play critical roles in tumor immunity; however, the functional roles of immune-related lncRNAs in breast cancer (BC) remain elusive. To further explore the immune−related lncRNAs in BC, whole genomic expression data and corresponding clinical information were obtained from multiple BC datasets. Based on correlation with the immune-related genes within the training set, we screened out the most promising immune-related lncRNAs. Subsequently, Lasso penalized Cox regression analysis followed by stepwise multivariate Cox regression analysis identified six survival-related lncRNAs (AC116366.1, AC244502.1, AC100810.1, MIAT, AC093297.2, and AL356417.2) and constructed a prognostic signature. The cohorts in the high−risk group had significantly poor survival time compared to those in the low−risk group. In addition, a nomogram integrated with clinical features and the prognostic signature was developed on the basis of the training set. Importantly, all the findings had a similar performance in three validated datasets. In the following studies, our integrative analyses indicated that the infiltration of CD8-positive (CD8) T cells associated with a good prognosis was strikingly activated in the low−risk group. To further provide an interpretation of biological mechanisms for the prognostic signature, we performed weighted gene co−expression network analysis (WGCNA) followed by KEGG pathway-enrichment analysis. Our results showed that the antigen presentation pathway involved in protein processing in endoplasmic reticulum and antigen processing and presentation was markedly altered in the high-risk group, which might promote tumor immune evasion and associate with poor clinical outcomes in BC patients with high risk scores. In conclusion, we aimed to take advantage of bioinformatics analyses to explore immune−related lncRNAs, which could function as prognostic indicators and promising therapeutic targets for BC patients.
format Online
Article
Text
id pubmed-7358358
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-73583582020-07-29 Identification of a Six-Immune-Related Long Non-coding RNA Signature for Predicting Survival and Immune Infiltrating Status in Breast Cancer Li, Zheng Li, Yaming Wang, Xiaolong Yang, Qifeng Front Genet Genetics Long non-coding RNAs (lncRNAs) play critical roles in tumor immunity; however, the functional roles of immune-related lncRNAs in breast cancer (BC) remain elusive. To further explore the immune−related lncRNAs in BC, whole genomic expression data and corresponding clinical information were obtained from multiple BC datasets. Based on correlation with the immune-related genes within the training set, we screened out the most promising immune-related lncRNAs. Subsequently, Lasso penalized Cox regression analysis followed by stepwise multivariate Cox regression analysis identified six survival-related lncRNAs (AC116366.1, AC244502.1, AC100810.1, MIAT, AC093297.2, and AL356417.2) and constructed a prognostic signature. The cohorts in the high−risk group had significantly poor survival time compared to those in the low−risk group. In addition, a nomogram integrated with clinical features and the prognostic signature was developed on the basis of the training set. Importantly, all the findings had a similar performance in three validated datasets. In the following studies, our integrative analyses indicated that the infiltration of CD8-positive (CD8) T cells associated with a good prognosis was strikingly activated in the low−risk group. To further provide an interpretation of biological mechanisms for the prognostic signature, we performed weighted gene co−expression network analysis (WGCNA) followed by KEGG pathway-enrichment analysis. Our results showed that the antigen presentation pathway involved in protein processing in endoplasmic reticulum and antigen processing and presentation was markedly altered in the high-risk group, which might promote tumor immune evasion and associate with poor clinical outcomes in BC patients with high risk scores. In conclusion, we aimed to take advantage of bioinformatics analyses to explore immune−related lncRNAs, which could function as prognostic indicators and promising therapeutic targets for BC patients. Frontiers Media S.A. 2020-07-07 /pmc/articles/PMC7358358/ /pubmed/32733537 http://dx.doi.org/10.3389/fgene.2020.00680 Text en Copyright © 2020 Li, Li, Wang and Yang. http://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 Genetics
Li, Zheng
Li, Yaming
Wang, Xiaolong
Yang, Qifeng
Identification of a Six-Immune-Related Long Non-coding RNA Signature for Predicting Survival and Immune Infiltrating Status in Breast Cancer
title Identification of a Six-Immune-Related Long Non-coding RNA Signature for Predicting Survival and Immune Infiltrating Status in Breast Cancer
title_full Identification of a Six-Immune-Related Long Non-coding RNA Signature for Predicting Survival and Immune Infiltrating Status in Breast Cancer
title_fullStr Identification of a Six-Immune-Related Long Non-coding RNA Signature for Predicting Survival and Immune Infiltrating Status in Breast Cancer
title_full_unstemmed Identification of a Six-Immune-Related Long Non-coding RNA Signature for Predicting Survival and Immune Infiltrating Status in Breast Cancer
title_short Identification of a Six-Immune-Related Long Non-coding RNA Signature for Predicting Survival and Immune Infiltrating Status in Breast Cancer
title_sort identification of a six-immune-related long non-coding rna signature for predicting survival and immune infiltrating status in breast cancer
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7358358/
https://www.ncbi.nlm.nih.gov/pubmed/32733537
http://dx.doi.org/10.3389/fgene.2020.00680
work_keys_str_mv AT lizheng identificationofasiximmunerelatedlongnoncodingrnasignatureforpredictingsurvivalandimmuneinfiltratingstatusinbreastcancer
AT liyaming identificationofasiximmunerelatedlongnoncodingrnasignatureforpredictingsurvivalandimmuneinfiltratingstatusinbreastcancer
AT wangxiaolong identificationofasiximmunerelatedlongnoncodingrnasignatureforpredictingsurvivalandimmuneinfiltratingstatusinbreastcancer
AT yangqifeng identificationofasiximmunerelatedlongnoncodingrnasignatureforpredictingsurvivalandimmuneinfiltratingstatusinbreastcancer