Cargando…

Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

BACKGROUND: Renal cell carcinoma (RCC) is one of the most common aggressive malignant tumors in urogenital system, and the clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma. Immune related long non-coding RNAs (IRlncRs) plentiful in immune cells and immune microen...

Descripción completa

Detalles Bibliográficos
Autores principales: Jiang, Yuanbin, Gou, Xin, Wei, Zongjie, Tan, Jianyu, Yu, Haitao, Zhou, Xiang, Li, Xinyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222502/
https://www.ncbi.nlm.nih.gov/pubmed/32435157
http://dx.doi.org/10.1186/s12935-020-01242-7
_version_ 1783533589334327296
author Jiang, Yuanbin
Gou, Xin
Wei, Zongjie
Tan, Jianyu
Yu, Haitao
Zhou, Xiang
Li, Xinyuan
author_facet Jiang, Yuanbin
Gou, Xin
Wei, Zongjie
Tan, Jianyu
Yu, Haitao
Zhou, Xiang
Li, Xinyuan
author_sort Jiang, Yuanbin
collection PubMed
description BACKGROUND: Renal cell carcinoma (RCC) is one of the most common aggressive malignant tumors in urogenital system, and the clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma. Immune related long non-coding RNAs (IRlncRs) plentiful in immune cells and immune microenvironment (IME) are potential in evaluating prognosis and assessing the effects of immunotherapy. A completed and meaningful IRlncRs analysis based on abundant ccRCC gene samples from The Cancer Genome Atlas (TCGA) will provide insight in this field. METHODS: Based on the TCGA dataset, we integrated the expression profiles of IRlncRs and overall survival (OS) in the 611 ccRCC patients. The immune score of each sample was calculated based on the expression level of immune-related genes and used to identify the most meaningful IRlncRs. Survival-related IRlncRs (sIRlncRs) was estimated by calculating the algorithm of difference and COX regression analysis in ccRCC patients. Based on the median immune-related risk score (IRRS) developed from the screened sIRlncRs, the high-risk and low-risk components were distinguished. Functional annotation was detected by gene set enrichment analysis (GSEA) and principal component analysis (PCA), and the immune composition and purity of the tumor was evaluated by microenvironment cell population records. The expression levels of three sIRlncRs were verified in various tissues and cell lines. RESULTS: A total of 39 IRlncRs were collected by Pearson correlation analyses among immune score and the lncRNA expression. A total of 7 sIRlncRs were significantly associated with the clinical outcomes of ccRCC patients. Three sIRlncRs (ATP1A1-AS1, IL10RB-DT and MELTF-AS1) with the most significant prognostic values were enrolled to build the IRRS model in which the OS of in the high-risk group was shorter than that in the low-risk group. The IRRS was identified as an independent prognosis factor and correlated with the OS. The high-risk group and low-risk group illustrated different distributions in PCA and different immune status in GSEA. Besides, we found the more significant expression in certain ccRCC cell lines and tumor tissues of ccRCC patients compared with the HK-2 and adjacent tissues respectively. Additionally, the expression levels of lncR-MELTF-AS1 and IL10RB-DT were remarkably enhanced along the more advanced T-stages, but the lncR-ATP1A1-AS1 showed the inverse gradient. CONCLUSION: Our results demonstrate some sIRlncRs with remark clinical relevance show the latent monitoring and prognosis values for ccRCC patients and may provide new insight in immunological researches and treatment strategies of ccRCC patients.
format Online
Article
Text
id pubmed-7222502
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-72225022020-05-20 Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma Jiang, Yuanbin Gou, Xin Wei, Zongjie Tan, Jianyu Yu, Haitao Zhou, Xiang Li, Xinyuan Cancer Cell Int Primary Research BACKGROUND: Renal cell carcinoma (RCC) is one of the most common aggressive malignant tumors in urogenital system, and the clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma. Immune related long non-coding RNAs (IRlncRs) plentiful in immune cells and immune microenvironment (IME) are potential in evaluating prognosis and assessing the effects of immunotherapy. A completed and meaningful IRlncRs analysis based on abundant ccRCC gene samples from The Cancer Genome Atlas (TCGA) will provide insight in this field. METHODS: Based on the TCGA dataset, we integrated the expression profiles of IRlncRs and overall survival (OS) in the 611 ccRCC patients. The immune score of each sample was calculated based on the expression level of immune-related genes and used to identify the most meaningful IRlncRs. Survival-related IRlncRs (sIRlncRs) was estimated by calculating the algorithm of difference and COX regression analysis in ccRCC patients. Based on the median immune-related risk score (IRRS) developed from the screened sIRlncRs, the high-risk and low-risk components were distinguished. Functional annotation was detected by gene set enrichment analysis (GSEA) and principal component analysis (PCA), and the immune composition and purity of the tumor was evaluated by microenvironment cell population records. The expression levels of three sIRlncRs were verified in various tissues and cell lines. RESULTS: A total of 39 IRlncRs were collected by Pearson correlation analyses among immune score and the lncRNA expression. A total of 7 sIRlncRs were significantly associated with the clinical outcomes of ccRCC patients. Three sIRlncRs (ATP1A1-AS1, IL10RB-DT and MELTF-AS1) with the most significant prognostic values were enrolled to build the IRRS model in which the OS of in the high-risk group was shorter than that in the low-risk group. The IRRS was identified as an independent prognosis factor and correlated with the OS. The high-risk group and low-risk group illustrated different distributions in PCA and different immune status in GSEA. Besides, we found the more significant expression in certain ccRCC cell lines and tumor tissues of ccRCC patients compared with the HK-2 and adjacent tissues respectively. Additionally, the expression levels of lncR-MELTF-AS1 and IL10RB-DT were remarkably enhanced along the more advanced T-stages, but the lncR-ATP1A1-AS1 showed the inverse gradient. CONCLUSION: Our results demonstrate some sIRlncRs with remark clinical relevance show the latent monitoring and prognosis values for ccRCC patients and may provide new insight in immunological researches and treatment strategies of ccRCC patients. BioMed Central 2020-05-13 /pmc/articles/PMC7222502/ /pubmed/32435157 http://dx.doi.org/10.1186/s12935-020-01242-7 Text en © The Author(s) 2020 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 Primary Research
Jiang, Yuanbin
Gou, Xin
Wei, Zongjie
Tan, Jianyu
Yu, Haitao
Zhou, Xiang
Li, Xinyuan
Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma
title Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma
title_full Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma
title_fullStr Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma
title_full_unstemmed Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma
title_short Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma
title_sort bioinformatics profiling integrating a three immune-related long non-coding rna signature as a prognostic model for clear cell renal cell carcinoma
topic Primary Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222502/
https://www.ncbi.nlm.nih.gov/pubmed/32435157
http://dx.doi.org/10.1186/s12935-020-01242-7
work_keys_str_mv AT jiangyuanbin bioinformaticsprofilingintegratingathreeimmunerelatedlongnoncodingrnasignatureasaprognosticmodelforclearcellrenalcellcarcinoma
AT gouxin bioinformaticsprofilingintegratingathreeimmunerelatedlongnoncodingrnasignatureasaprognosticmodelforclearcellrenalcellcarcinoma
AT weizongjie bioinformaticsprofilingintegratingathreeimmunerelatedlongnoncodingrnasignatureasaprognosticmodelforclearcellrenalcellcarcinoma
AT tanjianyu bioinformaticsprofilingintegratingathreeimmunerelatedlongnoncodingrnasignatureasaprognosticmodelforclearcellrenalcellcarcinoma
AT yuhaitao bioinformaticsprofilingintegratingathreeimmunerelatedlongnoncodingrnasignatureasaprognosticmodelforclearcellrenalcellcarcinoma
AT zhouxiang bioinformaticsprofilingintegratingathreeimmunerelatedlongnoncodingrnasignatureasaprognosticmodelforclearcellrenalcellcarcinoma
AT lixinyuan bioinformaticsprofilingintegratingathreeimmunerelatedlongnoncodingrnasignatureasaprognosticmodelforclearcellrenalcellcarcinoma