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Computational construction of TME‐related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma

BACKGROUND: The tumor microenvironment (TME) is closely related to clear cell renal cell carcinoma (ccRCC) prognosis, and immunotherapy response. In current study, comprehensive bio‐informative analysis was adopted to construct a TME‐related lncRNA signature for immune checkpoint inhibitors (ICIs) a...

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Autores principales: Zhou, Libin, Fang, Hualong, Guo, Fei, Yin, Min, Long, Huimin, Weng, Guobin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9396193/
https://www.ncbi.nlm.nih.gov/pubmed/35808868
http://dx.doi.org/10.1002/jcla.24582
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author Zhou, Libin
Fang, Hualong
Guo, Fei
Yin, Min
Long, Huimin
Weng, Guobin
author_facet Zhou, Libin
Fang, Hualong
Guo, Fei
Yin, Min
Long, Huimin
Weng, Guobin
author_sort Zhou, Libin
collection PubMed
description BACKGROUND: The tumor microenvironment (TME) is closely related to clear cell renal cell carcinoma (ccRCC) prognosis, and immunotherapy response. In current study, comprehensive bio‐informative analysis was adopted to construct a TME‐related lncRNA signature for immune checkpoint inhibitors (ICIs) and targeted drug responses in ccRCC patients. METHODS: The TME mRNAs were screened following the immune and stromal scores with the data from GSE15641, GSE29609, GSE36895, GSE46699, GSE53757, and The Cancer Genome Atlas (TCGA)‐kidney renal clear cell carcinoma (KIRC). And the TME‐related lncRNAs were recognized using correlation analysis. The TME‐related lncRNAs prognostic model was constructed using the training dataset. Kaplan–Meier analysis, principal‐component analysis, and time‐dependent receiver operating characteristic were used to evaluate the risk model. The immune cell infiltration in TME was evaluated using the single‐sample gene set enrichment analysis (ssGSEA), ESTIMATE, and microenvironment cell populations counter algorithm. The immunophenoscore (IPS) was used to assess the response to immunotherapy with the constructed model. RESULTS: In the current study, 364 TME‐related lncRNAs were selected based on the integrated bioinformatical analysis. Six TME‐related lncRNAs (LINC00460, LINC01094, AC008870.2, AC068792.1, and AC007637.1) were identified as the prognostic signature in the training dataset and subsequently verified in the testing and entire datasets. Patients in the high‐risk group exhibited poor overall survival and disease‐free survival than those in the low‐risk group. The 1‐, 3‐, and 5‐year areas under the curves of the prognostic signature in the entire dataset were 0.704, 0.683, and 0.750, respectively. The risk score independently predicted ccRCC survival based on univariate and multivariate Cox regression. GSEA analysis suggested that the high‐risk group was concentrated on immune‐related pathways. The high‐risk group were characterized by high immune cell infiltration, high TMB and somatic mutation counters, high IPS‐PD‐1 + CTLA4 scores, and immune checkpoints expression upregulation, reflecting the higher ICIs response. The half inhibitory concentrations of sunitinib, temsirolimus, and rapamycin were low in the high‐risk group. CONCLUSION: The TME‐related lncRNAs signature constructed could reliably predict the prognosis and immunotherapy response and targeted ccRCC patients' therapy.
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spelling pubmed-93961932022-08-24 Computational construction of TME‐related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma Zhou, Libin Fang, Hualong Guo, Fei Yin, Min Long, Huimin Weng, Guobin J Clin Lab Anal Research Articles BACKGROUND: The tumor microenvironment (TME) is closely related to clear cell renal cell carcinoma (ccRCC) prognosis, and immunotherapy response. In current study, comprehensive bio‐informative analysis was adopted to construct a TME‐related lncRNA signature for immune checkpoint inhibitors (ICIs) and targeted drug responses in ccRCC patients. METHODS: The TME mRNAs were screened following the immune and stromal scores with the data from GSE15641, GSE29609, GSE36895, GSE46699, GSE53757, and The Cancer Genome Atlas (TCGA)‐kidney renal clear cell carcinoma (KIRC). And the TME‐related lncRNAs were recognized using correlation analysis. The TME‐related lncRNAs prognostic model was constructed using the training dataset. Kaplan–Meier analysis, principal‐component analysis, and time‐dependent receiver operating characteristic were used to evaluate the risk model. The immune cell infiltration in TME was evaluated using the single‐sample gene set enrichment analysis (ssGSEA), ESTIMATE, and microenvironment cell populations counter algorithm. The immunophenoscore (IPS) was used to assess the response to immunotherapy with the constructed model. RESULTS: In the current study, 364 TME‐related lncRNAs were selected based on the integrated bioinformatical analysis. Six TME‐related lncRNAs (LINC00460, LINC01094, AC008870.2, AC068792.1, and AC007637.1) were identified as the prognostic signature in the training dataset and subsequently verified in the testing and entire datasets. Patients in the high‐risk group exhibited poor overall survival and disease‐free survival than those in the low‐risk group. The 1‐, 3‐, and 5‐year areas under the curves of the prognostic signature in the entire dataset were 0.704, 0.683, and 0.750, respectively. The risk score independently predicted ccRCC survival based on univariate and multivariate Cox regression. GSEA analysis suggested that the high‐risk group was concentrated on immune‐related pathways. The high‐risk group were characterized by high immune cell infiltration, high TMB and somatic mutation counters, high IPS‐PD‐1 + CTLA4 scores, and immune checkpoints expression upregulation, reflecting the higher ICIs response. The half inhibitory concentrations of sunitinib, temsirolimus, and rapamycin were low in the high‐risk group. CONCLUSION: The TME‐related lncRNAs signature constructed could reliably predict the prognosis and immunotherapy response and targeted ccRCC patients' therapy. John Wiley and Sons Inc. 2022-07-08 /pmc/articles/PMC9396193/ /pubmed/35808868 http://dx.doi.org/10.1002/jcla.24582 Text en © 2022 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Zhou, Libin
Fang, Hualong
Guo, Fei
Yin, Min
Long, Huimin
Weng, Guobin
Computational construction of TME‐related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma
title Computational construction of TME‐related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma
title_full Computational construction of TME‐related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma
title_fullStr Computational construction of TME‐related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma
title_full_unstemmed Computational construction of TME‐related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma
title_short Computational construction of TME‐related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma
title_sort computational construction of tme‐related lncrnas signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9396193/
https://www.ncbi.nlm.nih.gov/pubmed/35808868
http://dx.doi.org/10.1002/jcla.24582
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