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Clinical significance and immune landscape of cuproptosis-related lncRNAs in kidney renal clear cell carcinoma: a bioinformatical analysis

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is considered an immunogenic tumor. Cuproptosis is a newly identified copper-induced regulated cell death that relies on mitochondria respiration. Long noncoding RNAs (lncRNAs) have emerged as significant players in tumorigenesis and metastasis. H...

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Autores principales: Li, Ding, Wu, Xuan, Song, Wenping, Cheng, Cheng, Hao, Lidan, Zhang, Wenzhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9761138/
https://www.ncbi.nlm.nih.gov/pubmed/36544675
http://dx.doi.org/10.21037/atm-22-5204
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author Li, Ding
Wu, Xuan
Song, Wenping
Cheng, Cheng
Hao, Lidan
Zhang, Wenzhou
author_facet Li, Ding
Wu, Xuan
Song, Wenping
Cheng, Cheng
Hao, Lidan
Zhang, Wenzhou
author_sort Li, Ding
collection PubMed
description BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is considered an immunogenic tumor. Cuproptosis is a newly identified copper-induced regulated cell death that relies on mitochondria respiration. Long noncoding RNAs (lncRNAs) have emerged as significant players in tumorigenesis and metastasis. However, there is a huge knowledge gap on the prognostic role of cuproptosis-related lncRNAs in KIRC. And, the clinical value of them is still unknown. Here, we aimed to develop a cuproptosis-related lncRNA prognostic signature in KIRC. METHODS: The messenger RNA (mRNA)/lncRNA expression profiles and the clinical information including age, gender, tumor stage, grade, and overall survival (OS) were acquired from The Cancer Genome Atlas (TCGA) database. The included KIRC samples were further randomly assigned into training (n=258) or testing (n=257) data sets. We performed Pearson correlation analysis to identify the cuproptosis-related lncRNAs and then constructed the prognostic signature using Cox regression analysis and LASSO algorithm. Subsequently, Kaplan-Meier survival analysis, a nomogram, and receiver operating characteristic (ROC) curve were performed to assess the predictive performance of the signature. Moreover, the immune characteristics and drug sensitivity related to the signature were also explored. RESULTS: The signature comprised 7 cuproptosis-related lncRNAs. The patients with a low-risk score had superior OS compared with those with a high-risk score. The survival rates of the high- and low-risk groups were 44.96% and 83.72% (P<0.001). The area under the curve (AUC) value for 1-, 3-, 5-year survival rate reached 0.814, 0.762 and 0.825, respectively. In addition, a nomogram was also generated; the AUC was 0.785 for risk score, higher than that for age (0.593), gender (0.489), grade (0.679), and stage (0.721). The high-risk group had more enriched immune- and tumor-related genes. Patients with low-risk scores were more sensitive to immunotherapy and the small molecular drugs GSK1904529A, tipifarnib, BX-912, FR-180204, and GSK1070916. Meanwhile, the high-risk group tended to be more sensitive to pyrimethamine, MS-275, and CGP-60474. CONCLUSIONS: Collectively, we constructed a cuproptosis-related lncRNA prognostic signature with a higher predictive accuracy compared to multiple clinicopathological parameters, which may provide vital guidance for therapeutic strategies in KIRC. Combination of more prognostic biomarkers may further improve the accuracy.
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spelling pubmed-97611382022-12-20 Clinical significance and immune landscape of cuproptosis-related lncRNAs in kidney renal clear cell carcinoma: a bioinformatical analysis Li, Ding Wu, Xuan Song, Wenping Cheng, Cheng Hao, Lidan Zhang, Wenzhou Ann Transl Med Original Article BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is considered an immunogenic tumor. Cuproptosis is a newly identified copper-induced regulated cell death that relies on mitochondria respiration. Long noncoding RNAs (lncRNAs) have emerged as significant players in tumorigenesis and metastasis. However, there is a huge knowledge gap on the prognostic role of cuproptosis-related lncRNAs in KIRC. And, the clinical value of them is still unknown. Here, we aimed to develop a cuproptosis-related lncRNA prognostic signature in KIRC. METHODS: The messenger RNA (mRNA)/lncRNA expression profiles and the clinical information including age, gender, tumor stage, grade, and overall survival (OS) were acquired from The Cancer Genome Atlas (TCGA) database. The included KIRC samples were further randomly assigned into training (n=258) or testing (n=257) data sets. We performed Pearson correlation analysis to identify the cuproptosis-related lncRNAs and then constructed the prognostic signature using Cox regression analysis and LASSO algorithm. Subsequently, Kaplan-Meier survival analysis, a nomogram, and receiver operating characteristic (ROC) curve were performed to assess the predictive performance of the signature. Moreover, the immune characteristics and drug sensitivity related to the signature were also explored. RESULTS: The signature comprised 7 cuproptosis-related lncRNAs. The patients with a low-risk score had superior OS compared with those with a high-risk score. The survival rates of the high- and low-risk groups were 44.96% and 83.72% (P<0.001). The area under the curve (AUC) value for 1-, 3-, 5-year survival rate reached 0.814, 0.762 and 0.825, respectively. In addition, a nomogram was also generated; the AUC was 0.785 for risk score, higher than that for age (0.593), gender (0.489), grade (0.679), and stage (0.721). The high-risk group had more enriched immune- and tumor-related genes. Patients with low-risk scores were more sensitive to immunotherapy and the small molecular drugs GSK1904529A, tipifarnib, BX-912, FR-180204, and GSK1070916. Meanwhile, the high-risk group tended to be more sensitive to pyrimethamine, MS-275, and CGP-60474. CONCLUSIONS: Collectively, we constructed a cuproptosis-related lncRNA prognostic signature with a higher predictive accuracy compared to multiple clinicopathological parameters, which may provide vital guidance for therapeutic strategies in KIRC. Combination of more prognostic biomarkers may further improve the accuracy. AME Publishing Company 2022-11 /pmc/articles/PMC9761138/ /pubmed/36544675 http://dx.doi.org/10.21037/atm-22-5204 Text en 2022 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Li, Ding
Wu, Xuan
Song, Wenping
Cheng, Cheng
Hao, Lidan
Zhang, Wenzhou
Clinical significance and immune landscape of cuproptosis-related lncRNAs in kidney renal clear cell carcinoma: a bioinformatical analysis
title Clinical significance and immune landscape of cuproptosis-related lncRNAs in kidney renal clear cell carcinoma: a bioinformatical analysis
title_full Clinical significance and immune landscape of cuproptosis-related lncRNAs in kidney renal clear cell carcinoma: a bioinformatical analysis
title_fullStr Clinical significance and immune landscape of cuproptosis-related lncRNAs in kidney renal clear cell carcinoma: a bioinformatical analysis
title_full_unstemmed Clinical significance and immune landscape of cuproptosis-related lncRNAs in kidney renal clear cell carcinoma: a bioinformatical analysis
title_short Clinical significance and immune landscape of cuproptosis-related lncRNAs in kidney renal clear cell carcinoma: a bioinformatical analysis
title_sort clinical significance and immune landscape of cuproptosis-related lncrnas in kidney renal clear cell carcinoma: a bioinformatical analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9761138/
https://www.ncbi.nlm.nih.gov/pubmed/36544675
http://dx.doi.org/10.21037/atm-22-5204
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