Cargando…
A cuproptosis-related lncRNAs risk model to predict prognosis and guide immunotherapy for lung adenocarcinoma
BACKGROUND: Cuproptosis, one of the newest forms of cell death induction, is attracting mounting attention. However, the role of cuproptosis in lung cancer is currently unclear. In this study, we constructed a prognostic signature utilizing cuproptosis-related long noncoding RNAs (CRL) in lung adeno...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10061461/ https://www.ncbi.nlm.nih.gov/pubmed/37007546 http://dx.doi.org/10.21037/atm-22-3195 |
_version_ | 1785017297347280896 |
---|---|
author | Li, Qixuan Wang, Tianyi Zhu, Jiaqi Zhang, Anping Wu, Anqi Zhou, Youlang Shi, Jiahai |
author_facet | Li, Qixuan Wang, Tianyi Zhu, Jiaqi Zhang, Anping Wu, Anqi Zhou, Youlang Shi, Jiahai |
author_sort | Li, Qixuan |
collection | PubMed |
description | BACKGROUND: Cuproptosis, one of the newest forms of cell death induction, is attracting mounting attention. However, the role of cuproptosis in lung cancer is currently unclear. In this study, we constructed a prognostic signature utilizing cuproptosis-related long noncoding RNAs (CRL) in lung adenocarcinoma (LUAD) and researched its clinical and molecular function. METHODS: RNA-related and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed CRLs were screened using the ‘limma’ package of R software. We used coexpression analysis and univariate Cox analysis to further identify prognostic CRLs. Applying least absolute shrinkage and selection operator (LASSO) regression and Cox regression models, a prognostic risk model based on 16 prognostic CRLs was constructed. To validate prognostic CRL function in LUAD, vitro experiments were conducted to explore the expression of GLIS2-AS1, LINC01230, and LINC00592 in LUAD. Subsequently, according to a formula, patients in the training, test, and overall groups were split into high- and low-risk groups. Kaplan-Meier and receiver operating characteristic (ROC) analyses were applied to assess the predictability of the risk model. Finally, the associations between risk signature and immunity-related analysis, somatic mutation, principal component analysis (PCA), enriched molecular pathways, and drug sensitivity was investigated. RESULTS: A cuproptosis-related long noncoding RNAs (lncRNAs) signature was constructed. Using quantitative polymerase chain reaction (qPCR) trial, we verified that the expressions of GLIS2-AS1, LINC01230, and LINC00592 in LUAD cell lines and tissues were consistent with the above screening results. Based on this signature, a total of 471 LUAD samples from TCGA data set were split into two risk groups based on the computed risk score. The risk model showed a better capacity in predicting prognosis than traditional clinicopathological features. Moreover, significant differences were found in immune cell infiltration, drug sensitivity, and immune checkpoint expression between the two risk groups. CONCLUSIONS: The CRLs signature was shown to be a prospective biomarker to forecast prognosis in patients with LUAD and presents new insights for personalized treatment of LUAD. |
format | Online Article Text |
id | pubmed-10061461 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-100614612023-03-31 A cuproptosis-related lncRNAs risk model to predict prognosis and guide immunotherapy for lung adenocarcinoma Li, Qixuan Wang, Tianyi Zhu, Jiaqi Zhang, Anping Wu, Anqi Zhou, Youlang Shi, Jiahai Ann Transl Med Original Article BACKGROUND: Cuproptosis, one of the newest forms of cell death induction, is attracting mounting attention. However, the role of cuproptosis in lung cancer is currently unclear. In this study, we constructed a prognostic signature utilizing cuproptosis-related long noncoding RNAs (CRL) in lung adenocarcinoma (LUAD) and researched its clinical and molecular function. METHODS: RNA-related and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed CRLs were screened using the ‘limma’ package of R software. We used coexpression analysis and univariate Cox analysis to further identify prognostic CRLs. Applying least absolute shrinkage and selection operator (LASSO) regression and Cox regression models, a prognostic risk model based on 16 prognostic CRLs was constructed. To validate prognostic CRL function in LUAD, vitro experiments were conducted to explore the expression of GLIS2-AS1, LINC01230, and LINC00592 in LUAD. Subsequently, according to a formula, patients in the training, test, and overall groups were split into high- and low-risk groups. Kaplan-Meier and receiver operating characteristic (ROC) analyses were applied to assess the predictability of the risk model. Finally, the associations between risk signature and immunity-related analysis, somatic mutation, principal component analysis (PCA), enriched molecular pathways, and drug sensitivity was investigated. RESULTS: A cuproptosis-related long noncoding RNAs (lncRNAs) signature was constructed. Using quantitative polymerase chain reaction (qPCR) trial, we verified that the expressions of GLIS2-AS1, LINC01230, and LINC00592 in LUAD cell lines and tissues were consistent with the above screening results. Based on this signature, a total of 471 LUAD samples from TCGA data set were split into two risk groups based on the computed risk score. The risk model showed a better capacity in predicting prognosis than traditional clinicopathological features. Moreover, significant differences were found in immune cell infiltration, drug sensitivity, and immune checkpoint expression between the two risk groups. CONCLUSIONS: The CRLs signature was shown to be a prospective biomarker to forecast prognosis in patients with LUAD and presents new insights for personalized treatment of LUAD. AME Publishing Company 2023-03-07 2023-03-15 /pmc/articles/PMC10061461/ /pubmed/37007546 http://dx.doi.org/10.21037/atm-22-3195 Text en 2023 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, Qixuan Wang, Tianyi Zhu, Jiaqi Zhang, Anping Wu, Anqi Zhou, Youlang Shi, Jiahai A cuproptosis-related lncRNAs risk model to predict prognosis and guide immunotherapy for lung adenocarcinoma |
title | A cuproptosis-related lncRNAs risk model to predict prognosis and guide immunotherapy for lung adenocarcinoma |
title_full | A cuproptosis-related lncRNAs risk model to predict prognosis and guide immunotherapy for lung adenocarcinoma |
title_fullStr | A cuproptosis-related lncRNAs risk model to predict prognosis and guide immunotherapy for lung adenocarcinoma |
title_full_unstemmed | A cuproptosis-related lncRNAs risk model to predict prognosis and guide immunotherapy for lung adenocarcinoma |
title_short | A cuproptosis-related lncRNAs risk model to predict prognosis and guide immunotherapy for lung adenocarcinoma |
title_sort | cuproptosis-related lncrnas risk model to predict prognosis and guide immunotherapy for lung adenocarcinoma |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10061461/ https://www.ncbi.nlm.nih.gov/pubmed/37007546 http://dx.doi.org/10.21037/atm-22-3195 |
work_keys_str_mv | AT liqixuan acuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT wangtianyi acuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT zhujiaqi acuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT zhanganping acuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT wuanqi acuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT zhouyoulang acuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT shijiahai acuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT liqixuan cuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT wangtianyi cuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT zhujiaqi cuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT zhanganping cuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT wuanqi cuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT zhouyoulang cuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma AT shijiahai cuproptosisrelatedlncrnasriskmodeltopredictprognosisandguideimmunotherapyforlungadenocarcinoma |