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Risk coefficient model of necroptosis-related lncRNA in predicting the prognosis of patients with lung adenocarcinoma
Model algorithms were used in constructing the risk coefficient model of necroptosis-related long non-coding RNA in identifying novel potential biomarkers in the prediction of the sensitivity to chemotherapeutic agents and prognosis of patients with lung adenocarcinoma (LUAD). Clinic and transcripto...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243036/ https://www.ncbi.nlm.nih.gov/pubmed/35768485 http://dx.doi.org/10.1038/s41598-022-15189-4 |
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author | Chen, HuiWei Xie, Zhimin Li, QingZhu Qu, GenYi Tan, NianXi Zhang, YuLong |
author_facet | Chen, HuiWei Xie, Zhimin Li, QingZhu Qu, GenYi Tan, NianXi Zhang, YuLong |
author_sort | Chen, HuiWei |
collection | PubMed |
description | Model algorithms were used in constructing the risk coefficient model of necroptosis-related long non-coding RNA in identifying novel potential biomarkers in the prediction of the sensitivity to chemotherapeutic agents and prognosis of patients with lung adenocarcinoma (LUAD). Clinic and transcriptomic data of LUAD were obtained from The Cancer Genome Atlas. Differently expressed necroptosis-related long non-coding RNAs got identified by performing both the univariate and co-expression Cox regression analyses. Subsequently, the least absolute shrinkage and selection operator technique was adopted in constructing the nrlncRNA model. We made a comparison of the areas under the curve, did the count of the values of Akaike information criterion of 1-year, 2-year, as well as 3-year receiver operating characteristic curves, after which the cut-off value was determined for the construction of an optimal model to be used in identifying high risk and low risk patients. Genes, tumor-infiltrating immune cells, clinical correlation analysis, and chemotherapeutic agents data of both the high-risk and low-risk subgroups were also performed. We identified 26 DEnrlncRNA pairs, which were involved in the Cox regression model constructed. The curve areas under survival periods of 1 year, 2 years, and 3 years of patients with LUAD were 0.834, 0.790, and 0.821, respectively. The cut-off value set was 2.031, which was used in the identification of either the high-risk or low-risk patients. Poor outcomes were observed in patients belonging to the high-risk group. The risk score was the independent predictor of the LUAD outcome (p < 0.001). The expression levels of immune checkpoint and infiltration of specific immune cells were anticipated by the gene risk model. The high-risk group was found to be highly sensitive to docetaxel, erlotinib, cisplatin, and paclitaxel. The model established through nrlncRNA pairs irrespective of the levels of expression could give a prediction on the LUAD patients’ prognosis and assist in identifying the patients who might gain more benefit from chemotherapeutic agents. |
format | Online Article Text |
id | pubmed-9243036 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92430362022-07-01 Risk coefficient model of necroptosis-related lncRNA in predicting the prognosis of patients with lung adenocarcinoma Chen, HuiWei Xie, Zhimin Li, QingZhu Qu, GenYi Tan, NianXi Zhang, YuLong Sci Rep Article Model algorithms were used in constructing the risk coefficient model of necroptosis-related long non-coding RNA in identifying novel potential biomarkers in the prediction of the sensitivity to chemotherapeutic agents and prognosis of patients with lung adenocarcinoma (LUAD). Clinic and transcriptomic data of LUAD were obtained from The Cancer Genome Atlas. Differently expressed necroptosis-related long non-coding RNAs got identified by performing both the univariate and co-expression Cox regression analyses. Subsequently, the least absolute shrinkage and selection operator technique was adopted in constructing the nrlncRNA model. We made a comparison of the areas under the curve, did the count of the values of Akaike information criterion of 1-year, 2-year, as well as 3-year receiver operating characteristic curves, after which the cut-off value was determined for the construction of an optimal model to be used in identifying high risk and low risk patients. Genes, tumor-infiltrating immune cells, clinical correlation analysis, and chemotherapeutic agents data of both the high-risk and low-risk subgroups were also performed. We identified 26 DEnrlncRNA pairs, which were involved in the Cox regression model constructed. The curve areas under survival periods of 1 year, 2 years, and 3 years of patients with LUAD were 0.834, 0.790, and 0.821, respectively. The cut-off value set was 2.031, which was used in the identification of either the high-risk or low-risk patients. Poor outcomes were observed in patients belonging to the high-risk group. The risk score was the independent predictor of the LUAD outcome (p < 0.001). The expression levels of immune checkpoint and infiltration of specific immune cells were anticipated by the gene risk model. The high-risk group was found to be highly sensitive to docetaxel, erlotinib, cisplatin, and paclitaxel. The model established through nrlncRNA pairs irrespective of the levels of expression could give a prediction on the LUAD patients’ prognosis and assist in identifying the patients who might gain more benefit from chemotherapeutic agents. Nature Publishing Group UK 2022-06-29 /pmc/articles/PMC9243036/ /pubmed/35768485 http://dx.doi.org/10.1038/s41598-022-15189-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Chen, HuiWei Xie, Zhimin Li, QingZhu Qu, GenYi Tan, NianXi Zhang, YuLong Risk coefficient model of necroptosis-related lncRNA in predicting the prognosis of patients with lung adenocarcinoma |
title | Risk coefficient model of necroptosis-related lncRNA in predicting the prognosis of patients with lung adenocarcinoma |
title_full | Risk coefficient model of necroptosis-related lncRNA in predicting the prognosis of patients with lung adenocarcinoma |
title_fullStr | Risk coefficient model of necroptosis-related lncRNA in predicting the prognosis of patients with lung adenocarcinoma |
title_full_unstemmed | Risk coefficient model of necroptosis-related lncRNA in predicting the prognosis of patients with lung adenocarcinoma |
title_short | Risk coefficient model of necroptosis-related lncRNA in predicting the prognosis of patients with lung adenocarcinoma |
title_sort | risk coefficient model of necroptosis-related lncrna in predicting the prognosis of patients with lung adenocarcinoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243036/ https://www.ncbi.nlm.nih.gov/pubmed/35768485 http://dx.doi.org/10.1038/s41598-022-15189-4 |
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