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Characteristics of m(6)A-related LncRNAs in breast cancer as prognostic biomarkers and immunotherapy
N6-methyladenosine (m(6)A) is a common RNA modification in coding and non-coding RNAs and plays an important role in the occurrence and development of breast cancer (BC). However, the role of m(6)A-related lncRNAs in breast cancer prognosis is unclear. This study aimed to help verify the biological...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Ivyspring International Publisher
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539557/ https://www.ncbi.nlm.nih.gov/pubmed/37781080 http://dx.doi.org/10.7150/jca.87079 |
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author | Han, Xinwei Chen, Yu Xie, Jiaogui Wang, Yichao |
author_facet | Han, Xinwei Chen, Yu Xie, Jiaogui Wang, Yichao |
author_sort | Han, Xinwei |
collection | PubMed |
description | N6-methyladenosine (m(6)A) is a common RNA modification in coding and non-coding RNAs and plays an important role in the occurrence and development of breast cancer (BC). However, the role of m(6)A-related lncRNAs in breast cancer prognosis is unclear. This study aimed to help verify the biological function of m(6)A-related lncRNAs in breast cancer prognosis through bio-informatics techniques. First, we screened 18 m(6)A-related lncRNAs from the TCGA database: AL137847.1, AC137932.2, OTUD6B-AS1, MORF4L2-AS1, AC078846.1, AC012442.1, AL118556.1, AL138955.1, AC009754.1, AC024257.4, AL391095.1, AC024270.3, AC087392.1, LINC02649, AC090948.2, AL158212.1, ITGA6-AS1, AL133243.2 and constructed a risk-prognosis model based on this. Based on the model's median risk score, BC patients were divided into high-risk and low-risk groups. Then, the predictive value of the model was verified by Cox regression, Lasso regression, Kaplan-Meier curve and ROC curve analysis, and biological differences between the two groups were verified by GO enrichment analysis, tumor mutation burden, immune indications and in vitro tests. Importantly, the risk score of this prognostic model is an excellent independent prognostic factor, and m(6)A regulators are differentially expressed in patients with different risks. In addition, based on patients' different sensitivities to drugs, some drug candidates for different risk populations are screened to provide targets for breast cancer treatment. The difference in immune function between high-risk and low-risk patients also affected the sensitivity to immunotherapy. In the validation of clinical samples, we analyzed the expression of relevant lncRNAs in different risk groups and speculated the possible impact on the prognosis of breast cancer patients. The risk assessment tool built based on the full analysis of these m(6)A-related genes and m(6)A-related lncRNA libraries, as well as the m(6)A-related lncRNAs, has a high prognostic prediction ability, which may provide a supplementary screening method for accurately judging the prognosis of BC and a new perspective for personalized treatment of breast cancer patients. |
format | Online Article Text |
id | pubmed-10539557 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Ivyspring International Publisher |
record_format | MEDLINE/PubMed |
spelling | pubmed-105395572023-09-30 Characteristics of m(6)A-related LncRNAs in breast cancer as prognostic biomarkers and immunotherapy Han, Xinwei Chen, Yu Xie, Jiaogui Wang, Yichao J Cancer Research Paper N6-methyladenosine (m(6)A) is a common RNA modification in coding and non-coding RNAs and plays an important role in the occurrence and development of breast cancer (BC). However, the role of m(6)A-related lncRNAs in breast cancer prognosis is unclear. This study aimed to help verify the biological function of m(6)A-related lncRNAs in breast cancer prognosis through bio-informatics techniques. First, we screened 18 m(6)A-related lncRNAs from the TCGA database: AL137847.1, AC137932.2, OTUD6B-AS1, MORF4L2-AS1, AC078846.1, AC012442.1, AL118556.1, AL138955.1, AC009754.1, AC024257.4, AL391095.1, AC024270.3, AC087392.1, LINC02649, AC090948.2, AL158212.1, ITGA6-AS1, AL133243.2 and constructed a risk-prognosis model based on this. Based on the model's median risk score, BC patients were divided into high-risk and low-risk groups. Then, the predictive value of the model was verified by Cox regression, Lasso regression, Kaplan-Meier curve and ROC curve analysis, and biological differences between the two groups were verified by GO enrichment analysis, tumor mutation burden, immune indications and in vitro tests. Importantly, the risk score of this prognostic model is an excellent independent prognostic factor, and m(6)A regulators are differentially expressed in patients with different risks. In addition, based on patients' different sensitivities to drugs, some drug candidates for different risk populations are screened to provide targets for breast cancer treatment. The difference in immune function between high-risk and low-risk patients also affected the sensitivity to immunotherapy. In the validation of clinical samples, we analyzed the expression of relevant lncRNAs in different risk groups and speculated the possible impact on the prognosis of breast cancer patients. The risk assessment tool built based on the full analysis of these m(6)A-related genes and m(6)A-related lncRNA libraries, as well as the m(6)A-related lncRNAs, has a high prognostic prediction ability, which may provide a supplementary screening method for accurately judging the prognosis of BC and a new perspective for personalized treatment of breast cancer patients. Ivyspring International Publisher 2023-09-11 /pmc/articles/PMC10539557/ /pubmed/37781080 http://dx.doi.org/10.7150/jca.87079 Text en © The author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. |
spellingShingle | Research Paper Han, Xinwei Chen, Yu Xie, Jiaogui Wang, Yichao Characteristics of m(6)A-related LncRNAs in breast cancer as prognostic biomarkers and immunotherapy |
title | Characteristics of m(6)A-related LncRNAs in breast cancer as prognostic biomarkers and immunotherapy |
title_full | Characteristics of m(6)A-related LncRNAs in breast cancer as prognostic biomarkers and immunotherapy |
title_fullStr | Characteristics of m(6)A-related LncRNAs in breast cancer as prognostic biomarkers and immunotherapy |
title_full_unstemmed | Characteristics of m(6)A-related LncRNAs in breast cancer as prognostic biomarkers and immunotherapy |
title_short | Characteristics of m(6)A-related LncRNAs in breast cancer as prognostic biomarkers and immunotherapy |
title_sort | characteristics of m(6)a-related lncrnas in breast cancer as prognostic biomarkers and immunotherapy |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539557/ https://www.ncbi.nlm.nih.gov/pubmed/37781080 http://dx.doi.org/10.7150/jca.87079 |
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