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Scoring model based on the signature of non-m6A-related neoantigen-coding lncRNAs assists in immune microenvironment analysis and TCR-neoantigen pair selection in gliomas

BACKGROUND: Small peptides encoded by long non-coding RNAs (lncRNAs) have attracted attention for their various functions. Recent studies indicate that these small peptides participate in immune responses and antigen presentation. However, the significance of RNA modifications remains unclear. METHO...

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Autores principales: Zhao, Wenbo, Wu, Yibo, Zhao, Feihu, Xue, Zhiyi, Liu, Wenyu, Cao, Zenxin, Zhao, Zhimin, Huang, Bin, Han, Mingzhi, Li, Xingang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9617417/
https://www.ncbi.nlm.nih.gov/pubmed/36309750
http://dx.doi.org/10.1186/s12967-022-03713-z
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author Zhao, Wenbo
Wu, Yibo
Zhao, Feihu
Xue, Zhiyi
Liu, Wenyu
Cao, Zenxin
Zhao, Zhimin
Huang, Bin
Han, Mingzhi
Li, Xingang
author_facet Zhao, Wenbo
Wu, Yibo
Zhao, Feihu
Xue, Zhiyi
Liu, Wenyu
Cao, Zenxin
Zhao, Zhimin
Huang, Bin
Han, Mingzhi
Li, Xingang
author_sort Zhao, Wenbo
collection PubMed
description BACKGROUND: Small peptides encoded by long non-coding RNAs (lncRNAs) have attracted attention for their various functions. Recent studies indicate that these small peptides participate in immune responses and antigen presentation. However, the significance of RNA modifications remains unclear. METHODS: Thirteen non-m6A-related neoantigen-coding lncRNAs were selected for analysis from the TransLnc database. Next, a neoantigen activation score (NAS) model was established based on the characteristics of the lncRNAs. Machine learning was employed to expand the model to two additional RNA-seq and two single-cell sequencing datasets for further validation. The DLpTCR algorithm was used to predict T cell receptor (TCR)-peptide binding probability. RESULTS: The non-m6A-related NAS model predicted patients’ overall survival outcomes more precisely than the m6A-related NAS model. Furthermore, the non-m6A-related NAS was positively correlated with tumor cells’ evolutionary level, immune infiltration, and antigen presentation. However, high NAS gliomas also showed more PD-L1 expression and high mutation frequencies of T-cell positive regulators. Interestingly, results of intercellular communication analysis suggest that T cell-high neoplastic cell interaction is weaker in both of the NAS groups which might arise from decreased IFNGR1 expression. Moreover, we identified unique TCR-peptide pairs present in all glioma samples based on peptides encoded by the 13 selected lncRNAs. And increased levels of neoantigen-active TCR patterns were found in high NAS gliomas. CONCLUSIONS: Our work suggests that non-m6A-related neoantigen-coding lncRNAs play an essential role in glioma progression and that screened TCR clonotypes might provide potential avenues for chimeric antigen receptor T cell (CAR-T) therapy for gliomas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03713-z.
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spelling pubmed-96174172022-10-30 Scoring model based on the signature of non-m6A-related neoantigen-coding lncRNAs assists in immune microenvironment analysis and TCR-neoantigen pair selection in gliomas Zhao, Wenbo Wu, Yibo Zhao, Feihu Xue, Zhiyi Liu, Wenyu Cao, Zenxin Zhao, Zhimin Huang, Bin Han, Mingzhi Li, Xingang J Transl Med Research BACKGROUND: Small peptides encoded by long non-coding RNAs (lncRNAs) have attracted attention for their various functions. Recent studies indicate that these small peptides participate in immune responses and antigen presentation. However, the significance of RNA modifications remains unclear. METHODS: Thirteen non-m6A-related neoantigen-coding lncRNAs were selected for analysis from the TransLnc database. Next, a neoantigen activation score (NAS) model was established based on the characteristics of the lncRNAs. Machine learning was employed to expand the model to two additional RNA-seq and two single-cell sequencing datasets for further validation. The DLpTCR algorithm was used to predict T cell receptor (TCR)-peptide binding probability. RESULTS: The non-m6A-related NAS model predicted patients’ overall survival outcomes more precisely than the m6A-related NAS model. Furthermore, the non-m6A-related NAS was positively correlated with tumor cells’ evolutionary level, immune infiltration, and antigen presentation. However, high NAS gliomas also showed more PD-L1 expression and high mutation frequencies of T-cell positive regulators. Interestingly, results of intercellular communication analysis suggest that T cell-high neoplastic cell interaction is weaker in both of the NAS groups which might arise from decreased IFNGR1 expression. Moreover, we identified unique TCR-peptide pairs present in all glioma samples based on peptides encoded by the 13 selected lncRNAs. And increased levels of neoantigen-active TCR patterns were found in high NAS gliomas. CONCLUSIONS: Our work suggests that non-m6A-related neoantigen-coding lncRNAs play an essential role in glioma progression and that screened TCR clonotypes might provide potential avenues for chimeric antigen receptor T cell (CAR-T) therapy for gliomas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03713-z. BioMed Central 2022-10-29 /pmc/articles/PMC9617417/ /pubmed/36309750 http://dx.doi.org/10.1186/s12967-022-03713-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhao, Wenbo
Wu, Yibo
Zhao, Feihu
Xue, Zhiyi
Liu, Wenyu
Cao, Zenxin
Zhao, Zhimin
Huang, Bin
Han, Mingzhi
Li, Xingang
Scoring model based on the signature of non-m6A-related neoantigen-coding lncRNAs assists in immune microenvironment analysis and TCR-neoantigen pair selection in gliomas
title Scoring model based on the signature of non-m6A-related neoantigen-coding lncRNAs assists in immune microenvironment analysis and TCR-neoantigen pair selection in gliomas
title_full Scoring model based on the signature of non-m6A-related neoantigen-coding lncRNAs assists in immune microenvironment analysis and TCR-neoantigen pair selection in gliomas
title_fullStr Scoring model based on the signature of non-m6A-related neoantigen-coding lncRNAs assists in immune microenvironment analysis and TCR-neoantigen pair selection in gliomas
title_full_unstemmed Scoring model based on the signature of non-m6A-related neoantigen-coding lncRNAs assists in immune microenvironment analysis and TCR-neoantigen pair selection in gliomas
title_short Scoring model based on the signature of non-m6A-related neoantigen-coding lncRNAs assists in immune microenvironment analysis and TCR-neoantigen pair selection in gliomas
title_sort scoring model based on the signature of non-m6a-related neoantigen-coding lncrnas assists in immune microenvironment analysis and tcr-neoantigen pair selection in gliomas
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9617417/
https://www.ncbi.nlm.nih.gov/pubmed/36309750
http://dx.doi.org/10.1186/s12967-022-03713-z
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