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Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer
Presenting with a poor prognosis, gastric cancer (GC) remains one of the leading causes of disease and death worldwide. Long non-coding RNAs (lncRNAs) regulate tumor formation and have been long used to predict tumor prognosis. N7-methylguanosine (m7G) is the most prevalent RNA modification. m7G-lnc...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10024859/ https://www.ncbi.nlm.nih.gov/pubmed/36935487 http://dx.doi.org/10.1186/s12859-023-05228-w |
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author | Zhao, Bin Fang, Fang Liao, Yiqun Chen, Yuji Wang, Fei Ma, Yichao Wei, Chen Zhao, Jiahao Ji, Hao Wang, Daorong Tang, Dong |
author_facet | Zhao, Bin Fang, Fang Liao, Yiqun Chen, Yuji Wang, Fei Ma, Yichao Wei, Chen Zhao, Jiahao Ji, Hao Wang, Daorong Tang, Dong |
author_sort | Zhao, Bin |
collection | PubMed |
description | Presenting with a poor prognosis, gastric cancer (GC) remains one of the leading causes of disease and death worldwide. Long non-coding RNAs (lncRNAs) regulate tumor formation and have been long used to predict tumor prognosis. N7-methylguanosine (m7G) is the most prevalent RNA modification. m7G-lncRNAs regulate GC onset and progression, but their precise mechanism in GC is unclear. The objective of this research was the development of a new m7G-related lncRNA signature as a biomarker for predicting GC survival rate and guiding treatment. The Cancer Genome Atlas database helped extract gene expression data and clinical information for GC. Pearson correlation analysis helped point out m7G-related lncRNAs. Univariate Cox analysis helped in identifying m7G-related lncRNA with predictive capability. The Lasso-Cox method helped point out seven lncRNAs for the purpose of establishing an m7G-related lncRNA prognostic signature (m7G-LPS), followed by the construction of a nomogram. Kaplan–Meier analysis, univariate and multivariate Cox regression analysis, calibration plot of the nomogram model, receiver operating characteristic curve and principal component analysis were utilized for the verification of the risk model’s reliability. Furthermore, q-PCR helped verify the lncRNAs expression of m7G-LPS in-vitro. The study subjects were classified into high and low-risk groups based on the median value of the risk score. Gene enrichment analysis confirmed the constructed m7G-LPS’ correlation with RNA transcription and translation and multiple immune-related pathways. Analysis of the clinicopathological features revealed more progressive features in the high-risk group. CIBERSORT analysis showed the involvement of m7G-LPS in immune cell infiltration. The risk score was correlated with immune checkpoint gene expression, immune cell and immune function score, immune cell infiltration, and chemotherapy drug sensitivity. Therefore, our study shows that m7G-LPS constructed using seven m7G-related lncRNAs can predict the survival time of GC patients and guide chemotherapy and immunotherapy regimens as biomarker. |
format | Online Article Text |
id | pubmed-10024859 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-100248592023-03-20 Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer Zhao, Bin Fang, Fang Liao, Yiqun Chen, Yuji Wang, Fei Ma, Yichao Wei, Chen Zhao, Jiahao Ji, Hao Wang, Daorong Tang, Dong BMC Bioinformatics Research Presenting with a poor prognosis, gastric cancer (GC) remains one of the leading causes of disease and death worldwide. Long non-coding RNAs (lncRNAs) regulate tumor formation and have been long used to predict tumor prognosis. N7-methylguanosine (m7G) is the most prevalent RNA modification. m7G-lncRNAs regulate GC onset and progression, but their precise mechanism in GC is unclear. The objective of this research was the development of a new m7G-related lncRNA signature as a biomarker for predicting GC survival rate and guiding treatment. The Cancer Genome Atlas database helped extract gene expression data and clinical information for GC. Pearson correlation analysis helped point out m7G-related lncRNAs. Univariate Cox analysis helped in identifying m7G-related lncRNA with predictive capability. The Lasso-Cox method helped point out seven lncRNAs for the purpose of establishing an m7G-related lncRNA prognostic signature (m7G-LPS), followed by the construction of a nomogram. Kaplan–Meier analysis, univariate and multivariate Cox regression analysis, calibration plot of the nomogram model, receiver operating characteristic curve and principal component analysis were utilized for the verification of the risk model’s reliability. Furthermore, q-PCR helped verify the lncRNAs expression of m7G-LPS in-vitro. The study subjects were classified into high and low-risk groups based on the median value of the risk score. Gene enrichment analysis confirmed the constructed m7G-LPS’ correlation with RNA transcription and translation and multiple immune-related pathways. Analysis of the clinicopathological features revealed more progressive features in the high-risk group. CIBERSORT analysis showed the involvement of m7G-LPS in immune cell infiltration. The risk score was correlated with immune checkpoint gene expression, immune cell and immune function score, immune cell infiltration, and chemotherapy drug sensitivity. Therefore, our study shows that m7G-LPS constructed using seven m7G-related lncRNAs can predict the survival time of GC patients and guide chemotherapy and immunotherapy regimens as biomarker. BioMed Central 2023-03-19 /pmc/articles/PMC10024859/ /pubmed/36935487 http://dx.doi.org/10.1186/s12859-023-05228-w Text en © The Author(s) 2023 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, Bin Fang, Fang Liao, Yiqun Chen, Yuji Wang, Fei Ma, Yichao Wei, Chen Zhao, Jiahao Ji, Hao Wang, Daorong Tang, Dong Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer |
title | Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer |
title_full | Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer |
title_fullStr | Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer |
title_full_unstemmed | Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer |
title_short | Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer |
title_sort | novel m7g-related lncrna signature for predicting overall survival in patients with gastric cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10024859/ https://www.ncbi.nlm.nih.gov/pubmed/36935487 http://dx.doi.org/10.1186/s12859-023-05228-w |
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