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N7-methylguanosine methylation-related regulator genes as biological markers in predicting prognosis for melanoma

The aim of this study is to find those N7-methylguanosine (m7G) methylation-related regulator genes (m7GMRRGs) which were associated with melanoma prognosis and use them to develop a prognostic prediction model. Clinical information was retrieved online from The Cancer Gene Atlas (TCGA) and the Gene...

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Autores principales: Deng, Jiehua, Lin, Jiahua, Liu, Chang, Li, Jiasong, Cai, Jun, Zhou, Xiyu, Li, Xiong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9726938/
https://www.ncbi.nlm.nih.gov/pubmed/36473947
http://dx.doi.org/10.1038/s41598-022-25698-x
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author Deng, Jiehua
Lin, Jiahua
Liu, Chang
Li, Jiasong
Cai, Jun
Zhou, Xiyu
Li, Xiong
author_facet Deng, Jiehua
Lin, Jiahua
Liu, Chang
Li, Jiasong
Cai, Jun
Zhou, Xiyu
Li, Xiong
author_sort Deng, Jiehua
collection PubMed
description The aim of this study is to find those N7-methylguanosine (m7G) methylation-related regulator genes (m7GMRRGs) which were associated with melanoma prognosis and use them to develop a prognostic prediction model. Clinical information was retrieved online from The Cancer Gene Atlas (TCGA) and the Gene Expression Omnibus (GEO). R software was used to extract m(7)GMRRGs by differential expression analysis. To create a prognostic risk model, univariate and multivariate Cox regression analyses were employed for the evaluation of the prognostic significance of m(7)G methylation modifiers. Internal validation using cohort from TCGA (training set) and external validation using cohort from GEO (validation set) of the model were carried out. The model’s predictive performance was confirmed by using the Kaplan–Meier, univariate, and multivariate Cox regression, and receiver operating characteristic curve (ROC) by constructing column line plots incorporating clinical factor characteristics. Immune infiltration analyses were performed to assess the immune function of m(7)GMRRGs. Drug sensitivity analysis was conducted to study chemotherapeutic drug treatment cues. Prognostic models using four m(7)GMRRGs (EIF4E3, LARP1, NCBP3, and IFIT5) showed good prognostic power in training and validation sets. The area under the curve (AUC) at 1, 3, and 5 years for GEO-melanoma were 0.689, 0.704, and 0.726, respectively. The prediction model could distinctly classify patients with melanoma into different risk subgroups (P < 0.001 for TCGA-melanoma and P < 0.05 for GEO-melanoma). Clinical characteristics were taken into account in Cox regression and AUC analysis, which highlighted that the risk score served as an independent risk factor determining the prognosis of patients with melanoma. Immuno-infiltration analysis showed that m(7)GMRRGs could potentially regulate CD8(+) T cells as well as regulatory T cells (Treg cells). Results of our study indicate a association between m(7)GMRRGs and melanoma prognosis, and the prognostic prediction model using m(7)GMRRGs may predict the prognosis of patients with melanoma well. Nevertheless, these results may provide a clue for potential better options of melanoma treatment but need further validation in futural studies.
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spelling pubmed-97269382022-12-08 N7-methylguanosine methylation-related regulator genes as biological markers in predicting prognosis for melanoma Deng, Jiehua Lin, Jiahua Liu, Chang Li, Jiasong Cai, Jun Zhou, Xiyu Li, Xiong Sci Rep Article The aim of this study is to find those N7-methylguanosine (m7G) methylation-related regulator genes (m7GMRRGs) which were associated with melanoma prognosis and use them to develop a prognostic prediction model. Clinical information was retrieved online from The Cancer Gene Atlas (TCGA) and the Gene Expression Omnibus (GEO). R software was used to extract m(7)GMRRGs by differential expression analysis. To create a prognostic risk model, univariate and multivariate Cox regression analyses were employed for the evaluation of the prognostic significance of m(7)G methylation modifiers. Internal validation using cohort from TCGA (training set) and external validation using cohort from GEO (validation set) of the model were carried out. The model’s predictive performance was confirmed by using the Kaplan–Meier, univariate, and multivariate Cox regression, and receiver operating characteristic curve (ROC) by constructing column line plots incorporating clinical factor characteristics. Immune infiltration analyses were performed to assess the immune function of m(7)GMRRGs. Drug sensitivity analysis was conducted to study chemotherapeutic drug treatment cues. Prognostic models using four m(7)GMRRGs (EIF4E3, LARP1, NCBP3, and IFIT5) showed good prognostic power in training and validation sets. The area under the curve (AUC) at 1, 3, and 5 years for GEO-melanoma were 0.689, 0.704, and 0.726, respectively. The prediction model could distinctly classify patients with melanoma into different risk subgroups (P < 0.001 for TCGA-melanoma and P < 0.05 for GEO-melanoma). Clinical characteristics were taken into account in Cox regression and AUC analysis, which highlighted that the risk score served as an independent risk factor determining the prognosis of patients with melanoma. Immuno-infiltration analysis showed that m(7)GMRRGs could potentially regulate CD8(+) T cells as well as regulatory T cells (Treg cells). Results of our study indicate a association between m(7)GMRRGs and melanoma prognosis, and the prognostic prediction model using m(7)GMRRGs may predict the prognosis of patients with melanoma well. Nevertheless, these results may provide a clue for potential better options of melanoma treatment but need further validation in futural studies. Nature Publishing Group UK 2022-12-06 /pmc/articles/PMC9726938/ /pubmed/36473947 http://dx.doi.org/10.1038/s41598-022-25698-x 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
Deng, Jiehua
Lin, Jiahua
Liu, Chang
Li, Jiasong
Cai, Jun
Zhou, Xiyu
Li, Xiong
N7-methylguanosine methylation-related regulator genes as biological markers in predicting prognosis for melanoma
title N7-methylguanosine methylation-related regulator genes as biological markers in predicting prognosis for melanoma
title_full N7-methylguanosine methylation-related regulator genes as biological markers in predicting prognosis for melanoma
title_fullStr N7-methylguanosine methylation-related regulator genes as biological markers in predicting prognosis for melanoma
title_full_unstemmed N7-methylguanosine methylation-related regulator genes as biological markers in predicting prognosis for melanoma
title_short N7-methylguanosine methylation-related regulator genes as biological markers in predicting prognosis for melanoma
title_sort n7-methylguanosine methylation-related regulator genes as biological markers in predicting prognosis for melanoma
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9726938/
https://www.ncbi.nlm.nih.gov/pubmed/36473947
http://dx.doi.org/10.1038/s41598-022-25698-x
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