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Establishment of a new molecular subtyping and prognostic signature with m6A/m5C/m1A/m7G regulatory genes for hepatocellular carcinoma

BACKGROUND: RNA modification, including m6A, m5C, m1A, and m7G, participated in tumor progress. Therefore, the purpose of the present study was to explore the role of m6A/m5C/m1A/m7G regulatory genes in the prognosis and tumor microenvironment (TME) for hepatocellular carcinoma (HCC). METHODS: 71 m6...

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Autores principales: Liu, Ting, Wang, Yang, Li, Zhizhao, Sun, Lei, Yang, Kun, Chen, Jiamin, Han, Xiaoyi, Qi, Liming, Zhou, Xingang, Wang, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10660009/
https://www.ncbi.nlm.nih.gov/pubmed/38027812
http://dx.doi.org/10.1016/j.heliyon.2023.e21285
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author Liu, Ting
Wang, Yang
Li, Zhizhao
Sun, Lei
Yang, Kun
Chen, Jiamin
Han, Xiaoyi
Qi, Liming
Zhou, Xingang
Wang, Peng
author_facet Liu, Ting
Wang, Yang
Li, Zhizhao
Sun, Lei
Yang, Kun
Chen, Jiamin
Han, Xiaoyi
Qi, Liming
Zhou, Xingang
Wang, Peng
author_sort Liu, Ting
collection PubMed
description BACKGROUND: RNA modification, including m6A, m5C, m1A, and m7G, participated in tumor progress. Therefore, the purpose of the present study was to explore the role of m6A/m5C/m1A/m7G regulatory genes in the prognosis and tumor microenvironment (TME) for hepatocellular carcinoma (HCC). METHODS: 71 m6A/m5C/m1A/m7G regulatory genes expression for HCC was detected, differentially expressed genes were screened, and molecular forms were classified by unsupervised consensus clustering. Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) analysis were applied to establish a prognostic signature. Time-dependent receiver operating characteristic (ROC) curves were evaluated for clinical effectiveness and accuracy of the prognostic hazard model. In cluster subtypes and risk models, the differences in prognosis, immune cell infiltration, immune checkpoint, immunotherapy, and drug sensitivity between different subtypes were evaluated. RESULTS: HCC patients were classified into two clusters (cluster 1 and cluster 2) according to the expression of 71 m6A/m5C/m1A/m7G regulatory genes. Cluster 1 had a poor prognosis and different immune cell infiltration. Cluster 1 had higher immune checkpoint expression and TIDE score than cluster 2. Subsequently, we construct a five-gene prognostic model of m6A/m5C/m1A/m7G regulatory genes (YTHDF2, YTHDF1,YBX1, TRMT61A, TRMT10C). The Kaplan-Meier and ROC curve analysis showed that the prognostic signature exhibited good predictability. The risk score was considered an independent poor prognostic index. The high-risk group had higher immune checkpoint expression and higher TIDE scores. 5-Fluorouracil, docetaxel, doxorubicin, etoposide, gemcitabine, paclitaxel, sorafenib, and vinblastine were more suitable for high-risk patients. ECM receptor interaction, cell cycle, and Leishmania infection were enriched in the high-risk group. CONCLUSION: The clustering subgroups and prognostic model of m6A/m5C/m1A/m7G regulatory genes were linked with bad prognosis and TME for HCC, and had the potential to be a novel tool to evaluate the outcomes of HCC patients.
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spelling pubmed-106600092023-10-30 Establishment of a new molecular subtyping and prognostic signature with m6A/m5C/m1A/m7G regulatory genes for hepatocellular carcinoma Liu, Ting Wang, Yang Li, Zhizhao Sun, Lei Yang, Kun Chen, Jiamin Han, Xiaoyi Qi, Liming Zhou, Xingang Wang, Peng Heliyon Research Article BACKGROUND: RNA modification, including m6A, m5C, m1A, and m7G, participated in tumor progress. Therefore, the purpose of the present study was to explore the role of m6A/m5C/m1A/m7G regulatory genes in the prognosis and tumor microenvironment (TME) for hepatocellular carcinoma (HCC). METHODS: 71 m6A/m5C/m1A/m7G regulatory genes expression for HCC was detected, differentially expressed genes were screened, and molecular forms were classified by unsupervised consensus clustering. Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) analysis were applied to establish a prognostic signature. Time-dependent receiver operating characteristic (ROC) curves were evaluated for clinical effectiveness and accuracy of the prognostic hazard model. In cluster subtypes and risk models, the differences in prognosis, immune cell infiltration, immune checkpoint, immunotherapy, and drug sensitivity between different subtypes were evaluated. RESULTS: HCC patients were classified into two clusters (cluster 1 and cluster 2) according to the expression of 71 m6A/m5C/m1A/m7G regulatory genes. Cluster 1 had a poor prognosis and different immune cell infiltration. Cluster 1 had higher immune checkpoint expression and TIDE score than cluster 2. Subsequently, we construct a five-gene prognostic model of m6A/m5C/m1A/m7G regulatory genes (YTHDF2, YTHDF1,YBX1, TRMT61A, TRMT10C). The Kaplan-Meier and ROC curve analysis showed that the prognostic signature exhibited good predictability. The risk score was considered an independent poor prognostic index. The high-risk group had higher immune checkpoint expression and higher TIDE scores. 5-Fluorouracil, docetaxel, doxorubicin, etoposide, gemcitabine, paclitaxel, sorafenib, and vinblastine were more suitable for high-risk patients. ECM receptor interaction, cell cycle, and Leishmania infection were enriched in the high-risk group. CONCLUSION: The clustering subgroups and prognostic model of m6A/m5C/m1A/m7G regulatory genes were linked with bad prognosis and TME for HCC, and had the potential to be a novel tool to evaluate the outcomes of HCC patients. Elsevier 2023-10-30 /pmc/articles/PMC10660009/ /pubmed/38027812 http://dx.doi.org/10.1016/j.heliyon.2023.e21285 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Liu, Ting
Wang, Yang
Li, Zhizhao
Sun, Lei
Yang, Kun
Chen, Jiamin
Han, Xiaoyi
Qi, Liming
Zhou, Xingang
Wang, Peng
Establishment of a new molecular subtyping and prognostic signature with m6A/m5C/m1A/m7G regulatory genes for hepatocellular carcinoma
title Establishment of a new molecular subtyping and prognostic signature with m6A/m5C/m1A/m7G regulatory genes for hepatocellular carcinoma
title_full Establishment of a new molecular subtyping and prognostic signature with m6A/m5C/m1A/m7G regulatory genes for hepatocellular carcinoma
title_fullStr Establishment of a new molecular subtyping and prognostic signature with m6A/m5C/m1A/m7G regulatory genes for hepatocellular carcinoma
title_full_unstemmed Establishment of a new molecular subtyping and prognostic signature with m6A/m5C/m1A/m7G regulatory genes for hepatocellular carcinoma
title_short Establishment of a new molecular subtyping and prognostic signature with m6A/m5C/m1A/m7G regulatory genes for hepatocellular carcinoma
title_sort establishment of a new molecular subtyping and prognostic signature with m6a/m5c/m1a/m7g regulatory genes for hepatocellular carcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10660009/
https://www.ncbi.nlm.nih.gov/pubmed/38027812
http://dx.doi.org/10.1016/j.heliyon.2023.e21285
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