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Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer

Endometrial cancer (EC) is women’s fourth most common malignant tumor. Neddylation plays a significant role in many diseases; however, the effect of neddylation and neddylation-related genes (NRGs) on EC is rarely reported. In this study, we first used MLN4924 to affect the activation of neddylation...

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Autores principales: Li, Yi, Niu, Jiang-Hua, Wang, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992729/
https://www.ncbi.nlm.nih.gov/pubmed/36910623
http://dx.doi.org/10.3389/fonc.2023.1084523
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author Li, Yi
Niu, Jiang-Hua
Wang, Yan
author_facet Li, Yi
Niu, Jiang-Hua
Wang, Yan
author_sort Li, Yi
collection PubMed
description Endometrial cancer (EC) is women’s fourth most common malignant tumor. Neddylation plays a significant role in many diseases; however, the effect of neddylation and neddylation-related genes (NRGs) on EC is rarely reported. In this study, we first used MLN4924 to affect the activation of neddylation in different cell lines (Ishikawa and HEC-1-A) and determined the critical role of neddylation-related pathways for EC progression. Subsequently, we screened 17 prognostic NRGs based on expression files of the TCGA-UCEC cohort. Based on unsupervised consensus clustering analysis, patients with EC were classified into two neddylation patterns (C1 and C2). In terms of prognosis, substantial differences were observed between the two patterns. Compared with C2, C1 exhibited low levels of immune infiltration and promoted tumor progression. More importantly, based on the expression of 17 prognostic NRGs, we transformed nine machine-learning algorithms into 89 combinations. The random forest (RSF) was selected to construct the neddylation-related risk score according to the average C-index of different cohorts. Notably, our score had important clinical implications for EC. Patients with high scores have poor prognoses and a cold tumor state. In conclusion, neddylation-related patterns and scores can distinguish tumor microenvironment (TME) and prognosis and guide personalized treatment in patients with EC.
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spelling pubmed-99927292023-03-09 Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer Li, Yi Niu, Jiang-Hua Wang, Yan Front Oncol Oncology Endometrial cancer (EC) is women’s fourth most common malignant tumor. Neddylation plays a significant role in many diseases; however, the effect of neddylation and neddylation-related genes (NRGs) on EC is rarely reported. In this study, we first used MLN4924 to affect the activation of neddylation in different cell lines (Ishikawa and HEC-1-A) and determined the critical role of neddylation-related pathways for EC progression. Subsequently, we screened 17 prognostic NRGs based on expression files of the TCGA-UCEC cohort. Based on unsupervised consensus clustering analysis, patients with EC were classified into two neddylation patterns (C1 and C2). In terms of prognosis, substantial differences were observed between the two patterns. Compared with C2, C1 exhibited low levels of immune infiltration and promoted tumor progression. More importantly, based on the expression of 17 prognostic NRGs, we transformed nine machine-learning algorithms into 89 combinations. The random forest (RSF) was selected to construct the neddylation-related risk score according to the average C-index of different cohorts. Notably, our score had important clinical implications for EC. Patients with high scores have poor prognoses and a cold tumor state. In conclusion, neddylation-related patterns and scores can distinguish tumor microenvironment (TME) and prognosis and guide personalized treatment in patients with EC. Frontiers Media S.A. 2023-02-22 /pmc/articles/PMC9992729/ /pubmed/36910623 http://dx.doi.org/10.3389/fonc.2023.1084523 Text en Copyright © 2023 Li, Niu and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Li, Yi
Niu, Jiang-Hua
Wang, Yan
Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer
title Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer
title_full Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer
title_fullStr Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer
title_full_unstemmed Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer
title_short Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer
title_sort machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992729/
https://www.ncbi.nlm.nih.gov/pubmed/36910623
http://dx.doi.org/10.3389/fonc.2023.1084523
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AT wangyan machinelearningbasedneddylationlandscapeindicatesdifferentprognosisandimmunemicroenvironmentinendometrialcancer