Cargando…

N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease that, at present, has no well-characterised prognostic biomarker. In this study, two clusters were identified based on 28 N1-methyladenosine- (m1A-) related long noncoding RNAs (lncRNAs), of which cluster 1 was related to immune path...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Jinhui, Geng, Rui, Zhong, Zihang, Zhang, Yixin, Ni, Senmiao, Liu, Wen, Du, Mulong, Bai, Jianling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372539/
https://www.ncbi.nlm.nih.gov/pubmed/35965688
http://dx.doi.org/10.1155/2022/2754836
_version_ 1784767407496101888
author Liu, Jinhui
Geng, Rui
Zhong, Zihang
Zhang, Yixin
Ni, Senmiao
Liu, Wen
Du, Mulong
Bai, Jianling
author_facet Liu, Jinhui
Geng, Rui
Zhong, Zihang
Zhang, Yixin
Ni, Senmiao
Liu, Wen
Du, Mulong
Bai, Jianling
author_sort Liu, Jinhui
collection PubMed
description Uterine corpus endometrial carcinoma (UCEC) is a malignant disease that, at present, has no well-characterised prognostic biomarker. In this study, two clusters were identified based on 28 N1-methyladenosine- (m1A-) related long noncoding RNAs (lncRNAs), of which cluster 1 was related to immune pathways according to the results of an enrichment analysis. We further observed better prognosis in patients with higher levels of immune cell infiltration, tumor mutation burden, microsatellite instability, and immune checkpoint gene expression. In addition, through Cox regression analysis and least absolute shrinkage and selection operator regression analysis, 10 m1A-related lncRNAs (mRLs) were employed to build a prognosis model. We found that people in higher risk categories had a poorer survival probability than those in lower risk. Low-risk samples were enriched with immune-related pathways, while the high-risk group was similar to the definition of the “immune desert” phenotype, which was associated with decreased immune infiltration, T cell failure, and decreased tumor mutation burden, while also being insensitive to immunotherapy and chemotherapy. This mRL-based model has the ability to accurately predict the prognosis of UCEC patients, and the mRLs could become promising therapeutic targets in enhancing the response of immunotherapy.
format Online
Article
Text
id pubmed-9372539
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-93725392022-08-13 N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma Liu, Jinhui Geng, Rui Zhong, Zihang Zhang, Yixin Ni, Senmiao Liu, Wen Du, Mulong Bai, Jianling Oxid Med Cell Longev Research Article Uterine corpus endometrial carcinoma (UCEC) is a malignant disease that, at present, has no well-characterised prognostic biomarker. In this study, two clusters were identified based on 28 N1-methyladenosine- (m1A-) related long noncoding RNAs (lncRNAs), of which cluster 1 was related to immune pathways according to the results of an enrichment analysis. We further observed better prognosis in patients with higher levels of immune cell infiltration, tumor mutation burden, microsatellite instability, and immune checkpoint gene expression. In addition, through Cox regression analysis and least absolute shrinkage and selection operator regression analysis, 10 m1A-related lncRNAs (mRLs) were employed to build a prognosis model. We found that people in higher risk categories had a poorer survival probability than those in lower risk. Low-risk samples were enriched with immune-related pathways, while the high-risk group was similar to the definition of the “immune desert” phenotype, which was associated with decreased immune infiltration, T cell failure, and decreased tumor mutation burden, while also being insensitive to immunotherapy and chemotherapy. This mRL-based model has the ability to accurately predict the prognosis of UCEC patients, and the mRLs could become promising therapeutic targets in enhancing the response of immunotherapy. Hindawi 2022-07-31 /pmc/articles/PMC9372539/ /pubmed/35965688 http://dx.doi.org/10.1155/2022/2754836 Text en Copyright © 2022 Jinhui Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Jinhui
Geng, Rui
Zhong, Zihang
Zhang, Yixin
Ni, Senmiao
Liu, Wen
Du, Mulong
Bai, Jianling
N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma
title N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma
title_full N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma
title_fullStr N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma
title_full_unstemmed N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma
title_short N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma
title_sort n1-methyladenosine-related lncrnas are potential biomarkers for predicting prognosis and immune response in uterine corpus endometrial carcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372539/
https://www.ncbi.nlm.nih.gov/pubmed/35965688
http://dx.doi.org/10.1155/2022/2754836
work_keys_str_mv AT liujinhui n1methyladenosinerelatedlncrnasarepotentialbiomarkersforpredictingprognosisandimmuneresponseinuterinecorpusendometrialcarcinoma
AT gengrui n1methyladenosinerelatedlncrnasarepotentialbiomarkersforpredictingprognosisandimmuneresponseinuterinecorpusendometrialcarcinoma
AT zhongzihang n1methyladenosinerelatedlncrnasarepotentialbiomarkersforpredictingprognosisandimmuneresponseinuterinecorpusendometrialcarcinoma
AT zhangyixin n1methyladenosinerelatedlncrnasarepotentialbiomarkersforpredictingprognosisandimmuneresponseinuterinecorpusendometrialcarcinoma
AT nisenmiao n1methyladenosinerelatedlncrnasarepotentialbiomarkersforpredictingprognosisandimmuneresponseinuterinecorpusendometrialcarcinoma
AT liuwen n1methyladenosinerelatedlncrnasarepotentialbiomarkersforpredictingprognosisandimmuneresponseinuterinecorpusendometrialcarcinoma
AT dumulong n1methyladenosinerelatedlncrnasarepotentialbiomarkersforpredictingprognosisandimmuneresponseinuterinecorpusendometrialcarcinoma
AT baijianling n1methyladenosinerelatedlncrnasarepotentialbiomarkersforpredictingprognosisandimmuneresponseinuterinecorpusendometrialcarcinoma