Cargando…

Prognostic model based on m6A-associated lncRNAs in esophageal cancer

BACKGROUND: This research aimed to build an m6A-associated lncRNA prognostic model of esophageal cancer that can be used to predict outcome in esophageal cancer patients. METHODS: RNA sequencing transcriptome data and clinical information about patients with esophageal cancer were obtained according...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Weidong, Dong, Danhong, Yu, Pengfei, Chen, Tong, Gao, Ruiqi, Wei, Jiangpeng, Mo, Zhenchang, Zhou, Haikun, Yang, Qinchuan, Yue, Chao, Yang, Xisheng, Li, Xiaohua, Ji, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9468245/
https://www.ncbi.nlm.nih.gov/pubmed/36111294
http://dx.doi.org/10.3389/fendo.2022.947708
_version_ 1784788368191651840
author Wang, Weidong
Dong, Danhong
Yu, Pengfei
Chen, Tong
Gao, Ruiqi
Wei, Jiangpeng
Mo, Zhenchang
Zhou, Haikun
Yang, Qinchuan
Yue, Chao
Yang, Xisheng
Li, Xiaohua
Ji, Gang
author_facet Wang, Weidong
Dong, Danhong
Yu, Pengfei
Chen, Tong
Gao, Ruiqi
Wei, Jiangpeng
Mo, Zhenchang
Zhou, Haikun
Yang, Qinchuan
Yue, Chao
Yang, Xisheng
Li, Xiaohua
Ji, Gang
author_sort Wang, Weidong
collection PubMed
description BACKGROUND: This research aimed to build an m6A-associated lncRNA prognostic model of esophageal cancer that can be used to predict outcome in esophageal cancer patients. METHODS: RNA sequencing transcriptome data and clinical information about patients with esophageal cancer were obtained according to TCGA. Twenty-four m6A-associated genes were selected based on previous studies. m6A-associated lncRNAs were determined through Pearson correlation analysis. Three m6A-associated lncRNA prognostic signatures were built through analysis of the training set using univariate, LASSO, and multivariate Cox regression. To validate the stabilization of the risk signature, Kaplan–Meier and ROC curve analyses were performed on the testing and complete sets. The prognoses of EC patients were predicted quantitatively by building a nomogram. GSEA was conducted to analyze the underlying signaling pathways and biological processes. To identify the underlying mechanisms through which the lncRNAs act, we constructed a PPI network and a ceRNA network and conducted GO and KEGG pathway analyses. EC samples were evaluated using the ESTIMATE algorithm to compute stromal, immune, and estimate scores. The ssGSEA algorithm was used to quantitatively infer immune cell infiltration and immune functions. The TIDE algorithm was performed to simulate immune evasion and predict the response to immunotherapy. RESULTS: We identified and validated an m6A-associated lncRNA risk model in EC that could correctly and reliably predict the OS of EC patients. The ceRNA network, PPI network, and GO and KEGG pathway analyses confirmed and the underlying mechanisms and functions provided enlightenment regarding therapeutic strategies for EC. Immunotherapy responses were better in the low-risk subgroup, and PD-1 and CTLA4 checkpoint immunotherapy benefited the patients in the low-risk subgroup. CONCLUSIONS: We constructed a new m6A-related lncRNA prognostic risk model of EC, based on three m6A-related lncRNAs: LINC01612, AC025166.1 and AC016876.2, that can predict the prognoses of EC patients.
format Online
Article
Text
id pubmed-9468245
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-94682452022-09-14 Prognostic model based on m6A-associated lncRNAs in esophageal cancer Wang, Weidong Dong, Danhong Yu, Pengfei Chen, Tong Gao, Ruiqi Wei, Jiangpeng Mo, Zhenchang Zhou, Haikun Yang, Qinchuan Yue, Chao Yang, Xisheng Li, Xiaohua Ji, Gang Front Endocrinol (Lausanne) Endocrinology BACKGROUND: This research aimed to build an m6A-associated lncRNA prognostic model of esophageal cancer that can be used to predict outcome in esophageal cancer patients. METHODS: RNA sequencing transcriptome data and clinical information about patients with esophageal cancer were obtained according to TCGA. Twenty-four m6A-associated genes were selected based on previous studies. m6A-associated lncRNAs were determined through Pearson correlation analysis. Three m6A-associated lncRNA prognostic signatures were built through analysis of the training set using univariate, LASSO, and multivariate Cox regression. To validate the stabilization of the risk signature, Kaplan–Meier and ROC curve analyses were performed on the testing and complete sets. The prognoses of EC patients were predicted quantitatively by building a nomogram. GSEA was conducted to analyze the underlying signaling pathways and biological processes. To identify the underlying mechanisms through which the lncRNAs act, we constructed a PPI network and a ceRNA network and conducted GO and KEGG pathway analyses. EC samples were evaluated using the ESTIMATE algorithm to compute stromal, immune, and estimate scores. The ssGSEA algorithm was used to quantitatively infer immune cell infiltration and immune functions. The TIDE algorithm was performed to simulate immune evasion and predict the response to immunotherapy. RESULTS: We identified and validated an m6A-associated lncRNA risk model in EC that could correctly and reliably predict the OS of EC patients. The ceRNA network, PPI network, and GO and KEGG pathway analyses confirmed and the underlying mechanisms and functions provided enlightenment regarding therapeutic strategies for EC. Immunotherapy responses were better in the low-risk subgroup, and PD-1 and CTLA4 checkpoint immunotherapy benefited the patients in the low-risk subgroup. CONCLUSIONS: We constructed a new m6A-related lncRNA prognostic risk model of EC, based on three m6A-related lncRNAs: LINC01612, AC025166.1 and AC016876.2, that can predict the prognoses of EC patients. Frontiers Media S.A. 2022-08-30 /pmc/articles/PMC9468245/ /pubmed/36111294 http://dx.doi.org/10.3389/fendo.2022.947708 Text en Copyright © 2022 Wang, Dong, Yu, Chen, Gao, Wei, Mo, Zhou, Yang, Yue, Yang, Li and Ji 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 Endocrinology
Wang, Weidong
Dong, Danhong
Yu, Pengfei
Chen, Tong
Gao, Ruiqi
Wei, Jiangpeng
Mo, Zhenchang
Zhou, Haikun
Yang, Qinchuan
Yue, Chao
Yang, Xisheng
Li, Xiaohua
Ji, Gang
Prognostic model based on m6A-associated lncRNAs in esophageal cancer
title Prognostic model based on m6A-associated lncRNAs in esophageal cancer
title_full Prognostic model based on m6A-associated lncRNAs in esophageal cancer
title_fullStr Prognostic model based on m6A-associated lncRNAs in esophageal cancer
title_full_unstemmed Prognostic model based on m6A-associated lncRNAs in esophageal cancer
title_short Prognostic model based on m6A-associated lncRNAs in esophageal cancer
title_sort prognostic model based on m6a-associated lncrnas in esophageal cancer
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9468245/
https://www.ncbi.nlm.nih.gov/pubmed/36111294
http://dx.doi.org/10.3389/fendo.2022.947708
work_keys_str_mv AT wangweidong prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer
AT dongdanhong prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer
AT yupengfei prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer
AT chentong prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer
AT gaoruiqi prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer
AT weijiangpeng prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer
AT mozhenchang prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer
AT zhouhaikun prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer
AT yangqinchuan prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer
AT yuechao prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer
AT yangxisheng prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer
AT lixiaohua prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer
AT jigang prognosticmodelbasedonm6aassociatedlncrnasinesophagealcancer