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Identification of markers for predicting prognosis and endocrine metabolism in nasopharyngeal carcinoma by miRNA–mRNA network mining and machine learning

BACKGROUND: Nasopharyngeal cancer (NPC) has a high incidence in Southern China and Asia, and its survival is extremely poor in advanced patients. MiRNAs play critical roles in regulating gene expression and serve as therapeutic targets in cancer. This study sought to disclose key miRNAs and target g...

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Autores principales: Zhang, Xixia, Li, Xiao, Wang, Caixia, Wang, Shuang, Zhuang, Yuan, Liu, Bing, Lian, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10396331/
https://www.ncbi.nlm.nih.gov/pubmed/37538797
http://dx.doi.org/10.3389/fendo.2023.1174911
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author Zhang, Xixia
Li, Xiao
Wang, Caixia
Wang, Shuang
Zhuang, Yuan
Liu, Bing
Lian, Xin
author_facet Zhang, Xixia
Li, Xiao
Wang, Caixia
Wang, Shuang
Zhuang, Yuan
Liu, Bing
Lian, Xin
author_sort Zhang, Xixia
collection PubMed
description BACKGROUND: Nasopharyngeal cancer (NPC) has a high incidence in Southern China and Asia, and its survival is extremely poor in advanced patients. MiRNAs play critical roles in regulating gene expression and serve as therapeutic targets in cancer. This study sought to disclose key miRNAs and target genes responsible for NPC prognosis and endocrine metabolism. MATERIALS AND METHODS: Three datasets (GSE32960, GSE70970, and GSE102349) of NPC samples came from Gene Expression Omnibus (GEO). Limma and WGCNA were applied to identify key prognostic miRNAs. There were 12 types of miRNA tools implemented to study potential target genes (mRNAs) of miRNAs. Univariate Cox regression and stepAIC were introduced to construct risk models. Pearson analysis was conducted to analyze the correlation between endocrine metabolism and RiskScore. Single-sample gene set enrichment analysis (ssGSEA), MCP-counter, and ESTIMATE were performed for immune analysis. The response to immunotherapy was predicted by TIDE and SubMap analyses. RESULTS: Two key miRNAs (miR-142-3p and miR-93) were closely involved in NPC prognosis. The expression of the two miRNAs was dysregulated in NPC cell lines. A total of 125 potential target genes of the key miRNAs were screened, and they were enriched in autophagy and mitophagy pathways. Five target genes (E2F1, KCNJ8, SUCO, HECTD1, and KIF23) were identified to construct a prognostic model, which was used to divide patients into high group and low group. RiskScore was negatively correlated with most endocrine-related genes and pathways. The low-risk group manifested higher immune infiltration, anticancer response, more activated immune-related pathways, and higher response to immunotherapy than the high-risk group. CONCLUSIONS: This study revealed two key miRNAs that were highly contributable to NPC prognosis. We delineated the specific links between key miRNAs and prognostic mRNAs with miRNA–mRNA networks. The effectiveness of the five-gene model in predicting NPC prognosis as well as endocrine metabolism provided a guidance for personalized immunotherapy in NPC patients.
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spelling pubmed-103963312023-08-03 Identification of markers for predicting prognosis and endocrine metabolism in nasopharyngeal carcinoma by miRNA–mRNA network mining and machine learning Zhang, Xixia Li, Xiao Wang, Caixia Wang, Shuang Zhuang, Yuan Liu, Bing Lian, Xin Front Endocrinol (Lausanne) Endocrinology BACKGROUND: Nasopharyngeal cancer (NPC) has a high incidence in Southern China and Asia, and its survival is extremely poor in advanced patients. MiRNAs play critical roles in regulating gene expression and serve as therapeutic targets in cancer. This study sought to disclose key miRNAs and target genes responsible for NPC prognosis and endocrine metabolism. MATERIALS AND METHODS: Three datasets (GSE32960, GSE70970, and GSE102349) of NPC samples came from Gene Expression Omnibus (GEO). Limma and WGCNA were applied to identify key prognostic miRNAs. There were 12 types of miRNA tools implemented to study potential target genes (mRNAs) of miRNAs. Univariate Cox regression and stepAIC were introduced to construct risk models. Pearson analysis was conducted to analyze the correlation between endocrine metabolism and RiskScore. Single-sample gene set enrichment analysis (ssGSEA), MCP-counter, and ESTIMATE were performed for immune analysis. The response to immunotherapy was predicted by TIDE and SubMap analyses. RESULTS: Two key miRNAs (miR-142-3p and miR-93) were closely involved in NPC prognosis. The expression of the two miRNAs was dysregulated in NPC cell lines. A total of 125 potential target genes of the key miRNAs were screened, and they were enriched in autophagy and mitophagy pathways. Five target genes (E2F1, KCNJ8, SUCO, HECTD1, and KIF23) were identified to construct a prognostic model, which was used to divide patients into high group and low group. RiskScore was negatively correlated with most endocrine-related genes and pathways. The low-risk group manifested higher immune infiltration, anticancer response, more activated immune-related pathways, and higher response to immunotherapy than the high-risk group. CONCLUSIONS: This study revealed two key miRNAs that were highly contributable to NPC prognosis. We delineated the specific links between key miRNAs and prognostic mRNAs with miRNA–mRNA networks. The effectiveness of the five-gene model in predicting NPC prognosis as well as endocrine metabolism provided a guidance for personalized immunotherapy in NPC patients. Frontiers Media S.A. 2023-07-19 /pmc/articles/PMC10396331/ /pubmed/37538797 http://dx.doi.org/10.3389/fendo.2023.1174911 Text en Copyright © 2023 Zhang, Li, Wang, Wang, Zhuang, Liu and Lian 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
Zhang, Xixia
Li, Xiao
Wang, Caixia
Wang, Shuang
Zhuang, Yuan
Liu, Bing
Lian, Xin
Identification of markers for predicting prognosis and endocrine metabolism in nasopharyngeal carcinoma by miRNA–mRNA network mining and machine learning
title Identification of markers for predicting prognosis and endocrine metabolism in nasopharyngeal carcinoma by miRNA–mRNA network mining and machine learning
title_full Identification of markers for predicting prognosis and endocrine metabolism in nasopharyngeal carcinoma by miRNA–mRNA network mining and machine learning
title_fullStr Identification of markers for predicting prognosis and endocrine metabolism in nasopharyngeal carcinoma by miRNA–mRNA network mining and machine learning
title_full_unstemmed Identification of markers for predicting prognosis and endocrine metabolism in nasopharyngeal carcinoma by miRNA–mRNA network mining and machine learning
title_short Identification of markers for predicting prognosis and endocrine metabolism in nasopharyngeal carcinoma by miRNA–mRNA network mining and machine learning
title_sort identification of markers for predicting prognosis and endocrine metabolism in nasopharyngeal carcinoma by mirna–mrna network mining and machine learning
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10396331/
https://www.ncbi.nlm.nih.gov/pubmed/37538797
http://dx.doi.org/10.3389/fendo.2023.1174911
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