AUTHOR=Zhang Xixia , Li Xiao , Wang Caixia , Wang Shuang , Zhuang Yuan , Liu Bing , Lian Xin 

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

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1174911

DOI=10.3389/fendo.2023.1174911

ISSN=1664-2392

ABSTRACT=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. 12 types of miRNA tools were implemented to study potential target genes (mRNAs) of miRNAs. Univariate Cox regression and stepAIC were introduced to construct risk models. Pearson analysis was conduct to analyze the correlation between endocrine metabolism and RiskScore. Single sample gene set enrichment analysis (ssGSEA), MCP-counter, and ESTIMATE performed immune analysis. The response to immunotherapy was predicted by TIDE and SubMap analysis.
Results: Two key miRNAs (miR-142-3p and miR-93) were closely involved in NPC prognosis. The two miRNAs expression 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. 5 target genes (E2F1, KCNJ8, SUCO, HECTD1, and KIF23) were identified to construct a prognostic model, which was used to divided patients into high group and low group. RiskScore was negatively correlated to most endocrine related genes and pathways. Low-risk group manifested higher immune infiltration, anti-cancer response, more activated immune-related pathways, and higher response to immunotherapy than 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 5-gene model in predicting NPC prognosis as well as endocrine metabolism provided a guidance for personalized immunotherapy in NPC patients.