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

Xixia Zhang; Xiao Li; Caixia Wang; Shuang Wang; Yuan Zhuang; Bing Liu; Xin Lian

doi:10.3389/fendo.2023.1174911

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

Front Endocrinol (Lausanne). 2023 Jul 19:14:1174911. doi: 10.3389/fendo.2023.1174911. eCollection 2023.

Authors

Xixia Zhang¹, Xiao Li², Caixia Wang², Shuang Wang², Yuan Zhuang², Bing Liu¹, Xin Lian¹

Affiliations

¹ Department of Otolaryngology Head and Neck Surgery, Shengjing Hospital of China Medical University, Shenyang, China.
² Department Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.

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. 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.

Keywords: endocrine; immune checkpoint blockade; immunotherapy; miRNA-mRNA network; micro RNAs; nasopharyngeal cancer; risk model.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Carcinoma*
Gene Expression Profiling
Gene Regulatory Networks
Humans
Machine Learning
MicroRNAs* / metabolism
Nasopharyngeal Carcinoma / genetics
Nasopharyngeal Neoplasms* / genetics
Nasopharyngeal Neoplasms* / metabolism
Nasopharyngeal Neoplasms* / pathology
Prognosis
RNA, Messenger / metabolism

Substances

MicroRNAs
RNA, Messenger

Grants and funding

This work was supported by Key Projects of Intergovernmental Cooperation in National Key R&D Programs (Grant no. 2022YFE0131800), Natural Science Foundation of Liaoning Province (Grant no. 2022-MS-235), and Project of Shenyang Science and Technology Bureau (Grant no. RC210316).