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

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 correlat

SUBMITTER: Zhang X 

PROVIDER: S-EPMC10396331 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature