Bioinformatic Analysis Identified Hub Genes Associated with Heterocyclic Amines Induced Cytotoxicity of Peripheral Blood Mononuclear Cells

Abstract

Heterocyclic amines (HCAs) are a set of food contaminants that may exert a cytotoxic effect on human peripheral blood mononuclear cells (PBMC). However, the genetic mechanism underlying the cytotoxicity of HCAs on PBMC has not been investigated. In the study, bioinformatic analysis on gene dataset GSE19078 was performed. The results of weighted correlation network analysis and linear models for microarray and RNA-seq data analysis showed that four gene modules were relevant to 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) exposure while one gene module was correlated with 2-amino-3-methyl-3H-imidazo[4,5f]quinoline (IQ) exposure. Gene functional analysis showed that the five modules were annotated mainly with mRNA transcriptional regulation, mitochondrial function, RNA catabolic process, protein targeting, and immune function. Five genes, MIER1, NDUFA4, MLL3, CD53 and CSF3 were recognized as the feature genes for each hub gene network of the corresponding gene module, and the expression of feature genes was observed with a significant difference between the PhIP/IQ samples and the other samples. Our results provide novel genes and promising mechanisms for exploration on the genetic mechanism of HCAs on PBMC.

Identification of Potential Diagnostic Biomarkers and Biological Pathways in Hypertrophic Cardiomyopathy Based on Bioinformatics Analysis

“…The Gene Expression Omnibus (GEO) database is a public database that contains a large resource of high-throughput gene expression data submitted by researchers around the world. Weighted gene coexpression network analysis (WGCNA) is a systems biology method for describing the correlation patterns among genes across microarray samples, and this method can be employed to identify candidate biomarkers or therapeutic targets [ 7 ], which has been widely used in various disease research studies [ 8 , 9 , 10 ]. The least absolute shrinkage and selection operator (LASSO) is a machine learning method to identify the best feature from high-dimensional data with a high predictive value and low correlation [ 11 ], which can significantly improve the accuracy and predictive value of key genes identified from microarray and high-throughput data [ 12 ].…”

The exploration of the pathogenesis and causative relationship of hypertrophic cardiomyopathy mediated by STAT3 through bioinformatics and Mendelian randomization

“…WGCNA represents a systematic biological approach that has proven instrumental in identifying candidate biomarkers or therapeutic targets 19 . This method has found wide application across diverse disease studies [20][21][22] . In this study, we initiated our investigation by retrieving datasets related to patients with HCM and healthy controls, speci cally GSE36961 and GSE32452, from the GEO database.…”

Mitochondrial respiratory chain component NDUFA4: a promising therapeutic target for gastrointestinal cancer

“…Besides, NDUFA4 gene silencing is associated with the response to abatacept (AB) in patients with rheumatoid arthritis (RA) [ 20 ]. Similarly, the NDUFA4 gene has also been identified as a central gene associated with heterocyclic amine-induced cytotoxicity in peripheral blood monocytes [ 21 ]. High expression of NDUFA4 is significantly associated with poorer overall survival (OS) in patients with bacterial sepsis [ 22 ].…”