Alterations of miRNAs and Their Potential Roles in Arsenite-Induced Transformation of Human Bronchial Epithelial Cells

Abstract

The alterations of micro RNAs (miRNAs) and their potential roles in arsenite-induced tumorigenesis are still poorly understood. In this study, miRNA Array was used to detect the expression level of miRNAs in human bronchial epithelial (HBE) cells that were transformed by 2.5 μM arsenite for 13 weeks. These cells exhibited a neoplastic phenotype manifested by increased levels of cellular proliferation and migration and clone formation. Subsequently, 191 dysregulated miRNAs were identified to be associated with arsenite-induced transformation by miRNA Array. Among them, six miRNAs were validated by their expression levels with quantitative real-time polymerase chain reaction (qPCR), and 17 miRNAs were further explored via their target genes as well as regulatory network. Three databases, TargetMiner, miRDB, and TarBase, were used to predict the target genes of the 17 miRNAs, and a total of 954 common genes were sorted. Results of Gene Ontology (GO) analyses showed that the 954 genes were involved in diverse terms of GO categories, such as positive regulation of macroautophagy, epithelial cell maturation, and synaptic vesicle clustering. Moreover, results of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses demonstrated that most of these target genes were enriched in various cancer-related pathways, including non-small cell lung cancer, Wnt signaling pathway, cell cycle, and p53 signaling pathway. The miRNA-gene regulatory network, which was constructed by cytoscape software with miRNAs and their target genes, showed that miR-15b-5p, miR-106b-5p, and miR-320d were the core hubs. Collectively, our results provide new insights into miRNA-mediated mechanisms underlying arsenite-induced transformation, although more experimental verification is still needed to prove these predictions.

The Role of microRNAs in Arsenic-Induced Human Diseases: A Review

“…Therefore, miR-106b, miR-15b, miR-192b, miR-33b, miR-200b, and miR-141 were involved in the malignant transformation of arsenicinduced lung cancer. 132 The role of HIF-2α regulation of miR-191 was investigated to reveal the mechanisms of angiogenesis and metastasis of arsenic-induced lung cancer. 133 In HBE and HUVECs, HIF-2α up-regulated miR-191 via binding to the hypoxia response element (HRE) on the region of miR-191 promoter.…”

“…The target genes were enriched in multiple cancer pathways, such as cell cycle, Wnt, and p53 signaling pathways. Therefore, miR-106b, miR-15b, miR-192b, miR-33b, miR-200b, and miR-141 were involved in the malignant transformation of arsenic-induced lung cancer . The role of HIF-2α regulation of miR-191 was investigated to reveal the mechanisms of angiogenesis and metastasis of arsenic-induced lung cancer .…”

MicroRNA–Gene Interactions Impacted by Toxic Metal(oid)s during EMT and Carcinogenesis

“…Recent studies have identified new miRNA targets in arsenic carcinogenesis. A miRNA microarray study using arsenite-transformed cells found that miR-33b was downregulated [ 63 ]. miR-33b is a tumor-suppressing miRNA and has been shown to bind to the 3′-UTR of ZEB1, inhibiting its expression, and suppressing Wnt/β-catenin signaling [ 64 ].…”

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

“…As a novel approach with a large advantage in biomedical research, more and more toxicological studies have employed bioinformatic analysis as a novel method exploring the toxicology, especially in the terms of the toxicological mechanism. The authors have successfully used bioinformatic analysis in the exploration of mechanism toxicology of PhIP and other carcinogens [ 12 , 13 ], and found interesting changes in colonic transcriptome induced by PhIP [ 12 ], suggesting that bioinformatic analysis might be a good utility for exploring the toxicological mechanism of food contaminants. In the last ten years, more and more novel methods of bioinformatic analysis, such as weighted gene correlation network analysis (WGCNA) and linear models for microarray and RNA-seq data (LIMMA) analysis, have been established to identify the potential signaling pathways and plausible central modulating genes (hub genes) [ 14 ] in biomedical research.…”