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Epithelial-mesenchymal transition and cancer stem cells, mediated by a long non-coding RNA, HOTAIR, are involved in cell malignant transformation induced by cigarette smoke extract.

Toxicology and applied pharmacology | 2015

The incidence of lung diseases, including cancer, caused by cigarette smoke is increasing, but the molecular mechanisms of gene regulation induced by cigarette smoke remain unclear. This report describes a long noncoding RNA (lncRNA) that is induced by cigarette smoke extract (CSE) and experiments utilizing lncRNAs to integrate inflammation with the epithelial-mesenchymal transition (EMT) in human bronchial epithelial (HBE) cells. The present study shows that, induced by CSE, IL-6, a pro-inflammatory cytokine, leads to activation of STAT3, a transcription activator. A ChIP assay determined that the interaction of STAT3 with the promoter regions of HOX transcript antisense RNA (HOTAIR) increased levels of HOTAIR. Blocking of IL-6 with anti-IL-6 antibody, decreasing STAT3, and inhibiting STAT3 activation reduced HOTAIR expression. Moreover, for HBE cells cultured in the presence of HOTAIR siRNA for 24h, the CSE-induced EMT, formation of cancer stem cells (CSCs), and malignant transformation were reversed. Thus, IL-6, acting on STAT3 signaling, which up-regulates HOTAIR in an autocrine manner, contributes to the EMT and to CSCs induced by CSE. These data define a link between inflammation and EMT, processes involved in the malignant transformation of cells caused by CSE. This link, mediated through lncRNAs, establishes a mechanism for CSE-induced lung carcinogenesis.

Pubmed ID: 25447409 RIS Download

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RRID:SCR_005012

Functional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.

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