Significant Association Among the Epigenetic Alterations in EGFR and ALK Genes and Non–Small Cell Lung Cancer: A Prospective Emerging Molecular Biomarker
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引用次数: 0
Abstract
Background
Non–small cell lung cancer (NSCLC) is increasingly recognized as a heterogeneous set of diseases at the molecular level, and these differences likely could drive therapeutic decision-making. The anaplastic lymphoma kinase (ALK) and epidermal growth factor receptor (EGFR) genes are two key oncogenes of tyrosine kinase that their expression is increased in lung cancer and activates their downstream signaling cascade. These two genes have been identified as therapeutic targets, and targeted therapies for these genes by tyrosine kinase inhibitors have been approved by the FDA.
Objective
We aimed to elucidate the epigenetic alterations in ALK and EGFR genes in NSCLC patients.
Methods
Fifty tissue samples of the paired NSCLC samples and adjacent normal tissues were evaluated by methylation-specific polymerase chain reaction (MS-PCR). Subsequently, the methylation status of the EGFR and ALK genes promoter was examined by bioinformatics tools such as UCSC, GTEx portal, and iMETHYL servers.
Results
There was a significant difference in the methylation pattern of EGFR (Region II) (p = 0.02) between the case and control groups and also that patients who were methylated in this region of the EGFR promoter had a higher stage than non-methylated patients, which was statistically significant (p = 0.03).
Conclusion
We analyzed methylation changes of EGFR and ALK genes in patients suffering from NSCLC. Alteration of EGFR gene methylation pattern could play an important role in the pathogenesis of NSCLC. To delineate the potential role of ALK somatic alterations, further investigations are warranted.
期刊介绍:
Annals of Human Genetics publishes material directly concerned with human genetics or the application of scientific principles and techniques to any aspect of human inheritance. Papers that describe work on other species that may be relevant to human genetics will also be considered. Mathematical models should include examples of application to data where possible.
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