In Silico Data Mining of Single Nucleotide Polymorphisms in EZH2 and Their Role in Cancer

Background: Enhancer of zeste homolog 2 (EZH2) is a catalytic subunit of Polycomb Repressor Complex 2. PRC2 catalyzes methylation of H3K27me and it silences specific gene transcriptions. EZH2 is known to play a vital role in cancer initiation, development, progression, metastasis, and drug resistance. The expression of EZH2 is regulated by a variety of oncogenic transcription factors, tumor suppressor micro-RNAs, and cancer-associated non-coding RNAs. Post-translational modifications also control EZH2 activity. The altered expression of EZH2 has major implication in altering cellular plasticity and, hence, understanding various deleterious mutations can help comprehend its role in cancer metastasis. Objectives: The aim of this study is to summarize the data from COSMIC into useful information from the perspective of severity of the mutations in EZH2 and their contributory role as a potential biomarker in diagnosis and therapeutics associated cancers. Methods: Data mining was carried out for various SNPs in EZH2 SET domain from COSMIC, and the severity of each mutation on the functionality of the enzyme was analyzed, using multiple online in-silico tools. The frequently deleterious SNPs were further subjected to advanced tools to understand the changes which render the enzyme functionally erratic during cancer. Results: The results obtained enhanced the understanding of EZH2 mutation and predicted the plausible biomarkers that could be targeted for the purpose of diagnosis and therapeutics. About 14 prospective biomarkers for various cancers were identified and, further, their role in altering the EZH2 function was discussed. Conclusions: The various predictive and prognostic impacts of these SNPs in the selected residues are discussed which can be efficiently targeted for an improved cancer diagnosis and designing appropriate treatment strategies.