Identification and analysis of single nucleotide polymorphisms in matrix metallopeptidase 2 and 3 genes in Malaysian breast cancer patients - Chan, Soon Choy (2013)
Breast cancer is the most common cancer among women worldwide as well as in Malaysia. However, it is the process of metastasis in which cancerous cells spread to distant sites from its site of origin that has contributed up to 90% of cancer related mortality. In breast cancer, the matrix metallopeptidase 2 (MMP2) and matrix metallopeptidase 3 (MMP3) genes have been reported to be involved in metastasisand this is supported by both in vitro and in vivo studies. However, the existing literature has not addressed the influence of coding single nucleotide polymorphisms (SNPs) in these genes towards breast cancer metastasis. Hence, this study is designed to be exploratory in nature, utilising the candidate gene approach to investigate the influence of SNP and its haplotype in MMP2 and MMP3 genes on metastasis in Malaysian breast cancer patients. The combination of high resolution melting (HRM) analysis and DNA sequencing was established as a SNP detection strategy, which successfully identified 26 known SNPs, 10 novel SNPs and 1 novel deletion in both MMP2 and MMP3 genes. All the novel SNPs and the novel deletion have been deposited into the SNP Database (dbSNP) and have been released in database version of Build 132. Comparison of SNP genotypes across three different sources of DNA consisting of blood, adjacent normal tissue, and carcinoma tissue shows 100% concordance. This finding suggests that no somatic mutation occurred in both the MMP genes. It could be implied that any statistically significant SNPs identified in subsequent analysis are inherited lowpenetrant variants that can potentially serve as predictive markers. Statistical analysis identified SNPs that may confer protective effect against metastasis of breast cancer patients. The identified SNPs are c.678G>C of MMP2 gene, and c.133A>G, c.288T>C, c.626‐14A>G of MMP3 gene. In addition, a logistic regression model for predicting metastasis status of the patients was built and the overall accuracy of the model was 76.7%. Bioinformatics analysis predicted four SNPs in both MMP2 (c.678G>C, c.750C>T, c.1806C>T, and c.1842C>G) and MMP3 (c.133A>G, c.288T>C, c.306C>G, and c.*129T>C) to exert major effects in changing the secondary structure of its mRNA. Such mRNA structural changes could possibly lead to lower expression levels due to their instable structure. Phylogenetics analysis showed that negative (purifying) selection acted upon both MMP2 and MMP3 genes in eliminating deleterious non‐synonymous SNPs from the breast cancer patient population. This explained the identification of only three non‐synonymous SNPs (MMP2: c.344G>A and c.1499G>A; MMP3: c.133A>G) among the breast cancer patients. Additionally, it is suggested that deleterious synonymous SNPs that confer protective effect against metastasis may possibly be experiencing balancing (positive) selection. It is hoped that findings from this study have contributed towards new knowledge on the genetic basis of SNPs in MMP2 and MMP3 genes in breast cancer metastasis.