Matrix Metalloproteinase-9 (MMP-9) polymorphisms in patients with cutaneous malignant melanoma

BACKGROUND: Cutaneous Malignant Melanoma causes over 75% of skin cancer-related deaths, and it is clear that many factors may contribute to the outcome. Matrix Metalloproteinases (MMPs) play an important role in the degradation and remodeling of the extracellular matrix and basement membrane that, in turn, modulate cell division, migration and angiogenesis. Some polymorphisms are known to influence gene expression, protein activity, stability, and interactions, and they were shown to be associated with certain tumor phenotypes and cancer risk. METHODS: We tested seven polymorphisms within the MMP-9 gene in 1002 patients with melanoma in order to evaluate germline genetic variants and their association with progression and known risk factors of melanoma. The polymorphisms were selected based on previously published reports and their known or potential functional relevance using in-silico methods. Germline DNA was then genotyped using pyrosequencing, melting temperature profiles, heteroduplex analysis, and fragment size analysis. RESULTS: We found that reference alleles were present in higher frequency in patients who tend to sunburn, have family history of melanoma, higher melanoma stage, intransit metastasis and desmoplastic melanomas among others. However, after adjustment for age, sex, phenotypic index, moles, and freckles only Q279R, P574R and R668Q had significant associations with intransit metastasis, propensity to tan/sunburn and primary melanoma site. CONCLUSION: This study does not provide strong evidence for further investigation into the role of the MMP-9 SNPs in melanoma progression

Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.</p> <p>Results</p> <p>This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|H_i), which is used as confidence level. The unit network with higher P(D|H_i) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|H_i), which is a unique property of the proposed algorithm.</p> <p>The algorithm is evaluated with synthetic and <it>Saccharomyces cerevisiae </it>expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.</p> <p>The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and <it>Saccharomyces cerevisiae </it>expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.</p> <p>Conclusion</p> <p>From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.</p

Genetic polymorphisms in MMP 2, 3, 7, and 9 genes and the susceptibility and clinical outcome of cervical cancer in a Chinese Han population

Matrix metalloproteases (MMPs) are proteolytic enzymes that contribute to all stages of tumor progression, including the invasion and metastasis. However, there are no data about the role of MMP polymorphism in the development of cervical cancer. A hospital-based case–control study was conducted in 230 patients with cervical cancer and 230 healthy controls to investigate the possible association between the MMP2 rs243865, MMP3 rs3025058, MMP7 rs11568818, and MMP9 rs3918242 polymorphisms, respectively, and the risk of cervical cancer. Our results suggested that the MMP2 rs243865-1306 C/T was significantly associated with an increased risk of cervical cancer (CT vs. CC, OR = 1.46; 95 % CI 1.18–3.55; P = 0.032; TT vs. CC, OR = 1.72; 95 % CI 1.28–4.02; P = 0.031; CT + TT vs. CC, OR = 1.43; 95 % CI 1.21–3.44; P = 0.029). Similarly, the MMP7 rs11568818-181A/G genotypes can also elevate the risk of cervical cancer in all genetic models. However, the genotype and allele frequencies of MMP3 rs3025058 and MMP9 rs3918242 polymorphisms in cervical cancer patients were not significantly different from controls. Further analysis showed MMP2 rs243865 and MMP7 rs11568818 genotypes were associated with advanced tumor stages of cervical cancer patients. More interestingly, the MMP2 rs243865 and MMP7 rs11568818 genotype was statistically significantly associated with a poor survival in cervical cancer patients. Our results showed that the MMP2 rs243865 and MMP7 rs11568818 genotypes e were associated with increased susceptibility and development of cervical cancer in Chinese Han population