METHODS: Demographic, clinical and genotype data were determined for 1122 women (267 cases and 855 controls) recruited from the University of Malaya Medical Centre in the Klang Valley, Kuala Lumpur. Relevant articles were identified from Pubmed, Embase, MEDLINE, and Web of Science. Extraction of data was carried out and summary estimates of the association between rs780094 and GDM were examined.
RESULTS: The frequency of risk allele C was significantly higher in the cases than controls (OR 1.34, 95% CI 1.09-1.66, P = 0.006). The C allele was also associated with increased level of random 2-hour fasting plasma glucose and pregravid body mass index. Meta-analysis further confirmed the association of the GCKR rs780094 with GDM (OR 1.32, 95% CI 1.14-1.52, P = 0.0001).
CONCLUSION: This study strongly suggests that GCKR rs780094-C is associated with increased risk of GDM.
AIM: To investigate the genotype frequencies of MLH1 promoter polymorphism -93G>A and to determine whether it could play any role in modulating familial and sporadic CRC susceptibility risk.
METHODS: A case-control study comprising of 104 histopathologically confirmed CRC patients as cases (52 sporadic CRC and 52 Lynch syndrome patients) and 104 normal healthy individuals as controls was undertaken. DNA was extracted from peripheral blood and the polymorphism was genotyped employing PCR-RFLP methods. The genotypes were categorized into homozygous wild type, heterozygous and homozygous variants. The risk association between these polymorphisms and CRC susceptibility risk was calculated using binary logistic regression analysis and deriving odds ratios (ORs).
RESULTS: When risk association was investigated for all CRC patients as a single group, the heterozygous (G/A) genotype showed a significantly higher risk for CRC susceptibility with an OR of 2.273, (95%CI: 1.133-4.558 and p-value=0.021). When analyzed specifically for the 2 types of CRC, the heterozygous (G/A) genotype showed significantly higher risk for sporadic CRC susceptibility with and OR of 3.714, (95%CI: 1.416-9.740 and p-value=0.008). Despite high OR value was observed for Lynch syndrome (OR: 1.600, 95%CI: 0.715-3.581), the risk was not statistically significant (P=0.253).
CONCLUSION: Our results suggest an influence of MLH1 promoter polymorphism -93G>A in modulating susceptibility risk in Malaysian CRC patients, especially those with sporadic disease.
AIMS: The aim of this study was to analyze the mutations in genes involved in CRC including MLH1, MSH2, KRAS, and APC genes.
METHODS: A total of 76 patients were recruited. We used the polymerase chain reaction-denaturing high-performance liquid chromatography for the detection of mutations in the mismatch repair (MMR) and APC genes and the PCR single-strand conformation polymorphism for screening of the KRAS gene mutations.
RESULTS: We identified 17 types of missense mutations in 38 out of 76 patients in our patients. Nine mutations were identified in the APC gene, five mutations were detected in the KRAS gene, and two mutations were identified in the MSH2 gene. Only one mutation was identified in MLH1. Out of these 17 mutations, eight mutations (47 %) were predicted to be pathogenic. Seven patients were identified with multiple mutations (3: MSH2 and KRAS, 1: KRAS and APC, 1: MLH1 and APC, 2: APC and APC).
CONCLUSIONS: We have established the PCR-DHPLC and PCR-SSCP for screening of mutations in CRC patients. This study has given a snapshot of the spectrum of mutations in the four genes that were analyzed. Mutation screening in patients and their family members will help in the early detection of CRC and hence will reduce mortality due to CRC.
METHODS: PNMA5 mutants were generated through deletion or site-directed mutagenesis and transiently expressed in human cancer cell lines to investigate their role in apoptosis, subcellular localization, and potential interaction with MOAP-1 through apoptosis assays, fluorescence microscopy, and co-immunoprecipitation studies, respectively.
RESULTS: Over-expressed human PNMA5 exhibited nuclear localization pattern in both MCF-7 and HeLa cells. Deletion mapping and mutagenesis studies showed that C-terminus of PNMA5 is responsible for nuclear localization, while the amino acid residues (391KRRR) within the C-terminus of PNMA5 are required for nuclear targeting. Deletion mapping and co-immunoprecipitation studies showed that PNMA5 interacts with MOAP-1 and N-terminal domain of PNMA5 is required for interaction with MOAP-1. Furthermore, co-expression of PNMA5 and MOAP-1 in MCF-7 cells significantly enhanced chemo-sensitivity of MCF-7 to Etoposide treatment, indicating that PNMA5 and MOAP-1 interact synergistically to promote apoptotic signaling in MCF-7 cells.
CONCLUSIONS: Our results show that PNMA5 promotes apoptosis signaling in HeLa and MCF-7 cells and interacts synergistically with MOAP-1 through its N-terminal domain to promote apoptosis and chemo-sensitivity in human cancer cells. The C-terminal domain of PNMA5 is required for nuclear localization; however, both N-and C-terminal domains of PNMA5 appear to be required for pro-apoptotic function.
RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.