Insights from Studies with Oral Cleft Genes Suggest Associations between WNT-pathway Genes and Risk of Oral Cancer

Oral squamous cell carcinoma (OSCC) accounts for more than 90% of the malignant neoplasms that arise in the mucosa of the upper aerodigestive tract. Recent studies of cleft lip/palate have shown the association of genes involved in cancer. WNT pathway genes have been associated with several types of cancer and recently with cleft lip/palate. To investigate if genes associated with cleft lip/palate were also associated with oral cancer, we genotyped 188 individuals with OSCC and 225 control individuals for markers in AXIN2, AXIN1, GSK3 beta, WNT3A, WNT5A, WNT8A, WNT11, WNT3, and WNT9B. Statistical analysis was performed with PLINK 1.06 software to test for differences in allele frequencies of each polymorphism between cases and controls. We found association of SNPs in GSK3B (p = 0.0008) and WNT11 (p = 0.03) with OSCC. We also found overtransmission of GSK3B haplotypes in OSCC cases. Expression analyses showed up-regulation of WNT3A, GSK3B, and AXIN1 and down-regulation of WNT11 in OSCC in comparison with control tissues (P < 0.001). Additional studies should focus on the identification of potentially functional variants in these genes as contributors to human clefting and oral cancer.

The effects of normalisation on the ability of business end-users to detect data anomalies: An experimental evaluation

With the proliferation of relational database programs for PC's and other platforms, many business end-users are creating, maintaining, and querying their own databases. More importantly, business end-users use the output of these queries as the basis for operational, tactical, and strategic decisions. Inaccurate data reduce the expected quality of these decisions. Implementing various input validation controls, including higher levels of normalisation, can reduce the number of data anomalies entering the databases. Even in well-maintained databases, however, data anomalies will still accumulate. To improve the quality of data, databases can be queried periodically to locate and correct anomalies. This paper reports the results of two experiments that investigated the effects of different data structures on business end-users' abilities to detect data anomalies in a relational database. The results demonstrate that both unnormalised and higher levels of normalisation lower the effectiveness and efficiency of queries relative to the first normal form. First normal form databases appear to provide the most effective and efficient data structure for business end-users formulating queries to detect data anomalies.