Anti-CALCA monoclonal antibody, clone 5002

Although the occurrence of bladder cancer is common, the molecular events underlying the pathogenesis of this cancer remain ill-defined. A loss of heterozygosity (LOH) at specific chromosomal loci may predispose individuals to the development of bladder cancer but this has not been examined in detail. Furthermore, the role that deletion or inactivation of putative tumour suppressor genes might play in the genesis of bladder cancer has not been established. In this study, allelic deletion analysis on the short arm of chromosome 17 of patients with primary bladder tumours failed to show deletion at 17p13 (0/7), a region known to contain the p53 tumour suppressor gene. Chromosome 11p15 showed allelic deletion at the IGF2 locus (2/7: 29%) and the PTH locus (1/11:9%). However, no deletion was observed at the CALCA locus (0/6). LOH at 11p13, a region containing the Wilm's tumour suppressor gene (WT1), was also studied. Analysis of LOH at 11p13 showed deletion at the CAT locus (13/18: 72%), the DELTAJ/D11S414 locus (5115: 33%), the WT1 locus (7/14: 50%) and the FSHB locus (6/16: 38%). The significance of these findings is discussed.

Background. Symptoms and signs (symptoms in brief) are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM). To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. Methods. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. Results. The proposed method gets reliable gene rank list with AUC(area under curve) 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Conclusions. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.