IntroductionUrinary bladder carcinoma ranks the fourth position in malignancy incidence rates in men (6.1%) and the 17th position in women (1.6%). In general, neoplastic diseases should be approached from two perspectives: prevention with implementation of prophylactic measures and early diagnostics. Prophylactics is possible in the preclinical phase of neoplasm, being both justified and plausible in patients from high–risk groups. Thus, it is particularly important to select such groups, not only by referring to environmental carcinogenic factors (occupational and extra–occupational) but also from genetic predisposition, which may be conductive for neoplasm formation. The mutations / polymorphisms of CHEK2 and CYP1B1 genes predispose to neoplasm via multiorgan mechanisms, while the human papilloma virus (HPV) may participate in the neoplastic transformation as an environmental factor.Material and methods131 patients with diagnosed urinary bladder cancer were qualified to the study. Mutations/polymorphisms of CHEK2 (IVS2 + 1G > A gene, 1100delC, del5395, I157T) and CYP1B1– 355T/T were identified by the PCR in DNA isolated directly from the tumor and from peripheral blood. The ELISA test was used for the studies of 37 HPV genotypes in DNA, isolated tumour tissue.Results11 mutations of CHEK2 gene were found, 355T/T polymorphism if CYP1B1 gene occurred in 18 patients (12.9%). Oncogenic HPV was found in 36 (29.3%), out of 123 examined patients.ConclusionsThe concomitance of CHEK2 gene mutations or 355T/T polymorphism of CYP1B1 gene and the presence of oncogenic HPV types statistically significantly correlates with histological malignancy grades of urinary bladder carcinoma.

Kohonen self-organizing maps (SOMs) are unsupervised Artificial Neural Networks (ANNs) that are good for low-density data visualization. They easily deal with complex and nonlinear relationships between variables. We evaluated molecular events that characterize high- and low-grade BC pathways in the tumors from 104 patients. We compared the ability of statistical clustering with a SOM to stratify tumors according to the risk of progression to more advanced disease. In univariable analysis, tumor stage (log rank P = 0.006) and grade (P < 0.001), HPV DNA (P < 0.004), Chromosome 9 loss (P = 0.04) and the A148T polymorphism (rs 3731249) in CDKN2A (P = 0.02) were associated with progression. Multivariable analysis of these parameters identified that tumor grade (Cox regression, P = 0.001, OR.2.9 (95% CI 1.6–5.2)) and the presence of HPV DNA (P = 0.017, OR 3.8 (95% CI 1.3–11.4)) were the only independent predictors of progression. Unsupervised hierarchical clustering grouped the tumors into discreet branches but did not stratify according to progression free survival (log rank P = 0.39). These genetic variables were presented to SOM input neurons. SOMs are suitable for complex data integration, allow easy visualization of outcomes, and may stratify BC progression more robustly than hierarchical clustering.

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