Abstract Background. Helicobacter pylori infection is the main cause of gastric cancer. The disease progression is influenced by the host inflammatory responses, and cytokine single nucleotide polymorphisms (SNPs) may have a role in the course of the disease. The aim of our study was to investigate proinflammatory cytokine polymorphisms, previously associated with the development of gastric cancer, in a Slovenian population. Patients and methods. In total 318 patients and controls were selected for the study and divided into three groups: (i) patients with gastric cancer (n = 58), (ii) patients with chronic gastritis (n = 60) and (iii) healthy control group (n = 200). H. pylori infection in patient groups was determined by serology, histology and culture. Four proinflammatory gene polymorphisms were determined (IL-1β, IL-1rα, TNF-α, TLR-4) in all subjects. Results. We found a statistically significant difference between males and females for the groups (p = 0.025). Odds ratio (OR) for gastric cancer risk for females was 0.557 (95% confidence interval [CI]: 0.233―1.329) and for chronic gastritis 2.073 (95% CI: 1.005―4.277). IL-1B-511*T/T homozygous allele for cancer group had OR = 2.349 (95% CI: 0.583―9.462), heterozygous IL-1B-511*T had OR = 1.470 (95% CI: 0.583―3.709) and heterozygotes in TNF-A-308 genotype for chronic gastritis had OR = 1.402 (95% CI: 0.626―3.139). Other alleles had OR less than 1. Conclusions. We could not prove association between gastric cancer and chronic gastritis due to H. pylori in any cytokine SNPs studied in Slovenian population. Other SNPs might be responsible besides infection with H. pylori for the progression from atrophy to neoplastic transformation.

In this note we derive a simple Bayesian sampler for linear regression with the horseshoe hierarchy. A new interpretation of the horseshoe model is presented, and extensions to logistic regression and alternative hierarchies, such as horseshoe+, are discussed. Due to the conjugacy of the proposed hierarchy, Chib's algorithm may be used to easily compute the marginal likelihood of the model.