@article {T{\"U}RE-{\"O}ZDEMIR3697, author = {FILIZ T{\"U}RE-{\"O}ZDEMIR and MARIA GAZOULI and MICHALIS TZIVRAS and CHARALAMBOS PANAGOS and NIKOLAOS BOVARETOS and KALLIOPI PETRAKI and ATHANASSIOS GIANNAKOPOULOS and PENELOPE KORKOLOPOULOU and GERASSIMOS J. MANTZARIS}, title = {Association of Polymorphisms of NOD2, TLR4 and CD14 Genes with Susceptibility to Gastric Mucosa-associated Lymphoid Tissue Lymphoma}, volume = {28}, number = {6A}, pages = {3697--3700}, year = {2008}, publisher = {International Institute of Anticancer Research}, abstract = {Background: The clinical course of Helicobacter pylori infection is highly variable and is influenced by both microbial and host factors, including the genetic composition of the infecting strains and variations in the host immune responses. A genetic risk profile for gastric cancer has been identified, but genetic susceptibility to develop gastric mucosa-associated lymphoid tissue (MALT) lymphoma is unclear. The aim of this study was to evaluate the relationship between NOD2, TLR4 and CD14 genetic polymorphisms and the development of gastric MALT-lymphoma. Materials and Methods: Fifty-six patients with primary gastric MALT lymphoma and 51 patients with H. pylori infection were enrolled in this study. The polymorphisms were detected by the PCR-restriction fragment length polymorphism (RFLP) method of allele-specific PCR. Results: No polymorphisms in the NOD2 and TLR4 genes were found to be associated with the development of gastric MALT lymphoma. Carriers of the CD14 gene -159T allele had a marginally increased risk of developing gastric MALT lymphoma than the controls (p=0.042). Conclusion: The -159C/T genetic polymorphism of the CD14 gene may be implicated in the development of gastric MALT lymphoma. Copyright{\textcopyright} 2008 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved}, issn = {0250-7005}, URL = {https://ar.iiarjournals.org/content/28/6A/3697}, eprint = {https://ar.iiarjournals.org/content/28/6A/3697.full.pdf}, journal = {Anticancer Research} }