The goal of this study was to determine whether a panel of @entity5 suppressor gene markers of allelic loss could serve as a representative indicator of gene damage and thereby provide further discriminative power over current staging systems for recurrence-free prognostication in @entity1 undergoing liver transplantation in the presence of @entity157 . The paraffin blocks from 103 cases of @entity157 were obtained, and cellular targets were selected for tissue microdissection genotyping. @entity5 suppressor gene loss was based on loss of heterozygosity situated within or adjacent to specific genes of interest (APC, @entity986 , @entity46182 , MET, MYC1, @entity9552 , @entity13225 , @entity876 , @entity983 ). Microdissected tissue was amplified using polymerase chain reaction (PCR) with flanking oligonucleotides bearing fluorescent labels designed for GeneScan fragment analysis; PCR products were separated by capillary electrophoresis. Normal microdissected tissue samples for each case were evaluated for informative status with respect to individual alleles for 18 microsatellites at 10 genomic loci-1p, 3p, 5q, 7q, 8q, 9p, 10q, 17p, 17q, 18q. The measure of allelic loss of heterozygosity combined with @entity5 number, @entity5 size, vascular invasion, lobar distribution, and @entity1 gender provide a highly discriminatory model for predicting @entity5 recurrence after liver transplantation. Using our previously developed artificial @entity1274 model in combination with the genotyping results, unambiguous predictions were made for 91 of the103 @entity1 (88.3%). Of these, 1 was lost to follow-up, and 9 died recurrence-free less than 3 years posttransplantation. For the remaining 81, the combined models predicted @entity5 recurrence outcomes with complete accuracy. Microdissection genotyping provides powerful supplementary discriminative information for @entity5 -free survival.

Genotyping of XXXX in liver transplant recipients adds predictive power for determining recurrence-free survival.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity986 :: ('1029', 'Gene') :: ['CDKN2A'] @entity5 :: ('MESH:D009369', 'Disease') :: ['tumor', 'cancer', 'Tumor'] @entity13225 :: ('79719', 'Gene') :: ['p34'] @entity876 :: ('7157', 'Gene') :: ['p53'] @entity1274 :: ('MESH:D006319', 'Disease') :: ['neural network'] @entity983 :: ('5728', 'Gene') :: ['PTEN'] @entity157 :: ('MESH:D006528', 'Disease') :: ['hepatocellular carcinoma'] @entity9552 :: ('4968', 'Gene') :: ['OGG1'] @entity46182 :: ('1630', 'Gene') :: ['DCC']
@entity157 :: (MESH:D006528,Disease) :: ['hepatocellular carcinoma']
