The biochemical relapse of prostate cancer was determined if there were two consecutive
rises of prostate-specific antigen (PSA) level above 0.2 ng/mL or according to the attending physician’s opinion, there was a need for adjuvant treatment even with selleck kinase inhibitor onetime rise of PSA level above 0.2 ng/mL.\n\nResults. There was no significant difference between the HGPIN and non-HGPIN groups in terms of time to biochemical relapse and frequency of biochemical relapses, time before surgery, the timing of the HGPIN diagnosis, age, or PSA level. After radical prostatectomy, patients in the HGPIN group were found to have significantly more often poorer cancer cell differentiation according to the Gleason score (>= 7 vs. <7; P=0.001) buy LDN-193189 and higher TNM stage (T3a,b vs. T2a,b,c; P=0.001). Fewer positive resection
margins were diagnosed in the HGPIN group (P=0.05). The groups did not differ in terms of the degree of differentiation according to the Gleason score or perineural invasion (P=0.811 and P=0.282, respectively).\n\nConclusions. HGPIN was more often associated with the characteristics of the poor prognosis for relapse of prostate cancer: poorer tumor cell differentiation according to the Gleason score and more cases of higher TNM stage. HGPIN did not have any influence on biochemical relapse of the disease during the short-term follow-up.”
“Association mapping is a powerful approach for exploring the molecular basis of phenotypic variations in plants. A peanut (Arachis hypogaea L.) mini-core
collection in China comprising 298 accessions was genotyped using 109 simple sequence repeat selleck (SSR) markers, which identified 554 SSR alleles and phenotyped for 15 agronomic traits in three different environments, exhibiting abundant genetic and phenotypic diversity within the panel. A model-based structure analysis assigned all accessions to three groups. Most of the accessions had the relative kinship of less than 0.05, indicating that there were no or weak relationships between accessions of the mini-core collection. For 15 agronomic traits in the peanut panel, generally the Q+K model exhibited the best performance to eliminate the false associated positives compared to the Q model and the general linear model-simple model. In total, 89 SSR alleles were identified to be associated with 15 agronomic traits of three environments by the Q+K model-based association analysis. Of these, eight alleles were repeatedly detected in two or three environments, and 15 alleles were commonly detected to be associated with multiple agronomic traits. Simple sequence repeat allelic effects confirmed significant differences between different genotypes of these repeatedly detected markers. Our results demonstrate the great potential of integrating the association analysis and marker-assisted breeding by utilizing the peanut mini-core collection.