We have also performed the analysis on some samples from healthy

We have also performed the analysis on some samples from healthy tissues, to confirm that the background noise was inferior to 0.20 cut-off, such excluding false positive learn more results due to experimental procedure. Statistical analysis Fisher’s exact test was used to compare the frequency of promoter methylation in the two subgroups: Kinase Inhibitor Library recurrent tumors versus non recurrent tumors. Methylation status was considered as a dichotomic variable and genes showing methylation ≥ 20% were classified as positive. A difference was considered significant if it showed a two-tailed P value ≤0.05. The genes showing a significant p value in Fisher’s exact test were used to analyze the methylator phenotype.

Study endpoints were sensitivity (the proportion of recurrent cancer patients who were correctly identified by the test or procedures) and specificity (the proportion of non recurrent cancer patients who were correctly identified), with their 95% confidence intervals (CIs). We also evaluated overall accuracy, defined as the proportion

of the total number of patients correctly identified by the test. The student’s T test was used to assess the methylation index (MI), which was considered as a continuous variable. Logistic regression selleck inhibitor analysis was performed using the Epicalc of R to evaluate the performance of a panel of gene promoters (HIC1, RASSF1 and GSTP1) in discriminating between recurrent and non recurrent patients. We created logistic regression models with methylation levels of the three gene promoters (HIC1, RASSF1 and GSTP1). Probabilities

were calculated as follows: P = exp ((Σ(bixi) + c)/(1 + Σ(bixi) + c), where p is the probability of each case, i = 1 to n; b is the regression coefficient of a given gene, x is the log2-transformed methylation level and c is a constant generated by the model. The ROCR package was used to obtain the ROC curves of the models and area under the curve (AUC) old values. Recurrence-free survival was analyzed with the Log-rank test using SAS 9.3 software. All the molecular analyses were performed in a blind manner. Results MS-MLPA analysis was feasible in all samples. The methylation frequency in the overall series varied widely (1% to 50%) for the different genes (Table 3). A separate analysis as a function of recurrence showed lower gene methylation in recurring than non recurring tumors, with the exception of CDKN1B, FHIT and IGSF4 genes. However, a significant difference between recurrent and non recurrent tumors was only observed for GSTP1, HIC1 and RASSF1 locus 2 (Table 3), with lower methylation in relapsed than non relapsed patients (Figure 2). The methylation index (MI), evaluated as the number of methylated genes relative to the total number of analyzed genes, showed values from 0 to 0.68 in the overall series of 23 genes and a significantly lower median value in non recurrent (0.

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