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19 The PAM program uses nearest shrunken centroids to predict unknown samples with cross-validated training and testing and appears to be superior in performance for identifying smaller sets of genes compared with other class-prediction algorithms. The SPSS software package (version 16.0; SPSS, Inc., Chicago, Ill) and GraphPad Prism (GraphPad Software, San Diego, Calif) were used for statistical analyses. The chi-square test, Fisher exact test, and Mann-Whitney U test were used to analyze correlations between clinicopathologic features and the gene-expression signature. http://www.selleckchem.com/products/MG132.html Survival curves were plotted using the Kaplan-Meier method and were analyzed for statistical differences using log-rank tests. Multivariate logistic regression with stepwise variable selection was used to evaluate independent factors associated with PLNM. The patients were divided randomly into a training group and test group. Forty-four patients were assigned to the training group, which included 12 patients with http://www.selleck.cn/products/pfi-2.html PLNM and 32 patients without PLNM; and 44 patients were assigned to the test group, which included 11 patients with PLNM and 33 without PLNM. On the basis of microarray data from the training group, a preliminary profile consisting of 32 differentially expressed genes (19 down-regulated genes and 13 up-regulated genes) between the patients with and without PLNM was identified by using the SAM program. Hierarchical clustering with the 32 genes and 44 patients from the training group http://www.selleckchem.com/products/epacadostat-incb024360.html was performed, and 2 major clusters were identified, 1 cluster of patients without metastasis and the cluster of patients with metastasis (Fig. 1A). A similar result was achieved in the test group with the same 32 genes (Fig. 1B). To refine the expression profile, a subset of genes with high power for predicting PLNM was reselected from the 32 genes. This signature consisted of the following 11 genes: ribosomal protein L35 (RPL35); thymosin �� 10 (TMSB10); YWHAZ; biotinidase (BTD); LDHA; GUSB; SOD2; nuclear receptor subfamily 3, group C, member 2 (NR3C2); fructosamine 3 kinase (FN3K); x-ray repair cross-complementing 4 (XRCC4); and WNT2 (Table 2). The PLNM risk score for all patients in the training group was calculated using the formula with the 11-gene signature generated by PAM using BRB ArrayTools software. Then, the patients were divided into a high-risk group and a low-risk group based on calculating the probability for each patient using the formula with the 11-gene signature generated by PAM. Patients with a probability > .5 (range, 0-1) were included in the low-risk group; otherwise, patients were included in the high-risk group. Patients who had high-risk scores were associated with a higher rate of PLNM (P