Samples description and microarray quality.

<p>Type of tumor and delay to tumor freezing are shown. RNA integrity is evaluated through the RIN number. The ratio of centiles P95/P05 reflects the overall strength of the signal compared to the background. The Pearson correlation coefficient (r²) shows the correlation between log-expression levels of the central and peripheral samples, for each tumor and each time to cryopreservation.</p><p>HCC: HepatoCellular Carcinoma.</p><p>LC: Lung Carcinoma.</p><p>ND: Not Determined.</p

Average log-expression profiles of the 12 genes with significant up- or down-regulation over harvesting time (FDR<0.05) in HCC.

<p>The BRB-ArrayTools v4.2 time course analysis model was applied to whole-genome expression microarray data (HCC and LC samples) to identify significant individual deregulated genes over harvesting time. No significant deregulated genes in LC were observed. Individual log-expression profiles () and average log-expression line plots (3 HCC samples taken at the center and at the periphery) in relation to delay to tumor cryopreservation are displayed.</p

Gene expression for all probes and for restricted sets of probes.

<p>Over-all rate of expression changes for all probes in all samples combined, in LC and HCC samples and in peripheral and central samples are estimated as percent-change per hour. Expression levels changes are also estimated for different sets of probes (lowest and highest 5% of geometric mean expression, probes in warm ischemia genes and probes in HCC genes).</p

Time-course scatter-plots of HCC and LC genome-wide expression profiling quantile normalized data.

<p>Scatter plots for each tumor pair at t5 (HCC_5_AVG_Signal and LC_5_AVG_Signal on the X Axis) versus harvested tumor pairs at t15, t30 and t120 (on the Y Axis) were generated on a logarithmic scale. Genes showing greater than 2-fold change relative to the t5 sample from the same tumor were highlighted.</p

List of 34 HCC specific genes: comparison of gene expression data from the Liverome database and experimental dataset.

<p>Expression trend of 34 HCC specific genes reported as deregulated in more than 4 studies in the public Liverome database was compared to experimental expression trend.</p>a<p>average rate of expression from different Illumina probes.</p>b<p>discrepancies between Liverome studies results.</p><p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079826#pone-0079826-t003" target="_blank">Table 3</a> References.</p><p>1) Chan, K.Y., Lai, P.B., Squire, J.A., Beheshti, B., Wong, N.L., Sy, S.M., Wong, N., 2006. Positional expression profiling indicates candidate genes in deletion hotspots of hepatocellular carcinoma. Mod Pathol 19, 1546–1554.</p><p>2) Chung, E.J., Sung, Y.K., Farooq, M., Kim, Y., Im, S., Tak, W.Y., Hwang, Y.J., Kim, Y.I., Han, H.S., Kim, J.C., Kim, M.K., 2002. Gene expression profile analysis in human hepatocellular carcinoma by cDNA microarray. Mol Cells 14, 382–387.</p><p>3) Cui, X.D., Lee, M.J., Yu, G.R., Kim, I.H., Yu, H.C., Song, E.Y., Kim, D.G., 2010. EFNA1 ligand and its receptor EphA2: potential biomarkers for hepatocellular carcinoma. Int J Cancer 126, 940–949.</p><p>4) De Giorgi, V., Monaco, A., Worchech, A., Tornesello, M., Izzo, F., Buonaguro, L., Marincola, F.M., Wang, E., Buonaguro, F.M., 2009. Gene profiling, biomarkers and pathways characterizing HCV-related hepatocellular carcinoma. J Transl Med 7, 85.</p><p>5) Delpuech, O., Trabut, J.B., Carnot, F., Feuillard, J., Brechot, C., Kremsdorf, D., 2002. Identification, using cDNA macroarray analysis, of distinct gene expression profiles associated with pathological and virological features of hepatocellular carcinoma. Oncogene 21, 2926–2937.</p><p>6) Dong, H., Ge, X., Shen, Y., Chen, L., Kong, Y., Zhang, H., Man, X., Tang, L., Yuan, H., Wang, H., Zhao, G., Jin, W., 2009. Gene expression profile analysis of human hepatocellular carcinoma using SAGE and LongSAGE. BMC Med Genomics 2, 5.</p><p>7) Goldenberg, D., Ayesh, S., Schneider, T., Pappo, O., Jurim, O., Eid, A., Fellig, Y., Dadon, T., Ariel, I., de Groot, N., Hochberg, A., Galun, E., 2002. Analysis of differentially expressed genes in hepatocellular carcinoma using cDNA arrays. Mol Carcinog 33, 113–124.</p><p>8) Iizuka, N., Tsunedomi, R., Tamesa, T., Okada, T., Sakamoto, K., Hamaguchi, T., Yamada-Okabe, H., Miyamoto, T., Uchimura, S., Hamamoto, Y., Oka, M., 2006. Involvement of c-myc-regulated genes in hepatocellular carcinoma related to genotype-C hepatitis B virus. J Cancer Res Clin Oncol 132, 473–481.</p><p>9) Kato, K., Yamashita, R., Matoba, R., Monden, M., Noguchi, S., Takagi, T., Nakai, K., 2005. Cancer gene expression database (CGED): a database for gene expression profiling with accompanying clinical information of human cancer tissues. Nucleic Acids Res 33, D533–536.</p><p>10) Kim, B.Y., Lee, J.G., Park, S., Ahn, J.Y., Ju, Y.J., Chung, J.H., Han, C.J., Jeong, S.H., Yeom, Y.I., Kim, S., Lee, Y.S., Kim, C.M., Eom, E.M., Lee, D.H., Choi, K.Y., Cho, M.H., Suh, K.S., Choi, D.W., Lee, K.H., 2004. Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray. Biochim Biophys Acta 1739, 50–61.</p><p>11) Kurokawa, Y., Matoba, R., Takemasa, I., Nakamori, S., Tsujie, M., Nagano, H., Dono, K., Umeshita, K., Sakon, M., Ueno, N., Kita, H., Oba, S., Ishii, S., Kato, K., Monden, M., 2003. Molecular features of non-B, non-C hepatocellular carcinoma: a PCR-array gene expression profiling study. J Hepatol 39, 1004–1012.</p><p>12) Lee, M.J., Yu, G.R., Park, S.H., Cho, B.H., Ahn, J.S., Park, H.J., Song, E.Y., Kim, D.G., 2008. Identification of cystatin B as a potential serum marker in hepatocellular carcinoma. Clin Cancer Res 14, 1080–1089.</p><p>13) Li, Y., Tang, R., Xu, H., Qiu, M., Chen, Q., Chen, J., Fu, Z., Ying, K., Xie, Y., Mao, Y., 2002. Discovery and analysis of hepatocellular carcinoma genes using cDNA microarrays. J Cancer Res Clin Oncol 128, 369–379.</p><p>14) Okabe, H., Satoh, S., Kato, T., Kitahara, O., Yanagawa, R., Yamaoka, Y., Tsunoda, T., Furukawa, Y., Nakamura, Y., 2001. Genome-wide analysis of gene expression in human hepatocellular carcinomas using cDNA microarray: identification of genes involved in viral carcinogenesis and tumor progression. Cancer Res 61, 2129–2137.</p><p>15) Patil, M.A., Chua, M.S., Pan, K.H., Lin, R., Lih, C.J., Cheung, S.T., Ho, C., Li, R., Fan, S.T., Cohen, S.N., Chen, X., So, S., 2005. An integrated data analysis approach to characterize genes highly expressed in hepatocellular carcinoma. Oncogene 24, 3737–3747.</p><p>16) Shirota, Y., Kaneko, S., Honda, M., Kawai, H.F., Kobayashi, K., 2001. Identification of differentially expressed genes in hepatocellular carcinoma with cDNA microarrays. Hepatology 33, 832–840.</p><p>17) Tackels-Horne, D., Goodman, M.D., Williams, A.J., Wilson, D.J., Eskandari, T., Vogt, L.M., Boland, J.F., Scherf, U., Vockley, J.G., 2001. Identification of differentially expressed genes in hepatocellular carcinoma and metastatic liver tumors by oligonucleotide expression profiling. Cancer 92, 395–405.</p><p>18) Xu, L., Hui, L., Wang, S., Gong, J., Jin, Y., Wang, Y., Ji, Y., Wu, X., Han, Z., Hu, G., 2001a. Expression profiling suggested a regulatory role of liver-enriched transcription factors in human hepatocellular carcinoma. Cancer Res 61, 3176–3181.</p><p>19) Xu, X.R., Huang, J., Xu, Z.G., Qian, B.Z., Zhu, Z.D., Yan, Q., Cai, T., Zhang, X., Xiao, H.S., Qu, J., Liu, F., Huang, Q.H., Cheng, Z.H., Li, N.G., Du, J.J., Hu, W., Shen, K.T., Lu, G., Fu, G., Zhong, M., Xu, S.H., Gu, W.Y., Huang, W., Zhao, X.T., Hu, G.X., Gu, J.R., Chen, Z., Han, Z.G., 2001b. 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Hierarchical cluster analysis of all samples following log-transformation and quantile normalization of the microarray data.

<p>Dendrogram for clustering experiments was created using centred correlation and average linkage method. Length of nodes corresponds to correlation between samples. HCC4_5P: HCC from patient 4 taken at the periphery of the tumor and maintained at room temperature and then frozen in liquid nitrogen at t5 (min).</p

Within- and between-sample variance box-plot of microarray non-normalized fluorescent signals.

<p>The non-normalized fluorescent signals (AVG_Signal) have been generated by the Illumina Genome Studio V2010.2 for the 3 HepatoCellular Carcinomas (HCC) and the 3 Lung Carcinomas (LC ) samples taken at the center and at the periphery of the tumors and maintained at room temperature and then frozen in liquid nitrogen at different times: 5 minutes (t5, reference time), 15 minutes (t15), 30 minutes (t30) and 120 minutes (t120).</p

Rare genetic variants of <i>RAD51</i> identified in the BCFR.

*<p>A protein multiple sequence alignment (PMSA) including 15 sequences from Human to Drosophila (Dmel) was used to obtain scores for Align-GVGD and for SIFT (Median sequence conservation of 4.32 substitutions per position).</p><p>NA, not applicable.</p

<em>RAD51</em> and Breast Cancer Susceptibility: No Evidence for Rare Variant Association in the Breast Cancer Family Registry Study

<div><h3>Background</h3><p>Although inherited breast cancer has been associated with germline mutations in genes that are functionally involved in the DNA homologous recombination repair (HRR) pathway, including <em>BRCA1</em>, <em>BRCA2</em>, <em>TP53</em>, <em>ATM</em>, <em>BRIP1</em>, <em>CHEK2</em> and <em>PALB2,</em> about 70% of breast cancer heritability remains unexplained. Because of their critical functions in maintaining genome integrity and already well-established associations with breast cancer susceptibility, it is likely that additional genes involved in the HRR pathway harbor sequence variants associated with increased risk of breast cancer. <em>RAD51</em> plays a central biological function in DNA repair and despite the fact that rare, likely dysfunctional variants in three of its five paralogs, <em>RAD51C, RAD51D,</em> and <em>XRCC2,</em> have been associated with breast and/or ovarian cancer risk, no population-based case-control mutation screening data are available for the <em>RAD51</em> gene. We thus postulated that <em>RAD51</em> could harbor rare germline mutations that confer increased risk of breast cancer.</p> <h3>Methodology/Principal Findings</h3><p>We screened the coding exons and proximal splice junction regions of the gene for germline sequence variation in 1,330 early-onset breast cancer cases and 1,123 controls from the Breast Cancer Family Registry, using the same population-based sampling and analytical strategy that we developed for assessment of rare sequence variants in <em>ATM</em> and <em>CHEK2.</em> In total, 12 distinct very rare or private variants were characterized in <em>RAD51</em>, with 10 cases (0.75%) and 9 controls (0.80%) carrying such a variant. Variants were either likely neutral missense substitutions (3), silent substitutions (4) or non-coding substitutions (5) that were predicted to have little effect on efficiency of the splicing machinery.</p> <h3>Conclusion</h3><p>Altogether, our data suggest that <em>RAD51</em> tolerates so little dysfunctional sequence variation that rare variants in the gene contribute little, if anything, to breast cancer susceptibility.</p> </div

Stratified analyses of the <i>RAD51</i> −135G/C SNP on breast cancer risk in the BCFR.

a<p>Test for the difference in C allele frequency between cases and controls.</p>b<p>Results of the logistic regression assuming a log-additive model with study center and age included in the regression model as covariates in the combined analysis, and with race/ethnicity, study center and age as covariates in the stratified analysis.</p