Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome-0

<p><b>Copyright information:</b></p><p>Taken from "Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome"</p><p>http://www.biomedcentral.com/1471-2164/8/331</p><p>BMC Genomics 2007;8():331-331.</p><p>Published online 20 Sep 2007</p><p>PMCID:PMC2064937.</p><p></p>scores was plotted altogether. SEP was calculated with 231 genes correlated to 5-year recurrence outcome wit

Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome-2

<p><b>Copyright information:</b></p><p>Taken from "Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome"</p><p>http://www.biomedcentral.com/1471-2164/8/331</p><p>BMC Genomics 2007;8():331-331.</p><p>Published online 20 Sep 2007</p><p>PMCID:PMC2064937.</p><p></p>ssion profiles was increased from 1 to 100 one by one. Median areas were summarized from 10,000 re-samplings. Size-weighted average areas achieved by the combined dataset (red line) were generally larger than the corresponding areas achieved by individual datasets (blue line)

Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome-5

<p><b>Copyright information:</b></p><p>Taken from "Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome"</p><p>http://www.biomedcentral.com/1471-2164/8/331</p><p>BMC Genomics 2007;8():331-331.</p><p>Published online 20 Sep 2007</p><p>PMCID:PMC2064937.</p><p></p>pwise reduction procedure was applied to randomly select three genes and remove them from the initial profile at each step. The consequent changing of permutation median and 90% CI of ROC curve area was presented

Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome-4

<p><b>Copyright information:</b></p><p>Taken from "Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome"</p><p>http://www.biomedcentral.com/1471-2164/8/331</p><p>BMC Genomics 2007;8():331-331.</p><p>Published online 20 Sep 2007</p><p>PMCID:PMC2064937.</p><p></p>setta and Combined). The Kaplan-Meier survival curves corresponded to half of the patients who had SEP scores higher (green) or lower (red) than the median of all 286 scores. The contingency table, accuracy, and ROC curve area listed with plot were obtained after the Veridex patients were classified according to their 3-year prognosis as training patients

Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome-7

<p><b>Copyright information:</b></p><p>Taken from "Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome"</p><p>http://www.biomedcentral.com/1471-2164/8/331</p><p>BMC Genomics 2007;8():331-331.</p><p>Published online 20 Sep 2007</p><p>PMCID:PMC2064937.</p><p></p>scores was plotted altogether. SEP was calculated with 231 genes correlated to 5-year recurrence outcome wit

Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome-1

<p><b>Copyright information:</b></p><p>Taken from "Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome"</p><p>http://www.biomedcentral.com/1471-2164/8/331</p><p>BMC Genomics 2007;8():331-331.</p><p>Published online 20 Sep 2007</p><p>PMCID:PMC2064937.</p><p></p>00 re-samplings. ROC curves were built with SEP scores of testing patients at each re-sampling while SEP was calculated with expression profiles identified from the training patients. The size of expression profiles was increased from 1 to 100 one by one. Average curve area of two testing subgroups was weighted by their size

AdipoR1 could bond with TLR-4.

<p>(A) The results CO-IP’d (co-immunoprecipitated) assay was perfomed, and identified the interaction between AdipoR1 and TLR-4. (B) The confocal images demonstrated that both the two recombinant proteins of AdipoR1 and TLR-4 localized at cell membrane of HUVECs with overlaid exhibition.</p

Predicted consequential pairing of target region of AdipoR1 (top) and miRNA-6835 (bottom).

<p>Predicted consequential pairing of target region of AdipoR1 (top) and miRNA-6835 (bottom).</p

Relative abundances of the identified features for three healthy individuals: Left: Individual 1, Middle: 2, Right: 3.

<p>Relative abundances of the identified features for three healthy individuals: Left: Individual 1, Middle: 2, Right: 3.</p

MiR-6835 targeted at AdipoR1 in HUVECs.

<p>The results showed the obviously down-regulated 3’ UTR activities of AdipoR1 in HUVECs, but no alternations were found while AdipoR1 with mutation. Moreover, miR-6835 could not directly target at AMPK, SIRT-1, and TLR-4, respectively. The data are presented as means±SD from three independent experiments. *<i>P</i><0.05, **<i>P</i><0.01.</p