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-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-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-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

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

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

MiR-6835 inhibited clonogenicity and growth of HUVECs.

<p>(A) The proliferation of HUVECs was restrained by miR-6835 compared to control group. Furthermore, the inhibitors of miR-6835 promoted proliferation of HUVECs. (B and C) The clonogenicity of HUVECs was restrained by miR-6835 compared to control group. Furthermore, the inhibitors of miR-6835 promoted clonogenicity of HUVECs. The data are presented as means±SD from three independent experiments. *<i>P</i><0.05, **<i>P</i><0.01.</p

MiR-6835 promoted LPS-induced inflammation of HUVECs associated with the interaction between TLR-4 and AdipoR1 in lipid rafts

<div><p>Background</p><p>High mortality rate of critically-ill patients could be induced by sepsis and septic shock, which is the extremely life threatening. The purpose of this work is to identify and evaluate the potential regulatory mechanism of LPS-induced inflammation associated with miR-6835 and lipid rafts in HUVECs.</p><p>Methods</p><p>The 3’ UTR luciferase activity of AdipoR1 was detected, which was predicted the potential target gene of miR-6835. Moreover, the treated HUVECs with or without inhibitors or mimics of miR-6835 were used. Furthermore, the bio-functions of HUVECs were explored. The protein expression levels of SIRT-1, AMPK, and AdipoR1 were assessed, which were involved in the AdipoR1 signaling pathway. Then, the interaction between TLR-4 and AdipoR1 in lipid rafts and its mediation role on LPS-induced inflammation was investigated in HUVECs.</p><p>Results</p><p>MiR-6835 targeted directly on AdipoR1, and suppressed its expression in mRNA (mimics of miR-6835: 0.731±0.016 vs control: 1.527±0.015, <i>P</i><0.001) and proteins levels, then regulated protein expression of SIRT-1 and AMPK, which were the downstream target genes of AdipoR1 signaling pathway. MiR-6835 enhanced LPS-induced inflammation process in HUVECs (TNF-α: LPS+mimics of miR-6835: 1638.51±78.43 vs LPS: 918.73±39.73, <i>P</i><0.001; IL-6: LPS+mimics of miR-6835: 1249.35±69.51 vs LPS: 687.52±43.64, <i>P</i><0.001), which was associated with the interaction between TLR-4 and AdipoR1 in lipid rafts.</p><p>Conclusions</p><p>MiR-6835 is the key regulator of LPS-induced inflammation process in HUVECs. The interaction between TLR-4 and AdipoR1 mediated by lipid rafts at membrane of HUVECs with inflammation process induced by miR-6835. Our results demonstrated a hopeful strategy for treatment on sepsis by aiming at lipid rafts and miR-6835.</p></div