Depression and family support in breast cancer patients

MTS, migration and invasion assays in DCIS.COM cells that were previously transduced with scrambled control (Control) or BCL9 KD shRNA. The control cells and BCL9 KD cells were re-transduced with empty vector (EV), BCL9 overexpression (BCL9-OE) and BCL9 KD. BCL9-OE was achieved by transduction using the PCDH-BCL9 (BCL9-OE) acquired from Dr. Carrasco [11]. A Western blot analysis was performed using anti-BCL9, anti-vimentin, anti-E-cadherin antibodies, and anti-β-actin as a loading control. B MTS assay on control cells transduced with EV (control + EV), or BCL9-OE (control + BCL9-OE), BCL9-KD transduced with EV (BCL9 KD + EV), and BCL9-KD transduced with BCL9-OE (BCL9 KD + BCL9-OE). Bar graphs represent mean absorbance at 490 nm normalized to control ± standard error of the mean (SEM) (n = 6). C, D Representative images of the migration and invasion assays. Bar graph represents percent area of cells migrated (left) and invaded (right) under the membrane after 24 h. Invasion and migration were determined by ImageJ analysis of microscopic images per sample, the data are mean values normalized to control ± SEM (n = 3). E TopFlash and FopFlash reporter activity in DCIS.COM transduced as above that were either treated with Wnt3A or control conditioned medium (CM). Data represent mean ± SEM (n = 3, letters indicate statistically significant difference). (PDF 964 kb

Cross-dataset survival prediction (Sloan Kettering cancer genes).

<p>The first four columns of plots show the Kaplan-Meier survival curves for the two risk groups defined by Net-Cox (co-expression network), Net-Cox (functional linkage network), and . The fifth column of plots compare the time-dependent area under the ROC curves based on the estimated risk scores (PIs). The plots show the results for the death outcome by training with TCGA dataset and test on Tothill Dataset (A), the death outcome by training with TCGA dataset and test on Bonome Dataset (B), the tumor recurrence outcome by training with TCGA dataset and test on Tothill Dataset (C).</p

Consistency of signature genes on randomized co-expression networks.

<p>The x-axis is the number of selected signature genes ranked by each method. The y-axis is the percentage of the overlapped genes between the selected genes across the ovarian cancer datasets. The red curve reports the mean and the standard deviation of the percentages averaged over the experiments of 50 randomized networks. The plots show the results for the death outcome (A) and the tumor recurrence outcome (B).</p

Top-15 signature genes.

<p>The table lists the genes with over-expression indicating higher hazard of death or recurrence, identified by Net-Cox and in the consensus ranking across the three datasets.</p

Overview of Net-Cox.

<p>The patient gene expression data and the survival information specified by followup times and event indicators are illustrated on the left. The cost function of Net-Cox given in the box combines the total likelihood of Cox regression with a network regularization. The gene network shown is used as a constraint to encourage smoothness among correlated genes, i.e. the coefficients of the genes connected with edges of large weights are similarly weighted.</p

Log-rank test in cross-dataset evaluation (Sloan-Kettering cancer genes).

<p>The survival prediction performance on Tothill and Bonome datasets using the Cox models trained with TCGA dataset are reported.</p

Statistical analysis of log-partial likelihood.

<p>The optimal was fixed and is set to allow better evaluation of the network information. The log-partial likelihood computed by Net-Cox on the real co-expression network and on the randomized co-expression network are reported against tumor recurrence in the TCGA and Tothill datasets. The stars represent the results with the real co-expression networks, and the boxplots represent the results with the randomized networks.</p

Patient samples in the ovarian cancer datasets.

<p>The number of patients categorized by censoring and uncensoring for the death and recurrent events is reported in each dataset. Note that the Bonome dataset does not provide information on recurrence.</p

Protein-Protein interaction subnetworks of signature genes identified by Net-Cox on the TCGA dataset.

<p>(A) The PPI subnetworks identified by Net-Cox on the co-expression network. (B) The PPI subnetworks identified by Net-Cox on the functional linkage network.</p

Representative photomicrographs showing various levels of FBN1 expression in ovarian tumor arrays.

<p>The brown regions are stromal area showing expression of FBN1.</p