robust multi-array average (rma) algorithm with probe-level quintile normalization  (Partek)

Journal: Breast cancer research and treatment

Article Title: Transcriptome- and proteome-oriented identification of dysregulated eIF4G, STAT3, and Hippo pathways altered by PIK3CA H1047R in HER2/ER-positive breast cancer

doi: 10.1007/s10549-016-4011-9

Figure Lengend Snippet: Box plots showing the representative differential proteins in ER+PIK3CAH1047R versus ER+PIK3CAWT subgroups. a The top 5 upregulated and down-regulated proteins or phosphoproteins (y-axis by protein expression) in ER+PIK3CAH1047R patients versus ER+ PIK3CAWT subgroup, respectively. b Box plot view showing the ratio of phosphoprotein to total protein level for 2 representative proteins (AKT and YAP), phosphoprotein (STAT3) and protein (BCL-XL) differential expression, and mRNA differential expression for YAP1 and two YAP1 target genes (TEAD1 and CTGF) altered by PIK3CAH1047R in ER+ breast cancer. The phosphorylation sites in phosphoprotein were labeled by subscript text. The P values were calculated by Wilcoxon rank-sum test for protein differential expression analysis and by edgeR software for mRNA differential expression analysis. The detailed data are provided in Supplementary Table 3

Article Snippet: We also collected microarray gene expression data for breast cancer cell lines from two sources: (i) genecentric RMA-normalized mRNA expression data across 53 breast cancer cell lines was extracted from the Genomics of Drug Sensitivity in Cancer (GDSC) database (version: updated in October, 2012) [ 24 , 25 ]; and (ii) microarray gene expression profiles across 45 breast cancer cell lines were collected from the Stand Up to Cancer (SU2C) database [ 26 ].

Techniques: Expressing, Quantitative Proteomics, Phospho-proteomics, Labeling, Software

Journal: Breast cancer research and treatment

Article Title: Transcriptome- and proteome-oriented identification of dysregulated eIF4G, STAT3, and Hippo pathways altered by PIK3CA H1047R in HER2/ER-positive breast cancer

doi: 10.1007/s10549-016-4011-9

Figure Lengend Snippet: A gene co-expression-weighted protein interaction subnetwork for YAP1 and the relationship between mRNA expression and drug responses in breast cancer cell lines based on the GDSC dataset [55]. a A gene co-expression-weighted protein interaction subnetwork connecting YAP1 and its 58 interacting proteins. The red edges denote positive co-expression and blue edges denote negative co-expression. The different color keys on nodes represent significance (P values) of gene co-expression measured by F statistics. Gene co-expression analysis was performed based on ER+PIK3CAH1047R breast invasive carcinoma dataset (RNA-seq with V2 RSEM) from TCGA. b Heat map showing the Pearson correlation coefficient (color key) between drug responses and gene mRNA expression for YAP1 and its 58 interacting genes based on the GDSC dataset [55] including 130 drugs’ response data (IC50, the natural log micromolar) and microarray expression across 53 breast cancer cell lines. The labels at the right side are genes and in the bottom are drugs. YAP1 and its two important target genes (TEAD1 and CTGF) were highlighted by red. c Four significant correlation (r: Pearson correlation coefficient) pairs between YAP1 mRNA expression and drug resistance. The P values were performed by F statistics

Article Snippet: We also collected microarray gene expression data for breast cancer cell lines from two sources: (i) genecentric RMA-normalized mRNA expression data across 53 breast cancer cell lines was extracted from the Genomics of Drug Sensitivity in Cancer (GDSC) database (version: updated in October, 2012) [ 24 , 25 ]; and (ii) microarray gene expression profiles across 45 breast cancer cell lines were collected from the Stand Up to Cancer (SU2C) database [ 26 ].

Techniques: Expressing, RNA Sequencing, Microarray

Journal: Breast cancer research and treatment

Article Title: Transcriptome- and proteome-oriented identification of dysregulated eIF4G, STAT3, and Hippo pathways altered by PIK3CA H1047R in HER2/ER-positive breast cancer

doi: 10.1007/s10549-016-4011-9

Figure Lengend Snippet: Relationship between YAP1 mRNA expression and drug responses in breast cancer cell lines based on the SU2C dataset [26]. a Heat map showing the Pearson correlation coefficient (color key) between drug responses and gene expression for YAP1 and its 58 interacting genes based on the SU2C dataset [26] containing 73 drugs’ response data (−log10(GI50)) and microarray expression across 45 breast cancer cell lines. The labels at the right side are genes and in the bottom are drugs. b Three significant correlation (r: Pearson correlation coefficient) pairs between YAP1 mRNA expression and drug sensitivity. The P values were performed by F statistics

Article Snippet: We also collected microarray gene expression data for breast cancer cell lines from two sources: (i) genecentric RMA-normalized mRNA expression data across 53 breast cancer cell lines was extracted from the Genomics of Drug Sensitivity in Cancer (GDSC) database (version: updated in October, 2012) [ 24 , 25 ]; and (ii) microarray gene expression profiles across 45 breast cancer cell lines were collected from the Stand Up to Cancer (SU2C) database [ 26 ].

Techniques: Expressing, Gene Expression, Microarray