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Genentech inc adai microarray data set
(A) <t>Microarray</t> analysis demonstrates that SIX1 overexpression leads to increased VEGFC mRNA in both MCF7-SIX1 cells and MCF7-SIX1 tumors. (B) Quantitation of VEGFC gene expression in MCF7-Ctrl and MCF7-SIX1 using real-time PCR. (C) SIX1 induces VEGFC promoter activity. VEGFC promoter-luciferase reporter constructs were transiently transfected into MCF7 cells, along with increasing amounts of SIX1 and a constant amount of the SIX1 cofactor, EYA2. Luciferase activity was analyzed after 48 hours. (D) ChIP was performed to detect SIX1 presence on the VEGFC promoter in MCF7 cells transfected with SIX1 and EYA2. Protein-DNA complexes were precipitated with a SIX1-specific antibody as well as a control rabbit IgG antibody, after which real-time PCR was performed with primers that flank 5 predicted SIX1 binding sites within the VEGFC promoter (red circles denote the TGATAC binding sites; green triangles denote the ATCCTGA binding sites) as well as 1 upstream region with no predicted SIX1 binding site as a negative control. Dashed line indicates the background non-specific binding of SIX1 and x axis units are base pairs upstream of transcription start site. (E) Functional VEGF-C secreted by MCF7-Ctrl and MCF7-SIX1 cells was measured by ELISA and Western blot analysis (3 clonal isolates of MCF7-Ctrl cells [lanes 1–3] and 3 clonal isolates of MCF7-SIX1 cells [lanes 4–6]) in conditioned medium after serum starvation for 48 hours. (F) MCF7-SIX1 tumors express higher levels of VEGF-C in vivo compared with the MCF7-Ctrl tumors. Immunostaining was used to detect VEGF-C and VEGF-D. Original magnification, ×400. *P < 0.05; **P < 0.01.
Adai Microarray Data Set, supplied by Genentech inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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1) Product Images from "SIX1 induces lymphangiogenesis and metastasis via upregulation of VEGF-C in mouse models of breast cancer"

Article Title: SIX1 induces lymphangiogenesis and metastasis via upregulation of VEGF-C in mouse models of breast cancer

Journal: The Journal of Clinical Investigation

doi: 10.1172/JCI59858

(A) Microarray analysis demonstrates that SIX1 overexpression leads to increased VEGFC mRNA in both MCF7-SIX1 cells and MCF7-SIX1 tumors. (B) Quantitation of VEGFC gene expression in MCF7-Ctrl and MCF7-SIX1 using real-time PCR. (C) SIX1 induces VEGFC promoter activity. VEGFC promoter-luciferase reporter constructs were transiently transfected into MCF7 cells, along with increasing amounts of SIX1 and a constant amount of the SIX1 cofactor, EYA2. Luciferase activity was analyzed after 48 hours. (D) ChIP was performed to detect SIX1 presence on the VEGFC promoter in MCF7 cells transfected with SIX1 and EYA2. Protein-DNA complexes were precipitated with a SIX1-specific antibody as well as a control rabbit IgG antibody, after which real-time PCR was performed with primers that flank 5 predicted SIX1 binding sites within the VEGFC promoter (red circles denote the TGATAC binding sites; green triangles denote the ATCCTGA binding sites) as well as 1 upstream region with no predicted SIX1 binding site as a negative control. Dashed line indicates the background non-specific binding of SIX1 and x axis units are base pairs upstream of transcription start site. (E) Functional VEGF-C secreted by MCF7-Ctrl and MCF7-SIX1 cells was measured by ELISA and Western blot analysis (3 clonal isolates of MCF7-Ctrl cells [lanes 1–3] and 3 clonal isolates of MCF7-SIX1 cells [lanes 4–6]) in conditioned medium after serum starvation for 48 hours. (F) MCF7-SIX1 tumors express higher levels of VEGF-C in vivo compared with the MCF7-Ctrl tumors. Immunostaining was used to detect VEGF-C and VEGF-D. Original magnification, ×400. *P < 0.05; **P < 0.01.
Figure Legend Snippet: (A) Microarray analysis demonstrates that SIX1 overexpression leads to increased VEGFC mRNA in both MCF7-SIX1 cells and MCF7-SIX1 tumors. (B) Quantitation of VEGFC gene expression in MCF7-Ctrl and MCF7-SIX1 using real-time PCR. (C) SIX1 induces VEGFC promoter activity. VEGFC promoter-luciferase reporter constructs were transiently transfected into MCF7 cells, along with increasing amounts of SIX1 and a constant amount of the SIX1 cofactor, EYA2. Luciferase activity was analyzed after 48 hours. (D) ChIP was performed to detect SIX1 presence on the VEGFC promoter in MCF7 cells transfected with SIX1 and EYA2. Protein-DNA complexes were precipitated with a SIX1-specific antibody as well as a control rabbit IgG antibody, after which real-time PCR was performed with primers that flank 5 predicted SIX1 binding sites within the VEGFC promoter (red circles denote the TGATAC binding sites; green triangles denote the ATCCTGA binding sites) as well as 1 upstream region with no predicted SIX1 binding site as a negative control. Dashed line indicates the background non-specific binding of SIX1 and x axis units are base pairs upstream of transcription start site. (E) Functional VEGF-C secreted by MCF7-Ctrl and MCF7-SIX1 cells was measured by ELISA and Western blot analysis (3 clonal isolates of MCF7-Ctrl cells [lanes 1–3] and 3 clonal isolates of MCF7-SIX1 cells [lanes 4–6]) in conditioned medium after serum starvation for 48 hours. (F) MCF7-SIX1 tumors express higher levels of VEGF-C in vivo compared with the MCF7-Ctrl tumors. Immunostaining was used to detect VEGF-C and VEGF-D. Original magnification, ×400. *P < 0.05; **P < 0.01.

Techniques Used: Microarray, Over Expression, Quantitation Assay, Gene Expression, Real-time Polymerase Chain Reaction, Activity Assay, Luciferase, Construct, Transfection, Control, Binding Assay, Negative Control, Functional Assay, Enzyme-linked Immunosorbent Assay, Western Blot, In Vivo, Immunostaining

(A) SIX1 and VEGFC expression values were retrieved from an Oncomine microarray data set (as indicated in the figure) and were plotted according to different types of cell lines or by expression value. X and y axes of the right panel indicate mRNA expression analyzed on Affymetrix U133 Plus 2.0 microarrays. (B) Representative positive and negative staining of SIX1 and VEGF-C on human breast cancer tissue sections. Original magnification, ×100. (C) Model depicting the mechanism by which SIX1 promotes metastatic dissemination. Overexpression of SIX1 leads to increased VEGF-C and stimulates lymphangiogenesis, allowing for increased escape of tumor cells through the lymphatics and increased distant metastasis. However, SIX1 is also able to augment the later stages of metastasis of cancer cells that have traveled through the vasculature, thus contributing to metastatic spread via multiple mechanisms. An extension of this finding is that while inhibitors of the VEGF-C/VEGFR3 axis may prevent lymphatic spread, inhibitors of SIX1 are expected to inhibit metastasis at multiple stages, serving as powerful antimetastatic agents.
Figure Legend Snippet: (A) SIX1 and VEGFC expression values were retrieved from an Oncomine microarray data set (as indicated in the figure) and were plotted according to different types of cell lines or by expression value. X and y axes of the right panel indicate mRNA expression analyzed on Affymetrix U133 Plus 2.0 microarrays. (B) Representative positive and negative staining of SIX1 and VEGF-C on human breast cancer tissue sections. Original magnification, ×100. (C) Model depicting the mechanism by which SIX1 promotes metastatic dissemination. Overexpression of SIX1 leads to increased VEGF-C and stimulates lymphangiogenesis, allowing for increased escape of tumor cells through the lymphatics and increased distant metastasis. However, SIX1 is also able to augment the later stages of metastasis of cancer cells that have traveled through the vasculature, thus contributing to metastatic spread via multiple mechanisms. An extension of this finding is that while inhibitors of the VEGF-C/VEGFR3 axis may prevent lymphatic spread, inhibitors of SIX1 are expected to inhibit metastasis at multiple stages, serving as powerful antimetastatic agents.

Techniques Used: Expressing, Microarray, Negative Staining, Over Expression



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Image Search Results


Verification of expression level of hub genes and survival analysis of up-regulated genes. Part (A) represents the survival graph of each hub gene. Line color in black represents the survival of the patient when the expression of the genes is low; red line below represents the probability of the survival of the patient when genes with high expression. Part (B) represents the box plot of 2 hub genes constructed using TCGA and GTEx expression data. Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Different color code boxes represent the breast cancer tissue group, gray was the normal tissue group, and asterisk represented P < .01. The dots represented expression in each sample. Part (C) of figure represents the validity expression of 8 hub genes using test and control samples of GSE65194 data set. Different color code boxes represent the TNBC samples, and gray represents the normal sample. The dots represented expression in each sample.

Journal: Bioinformatics and Biology Insights

Article Title: Bioinformatics-Driven Investigations of Signature Biomarkers for Triple-Negative Breast Cancer

doi: 10.1177/11779322241271565

Figure Lengend Snippet: Verification of expression level of hub genes and survival analysis of up-regulated genes. Part (A) represents the survival graph of each hub gene. Line color in black represents the survival of the patient when the expression of the genes is low; red line below represents the probability of the survival of the patient when genes with high expression. Part (B) represents the box plot of 2 hub genes constructed using TCGA and GTEx expression data. Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Different color code boxes represent the breast cancer tissue group, gray was the normal tissue group, and asterisk represented P < .01. The dots represented expression in each sample. Part (C) of figure represents the validity expression of 8 hub genes using test and control samples of GSE65194 data set. Different color code boxes represent the TNBC samples, and gray represents the normal sample. The dots represented expression in each sample.

Article Snippet: Microarray data set GSE65194 from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus was used for identification of differentially expressed genes (DEGs) using R software.

Techniques: Expressing, Construct, Gene Expression, Control

Verification of expression level of hub genes and survival analysis of down-regulated genes. In Part (A), each case represents the survival graph of each hub gene. Line color in black represents the survival of the patient when the expression of the genes is high; red line below represents the probability of the survival of the patient when genes with low expression. Part (B) represents the box plot of 2 hub genes constructed using TCGA and GTEx expression data. Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Different color code boxes represent the breast cancer tissue group, gray was the normal tissue group, and asterisk represented P < .01. The dots represented expression in each sample. Part (C) of figure represents the validity expression of 8 hub genes using test and control samples of “GSE65194” data set. Different color code boxes represent the TNBC samples, and gray represents the normal sample. The dots represented expression in each sample.

Journal: Bioinformatics and Biology Insights

Article Title: Bioinformatics-Driven Investigations of Signature Biomarkers for Triple-Negative Breast Cancer

doi: 10.1177/11779322241271565

Figure Lengend Snippet: Verification of expression level of hub genes and survival analysis of down-regulated genes. In Part (A), each case represents the survival graph of each hub gene. Line color in black represents the survival of the patient when the expression of the genes is high; red line below represents the probability of the survival of the patient when genes with low expression. Part (B) represents the box plot of 2 hub genes constructed using TCGA and GTEx expression data. Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Different color code boxes represent the breast cancer tissue group, gray was the normal tissue group, and asterisk represented P < .01. The dots represented expression in each sample. Part (C) of figure represents the validity expression of 8 hub genes using test and control samples of “GSE65194” data set. Different color code boxes represent the TNBC samples, and gray represents the normal sample. The dots represented expression in each sample.

Article Snippet: Microarray data set GSE65194 from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus was used for identification of differentially expressed genes (DEGs) using R software.

Techniques: Expressing, Construct, Gene Expression, Control

Normalization of expression data is performed using RMA and DESeq2 algorithms on Affymetrix microarray (Set2) and RNA sequencing (Set3, Set4, and Set5) datasets respectively. BTK subtypes are generated using the same consensus clustering methodology in other four cohorts. BTK subtypes in each of these cohorts are significantly associated ( p < 0.05) with BTK mRNA Expression ( A -Set2, B -Set3, C -Set4 and D -Set5).

Journal: Blood Cancer Journal

Article Title: Transcriptomic clustering of chronic lymphocytic leukemia: molecular subtypes based on Bruton’s tyrosine kinase expression levels

doi: 10.1038/s41408-024-01196-3

Figure Lengend Snippet: Normalization of expression data is performed using RMA and DESeq2 algorithms on Affymetrix microarray (Set2) and RNA sequencing (Set3, Set4, and Set5) datasets respectively. BTK subtypes are generated using the same consensus clustering methodology in other four cohorts. BTK subtypes in each of these cohorts are significantly associated ( p < 0.05) with BTK mRNA Expression ( A -Set2, B -Set3, C -Set4 and D -Set5).

Article Snippet: Initial clustering and assessment of association with BTK mRNA expression was performed using Affymetrix Microarray data set that has 130 patients [ ] designated as Set1.

Techniques: Expressing, Microarray, RNA Sequencing Assay, Generated