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pmid:26260910

Prognostic significance of differential expression of angiogenic genes in women with high-grade serous ovarian carcinoma.

Sharareh Siamakpour-Reihani · Kouros Owzar · Chen Jiang · Taylor Turner · Yiwen Deng · Sarah M Bean · Janet K Horton · Andrew Berchuck · Jeffrey R Marks · Mark W Dewhirst · Angeles Alvarez Secord

Gynecologic oncology · 2015 doi:10.1016/j.ygyno.2015.08.001

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Data

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title
Prognostic significance of differential expression of angiogenic genes in women with high-grade serous ovarian carcinoma.
doi
10.1016/j.ygyno.2015.08.001
pmid
26260910
authors array · 11 items
[0]
Sharareh Siamakpour-Reihani
[1]
Kouros Owzar
[2]
Chen Jiang
[3]
Taylor Turner
[4]
Yiwen Deng
[5]
Sarah M Bean
[6]
Janet K Horton
[7]
Andrew Berchuck
[8]
Jeffrey R Marks
[9]
Mark W Dewhirst
[10]
Angeles Alvarez Secord
journal
Gynecologic oncology
year
2015
abstract
OBJECTIVES: To identify angiogenic biomarkers associated with tumor angiogenesis and clinical outcome in high-grade serous ovarian cancer (HGSC). METHODS: 51 HGSC samples were analyzed using Affymetrix HG-U133A microarray. Microvessel density (MVD) counts were determined using CD31 and CD105. Associations between mRNA expression levels and overall survival were assessed using rank score statistic. Effect size was estimated as a hazard ratio (HR) under a proportional hazard model. The Storey q-value method was used to account for multiple testing within the false-discovery rate (FDR) framework. Publicly available databases including TCGA and GSE were used for external confirmation. RESULTS: Thirty-one angiogenic-related genes were significantly associated with survival (q≤0.05). Of these 31 genes, 4 were also associated with outcome in the TCGA data: AKT1 (q=0.02; TCGA p=0.01, HR=0.8), CD44 (q=0.003; TCGA p=0.05, HR=0.9), EPHB2 (q=0.01; TCGA p=0.05, HR=1.2), and ERBB2 (q=0.02; TCGA p=0.05, HR=1.2). While 5 were associated with outcome in the GSE database: FLT1 (q=0.03; GSE26712 p=0.01, HR=3.1); PF4 (q=0.02; GSE26712 p=0.01, HR=3.0); NRP1 (q=0.02; GSE26712 p<0.04, HR>1.4); COL4A3 (q=0.04; GSE26712 p=0.03, HR=1.3); and ANGPTL3 (q=0.02; GSE14764 p=0.02, HR=1.5). High AKT1 and CD44 were associated with longer survival. In contrast, high expression of EPHB2, ERBB2, FLT1; PF4, NRP1, COL4A3, and ANGPTL3 were associated with shorter survival. CD105-MVD and CD31-MVD were not significantly associated with angiogenic gene expression. CONCLUSIONS: Thirty-one angiogenic-related genes were associated with survival in advanced HGSC and nine of these genes were confirmed in independent publicly available databases.
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The paper used the rank score statistic to assess associations between gene expression and overall survival, estimated effect sizes as hazard ratios under a proportional hazards model, and controlled for multiple testing using the Storey q-value method. The source data came from 51 HGSC samples analyzed using Affymetrix HG-U133A microarrays, with external confirmation obtained from publicly available TCGA and Gene Expression Omnibus datasets.
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sections[0] "Abstract" paragraph 2
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51 HGSC samples were analyzed using Affymetrix HG-U133A microarray.
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Association between mRNA expression levels and overall survival were assessed using rank score statistic. Effect size was estimated as a hazard ratio (HR) under a proportional hazards model. The Storey q-value method was used to account for multiple testing within the false-discovery rate (FDR) framework.
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Thirty-one angiogenic-related genes were associated with survival in advanced HGSC and nine of these genes were confirmed in independent publicly available databases.
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The paper used the rank score statistic to assess associations between gene expression and overall survival, estimated effect sizes as hazard ratios under a proportional hazards model, and controlled for multiple testing using the Storey q-value method. The source data came from 51 HGSC samples analyzed using Affymetrix HG-U133A microarrays, with external confirmation obtained from publicly available TCGA and Gene Expression Omnibus datasets.
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ples were analyzed using Affymetrix HG-U133A microarray. Microvessel density (MVD) counts were deter
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METHODS: 51 HGSC samples were analyzed using Affymetrix HG-U133A microarray. Microvessel density (MV
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d.[4] Statistical Analysis and validation of microarray results One hundred and forty-five angiogeni
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om the Bioconductor package was derived.[10] Affymetrix HG-U133A arrays were pre-processed using the
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METHODS Immunohistochemistry analysis of MVD Immunohistochemistry (IHC) w
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mistry analysis of MVD Immunohistochemistry (IHC) was performed for the MVD analysis using CD
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Complete file
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Microvessel density (MVD) counts were determined using CD31 and CD105. Associations between mRNA expression levels and overall survival were assessed using rank score statistic. Effect size was estimated as a hazard ratio (HR) under a proportional hazard model. The Storey q-value method was used to account for multiple testing within the false-discovery rate (FDR) framework. Publicly available databases including TCGA and GSE were used for external confirmation.\nRESULTS: Thirty-one angiogenic-related genes were significantly associated with survival (q≤0.05). Of these 31 genes, 4 were also associated with outcome in the TCGA data: AKT1 (q=0.02; TCGA p=0.01, HR=0.8), CD44 (q=0.003; TCGA p=0.05, HR=0.9), EPHB2 (q=0.01; TCGA p=0.05, HR=1.2), and ERBB2 (q=0.02; TCGA p=0.05, HR=1.2). While 5 were associated with outcome in the GSE database: FLT1 (q=0.03; GSE26712 p=0.01, HR=3.1); PF4 (q=0.02; GSE26712 p=0.01, HR=3.0); NRP1 (q=0.02; GSE26712 p<0.04, HR>1.4); COL4A3 (q=0.04; GSE26712 p=0.03, HR=1.3); and ANGPTL3 (q=0.02; GSE14764 p=0.02, HR=1.5). High AKT1 and CD44 were associated with longer survival. In contrast, high expression of EPHB2, ERBB2, FLT1; PF4, NRP1, COL4A3, and ANGPTL3 were associated with shorter survival. 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Microvessel density (MVD) counts were deter", "METHODS: 51 HGSC samples were analyzed using Affymetrix HG-U133A microarray. 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