ANALYSIS OF GENE EXPRESSION IN EARLY-STAGE OVARIAN CANCER (2)
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE8842 | GEO | 2007/08/25
SECONDARY ACCESSION(S): PRJNA102185
REPOSITORIES: GEO
Similar Datasets
Project description:Gene expression was analysed in 68 stage I ovarian cancers and 15 borderline samples using microarrays. Tumors were obtained directly at surgery and immediately frozen in liquid nitrogen until analysis. Glass arrays containing 16000 genes were used. Data was interpreted by unsupervised and supervised analysis. All tumors had a wild-type p53. Unsupervised analysis identified eight major clusters one of which was statistically associated to overall survival, grading and histotype (cluster C) and another with grading and histotype (cluster D). Supervised analysis showed a clear association between histotypes and gene expression profile and distinguished the four major histotypes (serous, clear cells, mucinous, endometrioid). The data also discriminated between the different grades of the tumors. The subset of 15 borderline tumors were undistinguishable from grade 1 tumors. When patients, relapsing or not, were analysed, a subset of genes able to differentiate them was identified. The genes identified in the major clusters belong to general classes of lipid biosynthesis, transport and metabolism, immunoglobulins (antigen presenting and processing) and genes acting as metal, ion and anion transporters including ATPase activity coupled with ion transport. Keywords: disease state analysis
2007-08-25 | GSE8841 | GEO
Project description:Gene expression was analysed in 68 stage I ovarian cancers and 15 borderline samples using microarrays. Tumors were obtained directly at surgery and immediately frozen in liquid nitrogen until analysis. Glass arrays containing 16000 genes were used. Data was interpreted by unsupervised and supervised analysis. All tumors had a wild-type p53. Unsupervised analysis identified eight major clusters one of which was statistically associated to overall survival, grading and histotype (cluster C) and another with grading and histotype (cluster D). Supervised analysis showed a clear association between histotypes and gene expression profile and distinguished the four major histotypes (serous, clear cells, mucinous, endometrioid). The data also discriminated between the different grades of the tumors. The subset of 15 borderline tumors were undistinguishable from grade 1 tumors. When patients, relapsing or not, were analysed, a subset of genes able to differentiate them was identified. The genes identified in the major clusters belong to general classes of lipid biosynthesis, transport and metabolism, immunoglobulins (antigen presenting and processing) and genes acting as metal, ion and anion transporters including ATPase activity coupled with ion transport. Keywords: disease state analysis Gene expression for 83 ovarian tumor samples was evaluated: 68 from patiens presenting stage I carcinoma and 15 with borderline desease. A common reference design was applied and each samples was cohybridizated with a universal reference. Also a dye swap control has been done so that for the two technical replicates of each sample an expression profile of raw data was obtained. These data were independently processed and combiend to have a unique normalized profile for each sample.
2007-08-24 | E-GEOD-8841 | biostudies-arrayexpress
Project description:Gene expression was analysed in 68 stage I ovarian cancers and 15 borderline samples using microarrays. Tumors were obtained directly at surgery and immediately frozen in liquid nitrogen until analysis. Glass arrays containing 16000 genes were used. Data was interpreted by unsupervised and supervised analysis. All tumors had a wild-type p53. Unsupervised analysis identified eight major clusters one of which was statistically associated to overall survival, grading and histotype (cluster C) and another with grading and histotype (cluster D). Supervised analysis showed a clear association between histotypes and gene expression profile and distinguished the four major histotypes (serous, clear cells, mucinous, endometrioid). The data also discriminated between the different grades of the tumors. The subset of 15 borderline tumors were undistinguishable from grade 1 tumors. When patients, relapsing or not, were analysed, a subset of genes able to differentiate them was identified. The genes identified in the major clusters belong to general classes of lipid biosynthesis, transport and metabolism, immunoglobulins (antigen presenting and processing) and genes acting as metal, ion and anion transporters including ATPase activity coupled with ion transport. Keywords: disease state analysis Gene expression for 83 ovarian tumor samples was evaluated: 68 from patiens presenting stage I carcinoma and 15 with borderline desease. A common reference design was applied and each samples was cohybridizated with a universal reference. Also a dye swap control has been done so that for the two technical replicates of each sample an expression profile of raw data was obtained. These data were independently processed and combiend to have a unique normalized profile for each sample.
2007-08-25 | E-GEOD-8842 | biostudies-arrayexpress
Project description:Ovarian cancer is characterized by multiple structural aberrations; most are passenger alterations which do not confer tumor growth. Like many cancers, it is a heterogeneous disease and till date, the histotype-specific copy number landscape has been difficult to elucidate. To dissect the heterogeneity of ovarian cancer and understand the pathogenesis of its various histotypes, we developed an in silico hypothesis-driven workflow to identify histotype-specific copy number aberrations across multiple datasets of epithelial ovarian cancer. In concordance with previous studies on global copy number changes, our study showed similar alterations. However, when the landscape was de-convoluted into histotypes, distinct alterations were observed. We report here a comprehensive histotype-specific copy number landscape of ovarian cancer and showed that there is genomic diversity between the histotypes; some involving well known cancer genes and some novel potential driver genes. Besides preferential occurrence of alterations in some histotypes, opposite trends of alteration were observed; such as ERBB2 amplification in mucinous but deletion in serous tumors. The landscape highlights the need for identifying histotype-specific aberrations in ovarian cancer and present potential to tailor management of ovarian cancer based on molecular signature of histotypes. 56 samples containing the 4 histotypes were used for this study. It contained 12 clear cell carcinoma, 6 endometrioid adenocarcinoma, 2 mucinous adenocarcinoma, 5 mucinous-borderline tumors, 26 serous adenocarcinoma, and 5 serous-borderline tumors.
2012-10-25 | E-GEOD-30274 | biostudies-arrayexpress
Project description:Ovarian cancer is characterized by multiple structural aberrations; most are passenger alterations which do not confer tumor growth. Like many cancers, it is a heterogeneous disease and till date, the histotype-specific copy number landscape has been difficult to elucidate. To dissect the heterogeneity of ovarian cancer and understand the pathogenesis of its various histotypes, we developed an in silico hypothesis-driven workflow to identify histotype-specific copy number aberrations across multiple datasets of epithelial ovarian cancer. In concordance with previous studies on global copy number changes, our study showed similar alterations. However, when the landscape was de-convoluted into histotypes, distinct alterations were observed. We report here a comprehensive histotype-specific copy number landscape of ovarian cancer and showed that there is genomic diversity between the histotypes; some involving well known cancer genes and some novel potential driver genes. Besides preferential occurrence of alterations in some histotypes, opposite trends of alteration were observed; such as ERBB2 amplification in mucinous but deletion in serous tumors. The landscape highlights the need for identifying histotype-specific aberrations in ovarian cancer and present potential to tailor management of ovarian cancer based on molecular signature of histotypes. 56 samples containing the 4 histotypes were used for this study. It contained 12 clear cell carcinoma, 6 endometrioid adenocarcinoma, 2 mucinous adenocarcinoma, 5 mucinous-borderline tumors, 26 serous adenocarcinoma, and 5 serous-borderline tumors. Data was pre-processed and normalized with Hapmap JPT using the Affymetric Genotyping Console.
2012-10-25 | E-GEOD-30283 | biostudies-arrayexpress
Project description:Ovarian cancer is characterized by multiple structural aberrations; most are passenger alterations which do not confer tumor growth. Like many cancers, it is a heterogeneous disease and till date, the histotype-specific copy number landscape has been difficult to elucidate. To dissect the heterogeneity of ovarian cancer and understand the pathogenesis of its various histotypes, we developed an in silico hypothesis-driven workflow to identify histotype-specific copy number aberrations across multiple datasets of epithelial ovarian cancer. In concordance with previous studies on global copy number changes, our study showed similar alterations. However, when the landscape was de-convoluted into histotypes, distinct alterations were observed. We report here a comprehensive histotype-specific copy number landscape of ovarian cancer and showed that there is genomic diversity between the histotypes; some involving well known cancer genes and some novel potential driver genes. Besides preferential occurrence of alterations in some histotypes, opposite trends of alteration were observed; such as ERBB2 amplification in mucinous but deletion in serous tumors. The landscape highlights the need for identifying histotype-specific aberrations in ovarian cancer and present potential to tailor management of ovarian cancer based on molecular signature of histotypes.
2012-10-25 | GSE30284 | GEO
Project description:Ovarian cancer is characterized by multiple structural aberrations; most are passenger alterations which do not confer tumor growth. Like many cancers, it is a heterogeneous disease and till date, the histotype-specific copy number landscape has been difficult to elucidate. To dissect the heterogeneity of ovarian cancer and understand the pathogenesis of its various histotypes, we developed an in silico hypothesis-driven workflow to identify histotype-specific copy number aberrations across multiple datasets of epithelial ovarian cancer. In concordance with previous studies on global copy number changes, our study showed similar alterations. However, when the landscape was de-convoluted into histotypes, distinct alterations were observed. We report here a comprehensive histotype-specific copy number landscape of ovarian cancer and showed that there is genomic diversity between the histotypes; some involving well known cancer genes and some novel potential driver genes. Besides preferential occurrence of alterations in some histotypes, opposite trends of alteration were observed; such as ERBB2 amplification in mucinous but deletion in serous tumors. The landscape highlights the need for identifying histotype-specific aberrations in ovarian cancer and present potential to tailor management of ovarian cancer based on molecular signature of histotypes.
2012-10-25 | GSE30300 | GEO
Project description:Ovarian cancer is characterized by multiple structural aberrations; most are passenger alterations which do not confer tumor growth. Like many cancers, it is a heterogeneous disease and till date, the histotype-specific copy number landscape has been difficult to elucidate. To dissect the heterogeneity of ovarian cancer and understand the pathogenesis of its various histotypes, we developed an in silico hypothesis-driven workflow to identify histotype-specific copy number aberrations across multiple datasets of epithelial ovarian cancer. In concordance with previous studies on global copy number changes, our study showed similar alterations. However, when the landscape was de-convoluted into histotypes, distinct alterations were observed. We report here a comprehensive histotype-specific copy number landscape of ovarian cancer and showed that there is genomic diversity between the histotypes; some involving well known cancer genes and some novel potential driver genes. Besides preferential occurrence of alterations in some histotypes, opposite trends of alteration were observed; such as ERBB2 amplification in mucinous but deletion in serous tumors. The landscape highlights the need for identifying histotype-specific aberrations in ovarian cancer and present potential to tailor management of ovarian cancer based on molecular signature of histotypes.
2012-10-25 | GSE30283 | GEO
Project description:Ovarian cancer is characterized by multiple structural aberrations; most are passenger alterations which do not confer tumor growth. Like many cancers, it is a heterogeneous disease and till date, the histotype-specific copy number landscape has been difficult to elucidate. To dissect the heterogeneity of ovarian cancer and understand the pathogenesis of its various histotypes, we developed an in silico hypothesis-driven workflow to identify histotype-specific copy number aberrations across multiple datasets of epithelial ovarian cancer. In concordance with previous studies on global copy number changes, our study showed similar alterations. However, when the landscape was de-convoluted into histotypes, distinct alterations were observed. We report here a comprehensive histotype-specific copy number landscape of ovarian cancer and showed that there is genomic diversity between the histotypes; some involving well known cancer genes and some novel potential driver genes. Besides preferential occurrence of alterations in some histotypes, opposite trends of alteration were observed; such as ERBB2 amplification in mucinous but deletion in serous tumors. The landscape highlights the need for identifying histotype-specific aberrations in ovarian cancer and present potential to tailor management of ovarian cancer based on molecular signature of histotypes.
2012-10-25 | GSE30274 | GEO
Project description:Ovarian cancer is characterized by multiple structural aberrations; most are passenger alterations which do not confer tumor growth. Like many cancers, it is a heterogeneous disease and till date, the histotype-specific copy number landscape has been difficult to elucidate. To dissect the heterogeneity of ovarian cancer and understand the pathogenesis of its various histotypes, we developed an in silico hypothesis-driven workflow to identify histotype-specific copy number aberrations across multiple datasets of epithelial ovarian cancer. In concordance with previous studies on global copy number changes, our study showed similar alterations. However, when the landscape was de-convoluted into histotypes, distinct alterations were observed. We report here a comprehensive histotype-specific copy number landscape of ovarian cancer and showed that there is genomic diversity between the histotypes; some involving well known cancer genes and some novel potential driver genes. Besides preferential occurrence of alterations in some histotypes, opposite trends of alteration were observed; such as ERBB2 amplification in mucinous but deletion in serous tumors. The landscape highlights the need for identifying histotype-specific aberrations in ovarian cancer and present potential to tailor management of ovarian cancer based on molecular signature of histotypes. 42 archived frozen tumor samples collected from Department of Obstetrics and Gynecology, Tri-Service General Hospital, Taiwan, containing 8 clear cell, 3 mucinous, and 31 serous.
2012-10-25 | E-GEOD-30284 | biostudies-arrayexpress