Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA393722 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR5815900_1.fastq.gz | Fastqsanger.gz | |||
| SRR5815900_2.fastq.gz | Fastqsanger.gz | |||
| SRR5815910_1.fastq.gz | Fastqsanger.gz | |||
| SRR5815910_2.fastq.gz | Fastqsanger.gz | |||
| SRR5815920_1.fastq.gz | Fastqsanger.gz |
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Project description:Transcriptomic and genomic profiling of early-stage ovarian carcinomas associated with histotype and overall survival
| PRJNA393720 | ENA
Project description:Transcriptomic and genomic profiling of early-stage ovarian carcinomas associated with histotype and overall survival [ARRAY]
| PRJNA393723 | ENA
Project description:Ovarian cancer is the most lethal gynecological malignancy in the western world. Despite recent efforts to characterize ovarian cancer using molecular profiling, few targeted treatment options are currently available. Here, we examined genetic variants, fusion transcripts, SNP genotyping, and gene expression patterns for early-stage (I, II) ovarian carcinomas (n=96) in relation to clinicopathological characteristics and clinical outcome, thereby identifying novel genetic features of ovarian carcinomas. Furthermore, mutation frequencies of specific genetic variants and/or their gene expression patterns were associated with histotype and overall survival, e.g. SLC28A2 (mucinous ovarian carcinoma histotype), ARCN1 (low expression in 0-2 year survival group), and tumor suppressor MTUS1 (mutation status and overall survival). The long non-coding RNA MALAT1 was identified as a highly promiscuous fusion transcript in ovarian carcinoma. Moreover, gene expression deregulation for 23 genes was associated with tumor aggressiveness. Taken together, the novel biomarkers identified here may improve ovarian carcinoma subclassification and patient stratification according to histotype and overall survival.
2018-10-04 | GSE101108 | GEO
Project description:Ovarian cancer is the most lethal gynecological malignancy in the western world. Despite recent efforts to characterize ovarian cancer using molecular profiling, few targeted treatment options are currently available. Here, we examined genetic variants, fusion transcripts, SNP genotyping, and gene expression patterns for early-stage (I, II) ovarian carcinomas (n=96) in relation to clinicopathological characteristics and clinical outcome, thereby identifying novel genetic features of ovarian carcinomas. Furthermore, mutation frequencies of specific genetic variants and/or their gene expression patterns were associated with histotype and overall survival, e.g. SLC28A2 (mucinous ovarian carcinoma histotype), ARCN1 (low expression in 0-2 year survival group), and tumor suppressor MTUS1 (mutation status and overall survival). The long non-coding RNA MALAT1 was identified as a highly promiscuous fusion transcript in ovarian carcinoma. Moreover, gene expression deregulation for 23 genes was associated with tumor aggressiveness. Taken together, the novel biomarkers identified here may improve ovarian carcinoma subclassification and patient stratification according to histotype and overall survival.
2018-10-04 | GSE101107 | GEO
Project description:BACKGROUND: Because of the distinct clinical presentation of early and advanced stage ovarian cancer, we aim to clarify whether these disease entities are solely separated by time of diagnosis or whether they arise from distinct molecular events. METHODS: Sixteen early and sixteen advanced stage ovarian carcinomas, matched for histological subtype and differentiation grade, were included. Genomic aberrations were compared for each early and advanced stage ovarian cancer by array comparative genomic hybridization. To study how the aberrations correlate to the clinical characteristics of the tumors we clustered tumors based on the genomic aberrations. RESULTS: The genomic aberration patterns in advanced stage cancer equalled those in early stage, but were more frequent in advanced stage (p?=?0.012). Unsupervised clustering based on genomic aberrations yielded two clusters that significantly discriminated early from advanced stage (p?=?0.001), and that did differ significantly in survival (p?=?0.002). These clusters however did give a more accurate prognosis than histological subtype or differentiation grade. CONCLUSION: This study indicates that advanced stage ovarian cancer either progresses from early stage or from a common precursor lesion but that they do not arise from distinct carcinogenic molecular events. Furthermore, we show that array comparative genomic hybridization has the potential to identify clinically distinct patients. Sixteen early and sixteen advanced stage ovarian carcinomas
2012-08-22 | E-GEOD-24418 | biostudies-arrayexpress
Project description:BACKGROUND: Because of the distinct clinical presentation of early and advanced stage ovarian cancer, we aim to clarify whether these disease entities are solely separated by time of diagnosis or whether they arise from distinct molecular events. METHODS: Sixteen early and sixteen advanced stage ovarian carcinomas, matched for histological subtype and differentiation grade, were included. Genomic aberrations were compared for each early and advanced stage ovarian cancer by array comparative genomic hybridization. To study how the aberrations correlate to the clinical characteristics of the tumors we clustered tumors based on the genomic aberrations. RESULTS: The genomic aberration patterns in advanced stage cancer equalled those in early stage, but were more frequent in advanced stage (p = 0.012). Unsupervised clustering based on genomic aberrations yielded two clusters that significantly discriminated early from advanced stage (p = 0.001), and that did differ significantly in survival (p = 0.002). These clusters however did give a more accurate prognosis than histological subtype or differentiation grade. CONCLUSION: This study indicates that advanced stage ovarian cancer either progresses from early stage or from a common precursor lesion but that they do not arise from distinct carcinogenic molecular events. Furthermore, we show that array comparative genomic hybridization has the potential to identify clinically distinct patients.
2012-08-22 | GSE24418 | GEO
Project description:High-grade serous ovarian cancer (HGSC) is the most lethal histotype of ovarian cancer and the majority of cases present with metastasis and late stage disease. We aimed to better characterize the distinctions between primary and metastatic tumors based on short- or long-term survival.
2023-04-27 | GSE218939 | GEO
Project description:A set of 45 surgical specimens has been profiled for miRNA expression to validate miRNA alterations associated to early relapse in advanced stage ovarian cancer patients. Fresh frozen samples were collected from a series of consecutive patients with high-grade advanced stage ovarian cancer who underwent primary surgery at INT-Milan. After surgery all patients received postoperative platinum-based chemotherapy. All patients signed an Institutional Review Board approved consent for bio-banking, clinical data collection and molecular analysis. Clinical codes: Histotype: according to WHO classification guidelines Stage: according to International Federation of Gynecological and Obstetrics (FIGO) guidelines Grading: according to WHO classification guidelines Debulking: NED: not evident disease; mRD: minimal residual disease; GRD: gross residual disease Therapy code: P: Platinum without taxanes; PT: Platinum/paclitaxel
2015-04-30 | E-GEOD-67819 | biostudies-arrayexpress
Project description:To date, a variety of studies have employed gene expression profiling to classify ovarian carcinomas in clinically relevant subtypes. These studies provided valuable first clues to molecular changes in ovarian cancer that might be exploited in new treatment strategies. However, most studies were of relatively limited size and the number of overlapping genes in the identified profiles was minimal. Although identification of gene expression profiles associated with clinically relevant subtypes in ovarian cancer is important, knowledge is now emerging rapidly on how genes interact in pathways, networks and complexes; this allows us to unravel those cellular pathways determining the biological behavior of ovarian cancer, that might be successfully targeted with drugs. The aim of our study was: 1) To develop a gene expression profile associated with overall survival in advanced stage serous ovarian cancer, 2) to assess the association of pathways and transcription factors with overall survival, and 3) to validate our identified profile and pathways/transcription factors in an independent set of ovarian cancers. According to a randomized design, profiling of 157 advanced stage serous ovarian cancers was performed in duplicate using ~35K 70-mer oligonucleotide microarrays. A continuous predictor of overall survival was built taking into account well-known issues in microarray analysis, such as multiple testing and overfitting. A functional class scoring analysis was utilized to assess pathways/transcription factors for their association with overall survival. The prognostic value of genes that constitute our overall survival profile was validated on a fully independent, publicly available data set of 118 well-defined primary serous ovarian cancers. Furthermore, functional class scoring analysis was also performed on this independent data set to assess the similarities with results from our own data set. An 86-gene overall survival profile discriminated between patients with unfavorable and favorable prognosis (median survival, 19 vs. 41 months, respectively; permutation p-value of log-rank statistic = 0.015) and maintained its independent prognostic value in multivariate analysis. Genes that comprised the overall survival profile were also able to discriminate between the two risk groups in the independent data set. In our dataset 17/167 pathways and 13/111 transcription factors were associated with overall survival of which 16 and 12 respectively were confirmed in the independent dataset. Our study provides new clues to genes, pathways and transcription factors which contribute to the clinical outcome of serous ovarian cancer and might be exploited in designing new treatment strategies. Keywords: Oncology/Gynecological Cancers, Genetics and Genomics/Cancer Genetics, Genetics and Genomics/Gene Expression, Genetics and Genomics/Genomics According to a randomized design, profiling of 157 advanced stage serous ovarian cancers was performed in duplicate using ~35K 70-mer oligonucleotide microarrays. Two randomly selected samples were hybridized together on the arrays for intensity-based instead of ratio-based analysis of the microarray data.
2008-12-10 | E-GEOD-13876 | biostudies-arrayexpress
Project description:To date, a variety of studies have employed gene expression profiling to classify ovarian carcinomas in clinically relevant subtypes. These studies provided valuable first clues to molecular changes in ovarian cancer that might be exploited in new treatment strategies. However, most studies were of relatively limited size and the number of overlapping genes in the identified profiles was minimal. Although identification of gene expression profiles associated with clinically relevant subtypes in ovarian cancer is important, knowledge is now emerging rapidly on how genes interact in pathways, networks and complexes; this allows us to unravel those cellular pathways determining the biological behavior of ovarian cancer, that might be successfully targeted with drugs. The aim of our study was: 1) To develop a gene expression profile associated with overall survival in advanced stage serous ovarian cancer, 2) to assess the association of pathways and transcription factors with overall survival, and 3) to validate our identified profile and pathways/transcription factors in an independent set of ovarian cancers. According to a randomized design, profiling of 157 advanced stage serous ovarian cancers was performed in duplicate using ~35K 70-mer oligonucleotide microarrays. A continuous predictor of overall survival was built taking into account well-known issues in microarray analysis, such as multiple testing and overfitting. A functional class scoring analysis was utilized to assess pathways/transcription factors for their association with overall survival. The prognostic value of genes that constitute our overall survival profile was validated on a fully independent, publicly available data set of 118 well-defined primary serous ovarian cancers. Furthermore, functional class scoring analysis was also performed on this independent data set to assess the similarities with results from our own data set. An 86-gene overall survival profile discriminated between patients with unfavorable and favorable prognosis (median survival, 19 vs. 41 months, respectively; permutation p-value of log-rank statistic = 0.015) and maintained its independent prognostic value in multivariate analysis. Genes that comprised the overall survival profile were also able to discriminate between the two risk groups in the independent data set. In our dataset 17/167 pathways and 13/111 transcription factors were associated with overall survival of which 16 and 12 respectively were confirmed in the independent dataset. Our study provides new clues to genes, pathways and transcription factors which contribute to the clinical outcome of serous ovarian cancer and might be exploited in designing new treatment strategies. Keywords: Oncology/Gynecological Cancers, Genetics and Genomics/Cancer Genetics, Genetics and Genomics/Gene Expression, Genetics and Genomics/Genomics
2008-12-11 | GSE13876 | GEO