Dataset Information

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.

ABSTRACT: To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC. VIDEO ABSTRACT.

SUBMITTER: Zhang H

PROVIDER: S-EPMC4967013 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.

Zhang Hui H Liu Tao T Zhang Zhen Z Payne Samuel H SH Zhang Bai B McDermott Jason E JE Zhou Jian-Ying JY Petyuk Vladislav A VA Chen Li L Ray Debjit D Sun Shisheng S Yang Feng F Chen Lijun L Wang Jing J Shah Punit P Cha Seong Won SW Aiyetan Paul P Woo Sunghee S Tian Yuan Y Gritsenko Marina A MA Clauss Therese R TR Choi Caitlin C Monroe Matthew E ME Thomas Stefani S Nie Song S Wu Chaochao C Moore Ronald J RJ Yu Kun-Hsing KH Tabb David L DL Fenyö David D Bafna Vineet V Wang Yue Y Rodriguez Henry H Boja Emily S ES Hiltke Tara T Rivers Robert C RC Sokoll Lori L Zhu Heng H Shih Ie-Ming IM Cope Leslie L Pandey Akhilesh A Zhang Bing B Snyder Michael P MP Levine Douglas A DA Smith Richard D RD Chan Daniel W DW Rodland Karin D KD

Cell 20160629 3

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the prote ...[more]

PMID: 27372738

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Project description:<a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Ovarian_JHU_Glycoproteome|TCGA_Ovarian_JHU_Proteome|TCGA_Ovarian_PNNL_Proteome|TCGA_Ovarian_PNNL_Phosphoproteome_Velos_Qexatvive" target="_blank">Explore This Study at the NCI Proteomic Data Commons</a><a href="http://www.ncbi.nlm.nih.gov/pubmed/27372738" target="_blank">Zhang H, Liu T, Zhang Z, Payne SH, Zhang B, McDermott JE et al., Cell (2016), DOI: 10.1016/j.cell.2016.05.069, Published online June 29, 2016.</a>To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC.Information on the complete TCGA Ovarian Serous Cystadenocarcinoma (OV) cohort can be found <a href="https://portal.gdc.cancer.gov/projects/TCGA-OV" target="_blank">here</a>. The Cancer Genome Atlas Research Network published on the Genomic characterization of the OV cohort in <a href="http://www.ncbi.nlm.nih.gov/pubmed/21720365" target="_blank">Nature</a> on June 30, 2011Genomic data for the 174 TCGA samples used in the CPTAC Proteome study can be downloaded from <a href="https://portal.gdc.cancer.gov/legacy-archive/search/f?filters=%7B%22op%22:%22and%22,%22content%22:%5B%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.program.name%22,%22value%22:%5B%22TCGA%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.project_id%22,%22value%22:%5B%22TCGA-OV%22%5D%7D%7D%5D%7D" target="_blank">here</a>.Peptide-Spectrum-Matches and Protein Reports from the CPTAC Common Data Analysis Pipeline (CDAP) can be downloaded from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S020" target="_blank">here</a>.<ul><li>Dataset imported into MassIVE from <a href="https://cptac-data-portal.georgetown.edu/cptac/s/S026">https://cptac-data-portal.georgetown.edu/cptac/s/S026</a> on 08/28/19</li><li>Dataset source: <a href="https://pdc.esacinc.com/pdc/browse/filters/study_name:TCGA_Ovarian_JHU_Glycoproteome|TCGA_Ovarian_JHU_Proteome|TCGA_Ovarian_PNNL_Proteome|TCGA_Ovarian_PNNL_Phosphoproteome_Velos_Qexatvive" target="_blank">https://pdc.esacinc.com/pdc/browse/filters/study_name:...</a></li></ul>

Project description:PurposeOvarian cancer is the leading cause of gynecologic cancer-related death worldwide. Early diagnosis of ovarian cancer can significantly improve patient prognosis. Hence, there is an urgent need to identify key diagnostic and prognostic biomarkers specific for ovarian cancer. Because high-grade serous ovarian cancer (HGSOC) is the most common type of ovarian cancer and accounts for the majority of deaths, we identified potential biomarkers for the early diagnosis and prognosis of HGSOC.MethodsSix datasets (GSE14001, GSE18520, GSE26712, GSE27651, GSE40595, and GSE54388) were downloaded from the Gene Expression Omnibus database for analysis. Differentially expressed genes (DEGs) between HGSOC and normal ovarian surface epithelium samples were screened via integrated analysis. Hub genes were identified by analyzing protein-protein interaction (PPI) network data. The online Kaplan-Meier plotter was utilized to evaluate the prognostic roles of these hub genes. The expression of these hub genes was confirmed with Oncomine datasets and validated by quantitative real-time PCR and Western blotting.ResultsA total of 103 DEGs in patients with HGSOC-28 upregulated genes and 75 downregulated genes-were successfully screened. Enrichment analyses revealed that the upregulated genes were enriched in cell division and cell proliferation and that the downregulated genes mainly participated in the Wnt signaling pathway and various metabolic processes. Ten hub genes were associated with HGSOC pathogenesis. Seven overexpressed hub genes were partitioned into module 1 of the PPI network, which was enriched in the cell cycle and DNA replication pathways. Survival analysis revealed that MELK, CEP55 and KDR expression levels were significantly correlated with the overall survival of HGSOC patients (P < 0.05). The RNA and protein expression levels of these hub genes were validated experimentally.ConclusionBased on an integrated analysis, we propose the further investigation of MELK, CEP55 and KDR as promising diagnostic and prognostic biomarkers of HGSOC.

Dataset Information

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.

Publications

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.

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