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Omics data ovarian cancer

Zhe Zhang, Ke Huang, Chenglei Gu, Luyang Zhao, Nan Wang, Xiaolei Wang, Dongsheng Zhao, Chenggang Zhang, Yiming Lu, Yuanguang Meng
Classification of ovarian cancer by morphologic features has a limited effect on serous ovarian cancer (SOC) treatment and prognosis. Here, we proposed a new system for SOC subtyping based on the molecular categories from the Cancer Genome Atlas project. We analyzed the DNA methylation, protein, microRNA, and gene expression of 1203 samples from 599 serous ovarian cancer patients. These samples were divided into nine subtypes based on RNA-seq data, and each subtype was found to be associated with the activation and/or suppression of the following four biological processes: immunoactivity, hormone metabolic, mesenchymal development and the MAPK signaling pathway...
2016: Scientific Reports
Prabhakar Chalise, Rama Raghavan, Brooke L Fridley
BACKGROUND AND OBJECTIVE: Integrative approaches for the study of biological systems have gained popularity in the realm of statistical genomics. For example, The Cancer Genome Atlas (TCGA) has applied integrative clustering methodologies to various cancer types to determine molecular subtypes within a given cancer histology. In order to adequately compare integrative or "systems-biology"-type methods, realistic and related datasets are needed to assess the methods. This involves simulating multiple types of 'omic data with realistic correlation between features of the same type (e...
May 2016: Computer Methods and Programs in Biomedicine
Paul A Rudnick, Sanford P Markey, Jeri Roth, Yuri Mirokhin, Xinjian Yan, Dmitrii V Tchekhovskoi, Nathan J Edwards, Ratna R Thangudu, Karen A Ketchum, Christopher R Kinsinger, Mehdi Mesri, Henry Rodriguez, Stephen E Stein
The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has produced large proteomics data sets from the mass spectrometric interrogation of tumor samples previously analyzed by The Cancer Genome Atlas (TCGA) program. The availability of the genomic and proteomic data is enabling proteogenomic study for both reference (i.e., contained in major sequence databases) and nonreference markers of cancer. The CPTAC laboratories have focused on colon, breast, and ovarian tissues in the first round of analyses; spectra from these data sets were produced from 2D liquid chromatography-tandem mass spectrometry analyses and represent deep coverage...
March 4, 2016: Journal of Proteome Research
Lin Zhang, Hui Liu, Yufei Huang, Xuesong Wang, Yidong Chen, Jia Meng
Different types of genomic aberration may simultaneously contribute to tumorigenesis. To obtain a more accurate prognostic assessment to guide therapeutic regimen choice for cancer patients, the heterogeneous multi-omic data should be integrated harmoniously, which can often be difficult. For this purpose, we propose a Gene Interaction Regularized Elastic Net (GIREN) model that predicts clinical outcome by integrating multiple data types. GIREN conveniently embraces both gene measurements and gene-gene interaction information under an elastic net formulation, enforcing structure sparsity and the "grouping effect" in solution to select the discriminate features with prognostic value...
December 23, 2015: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Haoming Xu, Mohammad Ali Moni, Pietro Liò
In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient...
December 2015: Computational Biology and Chemistry
Marie Denis, Mahlet G Tadesse
MOTIVATION: Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples. Each data type gives independent and complementary information that can explain the biological mechanisms of interest. While several studies performing independent analyses of each dataset have led to significant results, a better understanding of complex biological mechanisms requires an integrative analysis of different sources of data. RESULTS: Flexible modeling approaches, based on penalized likelihood methods and expectation-maximization (EM) algorithms, are studied and tested under various biological relationship scenarios between the different molecular features and their effects on a clinical outcome...
March 1, 2016: Bioinformatics
Xinhong Ye
OBJECTIVE: The study aimed to provide novel insight into the mechanism of platinum resistance of ovarian cancer. MATERIALS AND METHODS: RNA-seq data ERP000710 were obtained from Gene Expression Omnibus database, including specimens from six platinum sensitive samples and six platinum tolerance samples. The author analyzed the data of the 12 samples as a whole because of the low flux sequencing. Single nucleotide polymorphisms (SNPs) were identified between platinum-sensitive and platinum-tolerant samples using VARSCAN, followed by functional prediction of the SNPs...
2015: European Journal of Gynaecological Oncology
Zi Yang, George Michailidis
MOTIVATION: Recent advances in high-throughput omics technologies have enabled biomedical researchers to collect large-scale genomic data. As a consequence, there has been growing interest in developing methods to integrate such data to obtain deeper insights regarding the underlying biological system. A key challenge for integrative studies is the heterogeneity present in the different omics data sources, which makes it difficult to discern the coordinated signal of interest from source-specific noise or extraneous effects...
January 1, 2016: Bioinformatics
Lieven P C Verbeke, Jimmy Van den Eynden, Ana Carolina Fierro, Piet Demeester, Jan Fostier, Kathleen Marchal
The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples...
2015: PloS One
Anna Halama, Bella S Guerrouahen, Jennifer Pasquier, Ilhem Diboun, Edward D Karoly, Karsten Suhre, Arash Rafii
BACKGROUND: In this era of precision medicine, the deep and comprehensive characterization of tumor phenotypes will lead to therapeutic strategies beyond classical factors such as primary sites or anatomical staging. Recently, "-omics" approached have enlightened our knowledge of tumor biology. Such approaches have been extensively implemented in order to provide biomarkers for monitoring of the disease as well as to improve readouts of therapeutic impact. The application of metabolomics to the study of cancer is especially beneficial, since it reflects the biochemical consequences of many cancer type-specific pathophysiological processes...
2015: Journal of Translational Medicine
Shuji Ogino, Peter T Campbell, Reiko Nishihara, Amanda I Phipps, Andrew H Beck, Mark E Sherman, Andrew T Chan, Melissa A Troester, Adam J Bass, Kathryn C Fitzgerald, Rafael A Irizarry, Karl T Kelsey, Hongmei Nan, Ulrike Peters, Elizabeth M Poole, Zhi Rong Qian, Rulla M Tamimi, Eric J Tchetgen Tchetgen, Shelley S Tworoger, Xuehong Zhang, Edward L Giovannucci, Piet A van den Brandt, Bernard A Rosner, Molin Wang, Nilanjan Chatterjee, Colin B Begg
Disease classification system increasingly incorporates information on pathogenic mechanisms to predict clinical outcomes and response to therapy and intervention. Technological advancements to interrogate omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, interactomics, etc.) provide widely open opportunities in population-based research. Molecular pathological epidemiology (MPE) represents integrative science of molecular pathology and epidemiology. This unified paradigm requires multidisciplinary collaboration between pathology, epidemiology, biostatistics, bioinformatics, and computational biology...
July 2015: Cancer Causes & Control: CCC
Juan J Tarín, Miguel A García-Pérez, Toshio Hamatani, Antonio Cano
The present review aims to ascertain whether different infertility etiologies share particular genes and/or molecular pathways with other pathologies and are associated with distinct and particular risks of later-life morbidity and mortality. In order to reach this aim, we use two different sources of information: (1) a public web server named DiseaseConnect ( ) focused on the analysis of common genes and molecular mechanisms shared by diseases by integrating comprehensive omics and literature data; and (2) a literature search directed to find clinical comorbid relationships of infertility etiologies with only those diseases appearing after infertility is manifested...
2015: Reproductive Biology and Endocrinology: RB&E
Yuanshuai Zhou, Yongjing Liu, Kening Li, Rui Zhang, Fujun Qiu, Ning Zhao, Yan Xu
BACKGROUND: Over the last decade, an increasing number of integrative studies on cancer-related genes have been published. Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of gene expression and copy number to investigate the impact of copy number alteration on gene expression, and to predict and prioritize candidate oncogenes and tumor suppressor genes...
2015: PloS One
Ye Tian, Sean S Wang, Zhen Zhang, Olga C Rodriguez, Emanuel Petricoin, Ie-Ming Shih, Daniel Chan, Maria Avantaggiati, Guoqiang Yu, Shaozhen Ye, Robert Clarke, Chao Wang, Bai Zhang, Yue Wang, Chris Albanese
Ever growing "omics" data and continuously accumulated biological knowledge provide an unprecedented opportunity to identify molecular biomarkers and their interactions that are responsible for cancer phenotypes that can be accurately defined by clinical measurements such as in vivo imaging. Since signaling or regulatory networks are dynamic and context-specific, systematic efforts to characterize such structural alterations must effectively distinguish significant network rewiring from random background fluctuations...
November 2014: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Dokyoon Kim, Je-Gun Joung, Kyung-Ah Sohn, Hyunjung Shin, Yu Rang Park, Marylyn D Ritchie, Ju Han Kim
OBJECTIVE: Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes...
January 2015: Journal of the American Medical Informatics Association: JAMIA
Chen Meng, Bernhard Kuster, Aedín C Culhane, Amin Moghaddas Gholami
BACKGROUND: To leverage the potential of multi-omics studies, exploratory data analysis methods that provide systematic integration and comparison of multiple layers of omics information are required. We describe multiple co-inertia analysis (MCIA), an exploratory data analysis method that identifies co-relationships between multiple high dimensional datasets. Based on a covariance optimization criterion, MCIA simultaneously projects several datasets into the same dimensional space, transforming diverse sets of features onto the same scale, to extract the most variant from each dataset and facilitate biological interpretation and pathway analysis...
2014: BMC Bioinformatics
Yang Yang, Biao Ma, Ling Li, Ye Jin, Wei Ben, Dandan Zhang, Keping Jiang, Shujun Feng, Lu Huang, Jianhua Zheng
A large quantity of M2-polarized tumor-associated macrophages (TAMs) is present in the tissue, ascitic fluid and peritoneum of ovarian cancer patients. A thorough understanding of the roles of M2-TAM in the development of ovarian cancer may provide new insight into the treatment of this disease. The rapid advancement of omics techniques presents a great challenge to biologists to extract meaningful biological information from vast pools of data. In the present study, using microarray method, we identified 996 genes in SKOV3 ovarian carcinoma cells that underwent expression level changes under the influence of TAMs...
June 2014: Oncology Reports
Dokyoon Kim, Hyunjung Shin, Kyung-Ah Sohn, Anurag Verma, Marylyn D Ritchie, Ju Han Kim
In order to improve our understanding of cancer and develop multi-layered theoretical models for the underlying mechanism, it is essential to have enhanced understanding of the interactions between multiple levels of genomic data that contribute to tumor formation and progression. Although there exist recent approaches such as a graph-based framework that integrates multi-omics data including copy number alteration, methylation, gene expression, and miRNA data for cancer clinical outcome prediction, most of previous methods treat each genomic data as independent and the possible interplay between them is not explicitly incorporated to the model...
June 1, 2014: Methods: a Companion to Methods in Enzymology
Olivier Gevaert, Victor Villalobos, Branimir I Sikic, Sylvia K Plevritis
The increasing availability of multi-omics cancer datasets has created a new opportunity for data integration that promises a more comprehensive understanding of cancer. The challenge is to develop mathematical methods that allow the integration and extraction of knowledge from large datasets such as The Cancer Genome Atlas (TCGA). This has led to the development of a variety of omics profiles that are highly correlated with each other; however, it remains unknown which profile is the most meaningful and how to efficiently integrate different omics profiles...
August 6, 2013: Interface Focus
Sandra L Taylor, Gary S Leiserowitz, Kyoungmi Kim
Mass spectrometry is an important high-throughput technique for profiling small molecular compounds in biological samples and is widely used to identify potential diagnostic and prognostic compounds associated with disease. Commonly, this data generated by mass spectrometry has many missing values resulting when a compound is absent from a sample or is present but at a concentration below the detection limit. Several strategies are available for statistically analyzing data with missing values. The accelerated failure time (AFT) model assumes all missing values result from censoring below a detection limit...
December 2013: Statistical Applications in Genetics and Molecular Biology
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