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Omics data ovarian cancer

Sungjin Kwon, Hyosil Kim, Hyun Seok Kim
Current multiomics assay platforms facilitate systematic identification of functional entities that are mappable in a biological network, and computational methods that are better able to detect densely connected clusters of signals within a biological network are considered increasingly important. One of the most famous algorithms for detecting network subclusters is Molecular Complex Detection (MCODE). MCODE, however, is limited in simultaneous analyses of multiple, large-scale data sets, since it runs on the Cytoscape platform, which requires extensive computational resources and has limited coding flexibility...
2017: BioMed Research International
Abolfazl Doostparast Torshizi, Linda R Petzold
Objective: Data integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects of different biological data types can boost learning capabilities and lead to a better understanding of the underlying interactions among molecular levels. Methods: In this paper we present a graph-based semi-supervised classification algorithm that incorporates latent biological knowledge in the form of biological pathways with gene expression and DNA methylation data...
January 1, 2018: Journal of the American Medical Informatics Association: JAMIA
Debangana Chakravorty, Tanmoy Jana, Sukhen Das Mandal, Anuradha Seth, Anubrata Bhattacharya, Sudipto Saha
BACKGROUND: Myc is an essential gene having multiple functions such as in cell growth, differentiation, apoptosis, genomic stability, angiogenesis, and disease biology. A large number of researchers dedicated to Myc biology are generating a substantial amount of data in normal and cancer cells/tissues including Burkitt's lymphoma and ovarian cancer. RESULTS: MYCbase ( ) is a collection of experimentally supported functional sites in Myc that can influence the biological cellular processes...
April 28, 2017: BMC Bioinformatics
Xiao-Fei Zhang, Le Ou-Yang, Hong Yan
Motivation: Understanding how gene regulatory networks change under different cellular states is important for revealing insights into network dynamics. Gaussian graphical models, which assume that the data follow a joint normal distribution, have been used recently to infer differential networks. However, the distributions of the omics data are non-normal in general. Furthermore, although much biological knowledge (or prior information) has been accumulated, most existing methods ignore the valuable prior information...
August 15, 2017: Bioinformatics
Kiley Graim, Tiffany Ting Liu, Achal S Achrol, Evan O Paull, Yulia Newton, Steven D Chang, Griffith R Harsh, Sergio P Cordero, Daniel L Rubin, Joshua M Stuart
BACKGROUND: Patient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings...
March 31, 2017: BMC Medical Genomics
Dokyoon Kim, Ruowang Li, Anastasia Lucas, Shefali S Verma, Scott M Dudek, Marylyn D Ritchie
It is common that cancer patients have different molecular signatures even though they have similar clinical features, such as histology, due to the heterogeneity of tumors. To overcome this variability, we previously developed a new approach incorporating prior biological knowledge that identifies knowledge-driven genomic interactions associated with outcomes of interest. However, no systematic approach has been proposed to identify interaction models between pathways based on multi-omics data. Here we have proposed such a novel methodological framework, called metadimensional knowledge-driven genomic interactions (MKGIs)...
May 1, 2017: Journal of the American Medical Informatics Association: JAMIA
Zaixiang Tang, Yueping Shen, Xinyan Zhang, Nengjun Yi
Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data...
January 2017: Genetics
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
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