DNA microarray integromics analysis platform

Waller, Tomasz; Gubała, Tomasz; Sarapata, Krzysztof; Piwowar, Monika; Jurkowski, Wiktor

doi:10.1186/s13040-015-0052-6

Cited by 15 publications

(10 citation statements)

References 46 publications

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“…Classical statistical methods are adapted to process large volumes of data, or data may be preprocessed–with the use of biological knowledge–as a preliminary step in statistical processing pipelines [ 2 ] [ 3 ] [ 4 ]. It is also becoming more and more common to adopt an integrative approach based on specialied databases, with various configurations of analytical methods facilitating simple and efficient extraction of relevant information using custom analysis platforms [ 5 ] [ 6 ]. Nevertheless, in spite of the dynamic evolution of data analytics, the capabilities of existing IT frameworks lag behind the sheer volume of data sets produced by modern research tools.…”

Section: Introductionmentioning

confidence: 99%

Regularization and grouping -omics data by GCA method: A transcriptomic case

2018

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The paper presents the application of Grade Correspondence Analysis (GCA) and Grade Correspondence Cluster Analysis (GCCA) for ordering and grouping -omics datasets, using transcriptomic data as an example. Based on gene expression data describing 256 patients with Multiple Myeloma it was shown that the GCA method could be used to find regularities in the analyzed collections and to create characteristic gene expression profiles for individual groups of patients. GCA iteratively permutes rows and columns to maximize the tau-Kendall or rho-Spearman coefficients, which makes it possible to arrange rows and columns in such a way that the most similar ones remain in each other’s neighbourhood. In this way, the GCA algorithm highlights regularities in the data matrix. The ranked data can then be grouped using the GCCA method, and after that aggregated in clusters, providing a representation that is easier to analyze–especially in the case of large sets of gene expression profiles. Regularization of transcriptomic data, which is presented in this manuscript, has enabled division of the data set into column clusters (representing genes) and row clusters (representing patients). Subsequently, rows were aggregated (based on medians) to visualise the gene expression profiles for patients with Multiple Myeloma in each collection. The presented analysis became the starting point for characterisation of differentiated genes and biochemical processes in which they are involved. GCA analysis may provide an alternative analytical method to support differentiation and analysis of gene expression profiles characterising individual groups of patients.

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Section: Introductionmentioning

confidence: 99%

Regularization and grouping -omics data by GCA method: A transcriptomic case

2018

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“…This is especially true in molecular analyses [ 7–9 , 11 , 19 , 25–28 ], where tools such as Partek Genomics Suite (Partek Inc, St Louis, MO, USA), GeneSpring (Agilent Technologies, Santa Clara, CA, USA), tools that harness The Cancer Genome Atlas (TCGA) [ 25 , 26 ] data portal (e.g. cBioPortal for Cancer Genomics [developed and maintained by the Center for Molecular Oncology and the Computational Biology Center at Memorial Sloan-Kettering Cancer Center] [ 29 , 30 ], COSMIC [ 31 ] and the Broad Institute TCGA GDAC Firehose [ 32 ]) and others [ 33 , 34 ] can facilitate statistical analysis and biological contextualization of omics data. However, in the push towards development of precision medicine, there remains an urgent need for an integromics platform that takes into account non-molecular data.…”

Section: Introductionmentioning

confidence: 99%

Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Bankhead

Dunne

et al. 2016

Brief Bioinform

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Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating ‘big data’ across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.

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“…Molecular profiling of GC can be performed using microarray-based gene expression analysis [16-18]. We performed differential expression analysis using this technology to measure genomic or transcriptional expression of GC tissues (along with the adjacent normal gastric tissues).…”

Section: Introductionmentioning

confidence: 99%

Overexpression of Lymphocyte Antigen 6 Complex, Locus E in Gastric Cancer Promotes Cancer Cell Growth and Metastasis

Chen

et al. 2018

Cell Physiol Biochem

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Background/Aims: Lymphocyte antigen 6 complex, locus E (LY6E) is a member of the lymphostromal cell membrane Ly6 superfamily protein. The present study investigated the clinical significance and potential biological function of LY6E in gastric cancer (GC). Methods: LY6E mRNA and protein expressions in human GC tissues and GC cells were tested. Relationship between LY6E expression and the GC patients’ clinicopathologic characteristics was analyzed. LY6E was silenced by siRNA in the cultured GC cells. Results: The RNA expression microarray profiling assay results demonstrated that LY6E mRNA was significantly increased in multiple human GC tumor tissues. Immunohistochemistry (IHC) staining analysis revealed that 59 of 75 (78.7%) GC specimens were LY6E positive. LY6E over-expression in human GC was correlated with the histology grade, AJCC stage, N classification, lymphatic invasion, and tumor location. Notably, functional LY6E expression was also detected in AGS and other established GC cell lines. LY6E knockdown by targeted-siRNA inhibited AGS cell survival and proliferation. Meanwhile, the LY6E siRNA induced G1-S cell cycle arrest and apoptosis in AGC cells. Additionally, AGC cell migration was also inhibited by LY6E knockdown. Expressions of tumor-suppressing proteins, including PTEN (phosphatase and tensin homolog) and E-Cadherin, were increased in LY6E-silenced AGS cells. Conclusion: LY6E over-expression in GC is potentially required for cancer cell survival, proliferation and migration.

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DNA microarray integromics analysis platform

Cited by 15 publications

References 46 publications

Regularization and grouping -omics data by GCA method: A transcriptomic case

Regularization and grouping -omics data by GCA method: A transcriptomic case

Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Overexpression of Lymphocyte Antigen 6 Complex, Locus E in Gastric Cancer Promotes Cancer Cell Growth and Metastasis

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