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MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers

Multiple Myeloma (MM) is the second most frequent haematological malignancy in the world although the related pathogenesis remains unclear. The study of how gene expression profiling (GEP) is correlated with patients’ survival could be important for understanding the initiation and progression of MM...

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Autores principales: Settino, Marzia, Arbitrio, Mariamena, Scionti, Francesca, Caracciolo, Daniele, Di Martino, Maria Teresa, Tagliaferri, Pierosandro, Tassone, Pierfrancesco, Cannataro, Mario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304035/
http://dx.doi.org/10.1007/978-3-030-50420-5_42
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author Settino, Marzia
Arbitrio, Mariamena
Scionti, Francesca
Caracciolo, Daniele
Di Martino, Maria Teresa
Tagliaferri, Pierosandro
Tassone, Pierfrancesco
Cannataro, Mario
author_facet Settino, Marzia
Arbitrio, Mariamena
Scionti, Francesca
Caracciolo, Daniele
Di Martino, Maria Teresa
Tagliaferri, Pierosandro
Tassone, Pierfrancesco
Cannataro, Mario
author_sort Settino, Marzia
collection PubMed
description Multiple Myeloma (MM) is the second most frequent haematological malignancy in the world although the related pathogenesis remains unclear. The study of how gene expression profiling (GEP) is correlated with patients’ survival could be important for understanding the initiation and progression of MM. In order to aid researchers in identifying new prognostic RNA biomarkers as targets for functional cell-based studies, the use of appropriate bioinformatic tools for integrative analysis is required. The main contribution of this paper is the development of a set of functionalities, extending TCGAbiolinks package, for downloading and analysing Multiple Myeloma Research Foundation (MMRF) CoMMpass study data available at the NCI’s Genomic Data Commons (GDC) Data Portal. In this context, we present further a workflow based on the use of this new functionalities that allows to i) download data; ii) perform and plot the Array Array Intensity correlation matrix; ii) correlate gene expression and Survival Analysis to obtain a Kaplan–Meier survival plot.
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spelling pubmed-73040352020-06-19 MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers Settino, Marzia Arbitrio, Mariamena Scionti, Francesca Caracciolo, Daniele Di Martino, Maria Teresa Tagliaferri, Pierosandro Tassone, Pierfrancesco Cannataro, Mario Computational Science – ICCS 2020 Article Multiple Myeloma (MM) is the second most frequent haematological malignancy in the world although the related pathogenesis remains unclear. The study of how gene expression profiling (GEP) is correlated with patients’ survival could be important for understanding the initiation and progression of MM. In order to aid researchers in identifying new prognostic RNA biomarkers as targets for functional cell-based studies, the use of appropriate bioinformatic tools for integrative analysis is required. The main contribution of this paper is the development of a set of functionalities, extending TCGAbiolinks package, for downloading and analysing Multiple Myeloma Research Foundation (MMRF) CoMMpass study data available at the NCI’s Genomic Data Commons (GDC) Data Portal. In this context, we present further a workflow based on the use of this new functionalities that allows to i) download data; ii) perform and plot the Array Array Intensity correlation matrix; ii) correlate gene expression and Survival Analysis to obtain a Kaplan–Meier survival plot. 2020-05-22 /pmc/articles/PMC7304035/ http://dx.doi.org/10.1007/978-3-030-50420-5_42 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Settino, Marzia
Arbitrio, Mariamena
Scionti, Francesca
Caracciolo, Daniele
Di Martino, Maria Teresa
Tagliaferri, Pierosandro
Tassone, Pierfrancesco
Cannataro, Mario
MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers
title MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers
title_full MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers
title_fullStr MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers
title_full_unstemmed MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers
title_short MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers
title_sort mmrf-commpass data integration and analysis for identifying prognostic markers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304035/
http://dx.doi.org/10.1007/978-3-030-50420-5_42
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