Reference : Deconvolution of Transcriptomes and miRNomes by Independent Component Analysis Provid...
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
Systems Biomedicine
Deconvolution of Transcriptomes and miRNomes by Independent Component Analysis Provides Insights Into Biological Processes and Clinical Outcomes of Melanoma Patients
Nazarov, Petr V. [Luxembourg Institute of Health (LIH) > Quantitative Biology Unit]
Wienecke-Baldacchino, Anke K. [Laboratoire National de Santé > Epidemiology and Microbial Genomics Unit, Department of Microbiology]
Zinovyev, Andrei [PSL Research University > MINES ParisTech]
Czerwińska, Urszula [Université Paris Descartes > Centre de Recherches Interdisciplinaires]
Muller, Arnaud [Luxembourg Institute of Health (LIH) > Quantitative Biology Unit]
Nashan, Dorothée [Klinikum Dortmund GmbH]
Dittmar, Gunnar [Luxembourg Institute of Health (LIH) > Quantitative Biology Unit]
Azuaje, Francisco [Luxembourg Institute of Health (LIH) > Quantitative Biology Unit]
Kreis, Stephanie mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
BMC Medical Genomics
BioMed Central
12 (1)
Yes (verified by ORBilu)
United Kingdom
[en] Independent component analysis ; Deconvolution ; Transcriptomics ; Cancer ; Survival analysis
[en] Background: The amount of publicly available cancer-related“omics”data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. However, the integration of different datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Methods: Here we present a method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets. The approach is based on independent component analysis (ICA)–an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes transcriptomics datasets into expression profiles with minimal mutual dependency. Results: By applying the method to a small cohort of primary melanoma and control samples combined with a large discovery melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. We showed the potential of the method to predict cancer subtypes and estimate the activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation. ICA-based risk score was proposed and its connection to patient survival was validated with an independent cohort of patients. Additionally, through integration of components identified for mRNA and miRNA data, the proposed method helped deducing biological functions of miRNAs, which would otherwise not be possible. Conclusions: We present a method that can be used to map new transcriptomic data from cancer patient samples onto large discovery datasets. The method corrects technical biases, helps characterizing activity of biological processes or cell types in the new samples and provides the prognosis of patient survival
Luxembourg Ministry of Higher Educationand Research, the Luxembourg National Research Fund (C17/BM/11664971/DEMICS), the University of Luxembourg, IRP (R-AGR-0748-00) and by the Integrated Biobank of Luxembourg (IBBL)

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