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Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients
- Source :
- BMC Medical Genomics, BMC Medical Genomics, BioMed Central, 2019, 12 (1), pp.132. ⟨10.1186/s12920-019-0578-4⟩, BMC Medical Genomics, Vol 12, Iss 1, Pp 1-17 (2019)
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- 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.
- Subjects :
- Male
lcsh:Internal medicine
lcsh:QH426-470
Computer science
[SDV.CAN]Life Sciences [q-bio]/Cancer
Computational biology
Deconvolution
Independent component analysis
Biochemistry, biophysics & molecular biology [F05] [Life sciences]
Transcriptome
03 medical and health sciences
0302 clinical medicine
[SDV.CAN] Life Sciences [q-bio]/Cancer
[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN]
Databases, Genetic
Genetics
medicine
Humans
Biochimie, biophysique & biologie moléculaire [F05] [Sciences du vivant]
lcsh:RC31-1245
Transcriptomics
Melanoma
Genetics (clinical)
Survival analysis
030304 developmental biology
Cancer
0303 health sciences
Principal Component Analysis
Computational Biology
medicine.disease
Human genetics
3. Good health
lcsh:Genetics
MicroRNAs
Technical Advance
030220 oncology & carcinogenesis
[SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN]
Female
DNA microarray
Subjects
Details
- Language :
- English
- ISSN :
- 17558794
- Database :
- OpenAIRE
- Journal :
- BMC Medical Genomics, BMC Medical Genomics, BioMed Central, 2019, 12 (1), pp.132. ⟨10.1186/s12920-019-0578-4⟩, BMC Medical Genomics, Vol 12, Iss 1, Pp 1-17 (2019)
- Accession number :
- edsair.doi.dedup.....075d2dc54cd6683281b37e556edfd7cc