META-ANALYSIS OF CANCER TRANSCRIPTOMES USING INDEPENDENT COMPONENT ANALYSIS
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Date
2020
Authors
Seisenova, A.
Sharip, A.
Molkenov, A.
Daniyarov, A.
Karabayev, D.
Kairov, U.
Journal Title
Journal ISSN
Volume Title
Publisher
International conference "MODERN PERSPECTIVES FOR BIOMEDICAL SCIENCES: FROM BENCH TO BEDSIDE”; National Laboratory Astana
Abstract
Introduction: Independent Component Analysis (ICA) is a matrix factorization method for data dimension
reduction. ICA has been widely applied for the analysis of transcriptomic data for blind separation
of biological, environmental and technical factors affecting gene expression. This study aimed to analyze
cancer data using the ICA for identification and comprehensive analysis of reproducible signaling pathways
and molecular signatures in cancer.
Materials and Methods: In this study, four independent cancer transcriptomic datasets GSE26886,
GSE69925, GSE32701and GSE21293 (Affymetrix) from GEO databases were used. R Bioconductor and
Matlab have been used for normalization. A bioinformatics tool «BiODICA - Independent Component
Analysis of Big Omics Data» was applied to compute independent components (ICs). Gene Set Enrichment
Analysis (GSEA) and ToppGene uncovered the most significantly enriched pathways. Construction
and visualization of gene networks and graphs were performed using the OFTEN method, Cytoscape and
HPRD database.
Results: The correlation graph between decompositions into 30 ICs was built with absolute correlation
values exceeding 0.15. Clusters of components - pseudocliques were observed in the structure of the
correlation graph. Top 500 most contributing genes of each ICs in pseudocliques were mapped to the PPI
network to construct signaling pathways for gene interaction. Some cliques were composed of densely
interconnected nodes and included components common to most cancer types, while others were common
to some of them.
Conclusion: The results of this investigation may reveal potential biomarkers of carcinogenesis, functional
subsystems in the tumor cells, and helpful in predicting the early development of a tumor.
Description
Keywords
Research Subject Categories::MEDICINE, transcriptome, independent component analysis, cancer