Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis
Seisenova A. Daniyarov A. Molkenov A. Sharip A. Zinovyev A. Kairov U.
21 October 2021Frontiers Media S.A.
Frontiers in Genetics
2021#12
Independent Component Analysis 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. The study aimed to analyze the publicly available esophageal cancer data using the ICA for identification and comprehensive analysis of reproducible signaling pathways and molecular signatures involved in this cancer type. In this study, four independent esophageal cancer transcriptomic datasets from GEO databases were used. 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 Cytoscape, and HPRD database. The correlation graph between decompositions into 30 ICs was built with absolute correlation values exceeding 0.3. Clusters of components—pseudocliques were observed in the structure of the correlation graph. The top 1,000 most contributing genes of each ICs in the pseudocliques were mapped to the PPI network to construct associated signaling pathways. Some cliques were composed of densely interconnected nodes and included components common to most cancer types (such as cell cycle and extracellular matrix signals), while others were specific to EC. The results of this investigation may reveal potential biomarkers of esophageal carcinogenesis, functional subsystems dysregulated in the tumor cells, and be helpful in predicting the early development of a tumor. © Copyright
esophageal cancer , genomics , independent component analysis , meta-analysis , transcriptomics
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Laboratory of Bioinformatics and Systems Biology, National Laboratory Astana, Center for Life Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan
Department of Biology, School of Sciences and Humanities, Nazarbayev University, Nur-Sultan, Kazakhstan
Institut Curie, PSL Research University, INSERM U900, Paris, France
Laboratory of Advanced Methods for High-dimensional Data Analysis, Lobachevsky University, Nizhny Novgorod, Russian Federation
Laboratory of Bioinformatics and Systems Biology
Department of Biology
Institut Curie
Laboratory of Advanced Methods for High-dimensional Data Analysis
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