Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis.

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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.", "claims": [{"id": "claim-method", "statement": "ICA was applied to each transcriptomic dataset separately.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34795689", "locator": "sections[5] \"Independent Component Analysis\" paragraph 2", "quote": "ICA was applied to each transcriptomic dataset separately."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34795689", "locator": "sections[17] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ICA was applied to four publicly available esophageal cancer transcriptomic datasets from GEO to compute independent components and identify reproducible signaling pathways and molecular signatures.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34795689", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In this study, four independent esophageal cancer transcriptomic datasets from GEO databases were used."}, {"asset": "source:pmid:34795689", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A bioinformatics tool « BiODICA—Independent Component Analysis of Big Omics Data» was applied to compute independent components (ICs)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ICA was applied to four publicly available esophageal cancer transcriptomic datasets from GEO to compute independent components and identify reproducible signaling pathways and molecular signatures.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34795689"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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