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Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies

BACKGROUND: The broader use of high-throughput technologies has led to improved molecular characterization of hepatocellular carcinoma (HCC).  AIM: To comprehensively analyze and characterize all publicly available genomic, gene expression, methylation, miRNA and proteomic data in HCC, covering 85 s...

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Autores principales: Bhat, Mamatha, Pasini, Elisa, Pastrello, Chiara, Rahmati, Sara, Angeli, Marc, Kotlyar, Max, Ghanekar, Anand, Jurisica, Igor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856865/
https://www.ncbi.nlm.nih.gov/pubmed/33584989
http://dx.doi.org/10.4254/wjh.v13.i1.94
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author Bhat, Mamatha
Pasini, Elisa
Pastrello, Chiara
Rahmati, Sara
Angeli, Marc
Kotlyar, Max
Ghanekar, Anand
Jurisica, Igor
author_facet Bhat, Mamatha
Pasini, Elisa
Pastrello, Chiara
Rahmati, Sara
Angeli, Marc
Kotlyar, Max
Ghanekar, Anand
Jurisica, Igor
author_sort Bhat, Mamatha
collection PubMed
description BACKGROUND: The broader use of high-throughput technologies has led to improved molecular characterization of hepatocellular carcinoma (HCC).  AIM: To comprehensively analyze and characterize all publicly available genomic, gene expression, methylation, miRNA and proteomic data in HCC, covering 85 studies and 3355 patient sample profiles, to identify the key dysregulated genes and pathways they affect.  METHODS: We collected and curated all well-annotated and publicly available high-throughput datasets from PubMed and Gene Expression Omnibus derived from human HCC tissue. Comprehensive pathway enrichment analysis was performed using pathDIP for each data type (genomic, gene expression, methylation, miRNA and proteomic), and the overlap of pathways was assessed to elucidate pathway dependencies in HCC. RESULTS: We identified a total of 8733 abstracts retrieved by the search on PubMed on HCC for the different layers of data on human HCC samples, published until December 2016. The common key dysregulated pathways in HCC tissue across different layers of data included epidermal growth factor (EGFR) and β1-integrin pathways. Genes along these pathways were significantly and consistently dysregulated across the different types of high-throughput data and had prognostic value with respect to overall survival. Using CTD database, estradiol would best modulate and revert these genes appropriately. CONCLUSION: By analyzing and integrating all available high-throughput genomic, transcriptomic, miRNA, methylation and proteomic data from human HCC tissue, we identified EGFR, β1-integrin and axon guidance as pathway dependencies in HCC. These are master regulators of key pathways in HCC, such as the mTOR, Ras/Raf/MAPK and p53 pathways. The genes implicated in these pathways had prognostic value in HCC, with Netrin and Slit3 being novel proteins of prognostic importance to HCC. Based on this integrative analysis, EGFR, and β1-integrin are master regulators that could serve as potential therapeutic targets in HCC.
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spelling pubmed-78568652021-02-11 Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies Bhat, Mamatha Pasini, Elisa Pastrello, Chiara Rahmati, Sara Angeli, Marc Kotlyar, Max Ghanekar, Anand Jurisica, Igor World J Hepatol Basic Study BACKGROUND: The broader use of high-throughput technologies has led to improved molecular characterization of hepatocellular carcinoma (HCC).  AIM: To comprehensively analyze and characterize all publicly available genomic, gene expression, methylation, miRNA and proteomic data in HCC, covering 85 studies and 3355 patient sample profiles, to identify the key dysregulated genes and pathways they affect.  METHODS: We collected and curated all well-annotated and publicly available high-throughput datasets from PubMed and Gene Expression Omnibus derived from human HCC tissue. Comprehensive pathway enrichment analysis was performed using pathDIP for each data type (genomic, gene expression, methylation, miRNA and proteomic), and the overlap of pathways was assessed to elucidate pathway dependencies in HCC. RESULTS: We identified a total of 8733 abstracts retrieved by the search on PubMed on HCC for the different layers of data on human HCC samples, published until December 2016. The common key dysregulated pathways in HCC tissue across different layers of data included epidermal growth factor (EGFR) and β1-integrin pathways. Genes along these pathways were significantly and consistently dysregulated across the different types of high-throughput data and had prognostic value with respect to overall survival. Using CTD database, estradiol would best modulate and revert these genes appropriately. CONCLUSION: By analyzing and integrating all available high-throughput genomic, transcriptomic, miRNA, methylation and proteomic data from human HCC tissue, we identified EGFR, β1-integrin and axon guidance as pathway dependencies in HCC. These are master regulators of key pathways in HCC, such as the mTOR, Ras/Raf/MAPK and p53 pathways. The genes implicated in these pathways had prognostic value in HCC, with Netrin and Slit3 being novel proteins of prognostic importance to HCC. Based on this integrative analysis, EGFR, and β1-integrin are master regulators that could serve as potential therapeutic targets in HCC. Baishideng Publishing Group Inc 2021-01-27 2021-01-27 /pmc/articles/PMC7856865/ /pubmed/33584989 http://dx.doi.org/10.4254/wjh.v13.i1.94 Text en ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.
spellingShingle Basic Study
Bhat, Mamatha
Pasini, Elisa
Pastrello, Chiara
Rahmati, Sara
Angeli, Marc
Kotlyar, Max
Ghanekar, Anand
Jurisica, Igor
Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies
title Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies
title_full Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies
title_fullStr Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies
title_full_unstemmed Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies
title_short Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies
title_sort integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies
topic Basic Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856865/
https://www.ncbi.nlm.nih.gov/pubmed/33584989
http://dx.doi.org/10.4254/wjh.v13.i1.94
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