AIM To identify the key epigenetically modulated genes and pathways in HCC by performing an integrative meta-analysis of all major, well-annotated and publicly available methylation datasets using tools of network ana...AIM To identify the key epigenetically modulated genes and pathways in HCC by performing an integrative meta-analysis of all major, well-annotated and publicly available methylation datasets using tools of network analysis.METHODS Pub Med and Gene Expression Omnibus were searched for genome-wide DNA methylation datasets. Patient clinical and demographic characteristics were obtained. DNA methylation data were integrated using the Ingenuity Pathway Analysis, a software package for visualizing and analyzing biological networks. Pathway enrichment analysis was performed using IPA, which also provides literature-driven and computationallypredicted annotations for significant association of genes to curated molecular pathways.RESULTS From an initial 928 potential abstracts, we identified and analyzed 11 eligible high-throughput methylation datasets representing 354 patients. A significant proportion of studies did not provide concomitant clinical data. In the promoter region, HIST1H2AJ and SPDYA were the most commonly methylated, whereas HRNBP3 gene was the most commonly hypomethylated. ESR1 and ERK were central genes in the principal networks. The pathways most associated with the frequently methylated genes were G-protein coupled receptor and c AMP-mediated signalling. CONCLUSION Using an integrative network-based analysis approach of genome-wide DNA methylation data of both the promoter and body of genes, we identified G-protein coupled receptor signalling as the most highly associated with HCC. This encompasses a diverse range of cancer pathways, such as the PI3 K/Akt/m TOR and Ras/Raf/MAPK pathways, and is therefore supportive of previous literature on gene expression in HCC. However, there are novel targetable genes such as HIST1H2 AJ that are epigenetically modified, suggesting their potential as biomarkers and for therapeutic targeting of the HCC epigenome.展开更多
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...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 highthroughput 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)andβ1-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,andβ1-integrin are master regulators that could serve as potential therapeutic targets in HCC.展开更多
文摘AIM To identify the key epigenetically modulated genes and pathways in HCC by performing an integrative meta-analysis of all major, well-annotated and publicly available methylation datasets using tools of network analysis.METHODS Pub Med and Gene Expression Omnibus were searched for genome-wide DNA methylation datasets. Patient clinical and demographic characteristics were obtained. DNA methylation data were integrated using the Ingenuity Pathway Analysis, a software package for visualizing and analyzing biological networks. Pathway enrichment analysis was performed using IPA, which also provides literature-driven and computationallypredicted annotations for significant association of genes to curated molecular pathways.RESULTS From an initial 928 potential abstracts, we identified and analyzed 11 eligible high-throughput methylation datasets representing 354 patients. A significant proportion of studies did not provide concomitant clinical data. In the promoter region, HIST1H2AJ and SPDYA were the most commonly methylated, whereas HRNBP3 gene was the most commonly hypomethylated. ESR1 and ERK were central genes in the principal networks. The pathways most associated with the frequently methylated genes were G-protein coupled receptor and c AMP-mediated signalling. CONCLUSION Using an integrative network-based analysis approach of genome-wide DNA methylation data of both the promoter and body of genes, we identified G-protein coupled receptor signalling as the most highly associated with HCC. This encompasses a diverse range of cancer pathways, such as the PI3 K/Akt/m TOR and Ras/Raf/MAPK pathways, and is therefore supportive of previous literature on gene expression in HCC. However, there are novel targetable genes such as HIST1H2 AJ that are epigenetically modified, suggesting their potential as biomarkers and for therapeutic targeting of the HCC epigenome.
文摘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 highthroughput 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)andβ1-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,andβ1-integrin are master regulators that could serve as potential therapeutic targets in HCC.
