Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures

In melanoma, like in other cancers, both genetic alterations and epigenetic underlie the metastatic process. These effects are usually measured by changes in both methylome and transcriptome profiles, whose cross-correlation remains uncertain. We aimed to assess at systems scale the significance of...

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Veröffentlicht in:PloS one 2018-11, Vol.13 (11), p.e0206686-e0206686
Hauptverfasser: Jiang, Zhijie, Cinti, Caterina, Taranta, Monia, Mattioli, Elisabetta, Schena, Elisa, Singh, Sakshi, Khurana, Rimpi, Lattanzi, Giovanna, Tsinoremas, Nicholas F, Capobianco, Enrico
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container_start_page e0206686
container_title PloS one
container_volume 13
creator Jiang, Zhijie
Cinti, Caterina
Taranta, Monia
Mattioli, Elisabetta
Schena, Elisa
Singh, Sakshi
Khurana, Rimpi
Lattanzi, Giovanna
Tsinoremas, Nicholas F
Capobianco, Enrico
description In melanoma, like in other cancers, both genetic alterations and epigenetic underlie the metastatic process. These effects are usually measured by changes in both methylome and transcriptome profiles, whose cross-correlation remains uncertain. We aimed to assess at systems scale the significance of epigenetic treatment in melanoma cells with different metastatic potential. Treatment by DAC demethylation with 5-Aza-2'-deoxycytidine of two melanoma cell lines endowed with different metastatic potential, SKMEL-2 and HS294T, was performed and high-throughput coupled RNA-Seq and RRBS-Seq experiments delivered differential profiles (DiP) of both transcriptomes and methylomes. Methylation levels measured at both TSS and gene body were studied to inspect correlated patterns with wide-spectrum transcript abundance levels quantified in both protein coding and non-coding RNA (ncRNA) regions. The DiP were then mapped onto standard bio-annotation sources (pathways, biological processes) and network configurations were obtained. The prioritized associations for target identification purposes were expected to elucidate the reprogramming dynamics induced by the epigenetic therapy. The interactomic connectivity maps of each cell line were formed to support the analysis of epigenetically re-activated genes. i.e. those supposedly silenced by melanoma. In particular, modular protein interaction networks (PIN) were used, evidencing a limited number of shared annotations, with an example being MAPK13 (cascade of cellular responses evoked by extracellular stimuli). This gene is also a target associated to the PANDAR ncRNA, therapeutically relevant because of its aberrant expression observed in various cancers. Overall, the non-metastatic SKMEL-2 map reveals post-treatment re-activation of a richer pathway landscape, involving cadherins and integrins as signatures of cell adhesion and proliferation. Relatively more lncRNAs were also annotated, indicating more complex regulation patterns in view of target identification. Finally, the antigen maps matched to DiP display other differential signatures with respect to the metastatic potential of the cell lines. In particular, as demethylated melanomas show connected targets that grow with the increased metastatic potential, also the potential target actionability seems to depend to some degree on the metastatic state. However, caution is required when assessing the direct influence of re-activated genes over the identified targets. In
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These effects are usually measured by changes in both methylome and transcriptome profiles, whose cross-correlation remains uncertain. We aimed to assess at systems scale the significance of epigenetic treatment in melanoma cells with different metastatic potential. Treatment by DAC demethylation with 5-Aza-2'-deoxycytidine of two melanoma cell lines endowed with different metastatic potential, SKMEL-2 and HS294T, was performed and high-throughput coupled RNA-Seq and RRBS-Seq experiments delivered differential profiles (DiP) of both transcriptomes and methylomes. Methylation levels measured at both TSS and gene body were studied to inspect correlated patterns with wide-spectrum transcript abundance levels quantified in both protein coding and non-coding RNA (ncRNA) regions. The DiP were then mapped onto standard bio-annotation sources (pathways, biological processes) and network configurations were obtained. The prioritized associations for target identification purposes were expected to elucidate the reprogramming dynamics induced by the epigenetic therapy. The interactomic connectivity maps of each cell line were formed to support the analysis of epigenetically re-activated genes. i.e. those supposedly silenced by melanoma. In particular, modular protein interaction networks (PIN) were used, evidencing a limited number of shared annotations, with an example being MAPK13 (cascade of cellular responses evoked by extracellular stimuli). This gene is also a target associated to the PANDAR ncRNA, therapeutically relevant because of its aberrant expression observed in various cancers. Overall, the non-metastatic SKMEL-2 map reveals post-treatment re-activation of a richer pathway landscape, involving cadherins and integrins as signatures of cell adhesion and proliferation. Relatively more lncRNAs were also annotated, indicating more complex regulation patterns in view of target identification. Finally, the antigen maps matched to DiP display other differential signatures with respect to the metastatic potential of the cell lines. In particular, as demethylated melanomas show connected targets that grow with the increased metastatic potential, also the potential target actionability seems to depend to some degree on the metastatic state. However, caution is required when assessing the direct influence of re-activated genes over the identified targets. In light of the stronger treatment effects observed in non-metastatic conditions, some limitations likely refer to in silico data integration tools and resources available for the analysis of tumor antigens. Demethylation treatment strongly affects early melanoma progression by re-activating many genes. This evidence suggests that the efficacy of this type of therapeutic intervention is potentially high at the pre-metastatic stages. The biomarkers that can be assessed through antigens seem informative depending on the metastatic conditions, and networks help to elucidate the assessment of possible targets actionability.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0206686</identifier><identifier>PMID: 30485296</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Annotations ; Antigen (tumor-associated) ; Antigens ; Antigens - metabolism ; Antimetabolites, Antineoplastic - pharmacology ; Apoptosis ; Bioindicators ; Bioinformatics ; Biological activity ; Biological markers ; Biology and life sciences ; Biomarkers ; Biotechnology ; Cancer ; Cancer metastasis ; Cancer therapies ; Cancer treatment ; Care and treatment ; Cell adhesion ; Cell adhesion &amp; migration ; Cell cycle ; Cell Line, Tumor ; Correlation analysis ; Data integration ; Decitabine - pharmacology ; Demethylation ; Deoxyribonucleic acid ; Disease Progression ; DNA ; DNA methylation ; DNA Methylation - drug effects ; DNA repair ; Epigenesis, Genetic - drug effects ; Epigenetic inheritance ; Epigenetics ; Gene expression ; Gene Expression Regulation, Neoplastic - drug effects ; Gene mapping ; Genes ; Genetic aspects ; Genetic research ; Genetics ; Genomes ; Genomics ; Humans ; Integrins ; Lymphatic Metastasis - physiopathology ; Medicine and Health Sciences ; Melanoma ; Melanoma - drug therapy ; Melanoma - genetics ; Melanoma - metabolism ; Metastases ; Metastasis ; Methylation ; Non-coding RNA ; Physiology ; Protein Interaction Maps - drug effects ; Proteins ; Ribonucleic acid ; RNA ; RNA sequencing ; RNA, Long Noncoding - metabolism ; Signatures ; Skin Neoplasms - drug therapy ; Skin Neoplasms - genetics ; Skin Neoplasms - metabolism ; Target recognition ; Transcription ; Transcriptome - drug effects ; Tumor antigens ; Tumors</subject><ispartof>PloS one, 2018-11, Vol.13 (11), p.e0206686-e0206686</ispartof><rights>COPYRIGHT 2018 Public Library of Science</rights><rights>2018 Jiang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 Jiang et al 2018 Jiang et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-bde905ce1631c3bd9ea7a27f5d67a3b9bbabc2fbf378d5a46c83ea526cd36b9d3</citedby><cites>FETCH-LOGICAL-c692t-bde905ce1631c3bd9ea7a27f5d67a3b9bbabc2fbf378d5a46c83ea526cd36b9d3</cites><orcidid>0000-0001-9360-4767</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6261551/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6261551/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30485296$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Chammas, Roger</contributor><creatorcontrib>Jiang, Zhijie</creatorcontrib><creatorcontrib>Cinti, Caterina</creatorcontrib><creatorcontrib>Taranta, Monia</creatorcontrib><creatorcontrib>Mattioli, Elisabetta</creatorcontrib><creatorcontrib>Schena, Elisa</creatorcontrib><creatorcontrib>Singh, Sakshi</creatorcontrib><creatorcontrib>Khurana, Rimpi</creatorcontrib><creatorcontrib>Lattanzi, Giovanna</creatorcontrib><creatorcontrib>Tsinoremas, Nicholas F</creatorcontrib><creatorcontrib>Capobianco, Enrico</creatorcontrib><title>Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>In melanoma, like in other cancers, both genetic alterations and epigenetic underlie the metastatic process. These effects are usually measured by changes in both methylome and transcriptome profiles, whose cross-correlation remains uncertain. We aimed to assess at systems scale the significance of epigenetic treatment in melanoma cells with different metastatic potential. Treatment by DAC demethylation with 5-Aza-2'-deoxycytidine of two melanoma cell lines endowed with different metastatic potential, SKMEL-2 and HS294T, was performed and high-throughput coupled RNA-Seq and RRBS-Seq experiments delivered differential profiles (DiP) of both transcriptomes and methylomes. Methylation levels measured at both TSS and gene body were studied to inspect correlated patterns with wide-spectrum transcript abundance levels quantified in both protein coding and non-coding RNA (ncRNA) regions. The DiP were then mapped onto standard bio-annotation sources (pathways, biological processes) and network configurations were obtained. The prioritized associations for target identification purposes were expected to elucidate the reprogramming dynamics induced by the epigenetic therapy. The interactomic connectivity maps of each cell line were formed to support the analysis of epigenetically re-activated genes. i.e. those supposedly silenced by melanoma. In particular, modular protein interaction networks (PIN) were used, evidencing a limited number of shared annotations, with an example being MAPK13 (cascade of cellular responses evoked by extracellular stimuli). This gene is also a target associated to the PANDAR ncRNA, therapeutically relevant because of its aberrant expression observed in various cancers. Overall, the non-metastatic SKMEL-2 map reveals post-treatment re-activation of a richer pathway landscape, involving cadherins and integrins as signatures of cell adhesion and proliferation. Relatively more lncRNAs were also annotated, indicating more complex regulation patterns in view of target identification. Finally, the antigen maps matched to DiP display other differential signatures with respect to the metastatic potential of the cell lines. In particular, as demethylated melanomas show connected targets that grow with the increased metastatic potential, also the potential target actionability seems to depend to some degree on the metastatic state. However, caution is required when assessing the direct influence of re-activated genes over the identified targets. In light of the stronger treatment effects observed in non-metastatic conditions, some limitations likely refer to in silico data integration tools and resources available for the analysis of tumor antigens. Demethylation treatment strongly affects early melanoma progression by re-activating many genes. This evidence suggests that the efficacy of this type of therapeutic intervention is potentially high at the pre-metastatic stages. The biomarkers that can be assessed through antigens seem informative depending on the metastatic conditions, and networks help to elucidate the assessment of possible targets actionability.</description><subject>Annotations</subject><subject>Antigen (tumor-associated)</subject><subject>Antigens</subject><subject>Antigens - metabolism</subject><subject>Antimetabolites, Antineoplastic - pharmacology</subject><subject>Apoptosis</subject><subject>Bioindicators</subject><subject>Bioinformatics</subject><subject>Biological activity</subject><subject>Biological markers</subject><subject>Biology and life sciences</subject><subject>Biomarkers</subject><subject>Biotechnology</subject><subject>Cancer</subject><subject>Cancer metastasis</subject><subject>Cancer therapies</subject><subject>Cancer treatment</subject><subject>Care and treatment</subject><subject>Cell adhesion</subject><subject>Cell adhesion &amp; migration</subject><subject>Cell cycle</subject><subject>Cell Line, Tumor</subject><subject>Correlation analysis</subject><subject>Data integration</subject><subject>Decitabine - pharmacology</subject><subject>Demethylation</subject><subject>Deoxyribonucleic acid</subject><subject>Disease Progression</subject><subject>DNA</subject><subject>DNA methylation</subject><subject>DNA Methylation - drug effects</subject><subject>DNA repair</subject><subject>Epigenesis, Genetic - drug effects</subject><subject>Epigenetic inheritance</subject><subject>Epigenetics</subject><subject>Gene expression</subject><subject>Gene Expression Regulation, Neoplastic - drug effects</subject><subject>Gene mapping</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genetic research</subject><subject>Genetics</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Humans</subject><subject>Integrins</subject><subject>Lymphatic Metastasis - physiopathology</subject><subject>Medicine and Health Sciences</subject><subject>Melanoma</subject><subject>Melanoma - drug therapy</subject><subject>Melanoma - genetics</subject><subject>Melanoma - metabolism</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Methylation</subject><subject>Non-coding RNA</subject><subject>Physiology</subject><subject>Protein Interaction Maps - drug effects</subject><subject>Proteins</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>RNA sequencing</subject><subject>RNA, Long Noncoding - metabolism</subject><subject>Signatures</subject><subject>Skin Neoplasms - drug therapy</subject><subject>Skin Neoplasms - genetics</subject><subject>Skin Neoplasms - metabolism</subject><subject>Target recognition</subject><subject>Transcription</subject><subject>Transcriptome - drug effects</subject><subject>Tumor antigens</subject><subject>Tumors</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk01v1DAQhiMEoqXwDxBEQkJw2MWOYyfpAakqXytVVOLrak2cya6LEy-2A_TCb8fpptUG9YCsyJb9zDvx65kkeUzJkrKCvrqwg-vBLLe2xyXJiBCluJMc0oplC5ERdndvfZA88P6CEM5KIe4nB4zkJc8qcZj8-Yjhl3XfU_Aeve-wD6lt0wY7DJtLA0HbPg0OIVwd6T7t0EBvOzhO3-i2RRe3NZjIQO-V09tgO1zsouMqhb6JX9Br7NOts602mHq97iEMDv3D5F4LxuOjaT5Kvr57--X0w-Ls_P3q9ORsoUSVhUXdYEW4QioYVaxuKoQCsqLljSiA1VVdQ62ytm5ZUTYccqFKhsAzoRom6qphR8nTne7WWC8n67zMKKsoYVWeRWK1IxoLF3LrdAfuUlrQ8mrDurUEF7QyKHNeM8ZJSauS5gpIXfGYl5CGY8nalkSt11O2oe6wUdEiB2YmOj_p9Uau7U8pMkE5p1HgxSTg7I8BfZCd9gpNdB7tsPtvXuak4BF99g96--0mag3xArpvbcyrRlF5wgUTrCzESC1voeKI5aBVrLPx9eYBL2cBkQn4O6xh8F6uPn_6f_b825x9vsduEEzYeGuGsRr9HMx3oHLWe4ftjcmUyLFNrt2QY5vIqU1i2JP9B7oJuu4L9hdCrBDZ</recordid><startdate>20181128</startdate><enddate>20181128</enddate><creator>Jiang, Zhijie</creator><creator>Cinti, Caterina</creator><creator>Taranta, Monia</creator><creator>Mattioli, Elisabetta</creator><creator>Schena, Elisa</creator><creator>Singh, Sakshi</creator><creator>Khurana, Rimpi</creator><creator>Lattanzi, Giovanna</creator><creator>Tsinoremas, Nicholas F</creator><creator>Capobianco, Enrico</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-9360-4767</orcidid></search><sort><creationdate>20181128</creationdate><title>Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures</title><author>Jiang, Zhijie ; Cinti, Caterina ; Taranta, Monia ; Mattioli, Elisabetta ; Schena, Elisa ; Singh, Sakshi ; Khurana, Rimpi ; Lattanzi, Giovanna ; Tsinoremas, Nicholas F ; Capobianco, Enrico</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-bde905ce1631c3bd9ea7a27f5d67a3b9bbabc2fbf378d5a46c83ea526cd36b9d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Annotations</topic><topic>Antigen (tumor-associated)</topic><topic>Antigens</topic><topic>Antigens - metabolism</topic><topic>Antimetabolites, Antineoplastic - pharmacology</topic><topic>Apoptosis</topic><topic>Bioindicators</topic><topic>Bioinformatics</topic><topic>Biological activity</topic><topic>Biological markers</topic><topic>Biology and life sciences</topic><topic>Biomarkers</topic><topic>Biotechnology</topic><topic>Cancer</topic><topic>Cancer metastasis</topic><topic>Cancer therapies</topic><topic>Cancer treatment</topic><topic>Care and treatment</topic><topic>Cell adhesion</topic><topic>Cell adhesion &amp; migration</topic><topic>Cell cycle</topic><topic>Cell Line, Tumor</topic><topic>Correlation analysis</topic><topic>Data integration</topic><topic>Decitabine - pharmacology</topic><topic>Demethylation</topic><topic>Deoxyribonucleic acid</topic><topic>Disease Progression</topic><topic>DNA</topic><topic>DNA methylation</topic><topic>DNA Methylation - drug effects</topic><topic>DNA repair</topic><topic>Epigenesis, Genetic - drug effects</topic><topic>Epigenetic inheritance</topic><topic>Epigenetics</topic><topic>Gene expression</topic><topic>Gene Expression Regulation, Neoplastic - drug effects</topic><topic>Gene mapping</topic><topic>Genes</topic><topic>Genetic aspects</topic><topic>Genetic research</topic><topic>Genetics</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Humans</topic><topic>Integrins</topic><topic>Lymphatic Metastasis - physiopathology</topic><topic>Medicine and Health Sciences</topic><topic>Melanoma</topic><topic>Melanoma - drug therapy</topic><topic>Melanoma - genetics</topic><topic>Melanoma - metabolism</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>Methylation</topic><topic>Non-coding RNA</topic><topic>Physiology</topic><topic>Protein Interaction Maps - drug effects</topic><topic>Proteins</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>RNA sequencing</topic><topic>RNA, Long Noncoding - metabolism</topic><topic>Signatures</topic><topic>Skin Neoplasms - drug therapy</topic><topic>Skin Neoplasms - genetics</topic><topic>Skin Neoplasms - metabolism</topic><topic>Target recognition</topic><topic>Transcription</topic><topic>Transcriptome - drug effects</topic><topic>Tumor antigens</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Zhijie</creatorcontrib><creatorcontrib>Cinti, Caterina</creatorcontrib><creatorcontrib>Taranta, Monia</creatorcontrib><creatorcontrib>Mattioli, Elisabetta</creatorcontrib><creatorcontrib>Schena, Elisa</creatorcontrib><creatorcontrib>Singh, Sakshi</creatorcontrib><creatorcontrib>Khurana, Rimpi</creatorcontrib><creatorcontrib>Lattanzi, Giovanna</creatorcontrib><creatorcontrib>Tsinoremas, Nicholas F</creatorcontrib><creatorcontrib>Capobianco, Enrico</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing &amp; Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Zhijie</au><au>Cinti, Caterina</au><au>Taranta, Monia</au><au>Mattioli, Elisabetta</au><au>Schena, Elisa</au><au>Singh, Sakshi</au><au>Khurana, Rimpi</au><au>Lattanzi, Giovanna</au><au>Tsinoremas, Nicholas F</au><au>Capobianco, Enrico</au><au>Chammas, Roger</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2018-11-28</date><risdate>2018</risdate><volume>13</volume><issue>11</issue><spage>e0206686</spage><epage>e0206686</epage><pages>e0206686-e0206686</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>In melanoma, like in other cancers, both genetic alterations and epigenetic underlie the metastatic process. These effects are usually measured by changes in both methylome and transcriptome profiles, whose cross-correlation remains uncertain. We aimed to assess at systems scale the significance of epigenetic treatment in melanoma cells with different metastatic potential. Treatment by DAC demethylation with 5-Aza-2'-deoxycytidine of two melanoma cell lines endowed with different metastatic potential, SKMEL-2 and HS294T, was performed and high-throughput coupled RNA-Seq and RRBS-Seq experiments delivered differential profiles (DiP) of both transcriptomes and methylomes. Methylation levels measured at both TSS and gene body were studied to inspect correlated patterns with wide-spectrum transcript abundance levels quantified in both protein coding and non-coding RNA (ncRNA) regions. The DiP were then mapped onto standard bio-annotation sources (pathways, biological processes) and network configurations were obtained. The prioritized associations for target identification purposes were expected to elucidate the reprogramming dynamics induced by the epigenetic therapy. The interactomic connectivity maps of each cell line were formed to support the analysis of epigenetically re-activated genes. i.e. those supposedly silenced by melanoma. In particular, modular protein interaction networks (PIN) were used, evidencing a limited number of shared annotations, with an example being MAPK13 (cascade of cellular responses evoked by extracellular stimuli). This gene is also a target associated to the PANDAR ncRNA, therapeutically relevant because of its aberrant expression observed in various cancers. Overall, the non-metastatic SKMEL-2 map reveals post-treatment re-activation of a richer pathway landscape, involving cadherins and integrins as signatures of cell adhesion and proliferation. Relatively more lncRNAs were also annotated, indicating more complex regulation patterns in view of target identification. Finally, the antigen maps matched to DiP display other differential signatures with respect to the metastatic potential of the cell lines. In particular, as demethylated melanomas show connected targets that grow with the increased metastatic potential, also the potential target actionability seems to depend to some degree on the metastatic state. However, caution is required when assessing the direct influence of re-activated genes over the identified targets. In light of the stronger treatment effects observed in non-metastatic conditions, some limitations likely refer to in silico data integration tools and resources available for the analysis of tumor antigens. Demethylation treatment strongly affects early melanoma progression by re-activating many genes. This evidence suggests that the efficacy of this type of therapeutic intervention is potentially high at the pre-metastatic stages. The biomarkers that can be assessed through antigens seem informative depending on the metastatic conditions, and networks help to elucidate the assessment of possible targets actionability.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30485296</pmid><doi>10.1371/journal.pone.0206686</doi><tpages>e0206686</tpages><orcidid>https://orcid.org/0000-0001-9360-4767</orcidid><oa>free_for_read</oa></addata></record>
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subjects Annotations
Antigen (tumor-associated)
Antigens
Antigens - metabolism
Antimetabolites, Antineoplastic - pharmacology
Apoptosis
Bioindicators
Bioinformatics
Biological activity
Biological markers
Biology and life sciences
Biomarkers
Biotechnology
Cancer
Cancer metastasis
Cancer therapies
Cancer treatment
Care and treatment
Cell adhesion
Cell adhesion & migration
Cell cycle
Cell Line, Tumor
Correlation analysis
Data integration
Decitabine - pharmacology
Demethylation
Deoxyribonucleic acid
Disease Progression
DNA
DNA methylation
DNA Methylation - drug effects
DNA repair
Epigenesis, Genetic - drug effects
Epigenetic inheritance
Epigenetics
Gene expression
Gene Expression Regulation, Neoplastic - drug effects
Gene mapping
Genes
Genetic aspects
Genetic research
Genetics
Genomes
Genomics
Humans
Integrins
Lymphatic Metastasis - physiopathology
Medicine and Health Sciences
Melanoma
Melanoma - drug therapy
Melanoma - genetics
Melanoma - metabolism
Metastases
Metastasis
Methylation
Non-coding RNA
Physiology
Protein Interaction Maps - drug effects
Proteins
Ribonucleic acid
RNA
RNA sequencing
RNA, Long Noncoding - metabolism
Signatures
Skin Neoplasms - drug therapy
Skin Neoplasms - genetics
Skin Neoplasms - metabolism
Target recognition
Transcription
Transcriptome - drug effects
Tumor antigens
Tumors
title Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures
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