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Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach
Background DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methyla...
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| Published in: | The EPMA journal 2024-06, Vol.15 (2), p.375-404 |
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| creator | Jia, Wenshuang Li, Na Wang, Jingjing Gong, Xiaoxia Ouedraogo, Serge Yannick Wang, Yan Zhao, Junkai Grech, Godfrey Chen, Liang Zhan, Xianquan |
| description | Background
DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC.
Method
Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified.
Results
A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (
p
|
| doi_str_mv | 10.1007/s13167-024-00359-3 |
| format | article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_3065272657</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A793095988</galeid><sourcerecordid>A793095988</sourcerecordid><originalsourceid>FETCH-LOGICAL-c393t-b6a80b970b31e0346528a118e824dae57e367ee70103b5542f0e38735b47db1c3</originalsourceid><addsrcrecordid>eNp9kstu1TAQhiMEolXpC7BAltiwSfEljp1lVUFbqQgWsLYcZ5K6OrEPtlPUJ-F1mTSlFQhhL3yZ7__Hl6mq14yeMErV-8wEa1VNeVNTKmRXi2fVIdNK15Jq-fxxrtRBdZzzDcUmuEbpy-pAaN2wlovD6uflPC8B6gQ7W2AgEwQgM5TrO1z7GMg-xSnEXLwjPuSSlhlCIWNMBBeIjN5toA0DKTZNsNqUBLbck3Ek8dYmbwNxNjhIZI88RjIp8YdNA7HD7RoYiPjyidg9JrTu-lX1YrS7DMcP41H17eOHr2cX9dXn88uz06vaiU6Uum-tpn2naC8YUNG0kmvLmAbNm8GCVCBaBaAoo6KXsuEjBaGVkH2jhp45cVS923wx7fcFcjGzzw52OxsgLtkIipaKt1Ih-vYv9CYuKeDpVqphmnMun6jJ7sD4MEZ8JreamlPVCdrJTmukTv5BYR9g9i4GGD3u_yHgm8ClmHOC0eyTn226M4yatSDMVhAGC8LcF4QRKHrzcOKln2F4lPz-fgTEBmQMhQnS05X-Y_sLfgHBMg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3064182225</pqid></control><display><type>article</type><title>Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach</title><source>Springer Link</source><creator>Jia, Wenshuang ; Li, Na ; Wang, Jingjing ; Gong, Xiaoxia ; Ouedraogo, Serge Yannick ; Wang, Yan ; Zhao, Junkai ; Grech, Godfrey ; Chen, Liang ; Zhan, Xianquan</creator><creatorcontrib>Jia, Wenshuang ; Li, Na ; Wang, Jingjing ; Gong, Xiaoxia ; Ouedraogo, Serge Yannick ; Wang, Yan ; Zhao, Junkai ; Grech, Godfrey ; Chen, Liang ; Zhan, Xianquan</creatorcontrib><description>Background
DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC.
Method
Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified.
Results
A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (
p
< 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC.
Conclusions
This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach.</description><identifier>ISSN: 1878-5077</identifier><identifier>ISSN: 1878-5085</identifier><identifier>EISSN: 1878-5085</identifier><identifier>DOI: 10.1007/s13167-024-00359-3</identifier><identifier>PMID: 38841623</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Biomarkers ; Biomedical and Life Sciences ; Biomedicine ; Cancer ; Cancer therapies ; Care and treatment ; Comparative analysis ; DNA ; DNA methylation ; Drug resistance ; Drug therapy ; Epigenetic inheritance ; Genes ; Genetic aspects ; Genetic research ; Genetic transcription ; Health aspects ; Medical research ; Medicine, Experimental ; Medicine/Public Health ; Methylation ; Ovarian cancer ; Pathogenesis ; Pharmacogenetics ; Protein-protein interactions ; Transferases</subject><ispartof>The EPMA journal, 2024-06, Vol.15 (2), p.375-404</ispartof><rights>The Author(s), under exclusive licence to European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>COPYRIGHT 2024 BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c393t-b6a80b970b31e0346528a118e824dae57e367ee70103b5542f0e38735b47db1c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38841623$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jia, Wenshuang</creatorcontrib><creatorcontrib>Li, Na</creatorcontrib><creatorcontrib>Wang, Jingjing</creatorcontrib><creatorcontrib>Gong, Xiaoxia</creatorcontrib><creatorcontrib>Ouedraogo, Serge Yannick</creatorcontrib><creatorcontrib>Wang, Yan</creatorcontrib><creatorcontrib>Zhao, Junkai</creatorcontrib><creatorcontrib>Grech, Godfrey</creatorcontrib><creatorcontrib>Chen, Liang</creatorcontrib><creatorcontrib>Zhan, Xianquan</creatorcontrib><title>Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach</title><title>The EPMA journal</title><addtitle>EPMA Journal</addtitle><addtitle>EPMA J</addtitle><description>Background
DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC.
Method
Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified.
Results
A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (
p
< 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC.
Conclusions
This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach.</description><subject>Biomarkers</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Care and treatment</subject><subject>Comparative analysis</subject><subject>DNA</subject><subject>DNA methylation</subject><subject>Drug resistance</subject><subject>Drug therapy</subject><subject>Epigenetic inheritance</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genetic research</subject><subject>Genetic transcription</subject><subject>Health aspects</subject><subject>Medical research</subject><subject>Medicine, Experimental</subject><subject>Medicine/Public Health</subject><subject>Methylation</subject><subject>Ovarian cancer</subject><subject>Pathogenesis</subject><subject>Pharmacogenetics</subject><subject>Protein-protein interactions</subject><subject>Transferases</subject><issn>1878-5077</issn><issn>1878-5085</issn><issn>1878-5085</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kstu1TAQhiMEolXpC7BAltiwSfEljp1lVUFbqQgWsLYcZ5K6OrEPtlPUJ-F1mTSlFQhhL3yZ7__Hl6mq14yeMErV-8wEa1VNeVNTKmRXi2fVIdNK15Jq-fxxrtRBdZzzDcUmuEbpy-pAaN2wlovD6uflPC8B6gQ7W2AgEwQgM5TrO1z7GMg-xSnEXLwjPuSSlhlCIWNMBBeIjN5toA0DKTZNsNqUBLbck3Ek8dYmbwNxNjhIZI88RjIp8YdNA7HD7RoYiPjyidg9JrTu-lX1YrS7DMcP41H17eOHr2cX9dXn88uz06vaiU6Uum-tpn2naC8YUNG0kmvLmAbNm8GCVCBaBaAoo6KXsuEjBaGVkH2jhp45cVS923wx7fcFcjGzzw52OxsgLtkIipaKt1Ih-vYv9CYuKeDpVqphmnMun6jJ7sD4MEZ8JreamlPVCdrJTmukTv5BYR9g9i4GGD3u_yHgm8ClmHOC0eyTn226M4yatSDMVhAGC8LcF4QRKHrzcOKln2F4lPz-fgTEBmQMhQnS05X-Y_sLfgHBMg</recordid><startdate>20240601</startdate><enddate>20240601</enddate><creator>Jia, Wenshuang</creator><creator>Li, Na</creator><creator>Wang, Jingjing</creator><creator>Gong, Xiaoxia</creator><creator>Ouedraogo, Serge Yannick</creator><creator>Wang, Yan</creator><creator>Zhao, Junkai</creator><creator>Grech, Godfrey</creator><creator>Chen, Liang</creator><creator>Zhan, Xianquan</creator><general>Springer International Publishing</general><general>BioMed Central Ltd</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>7X8</scope></search><sort><creationdate>20240601</creationdate><title>Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach</title><author>Jia, Wenshuang ; Li, Na ; Wang, Jingjing ; Gong, Xiaoxia ; Ouedraogo, Serge Yannick ; Wang, Yan ; Zhao, Junkai ; Grech, Godfrey ; Chen, Liang ; Zhan, Xianquan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c393t-b6a80b970b31e0346528a118e824dae57e367ee70103b5542f0e38735b47db1c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Biomarkers</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Cancer</topic><topic>Cancer therapies</topic><topic>Care and treatment</topic><topic>Comparative analysis</topic><topic>DNA</topic><topic>DNA methylation</topic><topic>Drug resistance</topic><topic>Drug therapy</topic><topic>Epigenetic inheritance</topic><topic>Genes</topic><topic>Genetic aspects</topic><topic>Genetic research</topic><topic>Genetic transcription</topic><topic>Health aspects</topic><topic>Medical research</topic><topic>Medicine, Experimental</topic><topic>Medicine/Public Health</topic><topic>Methylation</topic><topic>Ovarian cancer</topic><topic>Pathogenesis</topic><topic>Pharmacogenetics</topic><topic>Protein-protein interactions</topic><topic>Transferases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jia, Wenshuang</creatorcontrib><creatorcontrib>Li, Na</creatorcontrib><creatorcontrib>Wang, Jingjing</creatorcontrib><creatorcontrib>Gong, Xiaoxia</creatorcontrib><creatorcontrib>Ouedraogo, Serge Yannick</creatorcontrib><creatorcontrib>Wang, Yan</creatorcontrib><creatorcontrib>Zhao, Junkai</creatorcontrib><creatorcontrib>Grech, Godfrey</creatorcontrib><creatorcontrib>Chen, Liang</creatorcontrib><creatorcontrib>Zhan, Xianquan</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>The EPMA journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jia, Wenshuang</au><au>Li, Na</au><au>Wang, Jingjing</au><au>Gong, Xiaoxia</au><au>Ouedraogo, Serge Yannick</au><au>Wang, Yan</au><au>Zhao, Junkai</au><au>Grech, Godfrey</au><au>Chen, Liang</au><au>Zhan, Xianquan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach</atitle><jtitle>The EPMA journal</jtitle><stitle>EPMA Journal</stitle><addtitle>EPMA J</addtitle><date>2024-06-01</date><risdate>2024</risdate><volume>15</volume><issue>2</issue><spage>375</spage><epage>404</epage><pages>375-404</pages><issn>1878-5077</issn><issn>1878-5085</issn><eissn>1878-5085</eissn><abstract>Background
DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC.
Method
Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified.
Results
A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (
p
< 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC.
Conclusions
This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>38841623</pmid><doi>10.1007/s13167-024-00359-3</doi><tpages>30</tpages></addata></record> |
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| subjects | Biomarkers Biomedical and Life Sciences Biomedicine Cancer Cancer therapies Care and treatment Comparative analysis DNA DNA methylation Drug resistance Drug therapy Epigenetic inheritance Genes Genetic aspects Genetic research Genetic transcription Health aspects Medical research Medicine, Experimental Medicine/Public Health Methylation Ovarian cancer Pathogenesis Pharmacogenetics Protein-protein interactions Transferases |
| title | Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach |
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