Integrated epigenetic biomarkers in circulating cell-free DNA as a robust classifier for pancreatic cancer
The high lethal rate of pancreatic cancer is partly due to a lack of efficient biomarkers for screening and early diagnosis. We attempted to develop effective and noninvasive methods using 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) markers from circulating cell-free DNA (cfDNA) for th...
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description | The high lethal rate of pancreatic cancer is partly due to a lack of efficient biomarkers for screening and early diagnosis. We attempted to develop effective and noninvasive methods using 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) markers from circulating cell-free DNA (cfDNA) for the detection of pancreatic ductal adenocarcinoma (PDAC).
A 24-feature 5mC model that can accurately discriminate PDAC from healthy controls (area under the curve (AUC) = 0.977, sensitivity = 0.824, specificity = 1) and a 5hmC prediction model with 27 features demonstrated excellent detection power in two distinct validation sets (AUC = 0.992 and 0.960, sensitivity = 0.786 and 0.857, specificity = 1 and 0.993). The 51-feature model combining 5mC and 5hmC markers outperformed both of the individual models, with an AUC of 0.997 (sensitivity = 0.938, specificity = 0.955) and particularly an improvement in the prediction sensitivity of PDAC. In addition, the weighted diagnosis score (wd-score) calculated with the 5hmC model can distinguish stage I patients from stage II-IV patients.
Both 5mC and 5hmC biomarkers in cfDNA are effective in PDAC detection, and the 5mC-5hmC integrated model significantly improve the detection sensitivity. |
doi_str_mv | 10.1186/s13148-020-00898-2 |
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A 24-feature 5mC model that can accurately discriminate PDAC from healthy controls (area under the curve (AUC) = 0.977, sensitivity = 0.824, specificity = 1) and a 5hmC prediction model with 27 features demonstrated excellent detection power in two distinct validation sets (AUC = 0.992 and 0.960, sensitivity = 0.786 and 0.857, specificity = 1 and 0.993). The 51-feature model combining 5mC and 5hmC markers outperformed both of the individual models, with an AUC of 0.997 (sensitivity = 0.938, specificity = 0.955) and particularly an improvement in the prediction sensitivity of PDAC. In addition, the weighted diagnosis score (wd-score) calculated with the 5hmC model can distinguish stage I patients from stage II-IV patients.
Both 5mC and 5hmC biomarkers in cfDNA are effective in PDAC detection, and the 5mC-5hmC integrated model significantly improve the detection sensitivity.</description><identifier>ISSN: 1868-7075</identifier><identifier>ISSN: 1868-7083</identifier><identifier>EISSN: 1868-7083</identifier><identifier>EISSN: 1868-7075</identifier><identifier>DOI: 10.1186/s13148-020-00898-2</identifier><identifier>PMID: 32703318</identifier><language>eng</language><publisher>Germany: BioMed Central Ltd</publisher><subject>5-Methylcytosine - analogs & derivatives ; 5-Methylcytosine - blood ; Adenocarcinoma ; Adenocarcinoma - blood ; Analysis ; Biological markers ; Biomarkers ; Biomarkers, Tumor - blood ; Cancer genetics ; Carcinoma, Pancreatic Ductal - blood ; Cell-Free Nucleic Acids - blood ; Deoxyribonucleic acid ; Diagnosis ; DNA ; DNA methylation ; Epigenesis, Genetic - physiology ; Epigenetic inheritance ; Epigenetics ; Female ; Gene expression ; Genomes ; Genomics ; Humans ; Male ; Methods ; Middle Aged ; Mutation ; Pancreatic cancer ; Pancreatic Neoplasms - blood ; Prediction models ; Reproducibility of Results ; Sensitivity and Specificity</subject><ispartof>Clinical epigenetics, 2020-07, Vol.12 (1), p.112-112, Article 112</ispartof><rights>COPYRIGHT 2020 BioMed Central Ltd.</rights><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c497t-24c88c9b26ecf6dbe1c12ba71d218ac77ff41461d29de1b3068202235473467a3</citedby><cites>FETCH-LOGICAL-c497t-24c88c9b26ecf6dbe1c12ba71d218ac77ff41461d29de1b3068202235473467a3</cites><orcidid>0000-0001-5388-5541</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/PMC7376965/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7376965/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,27905,27906,53772,53774</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32703318$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cao, Feng</creatorcontrib><creatorcontrib>Wei, Ailin</creatorcontrib><creatorcontrib>Hu, Xinlei</creatorcontrib><creatorcontrib>He, Yijing</creatorcontrib><creatorcontrib>Zhang, Jun</creatorcontrib><creatorcontrib>Xia, Lin</creatorcontrib><creatorcontrib>Tu, Kailing</creatorcontrib><creatorcontrib>Yuan, Jue</creatorcontrib><creatorcontrib>Guo, Ziheng</creatorcontrib><creatorcontrib>Liu, Hongying</creatorcontrib><creatorcontrib>Xie, Dan</creatorcontrib><creatorcontrib>Li, Ang</creatorcontrib><title>Integrated epigenetic biomarkers in circulating cell-free DNA as a robust classifier for pancreatic cancer</title><title>Clinical epigenetics</title><addtitle>Clin Epigenetics</addtitle><description>The high lethal rate of pancreatic cancer is partly due to a lack of efficient biomarkers for screening and early diagnosis. We attempted to develop effective and noninvasive methods using 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) markers from circulating cell-free DNA (cfDNA) for the detection of pancreatic ductal adenocarcinoma (PDAC).
A 24-feature 5mC model that can accurately discriminate PDAC from healthy controls (area under the curve (AUC) = 0.977, sensitivity = 0.824, specificity = 1) and a 5hmC prediction model with 27 features demonstrated excellent detection power in two distinct validation sets (AUC = 0.992 and 0.960, sensitivity = 0.786 and 0.857, specificity = 1 and 0.993). The 51-feature model combining 5mC and 5hmC markers outperformed both of the individual models, with an AUC of 0.997 (sensitivity = 0.938, specificity = 0.955) and particularly an improvement in the prediction sensitivity of PDAC. In addition, the weighted diagnosis score (wd-score) calculated with the 5hmC model can distinguish stage I patients from stage II-IV patients.
Both 5mC and 5hmC biomarkers in cfDNA are effective in PDAC detection, and the 5mC-5hmC integrated model significantly improve the detection sensitivity.</description><subject>5-Methylcytosine - analogs & derivatives</subject><subject>5-Methylcytosine - blood</subject><subject>Adenocarcinoma</subject><subject>Adenocarcinoma - blood</subject><subject>Analysis</subject><subject>Biological markers</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - blood</subject><subject>Cancer genetics</subject><subject>Carcinoma, Pancreatic Ductal - blood</subject><subject>Cell-Free Nucleic Acids - blood</subject><subject>Deoxyribonucleic acid</subject><subject>Diagnosis</subject><subject>DNA</subject><subject>DNA methylation</subject><subject>Epigenesis, Genetic - physiology</subject><subject>Epigenetic inheritance</subject><subject>Epigenetics</subject><subject>Female</subject><subject>Gene expression</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Humans</subject><subject>Male</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Mutation</subject><subject>Pancreatic cancer</subject><subject>Pancreatic Neoplasms - blood</subject><subject>Prediction models</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><issn>1868-7075</issn><issn>1868-7083</issn><issn>1868-7083</issn><issn>1868-7075</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</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><recordid>eNptUsuOFCEUJUbjTMb5AReGxI2bGnkVj41JZ3xNMtGNrglFXUraamihysS_l7bH1jHCggv3nHNzyEHoKSVXlGr5slJOhe4IIx0h2uiOPUDnraE7RTR_eKpVf4Yua92StrgxhpLH6IwzRTin-hxtb9ICU3ELjBj2cYIES_R4iHnnylcoFceEfSx-nd0S04Q9zHMXCgB-_WGDXcUOlzysdcF-drXGEKHgkAveu-QLuIOabyWUJ-hRcHOFy7vzAn1---bT9fvu9uO7m-vNbeeFUUvHhNfam4FJ8EGOA1BP2eAUHRnVzisVgqBCtqsZgQ6cSM0IY7wXigupHL9Ar466-3XYweghLcXNdl9is_TDZhft_U6KX-yUv1vFlTSybwIv7gRK_rZCXewu1oNvlyCv1TLBFKdESNKgz_-BbvNaUrPXULxnQvba_EFNbgYbU8htrj-I2o3kRDBBe91QV_9BtT3CLvqcIMT2fo_AjgRfcq0FwskjJfYQEnsMiW0hsb9CYlkjPfv7d06U35HgPwFvL7ai</recordid><startdate>20200723</startdate><enddate>20200723</enddate><creator>Cao, Feng</creator><creator>Wei, Ailin</creator><creator>Hu, Xinlei</creator><creator>He, Yijing</creator><creator>Zhang, Jun</creator><creator>Xia, Lin</creator><creator>Tu, Kailing</creator><creator>Yuan, Jue</creator><creator>Guo, Ziheng</creator><creator>Liu, Hongying</creator><creator>Xie, Dan</creator><creator>Li, Ang</creator><general>BioMed Central Ltd</general><general>BioMed Central</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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-5388-5541</orcidid></search><sort><creationdate>20200723</creationdate><title>Integrated epigenetic biomarkers in circulating cell-free DNA as a robust classifier for pancreatic cancer</title><author>Cao, Feng ; Wei, Ailin ; Hu, Xinlei ; He, Yijing ; Zhang, Jun ; Xia, Lin ; Tu, Kailing ; Yuan, Jue ; Guo, Ziheng ; Liu, Hongying ; Xie, Dan ; Li, Ang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c497t-24c88c9b26ecf6dbe1c12ba71d218ac77ff41461d29de1b3068202235473467a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>5-Methylcytosine - analogs & derivatives</topic><topic>5-Methylcytosine - blood</topic><topic>Adenocarcinoma</topic><topic>Adenocarcinoma - blood</topic><topic>Analysis</topic><topic>Biological markers</topic><topic>Biomarkers</topic><topic>Biomarkers, Tumor - blood</topic><topic>Cancer genetics</topic><topic>Carcinoma, Pancreatic Ductal - blood</topic><topic>Cell-Free Nucleic Acids - blood</topic><topic>Deoxyribonucleic acid</topic><topic>Diagnosis</topic><topic>DNA</topic><topic>DNA methylation</topic><topic>Epigenesis, Genetic - physiology</topic><topic>Epigenetic inheritance</topic><topic>Epigenetics</topic><topic>Female</topic><topic>Gene expression</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Humans</topic><topic>Male</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Mutation</topic><topic>Pancreatic cancer</topic><topic>Pancreatic Neoplasms - blood</topic><topic>Prediction models</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cao, Feng</creatorcontrib><creatorcontrib>Wei, Ailin</creatorcontrib><creatorcontrib>Hu, Xinlei</creatorcontrib><creatorcontrib>He, Yijing</creatorcontrib><creatorcontrib>Zhang, Jun</creatorcontrib><creatorcontrib>Xia, Lin</creatorcontrib><creatorcontrib>Tu, Kailing</creatorcontrib><creatorcontrib>Yuan, Jue</creatorcontrib><creatorcontrib>Guo, Ziheng</creatorcontrib><creatorcontrib>Liu, Hongying</creatorcontrib><creatorcontrib>Xie, Dan</creatorcontrib><creatorcontrib>Li, Ang</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech 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>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</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>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Clinical epigenetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cao, Feng</au><au>Wei, Ailin</au><au>Hu, Xinlei</au><au>He, Yijing</au><au>Zhang, Jun</au><au>Xia, Lin</au><au>Tu, Kailing</au><au>Yuan, Jue</au><au>Guo, Ziheng</au><au>Liu, Hongying</au><au>Xie, Dan</au><au>Li, Ang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integrated epigenetic biomarkers in circulating cell-free DNA as a robust classifier for pancreatic cancer</atitle><jtitle>Clinical epigenetics</jtitle><addtitle>Clin Epigenetics</addtitle><date>2020-07-23</date><risdate>2020</risdate><volume>12</volume><issue>1</issue><spage>112</spage><epage>112</epage><pages>112-112</pages><artnum>112</artnum><issn>1868-7075</issn><issn>1868-7083</issn><eissn>1868-7083</eissn><eissn>1868-7075</eissn><abstract>The high lethal rate of pancreatic cancer is partly due to a lack of efficient biomarkers for screening and early diagnosis. We attempted to develop effective and noninvasive methods using 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) markers from circulating cell-free DNA (cfDNA) for the detection of pancreatic ductal adenocarcinoma (PDAC).
A 24-feature 5mC model that can accurately discriminate PDAC from healthy controls (area under the curve (AUC) = 0.977, sensitivity = 0.824, specificity = 1) and a 5hmC prediction model with 27 features demonstrated excellent detection power in two distinct validation sets (AUC = 0.992 and 0.960, sensitivity = 0.786 and 0.857, specificity = 1 and 0.993). The 51-feature model combining 5mC and 5hmC markers outperformed both of the individual models, with an AUC of 0.997 (sensitivity = 0.938, specificity = 0.955) and particularly an improvement in the prediction sensitivity of PDAC. In addition, the weighted diagnosis score (wd-score) calculated with the 5hmC model can distinguish stage I patients from stage II-IV patients.
Both 5mC and 5hmC biomarkers in cfDNA are effective in PDAC detection, and the 5mC-5hmC integrated model significantly improve the detection sensitivity.</abstract><cop>Germany</cop><pub>BioMed Central Ltd</pub><pmid>32703318</pmid><doi>10.1186/s13148-020-00898-2</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-5388-5541</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 5-Methylcytosine - analogs & derivatives 5-Methylcytosine - blood Adenocarcinoma Adenocarcinoma - blood Analysis Biological markers Biomarkers Biomarkers, Tumor - blood Cancer genetics Carcinoma, Pancreatic Ductal - blood Cell-Free Nucleic Acids - blood Deoxyribonucleic acid Diagnosis DNA DNA methylation Epigenesis, Genetic - physiology Epigenetic inheritance Epigenetics Female Gene expression Genomes Genomics Humans Male Methods Middle Aged Mutation Pancreatic cancer Pancreatic Neoplasms - blood Prediction models Reproducibility of Results Sensitivity and Specificity |
title | Integrated epigenetic biomarkers in circulating cell-free DNA as a robust classifier for pancreatic cancer |
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