circRNA, a novel diagnostic biomarker for coronary heart disease

ObjectiveThis study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA.MethodsThe microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiao Tong [verfasserIn] Xinyi Zhao [verfasserIn] Xuan Dang [verfasserIn] Yan Kou [verfasserIn] Junjie Kou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	diagnostic marker plasma exosome coronary heart disease ceRNA network differentially expressed genes

Übergeordnetes Werk:	In: Frontiers in Cardiovascular Medicine - Frontiers Media S.A., 2015, 10(2023)
Übergeordnetes Werk:	volume:10 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fcvm.2023.1070616

Katalog-ID:	DOAJ081255942

Internformat


LEADER	01000naa a22002652 4500
001	DOAJ081255942
003	DE-627
005	20230310211125.0
007	cr uuu---uuuuu
008	230310s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3389/fcvm.2023.1070616 \|2 doi
035			\|a (DE-627)DOAJ081255942
035			\|a (DE-599)DOAJdd2a6390fcc4444ead0ae105d8719045
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a RC666-701
100	0		\|a Xiao Tong \|e verfasserin \|4 aut
245	1	0	\|a circRNA, a novel diagnostic biomarker for coronary heart disease
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a ObjectiveThis study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA.MethodsThe microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and GEO database (GSE61144), respectively, to identify the differentially expressed genes (DEGs). Our findings detected the differentially expressed circRNAs and mRNAs and predicted their correlation with microRNAs using the microRNA target prediction website, thus ascertaining the corresponding circ-microRNA and micro-mRNAs. Then, we performed systematic Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the differentially expressed mRNA. Protein-Protein Interactions (PPI) of these DEGs were examined using STRING. The receiver operator characteristic (ROC) curve was used to validate the diagnostic efficacy of circRNA in patients with CHD. Finally, the RNAs identified in this study were verified by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 85 differentially expressed circRNAs (4 up-regulated and 81 down-regulated) were identified by screening the circRNAs in exosome of CHD patients. Based on the prediction data of circRNA, mRNA, and the corresponding microRNA, a ceRNA network was constructed, including 7 circRNA nodes, 5 microRNA nodes, and 2 mRNA nodes. Finally, validated by qRT-PCR testing, we found circRNA0001785, circRNA0000973, circRNA0001741, and circRNA0003922 to be the promising candidate for the effective prediction of CHD. These potential diagnostic markers can provide insight for further research on the occurrence of CHD or even acute coronary syndrome (ACS).
650		4	\|a diagnostic marker
650		4	\|a plasma exosome
650		4	\|a coronary heart disease
650		4	\|a ceRNA network
650		4	\|a differentially expressed genes
653		0	\|a Diseases of the circulatory (Cardiovascular) system
700	0		\|a Xinyi Zhao \|e verfasserin \|4 aut
700	0		\|a Xuan Dang \|e verfasserin \|4 aut
700	0		\|a Yan Kou \|e verfasserin \|4 aut
700	0		\|a Junjie Kou \|e verfasserin \|4 aut
773	0	8	\|i In \|t Frontiers in Cardiovascular Medicine \|d Frontiers Media S.A., 2015 \|g 10(2023) \|w (DE-627)793951607 \|w (DE-600)2781496-8 \|x 2297055X \|7 nnns
773	1	8	\|g volume:10 \|g year:2023
856	4	0	\|u https://doi.org/10.3389/fcvm.2023.1070616 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/dd2a6390fcc4444ead0ae105d8719045 \|z kostenfrei
856	4	0	\|u https://www.frontiersin.org/articles/10.3389/fcvm.2023.1070616/full \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2297-055X \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 10 \|j 2023

Indexfelder

author_variant	x t xt x z xz x d xd y k yk j k jk
matchkey_str	article:2297055X:2023----::icnaoedansiboakrocr
hierarchy_sort_str	2023
callnumber-subject-code	RC
publishDate	2023
allfields	10.3389/fcvm.2023.1070616 doi (DE-627)DOAJ081255942 (DE-599)DOAJdd2a6390fcc4444ead0ae105d8719045 DE-627 ger DE-627 rakwb eng RC666-701 Xiao Tong verfasserin aut circRNA, a novel diagnostic biomarker for coronary heart disease 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectiveThis study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA.MethodsThe microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and GEO database (GSE61144), respectively, to identify the differentially expressed genes (DEGs). Our findings detected the differentially expressed circRNAs and mRNAs and predicted their correlation with microRNAs using the microRNA target prediction website, thus ascertaining the corresponding circ-microRNA and micro-mRNAs. Then, we performed systematic Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the differentially expressed mRNA. Protein-Protein Interactions (PPI) of these DEGs were examined using STRING. The receiver operator characteristic (ROC) curve was used to validate the diagnostic efficacy of circRNA in patients with CHD. Finally, the RNAs identified in this study were verified by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 85 differentially expressed circRNAs (4 up-regulated and 81 down-regulated) were identified by screening the circRNAs in exosome of CHD patients. Based on the prediction data of circRNA, mRNA, and the corresponding microRNA, a ceRNA network was constructed, including 7 circRNA nodes, 5 microRNA nodes, and 2 mRNA nodes. Finally, validated by qRT-PCR testing, we found circRNA0001785, circRNA0000973, circRNA0001741, and circRNA0003922 to be the promising candidate for the effective prediction of CHD. These potential diagnostic markers can provide insight for further research on the occurrence of CHD or even acute coronary syndrome (ACS). diagnostic marker plasma exosome coronary heart disease ceRNA network differentially expressed genes Diseases of the circulatory (Cardiovascular) system Xinyi Zhao verfasserin aut Xuan Dang verfasserin aut Yan Kou verfasserin aut Junjie Kou verfasserin aut In Frontiers in Cardiovascular Medicine Frontiers Media S.A., 2015 10(2023) (DE-627)793951607 (DE-600)2781496-8 2297055X nnns volume:10 year:2023 https://doi.org/10.3389/fcvm.2023.1070616 kostenfrei https://doaj.org/article/dd2a6390fcc4444ead0ae105d8719045 kostenfrei https://www.frontiersin.org/articles/10.3389/fcvm.2023.1070616/full kostenfrei https://doaj.org/toc/2297-055X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023
spelling	10.3389/fcvm.2023.1070616 doi (DE-627)DOAJ081255942 (DE-599)DOAJdd2a6390fcc4444ead0ae105d8719045 DE-627 ger DE-627 rakwb eng RC666-701 Xiao Tong verfasserin aut circRNA, a novel diagnostic biomarker for coronary heart disease 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectiveThis study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA.MethodsThe microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and GEO database (GSE61144), respectively, to identify the differentially expressed genes (DEGs). Our findings detected the differentially expressed circRNAs and mRNAs and predicted their correlation with microRNAs using the microRNA target prediction website, thus ascertaining the corresponding circ-microRNA and micro-mRNAs. Then, we performed systematic Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the differentially expressed mRNA. Protein-Protein Interactions (PPI) of these DEGs were examined using STRING. The receiver operator characteristic (ROC) curve was used to validate the diagnostic efficacy of circRNA in patients with CHD. Finally, the RNAs identified in this study were verified by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 85 differentially expressed circRNAs (4 up-regulated and 81 down-regulated) were identified by screening the circRNAs in exosome of CHD patients. Based on the prediction data of circRNA, mRNA, and the corresponding microRNA, a ceRNA network was constructed, including 7 circRNA nodes, 5 microRNA nodes, and 2 mRNA nodes. Finally, validated by qRT-PCR testing, we found circRNA0001785, circRNA0000973, circRNA0001741, and circRNA0003922 to be the promising candidate for the effective prediction of CHD. These potential diagnostic markers can provide insight for further research on the occurrence of CHD or even acute coronary syndrome (ACS). diagnostic marker plasma exosome coronary heart disease ceRNA network differentially expressed genes Diseases of the circulatory (Cardiovascular) system Xinyi Zhao verfasserin aut Xuan Dang verfasserin aut Yan Kou verfasserin aut Junjie Kou verfasserin aut In Frontiers in Cardiovascular Medicine Frontiers Media S.A., 2015 10(2023) (DE-627)793951607 (DE-600)2781496-8 2297055X nnns volume:10 year:2023 https://doi.org/10.3389/fcvm.2023.1070616 kostenfrei https://doaj.org/article/dd2a6390fcc4444ead0ae105d8719045 kostenfrei https://www.frontiersin.org/articles/10.3389/fcvm.2023.1070616/full kostenfrei https://doaj.org/toc/2297-055X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023
allfields_unstemmed	10.3389/fcvm.2023.1070616 doi (DE-627)DOAJ081255942 (DE-599)DOAJdd2a6390fcc4444ead0ae105d8719045 DE-627 ger DE-627 rakwb eng RC666-701 Xiao Tong verfasserin aut circRNA, a novel diagnostic biomarker for coronary heart disease 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectiveThis study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA.MethodsThe microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and GEO database (GSE61144), respectively, to identify the differentially expressed genes (DEGs). Our findings detected the differentially expressed circRNAs and mRNAs and predicted their correlation with microRNAs using the microRNA target prediction website, thus ascertaining the corresponding circ-microRNA and micro-mRNAs. Then, we performed systematic Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the differentially expressed mRNA. Protein-Protein Interactions (PPI) of these DEGs were examined using STRING. The receiver operator characteristic (ROC) curve was used to validate the diagnostic efficacy of circRNA in patients with CHD. Finally, the RNAs identified in this study were verified by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 85 differentially expressed circRNAs (4 up-regulated and 81 down-regulated) were identified by screening the circRNAs in exosome of CHD patients. Based on the prediction data of circRNA, mRNA, and the corresponding microRNA, a ceRNA network was constructed, including 7 circRNA nodes, 5 microRNA nodes, and 2 mRNA nodes. Finally, validated by qRT-PCR testing, we found circRNA0001785, circRNA0000973, circRNA0001741, and circRNA0003922 to be the promising candidate for the effective prediction of CHD. These potential diagnostic markers can provide insight for further research on the occurrence of CHD or even acute coronary syndrome (ACS). diagnostic marker plasma exosome coronary heart disease ceRNA network differentially expressed genes Diseases of the circulatory (Cardiovascular) system Xinyi Zhao verfasserin aut Xuan Dang verfasserin aut Yan Kou verfasserin aut Junjie Kou verfasserin aut In Frontiers in Cardiovascular Medicine Frontiers Media S.A., 2015 10(2023) (DE-627)793951607 (DE-600)2781496-8 2297055X nnns volume:10 year:2023 https://doi.org/10.3389/fcvm.2023.1070616 kostenfrei https://doaj.org/article/dd2a6390fcc4444ead0ae105d8719045 kostenfrei https://www.frontiersin.org/articles/10.3389/fcvm.2023.1070616/full kostenfrei https://doaj.org/toc/2297-055X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023
allfieldsGer	10.3389/fcvm.2023.1070616 doi (DE-627)DOAJ081255942 (DE-599)DOAJdd2a6390fcc4444ead0ae105d8719045 DE-627 ger DE-627 rakwb eng RC666-701 Xiao Tong verfasserin aut circRNA, a novel diagnostic biomarker for coronary heart disease 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectiveThis study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA.MethodsThe microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and GEO database (GSE61144), respectively, to identify the differentially expressed genes (DEGs). Our findings detected the differentially expressed circRNAs and mRNAs and predicted their correlation with microRNAs using the microRNA target prediction website, thus ascertaining the corresponding circ-microRNA and micro-mRNAs. Then, we performed systematic Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the differentially expressed mRNA. Protein-Protein Interactions (PPI) of these DEGs were examined using STRING. The receiver operator characteristic (ROC) curve was used to validate the diagnostic efficacy of circRNA in patients with CHD. Finally, the RNAs identified in this study were verified by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 85 differentially expressed circRNAs (4 up-regulated and 81 down-regulated) were identified by screening the circRNAs in exosome of CHD patients. Based on the prediction data of circRNA, mRNA, and the corresponding microRNA, a ceRNA network was constructed, including 7 circRNA nodes, 5 microRNA nodes, and 2 mRNA nodes. Finally, validated by qRT-PCR testing, we found circRNA0001785, circRNA0000973, circRNA0001741, and circRNA0003922 to be the promising candidate for the effective prediction of CHD. These potential diagnostic markers can provide insight for further research on the occurrence of CHD or even acute coronary syndrome (ACS). diagnostic marker plasma exosome coronary heart disease ceRNA network differentially expressed genes Diseases of the circulatory (Cardiovascular) system Xinyi Zhao verfasserin aut Xuan Dang verfasserin aut Yan Kou verfasserin aut Junjie Kou verfasserin aut In Frontiers in Cardiovascular Medicine Frontiers Media S.A., 2015 10(2023) (DE-627)793951607 (DE-600)2781496-8 2297055X nnns volume:10 year:2023 https://doi.org/10.3389/fcvm.2023.1070616 kostenfrei https://doaj.org/article/dd2a6390fcc4444ead0ae105d8719045 kostenfrei https://www.frontiersin.org/articles/10.3389/fcvm.2023.1070616/full kostenfrei https://doaj.org/toc/2297-055X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023
allfieldsSound	10.3389/fcvm.2023.1070616 doi (DE-627)DOAJ081255942 (DE-599)DOAJdd2a6390fcc4444ead0ae105d8719045 DE-627 ger DE-627 rakwb eng RC666-701 Xiao Tong verfasserin aut circRNA, a novel diagnostic biomarker for coronary heart disease 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectiveThis study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA.MethodsThe microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and GEO database (GSE61144), respectively, to identify the differentially expressed genes (DEGs). Our findings detected the differentially expressed circRNAs and mRNAs and predicted their correlation with microRNAs using the microRNA target prediction website, thus ascertaining the corresponding circ-microRNA and micro-mRNAs. Then, we performed systematic Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the differentially expressed mRNA. Protein-Protein Interactions (PPI) of these DEGs were examined using STRING. The receiver operator characteristic (ROC) curve was used to validate the diagnostic efficacy of circRNA in patients with CHD. Finally, the RNAs identified in this study were verified by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 85 differentially expressed circRNAs (4 up-regulated and 81 down-regulated) were identified by screening the circRNAs in exosome of CHD patients. Based on the prediction data of circRNA, mRNA, and the corresponding microRNA, a ceRNA network was constructed, including 7 circRNA nodes, 5 microRNA nodes, and 2 mRNA nodes. Finally, validated by qRT-PCR testing, we found circRNA0001785, circRNA0000973, circRNA0001741, and circRNA0003922 to be the promising candidate for the effective prediction of CHD. These potential diagnostic markers can provide insight for further research on the occurrence of CHD or even acute coronary syndrome (ACS). diagnostic marker plasma exosome coronary heart disease ceRNA network differentially expressed genes Diseases of the circulatory (Cardiovascular) system Xinyi Zhao verfasserin aut Xuan Dang verfasserin aut Yan Kou verfasserin aut Junjie Kou verfasserin aut In Frontiers in Cardiovascular Medicine Frontiers Media S.A., 2015 10(2023) (DE-627)793951607 (DE-600)2781496-8 2297055X nnns volume:10 year:2023 https://doi.org/10.3389/fcvm.2023.1070616 kostenfrei https://doaj.org/article/dd2a6390fcc4444ead0ae105d8719045 kostenfrei https://www.frontiersin.org/articles/10.3389/fcvm.2023.1070616/full kostenfrei https://doaj.org/toc/2297-055X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023
language	English
source	In Frontiers in Cardiovascular Medicine 10(2023) volume:10 year:2023
sourceStr	In Frontiers in Cardiovascular Medicine 10(2023) volume:10 year:2023
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	diagnostic marker plasma exosome coronary heart disease ceRNA network differentially expressed genes Diseases of the circulatory (Cardiovascular) system
isfreeaccess_bool	true
container_title	Frontiers in Cardiovascular Medicine
authorswithroles_txt_mv	Xiao Tong @@aut@@ Xinyi Zhao @@aut@@ Xuan Dang @@aut@@ Yan Kou @@aut@@ Junjie Kou @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	793951607
id	DOAJ081255942
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ081255942</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310211125.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230310s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fcvm.2023.1070616</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ081255942</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJdd2a6390fcc4444ead0ae105d8719045</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC666-701</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Xiao Tong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">circRNA, a novel diagnostic biomarker for coronary heart disease</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">ObjectiveThis study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA.MethodsThe microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and GEO database (GSE61144), respectively, to identify the differentially expressed genes (DEGs). Our findings detected the differentially expressed circRNAs and mRNAs and predicted their correlation with microRNAs using the microRNA target prediction website, thus ascertaining the corresponding circ-microRNA and micro-mRNAs. Then, we performed systematic Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the differentially expressed mRNA. Protein-Protein Interactions (PPI) of these DEGs were examined using STRING. The receiver operator characteristic (ROC) curve was used to validate the diagnostic efficacy of circRNA in patients with CHD. Finally, the RNAs identified in this study were verified by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 85 differentially expressed circRNAs (4 up-regulated and 81 down-regulated) were identified by screening the circRNAs in exosome of CHD patients. Based on the prediction data of circRNA, mRNA, and the corresponding microRNA, a ceRNA network was constructed, including 7 circRNA nodes, 5 microRNA nodes, and 2 mRNA nodes. Finally, validated by qRT-PCR testing, we found circRNA0001785, circRNA0000973, circRNA0001741, and circRNA0003922 to be the promising candidate for the effective prediction of CHD. These potential diagnostic markers can provide insight for further research on the occurrence of CHD or even acute coronary syndrome (ACS).</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">diagnostic marker</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">plasma exosome</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">coronary heart disease</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ceRNA network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">differentially expressed genes</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Diseases of the circulatory (Cardiovascular) system</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xinyi Zhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xuan Dang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yan Kou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Junjie Kou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Cardiovascular Medicine</subfield><subfield code="d">Frontiers Media S.A., 2015</subfield><subfield code="g">10(2023)</subfield><subfield code="w">(DE-627)793951607</subfield><subfield code="w">(DE-600)2781496-8</subfield><subfield code="x">2297055X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2023</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fcvm.2023.1070616</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/dd2a6390fcc4444ead0ae105d8719045</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/articles/10.3389/fcvm.2023.1070616/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2297-055X</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2023</subfield></datafield></record></collection>
callnumber-first	R - Medicine
author	Xiao Tong
spellingShingle	Xiao Tong misc RC666-701 misc diagnostic marker misc plasma exosome misc coronary heart disease misc ceRNA network misc differentially expressed genes misc Diseases of the circulatory (Cardiovascular) system circRNA, a novel diagnostic biomarker for coronary heart disease
authorStr	Xiao Tong
ppnlink_with_tag_str_mv	@@773@@(DE-627)793951607
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	RC666-701
illustrated	Not Illustrated
issn	2297055X
topic_title	RC666-701 circRNA, a novel diagnostic biomarker for coronary heart disease diagnostic marker plasma exosome coronary heart disease ceRNA network differentially expressed genes
topic	misc RC666-701 misc diagnostic marker misc plasma exosome misc coronary heart disease misc ceRNA network misc differentially expressed genes misc Diseases of the circulatory (Cardiovascular) system
topic_unstemmed	misc RC666-701 misc diagnostic marker misc plasma exosome misc coronary heart disease misc ceRNA network misc differentially expressed genes misc Diseases of the circulatory (Cardiovascular) system
topic_browse	misc RC666-701 misc diagnostic marker misc plasma exosome misc coronary heart disease misc ceRNA network misc differentially expressed genes misc Diseases of the circulatory (Cardiovascular) system
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Frontiers in Cardiovascular Medicine
hierarchy_parent_id	793951607
hierarchy_top_title	Frontiers in Cardiovascular Medicine
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)793951607 (DE-600)2781496-8
title	circRNA, a novel diagnostic biomarker for coronary heart disease
ctrlnum	(DE-627)DOAJ081255942 (DE-599)DOAJdd2a6390fcc4444ead0ae105d8719045
title_full	circRNA, a novel diagnostic biomarker for coronary heart disease
author_sort	Xiao Tong
journal	Frontiers in Cardiovascular Medicine
journalStr	Frontiers in Cardiovascular Medicine
callnumber-first-code	R
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
author_browse	Xiao Tong Xinyi Zhao Xuan Dang Yan Kou Junjie Kou
container_volume	10
class	RC666-701
format_se	Elektronische Aufsätze
author-letter	Xiao Tong
doi_str_mv	10.3389/fcvm.2023.1070616
author2-role	verfasserin
title_sort	circrna, a novel diagnostic biomarker for coronary heart disease
callnumber	RC666-701
title_auth	circRNA, a novel diagnostic biomarker for coronary heart disease
abstract	ObjectiveThis study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA.MethodsThe microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and GEO database (GSE61144), respectively, to identify the differentially expressed genes (DEGs). Our findings detected the differentially expressed circRNAs and mRNAs and predicted their correlation with microRNAs using the microRNA target prediction website, thus ascertaining the corresponding circ-microRNA and micro-mRNAs. Then, we performed systematic Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the differentially expressed mRNA. Protein-Protein Interactions (PPI) of these DEGs were examined using STRING. The receiver operator characteristic (ROC) curve was used to validate the diagnostic efficacy of circRNA in patients with CHD. Finally, the RNAs identified in this study were verified by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 85 differentially expressed circRNAs (4 up-regulated and 81 down-regulated) were identified by screening the circRNAs in exosome of CHD patients. Based on the prediction data of circRNA, mRNA, and the corresponding microRNA, a ceRNA network was constructed, including 7 circRNA nodes, 5 microRNA nodes, and 2 mRNA nodes. Finally, validated by qRT-PCR testing, we found circRNA0001785, circRNA0000973, circRNA0001741, and circRNA0003922 to be the promising candidate for the effective prediction of CHD. These potential diagnostic markers can provide insight for further research on the occurrence of CHD or even acute coronary syndrome (ACS).
abstractGer	ObjectiveThis study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA.MethodsThe microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and GEO database (GSE61144), respectively, to identify the differentially expressed genes (DEGs). Our findings detected the differentially expressed circRNAs and mRNAs and predicted their correlation with microRNAs using the microRNA target prediction website, thus ascertaining the corresponding circ-microRNA and micro-mRNAs. Then, we performed systematic Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the differentially expressed mRNA. Protein-Protein Interactions (PPI) of these DEGs were examined using STRING. The receiver operator characteristic (ROC) curve was used to validate the diagnostic efficacy of circRNA in patients with CHD. Finally, the RNAs identified in this study were verified by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 85 differentially expressed circRNAs (4 up-regulated and 81 down-regulated) were identified by screening the circRNAs in exosome of CHD patients. Based on the prediction data of circRNA, mRNA, and the corresponding microRNA, a ceRNA network was constructed, including 7 circRNA nodes, 5 microRNA nodes, and 2 mRNA nodes. Finally, validated by qRT-PCR testing, we found circRNA0001785, circRNA0000973, circRNA0001741, and circRNA0003922 to be the promising candidate for the effective prediction of CHD. These potential diagnostic markers can provide insight for further research on the occurrence of CHD or even acute coronary syndrome (ACS).
abstract_unstemmed	ObjectiveThis study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA.MethodsThe microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and GEO database (GSE61144), respectively, to identify the differentially expressed genes (DEGs). Our findings detected the differentially expressed circRNAs and mRNAs and predicted their correlation with microRNAs using the microRNA target prediction website, thus ascertaining the corresponding circ-microRNA and micro-mRNAs. Then, we performed systematic Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the differentially expressed mRNA. Protein-Protein Interactions (PPI) of these DEGs were examined using STRING. The receiver operator characteristic (ROC) curve was used to validate the diagnostic efficacy of circRNA in patients with CHD. Finally, the RNAs identified in this study were verified by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 85 differentially expressed circRNAs (4 up-regulated and 81 down-regulated) were identified by screening the circRNAs in exosome of CHD patients. Based on the prediction data of circRNA, mRNA, and the corresponding microRNA, a ceRNA network was constructed, including 7 circRNA nodes, 5 microRNA nodes, and 2 mRNA nodes. Finally, validated by qRT-PCR testing, we found circRNA0001785, circRNA0000973, circRNA0001741, and circRNA0003922 to be the promising candidate for the effective prediction of CHD. These potential diagnostic markers can provide insight for further research on the occurrence of CHD or even acute coronary syndrome (ACS).
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	circRNA, a novel diagnostic biomarker for coronary heart disease
url	https://doi.org/10.3389/fcvm.2023.1070616 https://doaj.org/article/dd2a6390fcc4444ead0ae105d8719045 https://www.frontiersin.org/articles/10.3389/fcvm.2023.1070616/full https://doaj.org/toc/2297-055X
remote_bool	true
author2	Xinyi Zhao Xuan Dang Yan Kou Junjie Kou
author2Str	Xinyi Zhao Xuan Dang Yan Kou Junjie Kou
ppnlink	793951607
callnumber-subject	RC - Internal Medicine
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3389/fcvm.2023.1070616
callnumber-a	RC666-701
up_date	2024-07-03T19:06:10.846Z
_version_	1803585921586561024
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ081255942</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310211125.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230310s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fcvm.2023.1070616</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ081255942</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJdd2a6390fcc4444ead0ae105d8719045</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC666-701</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Xiao Tong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">circRNA, a novel diagnostic biomarker for coronary heart disease</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">ObjectiveThis study aimed to identify the potential diagnostic biomarkers of coronary heart disease (CHD) from exosome-derived circRNA.MethodsThe microarray data of circRNA derived from the exosomes of patients with CHD and mRNA in acute myocardial infarction was retrieved from exoRBase website and GEO database (GSE61144), respectively, to identify the differentially expressed genes (DEGs). Our findings detected the differentially expressed circRNAs and mRNAs and predicted their correlation with microRNAs using the microRNA target prediction website, thus ascertaining the corresponding circ-microRNA and micro-mRNAs. Then, we performed systematic Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the differentially expressed mRNA. Protein-Protein Interactions (PPI) of these DEGs were examined using STRING. The receiver operator characteristic (ROC) curve was used to validate the diagnostic efficacy of circRNA in patients with CHD. Finally, the RNAs identified in this study were verified by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 85 differentially expressed circRNAs (4 up-regulated and 81 down-regulated) were identified by screening the circRNAs in exosome of CHD patients. Based on the prediction data of circRNA, mRNA, and the corresponding microRNA, a ceRNA network was constructed, including 7 circRNA nodes, 5 microRNA nodes, and 2 mRNA nodes. Finally, validated by qRT-PCR testing, we found circRNA0001785, circRNA0000973, circRNA0001741, and circRNA0003922 to be the promising candidate for the effective prediction of CHD. These potential diagnostic markers can provide insight for further research on the occurrence of CHD or even acute coronary syndrome (ACS).</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">diagnostic marker</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">plasma exosome</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">coronary heart disease</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ceRNA network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">differentially expressed genes</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Diseases of the circulatory (Cardiovascular) system</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xinyi Zhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xuan Dang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yan Kou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Junjie Kou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Cardiovascular Medicine</subfield><subfield code="d">Frontiers Media S.A., 2015</subfield><subfield code="g">10(2023)</subfield><subfield code="w">(DE-627)793951607</subfield><subfield code="w">(DE-600)2781496-8</subfield><subfield code="x">2297055X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2023</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fcvm.2023.1070616</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/dd2a6390fcc4444ead0ae105d8719045</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/articles/10.3389/fcvm.2023.1070616/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2297-055X</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2023</subfield></datafield></record></collection>
score	7.399004

Nicht das Richtige dabei?

Schreiben Sie uns!

circRNA, a novel diagnostic biomarker for coronary heart disease

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?