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[PMID]: | 27495086 |
[Au] Autor: | Chen SJ; Liao DL; Shen TW; Yang HC; Chen KC; Chen CH |
[Ad] Endereço: | aInstitute of Medical Sciences, Tzu Chi University, Hualien bDepartment of Psychiatry, Mackay Memorial Hospital, Taitung Branch cDepartment of Health Executive Yuan, Bali Psychiatric Center dInstitute of Statistical Science, Academia Sinica, Taipei eDepartment of Psychiatry, Chang Gung Memorial Hospital at Linkou fDepartment and Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan. |
[Ti] Título: | Genetic signatures of heroin addiction. |
[So] Source: | Medicine (Baltimore);95(31):e4473, 2016 Aug. | [Is] ISSN: | 1536-5964 |
[Cp] País de publicação: | United States |
[La] Idioma: | eng |
[Ab] Resumo: | Heroin addiction is a complex psychiatric disorder with a chronic course and a high relapse rate, which results from the interaction between genetic and environmental factors. Heroin addiction has a substantial heritability in its etiology; hence, identification of individuals with a high genetic propensity to heroin addiction may help prevent the occurrence and relapse of heroin addiction and its complications. The study aimed to identify a small set of genetic signatures that may reliably predict the individuals with a high genetic propensity to heroin addiction. We first measured the transcript level of 13 genes (RASA1, PRKCB, PDK1, JUN, CEBPG, CD74, CEBPB, AUTS2, ENO2, IMPDH2, HAT1, MBD1, and RGS3) in lymphoblastoid cell lines in a sample of 124 male heroin addicts and 124 male control subjects using real-time quantitative PCR. Seven genes (PRKCB, PDK1, JUN, CEBPG, CEBPB, ENO2, and HAT1) showed significant differential expression between the 2 groups. Further analysis using 3 statistical methods including logistic regression analysis, support vector machine learning analysis, and a computer software BIASLESS revealed that a set of 4 genes (JUN, CEBPB, PRKCB, ENO2, or CEBPG) could predict the diagnosis of heroin addiction with the accuracy rate around 85% in our dataset. Our findings support the idea that it is possible to identify genetic signatures of heroin addiction using a small set of expressed genes. However, the study can only be considered as a proof-of-concept study. As the establishment of lymphoblastoid cell line is a laborious and lengthy process, it would be more practical in clinical settings to identify genetic signatures for heroin addiction directly from peripheral blood cells in the future study. |
[Mh] Termos MeSH primário: |
Predisposição Genética para Doença Dependência de Heroína/genética
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[Mh] Termos MeSH secundário: |
Adulto Proteína beta Intensificadora de Ligação a CCAAT/genética Proteínas Estimuladoras de Ligação a CCAAT/genética Estudos de Casos e Controles Perfilação da Expressão Gênica Genes jun/genética Seres Humanos Modelos Logísticos Linfócitos/citologia Masculino Fosfopiruvato Hidratase/genética Proteína Quinase C beta/genética RNA Ribossômico 18S/metabolismo Reação em Cadeia da Polimerase em Tempo Real Software Máquina de Vetores de Suporte
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[Pt] Tipo de publicação: | JOURNAL ARTICLE |
[Nm] Nome de substância:
| 0 (CCAAT-Enhancer-Binding Protein-beta); 0 (CCAAT-Enhancer-Binding Proteins); 0 (CCAAT-enhancer-binding protein-gamma); 0 (CEBPB protein, human); 0 (RNA, Ribosomal, 18S); EC 2.7.11.13 (PRKCB protein, human); EC 2.7.11.13 (Protein Kinase C beta); EC 4.2.1.11 (Phosphopyruvate Hydratase) |
[Em] Mês de entrada: | 1702 |
[Cu] Atualização por classe: | 170220 |
[Lr] Data última revisão:
| 170220 |
[Sb] Subgrupo de revista: | AIM; IM |
[Da] Data de entrada para processamento: | 160807 |
[St] Status: | MEDLINE |
[do] DOI: | 10.1097/MD.0000000000004473 |
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