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Plasma miRNA profiles associated with stable warfarin dosage in Chinese patients
BACKGROUND: We used bioinformatic analysis and quantitative reverse transcription polymerase chain reaction (RT-qPCR) assays to investigate the association between plasma microRNAs (miRNAs) and stable warfarin dosage in a Chinese Han population. METHODS: Bioinformatics analysis was used to screen ou...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7566751/ https://www.ncbi.nlm.nih.gov/pubmed/33083118 http://dx.doi.org/10.7717/peerj.9995 |
Sumario: | BACKGROUND: We used bioinformatic analysis and quantitative reverse transcription polymerase chain reaction (RT-qPCR) assays to investigate the association between plasma microRNAs (miRNAs) and stable warfarin dosage in a Chinese Han population. METHODS: Bioinformatics analysis was used to screen out potential warfarin dose-associated miRNAs. Three plasma miRNAs were validated in 99 samples by RT-qPCR. Kruskal–Wallis test and multivariate logistic regression were used to compare differences in plasma miRNAs expression levels between three warfarin dosage groups. RESULTS: There were significant between-group differences among the three dose groups for hsa-miR-133b expression (p = 0.005), but we observed an “n-shaped” dose-dependent curve rather than a linear relationship. Expression levels of hsa-miR-24-3p (p = 0.475) and hsa-miR-1276 (p = 0.558) were not significantly different in the multivariate logistic regression. CONCLUSION: miRNAs have received extensive attention as ideal biomarkers and possible therapeutic targets for various diseases. However, they are not yet widely used in precision medicine. Our results indicate that hsa-miR-133b may be a possible reference factor for the warfarin dosage algorithm. These findings emphasize the importance of a comprehensive evaluation of complex relationships in warfarin dose prediction models and provide new avenues for future pharmacogenomics studies. |
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