Circulating miRNA Signature as a Potential Biomarker for the Prediction of Analgesic Efficacy of Hydromorphone
Abstract
:1. Introduction
2. Results
2.1. Selection of MOR-Regulated miRNAs from Previous Healthy Subject Study Data
2.2. Study of Patients with Cancer
2.2.1. Demographics of Patients with Cancer
2.2.2. Analgesic Efficacy of Hydromorphone
2.2.3. Prediction of Analgesic Efficacy of Hydromorphone from Circulating miRNA Signatures
3. Discussion
4. Materials and Methods
4.1. Previously Published Studies in Healthy Subjects
Selection of miRNAs to Predict Analgesic Efficacy of Hydromorphone
4.2. Study in Patients with Cancer
4.2.1. Study Outline
4.2.2. miRNA qPCR and Data Analysis
4.2.3. Calculation of MOR Signal Score
4.2.4. Evaluation of Pain Intensity
4.2.5. Statistical Analysis
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
MOR | μ-opioid receptor |
VAS | Visual Analog Scale |
miRNA | microRNA |
CNS | Central nervous system |
ECOG | Eastern Cooperative Oncology Group |
SAE | Severe adverse event |
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Parameter | Dose Conversion Ratio | |
---|---|---|
1:5 (N = 12) | 1:8 (N = 13) | |
Age, years (mean ± SD) | 67.5 ± 11.9 | 69.2 ± 8.2 |
Gender | ||
Male | 10 | 9 |
Female | 2 | 4 |
Underlying diseases (tumor types) | ||
Urinary/ reproductive | 1 | 2 |
Lung | 3 | 6 |
Gastrointestinal | 6 | 2 |
Head and neck | 0 | 1 |
Breast | 1 | 1 |
Others | 1 | 1 |
Morphine daily dose level (mg) | ||
60 | 11 | 10 |
90 | 1 | 3 |
Baseline VAS (mm) | ||
Average ± SD | 14.9 ± 11.9 | 13.3 ± 7.3 |
Maximum | 36.7 | 25.3 |
Minimum | 1.7 | 0 |
Rescue dose frequency during the hydromorphone treatment period (times) | ||
Average ± SD | 3.2 ± 6.0 | 1.0 ± 1.8 |
Maximum | 19 | 6 |
Minimum | 0 | 0 |
ID | MOR−UP miRNAs | MOR−DOWN miRNAs | MOR Signal Score | Average ΔVAS | Dose Ratio | SAE | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
hsa-let-7f-5p | hsa-let-7a-5p | hsa-miR-423-3p | hsa-miR-26a-5p | hsa-miR-144-3p | hsa-miR-451a | hsa-miR-215 | hsa-miR-363-3p | |||||
#1 | −2.7 | −0.3 | −1.0 | 0.2 | −2.8 | 5.0 | −5.6 | −3.5 | 3.0 | 4.3 | 1:5 | |
#2 | −1.7 | 0.9 | −1.3 | 0.4 | −2.0 | 5.4 | −5.0 | −2.9 | 2.7 | 4.3 | 1:8 | |
#3 | −2.0 | 0.3 | −0.9 | 0.1 | −1.9 | 5.7 | −5.1 | −3.5 | 2.2 | 2.7 | 1:5 | |
#4 | −2.3 | 0.2 | −1.3 | −0.3 | −2.1 | 5.2 | −5.1 | −3.3 | 1.6 | −1.0 | 1:8 | |
#5 | −2.5 | −0.1 | −1.3 | 0.2 | −2.0 | 5.8 | −4.7 | −3.2 | 0.5 | 1.7 | 1:8 | |
#6 | −2.0 | 0.2 | −0.9 | 0.0 | −1.6 | 5.7 | −4.1 | −2.4 | −0.2 | 1.7 | 1:8 | |
#7 | −1.9 | 0.4 | −1.3 | 0.2 | −1.6 | 5.9 | −2.7 | −3.3 | −0.9 | 6.0 | 1:8 | |
#8 | −2.1 | 0.1 | −1.1 | 0.3 | −0.6 | 6.3 | −4.4 | −2.8 | −1.2 | 6.7 | 1:5 | |
#9 | −1.3 | 0.5 | −1.8 | 0.1 | −1.3 | 6.6 | −3.6 | −2.4 | −1.9 | NA | 1:8 | + |
#10 | −2.5 | 0.0 | −1.2 | −0.2 | −1.3 | 5.8 | −3.1 | −2.8 | −2.4 | 1.0 | 1:5 | |
#11 | −2.1 | 0.5 | −1.9 | 0.2 | −0.4 | 6.5 | −4.1 | −2.7 | −2.7 | 2.3 | 1:5 | + |
#12 | −2.3 | 0.1 | −1.5 | −0.4 | −1.0 | 6.2 | −4.0 | −2.5 | −2.9 | 9.4 | 1:5 | + |
#13 | −1.9 | −0.2 | −1.8 | −0.2 | −0.5 | 7.0 | −5.1 | −2.5 | −3.0 | 0.7 | 1:5 | |
#14 | −1.5 | 1.0 | −1.7 | −0.1 | −0.1 | 6.8 | −2.4 | −3.1 | −3.3 | 2.7 | 1:8 | |
#15 | −1.9 | 0.8 | −2.2 | −0.4 | 0.0 | 7.0 | −4.9 | −2.4 | −3.4 | 0.3 | 1:8 | |
#16 | −2.6 | 0.0 | −2.1 | −0.4 | −1.2 | 6.1 | −4.0 | −2.5 | −3.4 | 0.0 | 1:8 | |
#17 | −3.1 | 0.0 | −1.1 | −0.2 | −1.5 | 5.7 | −0.9 | −3.1 | −4.7 | NA | 1:5 | + |
#18 | −2.5 | −0.1 | −1.4 | −0.3 | −0.6 | 6.4 | −3.6 | −1.6 | −4.9 | −13.0 | 1:8 | |
#19 | −2.5 | 0.1 | −1.9 | −0.5 | 0.0 | 6.2 | −3.3 | −2.2 | −5.4 | −1.3 | 1:5 | |
#20 | −2.6 | 0.2 | −2.2 | −0.5 | 0.1 | 7.5 | −3.4 | −2.1 | −7.2 | −0.3 | 1:8 | |
#21 | −2.7 | −0.3 | −1.5 | −0.6 | 0.2 | 7.4 | −3.5 | −1.8 | −7.6 | 7.3 | 1:5 | |
#22 | −2.7 | 0.7 | −2.4 | −0.7 | −0.1 | 7.6 | −2.1 | −1.6 | −8.8 | −2.0 | 1:8 | |
#23 | −2.5 | 0.1 | −2.5 | −1.2 | 0.4 | 7.4 | −3.4 | −1.1 | −9.4 | 0.0 | 1:8 | |
#24 | −3.8 | −0.1 | −3.6 | −1.7 | −0.4 | 7.2 | −3.9 | −2.5 | −9.7 | 0.0 | 1:5 | |
#25 | −3.8 | −1.2 | −2.4 | −3.2 | 1.5 | 8.2 | −3.3 | −0.6 | −16.4 | −1.7 | 1:5 |
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Kiyosawa, N.; Watanabe, K.; Toyama, K.; Ishizuka, H. Circulating miRNA Signature as a Potential Biomarker for the Prediction of Analgesic Efficacy of Hydromorphone. Int. J. Mol. Sci. 2019, 20, 1665. https://doi.org/10.3390/ijms20071665
Kiyosawa N, Watanabe K, Toyama K, Ishizuka H. Circulating miRNA Signature as a Potential Biomarker for the Prediction of Analgesic Efficacy of Hydromorphone. International Journal of Molecular Sciences. 2019; 20(7):1665. https://doi.org/10.3390/ijms20071665
Chicago/Turabian StyleKiyosawa, Naoki, Kenji Watanabe, Kaoru Toyama, and Hitoshi Ishizuka. 2019. "Circulating miRNA Signature as a Potential Biomarker for the Prediction of Analgesic Efficacy of Hydromorphone" International Journal of Molecular Sciences 20, no. 7: 1665. https://doi.org/10.3390/ijms20071665