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Novel single nucleotide polymorphism biomarkers to predict opioid effects for cancer pain.

ABSTRACT: There have been few studies on predictive biomarkers that may be useful to select the most suitable opioids to optimize therapeutic efficacy in individual patients with cancer pain. We recently investigated the efficacy of morphine and oxycodone using single nucleotide polymorphisms (SNPs) of the catechol-O-methyltransferase (COMT) rs4680 gene as a biomarker (RELIEF study). To explore additional biomarkers that may enable the selection of an appropriate opioid for individual patients with cancer pain, three SNPs were examined: C-C motif chemokine ligand 11 (CCL11; rs17809012), histamine N-methyltransferase (HNMT; rs1050891) and transient receptor potential V1 (TRPV1; rs222749), which were screened from 74 pain-related SNPs. These SNPs, which were identified as being significantly associated with the analgesic effect of morphine, were then used to genotype the 135 patients in the RELIEF study who had been randomized into a morphine group (n=69) or an oxycodone group (n=66). The present study then assessed whether the SNPs could also be used as selective biomarkers to predict which opioid(s) might be the most suitable to provide pain relief for patients with cancer. Oxycodone tended to provide superior analgesic effects over morphine in patients carrying the genotype AA for the CCL11 rs17809012 SNP (P=0.012 for interaction), suggesting that it could serve as a potential biomarker for personalized analgesic therapy for patients suffering with cancer pain.

SUBMITTER: Fujita Y

PROVIDER: S-EPMC10398630 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Novel single nucleotide polymorphism biomarkers to predict opioid effects for cancer pain.

Fujita Yoshihiko Y Matsuoka Hiromichi H Chiba Yasutaka Y Tsurutani Junji J Yoshida Takeshi T Sakai Kiyohiro K Nakura Miki M Sakamoto Ryo R Makimura Chihiro C Ohtake Yoichi Y Tanaka Kaoru K Hayashi Hidetoshi H Takeda Masayuki M Okuno Tatsuya T Takegawa Naoki N Haratani Koji K Takahama Takayuki T Tanizaki Junko J Koyama Atsuko A Nishio Kazuto K Nakagawa Kazuhiko K

Oncology letters 20230704 2

There have been few studies on predictive biomarkers that may be useful to select the most suitable opioids to optimize therapeutic efficacy in individual patients with cancer pain. We recently investigated the efficacy of morphine and oxycodone using single nucleotide polymorphisms (SNPs) of the catechol-O-methyltransferase (<i>COMT</i>) rs4680 gene as a biomarker (RELIEF study). To explore additional biomarkers that may enable the selection of an appropriate opioid for individual patients with ...[more]

PMID: 37545623

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Project description:The catechol-O-methyltransferase Val158Met polymorphism has been associated with alterations in pain perception, but the influence of the polymorphism on pain perception in patients with chronic pain receiving daily opioid therapy has not been previously reported. The primary aim of this study was to investigate the effects of the catechol-O-methyltransferase Val158Met polymorphism on heat pain perception in a cohort of adults receiving daily opioid therapy for chronic pain. Adults with chronic pain consecutively admitted to an outpatient pain rehabilitation program who met inclusion criteria and were receiving daily opioid therapy were recruited for study participation (N = 142). Individuals were genotyped for catechol-O-methyltransferase Val158Met (rs4680), and the polymorphism was analyzed using an additive and codominant genotype models. The distribution of the Val158Met genotypes was 25% for Val/Val, 41% for Val/Met and 34% for Met/Met (Hardy-Weinberg, P > 0.05). A main effect of genotype was observed for heat pain perception ( P = 0.028). Under the codominant model of allele effects, exploratory post hoc pairwise comparisons adjusted for morphine equivalent dose and pain catastrophizing demonstrated that individuals with the Val/Met genotype were hyperalgesic compared to individuals with the Val/Val ( P = 0.039) and Met/Met ( P = 0.023) genotypes. No significant association was observed between heat pain perception and genotype under the additive model of allele effects. Among patients with chronic pain who were receiving daily opioids, the Val/Met genotype was associated with hyperalgesia using a measure of heat pain perception that has been previously indicative of opioid-induced hyperalgesia in other heterogeneous samples of adults with chronic pain. This study contributes to the emerging understanding of how catechol-O-methyltransferase activity affects pain perception in the context of daily opioid use, and these findings may be useful in the design of future trials aimed at investigating the potential efficacy of ß-2 adrenergic receptor antagonism for opioid-induced hyperalgesia.

Project description:Background: Prostate cancer is one of the most common malignancies among men worldwide currently. However, specific mechanisms of prostate cancer were still not fully understood due to lack of integrated molecular analyses. We performed this study to establish an mRNA-single nucleotide polymorphism (SNP)-microRNA (miRNA) interaction network by comprehensive bioinformatics analysis, and search for novel biomarkers for prostate cancer. Materials and methods: mRNA, miRNA, and SNP data were acquired from Gene Expression Omnibus (GEO) database. Differential expression analysis was performed to identify differentially expressed genes (DEGs) and miRNAs (DEMs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, protein-protein interaction (PPI) analysis and expression quantitative trait loci (eQTL) analysis of DEGs were conducted. SNPs related to DEMs (miRSNPs) were downloaded from the open-source website MirSNP and PolymiRTS 3.0. TargetScan and miRDB databases were used for the target mRNA prediction of miRNA. The mRNA-SNP-miRNA interaction network was then constructed and visualized by Cytoscape 3.9.0. Selected key biomarkers were further validated using the Cancer Genome Atlas (TCGA) database. A nomogram model was constructed to predict the risk of prostate cancer. Results: In our study, 266 DEGs and 11 DEMs were identified. KEGG pathway analysis showed that DEGs were strikingly enriched in focal adhesion and PI3K-Akt signaling pathway. A total of 60 mRNA-SNP-miRNAs trios were identified to establish the mRNA-SNP-miRNA interaction network. Seven mRNAs in mRNA-SNP-miRNA network were consistent with the predicted target mRNAs of miRNA. These results were largely validated by the TCGA database analysis. A nomogram was constructed that contained four variables (ITGB8, hsa-miR-21, hsa-miR-30b and prostate-specific antigen (PSA) value) for predicting the risk of prostate cancer. Conclusion: Our study established the mRNA-SNP-miRNA interaction network in prostate cancer. The interaction network showed that hsa-miR-21, hsa-miR-30b, and ITGB8 may be utilized as new biomarkers for prostate cancer.

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Novel single nucleotide polymorphism biomarkers to predict opioid effects for cancer pain.

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Novel single nucleotide polymorphism biomarkers to predict opioid effects for cancer pain.

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