Next Article in Journal
Genome Mining and Genetic Manipulation Reveal New Isofuranonaphthoquinones in Nocardia Species
Previous Article in Journal
Ribosome Profiling and RNA Sequencing Reveal Translation and Transcription Regulation under Acute Heat Stress in Rainbow Trout (Oncorhynchus mykiss, Walbaum, 1792) Liver
Previous Article in Special Issue
OPRM1 Gene Polymorphism in Women with Alcohol Use Disorder
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 25
Issue 16
10.3390/ijms25168845
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

TMEM132C rs7296262 Single-Nucleotide Polymorphism Is Significantly Associated with Nausea Induced by Opioids Administered for Cancer Pain and Postoperative Pain

by
Yuna Kang
1,2,
Daisuke Nishizawa
1,3,
Seii Ohka
1,
Takeshi Terui
4,
Kunihiko Ishitani
4,
Ryozo Morino
5,
Miyuki Yokota
6,7,
Junko Hasegawa
1,
Kyoko Nakayama
1,3,
Yuko Ebata
1,
Kyotaro Koshika
3,
Tatsuya Ichinohe
3 and
Kazutaka Ikeda
1,3,*
1
Addictive Substance Project, Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo 156-8506, Japan
2
Department of Dental Anesthesiology, Tokyo Dental College, Chiyoda-ku, Tokyo 101-0061, Japan
3
Department of Neuropsychopharmacology, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
4
Division of Internal Medicine, Department of Medicine, Higashi-Sapporo Hospital, Sapporo 003-8585, Japan
5
Division of Anesthesiology, Koujinkai Daiichi Hospital, Tokyo 125-0041, Japan
6
Department of Anesthesiology, Cancer Institute Hospital, Tokyo 135-8550, Japan
7
Department of Anesthesiology, East Hokkaido Hospital, Kushiro 085-0036, Japan
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(16), 8845; https://doi.org/10.3390/ijms25168845
Submission received: 15 June 2024 / Revised: 9 August 2024 / Accepted: 12 August 2024 / Published: 14 August 2024
(This article belongs to the Special Issue Recent Progress of Opioid Research, 2nd Edition)
Review Reports Versions Notes

Abstract

:
Opioids are almost mandatorily used for analgesia for cancer pain and postoperative pain. Opioid analgesics commonly induce nausea as a side effect. However, the genetic factors involved are still mostly unknown. To clarify the genetic background of individual differences in the occurrence of nausea during opioid administration, the incidence of nausea was investigated in 331 patients (Higashi-Sapporo Hospital [HS] group) who received morphine chronically for cancer pain treatment and in 2021 patients (Cancer Institute Hospital [CIH] group) who underwent elective surgery under general anesthesia. We conducted a genome-wide association study of nausea in HS samples. Among the top 20 candidate single-nucleotide polymorphisms (SNPs), we focused on the TMEM132C rs7296262 SNP, which has been reportedly associated with psychiatric disorders. The rs7296262 SNP was significantly associated with nausea in both the HS and CIH groups (TT+TC vs. CC; HS group, p = 0.0001; CIH group, p = 0.0064). The distribution of nausea-prone genotypes for the rs7296262 SNP was reversed between HS and CIH groups. These results suggest that the TMEM132C rs7296262 SNP is significantly associated with nausea during opioid use, and the effect of the SNP genotype on nausea is reversed between chronic and acute phases of opioid use.

1. Introduction

Postoperative nausea and vomiting (PONV) is generally seen after major surgery. An estimated 30% of surgical patients will suffer from PONV during the first postoperative day [1]. Opioids are also used to treat cancer pain. Although opioids are the main treatment for cancer pain, opioid-related side effects, such as nausea and vomiting, may interfere with pain management and impair the quality of life of cancer patients [2]. Previously identified risk factors for PONV in adults include female sex, a history of PONV and/or motion sickness, nonsmoking status, and young age [3]. Anesthesia-related risk factors for PONV include volatile anesthetics, nitrous oxide, and postoperative opioids [4]. However, even patients at low PONV risk may suffer PONV, suggesting a genetic predisposition [5].
Opioids stimulate the medullary chemoreceptor trigger zone (CTZ), enhance vestibular sensitivity, and affect the gastrointestinal tract [2]. Nausea and vomiting are caused by various stimuli that act on the “vomiting center” in the medulla oblongata of the brain [6]. Four major areas—the CTZ, gastrointestinal tract, vestibular apparatus in the temporal lobe, and cerebral cortex—project to the vomiting center [6]. Opioids exert emetic effects, mainly through three mechanisms (i.e., direct stimulation of the CTZ, the inhibition of intestinal motility, and stimulation of the vestibular apparatus) [7]. Neurokinin-1 (NK-1) receptor, 5-hydroxytryptamine-2A (5-HT2A) receptor, and 5-HT3 receptor are ubiquitously expressed in human gastrointestinal vagal afferents and brain areas that are related to the vomiting reflex, such as the nucleus of the solitary tract [8]. Substance P is an endogenous ligand of NK-1 receptor and triggers NK-1 receptor signaling, causing nausea and vomiting [9]. Other receptors that are involved in nausea include dopamine D2 and D3 receptors and μ-opioid receptor [1].
Various single-nucleotide polymorphisms (SNPs) have been associated with nausea from acute or chronic opioid use, including OPRM1 rs9397685 [10], CHRM3 rs2165870 [11], and KCNB2 rs349358 [12] for acute opioid use and HTR3B rs1176744 [13], COMT rs165722 [13], CHRM3 rs10802789 [13], and HTR3B rs1672717 [13] for chronic opioid use. However, no SNPs have yet been reported to be commonly associated with nausea among acute and chronic opioid use. To clarify the general genetic background of individual differences in the occurrence of nausea during opioid administration, we investigated SNPs and their characteristics that are common to nausea during acute and chronic opioid administration.

2. Results

2.1. Impact of Clinical Variables on the Incidence of Nausea in Patients Who Were Treated with Opioids during the Treatment of Cancer Pain and in Patients Who Underwent Elective Surgery under General Anesthesia (Higashi-Sapporo Hospital [HS] and Cancer Institute Hospital [CIH] Groups)

In this study, samples with chronic opioid administration for cancer pain (HS group, n = 331) and samples with acute opioid administration in the perioperative period (CIH group, n = 2021) were analyzed. The background characteristics of the patients related to the surgery and anesthesia in the HS and CIH groups are shown in Tables S1–S4.
In the HS group, 331 patients were evaluated for nausea, 42% of whom developed nausea (with nausea: 138, without nausea: 193). In the CIH group, all 2021 patients were evaluated for nausea, 42% of whom developed nausea (with nausea: 850, without nausea: 1171). Next, we investigated the relationship between patient characteristics and the presence or absence of nausea using a Mann–Whitney-U test or χ2 test. The following items showed significant associations with the incidence of nausea: in the HS group, age (p = 1.918 × 10−2), morphine [mg] (p = 4.650 × 10−4), morphine (normalized with body weight) [mg/kg] (p = 1.448 × 10−4; Table 1); in the CIH group, gender (p = 5.640 × 10−17), height (p = 4.815 × 10−9), weight (p = 1.223 × 10−7), body mass index (p = 2.758 × 10−3), smoking history (p = 9.555 × 10−9), motion sickness (p = 4.604 × 10−11), total dosage of fentanyl (p = 1.217 × 10−9), total dosage of remifentanil (p = 1.250 × 10−5), use of pentazocine (p = 5.688 × 10−18), use of opioid after anesthesia (p = 3.817 × 10−15), anesthesia method (p = 2.605 × 10−7), and pain (p = 1.340 × 10−2; Table 2).
Logistic regression analysis of the presence or absence of nausea was performed for items that were significantly associated with the phenotype among the patient characteristics, and odds ratios (ORs) were calculated. The following items were significantly associated with nausea: in the HS group, age (p = 3.176 × 10−2, Table 3); in the CIH group, gender (p = 5.640 × 10−17), smoking history (p = 9.555 × 10−9), motion sickness (p = 4.604 × 10−11), total dosage of fentanyl (p = 1.074 × 10−7), total dosage of remifentanil (p = 1.583 × 10−4), use of pentazocine (p = 5.688 × 10−18), use of opioid after anesthesia (p = 3.817 × 10−15), anesthesia method (p = 2.605 × 10−7), and pain (p = 1.340 × 10−2; Table 4). In the CIH group, the following items had large ORs and were strongly associated with nausea: gender (OR = 2.281, 95% confidence interval [CI] = 1.877–2.773), use of pentazocine (OR = 2.252, 95% CI = 1.870–2.712), and use of opioid after anesthesia (OR = 2.045, 95% CI = 1.709–2.447; Table 4).

2.2. Exploration of Genetic Polymorphisms Associated with Nausea during Treatment of Cancer Pain by Genome-Wide Association Study (GWAS) in HS Samples

We comprehensively explored genetic variants that were associated with the presence or absence of nausea in the total of 331 patient subjects in the HS group who were treated with opioids for cancer pain. A total of 648,817 SNPs that met the quality control standards in the GWAS were examined for relationships with the phenotypes in the trend, dominant, and recessive models for minor alleles for each SNP. However, no SNPs showed genome-wide significant associations with the presence or absence of nausea, with the lowest p = 3.369 × 10−6 for the rs7282115 SNP in the trend model (Table 5). The top 20 candidate SNPs that were selected from the GWAS for nausea in the HS group did not include the reported SNPs that were associated with nausea (i.e., OPRM1 rs9397685 [10], CHRM3 rs2165870 [11], KCNB2 rs349358 [12], HTR3B rs1176744 [13], COMT rs165722 [13], CHRM3 rs10802789 [13], and HTR3B rs1672717 [13]).

2.3. Association between TMEM132C rs7296262 SNP and Nausea in Patients Who Were Treated with Opioids for Cancer Pain and in Patients Who Underwent Elective Surgery under General Anesthesia (HS and CIH Samples)

Among the top 20 gene-annotated SNPs in the HS GWAS with regard to the association with nausea (Table 5), we selected the TMEM132C rs7296262 SNP (i.e., the only SNP that was investigated in a previous study of a psychiatric disorder at the survey stage on 24 May 2024) [14]. The TMEM132C rs7296262 SNP is associated with bipolar disorder (BD) with a history of suicide attempts [14,15]. Physical symptoms are prevalent in BD (47.8%) [16] and may be an independent risk factor of disease severity and suicidal ideation [17]. Among physical symptoms, gastrointestinal symptoms, including nausea, have a strong relationship with the anxiety that BD patients experience during a depressive episode [18]. Thus, the TMEM132C rs7296262 SNP may feasibly be associated with nausea. To investigate this possibility, we performed statistical association analyses of the TMEM132C rs7296262 SNP using SPSS 28 software in the HS and CIH groups.
The genotypic distribution of the TMEM132C rs7296262 SNP did not significantly deviate from the theoretical Hardy–Weinberg equilibrium (HWE; HS group: p > 0.05, χ2 = 1.339; CIH group: p > 0.05, χ2 = 3.396). Linkage disequilibrium (LD) analysis was performed for HS and CIH samples (Figures S1 and S2, respectively). The rs7296262 SNP was present in an intron region. In the HS group, there were four other SNPs within the LD block that includes the rs7296262 SNP, all of which showed strong LD (D’ = 1) with each other (12:129095258, 12:129095945, kgp5245701, rs73161919). There were two SNPs with strong LD other than the LD block that includes the rs7296262 SNP (rs12321675, 12:129092485). All of these SNPs are located in intron regions. In the CIH group, there were two other SNPs within the LD block that includes the rs7296262 SNP, all of which showed strong LD with each other (rs10744383, rs1466642). There was one SNP with strong LD other than the LD block that includes the rs7296262 SNP (rs2220486). All of these SNPs are located in intron regions.
The TMEM132C rs7296262 SNP showed a significant association with nausea in the genotypic model in the HS and CIH groups (HS group: p = 7.000 × 10−4, Table 6; CIH group: p = 2.010 × 10−2, Table 7). In the recessive model, the TMEM132C rs7296262 SNP was significantly associated with nausea (HS group: p = 1.000 × 10−4, Table 6; CIH group: p = 6.400 × 10−3, Table 7). No significant association was found in the dominant model (HS group: p > 0.05, Table 6; CIH group: p > 0.05, Table 7). The prevalence of nausea among genotypes was then analyzed. In the HS group, a higher rate of nausea was observed in CC carriers than in TT+TC carriers (TT+TC/CC; with nausea, 77%/23%; without nausea, 92%/8%; Table 6). In the CIH group, a higher rate of nausea was observed in T-allele carriers (TT+TC/CC: with nausea, 88%/12%; without nausea, 83%/17%; Table 7). The results suggest that the nausea-prone genotype of the rs7296262 SNP was reversed in the HS and CIH groups.
To further investigate the effect of opioid use on the incidence of nausea, differences in the genotype distribution of opioid use were analyzed. For the amount of opioid use, the amount of opioids equivalent to oral morphine in the HS group and the amount of fentanyl use in the CIH group were analyzed. No significant difference was found in the genotype distribution of opioid use among genotypes in the HS and CIH groups (HS group: genotypic model, dominant model, recessive model, p > 0.05, Table 8; CIH group: genotypic model, dominant model, recessive model, p > 0.05, Table 9).

3. Discussion

We conducted a GWAS of nausea in the HS group (Table 5). Among the top 20 gene-annotated SNPs in the results of the HS GWAS in the recessive model with regard to the association with nausea, we selected the TMEM132C rs7296262 SNP for further analysis, which is reportedly associated with psychiatric disorders. The TMEM132C rs7296262 SNP was significantly associated with nausea during opioid use. In the HS group that chronically received opioids for cancer pain, the rs7296262 SNP was significantly associated with nausea in the recessive model. A higher rate of nausea was observed in CC carriers than in TT+TC carriers (Table 6). In the CIH group that received acute opioid administration during general anesthesia, the rs7296262 SNP was significantly associated with nausea in the recessive model. A higher rate of nausea was observed in TT+TC carriers than in CC carriers (Table 7). Thus, the nausea-prone genotype of the rs7296262 SNP was reversed with acute and chronic opioid use. These results suggest that the TMEM132C rs7296262 SNP is involved in the mechanisms of nausea during opioid use and the genotype reversal phenomenon between acute and chronic opioid use.
The TMEM132C rs7296262 SNP was significantly associated with nausea induced by opioids, consistent with the TMEM132C rs7296262 association with BD with suicide attempts, which are associated with nausea [14,15,16,17,18]. This implies that the TMEM132C rs7296262 SNP may also contribute to nausea in BD patients with suicide attempts.
Postoperative opioid use has been reported to increase the risk of PONV [19]. μ-Opioid receptor agonists, the most potent analgesics [20], were used for intraoperative anesthesia, and the κ-opioid receptor agonist pentazocine (30 mg) was administered for postoperative pain in the CIH group in this study [21]. Pentazocine was reported to cause a 17.2% incidence of nausea in a group that received 30 mg pentazocine [22], which is a lower rate than that caused by opioids in the present study (42% in both the HS and CIH groups), implying a minimal contribution of pentazocine to nausea. In the present study, there was a significant difference between the use and absence of use of pentazocine in the CIH group, depending on the presence or absence of nausea (Table 2), whereas the genotype distribution of the TMEM132C rs7296262 SNP showed no significant differences between the use and absence of use of pentazocine in the CIH group (Table 9). This means the same pentazocine administration rate induces nausea at different rates, depending on the genotype of the TMEM132C rs7296262 SNP. Although the contribution of pentazocine to nausea is minimal, we cannot exclude the possibility that the rs7296262 genotype is involved in nausea induced by the κ-opioid receptor agonist pentazocine.
The search for genomic functional regions revealed that human permissive enhancers exist around 6 kilobase pairs (kbp) upstream of the TMEM132C rs7296262 SNP (Table S5) [15], and the cap-analysis gene expression (CAGE) signal that is associated with active promoters and enhancers exists around 6 kbp upstream of the TMEM132C rs7296262 SNP (Table S6) [15]. Additionally, protein-coding transcripts have been reported that start from 67 kbp upstream and 83 kbp downstream of the rs7296262 SNP [23] (Table S7). Thus, the TMEM132C rs7296262 SNP may be associated with transcriptional activity for TMEM132C protein-coding transcripts. However, the TMEM132C rs7296262 SNP may also be associated with full-length transcripts or other transcripts. Further research is required to clarify the precise function of the TMEM132C rs7296262 SNP.
The TMEM132C rs7296262 SNP, which was associated with nausea in the present study, has been reported to be involved in BD and suicide [24]. One of the essential receptors related to nausea and vomiting is the NK-1 receptor. NK-1 receptor expression significantly decreased in monocytes in BD patients compared with healthy subjects [25]. Furthermore, 12 depressed patients, including 6 who committed suicide, showed lower NK-1 receptor expression in the orbitofrontal cortex compared with controls in a postmortem study [26]. Altogether, the TMEM132C rs7296262 SNP could be associated with BD and suicide, as well as nausea through NK-1 receptor signaling, although further research is needed.
NK-1 and 5-HT2A receptors are related to opioid-induced nausea and vomiting [1]. Acute morphine treatment was reported to upregulate the functional expression of NK-1 receptor in cortical neurons in morphine-treated rats [27], whereas chronic morphine treatment decreased substance P and NK-1 receptor immunoreactivity in the dorsal horn in morphine-treated rats [28]. Moreover, chronic morphine exposure increased 5-HT2A receptor [29]. Thus, acute or chronic morphine treatment influences the expression of NK-1 and 5-HT2A receptors and possibly has opposite effects on NK-1 receptor expression. In the present study, the distribution of nausea-prone genotypes for the TMEM132C rs7296262 SNP was reversed between the CIH and HS samples (i.e., between acute and chronic opioid administration), implying an association with the effect of acute or chronic morphine on these nausea-related receptors. However, further research is required to elucidate the mechanisms by which the TMEM132C rs7296262 SNP influences opioid-induced nausea through NK-1 and 5-HT2A receptor signaling.
Acute opioid administration is less likely to cause nausea in homozygote carriers of the C allele of the rs7296262 SNP, whereas chronic opioid administration is more likely to cause nausea in homozygote carriers of the C allele of the rs7296262 SNP, based on the present study. The allele frequencies of the rs7296262 SNP of the TMEM132C gene in different regional populations in the present study were the following: in the HS group, T-allele frequency of 64% and C-allele frequency of 36%; in the CIH group, T-allele frequency of 62% and C-allele frequency of 38%. The frequencies in the HS and CIH groups are similar to East Asian populations (T-allele frequency of 61% and C-allele frequency of 39%) and other regional populations (e.g., American populations: 57% T allele, 43% C allele; European populations: 55% T allele, 45% C allele; South Asian populations: 65% T allele, 35% C allele), except for African populations according to the 1000 Genomes study in the SNP database [24]. African populations show a T-allele frequency of 44% and C-allele frequency of 56%, resulting in small frequency reversals for African populations and others. These results suggest that the allele frequencies that are associated with nausea and vomiting with acute and chronic opioid administration have no large variations among regional populations.
The present study has limitations. First, some patients received the κ-opioid receptor agonist pentazocine in addition to opioids that mainly have affinity for μ-opioid receptor. Thus, the present study could not distinguish between the contributions of the different opioid receptor subtypes. Second, some patients received the volatile anesthetic desflurane for the maintenance of general anesthesia. The use of volatile anesthetics is associated with a risk of nausea [4]. Other supplementary analgesics, including other painkillers that have affinity for serotonin, norepinephrine, and dopamine receptors, were administered at the discretion of primary care doctors if required. Thus, the supplementary analgesics and their dosages were variable among patients. The present study cannot eliminate that possible unexpected side effects of these supplementary analgesics led to nausea.

4. Materials and Methods

4.1. Patients

4.1.1. Patients Who Were Treated with Opioids during the Treatment of Cancer Pain in the HS Group

We enrolled 428 adult Japanese patients (20–94 years old, 213 males and 215 females) who suffered from various types of cancer and were hospitalized at Higashi-Sapporo Hospital (Hokkaido, Japan) for the treatment of cancer pain in 2017–2019. Because the presence or absence of nausea could not be evaluated in 97 of the 428 patients, the analysis was performed for 331 patients (20–94 years old, 163 males and 168 females). Higashi-Sapporo Hospital specializes in cancer care, particularly palliative care [30,31]. All of the patients who were recruited in the present study were treated with opioid analgesics, and many were also appropriately treated with nonsteroidal anti-inflammatory drugs (NSAIDs) and/or other supplementary analgesics for the treatment of pain. We excluded patients who were considered unsuitable by their primary care doctors. Detailed demographic and clinical data of the subjects were provided in a previous report [32]. Peripheral blood samples were collected from these subjects for the gene analysis.
The study was conducted according to guidelines of the Declaration of Helsinki and approved by the Institutional Review Board or Ethics Committee of Higashi-Sapporo Hospital and Tokyo Metropolitan Institute of Medical Science (Tokyo, Japan; protocol code: 17-1). Written informed consent was obtained from all patients.

4.1.2. Patients Who Underwent Elective Surgery under General Anesthesia in the CIH Group

Enrolled in the study were 2021 adult patients (20–94 years old, 700 males and 1321 females) who were scheduled to undergo elective surgery for cancer under general anesthesia by TIVA with propofol or inhalational anesthesia with desflurane at The Cancer Institute Hospital of the Japanese Foundation for Cancer Research (CIH samples). The 2021 patients consisted of the previously reported 806 patients [21] and an additional 1215 patients. As detailed in the previous report [21], the exclusion criteria were the following: (1) patients to whom mild or more emetogenic antitumor agents were administered or who were scheduled to be administered from 6 days before the start of the study to 48 h after surgery; (2) patients with symptomatic brain metastases; (3) patients who used the following antiemetic drugs within 48 h before and during surgery: 5-HT3 receptor antagonists (granisetron, ondansetron, azasetron, etc.), phenothiazines (chlorpromazine, prochlorperazine, perphenazine, etc.), butyrophenone-based preparations (haloperidol, droperidol, etc.), benzamide preparations (sulpiride, tiapride, sultopride, etc.), dopamine receptor antagonists (metoclopramide, itopride, domperidone, etc.), antihistamines (hydroxyzine, dimenhydrinate, diphenhydramine), or NK-1 receptor antagonists (apireptant); (4) patients who were mentally unable to communicate; (5) patients who were pregnant; (6) patients who were judged to be inappropriate for inclusion in the study by the investigator; and (7) patients of the Head and Neck Department and Gastroenterology Department who needed advanced management in the postoperative intensive care unit. The major reasons for applying these exclusion criteria were the possible influence of these factors on the incidence and severity of PONV and the collection of accurate data. The cancellation criteria were the following: (1) patients for whom blood collection was not possible and (2) patients whose informed consent was withdrawn. All of the individuals who were included in the study were of Japanese origin. Peripheral blood samples were collected from these subjects for gene analysis. Detailed demographic and clinical data of the subjects were provided in a previous report [21]. The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Institutional Review Board or Ethics Committee of The Cancer Institute Hospital and Tokyo Metropolitan Institute of Medical Science (Tokyo, Japan; protocol code: 21-17). Written informed consent was obtained from all patients.

4.1.3. Patient Characteristics and Clinical Data for the HS Group

We obtained data on surgical history, treatment history, pain status (e.g., presence/absence of somatic pain, visceral pain, and neuropathic pain), drug treatments, and disease status (e.g., lung cancer, breast cancer, stomach cancer, etc. [32]). Some of the patients were afflicted with multiple diseases.
The treatment of pain was mainly conducted by administering opioid analgesics, including morphine, oxycodone, fentanyl, tapentadol, tramadol, methadone, and hydromorphone, which mainly activate the μ-opioid receptor [32]. Various types of drugs, such as NSAIDs (e.g., loxoprofen and diclofenac) and/or other supplementary analgesics (e.g., pregabalin and dexamethasone), were also administered at the discretion of primary care doctors if required. To allow intersubject comparisons of the opioid analgesic doses that were required for cancer pain treatment, the opioid doses were converted to equivalent doses of oral morphine, as described in a previous report [32]. The total dose of converted opioid analgesics administered was calculated daily, and the total dose of analgesics was calculated as a daily average based on the amount of 5 days of administration, 3–7 days before blood collection. This average total dose was used as the endpoint of opioid requirements for the genetic association analysis in the present study. Doses of analgesics that were administered were normalized to body weight. The detailed clinical data of the subjects are presented in Tables S1 and S3.

4.1.4. Patient Characteristics and Clinical Data for the CIH Group

We obtained data on patient characteristics (gender, age, height, weight, and body mass index), history of smoking, history of motion sickness, clinical data for the postoperative period (type of anesthesia, total dose of remifentanil, total dose of fentanyl, postoperative administration of opioid drugs, and postoperative opioid administration [including pentazocine]), the experience and frequency of postoperative pain, and nausea (Tables S2 and S4). The μ-opioid receptor agonists fentanyl and remifentanil were administered intraoperatively, and the κ-opioid receptor agonist pentazocine was administered for postoperative analgesia [21].

4.2. Whole-Genome Genotyping and Quality Control

For the HS and CIH samples, a total of 428 and 806 DNA samples from the patients, respectively, were used for genotyping. Total genomic DNA was extracted from whole-blood samples using standard procedures. The extracted DNA was dissolved in TE buffer (10 mM Tris-HCl and 1 mM ethylenediaminetetraacetic acid, pH 8.0). The DNA concentration was adjusted to 50 ng/μL and 100 ng/μL for whole-genome genotyping for the HS and CIH samples, respectively, using a NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA).
According to the manufacturer’s recommendations, whole-genome genotyping was performed by utilizing the Infinium Assay II with an iScan system (Illumina, San Diego, CA, USA), as described in previous reports [21,32]. An Infinium Asian Screening Array-24 v. 1.0 BeadChip was used to genotype all HS samples (total markers: 659,184). Three versions of HumanOmniExpressExome-8 Bead-Chips (v. 1.2, total markers: 964,193; v. 1.3, total markers: 958,497; v. 1.4, total markers: 960,919) were used to genotype 806 of the CIH samples. Approximately 946,000 common SNP markers were included in the three BeadChips versions. Numerous copy number variation markers were included in the BeadChips, but the majority of the Bead-Chips were for SNP markers on the human autosome or sex chromosome.
GenomeStudio with the Genotyping v. 2.0.4 module (Illumina) was used to examine data for samples that had their entire genomes genotyped to assess the quality of the findings. Following data cleaning, samples with genotype call rates less than 0.95 were not included in the remaining studies. No patient samples were consequently discarded for subsequent investigations (i.e., all of the samples met the 0.95 quality control cutoff, and all samples were considered for further association analyses). In the subsequent association analyses, markers with genotype call frequencies less than 0.95 and “Cluster sep” (a measure of genotype cluster separation) values less than 0.1 were not included. Markers were further filtered based on a test of HWE. Markers with p values (df = 1) less than 0.001 were considered to be deviated in the HWE tests and thus were excluded. After these filtration procedures, the patient samples retained a total of 648,817 and 651,086 SNP markers, as described in previous reports [21,32].
For the HS samples, all whole-genome genotyping data that passed the quality control criteria were used for the GWAS. For the CIH samples, only the genotype data for the selected rs7296262 SNP in the TMEM132C gene region among all of the genotyped SNPs were extracted and used for further association analyses.

4.3. TaqMan Genotyping

After whole-genome genotyping, we performed an additional TaqMan assay on 1215 CIH samples. The TaqMan allelic discrimination assay was conducted for genotyping the rs7296262 SNP, as described in previous reports [33,34]. To perform the TaqMan assay with a LightCycler 480 II (Roche Diagnostics, Basel, Switzerland), we used TaqMan SNP Genotyping Assays (Life Technologies, Carlsbad, CA, USA) that contained sequence-specific forward and reverse primers to amplify the polymorphic sequence and two probes that were labeled with VIC and FAM dye to detect both alleles of the rs7296262 SNPs (Assay ID: C_1179719_10). Real-time polymerase chain reaction was performed in a final volume of 10 μL that contained 2× LightCycler 480 II Probes Master (Roche Diagnostics), 40× TaqMan SNP Genotyping Assays, 5–50 ng genomic DNA as the template, and H2O (Roche Diagnostics). The thermal conditions were the following: 95 °C for 10 min, followed by 45 cycles of 95 °C for 10 s and 60 °C for 60 s, with final cooling at 50 °C for 30 s. Afterward, endpoint fluorescence was measured for each sample well, and each genotype was determined based on the presence or absence of each type of fluorescence.

4.4. Statistical Analysis

For the HS group, the presence or absence of nausea during the treatment period with opioids was evaluated. For the CIH group, the presence or absence of nausea during the 48 h postoperative period was evaluated. In the association studies, these criteria were used as indices of the vulnerability to nausea.
To explore associations between SNPs and the incidence of nausea, Fisher’s exact test or the Cochran–Armitage trend test was conducted for the HS samples, and genotype data were compared between subjects with and without the incidence of nausea. Trend, dominant, and recessive genetic models were used for the analyses. Male genotypes were not included in the analysis of X chromosome markers, whereas both male and female individuals were included in the association study for autosomal markers. PLINK v. 1.07 (https://zzz.bwh.harvard.edu/plink/index.shtml; accessed 30 October 2023) [35], gPLINK v. 2.050 [36], and Haploview v. 4.1 [37] were used to perform the statistical analyses and visualize the results. As in the previous GWAS of opioid analgesic requirements in the HS samples [32], the criterion for significance in the GWAS was set to p < 5 × 10−8, which is widely known to be a conventional criterion for the level of significance in GWASs [37,38].
To validate associations between the rs7296262 SNP and the incidence of nausea in the HS samples, an χ2 test was conducted for CIH samples with SPSS 28 software (IBM Japan, Tokyo, Japan), and genotype data between subjects with and without the incidence of nausea were compared. Genotypic, dominant, and recessive genetic models were used for the analyses. The criterion for significance in the association analysis was set to p < 0.05. Additionally, HWE was tested using the χ2 test (df = 1) for genotypic distributions of the rs7296262 SNP, with values of significant deviation set to p = 0.05.
To estimate the impact of clinical and genetic factors on the incidence of nausea, we used a multivariate analysis (i.e., logistic regression analysis). Variables that were significantly associated with PONV were subsequently included in the multivariable logistic regression model using a stepwise forward selection strategy. The dependent variable in both the HS and CIH groups was nausea (Yes/No). The independent variables in the HS group were patient age and morphine oral equivalent amount. Independent variables in the CIH group were gender (male/female), height, weight, body mass index, smoking history (Yes/No), motion sickness (Yes/No), total dosage of fentanyl, total dosage of remifentanil, use of pentazocine (Yes/No), use of opioid after anesthesia (Yes/No), anesthesia method (TIVA/inhalation anesthetic), and pain (Yes/No).

4.5. Additional In Silico Analysis

4.5.1. Power Analysis

Statistical power analyses were preliminarily performed using G*Power 3.1.3 software [39]. Power analyses for Fisher’s exact tests, with the allocation ratio set to 0.5, indicated that the expected power (1 minus type II error probability) was 80.0% for the type I error probability, which was set to 1.000 × 10−7 (closest to 5 × 10−8 in this software) when risk allele frequencies for patients with and without nausea were 0.3860 and 0.1000, respectively, and when the sample sizes for patients with and without nausea were 307 and 153, in the present study. However, for the same type I error probability and sample sizes of 310 and 155, the expected power decreased to 50.0% when the risk allele frequencies for patients with and without nausea were 0.3465 and 0.1000, respectively. Conversely, the estimated risk allele frequencies for patients with and without nausea were 0.4060 and 0.1000, respectively, for the same type I error probability, and sample sizes of 305 and 152 were required to achieve 90% power. Therefore, a single analysis in the present study might be expected to detect true associations with the phenotypes, with 80% statistical power for effect sizes from large to moderately medium but not small, although the exact effect size is poorly understood in cases of SNPs that significantly contribute to nausea.

4.5.2. LD Analysis

Data from whole-genome-genotyped samples were extracted using GenomeStudio 2.0 with the Genotyping v. 3.3.7 module to assess quality of the results for SNPs within the TMEM132C gene region. There were no SNPs with low typing rates, and SNPs with a minor allele frequency (MAF) greater than 0 were extracted. LD analysis was performed on 168 SNPs in the TMEM132C gene region in the SNP array in the HS group and on 178 SNPs in the TMEM132C gene region in the SNP array in the CIH group. To estimate the LD intensity between SNPs, the commonly used D’ and r2 values were calculated pairwise using the genotype dataset for each SNP. The LD block was defined among SNPs that showed “strong LD” based on the default algorithm of Gabriel et al. [40] with an upper limit of 0.98 and a lower limit of 0.7 for the 95% CI of D’ that indicated strong LD. TagSNPs in the LD blocks were determined using the Tagger software package that is incorporated in Haploview 4.2, which was detailed in a previous report [41].

4.5.3. Reference of Databases

Several databases and bioinformatic tools were referenced to more thoroughly examine the candidate SNP, which may be related to human opioid analgesic sensitivity, including the National Center for Biotechnology Information database on 31 January 2023 [24] and HaploReg v. 4.1 on 6 June 2024 [42,43]. HaploReg is a tool for investigating non-coding genomic annotations at variations in haplotype blocks, such as potential regulatory SNPs at disease-associated sites [42]. Data on human permissive enhancers and CAGE derived from FANTOM5 were extracted using ZENBU to investigate transcriptional regulation around the rs7296262 SNP [15] on 1 June 2024. Data on the transcripts starting around the rs7296262 SNP were extracted using European Molecular Biology Laboratory-European Bioinformatics Institute [23] on 6 June 2024.

5. Conclusions

The TMEM132C rs7296262 SNP was significantly associated with nausea during opioid use. The distribution of nausea-prone genotypes for the TMEM132C rs7296262 SNP was reversed between CIH and HS samples (i.e., between acute and chronic opioid administration), implying an association with the effect of acute or chronic morphine on these nausea-related receptors. However, further research is required to elucidate the mechanisms by which the TMEM132C rs7296262 SNP influences opioid-induced nausea signaling through nausea-related receptors.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ijms25168845/s1.

Author Contributions

Conceptualization, Y.K., D.N., S.O. and K.I. (Kazutaka Ikeda); Methodology, D.N., S.O. and K.I. (Kazutaka Ikeda); Validation, K.N. and Y.E.; Formal Analysis, Y.K., D.N., K.N. and Y.E.; Investigation, Y.K., J.H. and D.N.; Resources, T.T., K.I. (Kunihiko Ishitani), R.M. and M.Y.; Data Curation, D.N., J.H., K.N. and Y.E.; Writing—Original Draft Preparation, Y.K.; Writing—Review and Editing, D.N., S.O. and K.I. (Kazutaka Ikeda); Supervision, D.N., S.O., K.K., T.I. and K.I. (Kazutaka Ikeda); Project Administration, K.I. (Kazutaka Ikeda); Funding Acquisition, D.N., S.O. and K.I. (Kazutaka Ikeda). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by grants from the Japan Society for the Promotion of Science (JSPS) KAKENHI (C) (no. 23K06839 [S.O.], 20K07774 [S.O.], 20K09259 [D.N.], 17K08970 [D.N.]), (B) (no. 21H03028 [K.I. (Kazutaka Ikeda)], 17H04324 [K.I. (Kazutaka Ikeda)], 23K21457 [K.I. (Kazutaka Ikeda)]), and (AdAMS) (no. JP22H04922 [AdAMS] [K.I. (Kazutaka Ikeda)]) and the Japan Agency for Medical Research and Development (no. JP19ek0610011 [K.I. (Kazutaka Ikeda)]). The authors declare that they have no competing interests with regard to the research, authorship, or publication of this article.

Institutional Review Board Statement

The study was conducted according to guidelines of the Declaration of Helsinki and approved by the Institutional Review Board or Ethics Committee of Higashi-Sapporo Hospital (protocol code: none, date of approval: 20 November 2016), The Cancer Institute Hospital (protocol code: 2015-1063, date of approval: 25 November 2015), and Tokyo Metropolitan Institute of Medical Science (protocol code: 17-1,21-27, date of approval: 31 March 2017, and 31 March 2021).

Informed Consent Statement

Written informed consent was obtained from all participants involved in the study.

Data Availability Statement

The raw data in this study are shown in Datasets S1–S4.

Acknowledgments

We thank Michael Arends for editing the manuscript. We are grateful to the volunteers for their participation in the study and the anesthesiologists and surgeons for collecting the clinical data.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gan, T.J.; Belani, K.G.; Bergese, S.; Chung, F.; Diemunsch, P.; Habib, A.S.; Jin, Z.; Kovac, A.L.; Meyer, T.A.; Urman, R.D.; et al. Fourth consensus guidelines for the management of postoperative nausea and vomiting. Anesth. Analg. 2020, 131, 411–448. [Google Scholar] [CrossRef] [PubMed]
  2. Okamoto, Y.; Tsuneto, S.; Matsuda, Y.; Ono, Y.; Kurokawa, N.; Uejima, E. A retrospective chart review of the antiemetic effectiveness of risperidone in refractory opioid-induced nausea and vomiting in advanced cancer patients. J. Pain. Symptom Manag. 2007, 34, 217–222. [Google Scholar] [CrossRef] [PubMed]
  3. Apfel, C.C.; Laara, E.; Koivuranta, M.; Greim, C.A.; Roewer, N. A simplified risk score for predicting postoperative nausea and vomiting: Conclusions from cross-validations between two centers. Anesthesiology 1999, 91, 693–700. [Google Scholar] [CrossRef] [PubMed]
  4. Apfel, C.C.; Kranke, P.; Katz, M.H.; Goepfert, C.; Papenfuss, T.; Rauch, S.; Heineck, R.; Greim, C.A.; Roewer, N. Volatile anaesthetics may be the main cause of early but not delayed postoperative vomiting: A randomized controlled trial of factorial design. Br. J. Anaesth. 2002, 88, 659–668. [Google Scholar] [CrossRef] [PubMed]
  5. Klenke, S.; Frey, U.H. Genetic variability in postoperative nausea and vomiting: A systematic review. Eur. J. Anaesthesiol. 2020, 37, 959–968. [Google Scholar] [CrossRef] [PubMed]
  6. Herndon, C.M.; Jackson, K.C., 2nd; Hallin, P.A. Management of opioid-induced gastrointestinal effects in patients receiving palliative care. Pharmacotherapy 2002, 22, 240–250. [Google Scholar] [CrossRef] [PubMed]
  7. Porreca, F.; Ossipov, M.H. Nausea and vomiting side effects with opioid analgesics during treatment of chronic pain: Mechanisms, implications, and management options. Pain Med. 2009, 10, 654–662. [Google Scholar] [CrossRef] [PubMed]
  8. Liu, M.; Zhang, H.; Du, B.X.; Xu, F.Y.; Zou, Z.; Sui, B.; Shi, X.Y. Neurokinin-1 receptor antagonists in preventing postoperative nausea and vomiting. Medicine 2015, 94, 762. [Google Scholar] [CrossRef] [PubMed]
  9. Munoz, M.; Covenas, R. Involvement of substance P and the NK-1 receptor in human pathology. Amino Acids 2014, 46, 1727–1750. [Google Scholar] [CrossRef] [PubMed]
  10. Sugino, S.; Hayase, M.; Higuchi, M.; Saito, K.; Moriya, H.; Kumeta, Y.; Kurosawa, N.; Namiki, A.; Janicki, P.K. Association of μ-opioid receptor gene (OPRM1) haplotypes with postoperative nausea and vomiting. Exp. Brain Res. 2014, 232, 2627–2635. [Google Scholar] [CrossRef] [PubMed]
  11. Wang, J.; Li, Y.; Zheng, C.; Sun, Y.; Yang, J. CHRM3 rs2165870 polymorphism correlates with postoperative nausea and vomiting incidence and the efficacy of ondansetron in a Chinese Han population. Pharmgenom. Pers. Med. 2020, 13, 319–326. [Google Scholar] [CrossRef] [PubMed]
  12. Klenke, S.; de Vries, G.J.; Schiefer, L.; Seyffert, N.; Bachmann, H.S.; Peters, J.; Frey, U.H. Genetic contribution to PONV risk. Anaesth. Crit. Care. Pain Med. 2020, 39, 45–51. [Google Scholar] [CrossRef] [PubMed]
  13. Laugsand, E.A.; Fladvad, T.; Skorpen, F.; Maltoni, M.; Kaasa, S.; Fayers, P.; Klespstad, P. Clinical and genetic factors associated with nausea and vomiting in cancer patients receiving opioids. Eur. J. Cancer 2011, 47, 1682–1691. [Google Scholar] [CrossRef]
  14. Willour, V.L.; Seifuddin, F.; Mahon, P.B.; Jancic, D.; Pirooznia, M.; Steele, J.; Schweizer, B.; Goes, F.S.; Mondimore, F.M.; Mackinnon, D.F.; et al. A genome-wide association study of attempted suicide. Mol. Psychiatry 2012, 17, 433–444. [Google Scholar] [CrossRef] [PubMed]
  15. ZENBU Gene Browser. TMEM132C. Available online: https://fantom.gsc.riken.jp/zenbu/gLyphs/index.html#config=empty;loc=hg38::chr19:49657992..49666908+ (accessed on 1 June 2024).
  16. Edgcomb, J.B.; Tseng, C.H.; Kerner, B. Medically unexplained somatic symptoms and bipolar spectrum disorders: A systematic review and meta-analysis. J. Affect. Disord. 2016, 204, 205–213. [Google Scholar] [CrossRef] [PubMed]
  17. Edgcomb, J.B.; Kerner, B. Predictors and outcomes of somatization in bipolar I disorder: A latent class mixture modeling approach. J. Affect. Disord. 2018, 227, 681–687. [Google Scholar] [CrossRef] [PubMed]
  18. Haug, T.T.; Mykletun, A.; Dahl, A.A. Are anxiety and depression related to gastrointestinal symptoms in the general population? Scand. J. Gastroenterol. 2002, 37, 294–298. [Google Scholar] [CrossRef] [PubMed]
  19. Roberts, G.W.; Bekker, T.B.; Carlsen, H.H.; Moffatt, C.H.; Slattery, P.J.; McClure, A.F. Postoperative nausea and vomiting are strongly influenced by postoperative opioid use in a dose-related manner. Anesth. Analg. 2005, 101, 1343–1348. [Google Scholar] [CrossRef] [PubMed]
  20. Valentino, R.; Volkow, N.D. Untangling the complexity of opioid receptor function. Neuropsychopharmacology 2018, 43, 2514–2522. [Google Scholar] [CrossRef] [PubMed]
  21. Nishizawa, D.; Morino, R.; Inoue, R.; Ohka, S.; Kasai, S.; Hasegawa, J.; Ebata, Y.; Nakayama, K.; Sumikura, H.; Hayashida, M.; et al. Genome-wide association study identifies novel candidate variants associated with postoperative nausea and vomiting. Cancers 2023, 15, 4729. [Google Scholar] [CrossRef] [PubMed]
  22. Nonaka, A.; Suzuki, S.; Nagamine, N.; Furuya, A.; Abe, F. Postoperative nausea and vomiting after laparoscopic cholecystectomy under total intravenous anesthesia using propofol combined with fentanyl or pentazocine. Masui 2007, 56, 1343–1346. [Google Scholar] [PubMed]
  23. European Molecular Biology Laboratory-European Bioinformatics Institute. TMEM132C. Available online: https://grch37.ensembl.org/Homo_sapiens/Gene/Summary?db=core;g=ENSG00000181234;r=12:128751948-129192460 (accessed on 6 June 2024).
  24. National Center for Biotechnology Information. TMEM132C. Available online: https://www.ncbi.nlm.nih.gov/ (accessed on 31 January 2023).
  25. Amoruso, A.; Bardelli, C.; Cattaneo, C.I.; Fresu, L.G.; Manzetti, E.; Brunelleschi, S. Neurokinin (NK)-1 receptor expression in monocytes from bipolar disorder patients: A pilot study. J. Affect. Disord. 2015, 178, 188–192. [Google Scholar] [CrossRef] [PubMed]
  26. Hafizi, S.; Chandra, P.; Cowen, J. Neukinin-1 receptor antagonists as novel antidepressants: Trials and tribulations. Br. J. Psychiatry 2007, 191, 282–284. [Google Scholar] [CrossRef] [PubMed]
  27. Smith, H.S.; Smith, J.M.; Seidner, P. Opioid-induced nausea and vomiting. Ann. Palliat. Med. 2012, 1, 121–129. [Google Scholar] [CrossRef] [PubMed]
  28. Thomson, L.M.; Terman, G.W.; Zeng, J.; Lowe, J.; Chavkin, C.; Hermes, S.M.; Hegarty, D.M.; Aicher, S.A. Decreased substance P and NK1 receptor immunoreactivity and function in the spinal cord dorsal horn of morphine-treated neonatal rats. J. Pain 2009, 9, 11–19. [Google Scholar] [CrossRef] [PubMed]
  29. Pang, G.; Wu, X.; Tao, X.; Mao, R.; Liu, X.; Zhang, Y.M.; Li, G.; Stackman, R.W., Jr.; Dong, L.; Zhang, G. Blockade of serotonin 5-HT2A receptors suppresses behavioral sensitization and naloxone-precipitated withdrawal symptoms in morphine-treated mice. Front. Pharmacol. 2016, 7, 514. [Google Scholar] [CrossRef] [PubMed]
  30. Terui, T.; Koike, K.; Hirayama, Y.; Kusakabe, T.; Ono, K.; Mihara, H.; Kobayashi, K.; Takahashi, Y.; Nakajima, N.; Kato, J.; et al. Recent advances in palliative cancer care at a regional hospital in Japan. Am. J. Hosp. Palliat. Med. 2014, 31, 717–722. [Google Scholar] [CrossRef] [PubMed]
  31. Hirayama, Y.; Terui, T.; Kusakabe, T.; Koike, K.; Ono, K.; Kato, J.; Ishitani, K. A survey of patients who were referred to our palliative care division from other hospitals and appeared to have obvious indications for cancer chemotherapies. Am. J. Hosp. Palliat. Med. 2014, 31, 804–807. [Google Scholar] [CrossRef] [PubMed]
  32. Nishizawa, D.; Terui, T.; Ishitani, K.; Kasai, S.; Hasegawa, J.; Nakayama, K.; Ebata, Y.; Ikeda, K. Genome-wide association study identifies candidate loci associated with opioid analgesic requirements in the treatment of cancer pain. Cancers 2022, 14, 4692. [Google Scholar] [CrossRef]
  33. Nishizawa, D.; Fukuda, K.; Kasai, S.; Hasegawa, J.; Aoki, Y.; Nishi, A.; Saita, N.; Koukita, Y.; Nagashima, M.; Katoh, R.; et al. Genome-wide association study identifies a potent locus associated with human opioid sensitivity. Mol. Psychiatry 2014, 19, 55–62. [Google Scholar] [CrossRef] [PubMed]
  34. Ide, S.; Nishizawa, D.; Fukuda, K.; Kasai, S.; Hasegawa, J.; Hayashida, M.; Minami, M.; Ikeda, K. Association between genetic polymorphisms in Cav2.3 (R-type) Ca2+ channels and fentanyl sensitivity in patients undergoing painful cosmetic surgery. PLoS ONE 2013, 8, e70694. [Google Scholar] [CrossRef] [PubMed]
  35. Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.; Daly, M.J.; et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef] [PubMed]
  36. Barrett, J.C.; Fry, B.; Maller, J.; Daly, M.J. Haploview: Analysis and visualization of LD and haplotype maps. Bioinformatics 2005, 21, 263–265. [Google Scholar] [CrossRef] [PubMed]
  37. de Bakker, P.I.W.; Ferreira, M.A.R.; Jia, X.; Neale, B.M.; Raychaudhuri, S.; Voight, B.F. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum. Mol. Genet. 2008, 17, R122–R128. [Google Scholar] [CrossRef] [PubMed]
  38. Barsh, G.S.; Copenhaver, G.P.; Gibson, G.; Williams, S.M. Guidelines for genome-wide association studies. PLoS Genet. 2012, 8, e1002812. [Google Scholar] [CrossRef] [PubMed]
  39. Faul, F.; Erdfelder, E.; Lang, A.G.; Buchner, A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 2007, 39, 175–191. [Google Scholar] [CrossRef] [PubMed]
  40. Gabriel, S.B.; Schaffner, S.F.; Nguyen, H.; Moore, J.M.; Roy, J.; Blumenstiel, B.; Higgins, J.; DeFelice, M.; Lochner, A.; Faggart, M.; et al. The structure of haplotype blocks in the human genome. Science 2002, 296, 2225–2229. [Google Scholar] [CrossRef] [PubMed]
  41. de Bakker, P.I.W.; Yelensky, R.; Pe’er, L.; Gabriel, S.B.; Daly, M.; Altshuler, D. Efficiency and power in genetic association studies. Nat. Genet. 2005, 37, 1217–1223. [Google Scholar] [CrossRef] [PubMed]
  42. Ward, L.D.; Kellis, M. HaploReg: A resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012, 40, D930–D934. [Google Scholar] [CrossRef]
  43. HaploReg v4.2. Available online: https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php (accessed on 6 June 2024).
Table 1. Background characteristics of the patients and information related to the opioid administration and cancer pain of the HS group.
Table 1. Background characteristics of the patients and information related to the opioid administration and cancer pain of the HS group.
With Nausea (n = 138)Without Nausea (n = 193)p
Gender (male/female)66/72 (48%/52%)97/96 (50%/50%)6.624 × 10−1
Age (year)69.413 ± 12.61572.508 ± 11.9341.918 × 10−2 *
Height (cm)158.007 ± 8.828157.558 ± 8.1935.275 × 10−1
Weight (kg)50.019 ± 11.74551.109 ± 10.9022.468 × 10−1
Drinking (+/−)35/100 (26%/74%)45/142 (24% / 76%)7.028 × 10−1
Smoking history (+/−)54/81 (40%/60%)87/100 (47% / 53%)2.443 × 10−1
Morphine (mg) a101.721 ± 161.96771.625 ± 152.2734.650 × 10−4 *
Morphine (mg/kg) b1.989 ± 2.8631.464 ± 3.0381.448 × 10−4 *
Neuropathic pain (+/−)34/104 (25%/75%)50/143 (26%/74%)7.936 × 10−1
The data are expressed as numbers or the mean ± standard deviation (SD). * p < 0.05. a Total dose of analgesics as a daily average equivalent to oral morphine. b Total dose of analgesics as a daily average per body weight equivalent to oral morphine.
Table 2. Background characteristics of the patients and information related to the surgery and anesthesia of the CIH group.
Table 2. Background characteristics of the patients and information related to the surgery and anesthesia of the CIH group.
With Nausea (n = 850)Without Nausea (n = 1171)p
Gender (male/female)206/644 (24%/76%)494/677 (42%/58%)5.640 × 10−17 *
Age (year)57.035 ± 13.56757.594 ± 13.8362.747 × 10−1
Height (cm)159.151 ± 7.664161.339 ± 8.3784.815 × 10−9 *
Weight (kg)55.595 ± 11.31058.011 ± 11.1251.223 × 10−7 *
Body mass index (kg/m2)21.882 ± 3.79422.207 ± 3.4472.758 × 10−3 *
Smoking history (+/−)341/509 (40%/60%)621/550 (53%/47%)9.555 × 10−9 *
Motion sickness (+/−)450/399 (53%/47%)448/723 (38%/62%)4.604 × 10−11 *
Total dosage of fentanyl (μg)217.275 ± 131.081184.952 ± 127.4101.217 × 10−9 *
Total dosage of remifentanil (μg)2889.765 ± 2391.7262472.502 ± 2173.1961.250 × 10−5 *
Use of pentazocine (+/−)592/258 (70%/30%)591/580 (50%/50%)5.688 × 10−18 *
Use of opioid after anesthesia (+/−)493/357 (58%/42%)472/699 (40%/60%)3.817 × 10−15 *
Anesthesia method (TIVAa/inhalation anesthetic)218/632 (26%/74%)427/744 (36%/64%)2.605 × 10−7 *
Pain (+/−)617/233 (73%/27%)790/381 (67%/33%)1.340 × 10−2 *
The data are expressed as numbers or the mean ± standard deviation (SD). * p < 0.05. a General anesthesia using total intravenous anesthesia (TIVA).
Table 3. Factors behind the incidence of nausea in the multivariate analysis in the HS group.
Table 3. Factors behind the incidence of nausea in the multivariate analysis in the HS group.
VariableOR a95% CI bp
Age (year)0.9790.960–0.9983.176 × 10−2 *
Morphine (mg) c1.0020.996–1.008 5.693 × 10−1
Morphine (mg/kg) d0.9460.683–1.308 7.351 × 10−1
* p < 0.05. a Odds ratio. b Confidence interval. c Total dose of analgesics as a daily average equivalent to oral morphine. d Total dose of analgesics as a daily average per body weight equivalent to oral morphine.
Table 4. Factors behind the incidence of nausea in the multivariate analysis in the CIH group.
Table 4. Factors behind the incidence of nausea in the multivariate analysis in the CIH group.
VariableOR a95% CI bp
Gender (male/female)2.2811.877–2.7735.640 × 10−17 *
Height (cm)1.0370.962–1.1173.460 × 10−1
Weight (kg)1.0100.912–1.1198.504 × 10−1
Body mass index (kg/m2)1.0060.774–1.3089.644 × 10−1
Smoking history (+/−)0.5930.496–0.7129.555 × 10−9 *
Motion sickness (+/−)1.8201.522–2.1774.604 × 10−11 *
Total dosage of fentanyl (μg)0.9980.997–0.9991.074 × 10−7 *
Total dosage of remifentanil (μg)1.0000.998–0.9991.583 × 10−4 *
Use of pentazocine (+/−)2.2521.870–2.7125.688 × 10−18 *
Use of opioid after anesthesia (+/−)2.0451.709–2.4473.817 × 10−15 *
Anesthesia method (TIVA c/inhalation anesthetic)1.6641.370–2.0212.605 × 10−7 *
Pain (+/−)1.2771.052–1.5511.340 × 10−2 *
* p < 0.05. a Odds ratio. b Confidence interval. c General anesthesia using TIVA.
Table 5. Top 20 candidate SNPs selected from the GWAS for nausea in HS samples.
Table 5. Top 20 candidate SNPs selected from the GWAS for nausea in HS samples.
ModelRankCHRSNPPositionpRelated GeneGenotype (Nausea +) Genotype (Nausea −)
A/A A/B B/B A/A A/B B/B
Trend121rs7282115211599900.000003369NCAM2217740 1180102
Trend217rs2671822352706630.000008426-227046 1077106
Trend32020:55479012569039560.00001031-227109 879106
Trend43rs8909141599840190.00001213IL12A-AS1131105 013180
Trend588:1369467901359345470.0000136-013125 00193
Trend62020:58319409597443540.00001464PHACTR344787 130159
Trend74JHU_4.1334614511325402970.00002577-186951 619042
Trend822:1420965881413390190.00002735LRP1B95376 246145
Trend82JHU_2.1420967991413392310.00002735LRP1B95376 246145
Trend101818:38956090413761260.00002754-228107 1272108
Trend1111rs10742466391792580.00003006-012126 00193
Trend1212rs1861371973811560.00003233-74685 269176
Trend132rs2683834 *1413585160.00004015LRP1B95177 245146
Trend142kgp107497851525148710.000042FMNL2235101 117175
Trend157rs650974 *1037562360.00004238RELN466824 3110062
Trend161818:38953465413735010.00004501-229107 1272109
Trend1618rs8089014413743610.00004501-229107 1272109
Trend161818:38954459413744950.00004501-229107 1272109
Trend161818:38955349413753850.00004501-229107 1272109
Trend161818:38958695413787310.00004501-229107 1272109
Trend161818:38960113413801490.00004501-229107 1272109
Trend1618rs12454051413807140.00004501-229107 1272109
Dominant12020:55479012569039560.000005041-227109 879106
Dominant288:1369467901359345470.000008149-013125 00193
Dominant321rs7282115211599900.00001736NCAM2217740 1180102
Dominant411rs10742466391792580.00002063-012126 00193
Dominant41111:39300012392784620.00002063-111126 00193
Dominant63rs8909141599840190.00002983IL12A-AS1131105 013180
Dominant72020:58319409597443540.00003028PHACTR344787 130159
Dominant810rs3740337 *866623940.00003319OPN4196554 1458121
Dominant810rs17425484866665930.00003319-196554 1557121
Dominant102020:20330281203496370.00003408CFAP6165379 437152
Dominant1110rs7069923 *184414390.00003631CACNB2457419 408865
Dominant121818:38956090413761260.0000378-228107 1272108
Dominant132kgp107497851525148710.00003931FMNL2235101 117175
Dominant142kgp2534744 *751114930.00004175TACR1134103 115177
Dominant1510kgp1635663866734160.00005721LDB3297435 228091
Dominant1621rs2823824164664490.00006737MIR99AHG02136 025168
Dominant1621exm2272994 *165054740.00006737MIR99AHG02136 025168
Dominant181818:38953465413735010.00006826-229107 1272109
Dominant1818rs8089014413743610.00006826-229107 1272109
Dominant181818:38954459413744950.00006826-229107 1272109
Dominant181818:38955349413753850.00006826-229107 1272109
Dominant181818:38958695413787310.00006826-229107 1272109
Dominant181818:38960113413801490.00006826-229107 1272109
Dominant1818rs12454051413807140.00006826-229107 1272109
Recessive13kgp92421831079783090.00001306-175170 266125
Recessive13rs358871551079865120.00001306-175170 266125
Recessive323JHU_X.908222491141840.00002276-322416 145230
Recessive42kgp8484512479721890.00004133-157944 578055
Recessive516rs2075520 *212113510.00004225ZP2355152 1610472
Recessive62kgp8864616479713750.00004236-158043 578056
Recessive616rs11075364175610200.00004236-157845 579442
Recessive823rs664029290981510.00005369-322416 155130
Recessive914rs7147499253343280.00007244-133194 156136
Recessive102rs177207101473338090.000073-04593 1868107
Recessive114JHU_4.1334614511325402970.00008035-186951 619042
Recessive1219rs11083554403608150.00008898PLD3127056 498361
Recessive1323rs787620890830320.0001062-312516 155130
Recessive1422:1721391651712826550.0001304-104088 058135
Recessive1420rs18047959085460.0001304-103197 051142
Recessive1616kgp16425294770746470.0001489-04989 1654123
Recessive1715exm1158632 *451539580.0001921DUOX1466032 3010063
Recessive181919:35042712345518070.0002114-25977 237892
Recessive1912rs7296262 *1286105270.0002193TMEM132C325452 168988
Recessive2022kgp2905464293388080.0002205AP1B1255162 1073110
Model, the genetic model in which candidate SNPs were selected by the GWAS; CHR, chromosome number; Position, chromosomal position (bp); Related gene, the nearest gene from the SNP site; A/A, homozygote for the minor allele for each SNP; A/B, heterozygote for each SNP; B/B, homozygote for the major allele for each SNP; * SNP for which genotype data were available in CIH samples.
Table 6. Effects of genetic models of TMEM132C rs7296262 SNP on nausea in the HS group.
Table 6. Effects of genetic models of TMEM132C rs7296262 SNP on nausea in the HS group.
Genetic Models and
Nausea Occurrence		 Genotypes p
Genotypic model (TT, TC, CC)TT [n = 140] (%)TC [n = 143] (%)CC [n = 48] (%)
With nausea (n = 138)52 (38)54 (39)32 (23)7.000 × 10−4 *
Without nausea (n = 193)88 (46)89 (46)16 (8)
Dominant model (TT vs. TC+CC)TT [n = 140] (%)TC+CC [n = 191] (%)
With nausea (n = 138)52 (38)86 (62)1.507 × 10−1
Without nausea (n = 193)88 (46)105 (54)
Recessive model (TT+TC vs. CC)TT+TC [n = 283] (%)CC [n = 48] (%)
With nausea (n = 138)106 (77)32 (23)1.000 × 10−4 *
Without nausea (n = 193)177 (92)16 (8)
* p < 0.05.
Table 7. Effects of genetic models of TMEM132C rs7296262 SNP on nausea in the CIH group.
Table 7. Effects of genetic models of TMEM132C rs7296262 SNP on nausea in the CIH group.
Genetic Models and
Nausea Occurrence		 Genotypes p
Genotypic model (TT, TC, CC)TT [n = 805] (%)TC [n = 910] (%)CC [n = 306] (%)
With nausea (n = 850)355 (43)388 (45)107 (12)2.010 × 10−2 *
Without nausea (n = 1171)450 (39)522 (44)199 (17)
Dominant model (TT vs. TC+CC)TT [n = 805] (%)TC+CC [n = 1216] (%)
With nausea (n = 850)355 (42)495 (58)1.305 × 10−1
Without nausea (n = 1171)450 (38)721 (62)
Recessive model (TT+TC vs. CC)TT+TC [n = 1715] (%)CC [n = 306] (%)
With nausea (n = 850)743 (88)107 (12)6.400 × 10−3 *
Without nausea (n = 1171)972 (83)199 (17)
* p < 0.05.
Table 8. Amount of opioid required according to genetic models of TMEM132C rs7296262 SNP in the HS group.
Table 8. Amount of opioid required according to genetic models of TMEM132C rs7296262 SNP in the HS group.
Genetic Models and
Amount of Opioid Required		 Genotypes p
Genotypic model (TT, TC, CC)TT TC CC
Morphine (mg/kg)0.789 (0.419–1.861)0.853 (0.441–1.463)0.798 (0.382–1.492)9.403 × 10−1
Dominant model (TT vs. TC+CC)TTTC+CC
Morphine (mg/kg)0.798 (0.419–1.861)0.833 (0.417–1.463)9.705 × 10−1
Recessive model (TT+TC vs. CC)TT+TCCC
Morphine (mg/kg)0.825 (0.428–1.623)0.798 (0.382–1.492)7.533 × 10−1
The data are expressed as the median (25th–75th percentiles).
Table 9. Amount of opioid required according to genetic models of TMEM132C rs7296262 SNP in the CIH group.
Table 9. Amount of opioid required according to genetic models of TMEM132C rs7296262 SNP in the CIH group.
Genetic Models and
Amount of Opioid Required		 Genotypes p
Genotypic model (TT, TC, CC)TTTCCC
Total dosage of fentanyl (μg)200 (100–300)200 (100–300)200 (100–300)9.303 × 10−1
Total dosage of remifentanil (μg)2000 (1300–3500)2000 (1200–3375)2000 (1300–3400)8.598 × 10−1
Use of pentazocine (+/−)472/333540/370171/1355.673 × 10−1
Use of opioid after anesthesia (+/−)380/425441/469144/1628.440 × 10−1
Dominant model (TT vs. TC+CC)TTTC+CC
Total dosage of fentanyl (μg)200 (100–300)200 (100–300)8.850 × 10−1
Total dosage of remifentanil (μg)2000 (1300–3500)2000 (1200–3400)5.830 × 10−1
Use of pentazocine (+/−)472/333711/5059.419 × 10−1
Use of opioid after anesthesia (+/−)380/425585/6316.905 × 10−1
Recessive model (TT+TC vs. CC)TT+TCCC
Total dosage of fentanyl (μg)200 (100–300)200 (100–300)7.041 × 10−1
Total dosage of remifentanil (μg)2000 (1200–3500)2000 (1300–3400)8.690 × 10−1
Use of pentazocine (+/−)1012/703171/1353.065 × 10−1
Use of opioid after anesthesia (+/−)821/894144/1627.931 × 10−1
The data are expressed as the median (25th–75th percentiles).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kang, Y.; Nishizawa, D.; Ohka, S.; Terui, T.; Ishitani, K.; Morino, R.; Yokota, M.; Hasegawa, J.; Nakayama, K.; Ebata, Y.; et al. TMEM132C rs7296262 Single-Nucleotide Polymorphism Is Significantly Associated with Nausea Induced by Opioids Administered for Cancer Pain and Postoperative Pain. Int. J. Mol. Sci. 2024, 25, 8845. https://doi.org/10.3390/ijms25168845

AMA Style

Kang Y, Nishizawa D, Ohka S, Terui T, Ishitani K, Morino R, Yokota M, Hasegawa J, Nakayama K, Ebata Y, et al. TMEM132C rs7296262 Single-Nucleotide Polymorphism Is Significantly Associated with Nausea Induced by Opioids Administered for Cancer Pain and Postoperative Pain. International Journal of Molecular Sciences. 2024; 25(16):8845. https://doi.org/10.3390/ijms25168845

Chicago/Turabian Style

Kang, Yuna, Daisuke Nishizawa, Seii Ohka, Takeshi Terui, Kunihiko Ishitani, Ryozo Morino, Miyuki Yokota, Junko Hasegawa, Kyoko Nakayama, Yuko Ebata, and et al. 2024. "TMEM132C rs7296262 Single-Nucleotide Polymorphism Is Significantly Associated with Nausea Induced by Opioids Administered for Cancer Pain and Postoperative Pain" International Journal of Molecular Sciences 25, no. 16: 8845. https://doi.org/10.3390/ijms25168845

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop