Retrospective Cohort Study Open Access
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 14, 2021; 27(34): 5737-5752
Published online Sep 14, 2021. doi: 10.3748/wjg.v27.i34.5737
MTNR1B polymorphisms with CDKN2A and MGMT methylation status are associated with poor prognosis of colorectal cancer in Taiwan
Chia-Cheng Lee, Je-Ming Hu, Pi-Kai Chang, Chao-Yang Chen, Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
Chia-Cheng Lee, Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
Yu-Cheng Kuo, Fu-Huang Lin, Chih-Hsiung Hsu, Yu-Ching Chou, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
Chien-An Sun, Department of Public Health, College of Medicine, Fu-Jen Catholic University, New Taipei City 24205, Taiwan
Chien-An Sun, Big Data Research Center, College of Medicine, Fu-Jen Catholic University, New Taipei City 24205, Taiwan
Tsan Yang, Department of Health Business Administration, Meiho University, Pingtung 91202, Taiwan
Chuan-Wang Li, Department and Graduate Institute of Microbiology and Immunology, National Defense Medical Center, Taipei 114, Taiwan
Chuan-Wang Li, Institute of Preventive Medicine, National Defense Medical Center, New Taipei City 237, Taiwan
ORCID number: Chia-Cheng Lee (0000-0002-7450-504X); Yu-Cheng Kuo (0000-0002-7673-5164); Je-Ming Hu (0000-0002-7377-0984); Pi-Kai Chang (0000-0002-8641-3230); Chien-An Sun (0000-0001-9041-0537); Tsan Yang (0000-0002-8265-6438); Chuan-Wang Li (0000-0001-8108-8988); Chao-Yang Chen (0000-0002-2246-7635); Fu-Huang Lin (0000-0001-9878-9625); Chih-Hsiung Hsu (0000-0003-4423-4231); Yu-Ching Chou (0000-0003-4823-6541).
Author contributions: Hsu CH and Chou YC contributed equally to this work; Lee CC, Hsu CH and Chou YC designed the research; Sun CA, Yang T and Li CW performed the research; Hu JM, Chang PK and Chen CY collected the data; Lee CC, Kuo YC, Hsu CH, Lin FH and Chou YC analyzed the data; Lee CC, Hsu CH and Chou YC wrote the paper.
Supported by the grant from the Ministry of National Defense-Medical Affairs Bureau, Taiwan, No. MND-MAB-110-109 and No. MND-MAB-D-111059.
Institutional review board statement: This study was approved by the TSGH Institutional Review Board (TSGHIRB approval number: 098-05-292 and 2-105-05-129).
Informed consent statement: Written informed consent was obtained from all patients before enrollment into the study to evaluate their prognosis.
Conflict-of-interest statement: We have no financial relationships to disclose.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yu-Ching Chou, PhD, Professor, School of Public Health, National Defense Medical Center, No. 161 Sec. 6, Minquan E. Road, Neihu District, Taipei 114, Taiwan. trishow@mail.ndmctsgh.edu.tw
Received: April 15, 2021
Peer-review started: April 15, 2021
First decision: June 23, 2021
Revised: June 30, 2021
Accepted: August 23, 2021
Article in press: August 23, 2021
Published online: September 14, 2021

Abstract
BACKGROUND

Identifying novel colorectal cancer (CRC) prognostic biomarkers is crucial to helping clinicians make appropriate therapy decisions. Melatonin plays a major role in managing the circadian rhythm and exerts oncostatic effects on different kinds of tumours.

AIM

To explore the relationship between MTNR1B single-nucleotide polymorphism (SNPs) combined with gene hypermethylation and CRC prognosis.

METHODS

A total of 94 CRC tumour tissues were investigated. Genotyping for the four MTNR1B SNPs (rs1387153, rs2166706, rs10830963, and rs1447352) was performed using multiplex polymerase chain reaction. The relationships between the MTNR1B SNPs and CRC 5-year overall survival (OS) was assessed by calculating hazard ratios with 95%CIs.

RESULTS

All SNPs (rs1387153, rs2166706, rs10830963, and rs1447352) were correlated with decreased 5-year OS. In stratified analysis, rs1387153, rs10830963, and rs1447352 risk genotype combined with CDKN2A and MGMT methylation status were associated with 5-year OS. A strong cumulative effect of the four polymorphisms on CRC prognosis was observed. Four haplotypes of MTNR1B SNPs were also associated with the 5-year OS. MTNR1B SNPs combined with CDKN2A and MGMT gene methylation status could be used to predict shorter CRC survival.

CONCLUSION

The novel genetic biomarkers combined with epigenetic biomarkers may be predictive tool for CRC prognosis and thus could be used to individualise treatment for patients with CRC.

Key Words: Colorectal cancer, Melatonin, Hypermethylation, Polymorphism, Prognosis, Biomarker

Core Tip: In this retrospective cohort study, we found that MTNR1B single-nucleotide polymorphism were associated with a significantly increased risk of colorectal cancer (CRC) 5-year overall survival. A strong cumulative effect of the four polymorphisms on CRC prognosis was observed. This study indicated the novel genetic biomarkers, MTNR1B, combined with CDKN2A and MGMT gene methylation statuses, maybe a predictive tool for CRC prognosis.



INTRODUCTION

Colorectal cancer (CRC) is the third most newly diagnosed cancer and second most frequent cause of cancer-related deaths worldwide[1]. The sudden increase in the incidence of CRC in Taiwan may be associated with obesity, a sedentary lifestyle, and unhealthy dietary habits resulting from improvement of the economy[2]. However, these well-known risk factors cannot wholly account for the increased incidence of CRC. Extensive studies have demonstrated that genetic and epigenetic variations influence personal CRC susceptibility and prognosis[3-6]. Finding novel CRC prognostic biomarkers is crucial because it would help clinicians in making appropriate decisions. Melatonin, which plays a central role in the management of circadian rhythm, has been identified in the pineal retina, lymphocytes, bone marrow, and gastrointestinal tract[7]. Several epidemiological studies have indicated that melatonin exerts oncostatic effects, including antioxidant activity, stimulation of apoptosis, regulation of prosurvival signalling and tumour metabolism, inhibition of angiogenesis, metastasis, and induction of epigenetic alteration[8] on different types of tumours[9-11]. Melatonin prevents metastases, increases the 1-year longevity of patients with resected CRC, and enhances myelotoxicity, lymphocytopenia, and other undesirous haematological and immunological side effects. Furthermore, melatonin reduces neurotoxicity, weakness, insomnia, and psychological stress. These effects have been reported when melatonin is administered as a single pharmaceutical agent or coadministered with the usual first-line and second-line schedules for radiotherapy and chemotherapy[12]. The growing interest in understanding the association between melatonin and colorectal carcinogenesis has led to thorough studies regarding the presence of unique melatonin binding sites in the human colon's intestinal mucosa and submucosa. The melatonin receptor MT2, one of the largest superfamilies of G-protein linked receptors and encoded by MTNR1B, is generally responsible for mediating the downstream effects of melatonin[13]. In 2006, Ekmekcioglu et al[14] reported that MT2 was involved in the antiproliferative action of melatonin. Some research demonstrated that the levels of melatonin are regulated through its biosynthesis from the amino acid tryptophan, which is mediated by MT2[15]. Research analyses have demonstrated reduced expression of MT2 in tumour mucosa compared with the normal mucosa in patients with CRC[16,17]. This may be caused by MT2 downregulation, which reduces protection against CRC and facilitates the development of CRC tumours. In vitro findings indicate that melatonin triggers p53 phosphorylation through the activation of MT2[18].

Studies have reported an association between MTNR1B gene polymorphisms and impaired insulin secretion, higher fasting glucose level, increased risk of type 2 diabetes, and gestational diabetes[19,20]. However, the effect of MTNR1B gene polymorphisms on CRC sensitivity is little understood. Many studies have demonstrated that CDKN2A is more frequently methylated in poorly differentiated, lymphatic metastasis of CRC[21]. In addition, the hypermethylation state of DNA repair genes, MGMT and MLH1, which are silenced, and have been shown to be correlated with specific mutations in tumor DNA, such as KRAS mutations or microsatellite instability, respectively[22]. Furthermore, aberrant promoter methylation of the CDKN2A, MGMT, and MLH1 genes has been reported to be related to adenoma-carcinoma sequence and could serve as a diagnostic prognostic marker of CRC[23,24].

In the present study, a hospital-based retrospective cohort study was conducted to evaluate the effects of MTNR1B gene polymorphisms rs1387153, rs2166706, rs10830963, and rs1447352 and their haplotypes on the 5-year overall survival (OS) of patients with CRC and to analyse interactions based on the methylation status of the CDKN2A and MGMT genes. We hypothesised that the influence of MTNR1B gene variation combined with the hypermethylation of CDKN2A and MGMT genes would predict the prognosis and provide clinical recommendations for optimal treatment of CRC.

MATERIALS AND METHODS
Samples and DNA extraction

A retrospective cohort study, described in detail elsewhere[24-27], was conducted to predict the OS of patients with CRC in Taiwan. Among this cohort, 94 tumour tissues were collected from patients with CRC, which was diagnosed in the Tri-Service General Hospital (TSGH), Taiwan, from 2006 to 2010. The 5-year prognosis was assessed using the tumour tissues. The TSGH Colon and Rectum Division's clinical practice guideline requires enrolees to return once every 3 mo in the first year after surgical resection and once every 3–6 mo thereafter. Written informed consent was obtained from all patients before participation in the study. The TSGH Institutional Review Board approved this study (TSGHIRB approval number: 098-05-292 and 2-105-05-129). Data regarding registered patients—including sex, surgical age (permanent variable), adjuvant chemotherapy, histologic grade and location of the tumour, and survival—were collected from the TSGH’s cancer registry database and analyzed to investigate the association with MTNR1B genotyping. All methods were performed in accordance with the relevant guidelines and regulations. OS was defined as the time from the date of surgery to the date of death from any cause or the last follow-up date before December 31, 2010. Cellulose-coated magnetic beads were employed to extract genomic DNA from the tumour tissues stored at −80 °C in a liquid nitrogen tank by using the MagCore Compact Automated Nucleic Acid Extractor (catalogue no. MCA0801; RBC Bioscience, Taipei, Taiwan) and Genomic DNA Tissue Kit (catalogue no. 69504; Qiagen, Taipei, Taiwan).

MTNR1B genotyping

The MTNR1B gene polymorphisms rs1387153, rs2166706, rs10830963, and rs1447352 were screened using the Agena MassARRAY platform with iPLEX gold chemistry (Agena, San Diego, CA, United States). The detailed genotyping protocols have been reported elsewhere[28,29]. Following the manufacturer guide, the specific polymerase chain reaction (PCR) primer and extension primer sequences were designed using the Assay Designer software package (v.4.0). A 1-μL genomic DNA sample (10 ng/μL) was employed in multiplex PCR in 5-μL volumes containing 1 unit of Taq polymerase, 500 nmol of each PCR primer mix, and 2.5 mmol/L of each dNTP (Agena, PCR accessory and Enzyme kit). Thermocycling was performed at 94 °C for 4 min, which was followed by 45 cycles at 94 °C for 20 s, 56 °C for 30 s, 72 °C for 1 min, and then 72 °C for 3 min. Unincorporated dNTPs were deactivated using 0.3 U of shrimp alkaline phosphatase. Single-base extension reaction was performed using iPLEX enzyme, terminator mix, and extension primer mix; this was followed by 94 °C for 30 s, 40 cycles at 94 °C for 5 s, 5 inner-cycles at 56 °C for 5 s, 80 °C for 5 s, and finally 72 °C for 3 min (Agena, iPLEX gold kit). After cation exchange resin was added to remove residual salt from the reaction, 7 nL of the purified primer extension reaction was loaded onto the matrix pad of a SpectroCHIP (Agena). The SpectroCHIPs were analysed using a MassARRAY Analyzer 4, and clustering analysis was performed using TYPER 4.0 software.

Methylation-specific-PCR

We analyzed CDKN2A, hMLH1, and MGMT DNA methylation in the promoter regions through methylation-specific polymerase chain reaction (MS-PCR), as described in our earlier study[25]. Perform MS-PCR with 1.2-μL aliquots of forward and reverse primers, with 12.5 μL HotStart Taq Premix (RBC Bioscience) and bisulfite-converted DNA according to the manufacturer's protocol. The sequences, annealing temperature of individual primer used for amplification, and MS-PCR product sizes are illustrated in Table 1. The MS-PCR procedures were in accordance with the previous study[25]: first, 10 min at 95 °C; then, 35 cycles of 30-s denaturation at 95 °C, 30-s annealing, and 48-s extension at 72 °C; finally, 4-min extension at 72 °C. After the amplification, MS-PCR products were mixed with a loading buffer, electrophoresed on 2% agarose gel by using 0.2-μL gel-stained dye for 25 min, and visualized using an ultraviolet transilluminator.

Table 1 Primer sequences, annealing temperature and product size for methylation-specific polymerase of target genes.
Genes
Forward primer (5’→3’)
Annealing temperature (oC)
Product size (bp)
CDKN2AMF: TTATTAGAGGGTGGGGCGGATCGC62150
R: GACCCCGAACCGCGACCGTAA
UF: TTATTAGAGGGTGGGGTGGATTGT62151
R: CAACCCCAAACCACAACCATAA
MLH1MF: ACGTAGACGTTTTATTAGGGTCGC60118
R: CCTCATCGTAACTACCCGCG
UF: TTTTGATGTAGATGTTTTATTAGGGTTGT60124
R: ACCACCTCATCATAACTACCCACA
MGMTMF: TTTCGACGTTCGTAGGTTTTCGC5381
R: GCACTCTTCCGAAAACGAAACG
UF: TTTGTGTTTTGATGTTTGTAGGTTTTTGT5393
R: AACTCCACACTCTTCCAAAAACAAAACA
Statistical analysis

Student's t-tests were performed to analyse continuous variables, and χ2 tests were performed for statistical analyses of categorical variables (IBM SPSS Statistics 22). The existence of a Hardy–Weinberg equilibrium per single-nucleotide polymorphism (SNP) was assessed using two goodness-of-fit tests. Linkage disequilibrium (LD) among genotyped SNPs was obtained using the Haploview 4.2 programme. The minor allele frequencies of four MTNR1B gene polymorphisms (rs1387153, rs2166706, rs10830963, and rs1447352) are higher than 5% in an ethnic Chinese population according to data in the dbSNP database.

The relationships between the MTNR1B SNPs and 5-year OS of patients with CRC and between the cumulative effect of MTNR1B SNPs and 5-year OS of CRC were assessed using adjusted hazard ratios (aHRs) with 95%CIs, calculated using Cox proportional-hazards analyses and adjusted for all the aforementioned patient-level and hospital-level characteristics.

Haplotype frequencies for these SNPs combinations were first estimated using haplo.stats (version 12.1) for the R statistical package and then verified using Haploview 4.2. These software programmes employ expectation–maximisation algorithms when constructing the haplotypes. All statistical tests were two-sided, and P < 0.05 was considered statistically significant.

RESULTS

In this study, 94 CRC tumor samples from the TSGH tumor bank were analyzed. LD was evaluated for all MTNR1B SNP pairs. The Lewontin’s D’ values of the pairs were 1.00 (rs1387153: rs2166706), 0.90 (rs1387153: rs10830963), and 0.88 (rs1387153: rs1447352); and the R2 values were 0.98, 1.00, and 1.00, respectively. Four haplotypes with frequencies of 0.034 (T-C-G-A), 0.011 (C-T-C-A), 0.011 (C-T-C-G), and 0.472 (C-T-G-A) were selected for the haplotype association analysis. The relationship between the MTNR1B genotype and the demographic and clinicopathological features of patients with CRC was evaluated. Among the patients with CRC, 41.5% were men; the mean age at surgery was 64.2 ± 13.8 years; 51.0% were at stage III–IV; and 19.1% of the patients died during the study period. Table 2 and Table 3 show that certain possible CRC risk factors, such as age, sex, tumor-node-metastasis stage, and CDKN2A, MLH1 and MGMT genes methylation status, were not significantly associated with the MTNR1B genotype. However, tumor location and survival were associated with a MTNR1B polymorphism. The relationships between each MTNR1B genotype were analyzed to determine their association with 5-year OS in patients with CRC using Cox proportional-hazards models adjusted for age, sex, stage, adjuvant chemotherapy, tumor location, and the methylation status of the CDKN2A, MLH1 and MGMT genes (Table 4). All SNPs were associated with lower 5-year OS. The variant types of rs1387153 (TT vs CC + CT: aHR = 6.23, 95%CI = 2.01–19.3), rs2166706 (CC vs TT + TC: aHR = 6.40, 95%CI = 2.21–18.6), rs10830963 (GG vs CC + CG: aHR = 7.43, 95%CI = 2.63–21.1), and rs1447352 (AA vs GG + GA: aHR = 4.28, 95%CI = 1.32–13.9) decreased the 5-year OS in patients with CRC.

Table 2 Clinical characteristics of colorectal cancer patients and MTNR1B genotypes (rs1387153 and rs2166706).
No of subjectsTotalrs1387153 (C>T)
rs2166706 (T>C)
CC (%)
CT (%)
TT (%)
P value
CC + CT (%)
P value
TT (%)
TC (%)
CC (%)
P value
TT + TC (%)
P value
Sex
Male39 (41.5)8 (34.8)20 (41.7)10 (47.6)0.6928 (39.4)0.628 (36.4)20 (40.8)11 (47.8)0.7328 (39.4)0.63
Female55 (58.5)15 (65.2)28 (58.3)11 (52.4)43 (60.6)14 (63.6)29 (59.2)12 (52.2)43 (60.6)
Age at surgery
mean ± SD (yr)64.2 ± 13.866.8 ± 13.861.7 ± 13.867.0 ± 13.30.2063.3 ± 13.90.2967.4 ± 13.861.8 ± 13.865.6 ± 13.50.2463.6 ± 13.90.54
< 6550 (53.2)12 (52.2)27 (56.2)10 (47.6)0.8039 (54.9)0.6211 (50.0)27 (55.1)12 (52.2)0.9238 (53.5)1.00
≥ 6544 (46.8)11 (47.8)21 (43.8)11 (52.4)32 (45.1)11 (50.0)22 (44.9)11 (47.8)33 (46.5)
Stage
I12 (12.8)2 (8.7)6 (12.5)4 (19.0)0.318 (11.3)0.122 (9.1)6.1 (2.2)4 (17.4)0.248 (11.3)0.08
II34 (36.2)9 (39.1)18 (37.5)6 (28.6)27 (38.0)8 (36.4)19 (38.8)7 (30.4)27 (38.0)
III30 (31.9)10 (43.5)16 (33.3)4 (19.0)26 (36.6)10 (45.5)16 (32.7)4 (17.4)26 (36.6)
IV18 (19.1)2 (8.7)8 (16.7)7 (33.3)10 (14.1)2 (9.1)8 (16.3)8 (34.8)10 (14.1)
Adjuvant chemotherapya
No 23 (24.5)5 (22.7)12 (25.5)6 (33.3)0.7417 (24.6)0.555 (23.8)12 (25.5)6 (30.0)0.9017 (25.0)0.77
Yes 65 (69.1)17 (77.3)35 (74.5)12 (66.7)52 (75.4)16 (76.2)35 (74.5)4 (70.0)51 (75.0)
Tumor locationa
Colon74 (78.7)21 (95.5)41 (87.2)11 (61.1)< 0.00162 (89.9)< 0.00120 (95.2)41 (87.2)13 (65.0)0.0261 (89.7)0.01
Rectum14 (14.9)1 (4.5)6 (12.5)7 (38.9)7 (10.1)1 (4.8)6 (12.8)7 (35.0)7 (10.3)
CDKN2A gene
Unmethylation43 (45.7)13 (56.5)19 (39.6)10 (47.6)0.4032 (45.1)1.0012 (54.5)21 (42.9)10 (43.5)0.6433 (46.5)0.99
Methylation 51 (54.3)10 (43.5)29 (60.4)11 (52.4)39 (54.9)10 (45.5)28 (57.1)13 (56.5)38 (53.5)
MLH1 gene
Unmethylation77 (81.9)20 (87.0)38 (79.2)17 (81.0)0.7358 (81.7)1.0019 (86.4)39 (79.6)19 (82.6)0.7958 (81.7)1.00
Methylation 17 (18.1)3 (13.0)10 (20.8)4 (19.0)13 (18.3)3 (13.6)10 (20.4)4 (17.4)13 (18.3)
MGMT gene
Unmethylation46 (48.9)8 (34.8)26 (54.2)12 (57.1)0.2434 (47.9)0.628 (36.4)25 (51.0)13 (56.5)0.3733 (46.5)0.55
Methylation 48 (51.1)15 (65.2)22 (45.8)9 (42.9)37 (52.1)14 (63.6)24 (49.0)10 (43.5)38 (53.5)
Death in 5 yr
No 76 (80.9)21 (91.3)41 (85.4)13 (61.9)0.0362 (87.3)0.0220 (90.9)42 (85.7)14 (60.9)0.0262 (87.3)0.01
Yes 18 (19.1)2 (8.7)7 (14.6)8 (38.1)9 (12.7)2 (9.1)7 (14.3)9 (39.1)9 (12.7)
Table 3 Clinical characteristics of colorectal cancer patients and MTNR1B genotypes (rs10830963 and rs1447352).
VariablesTotalrs10830963 (C>G)
rs1447352 (A>G)
CC (%)
CG (%)
GG (%)
P value
CC + CG (%)
P value
GG (%)
GA (%)
AA (%)
P value
GG + GA (%)
P value
Sex
Male39 (41.5)7 (33.3)22 (44.9)10 (43.5)0.6629 (41.4)1.004 (66.7)16 (43.2)19 (38.0)0.4020 (46.5)0.53
Female55 (58.5)14 (66.7)27 (55.1)13 (56.5)41 (58.6)2 (33.3)21 (56.8)31 (62.0)23 (53.5)
Age at surgery
mean ± SD (yr)64.2 ± 13.867.0 ± 14.061.4 ± 13.966.8 ± 13.10.1663.1 ± 14.00.2669.7 ± 14.164.4 ± 14.063.0 ± 13.80.5365.1 ± 14.00.47
< 6550 (53.2)11 (52.4)28 (57.1)11 (47.8)0.7539 (55.7)0.633 (50.0)18 (48.6)29 (58.0)0.6821 (48.8)0.41
≥ 6544 (46.8)10 (47.6)21 (42.9)12 (52.2)31 (44.3)3 (50.0)19 (51.4)21 (42.0)22 (51.2)
Stage
I12 (12.8)2 (9.5)6 (12.2)4 (17.4)0.568 (11.4)0.300 (0)4 (10.8)8 (16.0)0.544 (9.3)0.60
II34 (36.2)8 (38.1)18 (36.7)7 (30.4)26 (37.1)1 (16.7)15 (40.5)17 (34.0)16 (37.2)
III30 (31.9)9 (42.9)16 (32.7)5 (21.7)25 (35.7)4 (66.7)12 (32.4)14 (28.0)16 (37.2)
IV18 (19.1)2 (9.5)9 (18.4)7 (30.4)11 (15.7)1 (16.7)6 (16.2)11 (22.0)7 (16.3)
Adjuvant chemotherapya
No 23 (24.5)5 (23.8)12 (25.5)6 (30.0)0.9017 (25.0)0.771 (16.7)8 (22.2)14 (30.4)0.619 (21.4)0.47
Yes 65 (69.1)16 (76.2)35 (74.5)14 (70.0)51 (75.0)5 (83.3)28 (77.8)32 (69.6)33 (78.6)
Tumor locationa
Colon74 (78.7)20 (95.2)41 (87.2)13 (65.0)0.0261 (89.7)0.016 (100)34 (94.4)34 (73.9)0.0240 (95.2)< 0.001
Rectum14 (14.9)1 (4.85)6 (12.8)7 (35.0)7 (10.3)0 (0)2 (5.6)12 (26.1)2 (4.8)
CDKN2A gene
Unmethylation43 (45.7)12 (57.1)17 (34.7)13 (56.5)0.1029 (41.4)0.314 (66.7)16 (43.2)22 (44.0)0.5520 (46.5)0.97
Methylation 51 (54.3)9 (42.9)32 (65.3)10 (43.5)41 (58.6)2 (33.3)21 (56.8)28 (56.0)23 (53.5)
MLH1 gene
Unmethylation77 (81.9)18 (85.7)39 (79.6)19 (82.6)0.8357 (81.4)1.005 (83.3)31 (83.8)40 (80.0)0.9036 (83.7)0.79
Methylation 17 (18.1)3 (14.3)10 (20.4)4 (17.4)13 (18.6)1 (16.7)6 (16.2)10 (20.0)7 (16.3)
MGMT gene
Unmethylation46 (48.9)8 (38.1)26 (53.1)12 (52.2)0.5034 (48.6)0.954 (66.7)18 (48.6)24 (48.0)0.6822 (51.2)0.92
Methylation 48 (51.1)13 (61.9)23 (46.9)11 (47.8)36 (51.4)2 (33.3)19 (51.4)26 (52.0)21 (48.8)
Death in 5 yr
No 76 (80.9)19 (90.5)43 (87.8)13 (56.5)< 0.00162 (88.6)< 0.0016 (100)33 (89.2)36 (72.0)0.0639 (90.7)0.03
Yes 18 (19.1)2 (9.5)6 (12.2)10 (43.5)8 (11.4)0 (0)4 (10.8)14 (28.0)4 (9.3)
Table 4 Relationship between MTNR1B single-nucleotide polymorphism and 5-year overall survival of colorectal cancer patients.
No. of subjectsNo. of cases (%)Crude
Adjusteda
HR
95%CI
HR
95%CI
rs1387153 (C>T)
CC232 (11.8)1.00Referent 1.00Referent
CT487 (41.2)1.65(0.34 to 7.95)1.98(0.40 to 9.82)
TT218 (47.1)6.03(1.28 to 28.4)10.6(1.87 to 59.5)
TT vs CC + CT 4.18(1.61 to 10.9)6.23(2.01 to 19.3)
rs2166706 (T>C)
TT222 (11.1)1.00Referent 1.00Referent
TC497 (38.9)1.55(0.32 to 7.47)1.91(0.39 to 9.45)
CC239 (50.0)5.74(1.24 to 26.6)10.5(1.96 to 56.4)
CC vs TT + TC4.15(1.65 to 10.5)6.40(2.21 to 18.6)
rs10830963 (C>G)
CC212 (11.1)1.00Referent 1.00Referent
CG496 (33.3)1.19(0.24 to 5.91)1.40(0.27 to 7.22)
GG2310 (55.6)5.79(1.27 to 26.5)9.46(1.90 to 47.1)
GG vs CC + CG5.09(2.01 to 12.9)7.43(2.63 to 21.1)
rs1447352 (A>G)
AA5014 (77.8)1.00Referent 1.00Referent
GA374 (22.2)0.36(0.12 to 1.08)0.31(0.10 to 0.99)
GG60 (0)N/AN/AN/AN/A
AA vs GG + GA3.37(1.11 to 10.2)4.28(1.32 to 13.9)

We further examined the relationship between each SNP and the 5-year OS, with data stratified by the methylation status of the CDKN2A and MGMT genes (Table 5). Particularly, 5-year OS was significantly reduced in the rs1387153 and rs1447352 polymorphism subgroups of unmethylation of the MGMT gene (aHR = 8.57, 95%CI = 1.67–44.1; aHR = 19.4, 95%CI = 2.94–128, respectively) compared with the opposite subgroups. In contrast, we determined that the rs1447352 polymorphism was related to a higher risk of mortality in the subjects with methylation of the CDKN2A gene (aHR = 9.40, 95%CI = 1.02 –86.8). Besides, rs10830963 exhibited a significant association with 5-year OS in the subgroups with hypermethylation of the CDKN2A gene (aHR = 27.2, 95%CI = 3.12–233). According to the small number of hypermethylation MLH1 gene, we could not perform the MLH1 gene methylation-stratified analysis.

Table 5 Stratified effect between gene promoter region methylation and MTNR1B genotypes for 5-year overall survival of colorectal cancer patients.
No. of subjectsNo. of cases (%)Crude
Adjusteda
HR
95%CI
HR
95%CI
rs1387153 (C>T)
TT vs CC + CTCDKN2A
U104 (40.0)5.01(1.33 to 18.9)10.4(1.17 to 92.4)
M114 (36.4)3.83(0.96 to 15.3)8.86(1.08 to 72.8)
MGMT
U125 (41.7)3.63(1.05 to 12.6)8.57(1.67 to 44.1)
M93 (33.3)4.93(1.10 to 22.1)3.05(0.33 to 28.0)
rs2166706 (T>C)
CC vs TT + TCCDKN2A
U104 (40.0)5.02(1.33 to 19.0)10.4(1.17 to 92.4)
M135 (38.5)3.96(1.06 to 14.7)10.1(1.49 to 68.0)
MGMT
U135 (38.5)3.07(0.89 to 10.7)6.28(1.54 to 25.7)
M239 (39.1)6.25(1.56 to 25.1)8.28(0.95 to 72.3)
rs10830963 (C>G)
GG vs CC + CGCDKN2A
U135 (38.5)4.63(1.24 to 17.3)8.47(1.57 to 45.6)
M105 (50.0)5.61(1.51 to 20.9)27.2(3.12 to 233)
MGMT
U125 (41.7)3.47(1.00 to 12.0)8.50(1.98 to 36.5)
M115 (45.5)7.91(1.89 to 33.2)9.80(1.42 to 67.5)
rs1447352 (A>G)
AA vs GG + GACDKN2A
U226 (27.3)2.05(0.51 to 8.22)2.28(0.51 to 10.2)
M288 (28.6)7.34(0.92 to 58.7)9.40(1.02 to 86.8)
MGMT
U248 (33.3)3.94(0.84 to 18.6)19.4(2.94 to 128)
M266 (23.1)2.77(0.56 to 13.7)2.04(0.35 to 12.0)

The cumulative effects of four SNPs were then evaluated. We selected polymorphism to classify the risk based on genotypes, in accordance with the findings summarised in Table 4: TT vs CC + CT for rs1387153, CC vs TT + TC for rs2166706, GG vs CC + CG for rs10830963, and AA vs GG + GA for rs1447352. The subjects were classified into five groups on the basis of their genotypical risk score (0, 1, 2, 3, and 4), and the significance of the linear trend was then evaluated. The risk of poor CRC prognosis significantly increased with an increase in the SNP risk genotypes (Ptrend < 0.001, Table 6). Furthermore, patients were divided into two groups on the basis of the number of risk genotypes, forming the < 2 and ≥ 2 SNP risk genotypes groups. The 5-year OS was significantly different between the group with two or more SNP risk genotypes and the comparison group (aHR = 5.81, 95%CI = 2.03-16.6).

Table 6 Cumulative effect of MTNR1B single-nucleotide polymorphism associated with 5-year overall survival of colorectal cancer patients.
No. of subjectsNo. of cases (%)Crude
Adjusteda
HR
95%CI
HR
95%CI
No. of SNP risk genotypes
0413 (7.3)1.00Referent 1.00Referent
1254 (16.0)2.27(0.51 to 10.1)2.60(0.55 to 12.2)
273 (42.9)6.40(1.29 to 317)6.89(1.16 to 41.0)
310 (0)N/AN/AN/AN/A
4187 (38.9)7.60(1.96 to 29.5)14.0(2.94 to 66.3)
P for trend< 0.001< 0.001
≥ 2 of SNP risk genotypes2610 (38.5)4.00(1.58 to 10.1)5.81(2.03 to 16.6)

Haplotype analysis was performed to determine the relationship between haplotypes of the studied SNPs (rs1387153, rs2166706, rs10830963, and rs1447352) and the 5-year OS. Four haplotypes were screened and two demonstrated significance. The T-C-G-A haplotype contributed to reduced 5-year OS (aHR = 2.75, 95%CI = 1.82–11.2), whereas the C-T-C-G haplotype reduced the risk of mortality (aHR = 0.21, 95%CI = 0.06–0.71, Table 7). No significant relationship with the 5-year OS was evident for the C-T-G-A haplotype (aHR = 1.96, 95%CI = 0.44–8.66) or the C-T-C-A haplotype (aHR = 0.70, 95%CI = 0.28–1.72).

Table 7 Relationship between haplotypes of MTNR1B single-nucleotide polymorphism and 5-year overall survival of colorectal cancer patients.
HaplotypesSurvival group (%)Death group (%)Crude
Adjusteda
HR
95%CI
HR
95%CI
T-C-G-A42.068.42.58(1.27 to 5.28)2.75(1.82 to 11.2)
C-T-G-A2.75.31.74(0.42 to 7.26)1.96(0.44 to 8.66)
C-T-C-G30.07.90.23(0.07 to 0.76)0.21(0.06 to 0.71)
C-T-C-A22.015.80.73(0.30 to 1.76)0.70(0.28 to 1.72)
DISCUSSION

In this retrospective cohort study, we examined the associations between four MTNR1B gene polymorphisms (rs1387153, rs2166706, rs10830963, and rs1447352) and CRC outcomes in terms of OS. Correlations between all SNPs and the 5-year OS were identified. In stratified analysis, the rs1387153 and rs1447352 risk genotypes were determined to be associated with 5-year OS in the unmethylation MGMT gene subgroup. In contrast, the rs10830963 and rs1447352 risk genotypes with hypermethylation CDKN2A gene had a higher risk of death in five years. Four haplotypes of MTNR1B SNPs were also determined to be associated with increased risk of mortality. This study is one of a few that has reported an association between MTNR1B SNPs and the 5-year OS in patients with CRC. The MTNR1B gene location of both rs1387153 and rs2166706 is > 11 kb upstream. The variant rs10830963 is located in an intronic region, whereas the variant rs1447352 is located at approximately 4.5 kb from the MTNR1B gene[30]. Qiu et al[31] indicated that these SNPs may influence the MTNR1B expression, causing a functional deficiency of melatonin. De Luis et al[32] demonstrated that rs10830963 was associated with an increased MTNR1B mRNA expression and the expression of other genes that may affect the energy balance role of melatonin[33]. However, the potential role and regulation of the other three SNPs in the MTNR1B expression are poorly understood[34].

Mechanisms involving the oncostatic effect of melatonin binding to MT1 and MT2 receptors in CRC have been reported in numerous studies. An in vitro study conducted by Karasek et al[35] determined that both MT1 and MT2 were part of the oncostatic action of melatonin on Colon 38 adenocarcinoma cells. Furthermore, activation of the tumour suppressor p53 gene by melatonin is reportedly directly controlled by MT1 and MT2. Melatonin’s suppression of cell proliferation and clonogenic activity is impaired because of the lack of either receptor[18]. León et al[16] demonstrated an association between reduced MT1 and MT2 expression and increased malignancy in CRC in 54 Spanish patients with CRC. Moreover, expression of the tumour markers CD44 and CD133 was negatively correlated with MT1 and MT2 expression in patients with CRC[17]. Furthermore, the role of the MT1 receptor in gastric adenocarcinoma was demonstrated in patients over the age of 50 years[36]. In the present study, we determined that four MTNR1B gene polymorphisms were significantly associated with the 5-year OS of patients with CRC, with a cumulative effect on prediction for poorer prognosis. Few studies have assessed the relationship between MTNR1B gene polymorphisms and CRC prognosis. However, some type 2 diabetes susceptibility genes are correlated with metastasis development[37]. Nasrabadi et al[38] indicated that high expression of MT2 was associated with gastric adenocarcinoma, because MT2 receptors enhance the secretion of bicarbonate by stimulating calcium release into the mucosa of enterochromaffin cells. Numerous reports have indicated that MTNR1B SNPs are associated with fasting glucose level, obesity, carbohydrate disorders, and type 2 diabetes, which are crucial metabolic risk factors for CRC[39,40]. Indeed, Johnson et al[41] reviewed the association between colorectal cancer and type 2 diabetes and indicated that there is a positive and observational correlation. Besides, the GG genotype of the variant rs10830963 was discovered to significantly increase the risk of breast cancer than the CC genotype[39,42]. Moreover, the AA genotype of the variant rs10765576 was correlated with lower risk of breast cancer compared with the GG or GA major allele among Chinese women[13].

Data concerning the effect of gene methylation modification on the association between MTNR1B gene polymorphisms and CRC prognosis are scarce. The case-only analysis performed by Das et al[43] assessed the potential interactions and associations between epigenetics, genetics, and the risk of oesophageal cancer. Das et al[43] determined that CDKN2A methylation and the p53 polymorphism were significantly associated with oesophageal cancer risk. DNA methylation can regulate gene expression by modifying chromatin complexes and recruiting methyl-CpG domain-binding proteins around CpG islands, and this is the most common epigenetic alteration. There was a clinical study had revealed the feasibility of using specific gene methylation statuses as biomarkers for CRC prognosis[44]. For instance, the hypermethylation of CDKN2A and MGMT promoters has been suggested to be independently correlated with poorer prognosis (including metastasis, recurrence, and mortality) in patients with CRC[45,46]. Our findings demonstrated that the risk of mortality in CDKN2A hypermethylation patients with rs10830963 or rs1447352 risk genotype was higher than that in the opposing subgroups. However, SNPs−rs1387153 and rs1447352 with unmethylation of the MGMT gene were associated with poorer CRC prognosis. All these findings regarding the correlation with gene promoter methylation status and polymorphisms suggest an overlap and crosstalk between the involved pathways, adversely affecting cancer prognosis, and indicate a strong correlation between genetic and epigenetic factors in the Taiwanese population. Therefore, CDKN2A and MGMT methylation status and MTNR1B SNPs may be used as molecular targets for predicting CRC prognosis. The efficiency of any single polymorphic site for risk detection is usually limited because of the multistep model of colorectal carcinogenesis. The benefits of using a combination of several SNPs are well-documented[47]. Our analysis revealed a significant cumulative effect, which was observed as MTNR1B SNPs correlating with the 5-year OS, which indicates that using more risk genotypes may improve the accuracy of CRC prognoses. Furthermore, the risk of mortality in individuals with the TT genotype rs1387153, CC genotype rs2166706, and GG genotype rs10830963 may be 2.75-fold higher than that in other haplotypes.

Certain limitations to the present study should be considered. First, limitations are inherent in any retrospective cohort study. Second, our sample size was not sufficiently large enough to provide a more precise estimate of the association between MTNR1B SNPs and CRC prognosis. Third, our preliminary retrospective cohort study was not designed to clarify the pathophysiology of how the risk genotype of the MTNR1B gene reduces postoperative survival. In addition, data regarding registered patients were collected from the TSGH's cancer registry database. Other potential risk factors, such as dietary habits, obesity, and combined primary diseases were unavailable from the database. Furthermore, this study did not include normal colorectal tissues, i.e. we could not describe MTNR1B polymorphic variants in normal colorectal tissues. The results of the present study should be verified using a large-scale study that controls for confounding variables, such as lifestyle, carcinogen exposure, and diet, among others.

CONCLUSION

In summary, we conducted a retrospective cohort study to investigate the association between MTNR1B SNPs and CRC prognosis in those with different gene methylation statuses. All polymorphisms were correlated with 5-year OS. Three SNPs (rs1387153, rs10830963, and rs1447352) were associated with enhanced mortality risk when combined with different CDKN2A or MGMT gene methylation status. Furthermore, we observed a strong cumulative effect of MTNR1B SNPs on the 5-year OS of patients with CRC. Our findings indicate that MTNR1B SNPs combined with CDKN2A and MGMT gene methylation statuses may be predictive biomarkers for CRC prognosis. This study offers insights into novel genetic and epigenetic biomarkers for the prediction of CRC prognosis, and the findings could be used to individualise the treatment of patients with CRC.

ARTICLE HIGHLIGHTS
Research background

Melatonin plays a central role in the management of circadian rhythm and was identified in the gastrointestinal tract. Epidemiological studies demonstrated that melatonin has oncostatic effects including induction of epigenetic alteration on different types of tumours. The melatonin receptor MT2 encoded by MTNR1B is generally responsible for mediating the downstream effects of melatonin. The expression of MT2 in tumour mucosa is lower than the normal mucosa in patients with colorectal cancer (CRC).

Research motivation

Growing studies have investigated the association between melatonin and CRC carcinogenesis. However, the relationship between MTNR1B gene polymorphisms and CRC sensitivity is not clear. To analyze the effects of MTNR1B gene polymorphisms on CRC prognosis and evaluate the interactions with aberrant promoter methylation of the CDKN2A and MGMT genes will be of great significance.

Research objectives

In our study, we aimed to explore the association between MTNR1B single-nucleotide polymorphism (SNPs) and the 5-year overall survival (OS) of CRC patients. To further assess the interaction between MTNR1B SNPs and CDKN2A and MGMT gene methylation, we examined the relationship between each SNP and the 5-year OS, with data stratified by the methylation status of the CDKN2A and MGMT gene.

Research methods

Ninety four CRC patients from Taiwan were enrolled to evaluate the association between MTNR1B SNPs, CDKN2A, MGMT gene hypermethylation and 5-year OS. The MTNR1B gene polymorphisms were screened using the Agena MassARRAY platform with iPLEX gold chemistry. The promoter methylation status of CDKN2A and MGMT was assessed using methylation-specific polymerase chain reaction. Associations of the genetic and epigenetic effect and 5-year OS were assessed using the Cox proportional hazards regression model.

Research results

In this retrospective cohort study, we found that MTNR1B SNPs was associated with a significantly increased risk of CRC 5-year OS. A strong cumulative effect of the four polymorphisms on CRC prognosis was observed. In stratified analysis, rs1387153, and rs1447352 risk genotype were determined to be associated with 5-year OS in the unmethylation MGMT gene subgroup. In contrast, rs10830963 and rs1447352 risk genotype with hypermethylation CDKN2A gene had a higher risk of death in five years. Four haplotypes of MTNR1B SNPs were also determined to be associated with increased risk of mortality.

Research conclusions

This study is one of few reports which demonstrated the association between MTNR1B SNPs and the 5-year OS in patients with CRC. Our data identified these novel genetic biomarkers combined with CDKN2A and MGMT methylation status for the prediction of CRC prognosis, and the findings could be used to individualise the treatment of patients with CRC.

Research perspectives

Based on our findings, the novel genetic biomarkers, MTNR1B, combined with CDKN2A and MGMT gene methylation statuses could be a predictive tool for CRC prognosis. The new set of markers may help physicians make treatment decisions based on the prognostic information and would improve the OS of patients with CRC. This study warrant further investigation of the underlying mechanisms related to oncostatic effects of MTNR1B on CRC.

Footnotes

Manuscript source: Invited manuscript

Specialty type: Oncology

Country/Territory of origin: Taiwan

Peer-review report’s scientific quality classification

Grade A (Excellent): 0

Grade B (Very good): B

Grade C (Good): C, C

Grade D (Fair): 0

Grade E (Poor): 0

P-Reviewer: Ji G, Mogulkoc R, Xie M S-Editor: Zhang H L-Editor: A P-Editor: Liu JH

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