Abstract
AIM: To investigate the associiations between the polymorphisms of cell cycle pathway genes and the risk of hepatocellular carcinoma (HCC).
METHODS: We enrolled 1127 cases newly diagnosed with HCC from the Tumor Hospital of Guangxi Medical University and 1200 non-tumor patients from the First Affiliated Hospital of Guangxi Medical University. General demographic characteristics, behavioral information, and hematological indices were collected by unified questionnaires. Genomic DNA was isolated from peripheral venous blood using Phenol-Chloroform. The genotyping was performed using the Sequenom MassARRAY iPLEX genotyping method. The association between genetic polymorphisms and risk of HCC was shown by P-value and the odd ratio (OR) with 95% confidence interval (CI) using the unconditional logistic regression after adjusting for age, sex, nationality, smoking, drinking, family history of HCC, and hepatitis B virus (HBV) infection. Moreover, stratified analysis was conducted on the basis of the status of HBV infection, smoking, and alcohol drinking.
RESULTS: The HCC risk was lower in patients with the MCM4 rs2305952 CC (OR = 0.22, 95%CI: 0.08-0.63, P = 0.01) and with the CHEK1 rs515255 TC, TT, TC/TT (OR = 0.73, 95%CI: 0.56-0.96, P = 0.02; OR = 0.67, 95%CI: 0.46-0.97, P = 0.04; OR = 0.72, 95%CI: 0.56-0.92, P = 0.01, respectively). Conversely, the HCC risk was higher in patients with the KAT2B rs17006625 GG (OR = 1.64, 95%CI: 1.01-2.64, P = 0.04). In addition, the risk was markedly lower for those who were carriers of MCM4 rs2305952 CC and were also HBsAg-positive and non-drinking and non-smoking (P < 0.05, respectively) and for those who were carriers of CHEK1 rs515255 TC, TT, TC/TT and were also HBsAg-negative and non-drinking (P < 0.05, respectively). Moreover, the risk was higher for those who were carriers of KAT2B rs17006625 GG and were also HBsAg-negative (P < 0.05).
CONCLUSION: Of 12 cell cycle pathway genes, MCM4, CHEK1 and KAT2B polymorphisms may be associated with the risk of HCC.
Keywords: Cell cycle pathway genes, Hepatocellular carcinoma, Single nucleotide polymorphism, Case-control study, Genetic susceptibility
Core tip: We analyzed the effects of polymorphisms of 12 cell cycle pathway genes on the risk of hepatocellular carcinoma (HCC) in a large population of 1019 HCC cases and 1138 controls. The results suggest that MCM4 rs2305952 CC and CHEK1 rs515255 TC, TT, TC/TT may be significantly associated with a decreased risk of HCC. KAT2B rs17006625 GG may increase the risk of HCC.
INTRODUCTION
Hepatocellular carcinoma (HCC) is a serious threat to human health worldwide. It is the fourth most common cancer and the second leading cause of cancer death, with nearly 746000 deaths per year[1]. The incidence of this fatal disease continues to increase. HCC occurrence and development are related to environmental factors, such as infection with hepatitis B virus (HBV) or hepatitis C virus (HCV), cigarette smoking, and alcohol consumption, as well as genetic susceptibility[2-4]. Many studies strongly support that single nucleotide polymorphisms (SNPs) of a variety of genes are associated with HCC[5-7]. However, the genetic mechanism underlying the inherited component of HCC is still not fully understood.
The cell cycle comprises the events that result in the formation of two daughter cells through division of the parent cell. Cell cycle progression, including cell division, is influenced by three different types of molecules: cyclin, cyclin-dependent kinases, and cyclin kinase inhibitors[8]. The associations between the genetic susceptibility of genes which regulate the cell cycle and the risk of cancer are well known. For instance, a polymorphism of the p27 generates an increased risk of squamous cell carcinoma of the head and neck[9], while polymorphisms of p27 and p21 are associated with a significantly increased risk of HCC[10]. Other cell cycle pathway genes implicated in cancer include cyclinD1[11], p53[12], CHEK2[13] and P21[14].
During the last several decades, an increasing number of studies have shown an association between genetic variants, mainly in the form of SNPs, and the risk of cancer, including breast[15], colorectal[16], cervical, and vulvar cancers[17], and HCC[18]. Despite investigations into the association of polymorphisms in cell cycle pathway genes with cancer susceptibility[19,20], in the case of HCC this association remains unclear. Therefore, in this hospital-based study we investigated the associations between the polymorphisms of SNPs in cell cycle pathway genes and the risk of HCC.
MATERIALS AND METHODS
Study population
For this case-control study, 2327 subjects were consecutively recruited from June 2007 to December 2013. The 1127 HCC patients were from the Tumor Hospital of Guangxi Medical University and were newly diagnosed with HCC based on biochemical (α-fetoprotein > 20 μg/L) and histopathological examinations. None had undergone radiotherapy or chemotherapy before blood sampling. The 1200 controls from the First Affiliated Hospital of Guangxi Medical University consisted of non-tumor patients admitted within the same period of time. Informed consent was obtained from all participants, who also agreed to truthfully complete the questionnaires.
Information and sample collection
General demographic and behavioral information, hematological indices, and data on the patients’ age, sex, nationality, drinking habit, smoking habit, HBV infection, and family history of HCC were obtained in face-to-face interviews by trained investigators. Peripheral venous blood was collected in a vacuum EDTA anticoagulant tube from each participant. Genomic DNA was extracted using a standard phenol-chloroform extraction method and stored at -80 °C.
SNP selection
From the GEO database (https://www.ncbi.nlm.nih.gov/geo/), we found three sets of whole genome expression microarray data which were related to HCC (GSE14520, GSE25097, and GSE12941). A total of 3826 different genes were selected using SPSS 16.0 software (SPSS Inc., Chicago, IL, United States) (P < 0.05). Gene ontology classification and pathway enrichment analysis were performed by blast2GO and DAVID (https://david.ncifcrf.gov/) and 40 cell cycle pathway genes involved in the cellular process were chose. The genotype information was downloaded from Hapmap website (http://hapmap.ncbi.nlm.nih.gov/), and functional SNPs were selected using Haploview 4.2 software (Cambridge, MA o2141, United States) based on a function prediction website (http://snpinfo.niehs.nih.gov/snpfunc.htm). Referring to the existing literature on these SNPs with HCC, 15 SNPs in 12 genes (MCM4 rs2305952, YWHAB rs2425675, CDKN2A rs3088440, TGFB3 rs3917148, RBL2 rs3929, RAD21 rs6987652, SMAD3 rs11556090, rs8025774, KAT2B rs17006625, rs4858770, MCM7 rs2070215, rs2261360, CDKN1A rs3176320, CDC25C rs3734166, and CHEK1 rs515255) were selected in this study. Information of selected SNPs is shown in Table 1.
Table 1.
Summarized information of selected single nucleotide polymorphisms in cell cycle pathway genes
| Genes | SNPs | Chromosome (position) | Allele | MAF (hapmap-HCB) |
| MCM4 | rs2305952 | 8 (47962049) | C/T | C = 0.18 |
| YWHAB | rs2425675 | 20 (44906293) | A/G | A = 0.20 |
| CDKN2A | rs3088440 | 9 (21968160) | A/G | A = 0.08 |
| TGFB3 | rs3917148 | 14 (75980178) | A/C | C = 0.10 |
| RBL2 | rs3929 | 16 (53490396) | C/G | C = 0.20 |
| RAD21 | rs6987652 | 8 (116870042) | A/G | A = 0.12 |
| SMAD3 | rs11556090 | 15 (67194045) | A/G | G = 0.09 |
| rs8025774 | 15 (67190938) | C/T | C = 0.45 | |
| KAT2B | rs17006625 | 3 (20119604) | A/G | G = 0.14 |
| rs4858770 | 3 (20152931) | C/T | T = 0.47 | |
| MCM7 | rs2070215 | 7 (100099174) | A/G | G = 0.29 |
| rs2261360 | 7 (100095370) | A/C | A = 0.37 | |
| CDKN1A | rs3176320 | 6 (36679011) | A/G | G = 0.17 |
| CDC25C | rs3734166 | 5 (138329634) | A/G | G = 0.38 |
| CHEK1 | rs515255 | 11 (125627250) | C/T | T = 0.44 |
MAF (minor allele frequency) was derived from HCB population in HapMap website (http://hapmap.ncbi.nlm.nih.gov/). SNPs: Single nucleotide polymorphisms.
SNP genotyping
Before genotyping, each DNA sample was quantified using a UV-Vis spectrophotometer Q5000 (Quawell Technology, Inc., United States) and diluted to a final concentration of 50 ng/μL. SNP genotyping was performed using a MassARRAY system (Sequenom, San Diego, CA, United States) and a matrix-assisted laser desorption ionization-time of flight mass spectrometry method according to the manufacturer’s instructions. Primers for PCR and extension were designed using the Assay Designer software package (Sequenom). For quality control, 5% of the samples were randomly chosen and genotyped twice for each locus. Among the 1127 patient samples and 1200 control samples, genotyping was successful for all 15 SNPs in both groups, with a success rate of 92.7%. Thus, all 1019 HCC patients and 1138 controls were included in the final analysis.
Statistical analysis
Statistical analyses were performed using the SPSS 16.0 software (SPSS Inc., Chicago, IL, United States). Continuous variables were evaluated using the two-sample t-test. Categorical variables and genotype frequencies between the HCC patients and controls were compared using the Pearson’s χ2 and Fisher’s exact test. Hardy-Weinberg equilibrium (HWE) was evaluated by a goodness-of-fit χ2 test to compare the observed genotype frequencies with the expected ones. The association between SNP genotypes and HCC risk was estimated using unconditional logistic regression analysis and an odds ratio (OR) with 95% confidence interval (CI). All statistical tests were two-sided. A P-value < 0.05 was considered to indicate statistical significance.
RESULTS
Characteristics of the participants
The 2157 unrelated Chinese subjects enrolled in this study included 881 (86.5%) males and 138 (13.5%) females with HCC. The mean age of these patients was 48.54 ± 11.44 years. The control group consisted of 982 (86.3%) males and 156 (13.7%) females, with a mean age of 48.01 ± 11.5 years. The general demographic characteristics and behavior information on the patients and controls are provided in Table 2. There were no significant differences between the HCC patients and the controls in terms of age, sex, and nationality; however, HCC patients had a significantly higher rate of a positive history of HBV infection, a family history of HCC, smoking, and drinking.
Table 2.
General demographic characteristics and behavioral information among hepatocellular carcinoma patients and controls
| Variable | HCC patients | Controls | t/χ2 | P value |
| n = 1019 | n = 1138 | |||
| Age | 48.54 ± 11.44 | 48.01 ± 11.50 | -1.076 | 0.28 |
| Gender | ||||
| Male | 881 | 982 | 0.013 | 0.91 |
| Female | 138 | 156 | ||
| Nationality | ||||
| Han | 673 | 708 | 3.591 | 0.17 |
| Zhuang | 332 | 410 | ||
| Others | 14 | 20 | ||
| Drinking | ||||
| Yes | 345 | 145 | 136.527 | < 0.001 |
| No | 674 | 993 | ||
| Smoking | ||||
| Yes | 355 | 158 | 130.222 | < 0.001 |
| No | 664 | 980 | ||
| Chronic HBV infection | ||||
| Yes | 794 | 109 | 1031.687 | < 0.001 |
| No | 225 | 1029 | ||
| Family history of HCC | ||||
| Yes | 80 | 2 | 86.597 | < 0.001 |
| No | 939 | 1136 | ||
HCC: Hepatocellular carcinoma.
Allele frequencies and genotype distribution
In the control group, the genotype frequencies of the 15 SNPs, all but CDKN1A rs3176320, were in line with the HWE (P > 0.05), which indicated that these study participants were from a homogeneous group. The allele frequencies and genotype distribution of SNPs among the HCC patients and controls from this study are listed in Table 3.
Table 3.
Allele frequencies and genotype distribution of single nucleotide polymorphisms n (%)
| SNP | Genotype | HCC patients | Control | χ2 | P value of HWE |
| n = 1019 | n = 1138 | ||||
| rs2305952 | TT | 801 (78.61) | 883 (77.59) | ||
| TC | 209 (20.51) | 238 (20.91) | 0.04 | 0.83 | |
| CC | 9 (0.88) | 17 (1.49) | |||
| rs2425675 | GG | 632 (62.02) | 724 (63.62) | ||
| AG | 348 (34.15) | 374 (32.86) | 0.96 | 0.33 | |
| AA | 39 (3.83) | 40 (3.51) | |||
| rs3088440 | GG | 750 (73.60) | 813 (71.44) | ||
| GA | 249 (24.44) | 300 (26.36) | 0.19 | 0.66 | |
| AA | 20 (1.96) | 25 (2.20) | |||
| rs3917148 | AA | 773 (75.86) | 882 (77.50) | ||
| CA | 233 (22.87) | 235 (20.65) | 1.32 | 0.25 | |
| CC | 13 (1.28) | 21 (1.85) | |||
| rs3929 | GG | 619 (60.75) | 688 (60.46) | ||
| GC | 349 (34.25) | 395 (34.71) | 0.03 | 0.86 | |
| CC | 51 (5.00) | 55 (4.83) | |||
| rs6987652 | GG | 743 (72.91) | 843 (74.08) | ||
| AG | 251 (24.63) | 270 (23.73) | 0.38 | 0.54 | |
| AA | 25 (2.45) | 25 (2.20) | |||
| rs11556090 | AA | 622 (61.04) | 749 (65.82) | ||
| AG | 352 (34.54) | 346 (30.40) | 0.15 | 0.70 | |
| GG | 45 (4.42) | 43 (3.78) | |||
| rs17006625 | AA | 526 (51.62) | 620 (54.48) | ||
| AG | 412 (40.43) | 446 (39.19) | 0.48 | 0.49 | |
| GG | 81 (7.95) | 72 (6.33) | |||
| rs2070215 | AA | 465 (45.63) | 554 (48.68) | ||
| AG | 424 (41.61) | 480 (42.18) | < 0.01 | 1.00 | |
| GG | 130 (12.76) | 104 (9.14) | |||
| rs2261360 | CC | 460 (45.14) | 484 (42.53) | ||
| CA | 433 (42.49) | 497 (43.67) | 2.61 | 0.11 | |
| AA | 126 (12.37) | 157 (13.80) | |||
| rs3176320 | AA | 579 (56.82) | 687 (60.37) | ||
| GA | 383 (37.59) | 377 (33.13) | 5.05 | 0.02 | |
| GG | 57 (5.59) | 74 (6.50) | |||
| rs3734166 | AA | 421 (41.32) | 421 (36.99) | ||
| GA | 481 (47.20) | 539 (47.36) | 0.06 | 0.8 | |
| GG | 117 (11.48) | 178 (15.64) | |||
| rs4858770 | CC | 445 (43.67) | 465 (40.86) | ||
| CT | 461 (45.24) | 515 (45.25) | 0.65 | 0.42 | |
| TT | 113 (11.09) | 158 (13.88) | |||
| rs515255 | CC | 408 (40.04) | 411 (36.12) | ||
| TC | 469 (46.03) | 553 (48.59) | 0.29 | 0.59 | |
| TT | 142 (13.94) | 174 (15.29) | |||
| rs8025774 | CC | 313 (30.72) | 335 (29.44) | ||
| CT | 514 (50.44) | 547 (48.07) | 1.32 | 0.25 | |
| TT | 192 (18.84) | 256 (22.50) |
HCC: Hepatocellular carcinoma; SNP: Single nucleotide polymorphism; HWE: Hardy-Weinberg equilibrium
Association analysis of genetic polymorphisms and HCC
The association between SNPs and the risk of HCC was examined using unconditional logistic regression analysis. According to the crude ORs and their 95%CIs, SMAD3 rs11556090 AG or AG/GG and MCM7 rs2070215 GG carried an increased risk of HCC when compared with the wild genotype SMAD3 rs11556090 AA and MCM7 rs2070215 AA, respectively. Individuals with CDC25C rs3734166 GG or GA/GG and KAT2B rs4858770 TT had a lower risk of HCC than those with the wild genotype CDC25C rs3734166 AA and KAT2B rs4858770 CC, respectively. However, the association disappeared after adjusting for age, sex, nationality, smoking, drinking, family history of HCC, and HBV infection. Using individuals with the wild genotype AA as the reference, individuals carrying the GG variant of KAT2B rs17006625 had a higher risk of HCC (adjusted OR = 1.64, 95%CI: 1.01-2.64, P = 0.04) after adjusting for confounding factors. In addition, compared with the wild genotypes MCM4 rs2305952 TT and CHEK1 rs515255 CC, individuals carrying the CC variant of MCM4 rs2305952 or the TC, TT, TC/TT variants of CHEK1 rs515255 had a significantly lower risk of HCC (adjusted OR = 0.22, 95%CI: 0.08-0.63, P = 0.01; adjusted OR = 0.73, 95%CI: 0.56-0.96, P = 0.02; adjusted OR = 0.67, 95%CI: 0.46-0.97, P = 0.04; adjusted OR = 0.72, 95%CI: 0.56-0.92, P = 0.01, respectively). The associations are shown in Table 4.
Table 4.
Associations between single nucleotide polymorphisms with the risk of hepatocellular carcinoma
| SNP | Genotype | OR (95%CI)1 | P value1 | OR (95%CI)2 | P value2 |
| rs2305952 | TT | Reference | Reference | ||
| TC | 0.97 (0.79-1.19) | 0.76 | 0.97 (0.72-1.32) | 0.85 | |
| CC | 0.58 (0.26-1.32) | 0.19 | 0.22 (0.08-0.63) | 0.01a | |
| TC/CC | 0.94 (0.77-1.16) | 0.57 | 0.89 (0.66-1.19) | 0.43 | |
| rs2425675 | GG | Reference | Reference | ||
| AG | 1.07 (0.89-1.28) | 0.49 | 1.92 (0.71-1.20) | 0.54 | |
| AA | 1.12 (0.71-1.76) | 0.63 | 0.97 (0.51-1.85) | 0.93 | |
| AG/AA | 1.07 (0.90-1.28) | 0.44 | 0.93 (0.72-1.20) | 0.56 | |
| rs3088440 | GG | Reference | Reference | ||
| GA | 0.90 (0.74-1.09) | 0.29 | 1.02 (0.76-1.35) | 0.92 | |
| AA | 0.87 (0.48-1.58) | 0.64 | 1.46 (0.62-3.44) | 0.38 | |
| GA/AA | 0.90 (0.74-1.09) | 0.26 | 1.04 (0.79-1.37) | 0.77 | |
| rs3917148 | AA | Reference | Reference | ||
| CA | 1.13 (0.92-1.39) | 0.24 | 1.18 (0.88-1.59) | 0.28 | |
| CC | 0.71 (0.35-1.42) | 0.33 | 1.05 (0.41-2.68) | 0.92 | |
| CA/CC | 1.10 (0.90-1.34) | 0.37 | 1.17 (0.88-1.56) | 0.29 | |
| rs3929 | GG | Reference | Reference | ||
| GC | 0.98 (0.82-1.18) | 0.84 | 0.97 (0.75-1.26) | 0.82 | |
| CC | 1.03 (0.69-1.53) | 0.88 | 1.39 (0.80-2.42) | 0.25 | |
| GC/CC | 0.99 (0.83-1.18) | 0.89 | 1.02 (0.79-1.30) | 0.90 | |
| rs6987652 | GG | Reference | Reference | ||
| AG | 1.06 (0.87-1.29) | 0.60 | 0.92 (0.69-1.23) | 0.59 | |
| AA | 1.14 (0.65-1.99) | 0.66 | 1.26 (0.55-2.88) | 0.59 | |
| AG/AA | 1.06 (0.88-1.29) | 0.54 | 0.95 (0.72-1.25) | 0.71 | |
| rs11556090 | AA | Reference | Reference | ||
| AG | 1.23 (1.02-1.47) | 0.03 | 1.11 (0.85-1.44) | 0.44 | |
| GG | 1.26 (0.82-1.94) | 0.29 | 1.02 (0.54-1.91) | 0.96 | |
| AG/GG | 1.23 (1.03-1.47) | 0.02 | 1.10 (0.85-1.42) | 0.47 | |
| rs17006625 | AA | Reference | Reference | ||
| AG | 1.09 (0.91-1.30) | 0.35 | 1.07 (0.83-1.38) | 0.61 | |
| GG | 1.33 (0.95-1.86) | 0.10 | 1.64 (1.01-2.64) | 0.04a | |
| AG/GG | 1.12 (0.95-1.33) | 0.18 | 1.14 (0.89-1.46) | 0.29 | |
| rs2070215 | AA | Reference | Reference | ||
| AG | 1.05 (0.88-1.26) | 0.58 | 0.95 (0.73-1.24) | 0.71 | |
| GG | 1.49 (1.12-1.98) | 0.01 | 1.39 (0.93-2.08) | 0.11 | |
| AG/GG | 1.13 (0.95-1.34) | 0.16 | 1.03 (0.81-1.32) | 0.81 | |
| rs2261360 | CC | Reference | Reference | ||
| CA | 0.92 (0.77-1.10) | 0.35 | 0.84 (0.64-1.09) | 0.19 | |
| AA | 0.84 (0.65-1.10) | 0.21 | 0.89 (0.60-1.31) | 0.55 | |
| CA/AA | 0.90 (0.76-1.07) | 0.22 | 0.85 (0.66-1.09) | 0.19 | |
| rs3734166 | AA | Reference | Reference | ||
| GA | 0.89 (0.74-1.07) | 0.22 | 0.92 (0.71-1.21) | 0.56 | |
| GG | 0.66 (0.50-0.86) | 0.002 | 0.86 (0.59-1.25) | 0.43 | |
| GA/GG | 0.83 (0.70-0.99) | 0.04 | 0.91 (0.71-1.17) | 0.45 | |
| rs4858770 | CC | Reference | Reference | ||
| CT | 0.94 (0.78-1.12) | 0.47 | 0.96 (0.74-1.24) | 0.74 | |
| TT | 0.75 (0.57-0.98) | 0.04 | 0.80 (0.54-1.20) | 0.28 | |
| CT/TT | 0.89 (0.75-1.06) | 0.19 | 0.92 (0.72-1.18) | 0.51 | |
| rs515255 | CC | Reference | Reference | ||
| TC | 0.85 (0.71-1.03) | 0.09 | 0.73 (0.56-0.96) | 0.02a | |
| TT | 0.82 (0.63-1.07) | 0.14 | 0.67 (0.46-0.97) | 0.04a | |
| TC/TT | 0.85 (0.71-1.01) | 0.06 | 0.72 (0.56-0.92) | 0.01a | |
| rs8025774 | CC | Reference | Reference | ||
| CT | 1.01 (0.83-1.22) | 0.95 | 0.95 (0.72-1.27) | 0.74 | |
| TT | 0.80 (0.63-1.02) | 0.08 | 0.94 (0.66-1.32) | 0.71 | |
| CT/TT | 0.94 (0.78-1.13) | 0.52 | 0.95 (0.73-1.24) | 0.69 |
OR and 95%CI without adjusting for confounding factors;
OR and 95%CI after adjusting for age, sex, nationality, smoking, drinking, family history of hepatocellular carcinoma, and HBV infection.
P < 0.05 was considered statistically significant. OR: Odds ratio; CI: Confidence interval; SNPs: Single nucleotide polymorphisms.
Association between SNPs and HCC risk stratified by behavioral factors
HBV infection, alcohol intake status, and smoking status are important behavioral factors that can increase the risk of HCC. To account for the role of these factors, a stratified analysis was conducted. Thus, when the patients were stratified, we found that the variant genotype CC of MCM4 rs2305952 was associated with a significantly lower risk of HCC among HBsAg-positive individuals, non-drinkers, and non-smokers (adjusted OR = 0.25, 95%CI: 0.08-0.80, P = 0.02; adjusted OR = 0.19, 95%CI: 0.06-0.60, P = 0.004; adjusted OR = 0.17, 95%CI: 0.05-0.56, P = 0.004, respectively). The variant genotypes TC, TT, and TC/TT of CHEK1 rs515255 were associated with a significantly lower risk of HCC in HBsAg-negative individuals (adjusted OR = 0.64, 95%CI: 0.46-0.89, P = 0.01; adjusted OR = 0.69, 95%CI: 0.36-0.96, P = 0.03; adjusted OR = 0.63, 95%CI: 0.46-0.86, P = 0.003) and in non-drinkers (adjusted OR = 0.73, 95%CI: 0.54-0.99, P = 0.05; adjusted OR = 0.56, 95%CI: 0.36-0.86, P = 0.01; adjusted OR = 0.69, 95%CI: 0.52-0.92, P = 0.01, respectively). Among smokers, those with the TC variant genotype of CHEK1 rs515255 had a significantly lower risk of HCC (adjusted OR = 0.54, 95%CI: 0.32-0.93, P = 0.03), while among non-smokers the risk was significantly lower in those with the TT variant genotype (adjusted OR = 0.60, 95%CI: 0.39-0.94, P = 0.03). In addition, the variant genotype GG of KAT2B rs17006625 was shown to carry a significantly higher risk of HCC among HBsAg-negative individuals (adjusted OR = 1.79, 95%CI: 1.02-3.12, P = 0.04). These findings are summarized in Tables 5, 6 and 7 (only significant SNPs are shown).
Table 5.
Stratified analysis on the association between single nucleotide polymorphism genotype and hepatocellular carcinoma risk according to hepatitis B virus infection status
| SNP |
HBsAg-positive |
HBsAg-negative |
||||||
| Case | Control | OR (95%CI)1 | P value1 | Case | Control | OR (95%CI)1 | P value1 | |
| rs2305952 | ||||||||
| TT | 624 | 80 | Reference | 177 | 803 | Reference | ||
| TC | 161 | 24 | 0.86 (0.53-1.42) | 0.56 | 48 | 214 | 1.05 (0.72-1.52) | 0.80 |
| CC | 9 | 5 | 0.25 (0.08-0.80) | 0.02a | 0 | 12 | - | 1.00 |
| TC/CC | 170 | 29 | 0.76 (0.48-1.21) | 0.25 | 48 | 226 | 0.99 (0.68-1.43) | 0.95 |
| rs17006625 | ||||||||
| AA | 411 | 60 | Reference | 115 | 560 | Reference | ||
| AG | 323 | 42 | 1.15 (0.75-1.76) | 0.54 | 89 | 404 | 1.07 (0.77-1.48) | 0.68 |
| GG | 60 | 7 | 1.36 (0.59-3.17) | 0.47 | 21 | 65 | 1.79 (1.02-3.12) | 0.04a |
| AG/GG | 383 | 49 | 1.18 (0.78-1.77) | 0.44 | 110 | 469 | 1.17 (0.86-1.59) | 0.32 |
| rs515255 | ||||||||
| CC | 301 | 39 | Reference | 107 | 372 | Reference | ||
| TC | 377 | 52 | 0.93 (0.59-1.46) | 0.75 | 92 | 501 | 0.64 (0.46-0.89) | 0.01a |
| TT | 116 | 18 | 0.81 (0.44-1.50) | 0.51 | 26 | 156 | 0.69 (0.36-0.96) | 0.03a |
| TC/TT | 493 | 70 | 0.90 (0.59-1.37) | 0.62 | 118 | 657 | 0.63 (0.46-0.86) | 0.003a |
OR and 95%CI after adjusting for age, sex, nationality, smoking, drinking and family history of hepatocellular carcinoma.
P < 0.05 was considered statistically significant. OR: Odds ratio; CI: Confidence interval; SNPs: Single nucleotide polymorphisms.
Table 6.
Stratified analysis on the association between single nucleotide polymorphism genotype and hepatocellular carcinoma risk according to drinking status
| SNP |
Drinking |
Non-drinking |
||||||
| Case | Control | OR (95%CI)1 | P value1 | Case | Control | OR (95%CI)1 | P value1 | |
| rs2305952 | ||||||||
| TT | 273 | 111 | Reference | 528 | 772 | Reference | ||
| TC | 69 | 33 | 0.82 (0.44-1.52) | 0.53 | 140 | 205 | 1.02 (0.72-1.44) | 0.93 |
| CC | 3 | 1 | 0.51 (0.03-9.74) | 0.66 | 6 | 16 | 0.19 (0.06-0.60) | 0.004a |
| TC/CC | 72 | 34 | 0.81 (0.44-1.49) | 0.49 | 146 | 221 | 0.91 (0.65-1.27) | 0.57 |
| rs515255 | ||||||||
| CC | 145 | 56 | Reference | 263 | 355 | Reference | ||
| TC | 154 | 71 | 0.69 (0.40-1.19) | 0.18 | 315 | 482 | 0.73 (0.54-0.99) | 0.05a |
| TT | 46 | 18 | 1.10 (0.50-2.43) | 0.82 | 96 | 156 | 0.56 (0.36-0.86) | 0.01a |
| TC/TT | 200 | 89 | 0.77 (0.46-1.29) | 0.31 | 411 | 638 | 0.69 (0.52-0.92) | 0.01a |
OR and 95%CI after adjusting for age, sex, nationality, smoking, family history of hepatocellular carcinoma, and hepatitis B virus infection.
P < 0.05 was considered statistically significant. OR: Odds ratio; CI: Confidence interval; SNPs: Single nucleotide polymorphisms.
Table 7.
Stratified analysis on the association between single nucleotide polymorphism genotype and hepatocellular carcinoma risk according to smoking status
| SNP |
Smoking |
Non-smoking |
||||||
| Case | Control | OR (95%CI)1 | P value1 | Case | Control | OR (95%CI)1 | P value1 | |
| rs2305952 | ||||||||
| TT | 274 | 124 | Reference | 527 | 759 | Reference | ||
| TC | 77 | 32 | 1.05 (0.58-1.91) | 0.87 | 132 | 206 | 0.94 (0.66-1.34) | 0.75 |
| CC | 4 | 2 | 0.54 (0.06-4.97) | 0.59 | 5 | 15 | 0.17 (0.05-0.56) | 0.004a |
| TC/CC | 81 | 34 | 1.01 (0.57-1.82) | 0.96 | 137 | 221 | 0.84 (0.60-1.19) | 0.33 |
| rs515255 | ||||||||
| CC | 145 | 53 | Reference | 263 | 358 | Reference | ||
| TC | 155 | 84 | 0.54 (0.32-0.93) | 0.03a | 314 | 469 | 0.81 (0.59-1.10) | 0.17 |
| TT | 55 | 21 | 0.87 (0.41-1.85) | 0.72 | 87 | 153 | 0.60 (0.39-0.94) | 0.03a |
| TC/TT | 210 | 105 | 0.61 (0.67-1.02) | 0.06 | 401 | 622 | 0.75 (0.56-1.01) | 0.06 |
OR and 95%CI after adjusting for age, sex, nationality, drinking, family history of hepatocellular carcinoma, and HBV infection.
P < 0.05 was considered statistically significant. OR: Odds ratio; CI: Confidence interval; SNPs: Single nucleotide polymorphisms.
DISCUSSION
We performed this case-control study to investigate the associations between the 15 SNPs in 12 cell cycle pathway genes and the risk of HCC. The KAT2B rs17006625 GG was associated with an increased risk of HCC. Furthermore, this harmful effect was more marked in HBsAg-negative carriers. Conversely, the CHEK1 rs515255 TC, TT, TC/TT and the MCM4 rs2305952 CC were associated with a decreased risk of HCC. In addition, the risk was markedly lower for those who were carriers of MCM4 rs2305952 CC and were also HBsAg-positive and non-drinking and non-smoking and for those who were carriers of the TC, TT, TC/TT genotype of CHEK1 rs515255 and were also HBsAg-negative and non-drinking. No significant associations were observed between other 12 SNPs and HCC risk.
The cell cycle pathway is one of the most important cellular signaling pathways, as it regulates both cell division and apoptosis. DNA damage readily leads to dysregulation of the cell cycle, which is an essential step in the initiation and development of human malignancies[21-23]. In the present study, we reported that three SNPs in cell cycle pathway genes (MCM4, CHEK1, and KAT2B) were significantly associated with the risk of HCC.
MCM4, a member of the mini-chromosome maintenance family of proteins, which interact with cell cycle checkpoints and recombinant proteins to stabilize the S phase, is essential for the initiation of eukaryotic genome replication[24,25]. Several reports have shown that MCM4 protein is overexpressed in esophageal carcinomas[26], cervical cancer[27], and cervical squamous cell carcinoma[28]. In our study, we found that the polymorphism of MCM4 rs2305952 was associated with a lower risk of HCC. However, the mechanism of MCM4 polymorphisms in HCC development remains unclear. Ishimi et al[29] found that MCM4 is one of the crucial targets of DNA replication checkpoint and the phosphorylation of MCM4, which is caused by the activation of ATR-CHK1 pathway and CDK2, results in the DNA replication through the inactivation of the MCM4/6/7 complex. It is also found that MCM4 mutations may cause tumors by affecting the formation of the MCM4/6/7 complex[30,31].
CHEK1 is a mediator of cell cycle arrest in response to DNA damage. In addition to controlling cell cycle progression[32], it regulates DNA repair[33] and coordinates cell survival and death[34,35]. It is reported that CHEK1 plays an important role in the checkpoint of DNA damage and DNA replication through the ATR-CHK1 pathway[36-38]. Lin et al[39] performed a meta-analysis to explore the association of CHEK1 SNPs with breast cancer in patients registered in the database of the Utah Breast Cancer Study. They found that CHEK1 polymorphisms are significantly associated with the risk of breast cancer. However, in that study common alleles of CHEK1 are not implicated in breast cancer risk or in the survival of breast cancer patients after meta-analysis. Our results showed an association between the CHEK1 rs515255 genetic variant and a decreased risk of HCC, after adjusting for age, sex, nationality, smoking, drinking, family history of HCC, and HBV infection. The conflicting results may reflect the different cancers evaluated and/or differences in the study population. This remains to be clarified in further investigations.
KAT2B, also known as PCAF, encodes the cofactor PCAF (P300/CBP associated factor) of activated nucleoprotein that is important in cell cycle regulation. KAT2B induces cell cycle arrest and/or apoptosis by regulating p53 and affects the acetylation and stability of E2F1 in the presence of DNA damage[40,41]. Overexpression of PCAF was reported in samples of both central nervous system tumors and Wilm’s tumors[42]. In addition, an association between KAT2B gene polymorphisms and several human diseases and behaviors has been reported. For example, the KAT2B SNP rs9829896 is associated with drug abuse in African Americans[43]. We also found that the risk of HCC was higher in individuals with the KAT2B rs17006625 GG genotype than with the AA genotype, after adjusting for age, sex, nationality, smoking, drinking, family history of HCC, and HBV infection.
HBV infection status, drinking status, and smoking status are well known to influence the occurrence and development of HCC[44-47]. Moreover, some genotypes have no effect on HCC risk when considered within a population as a whole, but the subgroup analysis may show an effect on HCC risk among alcohol drinkers and/or smokers[48,49]. Therefore, in our study, we evaluated the role of risk factors such as drinking status and smoking status in a stratified analysis and found that these environmental factors may interact with the analyzed SNPs.
Our study had several limitations. First, the research population was drawn only from the Guangxi Zhuang Autonomous Region. Whether the results apply to the Chinese population as a whole or to other ethnic groups remains to be seen. Second, because our study used a case-control format, recall bias was difficult to avoid. However, we sought to minimize recall bias by choosing patients newly diagnosed with HCC. Finally, the functional influence of the examined SNPs and the potential mechanisms need to be determined in functional validation tests.
In conclusion, MCM4 rs2305952 CC and CHEK1 rs515255 TC, TT, TC/TT may decrease the risk of HCC and KAT2B rs17006625 GG may increase the risk of HCC. In addition, we observed an increased risk associated with KAT2B rs17006625 GG in HBsAg-negative patients. Furthermore, we also observed a decreased risk associated with MCM4 rs2305952 CC in HBsAg-positive patients and in also non-drinking patients and non-smoking patients, and with CHEK1 rs515255 TC, TT, TC/TT in HBsAg-negative patients and in also non-drinking patients. Our results suggest that the genetic variants in the cell cycle pathway genes affect the risk of HCC, however, further studies are needed to confirm the findings.
ACKNOWLEDGMENTS
We sincerely thank the staff of the First Affiliated Hospital of Guangxi Medical University and the Tumor Hospital of Guangxi Medical University for their support in recruiting the study participants. We also thank Da-Fang Chen and his students at the Peking University Health Science Center for technical help.
COMMENTS
Background
The uncontrollable proliferation of cancer cells is a crucial mechanism in cancer development and progression. Previous studies have shown that polymorphisms of cell cycle pathway genes are associated with cancer. However, their relationship with hepatocellular carcinoma (HCC) is unclear.
Research frontiers
Despite reports of an association between polymorphisms in cell cycle pathway genes and cancer risk, little is known about the relationship between these polymorphisms and HCC risk.
Innovations and breakthroughs
This study enrolled 1127 cases newly diagnosed with HCC and 1200 non-tumor patients. It comprehensively investigated the relationship between 15 SNPs in 12 cell cycle pathway genes and HCC risk.
Applications
Since individuals with the KAT2B rs17006625 GG genotype may have an increased risk of HCC, they should be carefully monitored to reduce the occurrence and development of HCC.
Terminology
A single nucleotide polymorphism (SNP) is a variation in the genomic DNA sequence. SNPs in some genes may cause an increased or decreased risk of HCC.
Peer-review
The manuscript is interesting and provides relevant information. The study is a descriptive paper analyzing the polymorphism in HCC in a wide number of patients. The analyses are consistent with the results and the conclusions asserted in the manuscript.
Footnotes
Supported by National Natural Science Foundation of China, No. 81360448; Natural Science Foundation of Guangxi, No. 2014GXNSFAA118139; Fund of Key Laboratory of High-Incidence-Tumor Prevention and Treatment (Guangxi Medical University), Ministry of Education, No. GK2015-ZZ03 and No. GK2014-ZZ03; and Guangxi Outstanding Teacher Training Project for Colleges.
Institutional review board statement: The study was approved by the ethical review committee of Guangxi Medical University.
Informed consent statement: All study participants provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors have declared that they have no competing interests.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Peer-review started: March 7, 2016
First decision: April 14, 2016
Article in press: May 23, 2016
P- Reviewer: Alwahaibi NY, Patial V, Servillo G S- Editor: Yu J L- Editor: Wang TQ E- Editor: Ma S
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