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. 2018 May 29;11:3225–3235. doi: 10.2147/OTT.S162205

Association between KIF1B (rs17401966) polymorphism and hepatocellular carcinoma susceptibility: a meta-analysis

Ya-fei Zhang 1, Xian-ling Zeng 2, Hong-wei Lu 1, Hong Ji 1, Le Lu 1, Peng-di Liu 1, Ruo-feng Hong 1, Yi-ming Li 1,✉
PMCID: PMC5985779  PMID: 29881295

Abstract

Introduction

The results of the earlier published studies on the association between KIF1B (rs17401966) polymorphism and hepatocellular carcinoma (HCC) risk are inconclusive. Hence, we performed this meta-analysis to evaluate the relationship between KIF1B (rs17401966) polymorphism and HCC risk.

Methods

Databases including PubMed, Web of Science and the Cochrane Library and bibliographies of relevant papers were screened to identify relevant studies published up to March 25, 2018. Pooled ORs and 95% CIs were calculated to evaluate the association. The subgroup analysis was conducted based on ethnicity, age, region and environment. A total of 19 studies from 11 eligible articles published from 2010 to 2016, with 8,741 cases and 10,812 controls, were included.

Results

The pooled results indicated that the association between KIF1B (rs17401966) polymorphism and the decreased HCC risk was significant. Subgroup analysis stratified by ethnicity showed the same association in Chinese, but not in non-Chinese population. When stratified by age, both old and young patients showed a decrease in HCC risk. When stratified by region, we detected the same association in Chinese in southern China. Similarly when stratified by environment, we observed the same association in Chinese in inland areas; however, no statistically significant association was observed in those in coastal areas.

Conclusion

This meta-analysis suggested that KIF1B (rs17401966) polymorphism could decrease HCC risk in Chinese and in overall population, but not in non-Chinese. This association remained significant in Chinese in southern China and inland areas, but not in those in northern and central China and coastal areas. Further large-scale multicenter studies are warranted to confirm these findings.

Keywords: KIF1B, rs17401966, hepatocellular carcinoma, polymorphism

Background

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor and the second leading cause of cancer-related deaths in the world.1 The onset of HCC is relatively insidious; in most cases, HCC is diagnosed at advanced stages and is difficult to treat. Presently, surgical resection-based comprehensive treatment is the main treatment for HCC, but with less success rate and high rates of recurrence and metastasis.2 Therefore, improving the early diagnosis is particularly important in the prevention and treatment of HCC. Determining the association between KIF1B (rs17401966) polymorphism and HCC risk provides a promising approach to achieve this goal.

KIF1B is a member of the kinesin superfamily and belongs to N-kinesin, encoding two alternatively spliced isoforms, KIF1Bα and KIF1Bβ. Both the isoforms have the same 660 amino acid residues in the N terminal domain; the main difference between them is the end of the C binding domain, conferring different axonal cargo specificity.3 KIF1B is located in chromosome region 1p36.22 and is an important molecule for intracellular vesicle trafficking and organelle transporting.4,5 In addition to transport function, KIF1B also plays an important role in tumor suppression by promoting apoptosis.6 Studies have shown that deficiency of 1p36 region is very common in the individuals with early-onset HCC, but the phenomenon is not observed in individuals with chronic liver disease. It can be speculated that the abnormal chromosomal regions may be associated with the risk of HCC.7

Through genome-wide association study (GWAS), Zhang et al8 found a significant association between KIF1B rs17401966 polymorphism and HCC, showing that the polymorphism of the site has a protective effect on HCC. However, a consistent conclusion on the correlation between the gene polymorphism and HCC was not reached, which may be caused by differences in race or ethnicity, as well as the difference in sample size.8–18 Therefore, we carried out a meta-analysis of the whole included case–control studies to make a more accurate estimate of the association.

Methods

Literature searching strategy

A comprehensive literature searching for all relevant studies published before March 25, 2018 was conducted in PubMed, Web of Science and the Cochrane Library, using the following keywords: KIF1B/Kinesin family member 1B/rs17401966 and locus/mutation/variant*/genotype/polymorphism*/SNP and ([liver/hepatic/hepatocellular/hepatocellular and carcinom*/cancer/neoplasm*/malign*/tumor] or HCC or hepatoma*) and the combinations. The relevant bibliographies of identified studies were examined for additional articles. Abstracts and citations were screened by two researchers independently, and any disagreements were resolved by discussing with a third reviewer. The full text of all the eligible articles was reviewed during a second screening. There were no language limitations during the retrieval procedure.

Selection and exclusion criteria

All eligible studies included in this meta-analysis met the following inclusion criteria: 1) independent case–control studies performed on humans; 2) evaluated the association between KIF1B (rs17401966) polymorphism and HCC risk; 3) genotype frequencies in case and control groups were available for risk estimate; 4) the diagnosis of the cases was based on pathology; 5) control subjects had no cancer and history of radiotherapy or chemotherapy; and 6) genotype frequencies of the subjects in control groups were in accordance with Hardy–Weinberg equilibrium (HWE). We excluded abstracts, case reports, letters, comments, editorials, reviews, meta-analyses and studies lacking sufficient data. Simultaneously, if the researches were duplicated or shared in more than one study, the most recent publications were included.

Data extraction and synthesis

We used endnote bibliographic software (EndNote X6) to construct an electronic library of citations identified in the literature search. Duplicates were found automatically by endnote and deleted manually. All the extracted data were checked and evaluated twice according to the inclusion criteria listed above by two independent investigators. The following data were extracted from each study: first author, year of publication, country, ethnicity, genotyping method, number of cases and controls, genotype distribution of cases and controls and P-value of HWE in controls. Meanwhile, multicenter studies were divided into several separate studies according to the origin. A third reviewer participated if some disagreements emerged, and a final decision was not made until a consensus was reached.

Quality assessment

The methodological quality assessment was performed based on the modified scoring system used for studies on genetic epidemiological issues.19 Points were awarded on the basis of representativeness of cases, source of controls, HWE in controls, genotyping examination and association assessment. Total score ranged from 0 (lowest quality) to 8 (highest quality). A study with a score of ≥6 was classified to be of high quality.

Statistical analysis

All statistical analyses were carried out using STATA version 11.0 (StataCorp LP, College Station, TX, USA) and Review Manager version 5.2.0 (The Cochrane Collaboration, 2012). Chi-square test was applied to calculate P-value of HWE in controls, and P>0.05 was considered to be consistent with HWE.20 The association of KIF1B (rs17401966) polymorphism and HCC susceptibility was estimated by pooled ORs with 95% CIs under five different genetic models including allele model, dominant model, recessive model, homozygous genetic model and heterozygous genetic model. Z test was used to assess the significance of the ORs. Both Q-statistic test and I2 test were applied to assess the between-study heterogeneity in this meta-analysis. If there was significant heterogeneity among included studies (P-value of Q-statistic was <0.1, or I2 value was >75%), ORs with corresponding 95% CIs were calculated using the random effects model; otherwise, the fixed effects model was selected.20,21 The subgroup analysis was conducted based on ethnicity and age (>50 years or ≤50 years). For studies with Chinese population, we also conducted subgroup analysis by region and environment. Sensitivity analyses were performed to assess the stability of the results. Each study involved in this meta-analysis was deleted each time to reflect the influence of the individual data exerted on the pooled OR. We used Begg’s funnel plot and Egger’s test (P<0.05 was considered significant) to evaluate the publication bias.22,23 All statistical tests were two-sided, and P<0.05 indicated statistical significance.

Results

Characteristics of the included studies

The selection process of eligible studies is presented in Figure 1. A total of 59 relevant articles were preliminarily identified based on our selection strategy. We also identified one article through other sources.18 Thirty-five articles remained after eliminating duplicated literature. Subsequently, 16 obviously irrelevant articles were excluded unquestionably after reviewing their titles and abstracts. Based on the inclusion and exclusion criteria, eight articles were excluded after reviewing the full text. Finally, 11 studies were eventually included in this meta-analysis.8–18 The 11 case–control studies were published between 2010 and 2016. Among them, Zhang et al’s research consisting of five independent studies was divided into five studies.8 Similarly, Li et al’s and Sawai et al’s articles were divided into two and four studies, respectively.13,15 Thus, a total of 19 studies from 11 articles with 8,741 cases and 10,812 controls were included in this meta-analysis. A summary of the characteristics of the 19 studies, including first author, year of publication, country, ethnicity, genotyping method, age of cases, number of cases and controls, P-value of HWE and quality score, is shown in Table 1. Based on quality assessment, all studies were considered to be of high quality (quality scores of these studies were 6–8).

Figure 1.

Figure 1

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Flowchart of studies selection in this meta-analysis.

Table 1.

Characteristics of the studies included in the meta-analysis

First author Year Country Ethnicity Genotyping method Age Number (case/control) HWE Quality score
Chen et al9 2013 China Chinese (Beijing) TaqMan 53.9 503/772 0.646837 6
Chen et al10 2016 China Chinese (Guangdong) TaqMan 55.84 306/306 0.05846 7
Hu et al11 2012 China Chinese (Jiangsu) TaqMan 52.9 1,293/2,671 0.05058 6
Jiang et al12 2013 China Chinese (Jiangsu) TaqMan 51.6 1,161/1,353 0.982272 8
Li et al13 2012 China Chinese (Guangdong) iPLEX or TaqMan 49.3 1,058/981 0.975939 6
Li et al13 2012 China Chinese (Shanghai) iPLEX or TaqMan 49.3 480/484 0.962279 6
Pan et al14 2015 China Chinese (Fujian) MassARRAY Typer 4.0 61.7 376/403 0.132385 8
Sawai et al15 2012 Japan Japanese PCR 62 179/769 0.31108 7
Sawai et al15 2012 Japan Japanese TaqMan 61.3 142/251 0.970885 7
Sawai et al15 2012 Japan Korean TaqMan 52.2 164/144 0.325085 7
Sawai et al15 2012 Japan Chinese (Hong Kong) TaqMan 58 93/187 0.466716 7
Sopipong et al16 2013 Thailand Thais PCR 59.8 202/196 0.764716 6
Su et al17 2014 China Chinese (Fujian) MALDI-TOF-MS NR 160/160 0.71155 6
Su18 2015 China Chinese (Fujian) MALDI-TOF NR 314/346 0.405123 6
Zhang et al8 2010 China Chinese (Guangxi) Affymetrix 45.8 348/359 0.98702 7
Zhang et al8 2010 China Chinese (Beijing) Affymetrix 55.9 276/266 0.805902 7
Zhang et al8 2010 China Chinese (Jiangsu) Affymetrix 52.7 507/215 0.393367 7
Zhang et al8 2010 China Chinese (Guangdong) Affymetrix 49.3 751/509 0.906845 7
Zhang et al8 2010 China Chinese (Shanghai) Affymetrix 50.6 428/440 0.777482 7
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Abbreviations: HWE, Hardy–Weinberg equilibrium; NR, not reported; PCR, polymerase chain reaction; MALDI-TOF-MS, matrix-associated laser desorption ionization-time of flight-mass spectrometry.

Meta-analysis results

The genotype distribution and allele frequencies of KIF1B (rs17401966) polymorphism in cases and controls are listed in Table 2. The main results of our study are shown in Tables 3 and 4.

Table 2.

KIF1B (rs17401966) polymorphisms genotype distribution and allele frequency in cases and controls

First author Year Genotype (N)
Allele frequency (N)
Case
Control
Case
Control
Total AA AG GG Total AA AG GG A G A G
Chen et al9 2013 503 63 194 246 772 65 309 398 320 686 439 1,105
Chen et al10 2016 306 21 126 159 306 18 138 150 168 444 174 438
Hu et al11 2012 1,293 107 480 706 2,671 231 1,038 1,402 694 1,892 1,500 3,842
Jiang et al12 2013 1,161 84 458 619 1,353 106 546 701 626 1,696 758 1,948
Li et al13 2012 1,058 77 417 564 981 77 395 509 571 1,545 549 1,413
Li et al13 2012 480 35 189 256 484 41 199 244 259 701 281 687
Pan et al14 2015 376 34 138 204 403 53 167 183 206 546 273 533
Sawai et al15 2012 179 13 61 105 769 45 261 463 87 271 351 1,187
Sawai et al15 2012 142 5 46 91 251 14 91 146 56 228 119 383
Sawai et al15 2012 164 17 59 88 144 15 55 74 93 235 85 203
Sawai et al15 2012 93 10 39 44 187 13 80 94 59 127 106 268
Sopipong et al16 2013 202 21 81 100 196 16 83 97 123 281 115 277
Su et al17 2014 160 24 60 76 160 16 66 78 108 212 98 222
Su18 2015 314 32 153 129 346 26 149 171 217 411 201 491
Zhang et al8 2010 348 8 100 240 359 26 141 192 116 580 193 525
Zhang et al8 2010 276 5 86 185 266 24 109 133 96 456 157 375
Zhang et al8 2010 507 26 181 300 215 21 101 93 233 781 143 287
Zhang et al8 2010 751 26 228 497 509 35 195 279 280 1,222 265 753
Zhang et al8 2010 428 12 141 275 440 32 169 239 165 691 233 647
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Table 3.

Overall meta-analysis results with subgroup conducted by ethnicity and age

Outcome or subgroup Studies Participants Statistical method Effect estimate P-value Heterogeneity
I2 P-value
Allele model
 Overall 19 39,106 OR (M–H, random, 95% CI) 0.87 (0.78, 0.97) 0.01 80% <0.00001
 Chinese 15 35,012 OR (M–H, random, 95% CI) 0.84 (0.74, 0.96) 0.009 84% <0.00001
 Non-Chinese 4 4,094 OR (M–H, fixed, 95% CI) 0.98 (0.84, 1.15) 0.84 0% 0.53
 >50 years 13 27,206 OR (M–H, random, 95% CI) 0.86 (0.76, 0.98) 0.02 77% <0.00001
 ≤50 years 4 9,940 OR (M–H, random, 95% CI) 0.75 (0.59, 0.97) 0.03 85% 0.0001
Dominant model
 Overall 19 19,553 OR (M–H, random, 95% CI) 0.84 (0.74, 0.94) 0.003 72% <0.00001
 Chinese 15 17,506 OR (M–H, random, 95% CI) 0.81 (0.71, 0.93) 0.003 78% <0.00001
 Non-Chinese 4 2,047 OR (M–H, fixed, 95% CI) 0.95 (0.78, 1.16) 0.63 0% 0.71
 >50 years 13 13,603 OR (M–H, random, 95% CI) 0.83 (0.73, 0.95) 0.006 66% 0.0004
 ≤50 years 4 4,970 OR (M–H, random, 95% CI) 0.73 (0.56, 0.96) 0.03 82% 0.001
Recessive model
 Overall 19 19,553 OR (M–H, random, 95% CI) 0.85 (0.69, 1.04) 0.12 67% <0.0001
 Chinese 15 17,506 OR (M–H, random, 95% CI) 0.80 (0.63, 1.02) 0.08 73% <0.00001
 Non-Chinese 4 2,047 OR (M–H, fixed, 95% CI) 1.09 (0.75, 1.57) 0.66 0% 0.64
 >50 years 13 13,603 OR (M–H, random, 95% CI) 0.85 (0.66, 1.11) 0.23 67% 0.0003
 ≤50 years 4 4,970 OR (M–H, random, 95% CI) 0.64 (0.41, 0.99) 0.04 68% 0.03
Homozygous genetic model
 Overall 19 12,024 OR (M–H, random, 95% CI) 0.79 (0.62, 1.00) 0.05 74% <0.00001
 Chinese 15 10,714 OR (M–H, random, 95% CI) 0.74 (0.56, 0.98) 0.03 79% <0.00001
 Non-Chinese 4 1,310 OR (M–H, fixed, 95% CI) 1.06 (0.72, 1.54) 0.77 0% 0.58
 >50 years 13 8,366 OR (M–H, random, 95% CI) 0.79 (0.59, 1.06) 0.11 73% <0.0001
 ≤50 years 4 3,106 OR (M–H, random, 95% CI) 0.57 (0.34, 0.95) 0.03 76% 0.006
Heterozygote comparison
 Overall 19 18,059 OR (M–H, random, 95% CI) 0.84 (0.76, 0.93) 0.0009 56% 0.002
 Chinese 15 16,158 OR (M–H, random, 95% CI) 0.83 (0.74, 0.93) 0.001 64% 0.0003
 Non-Chinese 4 1,901 OR (M–H, fixed, 95% CI) 0.93 (0.76, 1.15) 0.52 0% 0.87
 >50 years 13 12,532 OR (M–H, random, 95% CI) 0.85 (0.76, 0.94) 0.002 39% 0.07
 ≤50 years 4 4,645 OR (M–H, random, 95% CI) 0.77 (0.60, 0.97) 0.03 74% 0.01
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Abbreviation: M–H, Mantel–Haenszel.

Table 4.

Subgroup meta-analysis results of Chinese conducted by region and environment

Outcome or subgroup Studies Participants Statistical method Effect estimate P-value Heterogeneity
I2 P-value
Allele model
 Overall 15 35,012 OR (M–H, random, 95% CI) 0.84 (0.74, 0.96) 0.009 84% <0.00001
 Northern China 2 3,634 OR (M–H, random, 95% CI) 0.77 (0.34, 1.78) 0.55 96% <0.00001
 Central China 8 21,582 OR (M–H, random, 95% CI) 0.88 (0.76, 1.01) 0.07 79% <0.0001
 Southern China 5 9,796 OR (M–H, random, 95% CI) 0.81 (0.63, 1.04) 0.1 84% <0.0001
 Inland areas 6 19,448 OR (M–H, random, 95% CI) 0.76 (0.61, 0.96) 0.02 90% <0.00001
 Coastal areas 9 15,564 OR (M–H, random, 95% CI) 0.90 (0.77, 1.05) 0.18 77% <0.0001
Dominant model
 Overall 15 17,506 OR (M–H, random, 95% CI) 0.81 (0.71, 0.93) 0.003 78% <0.00001
 Northern China 2 1,817 OR (M–H, random, 95% CI) 0.75 (0.34, 1.66) 0.48 93% 0.0001
 Central China 8 10,791 OR (M–H, random, 95% CI) 0.85 (0.72, 1.01) 0.06 74% 0.0003
 Southern China 5 4,898 OR (M–H, random, 95% CI) 0.77 (0.59, 1.01) 0.05 78% 0.001
 Inland areas 6 9,724 OR (M–H, random, 95% CI) 0.73 (0.58, 0.94) 0.01 86% <0.00001
 Coastal areas 9 7,782 OR (M–H, random, 95% CI) 0.87 (0.73, 1.03) 0.11 69% 0.001
Recessive model
 Overall 15 17,506 OR (M–H, random, 95% CI) 0.80 (0.63, 1.02) 0.08 73% <0.00001
 Northern China 2 1,817 OR (M–H, random, 95% CI) 0.57 (0.07, 4.64) 0.6 94% <0.0001
 Central China 8 10,791 OR (M–H, random, 95% CI) 0.84 (0.65, 1.08) 0.17 60% 0.01
 Southern China 5 4,898 OR (M–H, random, 95% CI) 0.76 (0.47, 1.24) 0.27 71% 0.008
 Inland areas 6 9,724 OR (M–H, random, 95% CI) 0.68 (0.44, 1.06) 0.09 83% <0.0001
 Coastal areas 9 7,782 OR (M–H, random, 95% CI) 0.87 (0.65, 1.17) 0.37 63% 0.006
Homozygous genetic model
 Overall 15 10,714 OR (M–H, random, 95% CI) 0.74 (0.56, 0.98) 0.03 79% <0.00001
 Northern China 2 1,119 OR (M–H, random, 95% CI) 0.51 (0.05, 5.19) 0.57 95% <0.0001
 Central China 8 6,556 OR (M–H, random, 95% CI) 0.78 (0.58, 1.06) 0.12 72% 0.0009
 Southern China 5 3,039 OR (M–H, random, 95% CI) 0.70 (0.40, 1.22) 0.2 77% 0.002
 Inland areas 6 5,981 OR (M–H, random, 95% CI) 0.60 (0.36, 0.98) 0.04 87% <0.00001
 Coastal areas 9 4,733 OR (M–H, random, 95% CI) 0.84 (0.59, 1.18) 0.31 71% 0.0006
Heterozygote comparison
 Overall 15 16,158 OR (M–H, random, 95% CI) 0.83 (0.74, 0.93) 0.001 64% 0.0003
 Northern China 2 1,660 OR (M–H, random, 95% CI) 0.77 (0.44, 1.36) 0.37 86% 0.008
 Central China 8 9,911 OR (M–H, random, 95% CI) 0.87 (0.75, 1.00) 0.06 62% 0.01
 Southern China 5 4,587 OR (M–H, random, 95% CI) 0.78 (0.63, 0.98) 0.03 66% 0.02
 Inland areas 6 8,958 OR (M–H, random, 95% CI) 0.77 (0.63, 0.94) 0.01 77% 0.0005
 Coastal areas 9 7,200 OR (M–H, random, 95% CI) 0.87 (0.75, 1.01) 0.06 53% 0.03
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Abbreviation: M–H, Mantel–Haenszel.

As shown in Table 3 and Figure 2, the pooled results indicated that the association between KIF1B (rs17401966) polymorphism and the decreased occurrence of HCC was significant in overall population in three genetic models: allele model (OR=0.87, 95% CI=0.78–0.97, P=0.01), dominant model (OR=0.84, 95% CI=0.74–0.94, P=0.003) and heterozygote comparison (OR=0.84, 95% CI=0.76–0.93, P=0.0009). The subgroup analysis stratified by ethnicity showed the same association in Chinese population (allele model: OR=0.84, 95% CI=0.74–0.96, P=0.009; dominant model: OR=0.81, 95% CI=0.71–0.93, P=0.003; homozygous genetic model: OR=0.74, 95% CI=0.56–0.98, P=0.03; heterozygote comparison: OR=0.83, 95% CI=0.74–0.93, P=0.001) (Figure 3), while no genetic model showed significant association in non-Chinese. When stratified by age, we found that both old (allele model: OR=0.86, 95% CI=0.76–0.98, P=0.02; dominant model: OR=0.83, 95% CI=0.73–0.95, P=0.006; heterozygote comparison: OR=0.85, 95% CI=0.76–0.94, P=0.002) and young patients (allele model: OR=0.75, 95% CI=0.59–0.97, P=0.03; dominant model: OR=0.73, 95% CI=0.56–0.96, P=0.03; recessive model: OR=0.64, 95% CI=0.41–0.99, P=0.04; homozygous genetic model: OR=0.57, 95% CI=0.34–0.95, P=0.03; heterozygote comparison: OR=0.77, 95% CI=0.60–0.97, P=0.03) showed a significant association between KIF1B (rs17401966) polymorphism and decreased HCC risk (Figure 4).

Figure 2.

Figure 2

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Forest plots of the KIF1B (rs17401966) polymorphism and hepatocellular carcinoma risk in overall population (heterozygous genetic model, AG vs GG).

Abbreviations: df, degrees of freedom; M–H, Mantel–Haenszel.

Figure 3.

Figure 3

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Forest plots of the KIF1B (rs17401966) polymorphism and hepatocellular carcinoma risk in Chinese subgroup (heterozygous genetic model, AG vs GG).

Abbreviations: df, degrees of freedom; M–H, Mantel–Haenszel.

Figure 4.

Figure 4

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Forest plots of the KIF1B (rs17401966) polymorphism and hepatocellular carcinoma risk in subgroup stratified by age (heterozygous genetic model, AG vs GG).

Abbreviations: df, degrees of freedom; M–H, Mantel–Haenszel; NR, not reported.

For studies with Chinese population, we also conducted subgroup analysis by region and environment. As shown in Table 4, when stratified by region (northern China, central China, southern China), we detected an association of the KIF1B (rs17401966) polymorphism with decreased HCC risk in Chinese in southern China based on heterozygote comparison (OR=0.78, 95% CI=0.63–0.98, P=0.03) (Figure 5). When stratified by environment (inland areas, coastal areas), we observed an association between decreased HCC risk and KIF1B (rs17401966) polymorphism in Chinese in inland areas (allele model: OR=0.76, 95% CI=0.61–0.96, P=0.02; dominant model: OR=0.73, 95% CI=0.58–0.94, P=0.01; homozygous genetic model: OR=0.60, 95% CI=0.36–0.98, P=0.04; heterozygote comparison: OR=0.77, 95% CI=0.63–0.94, P=0.01) (Figure 6); however, no statistically significant association was observed in those in coastal areas.

Figure 5.

Figure 5

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Forest plots of the KIF1B (rs17401966) polymorphism and hepatocellular carcinoma risk in subgroup stratified by region (heterozygous genetic model, AG vs GG).

Abbreviations: df, degrees of freedom; M–H, Mantel–Haenszel.

Figure 6.

Figure 6

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Forest plots of the KIF1B (rs17401966) polymorphism and hepatocellular carcinoma risk in subgroup stratified by environment (heterozygous genetic model, AG vs GG).

Abbreviations: df, degrees of freedom; M–H, Mantel–Haenszel.

Sensitivity analyses

As shown in Table 1, all the studies were in line with the balance of HWE in control groups. To evaluate the stability of our results, we performed sensitivity analysis to assess the effect of each individual study on the pooled ORs. After excluding each study sequentially, the corresponding ORs were not substantially changed, suggesting that the results of our meta-analysis were stable and reliable.

Heterogeneity analysis

Heterogeneity among studies was assessed by Q-statistic. Random effects models were applied if P-value of heterogeneity tests was ≤0.1 or I2 was ≥75% (P≤0.1 or I2≥75%), otherwise, fixed effects models were selected (Tables 3 and 4).

Publication bias

Begg’s test, Egger’s test and funnel plot were all used to evaluate the publication bias of the included studies. No significant publication bias was found in Begg’s and Egger’s test (P>0.05). Funnel plot also indicated that publication bias did not exist with no obvious asymmetry that could be observed (Figure 7).

Figure 7.

Figure 7

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Funnel plot assessing evidence of publication bias from 19 studies (heterozygous genetic model, AG vs GG).

Abbreviation: SE, standard error.

Discussion

GWASs have been shown to be unbiased and effective in exploring disease phenotype-associated single-nucleotide polymorphism (SNP). Currently, a large number of GWASs have been reported, most of which are about cancer.24 Epidemiological and experimental studies have shown that HCC is a complex disease that occurs due to multiple factors, including viral, environmental and genetic factors. With the same environmental background, a small number of people suffer from HCC, whereas others do not, which also shows the importance of genotype. GWASs have found a number of HCC-associated SNPs, such as K1F1B, MICA, HLA-DQA/DQB, SL47W and so on.12,13,25,26 The existence of genetic etiology of HCC is further confirmed. Identification of HCC susceptibility genes and gene-related molecular mechanisms will provide a theoretical basis for the prevention and clinical diagnosis of HCC and treatment of population at high HCC risk. It is expected to achieve early prevention and individualized treatment of HCC and to improve the therapeutic effect of HCC.

Through GWAS, Zhang et al8 found a significant association between KIF1B rs17401966 polymorphism and HCC, showing that the polymorphism of the site has a protective effect on HCC. However, a consistent conclusion on the correlation between the gene polymorphism and HCC was not reached.8–18 Hence, we performed this meta-analysis aiming to illuminate the association between KIF1B (rs17401966) polymorphism and HCC. The pooled results of our study indicated that the association was significant. Subgroup analysis stratified by ethnicity showed the same association in Chinese population, but not in non-Chinese. All the above results were consistent with the results of the meta-analysis of Zhang et al27 and Wang et al.28 However, the number of included papers in their analysis was less than that in our study. When stratified by age, both old and young patients showed decreased HCC risk, which was consistent with the results of Zhang et al’s27 study. When stratified by region (northern China, central China, southern China), we detected an association between KIF1B (rs17401966) polymorphism and decreased HCC risk in Chinese in southern China. When stratified by environment (inland areas, coastal areas), we observed the same association in Chinese in Inland areas; however, no statistically significant association was observed in those in coastal areas. It was the first subgroup analysis on Chinese population stratified by region and environment.

Zhang et al27 also performed subgroup analysis by gender and found that KIF1B rs17401966 polymorphism was significantly associated with HCC in men but not in women. However, the number of papers from which gender data were extracted for their study was only five, and the sample size of women was extremely small. Therefore, we should interpret the results of their study with caution. Zhang et al27 also performed subgroup analysis based on sample sizes and quality scores and found that rs17401966 polymorphism was significantly associated with reduced HCC risk in studies with large sample size and of high quality; however, no significant associations were found in studies with small sample size and of low quality. However, we should realize that small sample sizes and low-quality scores were sources for this heterogeneity, so subgroup analyses stratified by sample sizes and quality scores may not be appropriate.

Nevertheless, some limitations of our meta-analysis should be addressed. First, we could not obtain all the raw data of the patients and hence could not conduct subgroup analysis by sex, hepatitis, liver function and other variables. We also failed to clarify gene–gene and gene–environment interactions in the occurrence and development of HCC. Second, only published studies were included in this meta-analysis; however, some unpublished papers may exist and conform to our inclusion criteria. Therefore, publication bias may have appeared, although no statistical evidence was found. Third, our research is only a comprehensive analysis of existing data. We did not verify the association through basic experiments. Moreover, the included papers were mostly based on Chinese population; only four papers were about non-Chinese. Therefore, data from large-scale multicenter studies based on non-Chinese population are still needed to confirm the association between KIF1B (rs17401966) polymorphism and HCC.

Conclusion

Our meta-analysis indicates that KIF1B (rs17401966) polymorphism could decrease HCC risk in Chinese and in overall population, but not in non-Chinese. This association remained significant in Chinese in southern China and inland areas, but not in those in northern or central China and in coastal areas. Further large-scale multicenter studies are warranted to confirm our findings.

Acknowledgments

This study was funded by National Natural Science Foundation of China (grant numbers 81170454, 30772049 and 30571765).

Footnotes

Disclosure

The authors report no conflicts of interest in this work.

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