Introduction: Association between Cyclin D1 (CCND1) single nucleotide polymorphism (SNP) rs9344 and cancer risk is paradoxical. Thus, we performed a meta-analysis to explore the association between CCND1 variant and overall cancer risk in Indian population. Methods: Data from 12 published studies including 3739 subjects were collected using Pubmed and Embase. RevMan (Review Manager) 5.3 was used to perform the meta-analysis. OR with 95%CI were calculated to establish the association. Results: Overall, the cumulative findings demonstrated that CCND1 polymorphism (rs9344) was not significantly associated with cancer risk in all the genetic models studied (dominant model: GG vs GA+AA: OR (95%CI) = 0.81 (0.60–1.09), P=0.17; recessive model: GG+GA vs AA: OR (95%CI) = 1.23 (0.96–1.59), P=0.11; co-dominant model: GG vs AA: OR (95%CI) = 1.35 (0.93–1.97), P=0.12; co-dominant model: (GG vs GA: OR (95%CI) = 1.16 (0.85–1.59), P=0.34; allelic model: A vs G: OR (95%CI) = 1.20 (1.14–2.85), P=0.23; allelic model: G vs A: OR (95%CI) = 0.83 (0.62–1.12), P=0.23). Subgroup analysis according to cancer types presented significant association of CCND1 polymorphism and increased breast cancer risk in dominant model (GG vs GA+AA: OR = 2.75, 95%CI = 1.54–4.90, P=0.0006) and allelic model (G vs A: OR = 1.63, 95%CI = 1.22–2.19, P=0.001). An increased esophageal cancer risk in recessive model (GG+GA vs AA: OR = 1.51, 95%CI = 1.05–2.16, P=0.03) and co-dominant model (GG vs AA: OR = 2.51, 95%CI = 1.10–5.71, P=0.03) was detected. A higher risk for colorectal cancer was detected under both the co-dominant models (GG vs AA: OR = 2.46, 95%CI = 1.34–4.51, P=0.004 and GG vs GA: OR = 1.74, 95%CI = 1.14–2.67, P=0.01). However, in case of cervical cancer risk a non-significant association was reported under the recessive model (GG+GA vs AA: OR = 1.52, 95%CI = 0.60–3.90, P=0.38) with reference to CCND1 polymorphism (rs9344). The trial sequential analysis (TSA) showed that the cumulative Z-curve neither crossed the trial sequential monitoring boundary nor reached the required information size (RIS). Thus, present meta-analysis remained inconclusive due to insufficient evidence. Conclusion:CCND1 polymorphism rs9344 may not have a role in overall cancer susceptibility in Indian population. However, this polymorphism acts as a crucial risk factor for breast, esophageal, and colorectal cancer but not for cervical cancer. Future studies with larger sample size are required to draw a reliable conclusion.

Introduction

Cancer is a major global health problem and it is worse in case of low- and middle-income developing countries. According to India’s National Cancer Registry Program (NCRP), 1.45 million cases would occur in 2016 with 0.74 million deaths in India. This is expected to rise to 1.73 million cases and 0.88 million deaths in 2020 [1,2]. Cancer is considered the disease of abnormal cell division. Besides, many environmental cofactors (smoking, use of alcohol, exposure to UV radiations, infections with certain viruses) and host genetic makeup has been recognized as a pivotal risk factor for human cancers.

India ranks third in the world in terms of incidence rate of cancer cases amongst women after China and the U.S.A. According to the Globocan report 2012, there were ∼232000 breast cancer cases registered in the U.S.A., however in India, 145000 new cases were reported. The burden of breast cancer in India is approximately two-thirds of that of the U.S.A. and is growing progressively [3]. Breast cancer is one of the most common malignancies in women worldwide, and each year more than 1 million new cases are diagnosed [4]. The main risk factors for breast cancer are genetic predisposition, lifestyle, and environment [5–7]. Genetic polymorphisms have been identified as one of the crucial factor for determining inter-individual susceptibility to cancer [8]. The clinical importance of CCND1 gene lies in the fact that 5–20% of breast cancer cases present with either amplified or deleted version of the gene [9,10]. CCND1 also has documented oncogenic characteristics by manipulating the regulation of cell cycle machinery particularly at the transition phase of G1/S [11,12]. Cyclin D1 (CCND1) protein is found to be overexpressed in more than 50% of breast cancer cases [13]. An important functional single nucleotide polymorphism (SNP) in CCND1 gene (rs9344) G870A, may influence the breast cancer development [14]. Esophageal cancer is the eighth most common cancer overall. In 2012, worldwide, 456000 new cases have been estimated (3.2% of all incidence cancer cases). It is the sixth most common cause of death from cancer, with an estimated 400000 deaths in 2012 (4.9% of all cancer deaths) [3]. It is one of the most common and lethal type of cancer worldwide, with <20% of 5-year survival rate [15]. Colorectal cancer is the third most commonly diagnosed cancer in men and second in women, with >1.4 million new cases annually [16]. Geographical deviation in the incidence rates has been observed as developed world contributes to >50% of the cases. Though, mortality is more in the developing countries due to insufficient resources and health infrastructure [17]. In India, the age standardized rate (ASR) for colorectal cancer is 7.2 per 100000 men and 5.1 per 100000 women [3].

CCND1 is a key cell cycle regulatory gene which governs the G1/S checkpoint in cell cycle. It is one of the most frequently altered molecules in human carcinogenesis. A common G/A SNP [dbSNP ID rs9344] was first described by Betticher et al. (1995) [18]. This SNP rs9344 is located at codon 242 in the exon-4/intron boundary of CCND1 and responsible for alternate splicing of transcripts with different half-lives [18]. Since then many case–control studies have been conducted to explore the potential association between CCND1 SNP (rs9344) and cancer susceptibility. Occurrence of this nucleotide variation has been found to be coupled with the risk of various cancers including cervical, breast, oral, esophageal, lung, urinary bladder, prostate, and colorectal [19–29]. The outcomes of these studies were inconsistent in different ethnic groups. To overcome this conflict, several meta-analyses have been performed worldwide to see the effect of CCND1 polymorphism and risk for different types of cancer [30–35]. To the best of our knowledge, no report is available from India addressing the impact of CCND1 SNP and overall cancer risk. Hence, we aimed to investigate the role of CCND1 polymorphism G870A (rs9344) in overall cancer susceptibility amongst Indian population by conducting this meta-analysis. The present data could be helpful in enriching the existing knowledge with respect to involvement of CCND1 polymorphism and cancer susceptibility in Indian population.

Methods

Literature search strategy

Pubmed and Embase databases were searched with the keywords ‘CCND1’, ‘Cyclin D1’, ‘SNP’, ‘cancer’, ‘India’, and ‘polymorphism’ for literature published till September 2016. All studies included in the present meta-analysis met the following inclusion and exclusion criteria.

Inclusion criteria

(i) Prospective or case–control studies involving association analysis between CCND1 SNP G870A (rs9344) and cancer susceptibility, (ii) studies included Indian population, (iii) genotypic and allelic details are provided for both the cases and control groups, (iv) full text available, and (v) articles published in English language.

Exclusion criteria

(i) Studies published on populations other than Indian, (ii) articles published in languages except English, and (iii) articles not providing genotypic and allelic details.

Data retrieval

Data from all eligible studies were retrieved independently by two investigators (N.T. and S.K.). The retrieved data incorporated the following details: (i) PubMed IDentifier (PMID), (ii) name of the first author, (iii) year of publication, (iv) country, (v) sources of controls, (vi) methods for genotyping, and (vii) frequency of genotypic and allelic data.

Quality assessment

Quality of the included studies was assessed by assigning the quality scores as previously mentioned by He et al. (2014) [36]. The scores were assigned to each qualified studies between 0 and 10. Studies with >5 scores were included for the further analyses (Supplementary Table S1).

Meta-analysis

RevMan (Review Manager) is an easy tool to perform the meta-analyses and generate the graphs (forest plot, funnel plot) in publication standard. Meta-analysis of CCND1 gene G870A polymorphism (rs9344) was performed by RevMan 5.3 [37]. For statistical models, both fixed model and random model were included in the RevMan. For random models, DerSimonian and Laird random-effects models were used. Odds ratios (ORs) with 95% confidence intervals (95%CIs) were used to assess the strength of association between the CCND1-G870A polymorphisms and cancer risk. The pooled OR was evaluated by the Z-test and a P-value <0.05 suggests a significant association.

I2 was used to estimate total variation across studies due to heterogeneity in percentage. A percentage of <25% was considered as a low level of heterogeneity, 25–50% as a moderate level of heterogeneity, and >50% as a high level of heterogeneity. I2> 50% could suggest heterogeneity and suggest using a random-effect estimate [38]. Otherwise, the fixed-effect model was used to calculate pooled ORs [39].

Software RevMan 5.3 used in this meta-analysis is freely available at http://community.cochrane.org/tools/review-production-tools/revman-5/revman-5-download

Statistical analysis

The association between CCND1 polymorphism and cancer risk was analyzed by OR with 95%CI in different genetic models: dominant (GA+AA vs GG), recessive (AA vs GG+GA), co-dominant (GA vs GG and AA vs GG), and allelic (A vs G and G vs A). The P-value <0.05 was considered statistically significant. Subgroup analysis was done after stratification of data according to various cancer types.

Heterogeneity was calculated by chi-square test and the extent of heterogeneity was measured by the value of I2 statistic. The OR of different types of genetic models was evaluated by employing the fixed-effect model (when I2 < 50%) or random-effect model (when I2 > 50%). Egger’s bias test and Begg’s funnel plot was used to assess the publication bias [40,41]. It is a well-acknowledged fact that meta-analyses are vulnerable to random errors due to sparse data and repetitive testing of accrued data [42]. Hence, trial sequential analysis (TSA) was performed to minimize the type I error and random error as the present study had smaller sample size. TSA was performed as described previously by Fu et al. (2017) [43]. It was done by using TSA software version 0.9.5.10. (http://www.ctu.dk/tsa/) [44] to calculate the required information size (RIS) (meta-analysis sample size) by taking the control event proportion to 25.77%, experimental event proportion 21.55%, a relative risk reduction (RRR) 10%, power 80%, and type I error (α) 5%. The monitoring boundaries were constructed to determine whether present meta-analysis is sufficiently powered and conclusive. Therefore, it is able to reject false-positive reports from meta-analysis [45]. If the Z-curve crosses the TSA boundaries or futility area, there is sufficient information to support the conclusions and further trials are unlikely to change the findings. If the Z-curve does not cross the any of the boundaries or reach the RIS, evidence is insufficient to reach a firm conclusion.

Results

Study characteristics

Using the Pubmed and Embase database, a total of 12 studies were searched independently by two investigators (S.K. and N.T.) according to the methodology depicted in flow diagram (Figure 1).

Methodology flowchart for the selection of studies in the present meta-analysis

Figure 1
Methodology flowchart for the selection of studies in the present meta-analysis

*Since, data from study PMID 24604328 were extracted twice, hence total articles mentioned are 12 in the text.

Figure 1
Methodology flowchart for the selection of studies in the present meta-analysis

*Since, data from study PMID 24604328 were extracted twice, hence total articles mentioned are 12 in the text.

Data from one study with PMID 24604328 was extracted twice. All the 12 studies including 1791 cancer cases and 1948 controls met our inclusion criteria. The characteristics of included studies for the present meta-analysis from different cancers are presented in Table 1.

Table 1
Characteristics of the studies included in the meta-analysis
S.No. PMID Authors Publication year Country Ethnicity Source of control Cancer type Genotyping methods 
1. 16488657 Sathyan et al. [222006 India Asian Hospital based Oral cancer PCR-SSCP 
2. 17011980 Sobti et al [252006 India Asian Hospital based Lung cancer PCR 
3. 17561354 Jain et al. [232007 India Asian Hospital based Esophageal cancer PCR-RFLP 
4. 18548202 Kaur et al. [202008 India Asian Hospital based Cervical cancer PCR-RFLP 
5. 19489683 Thakur et al. [192009 India Asian Hospital based Cervical cancer PCR-RFLP 
6. 20380574 Gangwar et al. [262010 India Asian Hospital based Urinary bladder cancer PCR-RFLP 
7. 21268129 Hussain et al. [242011 India Asian Hospital based Esophageal squamous cell carcinoma PCR-RFLP 
8. 20822933 Mandal et al. [272012 India Asian Hospital based Prostate cancer PCR-RFLP 
9. 23354584 Sameer et al. [292013 India Asian Hospital based Colorectal cancer PCR-RFLP 
10. 24604328 Wasson et al. [212014 India Asian Hospital based Breast cancer PCR-RFLP 
11. 24604328* Wasson et al. [212014 India Asian Hospital based Breast cancer PCR-RFLP 
12. 25146682 Govatati et al. [282014 India Asian Hospital based Colorectal cancer PCR 
S.No. PMID Authors Publication year Country Ethnicity Source of control Cancer type Genotyping methods 
1. 16488657 Sathyan et al. [222006 India Asian Hospital based Oral cancer PCR-SSCP 
2. 17011980 Sobti et al [252006 India Asian Hospital based Lung cancer PCR 
3. 17561354 Jain et al. [232007 India Asian Hospital based Esophageal cancer PCR-RFLP 
4. 18548202 Kaur et al. [202008 India Asian Hospital based Cervical cancer PCR-RFLP 
5. 19489683 Thakur et al. [192009 India Asian Hospital based Cervical cancer PCR-RFLP 
6. 20380574 Gangwar et al. [262010 India Asian Hospital based Urinary bladder cancer PCR-RFLP 
7. 21268129 Hussain et al. [242011 India Asian Hospital based Esophageal squamous cell carcinoma PCR-RFLP 
8. 20822933 Mandal et al. [272012 India Asian Hospital based Prostate cancer PCR-RFLP 
9. 23354584 Sameer et al. [292013 India Asian Hospital based Colorectal cancer PCR-RFLP 
10. 24604328 Wasson et al. [212014 India Asian Hospital based Breast cancer PCR-RFLP 
11. 24604328* Wasson et al. [212014 India Asian Hospital based Breast cancer PCR-RFLP 
12. 25146682 Govatati et al. [282014 India Asian Hospital based Colorectal cancer PCR 

Abbreviation: RFLP, restriction fragment length polymorphism. *PMID24604328 taken twice.

Details of genotypic and allelic frequencies of CCND1 polymorphism is shown in Table 2.

Table 2
Distribution of CCND1-G870A genotypes and allelic frequency in cancer cases and controls
S.No. PMID Cancer type Case Control Case Control Case Control 
   n n GG GA AA GG GA AA 
1. 16488657 Oral cancer 146 137 36 71 39 40 61 36 0.51 0.49 0.49 0.51 
2. 17011980 Lung cancer 151 151 29 87 35 39 69 43 NA NA NA NA 
3. 17561354 Esophageal cancer 151 201 22 76 53 37 111 53 NA NA NA NA 
4. 18548202 Cervical cancer 150 150 33 64 53 30 65 55 NA NA NA NA 
5. 1948683 Cervical cancer 200 200 39 94 67 47 119 34 228 172 187 213 
6. 20380574 Urinary bladder cancer 212 250 48 85 79 58 119 73 243 181 265 235 
7. 20822933 Prostate cancer 192 224 38 65 89 58 93 73 243 141 239 209 
8. 21268129 Esophageal cancer 151 151 20 99 32 56 72 23 163 139 118 184 
9. 23354584 Colorectal cancer 130 160 19 70 41 41 76 43 NA NA NA NA 
10. 24604328 Breast cancer 151 83 33 77 41 07 47 29 159 143 105 61 
11. 24604328* Breast cancer 54 134 15 31 08 18 78 38 47 61 154 114 
12. 25146682 Colorectal cancer 103 107 54 39 10 71 33 03 59 147 39 175 
S.No. PMID Cancer type Case Control Case Control Case Control 
   n n GG GA AA GG GA AA 
1. 16488657 Oral cancer 146 137 36 71 39 40 61 36 0.51 0.49 0.49 0.51 
2. 17011980 Lung cancer 151 151 29 87 35 39 69 43 NA NA NA NA 
3. 17561354 Esophageal cancer 151 201 22 76 53 37 111 53 NA NA NA NA 
4. 18548202 Cervical cancer 150 150 33 64 53 30 65 55 NA NA NA NA 
5. 1948683 Cervical cancer 200 200 39 94 67 47 119 34 228 172 187 213 
6. 20380574 Urinary bladder cancer 212 250 48 85 79 58 119 73 243 181 265 235 
7. 20822933 Prostate cancer 192 224 38 65 89 58 93 73 243 141 239 209 
8. 21268129 Esophageal cancer 151 151 20 99 32 56 72 23 163 139 118 184 
9. 23354584 Colorectal cancer 130 160 19 70 41 41 76 43 NA NA NA NA 
10. 24604328 Breast cancer 151 83 33 77 41 07 47 29 159 143 105 61 
11. 24604328* Breast cancer 54 134 15 31 08 18 78 38 47 61 154 114 
12. 25146682 Colorectal cancer 103 107 54 39 10 71 33 03 59 147 39 175 
*

PMID: 24604328 repeated twice in our study. NA, not available.

Meta-analysis of CCND1 G/A polymorphism (rs9344)

A total of 12 studies were included in the analysis to evaluate the association between CCND1 polymorphism and cancer risk in Indian population. The results from meta-analysis of the association between CCND1 polymorphism (rs9344) and cancer risk in 12 case–control studies are shown in Figure 2 and Table 3. Values of ORs with 95%CI were as follows: dominant model (GG vs GA+AA: OR = 0.81, 95%CI = 0.60–1.09, P=0.17, I2= 72%); recessive model (GG+GA vs AA: OR = 1.23, 95%CI = 0.96–1.59, P=0.11, I2 = 64%); co-dominant model (GG vs AA: OR = 1.35, 95%CI = 0.93-1.97, P = 0.12, I2 = 72%); co-dominant model (GG vs GA: OR = 1.16, 95%CI = 0.85–1.59, P=0.34, I2 = 69%); allele model (A vs G: OR = 1.20, 95%CI = 1.14–2.85, P=0.23, I2 = 82%), and allele model (G vs A: OR = 0.83, 95%CI = 0.62–1.12, P=0.23, I2 = 82%) (Table 3). If the values of I2 were >50% then the random-effect model was applied, otherwise fixed-effect model was used to calculate the pooled ORs and 95%CI. In meta-analysis, PZ<0.05 was considered statistically significant. Here, we demonstrate that CCND1 polymorphism G870A (rs9344) is not associated with the risk for overall cancers in Indian population.

Forest plots describing the association of CCND1-G870A polymorphism with overall cancer risk

Figure 2
Forest plots describing the association of CCND1-G870A polymorphism with overall cancer risk

(A) dominant model (GG vs GA+AA); (B) recessive model (GG+GA vs AA); (C) co-dominant model (GG vs GA); (D) co-dominant model (GG vs AA); (E) allele model (A vs G); (F) allele model (G vs A).

Figure 2
Forest plots describing the association of CCND1-G870A polymorphism with overall cancer risk

(A) dominant model (GG vs GA+AA); (B) recessive model (GG+GA vs AA); (C) co-dominant model (GG vs GA); (D) co-dominant model (GG vs AA); (E) allele model (A vs G); (F) allele model (G vs A).

Table 3
Meta-analysis results based on different genetic models
S.No. Category OR [95%CI] PZ PH I2 (%) Statistical method 
1. Dominant model (GG vs GA+AA) 0.81 [0.60–1.09] 0.17 <0.0001 72% Random 
2. Recessive model (GG+GA vs AA) 1.23 [0.96–1.59] 0.11 0.001 64% Random 
3. Co-dominant model (AA vs GG) 1.35 [0.93, 1.97] 0.12 <0.0001 72% Random 
4. Co-dominant model (GA vs GG) 1.16 [0.85, 1.59] 0.34 0.0002 69% Random 
5. Allele model (A vs G) 1.20 [1.14–2.85] 0.23 <0.00001 82% Random 
6. Allele model (G vs A) 0.83 [0.62–1.12] 0.23 <0.00001 82% Random 
S.No. Category OR [95%CI] PZ PH I2 (%) Statistical method 
1. Dominant model (GG vs GA+AA) 0.81 [0.60–1.09] 0.17 <0.0001 72% Random 
2. Recessive model (GG+GA vs AA) 1.23 [0.96–1.59] 0.11 0.001 64% Random 
3. Co-dominant model (AA vs GG) 1.35 [0.93, 1.97] 0.12 <0.0001 72% Random 
4. Co-dominant model (GA vs GG) 1.16 [0.85, 1.59] 0.34 0.0002 69% Random 
5. Allele model (A vs G) 1.20 [1.14–2.85] 0.23 <0.00001 82% Random 
6. Allele model (G vs A) 0.83 [0.62–1.12] 0.23 <0.00001 82% Random 

Abbreviations: PH, P value for heterogeneity; PZ, P value for Z-test.

On subgroup analysis stratified according to cancer types showed significant association of CCND1 polymorphism and increased breast cancer risk in dominant model (GG vs GA+AA: OR = 2.75, 95%CI = 1.54–4.90, P=0.0006), allelic model (G vs A: OR = 1.63, 95%CI = 1.22–2.19, P=0.001). A statistically significant association with esophageal cancer risk was observed in recessive (GG+GA vs AA: OR = 1.51, 95%CI = 1.05–2.16, P=0.03) and co-dominant model (GG vs AA: OR = 2.51, 95%CI = 1.10–5.71, P=0.03). An increased risk for colorectal cancer was detected under both the co-dominant models (GG vs AA: OR = 2.46, 95%CI = 1.34–4.51, P=0.004 and GG vs GA: OR = 1.74, 95%CI = 1.14–2.67, P=0.01). Contrary to this, none of the genetic model reported a statistically significant association with cervical cancer risk. Although a non-significant association was observed in recessive model (GG+GA vs AA: OR = 1.52, 95%CI = 0.60–3.90, P=0.38) and co-dominant model (GG vs AA: OR = 1.45, 95%CI = 0.55–3.85, P=0.46) with reference to CCND1 polymorphism (rs9344) (Figures 36 and Table 4).

Forest plots describing the association of CCND1-G870A polymorphism with breast cancer risk

Figure 3
Forest plots describing the association of CCND1-G870A polymorphism with breast cancer risk

(A) dominant model (GG vs GA+AA); (B) recessive model (GG+GA vs AA); (C) co-dominant model (GG vs GA); (D) co-dominant model (GG vs AA); (E) allele model (A vs G); (F) allele model (G vs A).

Figure 3
Forest plots describing the association of CCND1-G870A polymorphism with breast cancer risk

(A) dominant model (GG vs GA+AA); (B) recessive model (GG+GA vs AA); (C) co-dominant model (GG vs GA); (D) co-dominant model (GG vs AA); (E) allele model (A vs G); (F) allele model (G vs A).

Forest plots describing the association of CCND1-G870A polymorphism with colorectal cancer risk

Figure 4
Forest plots describing the association of CCND1-G870A polymorphism with colorectal cancer risk

(A) dominant model (GG vs GA+AA); (B) recessive model (GG+GA vs AA); (C) co-dominant model (GG vs GA); (D) co-dominant model (GG vs AA).

Figure 4
Forest plots describing the association of CCND1-G870A polymorphism with colorectal cancer risk

(A) dominant model (GG vs GA+AA); (B) recessive model (GG+GA vs AA); (C) co-dominant model (GG vs GA); (D) co-dominant model (GG vs AA).

Forest plots describing the association of CCND1-G870A polymorphism with esophageal cancer risk

Figure 5
Forest plots describing the association of CCND1-G870A polymorphism with esophageal cancer risk

(A) dominant model (GG vs GA+AA); (B) recessive model (GG+GA vs AA); (C) co-dominant model (GG vs GA); (D) co-dominant model (GG vs AA).

Figure 5
Forest plots describing the association of CCND1-G870A polymorphism with esophageal cancer risk

(A) dominant model (GG vs GA+AA); (B) recessive model (GG+GA vs AA); (C) co-dominant model (GG vs GA); (D) co-dominant model (GG vs AA).

Forest plots describing the association of CCND1-G870A polymorphism with cervical cancer risk

Figure 6
Forest plots describing the association of CCND1-G870A polymorphism with cervical cancer risk

(A) dominant model (GG vs GA+AA); (B) recessive model (GG+GA vs AA) (C) co-dominant model (GG vs GA); (D) co-dominant model (GG vs AA).

Figure 6
Forest plots describing the association of CCND1-G870A polymorphism with cervical cancer risk

(A) dominant model (GG vs GA+AA); (B) recessive model (GG+GA vs AA) (C) co-dominant model (GG vs GA); (D) co-dominant model (GG vs AA).

Table 4
Subgroup analysis: meta-analysis results according to the type of cancer
Subgroup OR (95%CI) PZ PH I2 (%) Effects model 
Breast cancer       
Dominant model (GG vs GA+AA) 2.75 (1.54–4.90) 0.0006 0.73 0% Fixed 
Recessive model (GG+GA vs AA) 0.59 (0.37–0.95) 0.03 0.38 0% Fixed 
Co-dominant model (GG vs GA) 0.41 (0.23–0.74) 0.003 0.60 0% Fixed 
Co-dominant model (GG vs AA) 0.28 (0.14–0.56) 0.0003 0.81 0% Fixed 
Allele model (A vs G) 0.61 (0.46–0.82) 0.001 0.68 0% Fixed 
Allele model (G vs A) 1.63 (1.22–2.19) 0.001 0.68 0% Fixed 
Colorectal cancer       
Dominant model (GG vs GA+AA) 0.53 (0.35–0.80) 0.002 0.78 0% Fixed 
Recessive model (GG+GA vs AA) 1.81 (0.66–4.99) 0.25 0.13 56% Random 
Co-dominant model (GG vs GA) 1.74 (1.14–2.67) 0.01 0.58 0% Fixed 
Co-dominant model (GG vs AA) 2.46 (1.34–4.51) 0.004 0.32 0% Fixed 
Esophageal cancer       
Dominant model (GG vs GA+AA) 0.44 (0.15–1.26) 0.13 0.010 85% Random 
Recessive model (GG+GA vs AA) 1.51 (1.05–2.16) 0.03 0.98 0% Fixed 
Co-dominant model (GG vs GA) 2.11 (0.65–6.88) 0.22 0.005 87% Random 
Co-dominant model (GG vs AA) 2.51 (1.10–5.71) 0.03 0.09 64% Random 
Cervical cancer       
Dominant model (GG vs GA+AA) 0.92 (0.64–1.32) 0.64 0.34 0% Fixed 
Recessive model (GG+GA vs AA) 1.52 (0.60–3.90) 0.38 0.005 87% Random 
Co-dominant model (GG vs GA) 0.93 (0.63–1.37) 0.71 0.88 0% Fixed 
Co-dominant model (GG vs AA) 1.45 (0.55–3.85) 0.46 0.02 81% Random 
Subgroup OR (95%CI) PZ PH I2 (%) Effects model 
Breast cancer       
Dominant model (GG vs GA+AA) 2.75 (1.54–4.90) 0.0006 0.73 0% Fixed 
Recessive model (GG+GA vs AA) 0.59 (0.37–0.95) 0.03 0.38 0% Fixed 
Co-dominant model (GG vs GA) 0.41 (0.23–0.74) 0.003 0.60 0% Fixed 
Co-dominant model (GG vs AA) 0.28 (0.14–0.56) 0.0003 0.81 0% Fixed 
Allele model (A vs G) 0.61 (0.46–0.82) 0.001 0.68 0% Fixed 
Allele model (G vs A) 1.63 (1.22–2.19) 0.001 0.68 0% Fixed 
Colorectal cancer       
Dominant model (GG vs GA+AA) 0.53 (0.35–0.80) 0.002 0.78 0% Fixed 
Recessive model (GG+GA vs AA) 1.81 (0.66–4.99) 0.25 0.13 56% Random 
Co-dominant model (GG vs GA) 1.74 (1.14–2.67) 0.01 0.58 0% Fixed 
Co-dominant model (GG vs AA) 2.46 (1.34–4.51) 0.004 0.32 0% Fixed 
Esophageal cancer       
Dominant model (GG vs GA+AA) 0.44 (0.15–1.26) 0.13 0.010 85% Random 
Recessive model (GG+GA vs AA) 1.51 (1.05–2.16) 0.03 0.98 0% Fixed 
Co-dominant model (GG vs GA) 2.11 (0.65–6.88) 0.22 0.005 87% Random 
Co-dominant model (GG vs AA) 2.51 (1.10–5.71) 0.03 0.09 64% Random 
Cervical cancer       
Dominant model (GG vs GA+AA) 0.92 (0.64–1.32) 0.64 0.34 0% Fixed 
Recessive model (GG+GA vs AA) 1.52 (0.60–3.90) 0.38 0.005 87% Random 
Co-dominant model (GG vs GA) 0.93 (0.63–1.37) 0.71 0.88 0% Fixed 
Co-dominant model (GG vs AA) 1.45 (0.55–3.85) 0.46 0.02 81% Random 

Abbreviations: PZ, P-value for Z-test; PH, P-value for heterogeneity. Statistically significant values shown in bold.

Heterogeneity measurement

Heterogeneity value depicted as I2 was calculated for different genetic models and presented in Table 3. Heterogeneity was observed in all the genotypic and allelic models. For dominant model: GG vs GA+AA: I2 = 72%, P for heterogeneity <0.0001; recessive model: GG+GA vs AA: I2 = 64%, P for heterogeneity = 0.001; co-dominant model: GG vs AA: I2 = 69%, P for heterogeneity = 0.0002; co-dominant model: GG vs GA: I2 = 72%, P for heterogeneity = 0.0001; allelic model: A vs G: I2 = 82%, P for heterogeneity <0.00001 and allelic model: G vs A: I2 = 82%, P for heterogeneity <0.00001 were noted, respectively (Table 3).

Publication bias

Funnel plots were used in random-effect and fixed-effect models respectively to detect the publication bias. A relatively symmetric distribution in the funnel plot was observed, which indicates that there is no significant publication bias in the included studies (Figure 7).

Funnel plot assessing publication bias in recessive model (GG+GA vs AA)

Figure 7
Funnel plot assessing publication bias in recessive model (GG+GA vs AA)
Figure 7
Funnel plot assessing publication bias in recessive model (GG+GA vs AA)

TSA

The TSA for association between CCND1 polymorphism (rs9344) and overall cancer risk showed that only conventional boundary was crossed by Z-curve, however, it neither crossed the TSA boundary nor the futility area. And the total sample size (3739) did not reach the RIS (11375) (Figure 8). This result indicates that present meta-analysis is inconclusive at this level. Further studies/trials are needed to make this association valid.

TSA of association of CCND1 polymorphism (rs9344) and overall cancer risk in Indian population from 12 studies

Figure 8
TSA of association of CCND1 polymorphism (rs9344) and overall cancer risk in Indian population from 12 studies

The cumulative Z-curve was constructed by using random-effect model. We calculated α-spending adjusted RIS of 11375 patients using α = 0.05 (two-sided), β = 0.20 (power = 80%). Note: Z-curve (blue); Conventional boundary (green); TSA boundary (red).

Figure 8
TSA of association of CCND1 polymorphism (rs9344) and overall cancer risk in Indian population from 12 studies

The cumulative Z-curve was constructed by using random-effect model. We calculated α-spending adjusted RIS of 11375 patients using α = 0.05 (two-sided), β = 0.20 (power = 80%). Note: Z-curve (blue); Conventional boundary (green); TSA boundary (red).

Discussion

CCND1 is key driver of normal cell cycle regulation and genetic variation in this gene has been reported in many types of cancers. A SNP G870A (rs9344) located on exon-4–intron boundary of CCND1 has been studied extensively in several cancer types. Several reports from different parts of the world have been published with reference to CCND1 polymorphism and risk of various types of cancers including cervical, prostate, colorectal, urinary bladder, squamous cell carcinoma of the head and neck etc. [46–50]. Investigators from India also tried to explore the association of CCND1 polymorphism and susceptibility to different cancer types including cervical, breast, oral, esophageal, lung, urinary bladder, prostate, and colorectal [19–29]. However, these reports are conflicting thus we performed meta-analysis on the literature available in order to provide more accurate information on the role of CCND1 G870A (rs9344) polymorphism and overall cancer risk in Indian population. Although, various meta-analyses on individual cancer susceptibility have been published globally [30–35]. Pabalan et al. (2008) [51], performed a meta-analysis on role of CCND1 polymorphism in different types of cancers and populations. However, a comprehensive data are lacking from India with overall cancer risk. Hence, we have designed the present study focussed on Indian population.

The present meta-analysis, contained a total of 12 studies comprising 1791 cancer cases and 1948 controls [19–29] showed the lack of significant association between CCND1 G870A polymorphism (rs9344) and overall cancer risk in all the genetic models. These findings are consistent with the result of another study by Luo et al. (2016) [52], which ruled out the involvement of CCND1 polymorphism (G870A) with the risk of hepatocellular carcinoma. In the similar lines, study by Zheng et al. (2015) [53] suggested that CCND1 polymorphism may not be associated with the risk of prostate cancer. Similarly, Wang et al. (2018) [54] also found no significant association between the let-7i rs10877887 and let-7a-1/let-7f-1/let-7d rs13293512 polymorphisms and overall cancer risk. In disagreement with our findings a meta-analysis by Pabalan et al. (2008) [51], showed an increased cancer risk associated with CCND1-A870G polymorphism in the human population. Another study by Qin et al. (2014) [55] also indicated that CCND1 polymorphism may increase the risk of non-Hodgkin lymphoma but it was not true in case of leukemia. On the identical lines, Lin et al. (2014) [56] too observed the lack of association between CCND1 polymorphism (G870A) and head and neck cancer, however; they found that smokers carrying ‘A’ allele or ‘AA’ genotype for rs9344 SNP located on CCND1 may be at higher risk to head and neck cancer development.

Our subgroup analysis showed an increased risk (1.52-fold) for cervical cancer development but this association could not attain the limits of statistical significance (P=0.38). The possible explanation for this observation may be the small sample size of contributing studies. No promising association of this SNP has been established with the development of cervical cancer in Caucasian population by Yang et al. (2015) [57]. In another study, no significant association was reported between the CCND1 SNP (rs9344) and overall risk for cervical cancer in the Asian population but on stratification analysis by race, individuals carrying the AA or AA/AG genotypes showed a significant higher risk in comparison with GG carriers [32]. In parallel to the findings from the present study, Hu et al. (2014) [30], also did not find the association of CCND1 G870A polymorphism and cervical cancer risk amongst different ethnic groups including Asian, Caucasian, and mixed in a cumulative meta-analysis.

Additionally, a significant association between CCND1 polymorphism and increased risk for breast and esophageal cancer has been established. Similar to our results, Sergentanis and Economopoulos (2011) [58] found that the ‘A’ allele of the CCND1 G870A polymorphism is associated with higher risk for breast cancer. These findings are further strengthened by another meta-analyses conducted by Lu et al. (2009) [59] and Cui et al. (2012) [60] that showed the association of AA genotype of CCND1 G870A polymorphism with breast cancer susceptibility. Similarly, Soleimani et al. (2016) [61] showed a significant association between CCND1 G870A polymorphism and breast cancer risk but in Caucasians. A meta-analysis conducted Wen et al. (2014) [62] supported our data that CCND1 G870A polymorphism is a potential risk factor in the development of esophageal cancer. Other related meta-analysis by Cai et al. (2013) [63] is not in agreement with our findings and showed lack of potential association between CCND1 G870A polymorphism and esophageal cancer risk. Likewise, Tang et al. (2015) [64], also observed similar results describing that CCND1 SNP rs9344 is not having role in esophageal squamous cell carcinoma.The present study suggests that there is a significant correlation between this polymorphism and increased risk of colorectal cancer amongst Indian population. Recently, Xu et al. (2016) [34] suggested that this SNP may increase the risk for developing colorectal cancer with special emphasis to sporadic colorectal cancer in Caucasian population. The study by Jiang et al. (2006) [65] suggested that the CCND1 G870 AA genotype may increase the colorectal cancer risk compared with the GG+AG genotype (OR = 1.56, 95%CI = 1.10–2.21) in an Indian population. Similarly, Zhang et al. (2016) [33], suggested that CCND1 polymorphism is a risk factor for gastric cancer in Caucasians. According to the literature search, Dai et al. (2016) [35] also tried to establish the association between CCND1 polymorphism (rs678653) located on the 3′-UTR and susceptibility to cancer, but they have not studied the polymorphism under investigation G870A (rs9344).

The present study had some limitations, first, all of the included studies were hospital based which may not represent the true population. Second, environmental factors like smoking, use of alcohol, and infections with viruses were not included in the present meta-analysis. Finally, the sample size was reasonably small, which may be the reason for controversial results.

Conclusion

In conclusion, present meta-analysis showed that CCND1 SNP (rs9344) may not serve as a risk factor for overall cancer susceptibility in Indian population. However, a significant association between CCND1 SNP and increased risk for breast, esophageal, and colorectal cancer was found on subgroup analysis. Moreover, a non-significant increased risk for cervical cancer in relation to CCND1 polymorphism was observed in Indian population. Thus, CCND1 G870A (rs9344) polymorphism has a potential to be served as a prognostic biomarker for breast, esophageal, and colorectal cancer in Indian population. Still, larger and well-designed studies including other risk factors are warranted in future to validate the findings from present analysis.

Author contribution

NT: study design, literature survey, data extraction, analysis, interpretation of the results, manuscript writing. SK: literature survey, data extraction, analysis. RM: critically reviwed the manuscript.

Competing interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

The present study was not funded by any agency. Open access charges provided by the Institutional funds of NICPR (ICMR) Noida.

Abbreviations

     
  • CCND1

    cyclin D1

  •  
  • OR

    odds ratio

  •  
  • PMID

    PubMed IDentifier

  •  
  • RevMan

    Review Manager

  •  
  • RIS

    required information size

  •  
  • SNP

    single nucleotide polymorphism

  •  
  • TSA

    trial sequential analysis

  •  
  • 95%CI

    95% confidence interval

References

References
1
The National Center for Disease Informatics and Research http://www.ncdirindia.org [Accessed: 24 November 2018]
2
Gandhi
A.K
,
Kumar
P.
,
Bhandari
M.
,
Devnani
B.
and
Rath
G.K.
(
2016
)
Burden of preventable cancers in India: time to strike the cancer epidemic
.
J. Egypt. Natl. Cancer Inst.
,
29
,
11
18
,
PMID: 27591115
3
,
Globocan report 2012 http://globocan.iarc.fr/Default.aspx [Accessed: 24 November 2018]
4
Makki
J.
(
2015
)
Diversity of breast carcinoma: histological subtypes and clinical relevance
.
Clin. Med. Insights Pathol.
8
,
23
31
[PubMed]
5
Fabris
V.T.
(
2014
)
From chromosomal abnormalities to the identification of target genes in mouse models of breast cancer
.
Cancer Genet.
207
,
233
246
[PubMed]
6
Lee
M.M.
and
Lin
S.S.
(
2000
)
Dietary fat and breast cancer
.
Annu. Rev. Nutr.
20
,
221
248
[PubMed]
7
Strumylaitė
L.
,
Mechonošina
K.
and
Tamašauskas
S.
(
2010
)
Environmental factors and breast cancer
.
Medicina (Kaunas)
46
,
867
873
[PubMed]
8
Theodoropoulos
G.E.
,
Michalopoulos
N.V.
,
Pantou
M.P.
,
Kontogianni
P.
,
Gazouli
M.
,
Karantanos
T.
et al (
2012
)
Caspase 9 promoter polymorphisms confer increased susceptibility to breast cancer
.
Cancer Genet.
205
,
508
512
[PubMed]
9
Ormandy
C.J.
,
Musgrove
E.A.
,
Hui
R.
,
Daly
R.J.
and
Sutherland
R.L.
(
2003
)
Cyclin D1, EMS1 and 11q13 amplification in breast cancer
.
Breast Cancer Res. Treat.
78
,
323
335
[PubMed]
10
Gillett
C.
,
Fantl
V.
,
Smith
R.
,
Fisher
C.
,
Bartek
J.
,
Dickson
C.
et al (
1994
)
Amplification and overexpression of cyclin D1 in breast cancer detected by immunohistochemical staining
.
Cancer Res.
54
,
1812
1817
[PubMed]
11
Baldin
V.
,
Lukas
J.
,
Marcote
M.J.
,
Pagano
M.
and
Draetta
G.
(
1993
)
Cyclin D1 is a nuclear protein required for cell cycle progression in G1
.
Genes Dev.
7
,
812
821
[PubMed]
12
Prall
O.W.
,
Rogan
E.M.
,
Musgrove
E.A.
,
Watts
C.K.
and
Sutherland
R.L.
(
1998
)
C-Myc or cyclin D1 mimics estrogen effects on cyclin ECdk2 activation and cell cycle reentry
.
Mol. Cell. Biol.
18
,
4499
4508
[PubMed]
13
Buckley
M.F.
,
Sweeney
K.J.
,
Hamilton
J.A.
,
Sini
R.L.
,
Manning
D.L.
,
Nicholson
R.I.
et al (
1993
)
Expression and amplification of cyclin genes in human breast cancer
.
Oncogene
8
,
2127
2133
[PubMed]
14
Lu
C.
,
Dong
J.
,
Ma
H.
,
Jin
G.
,
Hu
Z.
,
Peng
Y.
et al (
2008
)
CCND1 G870A polymorphism contributes to breast cancer susceptibility: a meta-analysis
.
Breast Cancer Res. Treat.
116
,
571
575
[PubMed]
15
Jemal
A.
,
Siegel
R.
,
Ward
E.
,
Hao
Y.
et al (
2008
)
Cancer statistics
.
CA Cancer J. Clin.
58
,
71
96
[PubMed]
16
Torre
L.A.
,
Bray
F.
,
Siegel
R.L.
,
Ferlay
J.
,
Lortet-tieulent
J.
and
Jemal
A.
(
2015
)
Global cancer statistics, 2012
.
CA Cancer J. Clin.
65
,
87
108
17
Center
M.M.
,
Jemal
A.
,
Smith
R.A.
and
Ward
E.
(
2010
)
Worldwide variations in colorectal cancer
.
Dis. Colon Rectum
53
,
1099
18
Betticher
D.C.
,
Thatcher
N.
,
Altermatt
H.J.
,
Hoban
P.
,
Ryder
W.D.
and
Heighway
J.
(
1995
)
Alternate splicing produces a novel cyclin D1 transcript
.
Oncogene
11
,
1005
1011
[PubMed]
19
Thakur
N.
,
Hussain
S.
,
Kohaar
I.
,
Tabassum
R.
,
Nasare
V.
,
Tiwari
P.
et al (
2009
)
Genetic variant of CCND1: association with HPV-mediated cervical cancer in Indian population
.
Biomarkers
14
,
219
225
[PubMed]
20
Kaur
S.
,
Sobti
R.C.
,
Kaur
P.
,
Gupta
I.
and
Jain
V.
(
2008
)
Cyclin D1 (G870A) polymorphism and risk of cervix cancer: a case control study in north Indian population
.
Mol. Cell. Biochem.
315
,
151
157
[PubMed]
21
Wasson
M.K.
,
Chauhan
P.S.
,
Singh
L.C.
,
Katara
D.
,
Sharma
J.
,
Zomawia
E.
et al (
2014
)
Association of DNA repair and cell cycle gene variations with breast cancer risk in Northeast Indian population: a multiple interaction analysis
.
Tumour Biol.
35
,
5885
5894
[PubMed]
22
Sathyan
K.M.
,
Nalinakumari
K.R.
,
Abraham
T.
and
Kannan
S.
(
2006
)
Influence of single nucleotide polymorphisms in H-Ras and cyclin D1 genes on oral cancer susceptibility
.
Oral Oncol.
42
,
607
613
[PubMed]
23
Jain
M.
,
Kumar
S.
,
Lal
P.
,
Tiwari
A.
,
Ghoshal
U.C.
and
Mittal
B.
(
2007
)
Role of BCL2 (ala43thr), CCND1 (G870A) and FAS (A-670G) polymorphisms in modulating the risk of developing esophageal cancer
.
Cancer Detect. Prev.
31
,
225
232
[PubMed]
24
Hussain
S.
,
M
Y.
,
Thakur
N.
,
Salam
I.
,
Singh
N.
,
Mir
M.M.
et al (
2011
)
Association of cyclin D1 gene polymorphisms with risk of esophageal squamous cell carcinoma in Kashmir Valley: a high risk area
.
Mol. Carcinog.
50
,
487
498
[PubMed]
25
Sobti
R.C.
,
Kaur
P.
,
Kaur
S.
,
Singh
J.
,
Janmeja
A.K.
,
Jindal
S.K.
et al (
2006
)
Effects of cyclin D1 (CCND1) polymorphism on susceptibility to lung cancer in a North Indian population
.
Cancer Genet. Cytogenet.
15:170
,
108
114
26
Gangwar
R.
and
Mittal
R.D.
(
2010
)
Association of selected variants in genes involved in cell cycle and apoptosis with bladder cancer risk in North Indian population
.
DNA Cell Biol.
29
,
349
356
[PubMed]
27
Mandal
R.K.
and
Mittal
R.D.
(
2010
)
Are cell cycle and apoptosis genes associated with prostate cancer risk in North Indian population?
Urol. Oncol.
30
,
555
561
[PubMed]
28
Govatati
S.
,
Singamsetty
G.K.
,
Nallabelli
N.
,
Malempati
S.
,
Rao
P.S.
,
Madamchetty
V.K.
et al (
2014
)
Contribution of cyclin D1 (CCND1) and E-cadherin (CDH1) alterations to colorectal cancer susceptibility: a case-control study
.
Tumour Biol.
35
,
12059
12067
[PubMed]
29
Sameer
A.S.
,
Parray
F.Q.
,
Dar
M.A.
,
Nissar
S.
,
Banday
M.Z.
,
Rasool
S.
et al (
2013
)
Cyclin D1 G870A polymorphism and risk of colorectal cancer: a case control study
.
Mol. Med. Rep.
7
,
811
815
[PubMed]
30
Hu
Y.
,
Zheng
R.
,
Guo
C.
and
Niu
Y.
(
2014
)
Association between cyclin D1 G870A polymorphism and cervical cancer risk: a cumulative meta-analysis involving 2,864 patients and 3,898 controls
.
Diagn. Pathol.
9
,
168
31
Lu
S.C.
,
Zhong
J.H.
,
Tan
J.T.
,
Tang
H.L.
,
Liu
X.G.
,
Xiang
B.D.
et al (
2015
)
Association between COX-2 gene polymorphisms and risk of hepatocellular carcinoma development: a meta-analysis
.
BMJ Open
,
5
32
Ni
J.
,
Wang
M.
,
Wang
M.
,
Fu
S.
,
Zhou
D.
,
Zhang
Z.
et al (
2011
)
CCND1 G870A polymorphism and cervical cancer risk: a case-control study and meta-analysis
.
J. Cancer Res. Clin. Oncol.
137
,
489
494
[PubMed]
33
Zhang
Y.
,
Zeng
X.
,
Lu
H.
,
Ji
H.
,
Zhao
E.
and
Li
Y.
(
2016
)
Association between cyclin D1 (CCND1) G870A polymorphism and gastric cancer risk: a meta-analysis
.
Oncotarget
7
,
66109
66118
[PubMed]
34
Xu
X.M.
,
Ni
X.B.
,
Yang
G.L.
,
Luo
Z.C.
,
Niu
Y.M.
and
Shen
M.
(
2016
)
CCND1 G870A polymorphism and colorectal cancer risk: an updated meta-analysis
.
Mol. Clin. Oncol.
4
,
1078
1084
[PubMed]
35
Dai
X.
,
Zhang
X.
,
Wang
B.
,
Wang
C.
,
Jiang
J.
and
Wu
C.
(
2016
)
Association between polymorphism rs678653 in human cyclin D1 (CCND1) and susceptibility to cancer: a meta-analysis
.
Med. Sci. Monit.
22
,
863
874
[PubMed]
36
He
J.
,
Liao
X.
,
Zhu
J.
,
Xue
W.
,
Shen
G.
,
Huang
S.
et al (
2014
)
Association of MTHFR C677T and A1298C polymorphisms with non-Hodgkin lymphoma susceptibility: evidence from a meta-analysis
.
Sci. Rep.
,
37
(
2014
)
Review Manager (RevMan) [Computer program]. Version 5.3
,
The Nordic Cochrane Centre, The Cochrane Collaboration
,
Copenhagen
38
Mantel
N.
and
Haenszel
W.
(
1959
)
Statistical aspects of the analysis of data from retrospective studies of disease
.
J. Natl. Cancer Inst.
22
,
719
748
[PubMed]
39
DerSimonian
R.
and
Laird
N.
(
1986
)
Meta-analysis in clinical trials
.
Control. Clin. Trials
7
,
177
188
[PubMed]
40
Begg
C.B.
and
Mazumdar
M.
(
1994
)
Operating characteristics of a rank correlation test for publication bias
.
Biometrics
50
,
1088
1101
[PubMed]
41
Egger
M.
,
Smith
G.D.
,
Schneider
M.
and
Minder
C.
(
1997
)
Bias in meta-analysis detected by a simple, graphical test
.
BMJ
315
,
629
634
[PubMed]
42
Brok
J.
,
Thorlund
K.
,
Gluud
C.
and
Wetterslev
J.
(
2008
)
Trial sequential analysis reveals insufficient information size and potentially false positive results in many meta-analyses
.
J. Clin. Epidemiol.
61
,
763
769
[PubMed]
43
Fu
W.
,
Zhuo
Z.
,
Chen
Y.
,
Zhu
J.
,
Zhao
Z.
,
Jia
W.
et al (
2017
)
NFKB1 -94insertion/deletion ATTG polymorphism and cancer risk: Evidence from 50 case-control studies
.
Oncotarget
8
,
9806
9822
[PubMed]
44
Thorlund
K.
,
Engstrøm
J.
,
Wetterslev
J.
,
Brok
J.
,
Imberger
G.
and
Gluud
C.
(
2011
)
User Manual for Trial Sequential Analysis (TSA)
, pp.
1
115
,
Copenhagen Trial Unit, Centre for Clinical Intervention Research
,
Copenhagen, Denmark
,
45
Chen
R.
,
Chen
C.
,
Yu
J.
,
Huang
X.
,
Zhou
M.
and
Huang
Z.
(
2014
)
Trial sequence meta-analysis can reject false-positive result calculated from conventional meta-analysis
.
Hepatology
60
,
1142
1143
46
Catarino
R.
,
Matos
A.
,
Pinto
D.
,
Pereira
D.
,
Craveiro
R.
,
Vasconcelos
A.
et al (
2005
)
Increased risk of cervical cancer associated with cyclin D1 gene A870G polymorphism
.
Cancer Genet. Cytogenet.
160
,
49
54
[PubMed]
47
Koike
H.
,
Suzuki
K.
,
Satoh
T.
,
Ohtake
N.
,
Takei
T.
,
Nakata
S.
et al (
2003
)
Cyclin D1 gene polymorphism and familial prostate cancer: the AA genotype of A870G polymorphism is associated with prostate cancer risk in men aged 70 years or older and metastatic stage
.
Anticancer Res.
23
,
4947
4951
[PubMed]
48
Kong
S.
,
Amos
C.I.
,
Luthra
R.
,
Lynch
P.M.
,
Levin
B.
and
Frazer
M.L.
(
2000
)
Effects of cyclin D1 polymorphism on age of hereditary nonpolyposis colorectal cancer
.
Cancer Res.
60
,
249
252
[PubMed]
49
Wang
L.
,
Habuchi
T.
,
Takahashi
T.
,
Mitsumori
K.
,
Kamoto
T.
,
Kakehi
Y.
et al (
2002
)
Cyclin D1 gene polymorphism is associated with an increased risk of urinary bladder cancer
.
Carcinogenesis
23
,
257
264
[PubMed]
50
Zheng
Y.
,
Shen
H.
,
Sturgis
E.M.
,
Wang
L.E.
,
Eicher
S.A.
,
Strom
S.S.
et al (
2001
)
Cyclin D1 polymorphism and risk for Squamous cell carcinoma of the head and neck: a case-control study
.
Carcinogenesis
22
,
1195
1199
[PubMed]
51
Pabalan
N.
,
Bapat
B.
,
Sung
L.
,
Jarjanazi
H.
,
Pabalan
O.F.
and
Ozcelik
H.
(
2008
)
Cyclin D1 Pro241Pro (CC ND1-G870A) polymorphism is associated with increased cancer risk in human populations: a meta-analysis, cancer
.
Epidemiol. Biomarkers Prev.
17
,
2773
2781
52
Luo
T.
,
Chen
J.
,
Liu
J.J.
,
Li
H.
,
You
X.M.
,
Wang
H.L.
et al (
2016
)
Association between cyclin D1 G870A polymorphism and hepatocellular carcinoma risk: a meta-analysis
.
Onco. Targets Ther.
21
,
4483
4489
53
Zheng
M.
,
Wan
L.
,
He
X.
,
Qi
X.
,
Liu
F.
and
Zhang
D.H.
(
2015
)
Effect of the CCND1 A870G polymorphism on prostate cancer risk: a meta-analysis of 3,820 cases and 3,825 controls
.
World J. Surg. Oncol.
13
,
55
[PubMed]
54
Wang
B.
,
Jiang
L.
and
Xu
Q.
,
2018
A comprehensive evaluation for polymorphisms in let-7 family in cancer risk and prognosis: a system review and meta-analysis
.
Biosci. Rep.
,
38
,
BSR20180273
,
55
Qin
L.Y.
,
Zhao
L.G.
,
Chen
X.
,
Yang
Z.
and
Mo
W.N.
(
2014
)
The CCND1 G870A gene polymorphism and leukemia or non-Hodgkin lymphoma risk: a meta-analysis
.
Asian Pac. J. Cancer Prev.
15
,
6923
6928
[PubMed]
56
Lin
H.
,
Fang
L.
and
Lin
D.
(
2014
)
Association of cyclin D1 variants with head and neck cancer susceptibility: evidence from a meta-analysis
.
Asian Pac. J. Cancer Prev.
15
,
5645
5651
[PubMed]
57
Yang
M.
,
Zhu
H.
,
Hu
T.
,
Liu
S.
and
Wang
H.
(
2015
)
Association of CCND1 gene polymorphism with cervical cancer susceptibility in Caucasian population: a meta-analysis
.
Int. J. Clin. Exp. Med.
8
,
12983
12988
[PubMed]
58
Sergentanis
T.N.
and
Economopoulos
K.P.
(
2011
)
Cyclin D1 G870A polymorphism and breast cancer risk: a meta-analysis comprising 9,911 cases and 11,171 controls
.
Mol. Biol. Rep.
38
,
4955
4963
[PubMed]
59
Lu
C.
,
Dong
J.
,
Ma
H.
,
Jin
G.
,
Hu
Z.
,
Peng
Y.
et al (
2009
)
CCND1 G870A polymorphism contributes to breast cancer susceptibility: a meta-analysis
.
Breast Cancer Res. Treat.
116
,
571
575
[PubMed]
60
Cui
J.
,
Shen
L.
and
Wang
Y.
(
2012
)
Specific CCND1 G870A Alleles associated with breast cancer susceptibility: a meta-analysis of 5,528 cases and 5,353 controls
.
Asian Pacific J. Cancer Prev.
13
,
5023
5025
61
Soleimani
Z.
,
Kheirkhah
D.
,
Sharif
M.R.
,
Sharif
A.
,
Karimian
M.
and
Aftabi
Y.
(
2016
)
Association of CCND1 gene c.870G>A polymorphism with breast cancer risk: a case-control study and a meta-analysis
.
Pathol. Oncol. Res.
,
[PubMed]
62
Wen
L.
,
Hu
Y.Y.
,
Yang
G.L.
and
Liu
D.X.
(
2014
)
CCND1 G870A polymorphism contributes to the risk of esophageal cancer: An updated systematic review and cumulative meta-analysis
.
Biomed. Rep.
2
,
549
554
63
Cai
W.
,
Wang
Z.T.
,
Zhong
J.
and
Zhang
Y.
(
2013
)
Lack of association between Cyclin D1 gene G870A polymorphism and esophageal cancer: evidence from a meta-analysis
.
Genet. Mol. Res.
12
,
6636
6645
[PubMed]
64
Tang
W.
,
Yu
P.
,
Wang
Y.
,
Kang
M.
,
Sun
B.
,
Yin
J.
et al (
2015
)
Lack of association between cyclin D1 A870G (rs9344) polymorphism and esophageal squamous cell carcinoma risk: case-control study and meta-analysis
.
Int. J. Clin. Exp. Med.
8
,
12685
12695
[PubMed]
65
Jiang
J.
,
Wang
J.
,
Suzuki
S.
,
Gajalakshmi
V.
,
Kuriki
K.
,
Zhao
Y.
et al (
2006
)
Elevated risk of colorectal cancer associated with the AA genotype of the cyclin D1 A870G polymorphism in an Indian population
.
J. Cancer Res. Clin. Oncol.
132
,
193
199
[PubMed]
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