Abstract
Yu Ren1, Li Xiao2, Guobin Weng1 and Bingyi Shi2
1Department of Urologic Surgery, Ningbo Urology and Nephrology Hospital, Ningbo 315000, People’s Republic of China
2Department of Urologic Surgery, Chinese PLA General Hospital, The 309th Hospital of China People’s Liberation Army, Beijing 100094, People’s Republic of China
Correspondence to:
Guobin Weng, email: [email protected]
Keywords: RCC, p16INK4A, p14ARF, promoter methylation, clinical significance
Received: February 02, 2017 Accepted: June 02, 2017 Published: June 28, 2017
ABSTRACT
The inactivation of p16INK4A and p14ARF via promoter methylation has been investigated in various cancers. However, the clinical effects of p16INK4A and p14ARF promoter methylation on renal cell carcinoma (RCC) remain to be clarified. The pooled data were calculated and summarized. Finally, an investigation of 14 eligible studies with 1231 RCC patients and 689 control patients was performed. Methylated p16INK4A and p14ARF were observed to be significantly higher in RCC than in control subjects without malignancies (OR = 2.77, P = 0.005; OR = 11.73, P < 0.001, respectively). Methylated p16INK4A was significantly associated with the risk of RCC in the tissue subgroup, but not in the serum and urine subgroups. Methylated p16INK4A was significantly associated with tumor size. We did not find that p16INK4A promoter methylation was associated with sex, tumor grade, lymph node status, and tumor histology. Methylated p14ARF was significantly correlated with sex and tumor histology. Three studies reported that p16INK4A methylation was not significantly correlated with the prognosis of RCC. The results suggested that p16INK4A and p14ARF promoter methylation may be correlated with the carcinogenesis of RCC, and that methylated p14ARF, especially, can be a major susceptibility gene. We also found the different clinicopathological significance of 16INK4A and p14ARF in RCC. Additional studies with sufficient data are essential to further evaluate the clinical features and prognostic effect of p16INK4A and p14ARF promoter methylation in RCC.
INTRODUCTION
Renal cell carcinoma (RCC) is one of the most common cancers of the human urinary system. Based on cancer statistics, approximately 62,700 new cases will be reported in clinics, with approximately 14,240 deaths in the USA in 2016 [1]. Clear cell renal cell carcinoma (ccRCC) is the most common histological type of RCC, accounting for 70% to 75% of all RCCs [2]. Most patients with RCC are symptom-free in the early stage, and more than 50% of RCCs are found coincidentally by physical examination and imaging [3]. Approximately 30% of the patients with RCC have developed metastases, and the average 5-year survival rate is just 12.3% [4].
Epigenetic and genetic changes are identified to be significantly associated with cancer [5, 6]. DNA methylation is an important mechanism of epigenetic alterations involved in gene expression, which is closely associated with the carcinogenesis and progression of various carcinomas [7–9]. The transcription repression of the gene via CpG island methylation of the promoter can lead to the downregulation of gene expression [10, 11]. Located at chromosome 9p21, cyclin-dependent kinase inhibitor 2A (CDKN2A) has two alternative splicings, encoding the cell cycle regulatory proteins p16INK4A and p14ARF, which have a key function in regulating the activities of the retinoblastoma (RB) and p53 genes, respectively [12, 13]. p16INK4A and p14ARF as tumor suppressor genes are involved in the regulation of cell division and apoptosis, and the maintenance of cellular homeostasis [14]. The inactivation of p16INK4A and p14ARF through promoter methylation has been reported in many cancers [15–17]. Promoter methylation of p16INK4A and p14ARF has been shown in different sample types of RCC, including blood, urine, and tissue samples [18–21].
Although some studies involving p16INK4A and p14ARF promoter methylation included patients with RCC, the studies published in this field have had small sample sizes. In addition, whether p16INK4A and p14ARF promoter methylation is associated with clinical characteristics of RCC remains to be determined. Therefore, in this study, we performed a systematic meta-analysis to further evaluate the clinical significance of p16INK4A and p14ARF promoter methylation in RCC.
RESULTS
Study characteristics
One hundred sixty-six potentially relevant studies were identified by the initial literature search. According to the inclusion criteria, a total of 14 studies involving 1231 RCC patients and 689 control patients [18–31] were included in the current analysis (Figure 1). Of these studies, which involved p16INK4A and p14ARF gene promoter methylation, nine studies evaluated the association between p16INK4A promoter methylation and RCC risk, five studies assessed the correlation between p14ARF promoter methylation and RCC risk, ten studies evaluated the relation between p16INK4A promoter methylation and clinicopathological features, and four studies evaluated the relation between p14ARF promoter methylation and clinicopathological features. The general characteristics of included studies are presented in Table 1.
Table 1: General characteristics of all eligible studies
First author | Country | Ethnicity | Method | Sample | Cancer | Control | OS | DFS | Gene | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
M % | Total | M % | Total | ||||||||
Kawada 2000 [30] | Japan | Asians | MSP | Tissue | 2.2 | 91 | - | - | - | - | P14 |
Esteller 2001 [31] | USA | Caucasians | MSP | Tissue | 13.1 | 38 | 0 | 38 | - | - | P14 |
Battagli 2003 [26] | USA | Caucasians | MSP | Tissue | 18 | 50 | 0 | 27 | - | - | P14 |
Battagli 2003 [26] | USA | Caucasians | MSP | Urine | 18 | 50 | 0 | 12 | - | - | P14 |
Dulaimi 2004 [25] | USA | Caucasians | MSP | Tissue | 17 | 100 | 0 | 15 | NS | - | P14 |
Hoque 2004 [21] | USA | Caucasians | QMSP | Urine | 30.8 | 26 | 0 | 91 | - | - | P14 |
Hoque 2004 [21] | USA | Caucasians | QMSP | Serum | 5.55 | 18 | 3.33 | 30 | - | - | P14 |
Hori 2007 [20] | Japan | Asians | MSP | Tissue | 70.5 | 44 | - | - | - | - | P14 |
Hauser 2013 [18] | Germany | Caucasians | * | Serum | 14.3 | 35 | 0 | 54 | - | - | P14 |
Kawada 2000 [30] | Japan | Asians | MSP | Tissue | 3.3 | 91 | - | - | - | - | P16 |
Romanenko 2002 [29] | Spain | Caucasians | MSP | Tissue | 31.8 | 22 | - | - | - | - | P16 |
Morris 2003 [27] | UK | Caucasians | MSP | Tissue | 0 | 17 | 0 | 14 | - | - | P16 |
Sanz-Casla 2003 [28] | Spain | Caucasians | PCR | Tissue | 20 | 40 | - | - | - | - | P16 |
Battagli 2003 [26] | USA | Caucasians | MSP | Tissue | 10 | 50 | 0 | 27 | - | - | P16 |
Battagli 2003 [26] | USA | Caucasians | MSP | Urine | 8 | 50 | 0 | 12 | - | - | P16 |
Dulaimi 2004 [25] | USA | Caucasians | MSP | Tissue | 10 | 100 | 0 | 15 | NS | - | P16 |
Hoque 2004 [21] | USA | Caucasians | QMSP | Urine | 34.6 | 26 | 0 | 91 | - | - | P16 |
Hoque 2004 [21] | USA | Caucasians | QMSP | Serum | 22.2 | 18 | 0 | 30 | - | - | P16 |
Arai 2006 [24] | Japan | Asians | MSP | Tissue | 73.3 | 60 | 37 | 67 | - | - | P16 |
Hori 2007 [20] | Japan | Asians | MSP | Tissue | 6.8 | 44 | - | - | - | - | P16 |
Vidaurreta 2008 [23] | Spain | Caucasians | MSP | Tissue | 22.9 | 48 | 0 | 48 | NS | NS | P16 |
Onay 2009 [19] | Turkey | Caucasians | MSP | Tissue | 57.1 | 21 | 52.4 | 21 | - | - | P16 |
Martino 2012 [22] | Austria | Caucasians | qPCR | Serum | 46.5 | 157 | 44.2 | 43 | - | NS | P16 |
Hauser 2013 [18] | Germany | Caucasians | * | Serum | 25.7 | 35 | 16.7 | 54 | - | - | P16 |
“*” Denotes detection method using methylation-sensitive real-time polymerase chain reaction; “-” indicates data not available; MSP: methylation specific polymerase chain reaction; PCR: polymerase chain reaction; qPCR: quantitative polymerase chain reaction; QMSP: quantitative methylation-specific polymerase chain reaction; M: methylation; OS: overall survival; DFS: disease-free survival; NS: not significant.
Figure 1: Flow chart of study selection.
Association between p16INK4A and p14ARF promoter methylation and RCC risk
When cancer patients were compared to control subjects, the result of p16INK4A promoter methylation with strong heterogeneity was conducted using a random-effects model (I2 = 51.7% and p = 0.029); under a fixed-effects model, no obvious heterogeneity was found for p14ARF promoter methylation (I2 = 0.0%; and p = 0.667) (Figures 2 and 3).
A significant association was found between p16INK4A promoter methylation and RCC (OR = 2.77, 95% CI = 1.36 - 5.66, P = 0.005), including in 582 of the cancer patients and 422 of the controls (Figure 2). The pooled OR of p14ARF promoter methylation in RCCs was significantly higher than in controls (OR = 11.73, 95% CI = 4.11 - 33.47, P < 0.001), including in 317 of the cancer patients and 267 of the controls (Figure 3).
Figure 2: Forest plot showing the pooled OR from a random-effects model for p16INK4A promoter methylation in RCCs vs. nonmalignant controls.
Figure 3: Forest plot showing the pooled OR from a fixed-effects model for p14ARF promoter methylation in RCCs vs. nonmalignant controls.
Subgroup analyses of p16INK4A promoter methylation in cancer patients versus control patients
According to sample type (tissue, serum, or urine), ethnicity (Caucasian or Asian), and testing method [methylation-specific polymerase chain reaction (MSP) and non-MSP], subgroup analyses were performed for p16INK4A promoter methylation with significant heterogeneity (Table 2).
Table 2: The pooled OR of p16INK4A and p14ARF promoter methylation and RCC
Studies | Overall OR (95 CI %) | I2; P | P value | Cases | Controls | P (Egger test) | |
|---|---|---|---|---|---|---|---|
p16INK4A | 9 | 2.77 (1.36 - 5.66) | 51.7%; 0.029 | 0.005 | 582 | 422 | 0.011 |
p14ARF | 5 | 11.73 (4.11 - 33.47) | 0.0%; 0.667 | < 0.001 | 317 | 267 | 0.193 |
Subgroup (p16INK4A) | |||||||
Ethnicity | |||||||
Asians | 1 | 2.23 (1.06 - 4.71) | NA; NA | 0.036 | 60 | 67 | |
Caucasians | 8 | 3.40 (1.35 - 8.59) | 57.6%; 0.016 | 0.009 | 522 | 355 | |
Sample | |||||||
Tissue | 6 | 2.82 (1.61 - 4.95) | 22.9%; 0.269 | < 0.001 | 296 | 192 | |
Serum | 3 | 1.66 (0.66 - 4.16) | 45.1%; 0.162 | 0.28 | 210 | 127 | |
Urine | 2 | 15.82 (0.41 - 608.03) | 67.6%; 0.079 | 0.138 | 76 | 103 | |
Method | |||||||
MSP | 5 | 2.06 (1.14 - 3.75) | 0.0%; 0.815 | 0.017 | 298 | 156 | |
Non-MSP | 4 | 5.85 (1.28 - 26.77) | 77.2%; 0.002 | 0.023 | 284 | 266 |
MSP: methylation-specific polymerase chain reaction; NA: not applicable; OR: odds ratio; 95% confidence interval (95% CI).
Subgroup analyses based on sample types showed that p16INK4A promoter methylation was significantly associated with RCC risk in tissue (OR = 2.82, 95% CI = 1.61-4.95, P < 0.001), but not in serum or urine (OR = 1.66, 95% CI = 0.66-4.16, P = 0.28; OR = 15.82, 95% CI = 0.41-608.03, P = 0.138, respectively). Subgroup analyses based on ethnicity and testing methods suggested that p16INK4A promoter methylation was significantly correlated with RCC risk in different ethnicities and by different testing methods (all P < 0.05).
Meta regression and sensitivity analyses of p16INK4A promoter methylation in cancer patients versus control patients
Meta regression based on sample type (tissue, serum, or urine), ethnicity (Caucasian or Asian), and testing method (MSP or non-MSP) was performed to find the potential sources of heterogeneity (Table 3).
Table 3: Meta regression analysis of p16INK4A promoter methylation
Subgroup | Coefficient (95% CI) | t | P value |
|---|---|---|---|
Testing method | 2.835 (-0.681, 6.352) | 1.97 | 0.096 |
Ethnicity | -0.225 (-2.909, 2.459) | -0.20 | 0.845 |
Sample material | -1.521 (-3.347, 0. 305) | -2.04 | 0.088 |
The results of meta-regression analysis showed that sample types, ethnicity, and testing methods did not explore the potential sources of heterogeneity (coefficient = −1.521, P = 0.088; coefficient = −0.225, P = 0.845; coefficient = 2.835, P = 0.096, respectively).
A sensitivity analysis was also conducted to evaluate the stability of the overall OR and the change of heterogeneity by deleting a single study. When a study from Hoque 2004 et al. ([21], urine) was removed, the pooled OR was not significantly changed (OR = 2.06, 95% CI = 1.41-3.02), with no obvious heterogeneity (I2 = 23.2%, and P = 0.237).
Relation of p16INK4A and p14ARF promoter methylation and clinicopathological features
We further determined whether p16INK4A and p14ARF promoter methylation status was associated with clinicopathological characteristics, such as sex, tumor grade, tumor stage, tumor size, lymph node status, and tumor histology. The fixed-effects model was used in relation to clinicopathological characteristics in cancer (all p > 0.1) (Table 4).
Table 4: The pooled OR of p16INK4A and p14ARF promoter methylation with clinicopathological features in RCC
Gene | Studies | Overall OR (95 CI %) | I2; P | P value | M (n) | RCCs | M (n) | RCCs | P (Egger test) |
|---|---|---|---|---|---|---|---|---|---|
p14ARF | 3 | 0.48 (0.25 - 0.94) | 29.1%; 0.238 | 0.032 | 41 | 168 | 25 | 76 | 0.769 |
p16INK4A | 4 | 0.66 (0.31 - 1.38) | 2.5%; 0.392 | 0.266 | 20 | 201 | 13 | 91 | 0.715 |
Grade1-2 | Grade 3-4 | ||||||||
p14ARF | 3 | 2.13 (0.96 - 4.75) | 17.5%; 0.297 | 0.063 | 40 | 111 | 15 | 72 | 0.613 |
p16INK4A | 7 | 1.20 (0.58 - 2.45) | 0.0%; 0.620 | 0.625 | 41 | 212 | 13 | 102 | 0.644 |
Stage1-2 | Stage 3-4 | ||||||||
p14ARF | 1 | 1.03 (0.18 - 5.98) | NA; NA | 0.97 | 6 | 35 | 2 | 12 | NA |
p16INK4A | 4 | 1.00 (0.42 - 2.36) | 0.0%; 0.786 | 0.999 | 25 | 104 | 11 | 52 | 0.211 |
pT2-4 | pT1 | ||||||||
p14ARF | 4 | 0.92 (0.44 - 1.91) | 7.4%; 0.356 | 0.815 | 16 | 94 | 41 | 180 | 0.229 |
p16INK4A | 6 | 2.43 (1.10 - 5.35) | 0.0%; 0.615 | 0.028 | 28 | 132 | 12 | 203 | 0.36 |
Node+ | Node- | ||||||||
p14ARF | 2 | 0.35 (0.04 - 2.83) | 0.0%; 0.665 | 0.326 | 0 | 11 | 23 | 123 | NA |
p16INK4A | 5 | 0.69 (0.18 - 2.69) | 0.0%; 0.465 | 0.595 | 1 | 18 | 43 | 225 | 0.02 |
CCRCC | Non-CCRCC | ||||||||
p14ARF | 4 | 0.38 (0.18 - 0.81) | 0.0%; 0.607 | 0.012 | 34 | 185 | 25 | 100 | 0.294 |
p16INK4A | 7 | 0.54 (0.29 - 1.00) | 0.0%; 0.842 | 0.051 | 53 | 289 | 31 | 144 | 0.015 |
M: methylation; ccRCC: clear cell renal cell carcinoma; NA: not applicable; Node+: lymph node-positive status; Node-: lymph node-negative status; RCC: renal cancer carcinoma; pT: pathological T category of primary tumor; n: the number of samples; OR: odds ratio; 95% confidence interval (95% CI).
Association of p16INK4A and p14ARF methylation and gender in cancer
The pooled OR from four studies suggested that p16INK4A promoter methylation was not significantly correlated with gender in RCC (OR = 0.66, 95% CI = 0.31-1.38, P = 0.266), including in 201 males and 91 females (Table 4). The pooled OR from three studies involving 168 males and 76 females suggested that p14ARF promoter methylation was significantly correlated with gender in RCC (OR = 0.48, 95% CI = 0.25-0.94, P = 0.032) (Table 4), indicating that it was lower in males than in females.
Association of p16INK4A and p14ARF methylation and tumor grade in cancer
The pooled OR from seven studies and from three studies suggested that p16INK4A and p14ARF promoter methylation was not significantly correlated with tumor grade in RCC (OR = 1.20, 95% CI = 0.58-2.45, P = 0.625; OR = 2.13, 95% CI = 0.96-4.75, P = 0.063, respectively) (Table 4).
Association of p16INK4A and p14ARF methylation and tumor stage in cancer
The pooled OR from four studies and from one study showed that p16INK4A and p14ARF promoter methylation was not significantly associated with tumor stage in RCC (OR = 1.00, 95% CI = 0.42-2.36, P = 0.999; OR = 1.03, 95% CI = 0.18-5.98, P = 0.97, respectively) (Table 4).
Association of p16INK4A and p14ARF methylation and the pathological T category of primary tumor (pT) in cancer
The pooled OR from six studies including 132 pT2-4 patients and 203 pT1 patients suggested that p16INK4A promoter methylation was significantly correlated with tumor size in RCC (OR = 2.43, 95% CI = 1.10-5.35, P = 0.028) (Table 4), indicating that it was higher in pT2-4 than in pT1. The pooled OR from four studies suggested that p14ARF promoter methylation was not significantly correlated with tumor size in RCC (OR = 0.92, 95% CI = 0.44-1.91, P = 0.815), including 94 pT2-4 patients and 180 pT1 patients (Table 4).
Association of p16INK4A and p14ARF methylation and lymph node status in cancer
The pooled OR from five studies and two studies showed that p16INK4A and p14ARF promoter methylation was not significantly associated with lymph node status in RCC (OR = 0.69, 95% CI = 0.18-2.69, P = 0.595; OR = 0.35, 95% CI = 0.04-2.83, P = 0.326, respectively) (Table 4).
Association of p16INK4A and p14ARF methylation and tumor histology in cancer
The pooled OR from seven studies comprising 289 ccRCC and 144 non-ccRCC patients suggested that p16INK4A promoter methylation was not significantly associated with tumor histology in RCC (OR = 0.54, 95% CI = 0.29-1.00, P = 0.051) (Table 4). The pooled OR from four studies involving 185 ccRCC and 100 non-ccRCC patients demonstrated that p14ARF promoter methylation was significantly correlated with tumor histology in RCC (OR = 0.38, 95% CI = 0.18-0.81, P = 0.012) (Table 4), suggesting that it was lower in ccRCC than in non-ccRCC.
Prognostic value of p16INK4A and p14ARF gene promoter methylation in RCC
The detailed overall survival (OS), and disease-free survival (DFS) data on p16INK4A or p14ARF gene promoter methylation as a prognostic factor for RCC were insufficient. The mean follow-up time for the participants ranged from 28 months [22] to 76 months [23] in this meta-analysis. Dulaimi et al. 2004 [25], Vidaurreta et al. 2008 [23], and Martino et al. 2012 [22] reported that p16INK4A methylation was not significantly associated with the prognosis in DFS or OS (Table 1). Dulaimi et al. 2004 [25] reported that p14ARF methylation was not significantly associated with the prognosis in OS (Table 1). More studies with sufficient data are necessary to further evaluate the prognostic value of p16INK4A and p14ARF promoter methylation in RCC.
Publication bias
The Egger test was used to evaluate potential publication bias. The Egger test showed low publication bias for p16INK4A promoter methylation in cancer patients versus control patients, and in cancer in relation to lymph node status and tumor histology (P = 0.011, P = 0.02, P = 0.015, respectively) (Tables 2 and 4).
DISCUSSION
The p16INK4A is formed from an alternative transcript of exons 1α, 2, and 3, whereas p14ARF is translated from alternative reading frames (ARF) consisting of exons 1β, 2, and 3 [32]. The silencing of p16INK4A and p14ARF can result in uncontrollable cell proliferation and tumor growth [32, 33]. Methylated p16INK4A and p14ARF have been investigated in various cancers, including RCC [18], esophageal squamous cell carcinoma [34], melanoma [35, 36], and gliomas [37]. Although numerous studies have been conducted to evaluate the role of p16INK4A and p14ARF promoter methylation in RCC, the results are still inconsistent and controversial. Kasahara et al. [38] found that the frequency of the methylated p14ARF was 0% in RCC. Hori et al. [20] found that the frequency of the methylated p14ARF was 70.5% in RCC. Morris et al. [27] reported that the frequency of p16INK4A promoter methylation was 0% in RCC. Arai et al. [24] reported that the frequency of p16INK4A promoter methylation was 73.3% in RCC. Therefore, we conducted this study of all available articles to further evaluate the effects of p16INK4A and p14ARF promoter methylation in RCC.
Analysis of the pooled OR showed that p16INK4A and p14ARF promoter methylation were significantly higher in patients with RCC than in control subjects, suggesting that p16INK4A and p14ARF inactivation via promoter methylation may play an important role in the tumorigenesis of RCC. Interestingly, p14ARF promoter methylation had a higher OR value (OR = 11.73) than that of p16INK4A promoter methylation (OR = 2.77) in cancer patients versus control patients, suggesting that RCC can be more susceptible to p14ARF promoter methylation.
When RCCs were compared to nonmalignant samples, the heterogeneity of p16INK4A promoter methylation was high (I2 = 51.7%, P = 0.029). According to sample type (tissue, serum, or urine), ethnicity (Caucasian or Asian), and testing method (MSP and non-MSP), subgroup analyses and meta-regression were used to explore the possible sources of heterogeneity. Analysis showed that subgroup analyses and meta-regression failed to find heterogeneity. Moreover, based on subgroup analyses of sample types, a significant association was observed between p16INK4A promoter methylation and tissue subgroup, but not in the serum and urine subgroups. The results should be carefully considered as only one study or two studies with a small number of samples involved in subgroup analyses. A sensitivity analysis was also performed in our study; when we deleted a study (Hoque 2004 et al., urine) [21], the overall OR was not significantly changed, with no significant heterogeneity, suggesting that our result was stable and reliable.
We further determined whether p16INK4A and p14ARF promoter methylation were correlated with clinicopathological features. Methylated p14ARF was significantly associated with gender, in which it was lower in males than in females, suggesting that female RCC patients can be more susceptible to p14ARF promoter methylation, whereas methylated p16INK4A had a similar frequency in males and females. Methylated p16INK4A was significantly associated with tumor size, in which it was higher in pT2-4 patients than in pT1 patients, suggesting that p16INK4A promoter methylation may play a key role in the pathogenesis of T2-4, whereas methylated p14ARF was not significantly correlated with tumor size. Methylated p14ARF was significantly associated with tumor histology, and it was lower in ccRCC than in non-ccRCC, suggesting that p14ARF promoter methylation had a decreased risk of ccRCC; whereas methylated p16INK4A had a similar frequency in ccRCC and Non-ccRCC. In addition, our findings showed that p16INK4A and p14ARF promoter methylation were not significantly associated with tumor grade, tumor stage, and lymph node status.
The prognostic data involving the pooled hazard ratio (HR) were insufficient and not available, as only three studies reporting showed that p16INK4A and p14ARF gene promoter methylation were not significantly correlated with the prognosis of RCC in OS or DFS [22, 23, 25]. More studies with sufficient data need to be done in the future.
The current study had several potential limitations. First, analysis of p16INK4A promoter methylation showed a slight publication bias in cancer versus control, and in cancer in relation to lymph node status and tumor histology. The articles with positive results are more often published than articles with negative results. The study was restricted to literatures published in English, which can lead to bias. In addition, because fluid samples from serum, plasma, and urine were limited, additional studies will be essential to evaluate the value of fluid detection in the future. Finally, the primary ethnic groups were Asian and Caucasian; thus, further studies using a larger variety of ethnic groups are warranted.
In conclusion, our study showed that RCC had a higher p16INK4A and p14ARF gene promoter methylation than did nonmalignant control patients. RCC had a higher p16INK4A promoter methylation in pT2-4 than in pT1. However, RCC had a lower p14ARF promoter methylation in males than in females, and was also lower in ccRCC than in non-ccRCC. Further large-scale studies with well-designed research are necessary to validate the role of p16INK4A and p14ARF promoter methylation in the prognosis and clinical effects of RCC patients in the future.
MATERIALS AND METHODS
Literature search
This meta-analysis was conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement criteria [39] (Supplementary Table 1). We systematically searched for the relevant literature in the PubMed, EMBASE, EBSCO, and Cochrane Library databases without language restrictions. We used the following free text and their combinations: (kidney OR renal) AND (cancer OR tumor OR neoplasm OR carcinoma) AND (CDKN2A OR MTS1 OR P16 OR INK4A OR P14 OR ARF) AND (methylation OR epigene*) up to September 20, 2016. Finally, only full-text papers published in English were included in this study.
Inclusion criteria
Eligible studies were selected in this meta-analysis if they met the following criteria: 1) patients were diagnosed with primary RCC; 2) although tissue specimens used must include surgically resected primary tumor samples, other samples, such as serum, plasm and urine, were used; 3) CDKN2A methylation included p16INK4A and p14ARF promoter methylation; 4) studies with sufficient data on p16INK4A and p14ARF promoter methylation frequency were selected to assess the association between p16INK4A and p14ARF promoter methylation and RCC; 5) to avoid duplicated publications, only the most recent paper or the most complete paper was included in the current study.
Data extraction
We collected information from each eligible report regarding first author’s name, country, ethnicity, testing method, sample type, methylation frequency, the number of samples, gender, tumor grade, clinical staging, pT, lymph node status, tumor histology, OS, and DFS. The whole data extraction was conducted independently by two authors, and minor disparities were solved by discussion.
Data analyses
All statistical analyses were performed using STATA software (version 12.0, Stata Corporation, College Station, TX, USA). The pooled OR and 95 % confidence interval were calculated to assess the strength of the association between p16INK4A and p14ARF genes promoter methylation and RCC. Heterogeneity among studies was examined by Cochran test and the I2 test [40]. If I2 greater than 50% or p value less than 0.1 was considered as a measure of significant heterogeneity, then the random-effects model was applied in this study; otherwise, the fixed-effects model was used [41, 42]. The meta-regression and subgroup analyses were conducted to explore the source of heterogeneity. A sensitivity analysis was also performed to assess the contributions of an individual study on the overall OR by omitting one study [43]. Any possible publication bias was detected using the Egger linear regression test [44].
Author contributions
YR and GW contributed to the conception and design. YR, LX and BS contributed to the completion of articles, the extraction of data, the calculation of data and the design of figures and tables. All the authors approved the final manuscript.
ACKNOWLEDGMENTS
The research was supported by the grants from Natural Science Foundation of Zhejiang Province (LY13H160038).
CONFLICTS OF INTEREST
The authors declare no conflicts of interest.
REFERENCES
1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016; 66:7-30.
2. Shuch B, Amin A, Armstrong AJ, Eble JN, Ficarra V, Lopez-Beltran A, Martignoni G, Rini BI, Kutikov A. Understanding pathologic variants of renal cell carcinoma: distilling therapeutic opportunities from biologic complexity. Eur Urol. 2015; 67:85-97.
3. Escudier B, Porta C, Schmidinger M, Algaba F, Patard JJ, Khoo V, Eisen T, Horwich A; ESMO Guidelines Working Group. Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2014; 25:iii49-56.
4. Smaletz O. Current management and future directions in the treatment of advanced renal cell carcinoma-a latin american perspective: 10 years in review. Int Braz J Urol. 2015; 41:835-843.
5. Becket E, Chopra S, Duymich CE, Lin JJ, You JS, Pandiyan K, Nichols PW, Siegmund KD, Charlet J, Weisenberger DJ, Jones PA, Liang G. Identification of DNA methylationindependent epigenetic events underlying clear cell renal cell carcinoma. Cancer Res. 2016; 76:1954-1964.
6. Ye M, Huang T, Ying Y, Li J, Yang P, Ni C, Zhou C, Chen S. Detection of 14-3-3 sigma (σ) promoter methylation as a noninvasive biomarker using blood samples for breast cancer diagnosis. Oncotarget. 2017; 8:9230-9242. doi: 10.18632/oncotarget.13992.
7. Mendoza-Perez J, Gu J, Herrera LA, Tannir NM, Matin SF, Karam JA, Huang M, Chang DW, Wood CG, Wu X. Genomic DNA hypomethylation and risk of renal cell carcinoma: a case-control study. Clin Cancer Res. 2016; 22:2074-2082.
8. Carrio E, Suelves M. DNA methylation dynamics in muscle development and disease. Front Aging Neurosci. 2015; 7:19.
9. Henning SM, Wang P, Carpenter CL, Heber D. Epigenetic effects of green tea polyphenols in cancer. Epigenomics. 2013; 5:729-741.
10. Ibragimova I, Dulaimi E, Slifker MJ, Chen DY, Uzzo RG, Cairns P. A global profile of gene promoter methylation in treatment-naive urothelial cancer. Epigenetics. 2014; 9:760-773.
11. Yoshiura K, Kanai Y, Ochiai A, Shimoyama Y, Sugimura T, Hirohashi S. Silencing of the E-cadherin invasion-suppressor gene by CpG methylation in human carcinomas. Proc Natl Acad Sci U S A. 1995; 92:7416-7419.
12. Aguilo F, Zhou MM, Walsh MJ. Long noncoding RNA, polycomb, and the ghosts haunting INK4b-ARF-INK4a expression. Cancer Res. 2011; 71:5365-5369.
13. Ford JH. Saturated fatty acid metabolism is key link between cell division, cancer, and senescence in cellular and whole organism aging. Age (Dordr). 2010; 32:231-237.
14. Nelson AA, Tsao H. Melanoma and genetics. Clin Dermatol. 2009; 27:46-52.
15. Huang T, Chen X, Hong Q, Deng Z, Ma H, Xin Y, Fang Y, Ye H, Wang R, Zhang C, Ye M, Duan S. Meta-analyses of gene methylation and smoking behavior in non-small cell lung cancer patients. Sci Rep. 2015; 5:8897.
16. Fotouhi O, Adel Fahmideh M, Kjellman M, Sulaiman L, Hoog A, Zedenius J, Hashemi J, Larsson C. Global hypomethylation and promoter methylation in small intestinal neuroendocrine tumors: an in vivo and in vitro study. Epigenetics. 2014; 9:987-997.
17. Guney S, Jardin F, Bertrand P, Mareschal S, Parmentier F, Picquenot JM, Tilly H, Bastard C. Several mechanisms lead to the inactivation of the CDKN2A (P16), P14ARF, or CDKN2B (P15) genes in the GCB and ABC molecular DLBCL subtypes. Genes Chromosomes Cancer. 2012; 51:858-867.
18. Hauser S, Zahalka T, Fechner G, Muller SC, Ellinger J. Serum DNA hypermethylation in patients with kidney cancer: results of a prospective study. Anticancer Res. 2013; 33:4651-4656.
19. Onay H, Pehlivan S, Koyuncuoglu M, Kirkali Z, Ozkinay F. Multigene methylation analysis of conventional renal cell carcinoma. Urol Int. 2009; 83:107-112.
20. Hori Y, Oda Y, Kiyoshima K, Yamada Y, Nakashima Y, Naito S, Tsuneyoshi M. Oxidative stress and DNA hypermethylation status in renal cell carcinoma arising in patients on dialysis. J Pathol. 2007; 212:218-226.
21. Hoque MO, Begum S, Topaloglu O, Jeronimo C, Mambo E, Westra WH, Califano JA, Sidransky D. Quantitative detection of promoter hypermethylation of multiple genes in the tumor, urine, and serum DNA of patients with renal cancer. Cancer Res. 2004; 64:5511-5517.
22. de Martino M, Klatte T, Haitel A, Marberger M. Serum cell-free DNA in renal cell carcinoma: a diagnostic and prognostic marker. Cancer. 2012; 118:82-90.
23. Vidaurreta M, Maestro ML, Sanz-Casla MT, Maestro C, Rafael S, Veganzones S, Moreno J, Blanco J, Silmi A, Arroyo M. Inactivation of p16 by CpG hypermethylation in renal cell carcinoma. Urol Oncol. 2008; 26:239-245.
24. Arai E, Kanai Y, Ushijima S, Fujimoto H, Mukai K, Hirohashi S. Regional DNA hypermethylation and DNA methyltransferase (DNMT) 1 protein overexpression in both renal tumors and corresponding nontumorous renal tissues. Int J Cancer. 2006; 119:288-296.
25. Dulaimi E, Ibanez de Caceres I, Uzzo RG, Al-Saleem T, Greenberg RE, Polascik TJ, Babb JS, Grizzle WE, Cairns P. Promoter hypermethylation profile of kidney cancer. Clin Cancer Res. 2004; 10:3972-3979.
26. Battagli C, Uzzo RG, Dulaimi E, Ibanez de Caceres I, Krassenstein R, Al-Saleem T, Greenberg RE, Cairns P. Promoter hypermethylation of tumor suppressor genes in urine from kidney cancer patients. Cancer Res. 2003; 63:8695-8699.
27. Morris MR, Hesson LB, Wagner KJ, Morgan NV, Astuti D, Lees RD, Cooper WN, Lee J, Gentle D, Macdonald F, Kishida T, Grundy R, Yao M, et al. Multigene methylation analysis of Wilms' tumour and adult renal cell carcinoma. Oncogene. 2003; 22:6794-6801.
28. Sanz-Casla MT, Maestro ML, del Barco V, Zanna I, Moreno J, Vidaurreta M, Almansa I, Fernandez C, Blanco J, Maestro C, Resel L. Loss of heterozygosity and methylation of p16 in renal cell carcinoma. Urol Res. 2003; 31:159-162.
29. Romanenko A, Morell-Quadreny L, Lopez-Guerrero JA, Pellin A, Nepomnyaschy V, Vozianov A, Llombart-Bosch A. P16INK4A and p15INK4B gene alteration associated with oxidative stress in renal cell carcinomas after the chernobyl accident (pilot study). Diagn Mol Pathol. 2002; 11:163-169.
30. Kawada Y, Nakamura M, Ishida E, Shimada K, Oosterwijk E, Uemura H, Hirao Y, Chul KS, Konishi N. Aberrations of the p14(ARF) and p16(INK4a) genes in renal cell carcinomas. Jpn J Cancer Res. 2001; 92:1293-1299.
31. Esteller M, Cordon-Cardo C, Corn PG, Meltzer SJ, Pohar KS, Watkins DN, Capella G, Peinado MA, Matias-Guiu X, Prat J, Baylin SB, Herman JG. p14ARF silencing by promoter hypermethylation mediates abnormal intracellular localization of MDM2. Cancer Res. 2001; 61:2816-2821.
32. Berggren P, Kumar R, Sakano S, Hemminki L, Wada T, Steineck G, Adolfsson J, Larsson P, Norming U, Wijkstrom H, Hemminki K. Detecting homozygous deletions in the CDKN2A(p16(INK4a))/ARF(p14(ARF)) gene in urinary bladder cancer using real-time quantitative PCR. Clin Cancer Res. 2003; 9:235-242.
33. Crespo I, Vital AL, Gonzalez-Tablas M, Patino Mdel C, Otero A, Lopes MC, de Oliveira C, Domingues P, Orfao A, Tabernero MD. Molecular and genomic alterations in glioblastoma multiforme. Am J Pathol. 2015; 185:1820-1833.
34. Hoshimoto S, Takeuchi H, Ono S, Sim MS, Huynh JL, Huang SK, Marzese DM, Kitagawa Y, Hoon DS. Genome-wide hypomethylation and specific tumor-related gene hypermethylation are associated with esophageal squamous cell carcinoma outcome. J Thorac Oncol. 2015; 10:509-517.
35. Hyland PL, Burke LS, Pfeiffer RM, Rotunno M, Sun D, Patil P, Wu X, Tucker MA, Goldstein AM, Yang XR. Constitutional promoter methylation and risk of familial melanoma. Epigenetics. 2014; 9:685-692.
36. Venza M, Visalli M, Biondo C, Lentini M, Catalano T, Teti D, Venza I. Epigenetic regulation of p14ARF and p16INK4A expression in cutaneous and uveal melanoma. Biochim Biophys Acta. 2015; 1849:247-256.
37. Majchrzak-Celinska A, Paluszczak J, Szalata M, Barciszewska AM, Nowak S, Kleszcz R, Sherba A, Baer-Dubowska W. The methylation of a panel of genes differentiates low-grade from high-grade gliomas. Tumour Biol. 2015; 36:3831-3841.
38. Kasahara T, Bilim V, Hara N, Takahashi K, Tomita Y. Homozygous deletions of the INK4a/ARF locus in renal cell cancer. Anticancer Res. 2006; 26:4299-4305.
39. Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009; 339:b2535.
40. Coory MD. Comment on: heterogeneity in meta-analysis should be expected and appropriately quantified. Int J Epidemiol. 2010; 39:932; author reply 933.
41. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003; 327:557-560.
42. DerSimonian R. Meta-analysis in the design and monitoring of clinical trials. Stat Med. 1996; 15:1237-1248; discussion 1249-1252.
43. Lau J, Ioannidis JP, Schmid CH. Quantitative synthesis in systematic reviews. Ann Intern Med. 1997; 127:820-826.
44. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997; 315:629-634.
