Disease Markers
Volume 2016 (2016), Article ID 5259602, 13 pages
http://dx.doi.org/10.1155/2016/5259602

Review Article

Prognostic and Clinicopathological Significance of Downregulated p16 Expression in Patients with Bladder Cancer: A Systematic Review and Meta-Analysis

Xiaoning Gan,¹ Xiaomiao Lin,² Rongquan He,³ Xinggu Lin,³ Hanlin Wang,¹ Liyan Yan,¹ Hong Zhou,¹ Hui Qin,¹ and Gang Chen¹

¹Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
²Department of Children Rehabilitation Medicine, Guangxi Maternal and Child Health Hospital, 225 Xinyang Road, Nanning, Guangxi Zhuang Autonomous Region 530003, China
³Center of Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China

Received 15 September 2015; Accepted 27 March 2016

Academic Editor: Ja Hyeon Ku

Copyright © 2016 Xiaoning Gan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

p16, encoded by the CDKN2A gene, is a tumor suppressor that has been widely studied in cancer research. However, the relationship of p16 with prognostic and clinicopathological parameters in patients with bladder cancer remains unclear. Data inclusion criteria were articles reporting on the relationship between p16 expression and the prognosis or clinicopathology in patients with bladder cancer. Meta-analyses were performed with Stata software. Hazard ratios (HRs) or odds ratios (ORs) and 95% confidence intervals (95% CI) were calculated to evaluate the relative risks. The source of heterogeneity was analyzed by subgroup analysis. A total of 37 studies with 2246 cases were included and analyzed. The results identified an important link between downregulated p16 expression and poor prognosis in patients with bladder cancer in terms of recurrence-free survival (RFS), overall survival (OS), progression-free survival (PFS), and some clinicopathological parameters including clinical staging, pathological degree, and lymph node metastasis. Subgroup analysis also showed that low p16 expression could function as a warning sign for RFS and PFS in patients with early-stage (Ta–T1) bladder cancer. In conclusion, p16 might play an essential role in the deterioration of bladder cancer and could serve as a biomarker for the prediction for patients’ progression and prognosis.

1. Introduction

Bladder cancer is the most frequent malignancy of the urinary tract and the ninth most common cancer worldwide [1]. About 95% of bladder cancers are histologically transitional cell carcinoma, with rare cases of squamous cell carcinoma and adenocarcinoma. However, the pathogenesis of bladder cancer is still unclear, and its occurrence and development appear to be affected by multiple genes [2]. Serrano et al. first cloned the cDNA of the gene encoding the tumor suppressor protein p16 (CDNK2A) in 1993; since then it has been widely studied in the field of cancer research [3]. Previous studies have reported ubiquitous downregulation of p16 gene expression in bladder cancer, as a result of various alterations including complete deletion, point mutation, or promoter methylation [4–6]. Furthermore, p16 could compete with cyclin D1 for binding to Cyclin Dependent Kinase (CDK) 4/6, thus blocking the phosphorylation of retinoblastoma (Rb) protein and inhibiting release of the transcription factor E2F, preventing cell conversion from G₁ phase to S phase, and eventually suppressing cell proliferation. These results suggest that abnormal expression of the p16 gene in cells might be associated with tumorigenesis [6, 7].

Numerous studies to date have explored the clinicopathological and prognostic significance of p16 in patients with bladder cancer. However, as a result of differences in sample sizes, accuracies of the statistical data, study populations, and interventions, the results remain inconclusive, and evidence-based confirmation by large-scale clinical trials is still lacking. We therefore conducted an in-depth systematic review and meta-analysis to investigate the correlation between abnormal expression of p16 and clinicopathological features, as well as prognosis in patients with bladder cancer. The specific mechanisms are shown in Figure 1.

Figure 1: Main molecular pathways of bladder cancer (adapted from Mitra et al. [7]).

2. Materials and Methods

2.1. Literature Search

The terms and combinations including “Cyclin Dependent Kinase Inhibitor p16,” “CDKN2A Protein,” “p16INK4A Protein,” “MTS1 Protein,” “Cyclin Dependent Kinase Inhibitor 2A,” “Multiple Tumor Suppressor 1,” “Cdk4 Associated Protein p16,” “TP16,” and “urinary bladder neoplasms,” “bladder tumors,” “bladder cancers,” “bladder carcinomas,” and “,” “,” “follow-up,” “mortality,” “predict,” “course,” “outcome,” and “clinicopathological” were used to search the following domestic and international databases: PubMed, Wiley Online Library, Embase, Cochrane Central Register of Controlled Trials, Science Direct, EBSCO, Google Scholar, Ovid, LILACS, China National Knowledge Infrastructure (CNKI), China Biology Medicine disc (CBMdisc), CQVIP, and Wan Fang, with unified retrieval rules such as Boolean logic. The obtained search results were then analyzed, evaluated, reviewed, and manually screened to determine their relevance.

2.2. Inclusion and Exclusion Criteria

Inclusion criteria were as follows: (1) patients diagnosed with bladder cancer; (2) immunohistochemical (IHC) detection of p16 expression levels in the tissues; (3) relationships between abnormal expression of p16 and prognostic indicators such as recurrence-free survival (RFS), progression-free survival (PFS), and overall survival (OS) or associations between p16 and clinicopathological features that were evaluated; (4) hazard ratio (HR), odds ratio (OR), relative risk (RR), and 95% confidence intervals (CI) that could be obtained directly from the full article or indirectly calculated with relevant software based on the data provided in the graphics and tables; (5) only the newest studies or the ones with higher quality were retained if the data were repeated in different studies; and (6) studies in English or Chinese.

Exclusion criteria were as follows: (1) cell or animal studies, case reports, letters, reviews, and meta-analyses; (2) articles with similar content or using the same data or those with small sample sizes () and those with no directly or indirectly extractable HR, OR, and 95% CI data; and (3) articles that could not be understood because of language barriers.

2.3. Data Extraction

Two independent investigators (Xiaoning Gan and Rongquan He) reviewed the articles that met the criteria and extracted data on author, year of publication, nationality, sample size, patient age, detection method of p16, antibody source and dilution, clinical stage, pathological degree, other costudied prognosis-associated genes, cut-off value, outcome, and extraction method of the study subjects. Discrepancies between the two independent investigators in terms of data extraction were resolved by discussion among all the authors.

2.4. Statistical Analysis

Effects of p16 on the related prognostic indexes were detected by merging the HRs and 95% CI of the included literatures, which were evaluated through the Forest plot and related parameters after the merging. The HRs and 95% CI values mainly came from direct extraction of the original text or survival curve through extraction and calculation by software.

The relationships between p16 and the clinicopathological parameters were derived from the binary variable data extracted from the original articles. ORs and 95% CI values came from the binary variable data calculated by Stata software. The data were then combined, and their statistical significance was evaluated by Forest plot and related parameters, to clarify the relationship between p16 low-expression and clinicopathological parameters.

Heterogeneity was measured by statistics as follows: no heterogeneity: ; low heterogeneity: ; moderate heterogeneity: ; high heterogeneity: . If and , a fixed-effect model would be used in combination with HRs, ORs, and 95% CI; if and , then a random-effect model would be selected. Heterogeneity analysis was performed to assess the accuracy of the data, and subgroup and sensitivity analyses were carried out based on professional knowledge.

Publication bias was detected by Begg’s funnel plot and Egger’s test with Stata software. A two-sided value < 0.05 was considered to indicate statistical significance. Statistical analyses were carried out with StataSE 12.0, Engauge, Photoshop CS5, and Microsoft Office 2007.

3. Results

3.1. Eligible Studies

A total of 364 articles were identified from the databases, including 190 English and 174 Chinese articles, 222 of which were excluded because of discrepancies between the study theme and their abstracts. The full text of the remaining 142 articles was then reviewed for their fit with the current study, after which a further 105 articles were excluded because they met one or more of the exclusion criteria, such as the cell or animal studies, reviews, and letters and studies with identical data and no extractable HR, OR, and 95% CI data from the full text or language barrier. The remaining 37 articles [4, 5, 8–42] with 2246 cases were included in our study and consisted of 21 English [4, 5, 8–19, 21–25, 41, 42] and 16 Chinese [20, 26–40] articles. The screening process was demonstrated in Figure 2.

Figure 2: Flow diagram of studies selection procedure.

The basic features of the included studies were presented in Table 1. Among the 37 articles, 26 studies [4, 5, 8–21, 28–34, 39, 41, 42] investigated the relationship between low expression of p16 and prognostic parameters in bladder cancer patients (RFS, OS, PFS, and DSS/CSS), and 30 studies [4, 10–13, 15–19, 21–40] assessed the association between p16 and clinicopathological factors in patients with bladder cancer.

Table 1: Main features of all studies included in the meta-analysis.

Author
Year
Nation
No. (M/F)
Age
Stage
Grade
Cut-off value
Outcome
Data extraction
Other costudied genes
Antibody source (dilution)
Detection method of p16

Orlow et al. [4]
1999
Canada
120
NR
Ta–T1
G1–G3
Score = 3
RFS/CP
Reported
P14
Vector (1 : 500)
Immunohistochemistry

Bartoletti et al. [8]
2007
Italy
56 (50/6)
70.1 (45–89)
Ta–T1
G1-G2
10%
RFS
Reported
9p21
Bio-Optica (1 : 25)
Immunohistochemistry

Chakravarti et al. [9]
2005
USA
50 (36/14)
NR
T2–T4
High
20%
OS/FFS/DSS
Reported
Erb-1, Erb-2, P53, PRB
Zymed (NR)
Immunohistochemistry

Hitchings et al. [10]
2004
UK
78
66 (24–90)
Ta–T1
G1–G3
10%
PFS/RFS/CP
Reported
P53, PRB
Novocastra (1 : 50)
Immunohistochemistry

Krüger et al. [5]
2005
Germany
73 (60/13)
68 (NR)
T1
G2-G3
10%
RFS/PFS
Reported
NR
Biocarta (1 : 50)
Immunohistochemistry

Lee et al. [11]
2010
Korea
47 (4/43)
NR
Ta–T4
Low and high
Score = 5
OS/CP
Reported
P53, PRB
DAKO (1 : 200)
Immunohistochemistry

Mhawech et al. [12]
2004
Switzerland
49 (44/5)
70.3 (52–90)
T1
Low and high
Score = 3
PFS/CP
Reported
P21
DAKO (1 : 20)
Immunohistochemistry

Yang et al. [13]
2002
China
67
NR
T1-T2
G1–G3
5%
RFS/CP
Binary variable
Cyclin D1, CCNE, p27, p21, p53
Santa Cruz (NR)
Immunohistochemistry

Brunner et al. [14]
2008
Switzerland
99
NR
Ta–T4
Low and high
1.5% or 23%
OS/RFS
Survival curve
MTS
NeoMarkers (1 : 50)
Immunohistochemistry

Friedrich et al. [15]
2001
Germany
40
NR
Ta–T1
G1–G3
5%
RFS/CP
Survival curve
LOH
Pharmingen (1 : 100)
Immunohistochemistry

Korkolopoulou et al. [16]
2001
Greece
23
72 (35–92)
T3-T4
Low and high
5%
OS/CP
Survival curve
P53
Santa Cruz (1 : 100)
Immunohistochemistry

Niehans et al. [17]
1999
USA
78
64.7 (48–82)
T1–T4
G2–G4
Score = 4
DSS/CP
Survival curve
P53, PRB, cyclin D1
Pharmingen (1 : 400)
Immunohistochemistry

Røtterud et al. [18]
2002
Norway
59
64 (42–75)
T2–T4
G2-G3
Score = 3
CSS/CP
Survival curve
p21, p27
NeoMarkers (1 : 100)
Immunohistochemistry

Vallmanya Llena et al. [19]
2006
Spain
97
NR
Ta–T1
Low and high
15%
RFS/PFS/OS/CP
Survival curve
p53, p21
DakoCytomation (NR)
Immunohistochemistry

Sun et al. [20]
2000
China
60
NR
Tis–T4
G1–G3
Score = 4
OS
Survival curve
PRb
Santa Cruz (1 : 100)
Immunohistochemistry

Santos et al. [21]
2003
Portugal
56 (40/16)
70 (43–83)
Ta–T1
G1-G2
20%
RFS/CP
Binary variable
p27, pRb, p53, Ki-67
Pharmingen (1 : 500)
Immunohistochemistry

Yin et al. [22]
2008
USA
18
NR
T1–T4
Low and high
Score = 4
CP
Binary variable
9p21
Pharmingen (1 : 250)
Immunohistochemistry

Primdahl et al. [23]
2002
Denmark
69 (55/14)
71 (42–83)
Ta–T4
G1–G4
Score = 4
CP
Binary variable
Rb, p27, p21, L-myc
NeoMarkers (1 : 50)
Immunohistochemistry

Jin et al. [24]
2006
USA
39 (25/14)
65 (42–84)
T2–T4
G1–G4
10%
CP
Binary variable
P53, pRB
NR (1 : 50)
Immunohistochemistry

Tzai et al. [25]
2004
China (Taiwan)
65 (44/21)
61.5 (41–84)
T2–T4
G2-G3
Score = 4
CP
Binary variable
P53, pRB
Santa Cruz (1 : 20)
Immunohistochemistry

Jin et al. [26]
2004
China
62 (32/30)
61 (18–80)
Tis–T4
G1–G3
OC
CP
Binary variable
Cyclin D1, PCNA
NR
Immunohistochemistry

Fu and Li [27]
2011
China
50 (39/11)
59.3 (32–81)
Tis–T4
G1–G3
10%
CP
Binary variable
E-cadherin
NR
Immunohistochemistry

Shi et al. [28]
2001
China
62 (52/10)
58.5 (22–87)
Tis–T4
G1–G3
Score = 3
RFS/CP
Binary variable
PCNA
Zymed (1 : 50)
Immunohistochemistry

Wang [29]
2001
China
49 (39/10)
61 (22–89)
NR
G1–G3
10%
RFS/CP
Binary variable
NR
NR
Immunohistochemistry

Miao [30]
1999
China
50
NR
Tis–T4
G1–G3
OC
RFS/OS/CP
Binary variable
Cyclin D1
Santa Cruz (1 : 100)
Immunohistochemistry

Shi et al. [31]
2003
China
82 (65/17)
58.7 (24–72)
Tis–T4
G1–G3
OC
RFS/CP
Binary variable
Cyclin D1
NR
Immunohistochemistry

Yang [32]
2005
China
69 (62/7)
61 (42–75)
Tis–T4
G1–G3
5%
RFS/CP
Binary variable
P27/nm23
NR
Immunohistochemistry

Wang et al. [33]
2013
China
45 (30/15)
65 (38–80)
NR
H/L
5%
RFS/CP
Binary variable
PTEN/P53
NR
Immunohistochemistry

Leng et al. [34]
2000
China
51 (43/8)
53.4 (28–72)
Tis–T3
G1–G3
OC
RFS/OS/CP
Binary variable
bcl-2
Santa Cruz (1 : 50)
Immunohistochemistry

Bai and Xiong [35]
2014
China
65 (50/15)
(57.7 ± 8.2)
Tis–T4
H/L
5%
CP
Binary variable
mfn2
Zymed (NR)
Immunohistochemistry

Wang et al. [36]
2000
China
75 (62/13)
58.5 (24–81)
Tis–T4
G1–G3
OC
CP
Binary variable
c-erbB-2, p53
Maxim (1 : 50)
Immunohistochemistry

Wang et al. [37]
2006
China
55 (35/20)
63 (24–75)
Tis–T4
G1–G3
10%
CP
Binary variable
hTERT, cyclin D1, RB
NR
Immunohistochemistry

Lu et al. [38]
2008
China
40 (30/10)
54.2 (37–79)
Tis–T4
G1–G3
10%
CP
Binary variable
p53, PCNA
NR (1 : 50)
Immunohistochemistry

Xie et al. [39]
2003
China
72 (56/16)
NR (29–78)
Tis–T4
G1–G3
5%
RFS/CP
Binary variable
Rb, cyclin D1
Zymed (1 : 50)
Immunohistochemistry

Qiu et al. [40]
2006
China
53 (46/7)
61 (25–83)
Tis–T4
G1–G3
15%
CP
Binary variable
NR
NR
Immunohistochemistry

Rebouissou et al. [41]
2012
France
89
NR
Ta–T1
G1–G3
Score = 3
RFS/PFS
Survival curve
FGFR3
NR
FISH

Abat et al. [42]
2014
Turkey
34 (30/4)
NR
T1–T4
Low and high
OC
PFS
Reported
p53
NR
FISH

M: male; F: female; RFS: recurrence-free survival; OS: overall survival; PFS: progression-free survival; DSS: disease-specific survival; CSS: cancer-specific survival; CP: clinicopathological parameters; OC: other criteria; NR: not reported; No.: number of patients.

3.2. Relationship between Downregulated p16 Expression and RFS in Patients with Bladder Cancer

A total of 17 studies with 1032 subjects were included in the final analysis of RFS [4, 5, 8, 10, 13–15, 19, 21, 28–34, 39]. Low expression of p16 was related to poor RFS in patients with bladder cancer (HR = 1.63, 95% CI = 1.36~1.94, and ), with low observed heterogeneity (, ) (Figure 3(a)).

(a)

(b)

(c)

Figure 3: Forrest plot of hazard ratio (HR) for the association of p16 with recurrence-free survival (RFS) (a), overall survival (OS) (b), and progression-free survival (PFS) (c) in patients with bladder cancer.

Cumulative meta-analysis based on year of publication and sample size demonstrated that the results tended to stabilize with increasing sample size, but there was no obvious relationship between the results and year of publication.

Based on sensitivity analysis, the study by Yang et al. [13] was initially excluded because of a large difference in HR compared with the overall average, which was attributed to the selection of a different calculation method in the original article. Binary variable data were extracted and the HR and 95% CI were therefore recalculated with Stata software.

Subgroup analysis based on geographic region showed that low expression of p16 was associated with RFS in patients with bladder cancer both in Asia (HR = 1.44, 95% CI = 1.15~1.81, and ) and in Europe (HR = 1.90, 95% CI = 1.13~3.19, and ). The results of American studies (HR = 1.58, 95% CI = 0.77~3.25, and ) need to be confirmed with larger sample sizes. The heterogeneity of Asian studies (, ) was lower than the overall heterogeneity, while that of Europe (, ) was higher, calculated with the random-effect model.

Subgroup analysis based on clinical stage suggested that the effect of p16 on RFS was associated with clinical stage (Tis-T1 group: HR = 1.96, 95% CI = 1.23~3.14, and ; , ; Tis–T4 group: HR = 1.41, 95% CI = 1.12~1.77, and ; , ).

Subgroup analysis based on histopathological grade showed that heterogeneity decreased from G1-G2 (HR = 4.12, 95% CI = 2.48~6.83, and ; , ), G1–G3 (HR = 1.44, 95% CI = 1.18~1.75, and ; , ), and G2-G3 (HR = 1.37, 95% CI = 0.78~2.42, and ; , ), indicating that the effects of p16 on RFS in patients with bladder cancer were closely associated with pathological grade.

Subgroup analysis also showed an effect of cut-off value on the influence of p16 on RFS (cut-off value ≤ 10%: HR = 1.83, 95% CI = 1.34~2.51, and ; , ; cut-off > 10%: HR = 1.34, 95% CI = 0.86~2.09, and ; , ).

In addition, subgroup analysis of early-stage data from 430 subjects from eight studies also demonstrated that low expression of p16 significantly affected RFS in patients with early-stage (Ta–T1) bladder cancer (HR = 1.96, 95% CI = 1.23~3.14, and ; , ).

3.3. Relationship between the Low Expression of p16 and OS in Patients with Bladder Cancer

A total of 425 subjects in eight studies were included in the final analysis of OS [9, 11, 14, 16, 19, 20, 30, 34], which showed that low expression of p16 was associated with decreased OS in patients with bladder cancer (HR = 1.70, 95% CI = 1.16~2.50, and ), with no significant observed heterogeneity (, ) (Figure 3(b)).

Cumulative meta-analysis and sensitivity analysis indicated relatively low overall heterogeneity and no study with high sensitivity.

Subgroup analysis based on geographic area showed a subtle distinction between p16 expression and OS in patients with bladder cancer in Asia (HR = 1.61, 95% CI = 0.97~2.66, and ; , ) and Europe (HR = 2.54, 95% CI = 1.05~6.15, and ; , ).

Subgroup analysis was also performed based on clinicopathological stages. However, limitations of sample size led to the impossibility of determining if the effects of p16 expression on OS were associated with these parameters in patients with bladder cancer (Ta–T1 group: HR = 1.57, 95% CI = 0.32~7.75, and ; , ; Ta–T4 group: HR = 1.59, 95% CI = 0.98~2.60, and ; , ; T2–T4 group: HR = 1.96, 95% CI = 0.99~3.88, and ; , ; low-grade group: HR = 1.41, 95% CI = 0.18~10.90, and ; , ; G1–G3 group: HR = 1.82, 95% CI = 1.16~2.84, and ; , ; high-grade group: HR = 1.41, 95% CI = 0.62–3.19, and ; , ).

Subgroup analysis based on cut-off value indicated that the effects of p16 on OS in patients with bladder cancer were associated with cut-off value (cut-off value ≤ 10%: HR = 1.83, 95% CI = 1.17~2.86, and ; , ; cut-off value > 10%: HR = 1.40, 95% CI = 0.66~2.96, and ; , ).

3.4. Relationship between Low Expression of p16 and PFS in Patients with Bladder Cancer

A total of 470 subjects in seven studies were included in the ultimate analysis of PFS [5, 9, 10, 12, 19, 41, 42]. The results showed a correlation between low expression of p16 and poor PFS in patients with bladder cancer (HR = 2.18, 95% CI = 1.37~3.48, and ), with low heterogeneity detected (, ).

Cumulative meta-analysis revealed no obvious characteristics because of the limited range of publication dates and the sample sizes.

Sensitivity analysis identified two studies [41, 42] as having the highest heterogeneities. Further investigation revealed that this heterogeneity was caused by different methods of measuring p16 (fluorescence in situ hybridization) and studying the influence of hemizygous or homozygous deletion of p16 on patient prognosis. These two studies were finally excluded because of their incompatible study objectives, leaving a total of 347 subjects from six studies in the final analysis of PFS. The results showed that low expression of p16 was correlated with poor PFS in patients with bladder cancer, and the heterogeneity was eliminated (HR = 1.84, 95% CI = 1.13~3.01, and ; , ) (Figure 3(c)).

Despite a reduced sample size, subgroup analysis of the 347 subjects from five studies [5, 9, 10, 12, 19] demonstrated that the effects of p16 expression on PFS were affected by clinical stage (Ta–T1 group: HR = 2.09, 95% CI = 1.21~3.63, and ; , ; T2–T4 group: HR = 1.14, 95% CI = 0.39~3.31, and ; , ) and geographical location (Europe: HR = 2.09, 95% CI = 1.21~3.63, and ; , ; America: HR = 1.14, 95% CI = 0.39~3.31, and ).

Subgroup analysis based on cut-off value demonstrated some relationship between cut-off value and the influence of p16 expression on PFS (cut-off value ≤ 10%: HR = 2.61, 95% CI = 1.42~4.77, and ; , ; cut-off value > 10%: HR = 0.95, 95% CI = 0.41~2.18, and ; , ).

The results from 297 subjects with early-stage (Ta–T1) bladder cancer from four studies [5, 10, 12, 19] suggested that low expression of p16 was also significantly associated with poor PFS in early-stage bladder cancer (HR = 2.09, 95% CI = 1.21~3.63, and ; , ).

3.5. Relationship between Low Expression of p16 and DSS/CSS in Patients with Bladder Cancer

A total of 187 subjects from three studies were included in the DSS/CSS analysis [9, 17, 18]; limitation of the sample size caused the impossibility of demonstrating an association between low expression of p16 and DSS/CSS (HR = 1.52, 95% CI = 0.85~2.71, and ; , ).

3.6. Relationship between Low Expression of p16 and Clinicopathological Parameters in Patients with Bladder Cancer

The relationship between low expression of p16 and clinicopathological parameters [4, 10–13, 15–19, 21–40] was further explored by analysis of 30 studies including 1785 subjects. The results of statistical analyses were as follows: T2–T4/Ta–T1: OR = 3.13, 95% CI = 2.42~4.06, and ; , ; T1/Ta: OR = 1.55, 95% CI = 0.87~2.76, and ; , ; G3/G1-2 [43]: OR = 3.33, 95% CI = 2.51~4.42, and ; , ; and H/L [44]: OR = 1.20, 95% CI = 0.69~2.33, and ; , ; because of the high heterogeneity, a random-effects model was therefore applied. Meanwhile, these results demonstrated significant differences in the effects of low p16 expression in patients with bladder cancer between the two WHO clinical pathological grading methods in 1973 and 2004.

Analysis of the results for lymph node metastasis showed OR = 2.20, 95% CI = 1.26~3.83, and ; , . The small sample size caused the impossibility of demonstrating any significant influence of pathological parameters such as muscle invasion, tumor number (multiple/single), and tumor size on the effect of p16 expression (Table 2).

Table 2: Main meta-analysis results of p16 expression in patients with bladder cancer.

Analysis
No. (No.)
HR (95% CI)

Model
Heterogeneity
Publication bias

%

Begg’s
Egger’s

RFS
18 (1032)
1.63 (1.36–1.94)
5.40

F
42.6
0.029
0.405
0.246

Europe
8 (365)
1.90 (1.13–3.19)
2.43

R
53.1
0.037
1.000
0.749

Asia
9 (547)
1.44 (1.15–1.81)
3.15

F
30.7
0.173
0.348
0.020

America
1 (120)
1.58 (0.77–3.25)
1.24

F
0.0
/
/
/

Ta–T1
8 (430)
1.96 (1.23–3.14)
2.82

R
55.5
0.028
0.711
0.916

Ta–T4
10 (602)
1.41 (1.12–1.77)
2.96

F
10.2
0.348
1.000
0.062

G1-G2
2 (75)
4.12 (2.48–6.83)
5.49

F
0.0
0.924
1.000
/

G1–G3
14 (762)
1.44 (1.18–1.75)
3.50

F
11.9
0.323
0.584
0.031

G2-G3
2 (95)
1.37 (0.78–2.42)
1.10

F
0.0
0.802
1.000
/

Cut-off value (≤10%)
13 (741)
1.83 (1.34–2.51)
3.79

R
54.8
0.009
0.583
0.297

Cut-off value (>10%)
5 (291)
1.34 (0.86–2.09)
1.28

F
0.0
0.701
0.462
0.166

OS
9 (425)
1.70 (1.16–2.50)
2.71

F
0.0
0.584
0.602
0.165

Europe
4 (167)
2.54 (1.05–6.15)
2.07

F
27.0
0.250
1.000
0.289

Asia
4 (208)
1.61 (0.97–2.66)
1.85

F
0.0
0.703
0.734
0.166

America
1 (50)
1.41 (0.62–3.19)
0.83

F
0.0
/
/
/

Ta–T4
5 (230)
1.59 (0.98–2.60)
1.87

F
3.1
0.389
1.000
0.232

Ta–T1
2 (122)
1.57 (0.32–7.75)
0.55

F
0.0
0.750
1.000
/

T2–T4
2 (73)
1.96 (0.99–3.88)
1.94

R
52.1
0.148
1.000
/

G1–G3
7 (353)
1.82 (1.16–2.84)
2.62

F
3.9
0.397
/
/

L
1 (22)
1.41 (0.18–10.90)
0.33

F
/
/
/
/

H
1 (50)
1.41 (0.62–3.19)
0.83

F
/
/
/
/

Cut-off value (≤10%)
7 (278)
1.83 (1.17–2.86)
2.63

F
3.2
0.402
0.764
0.185

Cut-off value (>10%)
2 (147)
1.40 (0.66–2.96)
0.87

F
0.0
0.951
1.000
/

PFS
8 (470)
2.18 (1.37–3.48)
3.28

F
26.3
0.219
0.174
0.325

IHC
6 (347)
1.84 (1.13–3.01)
2.44

F
0.0
0.487
1.000
0.754

FISH
2 (123)
11.28 (2.45–51.83)
3.11

F
0.0
0.718
1.000
/

Europe
5 (297)
2.09 (1.21–3.63)
2.62

F
0.0
0.484
1.000
0.607

America
1 (50)
1.14 (0.39–3.31)
0.24

F
/
/
/
/

Ta–T1
5 (297)
2.09 (1.21–3.63)
2.62

F
0.0
0.484
1.000
0.607

T2–T4
1 (50)
1.14 (0.39–3.31)
0.24

F
/
/
/
/

G1–G3
5 (297)
2.09 (1.21–3.63)
2.62

F
0.0
0.484
1.000
0.607

H
1 (50)
1.14 (0.39–3.31)
0.24

F
/
/
/
/

Cut-off value (≤10%)
4 (200)
2.61 (1.42–4.77)
3.10

F
0.0
0.932
1.000
0.746

Cut-off value (>10%)
2 (147)
0.95 (0.41–2.18)
0.13

F
0.0
0.579
1.000
/

DSS/CSS
3 (187)
1.52 (0.85–2.71)
1.42

F
0.0
0.825
0.296
0.517

Clinicopathological parameters

OR (95% CI)

Stage (T2–T4 versus Ta–T1)
19 (1231)
3.13 (2.42–4.06)
8.63

F
1.4
0.440
0.529
0.377

Asia
14 (878)
3.41 (2.51–4.64)
7.87

F
0.0
0.800
0.661
0.650

Europe
3 (277)
3.17 (1.79–5.60)
3.96

F
63.7
0.064
1.000
0.994

America
2 (76)
1.15 (0.41–3.20)
0.26

F
0.0
0.604
1.000
/

Stage (T1 versus Ta)
5 (374)
1.55 (0.87–2.76)
1.50

F
40.5
0.152
0.806
0.402

Grade (G3 versus G1-2)
20 (1291)
3.33 (2.51–4.42)
8.32

F
0.0
0.519
0.206
0.805

Asia
15 (895)
3.36 (2.44–4.63)
7.41

F
18.6
0.246
/
/

Europe
3 (196)
2.62 (1.23–5.57)
2.50

F
0.0
0.984
/
/

America
2 (200)
4.51 (1.61–12.61)
2.87

F
0.0
0.659
/
/

Grade (H versus L)
8 (688)
1.20 (0.62–2.33)
0.55

R
61.8
0.011
0.063
0.080

Lymph node metastasis (yes versus no)
5 (319)
2.20 (1.26–3.83)
2.77

F
27.2
0.240
1.000
0.487

Muscle Invasive (yes versus no)
4 (248)
2.18 (0.72–6.62)
1.38

R
71.8
0.014
0.497
0.998

Number of tumors (multiple versus single)
2 (166)
1.11 (0.43–2.85)
0.22

F
0.0
0.984
1.000
/

Tumor size (>3 versus ≤3)
2 (193)
2.93 (0.40–21.36)
1.06

R
79.2
0.028
1.000
/

RFS: recurrence-free survival; OS: overall survival; PFS: progression-free survival; DSS: disease-specific survival; CSS: cancer-specific survival; HR: hazard ratio; OR: odds ratio; CI: confidence interval; No.: number of studies; No.: number of patients; : for the heterogeneity; F: fixed-effect model; R: random-effect model; L: low grade; H: high grade.

3.7. Retrospective Review

Three studies [6, 24, 25] were retrospectively reviewed because of differences between their prognosis results and the data required by the meta-analysis. As shown in Table 3, low expression of p16 was associated with poor prognosis in patients with bladder cancer. However, some of the values were <0.05 because of the small sample sizes.

Table 3: Relationship between low expression of p16 and other prognostic factors in patients with bladder cancer.

Author
Year
Nation
No. (M/F)
Age
Stage
Grade
Cut-off value
Other related biomarkers
Measuring method
Antibody source (dilution)
Outcome
value

Jin et al. [24]
2006
USA
39 (25/14)
65 (42–84)
T2–T4
G1–G4
10%
P53, pRB
Immunohistochemistry
NR (1 : 50)
OS/PFS (2-year survival)
0.001
<0.001

Tzai et al. [25]
2004
China (Taiwan)
65 (44/21)
61.5 (41–84)
T2–T4
G2-G3
Score = 4
P53, pRB
Immunohistochemistry
Santa Cruz (1 : 20)
PFS/DSS
0.74
0.49

Yurakh et al. [6]
2006
Spain
55
NR
Ta–T4
G1–G3
10%
9p21 (P14, P15, P16)
Immunohistochemistry
Santa Cruz (1 : 500)
RFS/OS/PFS (3-year survival)
0.31
0.022
0.012

M: male; F: female; RFS: recurrence-free survival; OS: overall survival; PFS: progression-free survival; DSS: disease-specific survival; NR: not reported; No.: number of patients.

3.8. Publication Bias

Publication bias was detected by Begg’s funnel plot and Egger’s test (Figure 4). The points representing studies were symmetrically arranged in a funnel shape in the funnel plot, and the values calculated from Egger’s test with higher detection effectiveness were >0.05, indicating no publication bias. The only exception was for RFS; the funnel plot was asymmetrical and with a few points outside the funnel. Publication bias was also detected by Egger’s test (G1–G3 group: ; Asia group: ), indicating potential publication bias in terms of RFS.

(a)

(b)

(c)

Figure 4: The funnel plot of the meta-analysis of the impact of p16 expression on recurrence-free survival (RFS) (a), overall survival (OS) (b), and progression-free survival (PFS) (c) in patients with bladder cancer.

4. Discussion

p16, also known as tumor suppressor gene I (multiple tumor suppressor, MTS I), is located in 9p21 and is composed of two introns and three exons [45]. It is a key gene in cell cycle regulation, with its expression product being involved in the negative regulation of cell proliferation. Studies have shown that downregulation of p16 gene expression resulted in the loss of its inhibitory effects on CDK4/CDK6, which in turn may lead to malignant, abnormal cell proliferation and accelerated tumor development [7, 46, 47]. Elucidation of the relationship between low expression of p16 and prognosis and clinicopathology in patients with bladder cancer is therefore important for its early diagnosis, treatment, and prognosis.

Pan et al. performed a meta-analysis of the prognostic significance of abnormal p16 and p21 expression in bladder cancer in 2006 [48]. However, the current study analyzed a larger sample size; Pan et al.’s study included 12 articles with 975 cases, compared with 37 articles and 2246 cases in our study, leading to more accurate and reliable results. Secondly, Pan et al.’s study involved a number of mixed factors with no clear listing of each prognostic index or subgroup discussion. In contrast, the current study included subgroup analyses for the different indicators including RFS, PFS, OS, and DSS/CSS, allowing more thorough insights into the relationships between p16 expression and the prognostic and clinicopathological parameters in bladder cancer patients. Thirdly, Pan et al. found no association between p16 expression and prognosis in early Ta–T1 stage (stage I) bladder cancer, possibly because of the omission of the study by Krüger et al. [5], which explored the significance of p16 as an independent tumor predictive factor for the development of T1 bladder cancer, and demonstrated the important clinical value of low p16 expression in the early diagnosis and prognosis of patients with early-stage bladder cancer.

The current study systematically analyzed the relationships between p16 expression and prognostic index and clinicopathological parameters in patients with bladder cancer and showed that low expression of p16 was closely correlated with poor prognosis (Figure 5). However, the included studies varied in terms of study subjects, design, sample size, interventions, outcomes, time of study, and publication date. We used cumulative meta-analysis, sensitivity analysis, and subgroup analysis to explore the effects of the main variables in the included studies. Overall, the results confirmed that the relationship between low expression of p16 and prognosis in patients with bladder cancer was affected by clinicopathological stage, geographic origin of the study subjects, detection method, and cut-off values. Based on these findings, we further analyzed the relationships between p16 expression and clinicopathological parameters and demonstrated associations between low expression of p16 and clinical stage and lymph node metastasis, implying that the p16 gene tended to exert its regulatory effects during the early stage of bladder carcinogenesis. Low expression of p16 was also correlated with poor PFS and RFS in early-stage (Ta–T1) bladder cancer. These results thus confirmed an important role for p16 in the occurrence and development of bladder cancer. Meanwhile, through Phase I and II clinical trials, studies have revealed that CDK4/6 is an attractive target in p16 related pathway for anticancer therapy [49–51]. Furthermore, previous study also suggested that p16 functional peptide, as a molecular targeting agent, showed effective reactions for the treatment of renal cell carcinoma [52]. The results of these researches and our current meta-analysis had the effect of mutual authentication. Therefore, a better understanding of the mechanism underlying the development and progression of bladder cancer may play a significant role in prevention, target therapy, and prognosis, particularly if more sensitive and specific correlative biomarkers can be discovered and verified.

Figure 5: Our results illustrated and improved the relationship between p16 and prognosis, as well as clinicopathological features.

The current study had some limitations. First, tumors are the result of both environmental and genetic factors, and p16 may thus be only one of several factors involved in the whole process of bladder carcinogenesis. Secondly, heterogeneity may result from differences in intervention measures (surgery, radiotherapy, chemotherapy, or combination), immunohistochemical techniques (different antibodies, evaluation standards, etc.), and the HR extraction methods used in the included studies. Finally, the exclusion of articles because of language barriers and of studies that were not published because of a lack of sufficient data may have led to potential publication bias.

In conclusion, the results of the current study provide evidence for a relationship between p16 expression and prognosis and clinicopathological features in patients with bladder cancer. The results of this meta-analysis will help to inform about the development of clinical guidelines promoting best medical care for patients with bladder cancer. Further studies are required to investigate the combined influence of genetic and environmental factors on the development and progression of bladder cancer.

Abbreviations

RFS: | Recurrence-free survival
OS: | Overall survival
PFS: | Progression-free survival
DSS: | Disease-specific survival
CSS: | Cancer-specific survival
HR: | Hazard ratio
OR: | Odds ratio
CI: | Confidence interval.

Competing Interests

The authors declare that they have no competing interests.

Authors’ Contributions

Xiaoning Gan, Xiaomiao Lin, Rongquan He, Xinggu Lin, Hanlin Wang, Liyan Yan, Hong Zhou, and Hui Qin performed the literature search, data extraction, and statistical analysis and drafted the paper. Gang Chen supervised the literature search, data extraction, and analysis and reviewed the paper. Xiaoning Gan, Xiaomiao Lin, Rongquan He, Xinggu Lin, Hanlin Wang, Liyan Yan, Hong Zhou, Hui Qin, and Gang Chen read and approved the final paper.

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