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 Table of Contents  
ORIGINAL ARTICLE
Year : 2018  |  Volume : 14  |  Issue : 9  |  Page : 381-387

Promoter methylation of WNT inhibitory factor-1 may be associated with the pathogenesis of multiple human tumors


1 Department of General Surgery, The Second Hospital of Shandong University, Jinan 250033, P. R. China
2 Department of Medical Imaging, The Second Hospital of Shandong University, Jinan 250033, P. R. China

Date of Web Publication29-Jun-2018

Correspondence Address:
Jianliang Zhang
Department of General Surgery, The Second Hospital of Shandong University, No. 247, North Park Street, Tianqiao District, Jinan 250033
P. R. China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1482.235357

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 > Abstract 

Aim: We investigated the association of WNT inhibitory factor-1 (WIF-1) gene methylation with the pathogenesis of multiple human tumors, using a meta-analysis based approach.
Materials and Methods: Electronic databases and manual search was additionally employed to retrieve relevant published literature. The cohort studies relating to tumor and WIF-1 were screened based on predefined selection criteria, and all extracted data from the selected studies were analyzed through STATA software.
Results: Sixteen studies were finally enrolled in our study involved 1112 tumor samples and 612 adjacent normal samples. The study result showed that WIF-1 gene methylations in tumor tissues were significantly higher compared with adjacent/normal tissues. The result of subgroup analysis on ethnicity revealed that in the Caucasians, Asians, and Africans, the methylation status of WIF-1 gene in tumor tissues was higher than adjacent/normal tissues. Further subgroup analysis on disease types revealed that WIF-1 gene methylation status is a widespread phenomenon that is, observed in tumor tissues of patients with multiple human tumors compared with that in adjacent/normal tissues. Interestingly, there was no significant difference in WIF-1 gene methylation between tumor tissues among patients with lung cancer, gastric cancer, astrocytoma, and adjacent/normal tissues, indicating the WIF-1 gene methylation not a general nonspecific phenomenon.
Conclusion: WIF-1 gene methylation in tumor tissues was significantly more frequent as compared to that in adjacent normal tissues, indicating that WIF-1 gene methylation may be an important event in the pathogenesis of multiple human tumors.

Keywords: Cohort study, meta-analysis, methylation, WNT inhibitory factor-1


How to cite this article:
Zhou Y, Li Z, Ding Y, Zhang P, Wang J, Zhang J, Wang H. Promoter methylation of WNT inhibitory factor-1 may be associated with the pathogenesis of multiple human tumors. J Can Res Ther 2018;14, Suppl S2:381-7

How to cite this URL:
Zhou Y, Li Z, Ding Y, Zhang P, Wang J, Zhang J, Wang H. Promoter methylation of WNT inhibitory factor-1 may be associated with the pathogenesis of multiple human tumors. J Can Res Ther [serial online] 2018 [cited 2019 Jul 20];14:381-7. Available from: http://www.cancerjournal.net/text.asp?2018/14/9/381/235357


 > Introduction Top


Human cancers account for 1 in 4 deaths in the United States, and invasive cancers in men (45%) is notably higher compared to that in women (38%), and collectively all human cancers continue to be a serious public health problem worldwide.[1] Lung cancer remains the leading cancer worldwide, both in case numbers (1.6 million patients, 12.7% of total) as well as cancer-related deaths per year (1.4 million deaths, 18.2%).[2] Breast cancer is the second most frequently noncutaneous malignancy, accounting for almost one in three carcinomas diagnosed among women in the United States, and the second leading factor of cancer morality around the world.[3],[4] Colorectal cancer is the third most commonly diagnosed cancer worldwide, accounting for over 1 million cases and 600,000 deaths per year.[5] Significant racial differences in incidence rates, treatment response, and clinical outcomes vary by cancer site as well as include disparities in exposure to potential risk factors, including historical smoking prevalence for lung cancer, access to high-quality screening (cervical, breast, and colorectal cancers), and timely diagnosis and treatment.[6] Genetic variations, especially gene methylations, are prominently linked with cancer susceptibility.

Abnormal methylation of promoter regions, which leads to silencing of gene expression, has been identified as a mechanism for inhibiting tumor suppressor genes in human cancers.[7],[8] The WNT (wingless and INT-1) signaling pathway plays a major role in regulation of cell proliferation, differentiation and migration, control of adult tissue homeostasis, embryogenesis and tumor progression.[9] The WNT inhibitory factor-1 (WIF-1) gene, mapped to 12q14 and initially identified in human retina, is a highly conserved gene with no sequence similarities to the cystine-rich domains of frizzled or secreted frizzled-related protein.[10],[11],[12]WIF-1 is regarded as a secreted antagonist that binds to WNT proteins to suppress WNT/β-catenin signaling, and plays an important role in early development of human diseases. With respect to the pathogenesis of human malignancies, WIF-1 is down-regulated through promoter hypermethylation, genomic loss, or genomic rearrangement in multiple human tumors, including lung, colon, breast, bladder, kidney, prostate and salivary gland tumors.[13],[14],[15],[16],[17],[18],[19] However, controversy still exists whether WIF-1 gene methylation contributes to human tumorigenesis.[20],[21] In this context, a meta-analysis was conducted to explore the association of WIF-1 methylation with the pathogenesis of multiple human tumors.


 > Materials and Methods Top


Data sources and keywords

A systematic literature search was performed using electronic databases of PubMed, EBSCO, Ovid, Wiley, Web of Science, Wanfang, China National Knowledge Infrastructure and VIP by employing search terms: (“Tumors” or “neoplasms” or “neoplasia”), (“cancer” or “carcinoma”) as well as (“WIF-1 protein, human” or “WIF-1”) and (“gene methylation”) to retrieve articles published prior to November, 2014. Manual search was also applied in order to obtain all relevant articles from cross-references.

Selection criteria

Studies were selected if they met the following criteria: (1) Research topic: The correlations of WIF-1 gene methylation with multiple human tumors; (2) study type: Cohort study; (3) study subject: All tumor tissues and adjacent/normal tissues; (4) end outcomes: Gene methylation ratio of WIF-1 in tumor tissues and adjacent/normal tissues; (5) languages: Either in Chinese or in English. The exclusion criteria were: (1) Review articles, letters, meta-analyses, comments, case reports, and abstracts only articles; (2) nonhuman research; (3) duplicated articles and unpublished articles; (4) the data from articles were incomplete; (5) multiple reports published by the same author (in this case, the article that included the most details and/or that was most recently published was obtained); (6) non English or Chinese reports; (7) nonmetastatic tumors.

Data extraction and statistical analysis

For data collection, the following information was selected:First author, year, country, language, ethnicity, disease, sample source, age, gender, number of patients, number of samples, study design, method, and gene methylation of WIF-1. The current study applied the relative risk (RR) with 95% confidence intervals (95% CI) to assess the correlations between WIF-1 methylation and multiple human tumors, and the overall effect size was evaluated applying Z-test. Statistical heterogeneity among studies was tested by Cochran's Q-statistic test (Cochran, 1954) (Ph < 0.05 was regarded as heterogeneity existed)[22] and the I2 statistics (0%, no heterogeneity; 100%, maximal heterogeneity).[23],[24] If homogeneity was not rejected, fixed-effect models were applied (I2 < 50%). If homogeneity was rejected, random-effects models were utilized (I2< 50%). If homogeneity was rejected, meta-regression analyses were used to estimate the potential sources of heterogeneity. Further determination was carried out applying Monte Carlo method.[25] Individual studies were eliminated, and sensitivity analysis was performed to assess the influence of single study on the final outcomes. Publication bias was estimated applying contour-enhanced funnel plots as well as Egger's linear regression test (P < 0.05 was considered significant).[26],[27] Analysis on current meta-analysis was carried out with the STATA software (version 12.0; Stata Corporation, College Station, TX, USA).


 > Results Top


A total of 91 papers were retrieved through electronic database search, combined with manual search. Following by exclusion of duplicates (n = 18), reviews, letters, or meta-analyses (n = 2), nonhuman studies (n = 7), and studies not related to research topics (n = 15), the remaining studies (n = 49) were reviewed for full text, and additional papers were excluded for incomplete data (n = 24) and irrelevance to our study (n = 9). Finally, 16 cohort studies (containing a total of 1112 tumor tissues), published between 2006 and 2014, were enrolled, and included 62 renal cancer patients, 110 esophageal cancer patients, 57 bladder cancer patients, 81 liver cancer patients, 243 colorectal cancer patients, 150 breast cancer patients, 68 nasopharyngeal cancer patients, 46 mesothelioma patients, 16 cervical cancer patients and 612 adjacent normal tissues.[9],[10],[11],[20],[28],[29],[30],[31],[32],[33],[34],[35],[36],[37],[38],[39] The detection method in the 16 studies was uniform and involved methylation-specific polymerase chain reaction to detect the methylation of WIF-1 in tissue samples. Demographic information and baseline characteristics of the included studies are presented in [Table 1]; the Critical Appraisal Skills Programme (CASP) scores were performed in [Figure 1].
Table 1: Main characteristics of all eligible studies

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Figure 1: Quality assessment of included studies by CASP scores

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The correlations between WNT inhibitory factor-1 gene methylation and multiple human tumors

Heterogeneity was found among the selected studies (I2 = 83.3%, Ph < 0.001), thus a random-effects model was applied. The outcome of our current meta-analysis showed that WIF-1 gene methylation in tumor tissues was significantly higher compared with that in adjacent normal tissues (RR = 10.26, 95% CI = 4.47–23.53, P < 0.001) [Figure 2]. The result of sub-group analysis on ethnicity revealed that the WIF-1 gene methylation in tumor tissues of the Caucasians, Asians, and Africans was all significantly higher than adjacent normal tissues (Caucasians: RR = 12.39, 95% CI = 1.89–80.96, P = 0.009; Asians: RR = 10.16, 95% CI = 2.99–34.47, P < 0.001; Africans: RR = 19.61, 95% CI = 1.31–294.51, P = 0.031). Additional sub-analysis on disease type indicated that WIF-1 gene methylation in tumor tissues among patients with renal cancer, esophageal cancer, bladder cancer, liver cancer, colorectal cancer, breast cancer, nasopharyngeal cancer, and mesothelioma cervical cancer was all substantially higher compared with adjacent normal tissues (all P < 0.05). Importantly, the degree of WIF-1 gene methylation in tumor tissues among patients with lung cancer, gastric cancer and astrocytomas showed no obvious difference compared with that in adjacent/normal tissues (all P > 0.05) as shown in [Figure 3].
Figure 2: Forest plots for the differences of WNT inhibitory factor-1 gene methylation between tumor tissue and normal tissue

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Figure 3: Forest plots of the subgroup analyses about WNT inhibitory factor-1 gene methylation between tumor tissue and normal tissue ((a) subgroup analysis on ethnicity; (b) subgroup analysis on disease)

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Sources of heterogeneity

The outcomes of sensitivity analysis demonstrated that any one study had no significant influence on the polled RR regarding the correlation of WIF-1 gene methylation and human tumor [Figure 4]a. The results from meta-regression analyses showed that that year, country, language, ethnicity, disease and sample size were not the main sources of heterogeneity or the key factors of overall effect sizes (all P > 0.05) shown in [Figure 5] and [Table 2]. Contour-enhanced funnel plots revealed that a majority of included studies interspersed among the scope of P < 0.01 showing publication bias. Egger's linear regression test also proved there was publication bias in the present study (P < 0.05) as shown in [Figure 4]b.
Figure 4: Sensitivity analysis and contour-enhanced funnel plot of publication bias on the differences of WNT inhibitory factor-1 gene methylation between tumor tissue and adjacent/normal tissues ((a) sensitivity analysis on the differences of WNT inhibitory factor-1 gene methylation between tumor tissue and adjacent/normal tissues; (b) contour-enhanced funnel plot of the differences of WNT inhibitory factor-1 gene methylation between tumor tissue and adjacent/normal tissues)

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Figure 5: Meta-regression analyses on publication year, country, language, ethnicity, disease and sample size ((a) meta-regression analyses on publication year; (b) meta-regression analyses on country; (c) meta-regression analyses on language; (d) meta-regression analyses on ethnicity; (e) meta-regression analyses on disease; (f) meta-regression analyses on sample size)

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Table 2: Meta-regression analyses of potential source of heterogeneity

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 > Discussion Top


The WNT signaling pathway plays a vital role in regulation of cell migration, proliferation and differentiation, and controlling embryogenesis, adult tissue homeostasis as well as tumor progression.[9] The binding WNT to an acceptor complex consisting of Frizzled/low-density lipoprotein receptor-related protein or to individual receptors activate either β-catenin-independent or β-catenin dependent signaling pathway, leading to stabilization and translocation of β-catenin to the nucleus where it interacts with T-cell factor/lymphoid enhancer factor to activate transcription.[40],[41] Aberrant activation of the WNT/β-catenin signaling pathway is observed in several human cancers, thus a significant clinical value is placed on strategies to inhibit the effects of WNT.[42] WIF-1 is a secreted antagonist that can strongly bind to WNT proteins to suppress WNT/β-catenin signaling.[43] Inactivation of WIF-1 caused by promoter methylation may be a mechanism underlying the down-regulation of WIF-1, thereby potentially releasing the inhibition of the WNT/β-catenin signaling pathway to drive cancer progression.[10] It has reported that aberrant methylation of CpG islands may be a crucial mechanism for inactivating tumor suppressor genes in cancer.[44] Previous studies observed that WIF-1 promoter was methylated in 69.4% of nonsmall cell lung cancer, 81.4% of early colorectal tumors and 92% of mesothelioma.[11],[12],[45] It has well documented that WIF-1 silencing associated with its promoter hypermethylation in both cancer cell lines as well as human nonsmall cell lung cancer primary tissues.[34] Moreover, WIF-1 can function as a negative regulator of WNT signaling; while hypermethylation of WIF-1 promoter will activate WNT signaling as well as promote the development of hepatocellular carcinoma.[46] In the present meta-analysis, our results indicated that WIF-1 gene methylation in tumor tissues is significantly higher than normal tissues, suggesting that WIF-1 gene methylation may contribute to tumor progression of several human cancers.

The results of subgroup analysis on ethnicity revealed that in Caucasians, Asians and Africans, the WIF-1 gene methylation in tumor tissues was higher than normal tissues, which is consistent with the result of our overall meta-analysis results, suggesting that ethnicity is not a source of heterogeneity. Further subgroup analysis on disease type revealed that WIF-1 gene methylations occur commonly in multiple tumor tissues and is found among patients with renal cancer, esophageal cancer, bladder cancer, liver cancer, colorectal cancer, breast cancer, nasopharyngeal cancer, and mesothelioma cervical cancer. Curiously, there was no significant difference in WIF-1 gene methylation degree between tumor tissues among patients with lung cancer, gastric cancer, and astrocytomas and normal tissues. Further studies are needed to thoroughly examine the WIF-1 status in these cancers. Nevertheless, the result may be related to publication bias, incomplete data and/or relatively small sample size.

Limitations of this meta-analysis should be noted. First, although we made efforts to incorporate all relevant studies representing diverse disease types, the diseases were still incomplete and we could not represent multiple human tumors. Future studies will address this issue. The second limitation was publication bias that may affect the outcomes in the present meta-analysis. Third, the data of enrolled studies was still insufficient. Fourth, the tumors of included studies were not classified into benign tumor or malignant tumor, except for two of them (mesothelioma and astrocytomas), thus detailed information on tumor characteristics should be incorporated in further studies. Fifth, language bias was a limitation since 11 studies were from the Asians, and only 4 studies were in Caucasians. Sixth, we only obtained one study on the Africans, the small sample size of the Africans was not enough for sub-group analysis, thus, further studies of sub-group analysis on ethnicity could be carried out. Finally, the sample size was comparatively small and could influence the reliability of our results. Thus, further research is needed to confirm our findings.


 > Summary Top


WIF-1 gene methylation in tumor tissues was significantly more frequent than that in adjacent normal tissues, indicating that WIF-1 gene methylation may contribute to the pathogenesis of tumor. However, our conclusion needs to be verified by more adequately designed study with large sample size, more tumor types with detailed clinical parameters and more appropriate multivariate analyses.

Acknowledgment

This project was supported by the Shandong University Second Hospital Youth Fund Project (Y2013010046), and The author is grateful for the critical comments of the reviewers on this manuscript.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

 
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