alexa Meta-Analysis of P73 Polymorphism and Risk of Non-Small Cell Lung Cancer

ISSN: 2161-105X

Journal of Pulmonary & Respiratory Medicine

Meta-Analysis of P73 Polymorphism and Risk of Non-Small Cell Lung Cancer

Xiangsong Cheng, Haiyang Liu, Yongan Song, Yunxia An, Weixia Xuan, Zheng Wang, Zhiwei Xu, Nan Wei and Xiaoju Zhang*
Department of Respiratory and Critical Care Medicine, The People’s Hospital of Zhengzhou University, Henan, China
*Corresponding Author: Xiaoju Zhang, Department of Respiratory and Critical Care Medicine, The People’s Hospital of Zhengzhou University, Henan, China, Tel: 8687160159, Email: [email protected]

Received Date: Feb 02, 2018 / Accepted Date: Feb 07, 2018 / Published Date: Feb 13, 2018

Abstract

Background: The relationship between p73 gene G4C14-to-A4T14 polymorphism and non-small cell lung cancer risk is unclear. Now we performed a meta-analysis to clarify the association of p73 polymorphism with nonsmall cell lung cancer.
Methods: To assess the association between p73 polymorphism and non-small cell lung cancer deeply, we searched Pubmed, Embase, CNKI, Wanfang and CBM databases. All analyses were done using RevMan 5.3 software which provided by the Cochrane Collaboration and Stata version 12.0. The statistical heterogeneity among studies was assessed with the chi-square-based Q test. We used the random effects model as well as the fixed effects model to calculate the pooled ORs.
Results: Our meta-analysis included 6 studies with a total of 1658 patients with non-small lung cancer and 2328 cancer-free control subjects. In all comparisons, we find none of genetic models shows significant relation with the risk of non-small lung cancer (recessive model: OR: 1.16, 95%CI: 0.94-1.43; dominant model: OR: 0.63, 95%CI: 0.37-1.06; co-dominant model: OR: 1.63, 95%CI: 0.94-2.83; allelic model: OR: 1.20, 95%CI: 0.98-1.48). However, when we proceeded to subgroup analysis according country, significantly increased risk was observed in a recessive models (OR: 1.35, 95%CI: 1.15-1.59), in a co-dominant model: (OR: 2.49, 95%CI: 1.76-3.53), in an allelic model (OR, 1.41, 95%CI, 1.24-1.61). Significantly decreased risk was observed in a dominant model (OR: 0.42, 95%CI: 0.30-0.59).
Conclusions: Our results indicate that p73 gene G4C14-to-A4T14 polymorphism is associated with the risk of non-small cell lung cancer in China. However, a large gene-to-environment research is required to confirm this conclusion.

Keywords: p73 polymorphism; Non-small cell lung cancer; Meta analysis

Background

Non-small lung cancer (NSCLC) was a subtype for lung cancer, one of the most common malignancies of the world, especially in male [1]. The incidence of NSCLC is largely affected by tobacco smoking. However, not every NSCLC patient has the smoking history [2,3]. As we all know, genetic factors play an important role in lung cancer. The different individual genetic susceptibility to tumor [4-6]. Therefore, the research of genetic variants related to NSCLC may contribute to forecasting an individual’s risk of suffering from this disease. Recently, p73 gene is identified a significant gene of tumorigenesis and lots of studies have demonstrated that the p73 gene have a pivotal role in the occurrence of various types of cancer, including lung cancer [7-10]. However, there are very few reports of literature about NSCLC, and the results are not conclusive. Akio found p73 polymorphism was related with the decreased risk of NSCLC in Japanese population [11]. However, Wang found the opposite conclusion [12]. Therefore, to further explore the relationship between p73 polymorphism and NSCLC, we utilized a meta-analysis including data from all eligible case-control studies to summarize the currently published data.

Methods

Literatures collection and screening We searched several databases including the Pubmed, Embase, CNKI (China National Knowledge Infrastructure), Wanfang database and Chinese Biomedicine databases for all articles on the relation between p73 G4C14-A4T14 polymorphism and risk of non-small lung cancer (last search update 10th August 2017) by using keywords of ‘p73’, ‘polymorphism’, ‘polymorphisms’ and ‘lung cancer’ without any restriction on language or publication year. We also searched the other publications of references quoted in review and related meta-analysis by hand. The inclusion and exclusion criteria of literatures Studies would be considered for inclusion if:

1. Independently published case-control or cohort studies where the relation between p73 G4C14-to-A4T14 polymorphism and NSCLC susceptibility was investigated;

2. The publication published available genotype data for odds ratio (OR) or relative risk (RR) values and 95%CI (confidence interval) estimation and

3. If several studies were published on the basis of the same case population, we selected the most recent or the largest study.

The major exclusion criteria were:

1. Duplicate data,

2. There is not available relevant data

3. In the control population there is heterogeneity of gene polymorphism.

Quality assessment and data extraction

Two investigators extracted information from all eligible publication independently according to the inclusion criteria. Any disagreement was coordinated by referring to a third investigator. We assessed the methodological quality of the included studies with the Cochrane Handbook 5.2 quality evaluation criteria. The essential information from various studies included publication year, the first author, ethnicity, and country, sample size of cases and controls and genotype frequency for the present polymorphism.

Statistical analysis

All analyses were done using RevMan 5.3 software and Stata version 12.0. A statistical significance was considered when P<0.05. Assessment of the relation between p73 G4C14-to-A4T14 polymorphism and NSCLC susceptibility was performed by calculating ORs with 95% CIs. The pooled ORs were performed for allele model (AT vs. GC), the homozygote genotypes (AT/AT vs. GC/GC), the heterozygote genotypes (AT/GC vs. GC/GC), the dominant model(AT/AT+AT/ GC vs. GC/GC), and the recessive model (AT/AT vs. AT/GC+GC/ GC). Subgroup analysis was conducted by different countries. The statistical heterogeneity among studies was assessed with the chisquare- based Q test (P<0.10 was considered significant) [13]. If there was no obvious heterogeneity, the fixed-effects model derived from the Mantel-Haenszel method was used to estimate the summary OR [14]; otherwise, the random-effects model using the DerSimonian and Laird method was used [15]. Using the Pearson chi-square test, we checked the Hardy-Weinberg equilibrium (HWE) for the controls of all studies. Besides, we performed sensitivity analysis to check if any single data had a greater impact on the overall conclusion. Funnel plots was applied to diagnose the potential publication bias [16].

Results

Study identification

As shown in Figure 1, 47 English literatures were reviewed through database searching. A total of thirty seven literatures were excluded because of different gene/SNP/cancer, duplicate publication and review or meta-analysis. Six literatures were excluded because of unavailable genotype data. Finally, a total of 6 literatures [11,12,17-21] were included in the analysis with a total of 1658 patients with NSCLC and 2328 control subjects. Features included in this literature were shown in Table 1.

pulmonary-respiratory-medicine-Flow-chart

Figure 1: Meta-analysis of genetic polymorphism and non-small lung cancer. (A) Recessive model; (B) Dominant model; (C) Co-dominat model; (D) Allelic model.

Authors Publication year Country Ethnicity Genotype(case/control) HWE
GC/GC GC/AT AT/AT YES
Akio [11] 2003 Japanese Asian 109/130 68/95 12/10 0.215
Wang [12] 2014 China Asian 87/102 57/68 8/25 0.24
Jun [17] 2007 Korea Asian 266/338 184/212 35/32 0.87
Wang [18] 2015 China Asian 108/104 68/68 10/26 YES
Wu [19] 2017 China Asian 294/490 149/361 17/71 0.691
Wen [20] 2017 China Asian 94/98 80/71 12/27 NO

Table 1: Characteristics of incorporated studies.

Quantitative synthesis

The main result of meta-analysis and heterogeneity are present in Figures 2 and 3. As significant heterogeneity was detected, we used the random effects model as well as the fixed effects model to calculate the pooled ORs. In overall comparisons, none of genetic models showed significant association with the risk of non-small lung cancer (recessive model: OR: 1.16, 95%CI: 0.94-1.43; dominant model: OR: 0.63, 95%CI: 0.37-1.06; co-dominant model: OR: 1.63, 95%CI: 0.94-2.83; allelic model: OR: 1.20, 95%CI: 0.98-1.48) (Figure 2). However, when we proceeded to subgroup analysis according country, significantly increased risk was observed in a recessive models (OR: 1.35, 95%CI: 1.15-1.59), in a co-dominant model: (OR: 2.49, 95%CI: 1.76-3.53), in a allelic model (OR: 1.41, 95%CI: 1.24-1.61). Significantly decreased risk was observed in a dominant model (OR: 0.42, 95%CI: 0.30-0.59) (Figure 3).

pulmonary-respiratory-medicine-Meta-analysis

Figure 2: Meta-analysis of genetic polymorphism and non-small lung cancer. (A) Recessive model; (B) Dominant model; (C) Co-dominat model; (D) Allelic model.

pulmonary-respiratory-medicine-Subgroup-analysis

Figure 3: Subgroup analysis by country. (A) Recessive model; (B) Dominant model; (C) Co-dominat model; (D) Allelic model.

Publication bias analysis

As depicted in Figure 4 (the dominant model), the funnel plot showed no evidence for obvious asymmetry. We used the Egger’s test to further quantitatively estimate the publication bias, the result indicated no existence of significant publication bias (P=0.273). This indicated that in the present study there is no publication bias, and the result is credible.

pulmonary-respiratory-medicine-funnel-plot

Figure 4: Begg’s funnel plot for Publication bias test for all included studies (GC/AT+AT/AT vs. GC/GC).

Discussion

In this study, we performed a meta-analysis to clarify the relation between p73 polymorphism and non-small cell lung cancer. We found significant association of p73 polymorphism with non-small cell lung cancer in a recessive, dominant, co-dominant and allelic model in Chinese population but not in Japanese and Korean. The p73 gene was identified at chromosome 1p36.33 in 1977 [21], a site which had been proved to commonly undergo loss of heterozygosity (LOH) in various kinds of cancer [21-23]. In addition, LOH had been reported to occur in 62% of lung cancer patients at the p73 gene [24], indicating that in the development of lung cancer p73 gene probably play an important role [25,26]. Two completely linked single-nucleotide polymorphisms (SNPs) at position 4(G→A) and (C→T) (designated as G4C14-to-A4T14, rs numbers 2273953 and 1801173) in intron 1 of the p73 gene have been certified to be related to the lung cancer risk [27,5]. Only three genotypes are possible: GC/GC, GC/AT and AT/AT, because these SNPs are in complete linage equilibrium. The association between polymorphism in previous studies and the development of various types of cancer risk have been reported. Studies have found that carriers with AT allele genotype have significantly increased risk of lung cancer than GC/GC genotype in the north of China [28] and non- Hispanic whites [29]. Another study found no significant relationship between the G4C14-to-A4T14 polymorphism and the risk of lung cancer in Korea [30] and Japan [31]. Otherwise, another study found that crowd with AT allele genotype has reduced risk of lung cancer in Chinese [32]. Liu’s meta-analysis suggested that p73 G4C14-to- A4T14 polymorphism may play a important role in development of lung cancer in Caucasians [33]. However, all the above articles did not classify lung cancer by pathology. Above all, association between p73 polymorphism and NSCLC was not clear. The present study has shown that significant association of p73 polymorphism with non-small cell lung cancer in a recessive, dominant, co-dominant and allelic model in Chinese population but not in Japanese and Korean. However, further research and screening of etiological relations between the functional polymorphism loci of p73 gene and the susceptibility of NSCLC is still needed. Some limitation of this meta-analysis should be addressed. Firstly, the total number of cases and controls was somewhat small to detect the potential exist of marginal effect. Therefore, more studies with larger sample size and providing more detail information are needed. Secondly, there was significant between-study heterogeneity from studies of p73 polymorphism, and the genotype distribution also showed deviation from HWE in some studies. Finally, potential publication bias may exist in this meta-analysis, as studies with negative results are more likely not to be published.

Conclusion

In conclusion, based on the combined data, this meta-analysis demonstrated that there was no clear association between p73 G4C14- to-A4T14 polymorphism and overall non-small lung cancer risk, but we observed increased risk of lung cancer in Chinese population.

Funding

This work was supported by Science and Technology Office of Henan province. The project name: Preparation and Screening and the Role in EMT of N-Cadherin, CD146. The project number: 154100510021.

Acknowledgements

The authors acknowledge Professor Yuanlin Song for his assistance in performing chart review.

References

Citation: Cheng X, Liu H, Song Y, An Y, Xuan W, et al. (2018) Meta-Analysis of P73 Polymorphism and Risk of Non-Small Cell Lung Cancer. J Pulm Respir Med 8: 445. DOI: 10.4172/2161-105X.1000446

Copyright: © 2018 Cheng X, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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