Association of glutathione S-transferase P1 gene polymorphism with the susceptibility of lung cancer

Goodness-of-fit testing for meta-analysis of rare binary events

Random-effects (RE) meta-analysis is a crucial approach for combining results from multiple independent studies that exhibit heterogeneity. Recently, two frequentist goodness-of-fit (GOF) tests were proposed to assess the fit of RE model. However, they tend to perform poorly when assessing rare binary events. Under a general binomial-normal framework, we propose a novel GOF test for the meta-analysis of rare events. Our method is based on pivotal quantities that play an important role in Bayesian model assessment. It further adopts the Cauchy combination idea proposed in a 2019 JASA paper, to combine dependent p-values computed using posterior samples from Markov Chain Monte Carlo. The advantages of our method include clear conception and interpretation, incorporation of all data including double zeros without the need for artificial correction, well-controlled Type I error, and generally improved ability in detecting model misfits compared to previous GOF methods. We illustrate the proposed method via simulation and three real data applications.

Bayesian estimation and testing in random-effects meta-analysis of rare binary events allowing for flexible group variability

Rare binary events data arise frequently in medical research. Due to lack of statistical power in individual studies involving such data, meta-analysis has become an increasingly important tool for combining results from multiple independent studies. However, traditional meta-analysis methods often report severely biased estimates in such rare-event settings. Moreover, many rely on models assuming a pre-specified direction for variability between control and treatment groups for mathematical convenience, which may be violated in practice. Based on a flexible random-effects model that removes the assumption about the direction, we propose new Bayesian procedures for estimating and testing the overall treatment effect and inter-study heterogeneity. Our Markov chain Monte Carlo algorithm employs Pólya-Gamma augmentation so that all conditionals are known distributions, greatly facilitating computational efficiency. Our simulation shows that the proposed approach generally reports less biased and more stable estimates compared to existing methods. We further illustrate our approach using two real examples, one using rosiglitazone data from 56 studies and the other using stomach ulcers data from 41 studies.

Individual and combined effects of GSTM1, GSTT1, and GSTP1 polymorphisms on lung cancer risk: A meta-analysis and re-analysis of systematic meta-analyses

Thirty-five previous meta-analyses have been reported on the individual glutathione S-transferase M1 (GSTM1) present/null, glutathione S-transferase T1 (GSTT1) present/null, and glutathione S-transferase P1 (GSTP1) IIe105Val polymorphisms with lung cancer (LC) risk. However, they did not appraise the credibility and explore the combined effects between the 3 genes and LC risk.We performed a meta-analysis and re-analysis of systematic previous meta-analyses to solve the above problems.Meta-analyses of Observational Studies in Epidemiology guidelines were used. Moreover, we employed false-positive report probability (FPRP), Bayesian false discovery probability (BFDP), and the Venice criteria to verify the credibility of current and previous meta-analyses.Significantly increased LC risk was considered as "highly credible" or "positive" for GSTM1 null genotype in Japanese (odds ratio (OR) = 1.30, 95% confidence interval (CI) = 1.17-1.44, I2 = 0.0%, statistical power = 0.997, FPRP = 0.008, BFDP = 0.037, and Venice criteria: AAB), for GSTT1 null genotype in Asians (OR = 1.23, 95% CI = 1.12-1.36, I2 = 49.1%, statistical power = 1.000, FPRP = 0.051, BFDP = 0.771, and Venice criteria: ABB), especially Chinese populations (OR = 1.31, 95% CI = 1.16-1.49, I2 = 48.9%, Statistical power = 0.980, FPRP = 0.039, BFDP = 0.673, and Venice criteria: ABB), and for GSTP1 IIe105Val polymorphism in Asians (Val vs IIe: OR = 1.28, 95% CI = 1.17-1.42, I2 = 30.3%, statistical power = 0.999, FPRP = 0.003, BFDP = 0.183, and Venice criteria: ABB). Significantly increased lung adenocarcinoma (AC) risk was also considered as "highly credible" or "positive" in Asians for the GSTM1 (OR = 1.35, 95% CI = 1.22-1.48, I2 = 25.5%, statistical power = 0.988, FPRP < 0.001, BFDP < 0.001, and Venice criteria: ABB) and GSTT1 (OR = 1.36, 95% CI = 1.17-1.58, I2 = 30.2%, statistical power = 0.900, FPRP = 0.061, BFDP = 0.727, and Venice criteria: ABB) null genotype.This study indicates that GSTM1 null genotype is associated with increased LC risk in Japanese and lung AC risk in Asians; GSTT1 null genotype is associated with increased LC risk in Chinese, and GSTP1 IIe105Val polymorphism is associated with increased LC risk in Asians.

Jackknife empirical likelihood confidence intervals for assessing heterogeneity in meta-analysis of rare binary event data

In meta-analysis, the heterogeneity of effect sizes across component studies is typically described by a variance parameter in a random-effects (Re) model. In the literature, methods for constructing confidence intervals (CIs) for the parameter often assume that study-level effect sizes are normally distributed. However, this assumption might be violated in practice, especially in meta-analysis of rare binary events. We propose to use jackknife empirical likelihood (JEL), a nonparametric approach that uses jackknife pseudo-values, to construct CIs for the heterogeneity parameter. To compute jackknife pseudo-values, we employ a moment-based estimator and consider two commonly used weighing schemes (i.e., equal and inverse variance weights). We prove that with each scheme, the resulting log empirical likelihood ratio follows a chi-square distribution asymptotically. We further examine the performance of the proposed JEL methods and compare them with existing CIs through simulation studies and data examples that focus on data of rare binary events. Our numerical results suggest that the JEL method with equal weights compares favorably to alternatives, especially when (observed) effect sizes are non-normal and the number of component studies is large. Thus, it is worth serious consideration in statistical inference.

Genetic polymorphisms and lung cancer risk: Evidence from meta-analyses and genome-wide association studies

A growing number of studies investigating the association between Single Nucleotide Polymorphisms (SNPs) and lung cancer risk have been published since over a decade ago. An updated integrative assessment on the credibility and strength of the associations is required. We searched PubMed, Medline, and Web of Science on or before August 29^th, 2016. A total of 198 articles were deemed eligible for inclusion, which addressed the associations between 108 variants and lung cancer. Among the 108 variants, 63 were reported to be significantly associated with lung cancer while the remaining 45 were reported non-significant. Further evaluation integrating the Venice Criteria and false-positive report probability (FPRP) was performed to determine the strength of cumulative epidemiological evidence for the 63 significant associations. As a result, 15 SNPs on or near 12 genes and one miRNA with strong evidence of association with lung cancer risk were identified, including TERT (rs2736098), CHRNA3 (rs1051730), AGPHD1 (rs8034191), CLPTM1L (rs401681 and rs402710), BAT3 (rs3117582), TRNAA (rs4324798), ERCC2 (Lys751Gln), miR-146a2 (rs2910164), CYP1B1 (Arg48Gly), GSTM1 (null/present), SOD2 (C47T), IL-10 (-592C/A and -819C/T), and TP53 (intron 6). 19 SNPs were given moderate rating and 17 SNPs were rated as having weak evidence. In addition, all of the 29 SNPs identified in 12 genome-wide association studies (GWAS) were proved to be noteworthy based on FPRP value. This review summarizes and evaluates the cumulative evidence of genetic polymorphisms and lung cancer risk, which can serve as a general and useful reference for further genetic studies.

Attenuation of lung fibrosis in mice with a clinically relevant inhibitor of glutathione-S-transferase π

Idiopathic pulmonary fibrosis (IPF) is a debilitating lung disease characterized by excessive collagen production and fibrogenesis. Apoptosis in lung epithelial cells is critical in IPF pathogenesis, as heightened loss of these cells promotes fibroblast activation and remodeling. Changes in glutathione redox status have been reported in IPF patients. S-glutathionylation, the conjugation of glutathione to reactive cysteines, is catalyzed in part by glutathione-S-transferase π (GSTP). To date, no published information exists linking GSTP and IPF to our knowledge. We hypothesized that GSTP mediates lung fibrogenesis in part through FAS S-glutathionylation, a critical event in epithelial cell apoptosis. Our results demonstrate that GSTP immunoreactivity is increased in the lungs of IPF patients, notably within type II epithelial cells. The FAS-GSTP interaction was also increased in IPF lungs. Bleomycin- and AdTGFβ-induced increases in collagen content, α-SMA, FAS S-glutathionylation, and total protein S-glutathionylation were strongly attenuated in Gstp-/- mice. Oropharyngeal administration of the GSTP inhibitor, TLK117, at a time when fibrosis was already apparent, attenuated bleomycin- and AdTGFβ-induced remodeling, α-SMA, caspase activation, FAS S-glutathionylation, and total protein S-glutathionylation. GSTP is an important driver of protein S-glutathionylation and lung fibrosis, and GSTP inhibition via the airways may be a novel therapeutic strategy for the treatment of IPF.

P53 codon 72 Arg/Pro polymorphism and lung cancer risk in Asians: an updated meta-analysis

The polymorphism of p53 codon 72, a transversion of G to C (Arg to Pro), has been demonstrated to be associated with the risk for lung cancer. However, individual studies conducted in Asians have provided conflicting and inconclusive findings. Thus, we performed a meta-analysis by pooling all currently available case–control studies to estimate the effect of p53 codon 72 Arg/Pro polymorphism on the development of lung cancer. The pooled odds ratios (ORs) with the corresponding 95 % confidence intervals (95 %CIs) were calculated to assess this effect. A total of 14 individual studies involving 7,929 cases and 5,924 controls were included into this meta-analysis according to the inclusion criteria. The overall OR for the dominant genetic model indicated that the p53 codon 72 Arg/Pro variant was positively correlated with lung cancer risk (OR_{Arg/Pro + Pro/Pro vs. Arg/Arg} = 1.14, 95 %CI 1.07–1.23, P_OR < 0.001). Similar results were found in the stratified analysis of population-based studies. The histological types of lung cancer and smoking status seemed to exert no effect on the lung cancer risk. Sensitivity analysis confirmed the stability of the above findings. The updated meta-analysis suggests that the p53 codon 72 Arg/Pro polymorphism is a risk factor for lung cancer in the Asian population. However, the potential role of gene–environment interaction in lung cancer susceptibility needs further investigation in future studies with high quality.

Glutathione S-transferase P1 Ile105Val polymorphism and oral cancer risk: a meta-analysis

Objective The glutathione S-transferase P1 (GSTP1) gene has been suggested to play an important role in the pathogenesis of oral cancer. However, the results have been inconsistent. In this study, we performed a meta-analysis to clarify the association of GSTP1 Ile105Val polymorphisms with oral cancer risk. Methods Published literature from PubMed and EMBASE were retrieved. Pooled odds ratio (OR) with 95% confidence interval (CI) was calculated using fixed- or random-effects model. Results 13 studies (1803 oral cancer cases and 2998 controls) for GSTP1 Ile105Val polymorphism were included in the meta-analysis. The results indicated that there was no significant association between GSTP1 Ile105Val polymorphism and oral cancer in the overall population (OR=1.30, 95%CI=0.92-1.38, I(2)=48.0%, p for heterogeneity=0.027). Further subgroup analysis by ethnicity suggested that GSTP1 Ile105Val polymorphism was significantly associated with oral cancer only in East Asians (OR=1.64, 95%CI=1.16-2.31, I(2)=0.0%, p for heterogeneity=0.525), but not in Caucasians (OR=1.16, 95%CI=0.73-1.82, I(2)=7.5%, p for heterogeneity=0.299), Africans (OR=1.10, 95%CI=0.37-3.28), South Asians (OR=1.20, 95%CI=0.69-2.08, I(2)=74.3%, p for heterogeneity=0.021) and mixed population (OR=0.91, 95%CI=0.70-1.20, I(2)=39.7%, p for heterogeneity=0.174). Conclusions The present meta-analysis has limited evidence to support the association of GSTP1 Ile105Val polymorphism with HCC risk in the overall population. However, GSTP1 Ile105Val polymorphism might be associated with risk of oral cancer in East Asians.