Meta-analysis of the relationship between p21 Ser31Arg polymorphism and lung cancer susceptibility

The cyclin-dependent kinase inhibitor 1A (also known as p21) is thought to be involved in tumor development by mediating cell cycle arrest through the inhibition of cyclin/CDK activity. To explore the relationship of Ser31Arg polymorphism in the p21 gene with the risk of developing lung cancer, we performed an overall and stratified meta-analysis based on ethnicity, lung cancer subtypes and source of controls, with six eligible studies (2366 cases and 3320 controls). No significant variation in lung cancer risk was detected in any of the genetic models in the overall, and the ethnicity-based and cancer subtype-based subgroup analyses. However, in the subgroup analysis based on source of controls, significant opposite associations were observed; a significantly increased lung cancer risk was observed in the hospital-based control subgroup, while a significantly decreased lung cancer risk was detected in the mixed-source control and unknown-source control subgroups. In summary, based on our meta-analysis, p21 Ser31Arg polymorphism does not appear to act as an independent lung cancer risk factor and is more likely to act together with other genetic and non-genetic factors in the development of lung cancer; this needs further investigation.     

Complex segregation analysis reveals a multigene model for lung cancer

Lung cancer risk is largely attributed to tobacco exposure, but genetic predisposition also plays an etiologic role. Several studies have investigated the involvement of genetic predisposition in lung cancer aggregation in affected families, although with inconsistent results. Some studies have provided evidence for Mendelian inheritance, whereas others have suggested that environmental models are most appropriate for lung cancer aggregation in families. To examine the genetic basis of lung cancer, we performed segregation analysis on 14,378 individuals from 1,561 lung cancer case families, allowing for the effects of smoking, sex, and age. Both a Mendelian decreasing model and a Mendelian codominant model were found to be the best fitting models for susceptibility. However, when we modeled age-of-onset, all Mendelian models and the environmental model were rejected suggesting that multiple genetic factors (possibly multiple genetic loci and interactions) contribute to the age-of-onset of lung cancer. The results provide evidence that multiple genetic factors contribute to lung cancer and may act as a guide in further studies to localize susceptibility genes in lung cancer.     

Analysis of microsatellite mutations in buccal cells from a case-control study for lung cancer

Exposure to tobacco carcinogens is the major cause of human lung cancer, but even heavy smokers have only about a 10% life-time risk of developing lung cancer. Currently used screening processes, based largely on age and exposure status, have proven to be of limited clinical utility in predicting cancer risk. More precise methods of assessing an individual's risk of developing lung cancer are needed. Because of their sensitivity to DNA damage, microsatellites are potentially useful for the assessment of somatic mutational load in normal cells. We assessed mutational load using hypermutable microsatellites in buccal cells obtained from lung carcinoma cases and controls to test if such a measure could be used to estimate lung cancer risk. There was no significant association between smoking status and mutation frequency with any of the markers tested. No significant association between case status and mutation frequency was observed. Age was significantly related to mutation frequency in the microsatellite marker D7S1482. These observations indicate that somatic mutational load, as measured using mutation frequency of microsatellites in buccal cells, increases with increasing age but that subjects who develop lung cancer have a similar mutational load as those who remain cancer free. This finding suggests that mutation frequency of microsatellite mutations in buccal cells may not be a promising biomarker for lung cancer risk.     

Interactions between Paraoxonase 1 Genetic Polymorphisms and Smoking and Their Effects on Oxidative Stress and Lung Cancer Risk in a Korean Population

Background

Few studies in epidemiology have evaluated the effects of gene-environment interaction on oxidative stress, even though this interaction is an important etiologic factor in lung carcinogenesis. We investigated the effects of the genetic polymorphisms of paraoxonase 1 (PON1), smoking, and the interaction between the two on lung cancer risk and oxidative stress.

Methods

This study’s subjects consisted of 416 newly diagnosed lung cancer patients and an equal number of matched controls. The GoldenGate assay was used for genotypic analyses of the PON1 gene. Urinary 8-hydroxydeoxyguanosine (8-OHdG) and thiobarbituric acid reactive substances levels were measured as indicators of oxidative stress.

Results

The PON1 rs662 AA genotype showed a significantly lower risk of lung cancer than the GG genotype (OR = 0.60, 95% CI: 0.36–0.99). The protective effect of the PON1 rs662 AA genotype on lung cancer risk was limited to non-smokers. Lung cancer patients who had the rs662 A allele showed a dose-dependent association between smoking status and oxidative stress markers. Among non-smoking lung cancer patients, urinary 8-OHdG levels were significantly lower in individuals with the rs662 GA and AA genotypes than in those with the GG genotype. Furthermore, we found a significant interaction effect between PON1 rs662 and smoking status on urinary 8-OHdG levels in lung cancer patients.

Conclusions

Our results suggest that the protective effect of PON1 rs662 SNP against lung carcinogenesis and the induction of oxidative stress might be modulated by the interaction between PON1 genetic polymorphisms and tobacco smoking.     

Polymorphisms associated with the risk of lung cancer in a healthy Mexican Mestizo population: Application of the additive model for cancer

Lung cancer is the leading cause of cancer mortality in Mexico and worldwide. In the past decade, there has been an increase in the number of lung cancer cases in young people, which suggests an important role for genetic background in the etiology of this disease. In this study, we genetically characterized 16 polymorphisms in 12 low penetrance genes (AhR, CYP1A1, CYP2E1, EPHX1, GSTM1, GSTT1, GSTPI, XRCC1, ERCC2, MGMT, CCND1 and TP53) in 382 healthy Mexican Mestizos as the first step in elucidating the genetic structure of this population and identifying high risk individuals. All of the genotypes analyzed were in Hardy-Weinberg equilibrium, but different degrees of linkage were observed for polymorphisms in the CYP1A1 and EPHX1 genes. The genetic variability of this population was distributed in six clusters that were defined based on their genetic characteristics. The use of a polygenic model to assess the additive effect of low penetrance risk alleles identified combinations of risk genotypes that could be useful in predicting a predisposition to lung cancer. Estimation of the level of genetic susceptibility showed that the individual calculated risk value (iCRV) ranged from 1 to 16, with a higher iCRV indicating a greater genetic susceptibility to lung cancer.     

A flexible Bayesian model for studying gene-environment interaction

An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene–environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene–environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene–environment interaction (P-value = 0.016), with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene–environment interaction based on the single-marker approach is far from significant.     

Methods for etiologic and early marker investigations in the PLCO trial

With the rapid development of biomarkers and new technologies, large-scale biologically-based cohort studies present expanding opportunities for population-based research on disease etiology and early detection markers. The prostate, lung, colorectal and ovarian cancer (PLCO) screening trial is a large randomized trial designed to determine if screening for these cancers leads to mortality reduction for these diseases. Within the Trial, the PLCO etiology and early marker study (EEMS) identifies risk factors for cancer and other diseases and evaluates biologic markers for the early detection of disease. EEMS includes 155,000 volunteers who provide basic risk factor information. Serial blood samples are collected at each of six screening rounds (including one collection for cryopreserved whole blood) from screening arm participants (77,000 subjects) and buccal cells are collected from those in the control arm of the trial. Etiologic studies consider environmental (e.g., diet), biochemical, and genetic factors. Early detection studies focus on blood-based biologic markers of early disease. Clinical epidemiology is also an important component of the PLCO trial.     

Molecular-genetic markers in lung cancer diagnostics

The major approaches to different lung cancer marker development are outlined in the review, including genetic, epigenetic, protein, transcryptomic, proteomic, metabolic, and miRNA markers. As far as epigenetic changes are among the earliest events in malignant transformation, methylated markers are thoroughly discussed. Special attention is given to minimally invasive tumor markers, which could be detected in easily accessible biological fluids, because they can be useful for screening and early diagnostics of cancer (before its clinical manifestation) as well as for verification of standard methods of diagnostics. Extracellular nucleic acids, circulating in blood (cirNA), are highlighted as the potential source of material for the early lung cancer diagnostics, prediction of antitumor treatment efficiency, post-treatment monitoring and disease prognosis.     

hOGG1 Ser326Cys polymorphism and lung cancer susceptibility: a meta-analysis

The Ser326Cys polymorphism in the human 8-oxogunaine glycosylase (hOGG1) gene with lung cancer susceptibility had been investigated by the approaches of PCR–RFLP, PCR–SSCP and ASA. Due to limited specimen and different approaches the conclusion was drawn toughly. To evaluate this correlation comprehensively, a meta-analysis was performed based on 30 case–control studies, including 10,327 cases and 12,148 controls. The random-effects model was used to estimate the odds ratios and 95 % confidence interval for various contrasts of this polymorphism. The combined results suggested that the hOGG1 Ser326Cys polymorphism was not associated with lung cancer susceptibility in different genetic models. Similarly, in the stratified analyses by ethnicity and source of control, no risk was observed between all the genetic models and lung cancer risk. Our meta-analysis revealed that there was little correlation between the hOGG1 Ser326Cys polymorphism and the risk of lung cancer.     

A Comprehensive Genetic Approach for Improving Prediction of Skin Cancer Risk in Humans

Prediction of genetic risk for disease is needed for preventive and personalized medicine. Genome-wide association studies have found unprecedented numbers of variants associated with complex human traits and diseases. However, these variants explain only a small proportion of genetic risk. Mounting evidence suggests that many traits, relevant to public health, are affected by large numbers of small-effect genes and that prediction of genetic risk to those traits and diseases could be improved by incorporating large numbers of markers into whole-genome prediction (WGP) models. We developed a WGP model incorporating thousands of markers for prediction of skin cancer risk in humans. We also considered other ways of incorporating genetic information into prediction models, such as family history or ancestry (using principal components, PCs, of informative markers). Prediction accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) estimated in a cross-validation. Incorporation of genetic information (i.e., familial relationships, PCs, or WGP) yielded a significant increase in prediction accuracy: from an AUC of 0.53 for a baseline model that accounted for nongenetic covariates to AUCs of 0.58 (pedigree), 0.62 (PCs), and 0.64 (WGP). In summary, prediction of skin cancer risk could be improved by considering genetic information and using a large number of single-nucleotide polymorphisms (SNPs) in a WGP model, which allows for the detection of patterns of genetic risk that are above and beyond those that can be captured using family history. We discuss avenues for improving prediction accuracy and speculate on the possible use of WGP to prospectively identify individuals at high risk.     

Identification of sequence polymorphisms in the D-loop region of mitochondrial DNA as a risk factor for lung cancer

Accumulation of single nucleotide polymorphisms (SNPs) in the displacement loop (D-loop) of mitochondrial DNA (mtDNA) may be associated with an increased cancer risk. We investigated the lung cancer risk profile of D-loop SNPs in a case-controlled study. The minor alleles of nucleotides 235A/G and 324A/G were associated with an increased risk for lung cancer patients. The minor alleles of the nucleotides 151C/T, 200A/G, 524C/CA, and 16274G/A were specifically associated with the cancer risk of squamous cell carcinoma, whereas the minor allele of nucleotide 16298T/C was specifically associated with the risk of small cell lung cancer. In conclusion, SNPs in mtDNA are potential modifiers of lung cancer risk. The analysis of genetic polymorphisms in the mitochondrial D-loop can help identify subgroups of patients who are at a high risk of developing lung cancer.     

A common genetic variant (97906C>A) of DAB2IP/AIP1 is associated with an increased risk and early onset of lung cancer in Chinese males

DOC-2/DAB2 interactive protein (DAB2IP) is a novel identified tumor suppressor gene that inhibits cell growth and facilitates cell apoptosis. One genetic variant in DAB2IP gene was reported to be associated with an increased risk of aggressive prostate cancer recently. Since DAB2IP involves in the development of lung cancer and low expression of DAB2IP are observed in lung cancer, we hypothesized that the variations in DAB2IP gene can increase the genetic susceptibility to lung cancer. In a case-control study of 1056 lung cancer cases and 1056 sex and age frequency-matched cancer-free controls, we investigated the association between two common polymorphisms in DAB2IP gene (-1420T>G, rs7042542; 97906C>A, rs1571801) and the risk of lung cancer. We found that compared with the 97906CC genotypes, carriers of variant genotypes (97906AC+AA) had a significant increased risk of lung cancer (adjusted odds ratio [OR] = 1.33, 95%CI = 1.04-1.70, P = 0.023) and the number of variant (risk) allele worked in a dose-response manner (P(trend) = 0.0158). Further stratification analysis showed that the risk association was more pronounced in subjects aged less than 60 years old, males, non-smokers, non-drinkers, overweight groups and in those with family cancer history in first or second-degree relatives, and the 97906A interacted with overweight on lung cancer risk. We further found the number of risk alleles (97906A allele) were negatively correlated with early diagnosis age of lung cancer in male patients (P = 0.003). However, no significant association was observed on the -1420T>G polymorphism. Our data suggested that the 97906A variant genotypes are associated with the increased risk and early onset of lung cancer, particularly in males.     

A comprehensive study of CD44 rs 187115 variant and cancer risk in a central Chinese population

The rs187115, an intronic variant of CD44 gene, has been previously reported to play a potential role in genetic susceptibility to cancer. Here, we comprehensively examined the association between CD44 rs187115 variant and cancer risk (breast cancer, cervical cancer, lung cancer, gastric cancer, liver cancer, colon cancer, and rectal cancer) in a central Chinese population. The rs187115 variant was genotyped with the polymerase chain reaction-restriction fragment length polymorphism assay. In this study, we observed that rs187115 was associated with the risk of cervical, lung, and liver cancer, but not with the risk of breast, gastric, colon, rectal or colorectal cancer. Of note, the G allele and G allele genotypes of rs187115 conferred an increased risk of cervical, lung, and liver cancer. To improve the statistical strength, a followed meta-analysis was conducted. The results demonstrated that rs187115 was significantly associated with cancer risk, and the significant association remained in the stratification analysis by ethnicity, genotyping method, and cancer type. Collectively, the CD44 rs187115 variant may be associated with the risk of cervical, lung, and liver cancer in the central Chinese population, and may be used as a potential biomarker for cancer predisposition in the Asian population, especially in the Chinese population.     

The accumulated evidence on lung cancer and environmental tobacco smoke.

OBJECTIVE: To estimate the risk of lung cancer in lifelong non-smokers exposed to environmental tobacco smoke. DESIGN: Analysis of 37 published epidemiological studies of the risk of lung cancer (4626 cases) in non-smokers who did and did not live with a smoker. The risk estimate was compared with that from linear extrapolation of the risk in smokers using seven studies of biochemical markers of tobacco smoke intake. MAIN OUTCOME MEASURE: Relative risk of lung cancer in lifelong non-smokers according to whether the spouse currently smoked or had never smoked. RESULTS: The excess risk of lung cancer was 24% (95% confidence interval 13% to 36%) in non-smokers who lived with a smoker (P < 0.001). Adjustment for the effects of bias (positive and negative) and dietary confounding had little overall effect; the adjusted excess risk was 26% (7% to 47%). The dose-response relation of the risk of lung cancer with both the number of cigarettes smoked by the spouse and the duration of exposure was significant. The excess risk derived by linear extrapolation from that in smokers was 19%, similar to the direct estimate of 26%. CONCLUSION: The epidemiological and biochemical evidence on exposure to environmental tobacco smoke, with the supporting evidence of tobacco specific carcinogens in the blood and urine of non-smokers exposed to environmental tobacco smoke, provides compelling confirmation that breathing other people''s tobacco smoke is a cause of lung cancer.     

Assessment of the relationship between combinations of polymorphic variants of xenobiotic-metabolizing enzyme genes and predisposition to lung cancer

An imbalance between the phases of biotransformation systems, such as activation, detoxification, and release of toxic substances, is one of the causes of multifactor pathology. Therefore, it is important to examine the impact of the total contribution of the polymorphic variants of xenobiotic-metabolizing enzyme genes at all three phases on predisposition to lung cancer. The purposes of the present work were to study the relationship between polymorphic variants of xenobiotic-metabolizing enzyme genes and risk of lung cancer and to identify molecular genetic markers of predisposition to the disease. It was shown that GSTT1 null-genotype plays a dominant role in the development of lung cancer predisposition in the Belarusian population, while the polymorphic variants of other genes of xenobiotic-metabolizing enzymes render a modifying effect on predisposition to this disease. Combination 734AA CYP1A2/GSTT1(?)/GSTM1(+)/“slow” acetylator has the greatest risk significance, and combination GSTT1(?)/GSTM1(+)/“slow” acetylator exerts a protective effect.     

CYP2A6 gene deletion reduces susceptibility to lung cancer.

CYP2A6 is an enzyme with a high ability to activate a nitrosamine, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), to its potent and ultimate carcinogen. In the present study, we investigated the relationship between genetic polymorphism of CYP2A6 and lung cancer risk in a case-control study of Japanese subjects. Genotyping of the CYP2A6 gene in both healthy volunteers and lung cancer patients was conducted. The frequency with which the subjects carried homozygotes of the CYP2A6 gene deletion-type mutation (deletion), which causes lack of the enzyme activity, was lower in the lung cancer patients than in the healthy control subjects. The odds ratio (OR) of the group homozygous for the deletion was significantly lower and calculated to be 0.25 (95% CI; 0.08-0.83) when the OR for the population with homozygotes of the CYP2A6 wild-type gene was defined as 1.00. In the allelic-base analysis, there was also a significant decrease in the OR for the deletion allele. These data suggest that deficient CYP2A6 activity due to genetic polymorphism reduces lung cancer risk.     

Frequency and rearrangements of alleles of proto-oncogene c-Ha-ras-1 and their association with development of various malignant tumors in man

The c-Ha-ras-1 locus in 84 cancer patients was examined for allelic restriction fragment length's polymorphism, as well as for distribution of four common c-Ha-ras-1 alleles (a1, a2, a3 and a4) in lung, ovarian and thyroid cancer patients. In approximately half (8 out of 15) lung and ovarian carcinomas possessing a4 allele, alterations in the Ha-ras locus (deletion, amplification and change in allele length) were detected, as compared to 2 cases of rearrangements out of 40 tumors lacking the a4 allele. An increased a4 allele frequency was found in individuals with lung and ovarian carcinomas, as compared to both controls--summarized literature data, and thyroid cancer patients. On the other hand, homozygosity for the a2 locus, resulting from deletion in another allele, and increased a2 allele frequency in thyroid cancer patients were observed. Thus, a4 and a2 alleles of the c-Ha-ras-1 may perhaps be viewed as genetic markers of predisposition to lung, ovarian and thyroid cancer, respectively, in combination with other clinical parameters.     

Establishment of apoptotic regulatory network for genetic markers of colorectal cancer and optimal selection of traditional Chinese medicine target

The paper aimed to screen out genetic markers applicable to early diagnosis for colorectal cancer and establish apoptotic regulatory network model for colorectal cancer, and to analyze the current situation of traditional Chinese medicine (TCM) target, thereby providing theoretical evidence for early diagnosis and targeted therapy of colorectal cancer. Taking databases including CNKI, VIP, Wanfang data, Pub Med, and MEDLINE as main sources of literature retrieval, literatures associated with genetic markers that are applied to early diagnosis of colorectal cancer were searched and performed comprehensive and quantitative analysis by Meta analysis, hence screening genetic markers used in early diagnosis of colorectal cancer. KEGG analysis was employed to establish apoptotic regulatory network model based on screened genetic markers, and optimization was conducted on TCM targets. Through Meta analysis, seven genetic markers were screened out, including WWOX, K-ras, COX-2, P53, APC, DCC and PTEN, among which DCC has the highest diagnostic efficiency. Apoptotic regulatory network was built by KEGG analysis. Currently, it was reported that TCM has regulatory function on gene locus in apoptotic regulatory network. The apoptotic regulatory model of colorectal cancer established in this study provides theoretical evidence for early diagnosis and TCM targeted therapy of colorectal cancer in clinic.     

Single nucleotide polymorphisms (SNPs) are inherited from parents and they measure heritable events

Single nucleotide polymorphisms (SNPs) are extensively used in case-control studies of practically all cancer types. They are used for the identification of inherited cancer susceptibility genes and those that may interact with environmental factors. However, being genetic markers, they are applicable only on heritable conditions, which is often a neglected fact. Based on the data in the nationwide Swedish Family-Cancer Database, we review familial risks for all main cancers and discuss the evidence for a heritable component in cancer. The available evidence is not conclusive but it is consistent in pointing to a minor heritable etiology in cancer, which will hamper the success of SNP-based association studies. Empirical familial risks should be used as guidance for the planning of SNP studies. We provide calculations for the assessment of familial risks for assumed allele frequencies and gene effects (odds ratios) for different modes of inheritance. Based on these data, we discuss the gene effects that could account for the unexplained proportion of familial breast and lung cancer. As a conclusion, we are concerned about the indiscriminate use of a genetic tool to cancers, which are mainly environmental in origin. We consider the likelihood of a successful application of SNPs in gene-environment studies small, unless established environmental risk factors are tested on proven candidate genes.