Introduction

Lung cancer is the most common malignancy worldwide [1]. In China, lung cancer is a major health problem and has been reported with high mortality rate for both men and women [2, 3]. Epidemiological studies have demonstrated that the high proportion of smokers in the general population and the polluted environment in cities with the process of urbanization and industrialization are primary etiologic factors for lung cancer occurrence. However, genetic factors may play an important role in determining susceptibility to lung cancer [4]. To investigate how genetic factors contribute to lung cancer susceptibility in the Han population, we conducted a case–control study and selected six tSNPs from five genes, which have previously been reported to be associated with lung cancer susceptibility in genome-wide association studies [5–7].

Materials and methods

Study participants

We recruited a total of 309 patients at the Affiliated Hospital of Tibet University for Nationalities from October 2011 to September 2012 (Xi’an City, China). All patients were newly diagnosed and histologically identified with lung cancer. None of them had a previous history of other cancers, chemotherapy, or radiotherapy. They were chosen without age, gender, or disease stage restrictions. We also selected 310 healthy unrelated individuals during the same time period from the medical examination center of the Affiliated Hospital of Tibet University for Nationalities based on standard recruitment and exclusion criteria. We ensured that they have no chronic or severe endocrinological, metabolic, and nutritional diseases. All study participants were Han Chinese living in Xi’an City or nearby.

Clinical data and demographic information

We use a standardized epidemiological questionnaire including residential region, age, gender, smoking status, alcohol use, ethnicity, education status, and family history of cancer to collect personal data in an in-person interview. We informed all participants of the purpose and experimental procedures of the study and obtained signed informed consent from each participant. The Human Research Committee of the Affiliated Hospital of Tibet University for Nationalities for Approval of Research Involving Human Subjects approved the use of human tissue in this study.

SNP selection and genotyping

Six SNPs in the five metabolic process genes selected were associated with lung cancer, with minor allele frequencies (MAF) >5 % in the HapMap Chinese Han Beijing (CHB) population. DNA was extracted from whole-blood samples using the GoldMag-Mini Whole Blood Genomic DNA Purification Kit (GoldMag Co. Ltd. Xi’an City, China). DNA concentrations were measured with the NanoDrop 2000 (Thermo Scientific, Waltham, MA, USA). Multiplexed SNP MassEXTENDED assay was designed by Sequenom MassARRAY Assay Design 3.0 Software (Sequenom Co. Ltd., San Diego, CA, USA) [8]. SNP genotyping with a standard protocol was performed using Sequenom MassARRAY RS1000 (Sequenom Inc., San Diego, CA, USA) [8]. Sequenom Typer 4.0 Software (Sequenom Inc., San Diego, CA, USA) was used to analyze the data [8, 9].

Statistical analysis

Statistical processing of our data was performed by SPSS 16.0 software (SPSS Chicago, IL, USA) and Microsoft Excel software. The p values presented in this study are all two-sided, and p = 0.05 was used as the threshold of statistical significance. The validation of each SNP frequency in control group was tested for departure from Hardy-Weinberg equilibrium (HWE). A χ 2 test was used to compare the allelic frequencies of case and control groups [10].

The genetic association between genotype and lung cancer risk was tested under different genetic models (codominant, dominant, recessive, overdominant, and log-additive) with SNPStats, a website software from http://bioinfo.iconcologia.net/snpstats/start.htm [11]. Testing of odds ratios (ORs) and 95 % confidence intervals (CIs) was performed using unconditional logistic regression analysis with adjustment for gender and age [12]. Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) were applied to estimate the best-fit model for each SNP. Furthermore, association between genotype and lung cancer risk in gender-specific populations under each model was performed using SNPStats software [11].

Results

We included a total of 619 participants, with 309 patients (235 males, 74 females; mean age at diagnosis 58 years, range 25–85 years) and 310 controls (197 males, 113 females; mean age at 50 years, range 29–75 years) for association analysis. Basic characteristics of the subjects were listed in Table 1. Six SNPs in five metabolic process genes in lung cancer patients and the healthy controls were genotyped. Table 2 listed the MAF of cases and controls. Rs36600 was excluded from further analysis because it derived from HWE at 1 % p level. We compared the differences in frequency distributions of alleles between cases and controls using χ 2 test and found one significant SNP in the TP63 gene, rs10937405, was associated with decreased lung cancer risk (OR = 0.72; 95 % CI, 0.56–0.92; p = 0.009).

Table 1 Basic characteristics of the subjects
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Table 2 Basic information of candidate SNPs in this study
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The genetic association between rs10937405 and lung cancer risk was tested under different genetic models listed in Table 3. In the codominant model, the genotypes “CT” (OR = 0.71; 95 % CI, 0.51–0.99; p = 0.031) and “TT” (OR = 0.53; 95 % CI, 0.30–0.95; p = 0.031) of rs10937405 were associated with decreased lung cancer risk. The genotype “CC-CT” of rs10937405 was associated with decreased lung cancer risk in the dominant model (OR = 0.67; 95 % CI, 0.49–0.92; p = 0.014) and in the log-additive model (OR = 0.72; 95 % CI, 0.56–0.92; p = 0.0085). Table 4 shows that the genotype “CC-CT” of rs10937405 confers a higher risk of lung cancer for males than females (OR = 2.04; 95 % CI, 1.41–2.96; p = 0.033).

Table 3 Relationship between rs10937405 and lung cancer risk
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Table 4 Association between sex and lung cancer risk with rs10937405 under recessive model (n = 619)
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Discussion

In the current case–control study in the Han population, we genotyped six SNPs previously reported to be associated with lung cancer risk and identified that rs10937405 in the TP63 gene has a strong association with reduced lung cancer risk.

The SNP rs10937405 maps to the TP63 gene whose product is the tumor protein p63, an important component of the p53 family of genes. The p53 pathway plays a critical role in cell-cycle regulation by functioning as a tumor suppressor in numerous cancers. In addition, p53 mutations occur in approximately two-thirds of all human tumors [13, 14]. Furthermore, p63 has been found to play an important role in cancer development and progression through its interaction with mutant p53 [15]. An isoform of TP63 has been proposed to have oncogenic properties based on its dominant negative effects on p53, and TP63 genomic gains have been identified as potential indicators of pre-invasive lung lesions and early lung cancer diagnosis [16]. This SNP was previously reported in a GWAS conducted in Japan and South Korea [17]. In addition, it has been confirmed that TP63 is associated with lung adenocarcinoma in the UK population [5]. The two researches both confirmed that rs10937405 increased the risk of lung adenocarcinoma. However, in our study, we did not conduct sub-grouped for the small samples and found the loci may decrease the risk of lung cancer.

Other genes were not found associations in our study. Maybe, the sample size was a little small. We really need more samples to further validate the findings.

The genotype “CC-CT” of rs10937405 confers a higher risk of lung cancer for males than females. In fact, many tumors tend to occur in males. The real mechanism remains unclear. Perhaps the activity of gene product has difference in males and females.

In conclusion, our study described the association between rs10937405 in the TP63 gene and lung cancer risk in the Han population. Our findings, combined with previous studies, suggest a potential genetic susceptibility in TP63 for lung cancer progression.