Skip to main content

Association of CYP19A1 rs28757157 polymorphism with lung cancer risk in the Chinese Han population

Abstract

Background

Lung cancer is the leading cause of cancer death globally. Recent studies have revealed that CYP19A1 gene plays a crucial role in cancer initiation and development. The aim of this study was to assess the association of CYP19A1 genetic polymorphisms with the risk of lung cancer in the Chinese Han population.

Methods

This study randomly recruited 489 lung cancer patients and 467 healthy controls. The genotypes of four single nucleotide polymorphisms (SNPs) of the CYP19A1 gene were identified by the Agena MassARRY technique. Genetic model analysis was used to assess the association between genetic variations and lung cancer risk. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to evaluate the effect of four selected SNPs on lung cancer risk.

Results

CYP19A1 rs28757157 might contribute to an increased risk of lung cancer (p = 0.025, OR = 1.30, 95% CI 1.03–1.64). In stratified analysis, rs28757157 was associated with an increased cancer risk in the population aged under 60 years, females, smokers, and drinkers. Besides, rs3751592 and rs59429575 were also identified as risk biomarkers in the population under 60 years and drinkers. Meanwhile, a relationship between an enhanced risk of squamous cell carcinoma and rs28757157 was found, while the rs3751592 CC genotype was identified as a risk factor for lung adenocarcinoma development.

Conclusions

This study has identified revealed that the three SNPs (rs28757157, rs3751592, and rs59429575) of CYP19A1 are associated with lung cancer in the Chinese Han population. These findings will provide theoretical support for further functional studies of CYP19A1 in lung cancer.

Background

Lung cancer is a kind of malignant tumor with high morbidity and mortality [1]. In China, this malignant tumor has the highest mortality rate, accounting for about 25% of cancer-related deaths in the world [2]. At present, many risk factors are found to increase the risk of lung cancer. Among them, smoking seems to be strongly associated with lung cancer risk [3]. However, a new research has shown that worldwide, 15–20% of men with lung cancer are non-smokers while over 50% of women with lung cancer are non-smokers [4], indicating the importance of other risk factors such as exogenous air pollution, environmental, and genetic factors. According to the latest statistics, genetic factors have been identified to be robustly associated with lung cancer [5]. If the family history of lung cancer is from a first-degree relative, the risk increases by 2–4 times even after controlling for smoking history [6].

Lung cancer is the leading cause of cancer mortality worldwide, in which women are less than half as likely to die of lung cancer as men [1]. Lung cancer in non-smokers tends to be more common in females [4]. These findings have drawn attention to investigate the effects of estrogen on lung cancer risk. It has been reported that both estrogen receptor and aromatase are present in human lung tumors [7, 8]. These results suggest that estrogen may play a role in the biological behavior of human lung cancer.

Cytochrome p450 (CYP450) enzymes are pivotal for biological homeostasis. CYP450 enzymes also play a key role in the metabolism of many endogenous substrates and exogenous carcinogens as well as aromatic and heterocyclic amines. They then covalently combine with DNA to form DNA adducts, which in turn cause cancer [9, 10]. The CYP450 family 19, subfamily A, and polypeptide 1 (CYP19A1) gene encodes aromatase, which is a member of the CYP450 superfamily and a key enzyme in oestradiol biosynthesis. Mutations in the CYP19A1 gene can result in either increased or decreased aromatase activity [11], and aromatase plays an important role in lung cancer [12]. This suggests that CYP19A1 genetic variations may indirectly affect the occurrence of lung cancer, but the exact mechanism is unclear. At the same time, many works of literature have reported an inseparable relationship between the genetic variant of CYP19A1 and lung-related diseases, including lung cancer [13]. Previously, CYP19A1 rs3764221 has been studied to be significantly associated with the multicentric development of lung adenocarcinomas [13]. Moreover, CYP19A1 rs727479 is also significantly associated with the incidence of lung cancer [14]. However, there are still a large number of single-nucleotide polymorphisms (SNPs) in CYP19A1 whose association with lung cancer risk has not been reported.

Based on Han Chinese in Beijing (CHB) population in 1000 genome database (http://www.internationalgenome.org/) and the dbSNP database (http://www.bioinfo.org.cn), four SNPs (rs28757157 (NG_007982.1:g.90395G > C), rs3751592 (NG_007982.1:g.29218A > G), rs3751591 (NG_007982.1:g.29086 T > C), and rs59429575 (NG_007982.1:g.28719G > A)) in CYP19A1 with the minor allele frequency more than 5% were randomly selected. These SNPs in this study have been reported in the genome-wide association studies (GWAS) chips of published GWAS studies about testicular germ cell tumor and breast cancer [15, 16], but not lung cancer. Here, this study aimed to investigate the association between these four SNPs in the CYP19A1 gene and lung cancer susceptibility through a case–control study.

Methods

Participants

In order to ensure the accuracy and credibility of the research results, we used G * Power 3.1.9.7 software (https://stats.idre.ucla.edu/other/gpower/) to estimate the sample size before we planned to conduct this study. The specific parameters we set were as follows: effect size d = 0.2; α error probability = 0.05; and power (1-β error probability) = 80%. This calculation produced a sample of at least 412 cases and 412 controls. Here, we recruited 489 cases and 467 controls in this study, larger than the total sample size recommended by G * Power. In the study, we recruited 489 pathologically confirmed lung cancer patients from Xuanwei City, Yunnan. All cases were diagnosed as lung cancer by histological examination according to the World Health Organization tumor classification system and confirmed by two independent pathologists. The exclusion criteria for patients were as follows: (1) history of other tumors; (2) family history of lung cancer; (3) chemotherapy or radiotherapy treatment; (4) hypertension, diabetes mellitus, or any endocrine metabolic diseases; and (5) other lung diseases. The control group was composed of 467 healthy subjects who were volunteer blood donors from the same city as the cases. Controls with a history of any cancers, other endocrine metabolic diseases, or other lung diseases should be excluded. Eligible study participants were screened by completing a specialized questionnaire, which included demographic characteristics, disease history, lung status, and family history of other types of tumors. All participants were of Chinese Han ancestry from northwest China. The research protocol according to the Helsinki Declaration was conducted with the approval of the First People’s Hospital of Yunnan Province Ethics Committee, and written informed consent from all subjects was attained.

SNP selection

Four SNPs (rs28757157 (NG_007982.1:g.90395G > C), rs3751592 (NG_007982.1:g.29218A > G), rs3751591 (NG_007982.1:g.29086 T > C), and rs59429575 (NG_007982.1:g.28719G > A)) in CYP19A1 were randomly selected based on the following: (1) the variations of CYP19A1 through the e!GRCh37 (http://asia.ensembl.org/Homo_sapiens/Info/Index) database in the CHB and CHS population; (2) Hardy–Weinberg Equilibrium (HWE) > 0.01, minor allele frequency (MAF) > 0.05, and min genotype > 75% using Haploview software; (3) combined MassARRAY primer design software, HWE > 0.05, MAF > 0.05, and the call rate > 95% in our study population; and (4) a MAF > 0.05 based on the database of 1000 genome (http://www.internationalgenome.org/) and dbSNP (http://www.bioinfo.org.cn) databases.

SNP genotyping

Genomic DNA was extracted from collected peripheral blood samples using a DNA purification extraction kit (GoldMag Xi’an, China). The concentration and purity of DNA were determined quantitatively by an ultraviolet spectrophotometer (Nanodrop 2000, Thermo, USA). Multiplexed SNP MassEXTEND assay was designed with the Agena Bioscience Assay Design Suite software, version 3.0 (Agena Bioscience, USA). SNP genotyping was conducted utilizing the MassARRAY platform (Agena Bioscience, USA). The principle of MassARRAY is matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS). First, a locus-specific PCR reaction was performed, followed by a locus-specific primer extension reaction (iPLEX assay), in which oligonucleotide primers were annealed directly upstream of the polymorphism of genotyping. In the iPLEX assay, primers and amplified target DNA were incubated with a large number of modified dideoxynucleotide terminators. The primer extension is made according to the sequence of mutation sites and is a single complementary mass-modifying base. The quality of the extended primers was determined by MALDI-TOF mass spectrometry. The quality of the primers indicates the sequence, therefore, the allele present at the polymorphic locus of interest. Using MALDI-TOF mass spectrometry, SNP alleles could be identified with different qualities of extended primers [17, 18]. Data processing was carried out with Agena Bioscience TYPER software, version 4.0 (Agena Bioscience, San Diego, CA, USA) [19]. A 10% randomly selected samples were re-analyzed with 100% consistency for quality control.

Statistical analysis and bioinformatics analysis

SPSS software (SPSS 22.0, USA) and Microsoft Excel were used for statistical analysis. Continuous variables were evaluated for normality using the Kolmogorov–Smirnov test. Continuous variables (age and body mass index (BMI)) with non-normal distribution as median with interquartile range (IQR) were compared using the Mann–Whitney U test. The differences in gender, smoking, and drinking distribution between the case and control groups were determined by the χ2 test. The χ2 test was used to determine whether individual polymorphisms were in HWE. In addition, χ2 test was used to detect the difference in allele and genotype frequencies between cases and controls. The SNPStats software (https://www.snpstats.net/start.htm?q=snpstats/start.htm) was adopted to define the relationship between polymorphisms and the risk of lung cancer in the Chinese Han population in different genetic model analyses (genotype, dominant, recessive, and additive models). Logistic regression analysis was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to evaluate the relationship of four selected SNPs with lung cancer risk [20,21,22]. Binary logistic regression was used for the two SNP interactions associated with lung cancer susceptibility. The p < 0.05 was considered statistically significant in all tests. The functionality of candidate SNPs was annotated using the HaploReg v4.1 (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php), RegulomeDB (https://regulome.stanford.edu/regulome-search/), and QTLbase (http://www.mulinlab.org/qtlbase/index.html) databases.

In multifactor dimensionality reduction (MDR) analysis, multilocus genotypes were classified into high- and low-risk groups. With this method, multidimensional genotype variables were transformed into single-dimensional ones [23]. In order to explore the association of high-order SNP-SNP interactions with the susceptibility to lung cancer, we used the MDR method including cross-validation and permutation-test procedures. Cross-validation could minimize the possibility of false-positive results by dividing the data into a training set and a testing set and repeating each part of the data. Balanced accuracy was used to assess model quality. The overall best model with the greatest accuracy in the testing data was selected. The cross-validation consistency (CVC) provided a list of the number of cross-validation intervals in which a particular model was found. The permutation testing indicated the cross-validation consistency and the prediction error are statistically significant at the 0.001 level. This indicates that among 1000 permuted datasets, no best models had a cross-validation consistency or a prediction error of the same magnitude as was observed for the original dataset. Higher numbers indicated more robust results. A permutation test was used to assess the significance of the best model [24]. The optimal CYP19A1 SNP-SNP interaction model for lung cancer susceptibility was performed through MDR 3.0.2 software.

Results

Study population

In this study, 489 lung cancer patients (337 males and 152 females) was involved as well as 467 healthy controls (326 males and 141 females). The median (IQR) ages of cases and controls were 61.00 (56.00–65.00) years old and 61.00 (55.00–65.00) years old, respectively (Table 1). In addition, the characteristics of the study population were collected for subsequent studies, including BMI, smoking, and drinking history, pathological type, pathological stage, and lymph node metastasis (LNM). There was no significant difference in age, gender, BMI, smoking, and drinking between the case group and the control group (p > 0.05).

Table 1 Characteristics of the study population

Genetic analyses of the selected SNPs with the risk of lung cancer

Four SNPs in CYP19A1 were genotyped among subjects. The representative spectrum of each SNP is displayed in Supplemental Fig. 1. The basic information about all candidate SNPs is listed in Table 2. All SNPs are located on chromosome 15 and in the different positions of the CYP19A1 gene. The deviation of Hardy–Weinberg equilibrium in the control group was evaluated, and the results showed that the candidate SNPs all met the expected p value (p > 0.05), and satisfied further study. In addition, under the allele model, there was a significant difference in the allele distribution of rs28757157 between the lung cancer cases (0.215) and healthy controls (0.174), and rs28757157 T allele might contribute to an increased risk of lung cancer (p = 0.025, OR = 1.30, 95% CI 1.03–1.64). Functional prediction of SNPs was conducted in HaploReg v4.1 and RegulomeDB databases to explore their regulatory effect. The results showed that four SNPs exhibited potential biological functions in gene regulation. Based on QTLbase database, the genotypes of CYP19A1 rs28757157 (p = 6.610e − 5) were related to the mRNA expression of CYP19A1 in the lungs (Fig. 1).

Fig. 1
figure 1

Overview of eQTL for rs28757157 (a) and trait-wise plot of eQTL for rs28757157 in the lung (b)

Table 2 Basic information of candidate SNPs CYP19A1

Under four genetic models, the relationship between CYP19A1 polymorphisms and the risk of lung cancer is listed in Table 3. Our results revealed an association between rs28757157 and increased risk of lung cancer in the genotype (p = 0.034, OR = 1.43, 95% CI 1.09–1.88), dominant (p = 0.011, OR = 1.41, 95% CI 1.08–1.85), and additive (p = 0.021, OR = 1.34, 95% CI 1.04–1.71) models.

Table 3 Analysis of the association between CYP19A1 polymorphisms and risk of lung cancer

Stratification analyses by demographic characteristics

In addition, we conducted a stratified analysis by demographic characteristics (age, gender, BMI, smoking, and drinking) to explore the risk effects of these SNPs in specific groups, as shown in Table 4. The results of age stratification indicated that rs28757157 (genotype: p = 0.018, OR = 1.83; dominant: p = 0.005, OR = 1.82; and additive: p = 0.006, OR = 1.78), rs3751592 (genotype: p = 0.032, OR = 1.87; dominant: p = 0.010, OR = 1.93; and additive: p = 0.009, OR = 1.81), and rs59429575 (genotype: p = 0.047, OR = 1.71; dominant: p = 0.014, OR = 1.75; and additive: p = 0.016, OR = 1.57) were associated with an increased susceptibility to lung cancer in people aged under 60 years. Moreover, rs28757157 exerted a risk role in the development of lung cancer among females in the dominant (p = 0.033, OR = 1.76), and additive (p = 0.036, OR = 1.70) models. In smokers, rs28757157 (dominant: p = 0.031, OR = 1.55; and additive: p = 0.042, OR = 1.46) might confer to a higher risk for the occurrence of lung cancer. In addition, rs28757157 (genotype: p = 0.033, OR = 2.03; dominant: p = 0.009, OR = 2.04; and additive: p = 0.010, OR = 1.99) and rs59429575 (dominant: p = 0.044, OR = 1.75; and additive: p = 0.044, OR = 1.63) were related to an increased risk of lung cancer in drinkers, whereas rs3751592 (p = 0.023, OR = 3.31) was identified as a genetic risk factor for lung cancer susceptibility in non-drinkers. However, no significant correlation between CYP19A1 polymorphisms and lung cancer risk after stratification by BMI was found.

Table 4 Stratification analyses by demographic characteristics for the association between CYP19A1 polymorphisms and the risk of lung cancer

Stratification analyses by clinical characteristics

As listed in Table 5, the correlation between CYP19A1 polymorphisms and lung cancer risk in the different groups (tumor type, LNM, and stage) was assessed. The stratified analysis by tumor type demonstrated a relationship between enhanced risk of squamous cell carcinoma and rs28757157 (dominant: p = 0.032, OR = 1.59; and additive: p = 0.042, OR = 1.48), while rs3751592 CC genotype was identified as a risk factor for lung adenocarcinoma development (genotype: p = 0.011, OR = 3.57; and recessive: p = 0.013, OR = 3.84). Regrettably, no significant association between CYP19A1 polymorphisms and lung cancer risk in the stratification analyses by LNM and tumor stage was observed.

Table 5 Stratification analyses by clinical characteristics for the association between CYP19A1 polymorphisms and the risk of lung cancer

The two SNP interactions associated with lung cancer susceptibility.

AS displayed in Table 6, rs28757157-rs3751592 (p < 0.001, OR = 2.03), rs28757157-rs3751591 (p < 0.001, OR = 1.75), rs28757157- rs59429575 (p < 0.001, OR = 1.55), and (p = 0.011, OR = 1.31) were associated with the higher lung cancer susceptibility.

Table 6 The two SNP interactions associated with lung cancer susceptibility

MDR analysis

The association between higher-order SNP–SNP interactions and the predisposition to lung cancer was examined by MDR, as summarized in Fig. 2 and Table 7. Figure 1 presented that these four polymorphisms exhibited strong redundancy effects on the risk of lung cancer, and rs28757157 had the information gain (2.22%) of individual attributes regarding the occurrence of lung cancer. Table 6 summarized that the most influential single-locus attributor for lung cancer risk was rs28757157 (testing balanced accuracy of 0.5503 and cross-validation consistency of 10/10).

Fig. 2
figure 2

The dendogram (a) and Fruchterman Rheingold (b) of CYP19A1 SNP-SNP interaction for the risk of lung cancer. Green and blue color indicated stronger redundant interactions. Values in nodes and between nodes represent the information gains of an individual attribute (main effects) and each pair of attributes (interaction effects), respectively

Table 7 SNP–SNP interaction models of CYP19A1 polymorphisms in lung cancer susceptibility

MDR analysis of gene-environment interaction also suggested that rs28757157 was the most influential single-factor attributor for lung cancer risk. Gender and smoking were found to be the most important environmental factor affecting lung cancer susceptibility. In addition, the gene-environment interaction model, composed of rs28757157, rs3751591, gender, BMI, and smoke showed higher testing-balanced accuracy (0.601) and cross-validation consistency (9/10), indicating that this interaction model was a candidate gene-environment model in our population. Figure 3 exhibited a strong synergy effect of gene-environment interaction on lung cancer risk.

Fig. 3
figure 3

The dendogram (a) and fruchterman Rheingold (b) of CYP19A1 gene environment. SNP-SNP interaction for the risk of lung cancer. Green and blue color indicated stronger redundant interactions. Values in nodes and between nodes represent the information gains of individual attributes (main effects) and each pair of attributes (interaction effects), respectively

Discussion

In this study, the association of four SNPs in the CYP19A1 gene with the susceptibility to lung cancer in the Chinese Han cohort was assessed. Statistical and bioinformatics results highlighted the important roles of rs28757157, rs3751592, and rs59429575 in the outset of lung cancer in the total or stratified population, which helped improve our understanding of CYP19A1 in this disease.

CYP19A1 gene, encoding aromatase and responsible for the final step in the biosynthesis of estrogens, estradiol (E2) and estrone (E1), has been intensively investigated [25, 26]. It has been identified that SNPs in the intron region of CYP19A1 play an important role in the transcriptional regulation and splicing of CYP19A1 and could produce some different enzymes with diverse enzyme activity compared with normal gene products [27]. The allele frequency of several CYP19A1 SNPs have been documented in different populations and ethnic groups around the world. SNPs in CYP19A1 were found to be associated with cancer risk [28]. In particular, CYP19A1 SNPs have been shown to be significantly associated with lung-related diseases.

A previous study has shown that SNP rs3764221 is significantly correlated with CYP19A1 expression in non-cancerous lung tissues and affects the susceptibility to lung adenocarcinoma. The authors suggested that CYP19A1 polymorphisms may lead to elevated levels of local estrogen surrounding the lungs, and this excess local estrogen production may be one of the factors associated with the polycentric development of adenocarcinoma [13]. The recent result has suggested that CYP19A1 polymorphism is involved in lung bronchioloalveolar carcinoma and atypical adenomatous hyperplasia by causing differences in estrogen levels [29]. It is clear that CYP19A1 polymorphism may cause changes in estrogen levels around the lungs, which in turn can affect the susceptibility of lung cancer. Our results firstly revealed an association between rs28757157 and increased risk of lung cancer in the genotype, dominant, and additive models. In bioinformatic analysis, results from HaploReg v4.1 database displayed that rs28757157 may be associated with enhancer histone marks, motifs changed, and selected eQTL hits [30]. Based on the QTLbase database, the genotypes of CYP19A1 rs28757157 (p = 6.610e − 5) were related to the mRNA expression of CYP19A1 in the lungs [31]. These results suggested that CYP19A1 rs28757157 may be involved in the carcinogenicity of lung cancer by affecting the expression or function of CYP19A1, which requires further experimental confirmation.

Notably, the demographic characteristics (age, gender, BMI, smoking, and drinking) might influence the genetic association on the occurrence of lung cancer [32]. Our research showed that CYP19A1-rs28757157 was associated with increased cancer risk in the population aged under 60 years, females, smokers, and drinkers. Besides, rs3751592 and rs59429575 were also identified as risk biomarkers in the population aged under 60 years and drinkers. These results indicated that the risk association of these polymorphisms might be age-, sex-, smoking-, and drinking-dependent, and gene-behavioral habit interactions might operate in the pathogenesis of lung cancer.

These SNPs are located in the intron region of the CYP19A1 gene. Combined with previous studies and database predictions, we speculated that CYP19A1 intron SNPs may alter mRNA splicing, thereby leading to changes in the activity of CYP19A1 and related estrogens, and may affect disease susceptibility. Since the statistical significance of the correlation between CYP19A1 gene polymorphisms and the risk of lung cancer is slightly weak, further experimental studies are needed to verify the results of this study.

Furthermore, the correlation between CYP19A1 polymorphisms and lung cancer risk in different groups (tumor type, LNM, and stage) was further assessed. Stratified analysis by tumor type demonstrated a relationship between enhanced risk of squamous cell carcinoma and rs28757157, while rs3751592 CC genotype was identified as a risk factor for lung adenocarcinoma development. These findings suggested that lung adenocarcinoma and squamous cell carcinoma may have different genetic pathological mechanisms, which need to be further confirmed.

Our study has several limitations. All subjects were enrolled from the same hospital and the limitations of sample selection may affect the accuracy of this experiment. Subsequently, due to the lack of adequate information on factors such as dietary habits, occupational exposure, and air pollution, this study failed to assess the impact of these factors on the association between CYP19A1 variants and lung cancer susceptibility. Additional studies that encompass more geographical regions, additional ethnic groups, and larger sample sizes with complete risk factor information should be performed. In order to verify the results of this study, it is necessary to clarify the relationship between the CYP19A1 gene and lung cancer through subsequent functional studies.

Conclusions

In summary, our study defined SNPs of CYP19A1 (rs28757157, rs3751592, and rs59429575), which were significantly associated with lung cancer susceptibility. These variants may be considered as markers for lung cancer risk assessment in the Chinese Han population.

Availability of data and materials

The datasets generated during the current study are available.

Abbreviations

CYP450:

Cytochrome p450

CYP19A1 :

CYP450 family 19, subfamily A, polypeptide 1

SNPs:

Single-nucleotide polymorphisms

BMI:

Body mass index

LNM:

Lymph node metastasis

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

    Article  Google Scholar 

  2. Xia C, Dong X, Li H, Cao M, Sun D, He S, Yang F, Yan X, Zhang S, Li N, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J. 2022;135(5):584–90.

    Article  Google Scholar 

  3. Nooreldeen R, Bach H. Current and future development in lung cancer diagnosis. Int J Mol Sci. 2021;22(16):8661.

  4. Dubin S, Griffin D. Lung cancer in non-smokers. Mo Med. 2020;117(4):375–9.

    Google Scholar 

  5. Bossé Y, Amos CI. A decade of GWAS results in lung cancer. Cancer Epidemiol Biomarkers Prev. 2018;27(4):363–79.

    Article  Google Scholar 

  6. Kanwal M, Ding XJ, Cao Y. Familial risk for lung cancer. Oncol Lett. 2017;13(2):535–42.

    Article  CAS  Google Scholar 

  7. Hsu LH, Chu NM, Kao SH. Estrogen, estrogen receptor and lung cancer. Int J Mol Sci. 2017;18(8):1713.

  8. Rodriguez-Lara V, Avila-Costa MR. An overview of lung cancer in women and the impact of estrogen in lung carcinogenesis and lung cancer treatment. Front Med. 2021;8:600121.

    Article  Google Scholar 

  9. Stipp MC, Acco A. Involvement of cytochrome P450 enzymes in inflammation and cancer: a review. Cancer Chemother Pharmacol. 2021;87(3):295–309.

    Article  CAS  Google Scholar 

  10. Sausville LN, Williams SM, Pozzi A. Cytochrome P450 epoxygenases and cancer: a genetic and a molecular perspective. Pharmacol Ther. 2019;196:183–94.

    Article  CAS  Google Scholar 

  11. Parween S, DiNardo G, Baj F, Zhang C, Gilardi G, Pandey AV. Differential effects of variations in human P450 oxidoreductase on the aromatase activity of CYP19A1 polymorphisms R264C and R264H. J Steroid Biochem Mol Biol. 2020;196:105507.

    Article  CAS  Google Scholar 

  12. Young PA, Pietras RJ. Aromatase inhibitors combined with aspirin to prevent lung cancer in preclinical models. Translational lung cancer research. 2018;7(Suppl 4):S373-s376.

    Article  CAS  Google Scholar 

  13. Ikeda K, Shiraishi K, Eguchi A, Osumi H, Matsuishi K, Matsubara E, Fujino K, Shibata H, Yoshimoto K, Mori T, et al. Association of a genetic variant of CYP19A1 with multicentric development of lung adenocarcinomas. Ann Surg Oncol. 2014;21(3):939–45.

    Article  Google Scholar 

  14. Zhang J, Yin Y, Niu XM, Liu Y, Garfield D, Chen SF, Wang R, Wang L, Chen HQ. CYP19A1 gene polymorphisms and risk of lung cancer. J Int Med Res. 2013;41(3):735–42.

    Article  CAS  Google Scholar 

  15. Kristiansen W, Andreassen KE, Karlsson R, Aschim EL, Bremnes RM, Dahl O, Fosså SD, Klepp O, Langberg CW, Solberg A, et al. Gene variations in sex hormone pathways and the risk of testicular germ cell tumour: a case-parent triad study in a Norwegian-Swedish population. Human reproduction (Oxford, England). 2012;27(5):1525–35.

    Article  CAS  Google Scholar 

  16. Kopp TI, Jensen DM, Ravn-Haren G, Cohen A, Sommer HM, Dragsted LO, Tjonneland A, Hougaard DM, Vogel U. Alcohol-related breast cancer in postmenopausal women - effect of CYP19A1, PPARG and PPARGC1A polymorphisms on female sex-hormone levels and interaction with alcohol consumption and NSAID usage in a nested case-control study and a randomised controlled trial. BMC Cancer. 2016;16:283.

    Article  Google Scholar 

  17. Ellis JA, Ong B. The MassARRAY(®) System for targeted SNP genotyping. Methods in molecular biology (Clifton, NJ). 2017;1492:77–94.

    Article  CAS  Google Scholar 

  18. Gabriel S, Ziaugra L, Tabbaa D: SNP genotyping using the Sequenom MassARRAY iPLEX platform. Current protocols in human genetics 2009, Chapter 2:Unit 2.12.

  19. Liang Y, Thakur A, Gao L, Wang T, Zhang S, Ren H, Meng J, Geng T, Jin T, Chen M. Correlation of CLPTM1L polymorphisms with lung cancer susceptibility and response to cisplatin-based chemotherapy in a Chinese Han population. Tumour Biol. 2014;35(12):12075–82.

    Article  CAS  Google Scholar 

  20. Gao L, Thakur A, Liang Y, Zhang S, Wang T, Chen T, Meng J, Wang L, Wu F, Jin T, et al. Polymorphisms in the TERT gene are associated with lung cancer risk in the Chinese Han population. Eur J Cancer Prev. 2014;23(6):497–501.

    Article  CAS  Google Scholar 

  21. Hu QY, Jin TB, Wang L, Zhang L, Geng T, Liang G, Kang LL. Genetic variation in the TP63 gene is associated with lung cancer risk in the Han population. Tumour Biol. 2014;35(3):1863–6.

    Article  CAS  Google Scholar 

  22. Xun X, Wang H, Yang H, Wang H, Wang B, Kang L, Jin T, Chen C. CLPTM1L genetic polymorphisms and interaction with smoking and alcohol drinking in lung cancer risk: a case-control study in the Han population from northwest China. Medicine. 2014;93(28):e289.

    Article  CAS  Google Scholar 

  23. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet. 2001;69(1):138–47.

    Article  CAS  Google Scholar 

  24. Hahn LW, Ritchie MD, Moore JH. Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics (Oxford, England). 2003;19(3):376–82.

    Article  CAS  Google Scholar 

  25. Chatuphonprasert W, Jarukamjorn K, Ellinger I. Physiology and pathophysiology of steroid biosynthesis, transport and metabolism in the human placenta. Front Pharmacol. 2018;9:1027.

    Article  Google Scholar 

  26. Ghosh D, Egbuta C, Kanyo JE, Lam TT. Phosphorylation of human placental aromatase CYP19A1. Biochem J. 2019;476(21):3313–31.

    Article  CAS  Google Scholar 

  27. El-Bayomi KM, Saleh AA, Awad A, El-Tarabany MS, El-Qaliouby HS, Afifi M, El-Komy S, Essawi WM, Almadaly EA, El-Magd MA. Association of CYP19A1 gene polymorphisms with anoestrus in water buffaloes. Reprod Fertil Dev. 2018;30(3):487–97.

    Article  CAS  Google Scholar 

  28. Umamaheswaran G, Kadambari D, Muthuvel SK, Kalaivani S, Devi J, Damodaran SE, Pradhan SC, Dubashi B, Dkhar SA, Adithan C. Association of CYP19A1 gene variations with adjuvant letrozole-induced adverse events in South Indian postmenopausal breast cancer cohort expressing hormone-receptor positivity. Breast Cancer Res Treat. 2020;182(1):147–58.

    Article  CAS  Google Scholar 

  29. Kohno T, Kakinuma R, Iwasaki M, Yamaji T, Kunitoh H, Suzuki K, Shimada Y, Shiraishi K, Kasuga Y, Hamada GS, et al. Association of CYP19A1 polymorphisms with risks for atypical adenomatous hyperplasia and bronchioloalveolar carcinoma in the lungs. Carcinogenesis. 2010;31(10):1794–9.

    Article  CAS  Google Scholar 

  30. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic acids research. 2012;40(Database issue):D930-934.

    Article  CAS  Google Scholar 

  31. Gong J, Mei S, Liu C, Xiang Y, Ye Y, Zhang Z, Feng J, Liu R, Diao L, Guo AY, et al. PancanQTL: systematic identification of cis-eQTLs and trans-eQTLs in 33 cancer types. Nucleic Acids Res. 2018;46(D1):D971-d976.

    Article  CAS  Google Scholar 

  32. Pierzynski JA, Ye Y, Lippman SM, Rodriguez MA, Wu X, Hildebrandt MAT. Socio-demographic, clinical, and genetic determinants of quality of life in lung cancer patients. Sci Rep. 2018;8(1):10640.

    Article  Google Scholar 

Download references

Acknowledgements

We thank all authors for their contributions and supports. We are also grateful to all participants for providing blood samples.

Animal studies

Not applicable.

Funding

This study was supported by the Kingming Medical University Joint Project (202001AY070001-111) and CAMS Innovation Fund for Medical Sciences (CIFMS, 2016-I2M-3-024); the Open Project of the Clinical Medicine Center of the First People's Hospital of Yunnan Province, Yunnan Province Clinical Center for Hematologic Disease, and Yunnan Province Clinical Research Center for Hematologic Disease (grant number: 2021LCZXKF-XY12 and 2022LCZXKF-XY04); and Kunming University of Science and Technology Medical Joint Project (KUST-KH2022028Y).

Author information

Authors and Affiliations

Authors

Contributions

Tonghua Yang designed the experiment. Chan Zhang, Yujing Cheng, and Wanlu Chen performed the experiment. Qi Li and Run Dai processed the data. Chan Zhang wrote the manuscript. Yajie Wang revised the manuscript. The authors have read and approved the manuscript.

Corresponding author

Correspondence to Tonghua Yang.

Ethics declarations

Ethics approval and consent to participate

All procedures performed in this study were in accordance with the ethical standards of the ethics committee from the First People’s Hospital of Yunnan Province and with the 1964 Helsinki Declaration and its later amendments. Informed consent was obtained from each participant at recruitment after fully describing our research to them.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Supplementary Figure 1.

The representativespectra of each SNP.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, C., Cheng, Y., Chen, W. et al. Association of CYP19A1 rs28757157 polymorphism with lung cancer risk in the Chinese Han population. World J Surg Onc 20, 400 (2022). https://doi.org/10.1186/s12957-022-02868-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12957-022-02868-9

Keywords

  • CYP19A1
  • Polymorphisms
  • Lung cancer
  • Case–control study
  • Chinese Han population