Association of Single-Nucleotide Polymorphism REX1 rs6815391, OCT4 rs13409 or rs3130932, and CTBP2 rs3740535 with Primary Lung Cancer Susceptibility: A Case-Control Study in a Chinese Population

Shen, Qi; Wang, Wen-xiang; Xu, Qiu-ping; Xiong, Wen-min; Liu, Zhi-qiang; Hu, Zhi-jian; Cai, Lin; He, Fei

doi:https://doi.org/10.1155/2019/4150263

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Abstract Introduction Materials Results Discussion Conclusion Data Availability Ethical Approval Conflicts of Interest Authors’ Contributions Acknowledgments Supplementary Materials References Copyright Related Articles

Research Article | Open Access

Volume 2019 | Article ID 4150263 | https://doi.org/10.1155/2019/4150263

Association of Single-Nucleotide Polymorphism REX1 rs6815391, OCT4 rs13409 or rs3130932, and CTBP2 rs3740535 with Primary Lung Cancer Susceptibility: A Case-Control Study in a Chinese Population

Qi Shen,^1,2Wen-xiang Wang,^1,2Qiu-ping Xu,^1,2Wen-min Xiong,^1,2Zhi-qiang Liu,^3,4Zhi-jian Hu,^1,2Lin Cai,^1,2and Fei He^1,2,5

Academic Editor: Chiara Nicolazzo

Received15 Nov 2018

Revised17 Feb 2019

Accepted10 Mar 2019

Published02 May 2019

Abstract

The purpose of the current study is to explore the contribution of single-nucleotide polymorphisms (SNPs) of REX1 rs6815391, OCT4 rs13409 or rs3130932, and CTBP2 rs3740535 to the risk of lung cancer. A questionnaire survey was used to obtain basic information of the included subjects. A case control study was performed in 1121 patients and 1121 controls. All subjects were subjected to blood sampling for genomic DNA extraction and genotyping of the cancer stem cell-associated gene SNPs, including REX1 rs6815391, OCT4 rs13409 or rs3130932, and CTBP2 rs3740535 by real-time PCR. The association with the risk of primary lung cancer and interaction with environmental factors were assessed using unconditional logistic regression for the odds ratios and corresponding 95% confidence intervals. The genotype frequency distribution of OCT4 rs13409 loci was statistically significant, but there was no significant difference in the rest of the loci between lung cancer patients and healthy controls. The OCT4 gene was also related with lung cancer susceptibility in the genetic model after adjusting for lung cancer-related factors. Despite the presence of the dominant or recessive model, the four loci polymorphisms were associated with pollution near the place of residence, house type, worse ventilation situation, smoking, passive smoking, cooking oil fumes (COF), and family history of cancer, which increased the risk of lung cancer. Nonmarried status, , COF, smoking, passive smoking, family history of cancer, and history of lung disease were independent risk factors of lung cancer susceptibility. Additionally, college degree or above, no pollution near the place of residence, protective genotype 1 or 2, and well ventilation can reduce the occurrence of lung cancer. There is an interaction between the four loci and environmental factors, and OCT4 rs13409 is a risk factor of primary lung cancer.

1. Introduction

Lung cancer is a common malignancy and the most frequent reason of malignancy-associated death worldwide; additionally, approximately 1.8 million new lung cancer patients were diagnosed in 2012, accounting for approximately 13% of total malignancy diagnoses [1]. In 2015, there were 733,000 new lung cancer cases and 660,000 cases of death in China [2]. Smoking is the key risk factor of pulmonary cancer [3]; nevertheless, despite a lower popularity of tobacco use in China, lung cancer prevalence in Chinese females is higher than in females of several European countries [1]. This indicated that other risk factors, for example genetic factors, may be part of the cause in the development of lung cancer.

Lung cancer is caused by a variety of factors. Single-nucleotide polymorphisms (SNPs) play fundamental roles in tumorigenesis, cancer development, and prognosis. SNPs can modify gene product sequences, regulate gene expression, and influence gene function to change the phenotype.

The pluripotency and self-renewal characteristics of embryonic cells involved multiple levels of cells and a variety of factors, and the coding of stem cell signaling molecules in the genetic polymorphism may be involved in cancer occurrence [4–6]. Cancer stem cells (CSCs) have been recognized as the major source of cancer initiation and recurrence. Elevated OCT4 expression has been reported in many tumor types including NSCLC, and the expression levels of OCT4 mRNA and protein were significantly higher in tumor tissues compared with adjacent normal tissues [7]. Researchers revealed that OCT4 is linked to chemoresistance to cisplatin in NSCLC cells, suggesting that OCT4 inhibition may be a potential strategy for chemosensitization of NSCLC cells [8]. Clinical studies further validated the importance of the OCT4/NEAT1/MALAT1 signaling axis in lung cancer progression [9].

OCT4, the “core transcription factor,” plays important roles in self-renewal, tumorigenesis, invasiveness, and drug resistance of cancer stem cells [10]. OCT4 is associated with many types of cancer, including lung cancer [11], germ cell tumors [12], breast cancer [13], cervical cancer [14], prostate cancer [15], stomach cancer [16], liver cancer [17], and ovarian cancer [18]. In embryonic stem cells, OCT4 has been identified to regulate the transcriptions of other transcription factors, chromatin modifiers, long noncoding RNAs (lncRNAs), and microRNAs [19]. The OCT4gene binding inhibitory complex makes stem cells lose pluripotency. For example, miR-45 as acts as a tumor inhibitor by regulating the expression of OCT4 to reduce lung cancer initiation by tumor stem cells as well as their epithelial mesenchymal transition (epithelial-mesenchymal transition (EMT)). miR-45 also inhibited tumor growth and metastasis in lung adenocarcinoma [20]. Additionally, genes involved in the maintenance of pluripotency, such as Fgf4, Utf1, Zfp42/REX1, and Opn, are all downstream genes of OCT4 [21].

REX1 regulates the growth and development of embryonic stem cells and reacts with OCT4 [22]. REX1 not only inhibits the transcriptional activation of OCT4 to induce stem cell apoptosis but also independently regulates growth inhibition, apoptotic cell death, cell cycle arrest, and DNA damage [23].

The whole genome sequence analysis and functional experiments showed that the most prominent synergistic factor in the transcriptional repression complex was NuRD, and CTBP is an important ligand for the transcriptional inhibition of NuRD [24]. The combination of NuRD and CTBP2 leads to the deacetylation of embryonic stem cells and the disappearance of the pluripotency of stem cells. A new study showed that CtBP2 knockdown enhanced NSCLC cell sensitivity to CDDP through inhibition of the Wnt/β-catenin pathway, which suggests that CtBP2 depletion can provide a new target for NSCLC treatment [25]. REX1 (upstream of OCT4), CTBP2 (downstream of OCT4), and OCT4 synthetically can inhibit stem cell proliferation and regulate cancer stem cell apoptosis. We intended to explore the association between SNPS of three genes in this pathway and lung cancer susceptibility. The SNP rs13409 of the OCT4 gene showed significant associations with multiple myeloma risks from a previously published genome-wide association study (GWAS); the OCT4 rs3130932 was associated with gastric cancer and breast cancer. However, there are few studies on REX1 rs6815391 and CTBP2 rs3740535. Thus, we selected REX1 rs6815391, OCT4 rs13409, rs3130932, and CTBP2 rs3740535.

2. Materials and Method

2.1. Subjects

1121 newly diagnosed (enrolled in the study at the time of cancer diagnosis) primary lung cancer patients were recruited from three hospitals of the First Clinical Medical College of Fujian Medical University, Fuzhou General Hospital, and the Affiliated Union Hospital of Fujian Medical University, from July 2006 to February 2013. The control group consisted of 1121 age-matched (±2 years) cancer-free individuals recruited from medical examination centers or hospital nononcology departments during the same period. All subjects were Chinese Han who were living in Fujian for >10 years and were able to answer questions clearly. The response rate for subjects was 92.68%. This study was approved by the Institutional Review Board of Fujian Medical University (Fuzhou, China), and all participants signed the informed consent forms.

2.2. Questionnaire

A unified standard questionnaire according to the principle of informed consent was used. The investigators accepted a unified training for the face-to-face interview survey. The survey includes the general situation (age, gender, ethnicity, education, marital status, height, weight, etc.), living environment, diet history, alcohol drinking history, smoking history, tea drinking history, lung disease history, family history of cancer, and history of physical activity.

The education degree was divided into three levels: primary school or below, junior and senior high school, and college degree or above. The marital status included three grades: married, others (divorced and widowed), and single. We calculated the body mass index (BMI) as body weight (kg)/height² (m²). House types included bungalow, building, and others. There were three different ventilation situations, well, general, or worse, which was judged according to the ventilation of the bedroom. To evaluate cooking oil fume (COF) exposure, the fumes in the kitchen during cooking were classified into no fumes, little fumes, some fumes, or much fumes. Redecoration within 10 years, smoking, passive smoking, tea drinking, history of lung disease, and family history of cancer were provided with a binary response (“no” or “yes”). The kitchen ventilator was a machine that sucked cooking oil fumes out of the kitchens. Redecoration within 10 years indicated that the interior had been redecorated or painted within the last 10 years. Smoking status was defined as individuals who had smoked at least 100 cigarettes during their lifetime. Passive smoking was defined as exposure to other environmental sources of tobacco smoke at home and/or at work for more than 15 minutes per day. Drinking alcohol was defined as drinking at least once per week for more than half a year. Tea drinkers were defined as those who consumed at least 1 cup of tea per day, for at least 6 consecutive months. History of lung disease referred to a history of chronic bronchitis, emphysema, tuberculosis, and other chronic respiratory diseases. Family history of cancer was any type of cancer in any immediate family member.

2.3. Study Protocol

2.3.1. Sample Collection and DNA Isolation

Peripheral blood samples were obtained from both patients and control subjects included in this study and stored in EDTA tubes. Protease K digestion and phenol-chloroform extraction were used to extract the genomic DNA from blood samples. The purified genomic DNA was stored at -20°C until being used for SNP genotyping.

2.3.2. Genotyping

Genotyping [26] was performed at the UCLA Genotype and Sequencing Core, with a customized Fluidigm Dynamic 96.96 Array™ Assay (Fluidigm, South San Francisco, CA). The assays were based on allele-specific PCR SNP detection chemistry with Dynamic Array™ integrated fluidic circuits (IFCs). The SNP Type Assay employed tagged, allele-specific PCR primers and a common reverse primer. A universal probe set was used in every reaction, producing uniform fluorescence. Fluidigm provided locus-specific primer sequences that allowed one to confirm target locations.

The genomic DNA was used for SNP genotyping with the Sequenom platform in accordance with the manufacturer’s iPLEX Application Guide (Sequenom Inc., San Diego, CA). The samples were scanned using a matrix-assisted laser desorption ionization-time of flight mass spectrometry system and genotyped with a MassARRAY Typer 3.4 (Sequenom Inc., San Diego, CA). For quality control, approximately 10% of randomly selected samples were rerun. The concordance rate was 99.5%, and the genotyping call rates reached >90%.

2.4. Statistical Analysis

IBM SPSS 21.0 software (Armonk, NY, USA) was used, and the statistical significance was set to 0.05. Demographic features between lung cancer patients and healthy controls were compared by a 2-sided chi-squared test to identify the potential differences. An online calculator with of 0.05 was used to evaluate the Hardy-Weinberg equilibrium for each SNP. The odds ratio (OR) was calculated with an unconditional logistic regression model, and the risk of lung cancer developing for polymorphisms among study subjects was estimated by 95% confidence interval (CI); the possible confounding factors, for example age and education, were controlled to analyze the adjusted OR. The relative excess risk because of interaction, the attributable proportion because of interaction, the synergy index, and its 95% CI were used to evaluate the association between the four SNPs and lung cancer.

2.5. Ethics Statement

Our study was approved by the Ethical Committee of Fujian Medical University (Fuzhou, China) ([2014] Fu Yi Ethics Review (No. 98)), and all participants signed the informed consent forms.

3. Results

3.1. Demographic Characteristics and Environmental Factors

The demographic characteristics of 1121 patients and 1121 controls are listed in Table 1. There were no significant differences () between the patient group and control group including age, gender, redecoration within 10 years, and drinking tea. However, there was a significant difference in the distribution of the educational history, marital status, BMI, pollution near the place of residence, house type, ventilation situation, COF, kitchen ventilator, smoking, passive smoking, drinking alcohol, history of lung disease, and family history of cancer between cases and controls () (Table 1).

3.2. The Relationship between the Four SNPs and Susceptibility to Lung Cancer

The results of unconditional logistic regression analysis showed that there was no association between REX1 rs6815391, OCT4 rs3130932, or CTBP2 rs3740535 polymorphisms with susceptibility to lung cancer after adjusting for lung cancer-related factors, whether in co-dominant or dominant, recessive, additive, or genetic models. Only OCT4 rs13409 was associated with lung cancer occurrence. In the co-dominant model, the lung cancer risk of the OCT4 rs13409 CT genotype carriers was 0.731-fold that of the CC genotype carriers (95% CI: 0.595-0.899, ); in the dominant model, the risk of lung cancer in the CT+TT genotype carriers was 0.765-fold that of the CC carriers (95% CI: 0.629-0.930, ) (Tables 2 and 3).

3.3. Pathological Stratification Analysis

There was no significant difference between CTBP2 rs3740535 and REX1 rs6815391 polymorphism with lung cancer occurrence regardless of adenocarcinoma, squamous, or any type of lung cancer.

For the adenocarcinoma patients with OCT4 rs13409, the risk of lung cancer in CT genotype carriers was 0.689-fold (95% CI: 0.536-0.885, ) that of the CC carriers according to the co-dominant model; the lung cancer risk of CT+TT carriers was 0.745-fold that of the CC carriers (95% CI: 0.589-0.941, ) by the dominant model. For all lung cancer types with OCT4 rs13409, a risk of lung cancer in the CT genotype carriers was 0.734-fold (95% CI: 0.610-0.884, ) that of the CC carriers according to the co-dominant model; the lung cancer risk of CT+TT carriers was 0.781-fold that of the CC carriers (95% CI: 0.656-0.930, ).

Regarding squamous cell carcinoma, the risk of lung cancer in OCT4 rs3130932 GG carriers was 1.569-fold (95% CI: 1.016-2.425, ) that of the GT+TT carriers according to the recessive models (Table 4).

3.4. Combination and Interaction Analysis

We analyzed the interactions between the four genotypes and the environment factors including pollution near the place of residence, house type, COF, ventilation situation, kitchen ventilator, smoking, passive smoking, drink alcohol, history of lung disease, and family history of cancer (Tables 5 and 6).

The interaction analysis for both the dominant or recessive models showed that there was no additive interaction between the REX1 rs6815391, OCT4 rs13409 or rs3130932, or CTBP2 rs3740535 polymorphisms with smoking (Table 7).

3.5. Multivariate Analysis

As can be observed in Tables 2 and 3, REX1 rs6815391 CT+CC, OCT4 rs13409 CT+TT, and rs3130932 GT+TT were three protective genotypes, and the combination could be divided into three classes. In a collinear diagnosis, the results of multivariate analysis, marital status, BMI, COF, smoking, passive smoking, family history of cancer, history of lung disease, and lung cancer susceptibility were all independent risk factors. College or above, well ventilation, one or two protective genotypes, and no pollution near the place of residence reduced the occurrence of lung cancer (Table 8).

4. Discussion

In our current study, the relationship between each of the four SNPs and the risk of lung cancer among 1121 patients and 1121 controls were estimated. The unconditional logistic regression analysis results showed no association between REX1 rs6815391, OCT4 rs3130932, or CTBP2 rs3740535 with lung cancer susceptibility, whereas OCT4 rs13409 was associated with lung cancer susceptibility. In the co-dominant model, the lung cancer risk of the OCT4 rs13409 CT genotype carriers was 0.731-fold that of the CC genotype carriers (95% CI: 0.595-0.899); in the dominant model, the risk of lung cancer in the CT+TT genotype carriers was 0.765-fold that of the CC carriers (95% CI: 0.629-0.930).

REX1, also known as Zfp42, belongs to the zinc finger protein C2H2 family and is one of the subgroup members of the transcription factor YY1 (Yin Yang 1) [27]. The expression of the REX1 molecule occurs mainly in the early stages of embryonic development, and its deletion may not directly affect the differentiation direction of ES cells but may play a role in the late developmental stage [28].

The expression of REX1 was detected in different cells, such as bone marrow, heart, human epidermal keratinocytes, prostate, and lung-derived epithelial cells but disappeared with the increase in cell passage times, suggesting that REX1 expression is closely related to cell self-renewal [15]. The REX1 promoter contains binding sites for multiple core transcription factors and has a two-way regulatory effect with the OCT4 gene, which plays a role in the pluripotency of stem cells. Some scholars [29] have found a novel mechanism between OCT4 and REX1 in which Drp1 fission activity partially contributes to the pluripotency in hESCs (human embryonic stem cells). REX1 achieves mitochondrial fission by reducing the reprogramming barrier (growth stagnation and apoptosis), as well as the process of converting from oxidative phosphorylation to glycolytic metabolism, which depends on the cyclin B1/B2-DRP1 pathway, altering cell cycle progression and the metabolism state to allow stem cells to enter and exit pluripotency [30].

Studies on the association between the REX1 gene and tumors are rare. The current study found no association between REX1 rs6815391 polymorphism and lung cancer susceptibility. The stratified analysis of different pathological types of adenocarcinoma also showed no significant difference. However, our results suggested that the TT genotype may be associated with other risks of lung cancer, such as pollution near the place of residence, house type, ventilation situation, COF, smoking, passive smoking, lung disease history, and family history of cancer.

OCT4 is encoded by Pou5f1 and belongs to the POU (Pit-Oct-Unc) transcription factor family and participates in the regulation of downstream genes by binding an octa-base conserved sequence containing ATGCAAAT [31]. The OCT4 gene is the “core” of stem cells. Some studies [32] have found that the differentiation and maturation of tumor cells are related to the downregulation of OCT4 expression. Stem cells can also secrete OCT4 and VEGF to promote the origin of tumor epithelial cells into vascular epithelial cells and form small blood vessels for maintaining the survival of dry cells under hypoxia to induce tumor blood transfer for the foundation of resistance to chemotherapy [33]. Our study found that in adenocarcinoma, OCT4 rs13409 in a co-dominant model CT genotype showed 0.689-fold the risk of lung cancer in CC carriers (95% CI: 0.536-0.885); in dominant models, the risk of lung cancer in CT+TT carriers was 0.745-fold higher than that of CC carriers (95% CI: 0.589-0.941). In all subtypes of lung cancers, the risk of OCT4 rs13409 CT genotype carriers in the co-dominant genetic model was 0.734-fold that of the CC carriers (95% CI: 0.610-0.884), and the risk of CT+TT carriers in the recessive model was 0.781-fold that of the CC carriers (95% CI: 0.656-0.930). In squamous carcinoma, the risk of lung cancer in the OCT4 rs3130932 GG carriers in the recessive model was 1.569-fold as high as that of the GT+TT carriers (95% CI: 1.016-2.425).

After analyzing the interaction between the two genotypes of OCT4 and environmental factors (pollution near the place of residence, house type, COF, ventilation situation, kitchen ventilator, smoking, passive smoking, alcohol drinking, history of lung disease, and family history of cancer), our results showed that the OCT4 rs13409 dominant model had a combined effect with these environmental factors, while two genotypes of OCT4 in the recessive model had an interaction with the environmental factors except for alcohol drinking and kitchen ventilator. The OCT4 rs3130932 dominant model interacted with these environmental factors with the exception of alcohol drinking, kitchen ventilator, and history of lung disease. The OCT4 rs13409CC and OCT4 rs3130932GG genotypes were also found to be risk factors for lung cancer. OCT4 rs13409 showed [34] a significant association with multiple myeloma risk in 1,832 controls and 2,894 MM from seven European countries and Israel. In northern Iran [35], the OCT4 rs3130932G allele was associated with the incidence of gastric cancer; genotypes AC [, (0.44-0.91)] and AC+CC [, (0.48-0.95)] had protective effects on patients in north India [36]. In Greece [37], however, there was no significant association between the genotype tested and the risk of breast, ovarian, or lung cancer.

Vertebrates contain two different CTBP2 genes, which are located on human chromosomes 4p16 and l0q26.13, encoding CTBP1 and CTBP2 proteins, and both are structurally highly homologous [38]. A variety of transcription factors interact directly or indirectly with CTBP, and some of these are related to stem cell maintenance or development [39]. CTBP2 is a transcriptional corepressor and a regulator during exit from pluripotency [40]. CTBP2 binds transcription factors through a Pro-X-Asp-Leu-Ser motif and recruits epigenetic remodelers to form the complex [41]. The overexpression of the CtBP2 protein in NSCLC tissues indicates that CtBP2 is closely related to the occurrence of NSCLC, especially in the lung adenocarcinoma tissues, which is 100% overexpressed, indicating that CtBP2 is highly likely to play an important role in the occurrence and development of lung adenocarcinoma [42]. CTBP2 knockdown results in the activation of p53-dependent apoptosis arrest [43]. A recent study [44] showed that CTBP2 regulates deacetylation of H3K27 to fine control the withdrawal of pluripotency. One of the Ctbp2-binding zinc finger proteins, Zfp217, is known as an oncogenic protein associated with various cancers [45]. Ctbp2 binds to the enhancers and promoters of actively transcribed genes in undifferentiated ESCs that Ctbp2 associates with core components of NuRD and Oct4. These results suggest that Ctbp2 regulates ESC fate by binding to actively transcribed genes [46]. Also, CTBP2 plays a role in cell migration, signal pathways, and the cell cycle. Previous reports indicated that CtBP2 could promote H1299 lung cancer cell migration via repressing PTEN expression and stimulating phosphatidylinositol 3-kinase activity, thereby promoting cell migration [47]. Its overexpression could not only repress the epithelial marker E-cadherin but also upregulate the mesenchymal marker N-cadherin as well as vimentin to enhance migration [48]; in lung cancer cells, it can inhibit the Wnt signaling pathway to promote cell apoptosis and affect the cell cycle [25].

This study found that the genotype frequency distribution of CTBP2 rs3740535 was different between the patient group and the control group. The dominant or recessive genetic model showed that the risk of lung cancer was increased when combined with pollution near the place of residence, house type, COF, ventilation situation, smoking, passive smoking, history of lung disease, or family history of cancer.

Our results showed the combined effect of the four genotypes with the environmental factors of pollution near the place of residence, worse ventilation situation, smoking, passive smoking, COF, and family history of cancer. These factors together increase the risk of lung cancer in the population. We did not see an additive interaction between the four locus polymorphisms and smoking. The genotypes were related to the survival outcome of lung cancer patients. The number of protective genotypes of the four loci divided into different groups to evaluate its combined effect with the factors influencing lung cancer in the univariate analysis was also included in the logistic multivariate analysis. Our results showed that unmarried status, BMI, COF, smoking, passive smoking, family history of cancer, and history of lung disease were independent risk factors of lung cancer; college degree or above, no pollution near the place of residence, 1 or 2 protective genotypes, and well ventilation can reduce the occurrence of lung cancer.

The sample size was large, and the power of the results was high in the current study. However, our study had limitations. First, we could not completely rule out selection bias, although this was one of the objectives of using three of the largest flow hospitals. Second, the functional studies should be confirmed. Third, environmental factors were significantly different between the two groups, so the relationship between lung cancer and SNPs could not be directly determined. Therefore, the multivariate regression analysis was used for removing the influence of confounders and to explore the relationship between SNPs and lung cancer.

5. Conclusion

In conclusion, our study indicated that the OCT4 rs13409 locus was associated with susceptibility to lung cancer, and OCT4 rs13409 CC genotype carriers showed an increased risk of lung adenocarcinoma, whereas OCT4 rs3130932 GG carriers showed an increased risk of squamous carcinoma. However, the conclusion should be further demonstrated in a larger sample size.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethical Approval

This study was approved by the Institutional Review Board of Fujian Medical University (Fuzhou, China), and all participants signed informed consent forms.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

SQ carried out the experiments and helped draft the manuscript. LZQ, XQP, and QML carried out the experiments and collected the samples. HZJ and WWX participated in the design of the study and performed the statistical analysis. HF and LC conceived of the study and participated in its design and coordination and helped draft the manuscript. All authors read and approved the final manuscript.

Acknowledgments

This study was supported by grants from the National Natural Science Foundation of China (No. 81402738), Fujian Provincial Natural Science Foundation Project (No. 2016J01355), Fujian Program for Outstanding Young Researchers in University awarded by the Education Department of Fujian (No. 2017B019), and the National Key Research and Development Program of China (No. 2017YFC0907100). We thank all the staffs from the Department of Thoracic Surgery, The First Affiliated Hospital of Fujian Medical University. We would also like to express our appreciation to the patients who participated in our study.

Supplementary Materials

Supplementary Table 1: The genomic location of the four SNPs. (Supplementary Materials)

References

R. Siegel, D. Naishadham, and A. Jemal, “Cancer statistics, 2012,” CA: A Cancer Journal for Clinicians, vol. 62, no. 1, pp. 10–29, 2012.
View at: Publisher Site | Google Scholar
W. Chen, “Cancer statistics: updated cancer burden in China,” Chinese Journal of Cancer Research, vol. 27, no. 1, p. 1, 2015.
View at: Publisher Site | Google Scholar
G. Wenzheng, M. Hu, J. Wu et al., “Gprc5a depletion enhances the risk of smoking-induced lung tumorigenesis and mortality,” Biomedicine & Pharmacotherapy, vol. 114, 2019.
View at: Publisher Site | Google Scholar
N. Takebe, L. Miele, P. J. Harris et al., “Targeting Notch, hedgehog, and Wnt pathways in cancer stem cells: clinical update,” Nature Reviews Clinical Oncology, vol. 12, no. 8, pp. 445–464, 2015.
View at: Publisher Site | Google Scholar
X. Fu and Y. Xu, “Self-renewal and scalability of human embryonic stem cells for human therapy,” Regenerative Medicine, vol. 6, no. 3, pp. 327–334, 2011.
View at: Publisher Site | Google Scholar
V. Venkatesh, R. Nataraj, G. S. Thangaraj et al., “Targeting Notch signalling pathway of cancer stem cells,” Stem Cell Investigation, vol. 5, pp. 5–5, 2018.
View at: Publisher Site | Google Scholar
S. Zhao, Q. Yuan, H. Hao et al., “Expression of OCT4 pseudogenes in human tumours: lessons from glioma and breast carcinoma,” The Journal of Pathology, vol. 223, no. 5, pp. 672–682, 2011.
View at: Publisher Site | Google Scholar
X. Liu, M. Ma, X. Duan, H. Zhang, and M. Yang, “Knockdown of OCT4 may sensitize NSCLC cells to cisplatin,” Clinical & Translational Oncology, vol. 19, no. 5, pp. 587–592, 2017.
View at: Publisher Site | Google Scholar
J. Jen, Y.-A. Tang, Y.-H. Lu, C.-C. Lin, W.-W. Lai, and Y.-C. Wang, “Oct4 transcriptionally regulates the expression of long non-coding RNAs NEAT1 and MALAT1 to promote lung cancer progression,” Molecular Cancer, vol. 16, no. 1, p. 104, 2017.
View at: Publisher Site | Google Scholar
L. A. Boyer, T. I. Lee, M. F. Cole et al., “Core transcriptional regulatory circuitry in human embryonic stem cells,” Cell, vol. 122, no. 6, pp. 947–956, 2005.
View at: Publisher Site | Google Scholar
C. Xu, D. Xie, S. C. Yu et al., “β-Catenin/POU5F1/SOX2 transcription factor complex mediates IGF-I receptor signaling and predicts poor prognosis in lung adenocarcinoma,” Cancer Research, vol. 73, no. 10, pp. 3181–3189, 2013.
View at: Publisher Site | Google Scholar
L. H. J. Looijenga, H. Stoop, H. P. de Leeuw et al., “POU5F1 (OCT3/4) identifies cells with pluripotent potential in human germ cell tumors,” Cancer Research, vol. 63, no. 9, pp. 2244–2250, 2003.
View at: Google Scholar
T. Liu, B. Sun, X. Zhao et al., “OCT4 expression and vasculogenic mimicry formation positively correlate with poor prognosis in human breast cancer,” International Journal of Molecular Sciences, vol. 15, no. 11, pp. 19634–19649, 2014.
View at: Publisher Site | Google Scholar
J. Ji, X. Wei, and Y. Wang, “Embryonic stem cell markers Sox-2 and OCT4 expression and their correlation with WNT signal pathway in cervical squamous cell carcinoma,” International Journal of Clinical and Experimental Pathology, vol. 7, no. 5, pp. 2470–2476, 2014.
View at: Google Scholar
J. P. Breyer, D. C. Dorset, T. A. Clark et al., “An expressed retrogene of the master embryonic stem cell gene POU5F1 is associated with prostate cancer susceptibility,” American Journal of Human Genetics, vol. 94, no. 3, pp. 395–404, 2014.
View at: Publisher Site | Google Scholar
D. Kong, G. Su, L. Zha et al., “Coexpression of HMGA2 and Oct4 predicts an unfavorable prognosis in human gastric cancer,” Medical Oncology, vol. 31, no. 8, p. 130, 2014.
View at: Publisher Site | Google Scholar
Y. W. Zheng, Y. Z. Nie, and H. Taniguchi, “Cellular reprogramming and hepatocellular carcinoma development,” World Journal of Gastroenterology, vol. 19, no. 47, pp. 8850–8860, 2013.
View at: Publisher Site | Google Scholar
J. Di, T. Duiveman-de Boer, P. L. M. Zusterzeel, C. G. Figdor, L. F. A. G. Massuger, and R. Torensma, “The stem cell markers Oct4A, Nanog and c-Myc are expressed in ascites cells and tumor tissue of ovarian cancer patients,” Cellular Oncology (Dordrecht), vol. 36, no. 5, pp. 363–374, 2013.
View at: Publisher Site | Google Scholar
T. Wu, H. B. Pinto, Y. F. Kamikawa, and M. E. Donohoe, “The BET family member BRD4 interacts with OCT4 and regulates pluripotency gene expression,” Stem Cell Reports, vol. 4, no. 3, pp. 390–403, 2015.
View at: Publisher Site | Google Scholar
J. Hu, M. Qiu, F. Jiang et al., “MiR-145 regulates cancer stem-like properties and epithelial-to-mesenchymal transition in lung adenocarcinoma-initiating cells,” Tumour Biology, vol. 35, no. 9, pp. 8953–8961, 2014.
View at: Publisher Site | Google Scholar
J. Ding, H. Xu, F. Faiola, A. Ma'ayan, and J. Wang, “Oct4 links multiple epigenetic pathways to the pluripotency network,” Cell Research, vol. 22, no. 1, pp. 155–167, 2012.
View at: Publisher Site | Google Scholar
S. Masui, S. Ohtsuka, R. Yagi, K. Takahashi, M. S. H. Ko, and H. Niwa, “Rex1/Zfp42 is dispensable for pluripotency in mouse ES cells,” BMC Developmental Biology, vol. 8, no. 1, p. 45, 2008.
View at: Publisher Site | Google Scholar
J. Wang, S. Rao, J. Chu et al., “A protein interaction network for pluripotency of embryonic stem cells,” Nature, vol. 444, no. 7117, pp. 364–368, 2006.
View at: Publisher Site | Google Scholar
M. Pardo, B. Lang, L. Yu et al., “An expanded Oct4 interaction network: implications for stem cell biology, development, and disease,” Cell Stem Cell, vol. 6, no. 4, pp. 382–395, 2010.
View at: Publisher Site | Google Scholar
D. P. Wang, L. L. Gu, Q. Xue, H. Chen, and G. X. Mao, “CtBP2 promotes proliferation and reduces drug sensitivity in non-small cell lung cancer via the Wnt/β-catenin pathway,” Neoplasma, vol. 65, no. 6, pp. 888–897, 2018.
View at: Publisher Site | Google Scholar
X. Liu, “Genetic susceptibility and environmental risk factors of liver cancer, a population-based case-control study in Jiangsu Province, China,” Tech. Rep., Dissertations & Theses - Gradworks, 2015.
View at: Google Scholar
N. P. Mongan, K. M. Martin, and L. J. Gudas, “The putative human stem cell marker, Rex-1(Zfp42): structural classification and expression in normal human epithelial and carcinoma cell cultures,” Molecular Carcinogenesis, vol. 45, no. 12, pp. 887–900, 2006.
View at: Publisher Site | Google Scholar
J. D. Kim, H. Kim, M. B. Ekram, S. Yu, C. Faulk, and J. Kim, “Rex1/Zfp42 as an epigenetic regulator for genomic imprinting,” Human Molecular Genetics, vol. 20, no. 7, pp. 1353–1362, 2011.
View at: Publisher Site | Google Scholar
M. Y. Son, H. Choi, Y. M. Han, and Y. Sook Cho, “Unveiling the critical role of REX1 in the regulation of human stem cell pluripotency,” Stem Cells, vol. 31, no. 11, pp. 2374–2387, 2013.
View at: Publisher Site | Google Scholar
J. Xu, R. Sylvester, A. P. Tighe, S. Chen, and L. J. Gudas, “Transcriptional activation of the suppressor of cytokine signaling-3 (SOCS-3) gene via STAT3 is increased in F9 REX1 (ZFP-42) knockout teratocarcinoma stem cells relative to wild-type cells,” Journal of Molecular Biology, vol. 377, no. 1, pp. 28–46, 2008.
View at: Publisher Site | Google Scholar
Y. J. Wang and B. Kang, “OCT4 (octamer-binding transcription factor 4),” in Encyclopedia of Signaling Molecules, Springer, New York, NY, 2017.
View at: Publisher Site | Google Scholar
X. L. Li, L. L. Jia, M. M. Shi et al., “Downregulation of KPNA2 in non-small-cell lung cancer is associated with Oct4 expression,” Journal of Translational Medicine, vol. 11, no. 1, pp. 232–232, 2013.
View at: Publisher Site | Google Scholar
A. Pezzolo, F. Parodi, D. Marimpietri et al., “Oct4+/tenascin C+ neuroblastoma cells serve as progenitors of tumor-derived endothelial cells,” Cell Research, vol. 21, no. 10, pp. 1470–1486, 2011.
View at: Publisher Site | Google Scholar
A. Macauda, D. Calvetti, G. Maccari et al., “Identification of miRSNPs associated with the risk of multiple myeloma,” International Journal of Cancer, vol. 140, no. 3, pp. 526–534, 2017.
View at: Publisher Site | Google Scholar
Z. Shahhoseini, F. Jeivad, N. Ahangar, and S. Abediankenari, “Different genotype of rs3130932 single nucleotide polymorphism between gastric cancer patients and normal subjects,” Journal of Gastrointestinal Cancer, vol. 48, no. 1, pp. 38–41, 2017.
View at: Publisher Site | Google Scholar
S. Tulsyan, G. Agarwal, P. Lal, and B. Mittal, “Significant association of combination of OCT4, NANOG, and SOX2 gene polymorphisms in susceptibility and response to treatment in north Indian breast cancer patients,” Cancer Chemotherapy and Pharmacology, vol. 74, no. 5, pp. 1065–1078, 2014.
View at: Publisher Site | Google Scholar
S. Katafigiotis, S. I. Papamichos, R. Katopodi et al., “A case–control study on the rs3130932 single nucleotide polymorphism in the OCT4B translation initiation codon in association with cancer state,” European Journal of Cancer Prevention, vol. 20, no. 3, pp. 248–251, 2011.
View at: Publisher Site | Google Scholar
G. Chinnadurai, “Transcriptional regulation by C-terminal binding proteins,” International Journal of Biochemistry & Cell Biology, vol. 39, no. 9, pp. 1593–1607, 2007.
View at: Publisher Site | Google Scholar
B. Liu, I. Shats, S. P. Angus, M. L. Gatza, and J. R. Nevins, “Interaction of E2F7 transcription factor with E2F1 and C-terminal-binding protein (CtBP) provides a mechanism for E2F7-dependent transcription repression,” The Journal of Biological Chemistry, vol. 288, no. 34, pp. 24581–24589, 2013.
View at: Publisher Site | Google Scholar
T. W. Kim, S. Kwak, J. Shin et al., “Ctbp2-mediated β-catenin regulation is required for exit from pluripotency,” Experimental & Molecular Medicine, vol. 49, no. 10, article e385, 2017.
View at: Publisher Site | Google Scholar
K. G. R. Quinlan, M. Nardini, A. Verger et al., “Specific recognition of ZNF217 and other zinc finger proteins at a surface groove of C-terminal binding proteins,” Molecular and Cellular Biology, vol. 26, no. 21, pp. 8159–8172, 2006.
View at: Publisher Site | Google Scholar
B. Li, ““Relationship between CtBP2 expression and non-small cell lung cancer,” Huazhong university of science and technology, 2014.
View at: Google Scholar
C. N. Birts, R. Harding, G. Soosaipillai et al., “Expression of CtBP family protein isoforms in breast cancer and their role in chemoresistance,” Biology of the Cell, vol. 103, no. 1, pp. 1–19, 2010.
View at: Publisher Site | Google Scholar
T. W. Kim, B. H. Kang, H. Jang et al., “Ctbp2 modulates NuRD-mediated deacetylation of H3K27 and facilitates PRC2-mediated H3K27me3 in active embryonic stem cell genes during exit from pluripotency,” Stem Cells, vol. 33, no. 8, pp. 2442–2455, 2015.
View at: Publisher Site | Google Scholar
S. Kwak, T. W. Kim, B. H. Kang et al., “Zinc finger proteins orchestrate active gene silencing during embryonic stem cell differentiation,” Nucleic Acids Research, vol. 46, no. 13, pp. 6592–6607, 2018.
View at: Publisher Site | Google Scholar
Y. S. Ang, S. Y. Tsai, D. F. Lee et al., “Wdr5 mediates self-renewal and reprogramming via the embryonic stem cell core transcriptional network,” Cell, vol. 145, no. 2, pp. 183–197, 2011.
View at: Publisher Site | Google Scholar
S. Paliwal, R. C. Kovi, B. Nath, Y. W. Chen, B. C. Lewis, and S. R. Grossman, “The alternative reading frame tumor suppressor antagonizes hypoxia-induced cancer cell migration via interaction with the COOH-terminal binding protein corepressor,” Cancer Research, vol. 67, no. 19, pp. 9322–9329, 2007.
View at: Publisher Site | Google Scholar
J. Zhang, J. Zhu, L. Yang et al., “Interaction with CCNH/CDK7 facilitates CtBP2 promoting esophageal squamous cell carcinoma (ESCC) metastasis via upregulating epithelial-mesenchymal transition (EMT) progression,” Tumor Biology, vol. 36, no. 9, pp. 6701–6714, 2015.
View at: Publisher Site | Google Scholar

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Copyright © 2019 Qi Shen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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