Skip to main content
Oncology Letters logoLink to Oncology Letters
. 2011 Nov 25;3(2):482–484. doi: 10.3892/ol.2011.494

Single nucleotide polymorphisms in the mitochondrial displacement loop and age-at-onset of esophageal squamous cell carcinoma

ZHANJUN GUO 1,*, HUA YANG 1,*, FENGBIN ZHANG 1, RUIXING ZHANG 1, CUIJU WANG 2,3,
PMCID: PMC3362473  PMID: 22740936

Abstract

The accumulation of single nucleotide polymorphisms (SNPs) in the displacement loop (D-loop) of mitochondrial DNA (mtDNA) has been described for various types of cancer, and their association with cancer risk and disease outcome has been extensively identified. In the present study, we investigated the association between age-at-onset and SNPs in the mitochondrial D-loop using a population-based series of esophageal squamous cell carcinoma (ESCC) patients. The SNP sites of nucleotides 16266 C/T were identified for their association with age-at-onset using the log-rank test. The age-at-onset of patients with the minor allele T genotype was significantly lower than that of patients with the C genotype at the 16266 site (p=0.036). Genetic polymorphisms in the D-loop are predictive markers for age-at-onset in ESCC patients. Accordingly, the analysis of genetic polymorphisms in the mitochondrial D-loop may help identify ESCC patient subgroups at high risk of early onset.

Keywords: esophageal squamous cell carcinoma, single nucleotide polymorphisms, displacement loop, age-at-onset

Introduction

Esophageal cancer is one of the most common types of cancer in the population of Northern Central China with an age-standardized annual incidence rate >125/100,000 (1). Cumulative mortality attributed to esophageal cancer is approximately 20% for females and 25% for males (2). Despite improved diagnosis and therapeutic strategies, the prognosis of esophageal cancer remains poor, mostly due to its aggressive nature. Certain epidemiological factors were identified as risk factors or outcome predictors for ESCC (3,4), but the true mechanism of action for this cancer remains unknown. A few studies have focused on genetic factors associated with the age-at-onset of cancer. However, these studies have only demonstrated the genetic prevalence of this disease (4).

The human mitochondrial genome is 16 kb in length and is a closed-circular duplex molecule containing 37 genes, including 2 ribosomal RNAs and a complete set of 22 tRNAs (5). mtDNA is believed to be more susceptible to DNA damage and acquires mutations at a higher rate than nuclear DNA, due to high levels of reactive oxygen species (ROS), a lack of protective histones and a limited capacity for DNA repair in the mitochondria (6,7).

In cancer patients, sequence changes accumulate extensively in the mitochondrial D-loop region, which is significant for regulating the replication and expression of the mitochondrial genome, as it contains the leading-strand origin of replication and the main promoter for transcription (811). Using blood samples from the ESCC patients, we sequenced the D-loop, which contains a length of 1122 bp (nucleotides 16024–16569 and 1–576), and referred to the mitochondria database (www.mitomap.org). A total of 88 single nucleotide polymorphisms (SNPs) were identified in the D-loop. We also identified cancer outcome-associated SNPs using the Kaplan-Meier method (12). The aim of this study was to assess the correlation between germline SNPs of the D-loop and age-at-onset in ESCC patients.

Materials and methods

Tissue specimens and DNA extraction

Blood samples were collected at the Fourth Hospital of Hebei University from 68 patients who underwent esophageal cancer resection in the Department of Thoracic Surgery between 2003 and 2005. The patients were selected when they received endoscopy examination and specimens were confirmed as ESCC by a pathologist. The patients were from the Hebei Province of China, which is a high-risk area for ESCC. From the blood samples, genomic DNA was immediately extracted using a Wizard Genomic DNA extraction kit (Promega, Madison, WI, USA). The study was approved by the Human Tissue Research Committee of the Fourth Hospital of Hebei Medical University. All patients provided written informed consent for the collection of samples and subsequent analysis.

PCR amplification and sequence analysis

The forward 5′-CCCCATGCTTACAAGCAAGT-3′ (nucleotide 16190–16209) and reverse primers 5′-GCTTTGAGGAGGTAA GCTAC-3′ (nucleotide 602–583) were used for the amplification of a 982 bp product from the mtDNA D-loop region, as previously described (13). PCR was performed according to the protocol of the PCR Master Mix kit (Promega) and purified prior to sequencing. Cycle sequencing was carried out using the Dye Terminator Cycle Sequencing Ready Reaction kit (Applied Biosystems, Foster City, CA, USA) and the products were then separated using the ABI PRISM Genetic Analyzer 3100 (Applied Biosystems). Polymorphisms were confirmed by repeated analyses from the two strands. SNPs were identified directly from the blood mitochondria.

Statistical analysis

The age-at-onset curve of ESCC patients was calculated using the Kaplan-Meier method at each SNP site and compared with the log-rank test. All of the statistical analysis was conducted using the SPSS 11.5 software package (SPSS Company, Chicago, IL, USA). P<0.05 was considered to indicate a statistically significant difference.

Results

Age-at-onset distribution in ESCC patients and clinical characteristics

A total of 68 patients who shared the same performance status (ECOG score, 0) were enrolled in this study. The age-at-onset distribution of the ESCC patients is listed in Table I. The patients analyzed included: 1 patient aged <40, 7 patients aged 40–50, 34 patients aged 51–60 and 26 patients aged >60 years old. None of these patients had received any adjuvant chemotherapy or radiation therapy following ESCC resection. The age-at-onset was analyzed against clinical characteristics using the Kaplan-Meier method and these were compared using the log-rank test. Gender, TNM stage and local lymphatic metastasis were not associated with age-at-onset at statistically significant levels (Table II).

Table I.

Age-at-onset distribution in ESCC patients.

No. of cases

Age Male Female
<40 1 0
41–50 6 1
51–60 19 15
>60 17 9

ESCC, esophageal squamous cell carcinoma.

Table II.

Clinical characteristics and their association with age-at-onset age in ESCC patients.

Characteristics No. of cases P-value
Gender 0.265
 Male 43
 Female 25
TNM classification 0.406
 I+II 48
 III 23
Lymphatic metastases 0.282
 Yes 15
 No 53

ESCC, esophageal squamous cell carcinoma.

Correlation between mtDNA genotype and survival

The correlation between the mtDNA genotype and survival was compared. SNPs with a minor allele frequency of <5% were included; thus, a remaining 22 SNPs were used for further analysis. The ESCC patients were divided into two groups based on their genotype at each SNP site. For each site, the age-at-onset curve was plotted using the Kaplan-Meier method for all ESCC patients. A marked difference in the age-at-onset appeared at nucleotide 16266, as revealed by the log-rank test (Fig. 1). This SNP was previously identified in the mitochondrial database (www.mitomap.org). The age-at-onset for patients with the minor allele 16266T genotype was significantly lower than that of patients with 16266C at the 16266 site (p=0.036) (Fig. 1).

Figure 1.

Figure 1

Comparison of the age-at-onset for ESCC patients according to the 16266C/T genotype in the D-loop. ESCC, esophageal squamous cell carcinoma.

Discussion

Selected SNPs in the D-loop region have been examined for their ability to predict cancer risk and outcome in other types of tumor (1419). The present study has extended previous analyses to determine the correlation between age-at-onset and germline SNPs in a continuous sequence of mtDNA between nucleotides 16190 and 583 in ESCC patients. The SNP 16266C/T was identified for its association with age-at-onset at a statistically significant level by the log-rank test.

We have performed a number of studies on D-loop SNPs in cancers of the digestive tract (18,19). In this study, we suggest for the first time that, apart from being a predictor for cancer risk and outcome, SNPs in the D-loop are also a predictor for age-at-onset in ESCC patients. Nucleotide 16266 is located between MT-5 and MT-3L on the mitochondrial genome. The functional significance of this SNP is not known, although we have found it to be associated with the risk of hepatocellular cell carcinoma by conducting case-control studies. Minor alleles of 16266T are associated with the early onset of ESCC; the age-at-onset curve decreased rapidly in patients carrying these alleles (Fig. 1). We compared the distribution frequency of this SNP between ESCC patients and normal controls. Among the 68 age-matched controls, only one carried the 16266T allele, whereas five ESCC patients carried the T allele. A much larger sample size is therefore required to perform a comparison for cancer risk at this site.

The D-loop region of mtDNA is significant for the regulation of mitochondrial genome replication and expression. SNPs in this region may affect mtDNA replication and lead to alteration of the electron transport chain, which is responsible for the release of highly reactive oxygen species (ROS), and may contribute to nuclear genome damage as well as cancer initiation and promotion (2022). This SNP may alter the transcription of the mitochondrial genome, causing the production of ROS to be enhanced (23). This ROS-mediated mechanism may accelerate tumor development.

In conclusion, SNPs in the D-loop were found to be biomarkers for ESCC age-at-onset. The analysis of genetic polymorphisms in the D-loop may help to identify patient subgroups at a high risk of an early onset in ESCC cancers, thereby helping to refine therapeutic decisions.

Acknowledgements

This work was supported by the National Natural Science Foundation of P.R. China No. 30801384. The research was supported in part by the Natural Science Foundation of Hebei Province No. C2008000958.

References

  • 1.Blot WJ, Li JY. Some considerations in the design of a nutrition intervention trial in Linxian, People’s Republic of China. Natl Cancer Inst Monogr. 1985;69:29–34. [PubMed] [Google Scholar]
  • 2.Abnet CC, Huppi K, Carrera A, Armistead D, McKenney K, Hu N, Tang ZZ, Taylor PR, Dawsey SM. Control region mutations and ‘common deletion’ are frequent in the mitochondrial DNA of patients with esophageal squamous cell carcinoma. BMC Cancer. 2004;4:30. doi: 10.1186/1471-2407-4-30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Blanchard P, Quero L, Hennequin C. Prognostic and predictive factor of oesophageal carcinoma. Bull Cancer. 2009;96:279–389. doi: 10.1684/bdc.2008.0778. [DOI] [PubMed] [Google Scholar]
  • 4.Lee CH, Wu DC, Wu IC, Goan YG, Lee JM, Chou SH, Chan TF, Huang HL, Hung YH, Huang MC, Lai TC, Wang TN, Lan CC, Tsai S, Lin WY, Wu MT. Genetic modulation of ADH1B and ALDH2 polymorphism with regard to alcohol and tobacco consumption for younger aged esophageal squamous cell carcinoma. Int J Cancer. 2009;125:1134–1142. doi: 10.1002/ijc.24357. [DOI] [PubMed] [Google Scholar]
  • 5.Shadel GS, Clayton DA. Mitochondrial DNA maintenance in vertebrates. Annu Rev Biochem. 1997;66:409–435. doi: 10.1146/annurev.biochem.66.1.409. [DOI] [PubMed] [Google Scholar]
  • 6.DiMauro S, Schon EA. Mitochondrial DNA mutations in human disease. Am J Med Genet. 2001;106:18–26. doi: 10.1002/ajmg.1392. [DOI] [PubMed] [Google Scholar]
  • 7.Beal MF. Mitochondria, free radicals, and neurodegeneration. Curr Opin Neurobiol. 1996;6:661–666. doi: 10.1016/s0959-4388(96)80100-0. [DOI] [PubMed] [Google Scholar]
  • 8.Yoneyama H, Hara T, Kato Y, Yamori T, Matsuura ET, Koike K. Nucleotide sequence variation is frequently in the mitochondrial DNA displacement loop region of individual human tumor cell. Mol Cancer Res. 2005;3:14–20. [PubMed] [Google Scholar]
  • 9.Nashikawa M, Nishiguchi S, Shiomi S, Tamori A, Koh N, Takeda T, Kubo S, Hirohashi K, Kinoshita H, Sato E, Inoue M. Somatic mutation of mitochondrial DNA in cancerous and noncancerous liver tissue in individuals with hepatocellular carcinoma. Cancer Res. 2001;61:1843–1845. [PubMed] [Google Scholar]
  • 10.Sanchez-Cespedes M, Parrella P, Nomoto S, Cohen D, Xiao Y, Esteller M, Jeronimo C, Jordan RC, Nicol T, Koch WM, Schoenberg M, Mazzarelli P, Fazio VM, Sidransky D. Identification of a mononucleotide repeats as a major target for mitochondrial DNA alterations in human tumors. Cancer Res. 2001;61:7015–7019. [PubMed] [Google Scholar]
  • 11.Taanman JW. The mitochondrial genome: structure, transcription, translation and replication. Biochim Biophys Acta. 1999;1410:103–123. doi: 10.1016/s0005-2728(98)00161-3. [DOI] [PubMed] [Google Scholar]
  • 12.Zhang R, Wang R, Zhang F, Wu C, Fan H, li Y, Wang C, Guo Z. Single nucleotide polymorphisms in the mitochondrial displacement loop and outcome of esophageal squamous cell carcinoma. J Exp Clin Cancer Res. 2010;29:155. doi: 10.1186/1756-9966-29-155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Lee HC, Li SH, Lin JC, Wu CC, Yeh DC, Wei YH. Somatic mutations in the D-loop and decrease in the copy number of mitochondrial DNA in human hepatocellular carcinoma. Mutant Res. 2004;547:71–78. doi: 10.1016/j.mrfmmm.2003.12.011. [DOI] [PubMed] [Google Scholar]
  • 14.Navaglia F, Basso D, Fogar P, Sperti C, Greco E, Zambon CF, Stranges A, Falda A, Pizzi S, Parenti A, Pedrazzoli S, Plebani M. Mitochondrial DNA D-loop in pancreatic cancer: somatic mutations are epiphenomena while the germline 16519T variant worsen metabolism and outcome. Am J Clin Pathol. 2006;126:593–601. doi: 10.1309/GQFCCJMH5KHNVX73. [DOI] [PubMed] [Google Scholar]
  • 15.Wang L, Bamlet WR, de Andrade M, Boardman LA, Cunningham JM, Thibodeau SN, Petersen GM. Mitochondrial genetic polymorphism and pancreatic cancer risk. Cancer Epidemiol Biomarkers Prev. 2007;16:1455–1459. doi: 10.1158/1055-9965.EPI-07-0119. [DOI] [PubMed] [Google Scholar]
  • 16.Wang L, McDonnell SK, Hebbring SJ, Cunningham JM, St Sauver J, Cerhan JR, Isaya G, Schaid DJ, Thibodeau SN. Polymorphism in mitochondrial genes and prostate cancer risk. Cancer Epidemiol Biomarkers Prev. 2008;17:3558–3566. doi: 10.1158/1055-9965.EPI-08-0434. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Bai RK, Leal SM, Covarrubias D, Liu A, Wong LJ. Mitochondrial genetic background modifies breast cancer risk. Cancer Res. 2007;67:4687–4694. doi: 10.1158/0008-5472.CAN-06-3554. [DOI] [PubMed] [Google Scholar]
  • 18.Zhang R, Zhang F, Wang C, Wang S, Shiao Y-H, Guo Z. Identification of sequence polymorphism in the D-loop region of mitochondria DNA as a risk factor for hepatocellular carcinoma with distinct etiology. J Exp Clin Cancer Res. 2010;29:130. doi: 10.1186/1756-9966-29-130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Wang C, Zhang F, Fan H, Peng L, Zhang R, Liu S, Guo Z. Single nucleotide polymorphisms in the mitochondrial displacement loop and hepatocellular carcinoma outcome. Biochem Biophys Res Commun. 2011;406:493–496. doi: 10.1016/j.bbrc.2011.02.088. [DOI] [PubMed] [Google Scholar]
  • 20.Bandy B, Davision AJ. Mitochondrial mutations may increase oxidative stress: implications for carcinogenesis and aging? Free Radic Biol Med. 1990;8:523–539. doi: 10.1016/0891-5849(90)90152-9. [DOI] [PubMed] [Google Scholar]
  • 21.Gille JJ, Joenje H. Cell culture models for oxidative stress: Superoxide and hydrogen peroxidative versus normobaric heperoxia. Mutat Res. 1992;275:405–414. doi: 10.1016/0921-8734(92)90043-o. [DOI] [PubMed] [Google Scholar]
  • 22.Shigenaga MK, Hagen TM, Ames BN. Oxidative damage and mitochondrial decay in aging. Proc Natl Acad Sci USA. 91:10771–10778. doi: 10.1073/pnas.91.23.10771. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Dement GA, Maloney SC, Reeves R. Nuclear HMGA1 nonhistone chromatin proteins directly influence mitochondrial transcription, maintenance, and function. Exp Cell Res. 2007;313:77–87. doi: 10.1016/j.yexcr.2006.09.014. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Oncology Letters are provided here courtesy of Spandidos Publications

RESOURCES