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. 2016 Oct 24;6:35859. doi: 10.1038/srep35859

Altered mitochondrial DNA copy number contributes to human cancer risk: evidence from an updated meta-analysis

Liwen Hu 1,*, Xinyue Yao 2,*, Yi Shen 1,a
PMCID: PMC5075889  PMID: 27775013

Abstract

Accumulating epidemiological evidence indicates that the quantitative changes in human mitochondrial DNA (mtDNA) copy number could affect the genetic susceptibility of malignancies in a tumor-specific manner, but the results are still elusive. To provide a more precise estimation on the association between mtDNA copy number and risk of diverse malignancies, a meta-analysis was conducted by calculating the pooled odds ratios (OR) and the 95% confidence intervals (95% CI). A total of 36 case-control studies involving 11,847 cases and 15,438 controls were finally included in the meta-analysis. Overall analysis of all studies suggested no significant association between mtDNA content and cancer risk (OR = 1.044, 95% CI = 0.866–1.260, P = 0.651). Subgroup analyses by cancer types showed an obvious positive association between mtDNA content and lymphoma and breast cancer (OR = 1.645, 95% CI = 1.117–2.421, P = 0.012; OR = 1.721, 95% CI = 1.130–2.622, P = 0.011, respectively), and a negative association for hepatic carcinoma. Stratified analyses by other confounding factors also found increased cancer risk in people with drinking addiction. Further analysis using studies of quartiles found that populations with the highest mtDNA content may be under more obvious risk of melanoma and that Western populations were more susceptible than Asians.

Mitochondria are organelles in eukaryotic cells that participate in a wide range of biological actions, such as energy metabolism, free oxygen radical generation, and cell apoptosis1. Usually thousands of mitochondria exist in each cell. Mitochondria possess separate copies of DNA (mtDNA), which are double-stranded, maternally inherited circular nucleotides with 16,569 base pairs encoding for 37 RNAs, including 13 mRNA, 22 transfer RNAs and two ribosomal RNAs. mtDNA content in cells typically ranges from 103 to 104 copies per cell and varies by cell type2.

Due to the lack of protection from introns and histones, and less efficient DNA repair mechanisms, mtDNA is particularly susceptible to reactive oxidative species (ROS) and other sources of genotoxic damage, which may finally lead to sequence mutations or copy number alterations3. Changes in mtDNA copy number could alter the expression of mitochondrial genes and result in abnormal mitochondrial functions, such as energy production, signaling transduction, apoptosis and cell growth. Therefore, aberrant mtDNA content could potentially lead to alterations in oxidative phosphorylation, and enhance the production of ROS in aerobic metabolism, which can cause chromosomal aberrations such as base pair mutation and chromosome rearrangements, and finally facilitate the malignant transformation4. Contribution of mtDNA copy number alterations to nuclear genome damage has been confirmed by previous studies. Corral et al. found that introduction of mtDNA molecules to chemically-induced rat hepatomas and HT-1080 fibrosarcoma cell line caused an increased number of copies of COI, COII and COIII pseudogenes in the nuclear genome5, while Delsite et al. demonstrated that a reduction in mitochondria in HeLa cells resulted in increased lipid peroxidation6. It has been shown that increased lipid peroxidation could induce damage directly to nuclear DNA7.

Previous studies have reported quantitative changes in mtDNA content in patients’ cancerous tissues compared with their normal counterparts, including an increase of mtDNA content in breast cancer and endometrial adenocarcinoma8,9, and a decrease in colorectal cancer, cervical cancer, osteosarcoma and Ewing’s sarcoma10,11,12,13. Moreover, the alteration of peripheral blood mtDNA content has also been found to be associated with tumor progression and patient prognosis. For example, lower peripheral blood leukocyte mtDNA copy number was correlated with aggressiveness and fast progression of prostate cancer14. Also, high mtDNA copy number in peripheral blood may predict a poor prognosis for hepatocellular carcinoma15, glioma16, colorectal cancer17, and head and neck cancer18. Taken together, these results suggested that the alteration of mtDNA content might play complicated roles in tumorigenesis and progression in a cancer-specific manner.

As above, the involvement of mtDNA content changes in carcinogenesis remains controversial and ambiguous. Meanwhile, a single study might have been underpowered to detect the overall effects. Thus in this study we carried out a meta-analysis to summarize the existing evidence and to make a more precise evaluation about the relationship between mtDNA copy number and cancer risk among different populations.

Methods

Literature search

Relevant studies that estimated the association between mtDNA copy number and risk of cancers were identified from the databases of PubMed, EMBASE, and China National Knowledge Infrastructure (CNKI) up to March 1, 2016 by two investigators independently. The following key words were used for searching: “mitochondrial DNA”, “risk” and “copy number”. Other alternative spellings of these key words such as “content” and “susceptibility” were also used; only those publications in English were retained.

Inclusion and Exclusion criteria

Studies were considered eligible for the current meta-analysis if they met the following criteria: (1) they assessed the relationship between mtDNA copy number (or mtDNA content) and risk (or susceptibility) of diverse cancer types; (2) used a cohort or case–control studies design; (3) had an appropriate description of mtDNA copy number in cases and controls; and (4) reported an odds ratio (OR) with a 95% confidence interval (CI) or other available data for calculating the OR (95% CI).

Exclusion criteria: (1) review articles and editorials; (2) case reports; (3) studies not focused on the mtDNA content and cancer risk; and (4) investigation of the functional role of mtDNA in carcinogenesis.

Data extraction

The following information from each eligible study was carefully extracted independently by two investigators: name of the first author, year of publication, country of origin, number of cases and controls, numbers of genotyped cases and controls, mitochondrial DNA haplotypes, subject population (categorized as East Asian, Indian, Western and Africa populations), source of controls (hospital-based [HB] and population-based [PB]), exposure to smoking, alcohol consumption, and body mass index (BMI).

Statistical analysis

The strength of the association between mtDNA content and risk of diverse cancers was measured by calculating the odds ratio (OR) with a 95% confidence interval (95% CI), and two-tailed P < 0.05 was considered as statistically significant. First, the overall association between mtDNA content and cancer risk was assessed by categorizing mtDNA content into high and low haplotypes based on cut-off points at the median, the 1st tertile and the 2nd quintile. Specifically, for those studies that categorized the mtDNA content into dichotomies or quartiles, the cut-off point was set at the median value in controls. For those studies categorizing mtDNA into tertiles, the 1st tertile was treated as the low mtDNA content group, while the 2nd and 3rd tertiles were seen as the high mtDNA content group. For those studies that categorized mtDNA into quintiles, the 1st and 2nd quintiles were treated as the low content group, while the rest were designated as the high content group. The detailed information for the categorization is presented in supplementary Table S1. Nested case-control studies and case-control studies were then combined to calculate the odds ratio in total and separately.

Further association analysis was performed by recruiting studies of quartiles to test if a dose-dependent relationship existed, under which condition the 1st quartile was designated as the reference. Then we compared the odds ratio for the other quartiles with the reference to test whether the higher mtDNA contents correlated with cancer risk more obviously.

Heterogeneity among studies was further examined with the I2 statistic, which was interpreted as the proportion of total variation contributed by between-study variation. The Mantel–Haenszel method was used to calculate the OR for the pooled data in a fixed effects model if there was no evidence for significant heterogeneity. Otherwise a random effects model was selected to combine the data. Several subgroup meta-analyses were performed in an attempt to assess the association between the mtDNA content and cancer risk based on the population, age, gender, smoking status, alcohol drinking and BMI.

A sensitivity analysis was also performed to examine the reliability of our study by excluding individual studies orderly. Cumulative analysis was performed to explore the tendency of the changes in risk association by publication year. Evidence of publication bias was determined by visual inspection of the funnel plot and Egger’s test. All statistical analyses were performed with STATA software (version 10.0; StataCorp, College Station, TX, USA). All P values are two-sided.

Results

Characteristics of eligible studies

Finally, a total of 36 articles involving 11,847 cases and 15,438 controls were used in the current meta-analysis. By the sample sources according to original records, we classified these 36 articles into 39 independent study subgroups, among which there were twenty-five studies from Western populations, eleven from East Asia, two from India and one from Africa (Table 1). There were five studies with hospital-based controls and the others included population-based controls.

Table 1. Study and host characteristics included in this meta-analysis.

Author Year Cancer type Sample source Country Ethnicity Sample size Ca/Co Gender
 Age, year old Ca/Co BMI, kg/m2 Ca/Co Ca/Co Smoking status
 Drinking status
Male Ca/Co Female Ever Ca/Co Never Ca/Co Ever Ca/Co Never Ca/Co
Lan 2008 lymphoma Population Finland Caucasian 104/104 Un Un 58/57 25.5/25.4 Un Un Un Un
Shen 2010 Breast cancer Population USA Caucasian 103/103 Un 103/103 58/56 27.8/26.4 52/51 Un 75/78 Un
Xing 2009 Renal cell carcinoma Population USA Caucasian 260/281 172/175 88/106 Un Un 134/126 125/155 Un Un
Bonner 2009 Lung cancer Population China Asian 113/107 73/68 40/39 54.9/54.5 Un Un Un Un Un
Hosgood 2010 Lung cancer Population Finland Caucasian 227/227 227/227 0/0 58.7/58.4 25.6/26.3 227/227 0/0 Un Un
Liao 2011 Gastric cancer Population China Asian 162/299 0/0 162/299 61.0/61.0 Un Un Un Un Un
Lynch 2011 pancreatic Population Finland Caucasian 203/656 203/656 0/0 58.0/58.0 26.2/25.7 203/656 0/0 Un Un
Qu 2011 Colorectal Population China Asian 320/320 169/151 169/151 58.4/58.2 23.8/23.6 126/105 194/215 42/35 278/285
Zhao 2011 Hepatocellular carcinoma Population China Asian 274/384 236/218 38/40 50.1/48.7 Un 154/132 120/126 98/42 176/216
Purdue 2012 Renal cell carcinoma Population USA Caucasian 445/379 256/247 189/132 Un Un 293/231 152/148 Un Un
      Population   African 158/224 109/110 49/114 Un Un 112/154 46/70 Un Un
Thyagarajan 2012 Colorectal Population Singapore Chinese Asian 422/874 250/380 172/494 66.1/57.6 23.0/22.8 181/228 241/646 Un Un
Mondal 2012 Oral Hospital India Asian 124/140 98/26 101/39 58/56 Un 89/75 35/65 Un Un
Thyagarajan 2013 Breast Population Singapore Chinese Asian 183/529 0/0 183/529 61.1/61.1 Un Un Un Un Un
Xie 2013 Soft tissue sarcoma Population USA Mixed 325/330 174/180 151/150 58.24/58.50 Un 122/117 174/212 Un Un
Xu 2013 Esophageal adenocarcinoma Population USA Caucasian 218/218 173/173 45/45 62.05/60.94 Un 150/122 68/96 Un Un
Ghosh 2014 Nasopharyngeal carcinoma Hospital India Asian 64/100 49/79 15/21 Un Un 45/26 19/74 Un Un
Hoffman 2014 Renal cell carcinoma Population USA Caucasian 230/468 164/331 66/137 Un Un 212/256 92/138 Un Un
Hosnijeh 2014 lymphoma Population European Caucasian 469/469 231/231 238/238 56.6/56.6 26.9/26.6 272/264 197/205 Un Un
Huang 2014 Colorectal cancer Population China Asian 444/1423 0/0 444/1423 58.6/55.2 24.6/24.4 11/46 433/1377 14/39 430/1384
Jiang 2014 Breast cancer Population China Asian 506/520 0/0 506/520 Un Un Un Un Un Un
Kim ATBC 2014 Lung cancer Population Finland Caucasian 227/227 227/227   56.67/58.41 25.59/26.35 227/227 Un Un Un
Kim PLCO     Population USA Mixed 426/436 259/267 167/169 64.07/63.67 26.84/27.43 380/238 Un Un Un
Kim SWHS     Population China Asian 221/222 0/0 221/222 59.2/59.2 24.58/25.01 17/11 Un Un Un
Sun 2014 gastric Population USA Caucasian 132/125 74/65 58/60 58.2/55.5 Un 55/82 77/43 Un Un
Zhang 2014 glioma Population China Asian 414/414 241/241 173/173 Un Un 96/85 318/329 Un Un
Zhou 2014 Prostate cancer Population China Asian 193/194 193/194 0/0 70.3/70.1 24.1/23.2 100/98 93/96 Un Un
Hyland 2014 Melanoma Population USA Mixed 136/302 130/63 172/73 Un Un Un Un Un Un
Kim ATBC 2015 lymphoma Population Finland Caucasian 142/142 142/142 Un Un Un 142/142 0/0 Un Un
Kim PLCO 2015 lymphoma Population USA Mixed 292/301 178/185 114/116 Un Un 135/155 157/146 Un Un
Lemnrau 2015 Breast Cancer Population UK Caucasian 1053/1053 Un Un Un Un 428/410 679/687 981/936 125/160
Melkonian 2015 Renal cell carcinoma Population USA Caucasian 608/629 400/418 208/211 58.46/58.57 Un 297/287 311/342 Un Un
Shen 2015 breast Population USA Caucasian 1000/1000 Un 1000/1000 58/56 27.8/26.4 315/288 685/712 720/750 280/250
Shen 2015 melanoma Population USA Mixed 500/500 238/262 238/262 Un Un Un Un Un Un
He 2014 Oralpremalignant lesion Population USA Mixed 143/357 87/215 56/142 57.3/58.5 Un 83/175 60/182 85/108 53/249
Sun 2015 Endometrial Hospital USA Caucasian 139/139 Un Un 62.24/61.76 Un 31/48 108/91 Un Un
Wang 2015 Liver Cirrhosis Hospital USA Caucasian 136/136 112/105 24/31 54.17/54.03 Un 55/54 81/82 68/60 68/76
Williams 2015 Bladder cancer Hospital USA Caucasian 926/926 738/738 188/188 Un Un 678/521 248/405 Un Un
Hashad 2015 Liver Carcinoma/Cirrhosis Population Egypt African 90/45 52/30 38/15 50.88/50 Un Un Un Un Un
Shen 2016 Glioma Population USA Caucasian 395/425 242/221 149/204 49/52 28.2/27.6 175/216 198/204 Un Un
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Un: Unknown data.

Among these 36 studies, five each focused on breast cancer, four each on renal clear cell carcinoma, three each on lung cancer, colorectal cancer, lymphoma and hepatic carcinoma /cirrhosis, two each on glioma, melanoma, oral and gastric cancer, and one each on nasopharyngeal cancer, endometrial cancer, prostate cancer, esophageal carcinoma, pancreatic carcinoma, soft tissue sarcoma and bladder cancer.

Most studies used ND1 and D-loop which were located on mtDNA as target regions to determine mtDNA copy number. Hemoglobin subunit beta (HBB), human globulin (HGB), 18 s RNA and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were the most widely used reference genes, and they are widely accepted single copy nuclear genes. All the information about the measurements applied in the studies can be obtained in supplementary Table S1.

Association between mtDNA content and cancer risk in overall and stratified analyses

When all studies were pooled for the overall meta-analysis by dichotomizing mtDNA copy number into high and low content groups, there was no significant association between mtDNA content and overall cancer risk (OR = 1.044, 95% CI 0.866–1.260, P = 0.651), and there was high heterogeneity (I2 = 92.4%), which may be explained by different cancer types and populations (Fig. 1).

Figure 1. Forest plot of the ORs for the overall cancer risk in dichotomizing analysis.

Figure 1

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The squares and horizontal lines correspond to the study-specific OR and 95% CI.

Results of the subgroup analyses are presented in Table 2. Stratification analysis by cancer types identified increased cancer risk associated with high mtDNA content in the subgroups of breast cancer (OR = 1.721, 95% CI = 1.130–2.622, p = 0.011) and lymphoma (OR = 1.645, 95% CI = 1.117–2.421, p = 0.012 respectively), with high and moderate heterogeneity, respectively (I2 = 92.6% and 51.6%). Marginal increased risk associated with high mtDNA content was found in melanoma and glioma, with an odds ratio of 1.555 (95% CI = 0.598–4.045, p = 0.365, I2 = 93.6%) and 2.510 (95% CI = 0.709–8.887, p = 0.154, I2 = 97.2%), respectively. In contrast, mtDNA content was negatively significantly correlated with hepatic malignancy (OR = 0.622, 95% CI = 0.486–0.798, p = 0.000). We further categorized the studies by organ systems, such as digestive, respiratory, gynecological and urinary carcinoma. However, no significant association was identified in any group.

Table 2. Overall and subgroup analyses of mtDNA copy number with cancer risk by dichotomizing mtDNA copy number into high and low groups.

Subgroups Research counts (n) Test of association
 95% CI P Heterogeneity
 I2 Tau-squared
Model OR χ p
Overall 39 Random 1.044 0.866-1.260 0.651 499.08 0.000 92.4% 0.3139
Cancer types
 Lymphoma 4 Random 1.645 1.117-2.421 0.012a 6.20 0.102 51.6% 0.0769
 Breast cancer 5 Random 1.721 1.130-2.622 0.011a 54.16 0.000 92.6% 0.2015
 Lung cancer 5 Random 0.982 0.788-1.223 0.871 7.84 0.097 49.0% 0.2015
 Colorectal cancer 3 Random 1.168 0.638-2.139 0.641 34.38 0.000 94.2% 0.2681
 Renal carcinoma 4 Random 0.856 0.598-1.226 0.397 19.71 0.000 84.8% 0.1126
 Melanoma 2 Random 1.555 0.598-4.045 0.365 15.73 0.000 93.6% 0.4456
 Glioma 2 Random 2.510 0.709-8.887 0.154 35.96 0.000 97.2% 0.8091
 Gastric 2 Random 0.412 0.054-3.134 0.392 25.76 0.000 96.1% 2.0587
 Hepatic 3 Fixed 0.622 0.486-0.798 0.000a 0.53 0.767 0.0%  
 Oral 2 Random 0.946 0.295-3.029 0.925 12.43 0.000 92.0% 0.6489
Organ systems
 Digestive system 12 Random 0.861 0.632-1.173 0.344 95.74 0.000 88.5% 0.2521
 Urogenital system 6 Random 0.876 0.635-1.209 0.420 43.03 0.000 88.4% 0.1398
 Respiratory system 6 Random 0.928 0.730-1.179 0.539 11.74 0.039 57.4% 0.0488
 Gynecologica system 6 Random 1.253 0.744-2.109 0.396 106.55 0.000 95.3% 0.3847
Population
 Western 25 Random 0.951 0.719-1.259 0.728 456.55 0.000 94.7% 0.4628
 East Asian 11 Random 1.296 0.901-1.866 0.162 147.15 0.000 93.2% 0.3481
 India 2 Fixed 0.508 0.335-0.771 0.001 0.01 0.938 0.0%  
 Africa 1   0.799            
Ageb
 Elder 10 Random 1.104 0.655-1.860 0.710 111.22 0.000 91.9% 0.6352
 Younger 10 Random 1.079 0.670-1.738 0.754 95.11 0.000 90.5% 0.5213
Gender
 Male 16 Random 1.036 0.755-1.420 0.828 155.98 0.000 90.4% 0.3578
 Female 22 Random 1.109 0.866-1.420 0.414 191.26 0.000 89.0% 0.2788
Life styles
 Smoking status
  Ever/Frequent 21 Random 0.984 0.765-1.267 0.902 106.17 0.000 81.2% 0.2465
  Never/Occasionally 18 Random 1.047 0.736-1.491 0.798 165.82 0.000 89.7% 0.4972
 Drinking Status
  Ever/Frequent 5 Fixed 1.261 1.076-1.479 0.004a 4.53 0.340 11.6%  
  Never/Occasionally 5 Random 1.179 0.971-1.432 0.096 42.69 0.000 90.6% 0.5403
 BMI
  Highc 4 Random 1.103 0.413-2.942 0.845 36.84 0.000 91.9% 0.9038
  Lowc 4 Random 1.077 0.550-2.109 0.828 15.12 0.002 80.2% 0.3609
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ap < 0.05;

bAge was classified as elder and younger according to publication records; cRelative value which means relatively high and relatively low BMI.

Further subgroup analyses were performed by stratifying studies according to population, age, and gender, as well as life styles such as smoking status, alcohol consumption and body mass index (Table 2). The results revealed that high mtDNA content may predict increased carcinogenesis risk in people with drinking addiction (OR = 1.261, 95% CI = 1.076–1.479, p = 0.004). A total of five studies that provided drinking-related data were included for stratified analysis, and it is notable that four of these studies came from digestive cancers, indicating that high mtDNA content accompanied by drinking addiction may contribute to elevated digestive cancer risk.

According to the study design, we classified the studies into nested case-control and case-control studies (supplementary Table S1), and we performed association analysis based on the study design as well (Table 3). The results from the nested case-control and case-control study did not differ significantly from the results above, which demonstrated the stability and reliability of our analysis. It was surprising to find that mtDNA content was negatively significantly correlated with renal cancer risk in the case-control studies, which was obscured in the stratified analysis in the previous section.

Table 3. Comparison of cancer risk association between nested case-control and case-control studies.

Subgroups Research counts (n) Test of association
 95% CI P Heterogeneity
 I2 Tau-squared
Model OR χ p
Overall
 Nested case-control 16 Random 1.133 0.957-1.342 0.651 62.08 0.000 75.8% 0.0816
 Case-control 23 Random 0.958 0.708-1.297 0.783 436.96 0.000 95.0% 0.5011
Cancer types
 Lymphomaa 4 Random 1.645 1.117-2.421 0.012c 6.20 0.102 51.6% 0.0769
 Breast cancer 2 Fixed 1.360 1.165-1.587 0.000c 1.77 0.183 43.6%  
 Breast cancerb 3 Random 2.001 0.897-4.468 0.090 46.98 0.000 95.7% 0.4645
 Lung cancer 4 Random 0.888 0.752-1.049 0.163 2.29 0.514 0.0% 0.2015
 Lung cancer 1   0.669            
 Colorectal cancer 2 Random 0.891 0.760-1.045 0.156 14.68 0.000 93.2%  
 Colorectal cancer 1   1.991            
 Renal carcinoma 1   1.584            
 Renal carcinoma 3 Fixed 0.711 0.611-0.827 0.000c 0.15 0.926 0.0%  
 Melanoma 2 Random 1.555 0.598-4.045 0.365 15.73 0.000 93.6% 0.4456
 Glioma 2 Random 2.510 0.709-8.887 0.154 35.96 0.000 97.2% 0.8091
 Gastric 2 Random 0.412 0.054-3.134 0.392 25.76 0.000 96.1% 2.0587
 Hepatic 1   0.600            
 Hepatic 2 Fixed 0.630 0.472-0.842 0.002c 0.50 0.479 0.0%  
 Oral 2 Random 0.946 0.295-3.029 0.925 12.43 0.000 92.0% 0.6489
Organ systems
 Digestive system 5 Random 0.920 0.673-1.258 0.602 20.22 0.204 80.2% 0.0975
 Digestive system 7 Random 0.711 0.399-1.266 0.247 75.74 0.000 92.1% 0.5452
 Urogenital system 1   1.584            
 Urogenital system 5 Random 0.808 0.637-1.025 0.079 16.11 0.003 75.2% 0.0533
 Respiratory system 4 Random 0.888 0.752-1.049 0.163 2.29 0.514 0.0% 0.2015
 Respiratory system 2 Random 0.942 0.279-3.181 0.924 7.97 0.005 87.4% 0.6742
 Gynecologica system 2 Fixed 1.360 1.165-1.587 0.000a 1.77 0.183 43.6%  
 Gynecologica system 4 Random 1.143 0.443-2.949 0.782 103.65 0.000 97.1% 0.8848
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aThe bold inclined letters indicate the nested case-control study.

bThe thin inclined letters indicate the case-control study.

cp < 0.05.

Cancer risk increased as mtDNA content elevated by quartile analyses

To investigate the possible dose-dependent relationship between mtDNA content and cancer risk, we enrolled the studies that categorized mtDNA copy number into quartiles for further analysis and assigned the 1st quartile as the reference. A total of 20 studies were included in the dose-dependent relationship analyses. No significant difference was found in the risk among carriers of the 2nd, 3rd and 4th quartiles in the overall analysis. However, stratified analysis by cancer types found that carriers of higher mtDNA content exhibited gradually increasing risk for breast cancer and melanoma (Table 4), which is consistent with the findings in the dichotomous meta-analysis, further supporting the idea that high mtDNA predisposes increased risk in breast cancer and melanoma. After grouping studies by population, the association of mtDNA content in 4th quartiles with cancer risk was more obvious in western populations than in Asians (OR = 1.122, p = 0.016 versus OR = 0.981, p = 0.896). There were no obvious differences in cancer risk among males and females or among frequent and infrequent smokers.

Table 4. Overall and subgroup analyses of mtDNA copy number with cancer risk in quartiles.

Study groups Studies (n) 4th versus 1st quartile
 p I2 3rd versus 1st quartile
 p I2 2nd versus 1st quartile
 p I2
OR 95%CI OR 95%CI OR 95%CI
Overall 21 1.025 0.765-1.373 0.870 90.4% 0.923 0.743-1.146 0.466 81.9% 0.925 0.777-1.101 0.382 71.9%
Cancer types
 Breast cancer 2 1.906 0.974-3.729 0.060 92.9% 1.573 0.973-2.542 0.065 85.8% 1.250 0.811-1.927 0.312 81.8%
 Lung cancer 4 1.068 0.783-1.456 0.677 40.5% 0.816 0.640-0.040 0.100 0.0% 1.111 0.880-1.401 0.376 0.0%
 Renal cancer 2 0.594 0.456- 0.775 0.000 97.4% 0.572 0.437-0.749 0.000 96.2% 0.511 0.387-0.674 0.000 93.0%
 Melanoma 2 1.792 1.313-2.445 0.000 87.9% 1.696 1.240-2.320 0.001 49.9% 1.544 1.124-2.119 0.007 10.2%
Organ systems
 Digestive cancers 5 0.997 0.806-1.233 0.978 78.7% 0.673 0.537-0.845 0.001 65.7% 0.622 0.495-0.780 0.000 48.3%
 Urogenital cancers 4 0.788 0.663-0.938 0.007 92.0% 0.860 0.724-1.021 0.085 84.2% 0.864 0.726-1.026 0.096 57.8%
Population
 Western 14 1.122 1.022-1.233 0.016 90.8% 0.916 0.831-1.008 0.073 84.1% 0.881 0.799-0.971 0.010 74.7%
 East Asian 5 0.981 0.739-1.303 0.896 80.8% 0.962 0.727-1.272 0.784 73.0% 0.962 0.727-1.272 0.784 73.0%
Gender
 Male 6 0.762 0.622-0.934 0.009 90.7% 0.662 0.535-0.819 0.000 63.7% 0.768 0.625-0.944 0.012 48.0%
 Female 6 0.720 0.563-0.920 0.009 80.0% 0.750 0.588-0.957 0.020 55.2% 0.733 0.572-0.939 0.014 70.5%
Life styles
 Smoking staus
  Ever/Frequent 5 1.121 1.005-1.250 0.040 43.5% 1.031 0.918-1.158 0.603 57.0% 1.036 0.919-1.167 0.563 0.0%
  Never/occasionally 5 1.117 1.001-1.247 0.048 0.0% 1.054 0.939-1.182 0.372 0.0% 1.018 0.906- 1.144 0.761 0.0%
 Drinking status
  Ever/Frequent 1 1.149 1.024-1.289 0.018   1.075 0.953-1.212 0.242   0.998 0.875-1.138 0.974  
  Never/occasionally 1 1.137 0.790-1.635 0.489   1.160 0.862-1.562 0.328   1.040 0.749-1.444 0.814  
 BMI
  High 1 1.398 1.131-1.728 0.002   1.227 0.976-1.543 0.080   1.053 0.796-1.391 0.719  
  Low 1 0.950 0.609-1.481 0.820   1.058 0.700-1.599 0.790   1.023 0.665-1.574 0.917  
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Cumulative analysis

The findings from the cumulative meta-analysis showed that the positive association was more obvious in early studies but decreased as the data increased by publication year, and the association strength remained stable in the 95% confidence intervals with increasing sample sizes in recent years (Fig. 2), which further demonstrated the complicated roles of mtDNA content in diverse carcinogenesis.

Figure 2. Cumulative analysis of the overall ORs.

Figure 2

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The results were sorted by publication years.

Sensitivity analyses

A sensitivity analysis was conducted to assess the influence of each study by sequential omission of each eligible study both in the overall meta-analysis and stratified analysis. The results showed that the significance of the OR was not affected by any single study (Fig. 3), indicating the reliability of our study.

Figure 3. Sensitivity analysis of the summary ORs on the association between mtDNA content and overall cancer risk.

Figure 3

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The results were calculated by omitting each eligible study. Meta-analysis random-effects estimates were used.

Evaluation of publication bias

Both Begg’s funnel plot and Egger’s test were performed to assess the publication bias. The shape of the funnel plots did not reveal any evidence of obvious asymmetry for any genetic model in the overall meta-analysis (Fig. 4). Next, Egger’s test was used to provide statistical evidence for the funnel plot symmetry, and the results still did not suggest any obvious evidence of publication bias (P = 0.868). Thus, there was no obvious risk of publication bias in this meta-analysis.

Figure 4. Begg’s funnel plot for publication bias analysis.

Figure 4

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Each point represents a separate study for the indicated association. Log[or], natural logarithm of the odds ratio. Horizontal line indicates effect size.

Discussion

Mitochondria are energy factories that produce adenosine triphosphate (ATP) through oxidative phosphorylation. Both increased and decreased mtDNA copy number in blood have been previously reported to be associated with cancer risk in a cancer-specific manner. For example, it has been reported that increased mtDNA copy number contributes to high risk for breast cancer and lymphoma, while decreased mtDNA content was relevant to renal and colorectal cancers. The opposite effects brought out by altered mtDNA content may represent diverse biological roles that mtDNA plays in malignancies.

Biologically, lowered mtDNA copy number can cause deficiency in oxidative phosphorylation and enhanced production of toxic metabolites in aerobic metabolism and glycolysis, resulting in the disruption of cellular functions. Also, it has been demonstrated that mtDNA reduction promotes cancer cells to become more resistant to apoptosis, and leads to epithelial-mesenchymal transition (EMT)19,20, an important process by which epithelial cells lose their cell polarity and cell-cell adhesion and acquire mesenchymal, fibroblast-like characteristics, accompanied by increased cell migratory and invasive properties, thus endowing the incipient cancer cell with invasive and metastatic properties21. On the other hand, high mtDNA content can be an indicative marker of oxidative stress, which is associated with environmental exposure to pollutants, tobacco, smoke, drugs, xenobiotics, or radiation. High mtDNA content is also an important marker of an impaired aerobic mechanism that is thought to be involved in the molecular mechanisms of carcinogenesis22. These observations together suggest a complicated role for the changes in mtDNA copy number on the modulation of cancer risk. In the present study, we performed this meta-analysis to achieve a more conclusive result.

Our analysis included 36 studies that focused on diverse cancer types from different origins around the world23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58. To reduce the uncertainty induced by confusing factors in the meta-analysis, we evaluated the association between mtDNA content with cancer risk in an overall analysis, as well as stratified analyses by cancer types, genders, ages, ethnicity and life styles.

Finally, we did not find an obvious association between mtDNA content and cancer risk in the overall analysis, which should be the result of the heterogeneity among different cancer types. Thus subgroup analyses were performed to further detect the association strength. Stratified analysis by cancer types provided evidence on the relationship between high mtDNA content and increased breast cancer and lymphoma risk. Dose-dependent effects were also found for melanoma risk. The biological contribution of increased mtDNA content in the peripheral blood to carcinogenesis remains obscured. Previous studies found a correlation between mtDNA content and markers of oxidative stress, such as thiobarbituric acid reactive substances and 8-hydroxyguanosine59. Moreover, lower levels of antioxidants were also detected in blood with an increased mtDNA copy number. Due to lack of sufficient protective mechanisms, mtDNA mutations may occur during the process of oxidative stress, and certain mtDNA mutations may lead to the generation of increased superoxide and nitric oxide, resulting in aberrant mitochondrial biogenesis60, which has been associated with deficient or defective apoptosis, and confers a replicative advantage to the cells59. Therefore it is plausible that high mtDNA content in peripheral blood leukocytes may be indicative of increased oxidative stress, impaired aerobic metabolism, and ROS-mediated DNA damage. mtDNA copy number may increase to compensate for mtDNA damage and mitochondrial dysfunction3. Thus it is more likely that the increased mtDNA content is a significant marker of carcinogenesis associated oxidative stress, but not a cause of cancer development.

Stratified analysis also found a significant negative association between mtDNA content and susceptibility of hepatic carcinoma, suggesting the complexity of the involvement of mtDNA changes in carcinogenesis in different populations, which should not be explained by any single mechanism. It has been demonstrated that mtDNA decrease alters mitochondrial gene expression, resulting in deficiency in oxidative phosphorylation, and causes a disturbance of cellular functions. Warburg proposed that damage to the respiratory chain was a critical event in carcinogenesis61, which demonstrates that cancer cells enhanced the generation of adenosine triphosphate by glycolysis62, resulting in a stronger tolerance to hypoxia and reducing the dependence of mitochondrial oxidative phosphorylation, thus conferring tumor cells the advantages of growth. Enhanced glycolysis also leads to excessive production of lactate and prosurvival proteins, which may initiate and promote cancer development. MtDNA reduction was also found to increase cancer cells’ resistance to apoptosis and lead to epithelial-mesenchymal transition, which are both common in tumor formation and metastatic progression. Some previous studies have partially explained the mechanism by which mtDNA reduction acts on apoptosis resistance and epithelial-mesenchymal transition. Biswas et al. reported that decreased mtDNA content could activate NFkappaB/Rel factors63. Activation of nuclear factor-kappa B signaling plays a critical role in apoptosis resistance and is clearly linked to various cancer malignant transformation64. In addition, activation of the AKT pathway by an mtDNA deficiency could also inhibit cell apoptosis65,66. As for the contribution to EMT, mtDNA loss may activate PI3K/Akt2, and Raf/MAPK pathways, which may finally lead to EMT and cancer metastasis67,68.

Stratified analysis by life styles, such as smoking, drinking and BMI status, suggested alcohol users with high mtDNA content may be more susceptible to cancer development, indicating an intrinsic relationship between mtDNA and alcohol degradation. Surprisingly, we did not find any association between altered mtDNA and smoking-related malignancies, although it has been widely accepted that smoking is an important stimulator of diverse cancers. Previous evidence has shown that the metabolites of cigarettes should be oxidative phosphorylated by mitochondria, so we proposed that abnormal mtDNA content may predict altered cancer risk in the smoking population. However, we did not obtain the suspected results, indicating that tumor-genesis of smoking related cancers involves much more cellular abnormality than mtDNA changes.

In summary, our study provides evidence that increased mtDNA content is statistically significantly associated with risk of lymphoma, breast cancer and melanoma, but is negatively associated with hepatic carcinoma. The mechanism for the tumor specific associations between mtDNA content and cancer risk remain to be illustrated, although they are likely to be regulated by a wide range of genetic, molecular, and cellular determinants. For instance, elevated mtDNA content has been significantly associated with altered oxidative stress, aging, immune response activation, and response to environmental exposure. Future studies are warranted to evaluate oxidative stress related factors that influence mtDNA copy number and to provide novel insights into the biological mechanisms of mtDNA copy number variation on the development of various cancers, especially on the opposite effects of increased and decreased mtDNA content in carcinogenesis. Furthermore, many more subjects are needed to confirm the association identified by our meta-analysis in different populations.

Additional Information

How to cite this article: Hu, L. et al. Altered mitochondrial DNA copy number contributes to human cancer risk: evidence from an updated meta-analysis. Sci. Rep. 6, 35859; doi: 10.1038/srep35859 (2016).

Supplementary Material

Supplementary Information
srep35859-s1.doc (291.5KB, doc)

Acknowledgments

This study was supported by the grants from the Basic Research Program of Jiangsu Province (BK20160606), and National Science Foundation of China (81172032).

Footnotes

Author Contributions L.H. and X.Y. contribute equally on data collection and analysis. Y. S. designed and directed the research.

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Supplementary Materials

Supplementary Information
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