Abstract
Background
Mitochondria play an important role in cellular energy metabolism, free radical production, and apoptosis and thus may be involved in cancer development.
Methods
We evaluated mtDNA copy number in peripheral leukocytes in relation to CRC risk in a case-control study of 444 CRC cases and 1,423 controls nested in the Shanghai Women's Health Study, a population-based, prospective cohort study. Relative mtDNA copy number was determined by a quantitative real-time PCR assay using peripheral leukocyte DNA samples collected at the time of study enrollment, prior to cancer diagnosis.
Results
We found that baseline mtDNA copy number was lower among women who subsequently developed CRC (geometric mean = 0.277, 95% CI: 0.269-0.285) than among women who remained cancer-free (geometric mean = 0.288, 95% CI: 0.284-0.293; P=0.0153). Multivariate adjusted odds ratios (ORs) were 1.26 (95% CI: 0.93-1.70) and 1.44 (95% CI: 1.06-1.94) for the middle and lower tertiles of mtDNA copy number, respectively, compared with the upper tertile (highest mtDNA copy number; P for trend=0.0204). The association varied little by the interval between blood collection and cancer diagnosis.
Conclusions
Our data suggest that mtDNA copy number measured in peripheral leukocytes may be a potential biomarker useful for CRC risk assessment.
Impact
If confirmed, mtDNA copy number measured in peripheral leukocytes may be a biomarker useful for colorectal cancer risk assessment.
Keywords: mtDNA copy number, colorectal cancer risk, prospective study, leukocytes
Introduction
Colorectal cancer (CRC) is the third most common cancer worldwide among men and the second most common cancer among women, with over 1.36 million new cancer cases and 694,000 deaths in 2012 (1). During the past two decades, CRC incidence and mortality have slightly declined in most Western countries, yet more than 140,000 new cases and 50,000 deaths are estimated to have occurred in the United States in 2013 (2). In Asian countries including China, Japan, South Korea, and Singapore, incidence of CRC has demonstrated a two- to fourfold increase in recent decades (3). Therefore, studies to identify biomarkers, including genetic factors, would help with prevention and early detection of CRC by identifying populations at high risk for CRC.
Mitochondria are eukaryotic organelles that have their own genome, i.e. mitochondrial DNA (mtDNA). Human mtDNA is a double-strand circular molecule consisting of 16,569 base pairs and containing 37 genes encoding 22 tRNA and 2 rRNA for translation machinery and 13 subunits of the respiratory complexes I, III, IV, and V essential for electron transport chains (4). In humans, the number of copies of mtDNA varies widely with cell type, presenting at 100-10,000 copies per cell (except in egg and sperm cells) (5). However, mtDNA copy number is tightly regulated, and specific tissues or individual cells contains a fairly constant number of mtDNA molecules (6, 7). The mechanism regulating mtDNA copy number is largely unknown, but it is presumably correlated with mitochondrial function and energy requirements (8-11). Consistent with this notion, alterations to mtDNA copy number are frequently observed in certain types of cancer and other mitochondria-associated diseases that present oxidative stress and metabolic abnormalities resulting from mitochondrial dysfunction (12-15).
Few studies have investigated the association of mtDNA copy number in peripheral leukocytes with cancer risk (16-21), and two studies that focused specifically on CRC (18, 21) had inconsistent results. To evaluate the association of leukocyte mtDNA copy number with CRC risk, we conducted a prospective study of 444 women diagnosed with CRC and 1,423 controls nested within the Shanghai Women's Health Study.
Materials and Methods
Study participants
All CRC cases and controls were participants of the Shanghai Women's Health Study (SWHS), a population-based prospective cohort study. Details on the methodology for the SWHS have been published previously (22). In brief, from 1996 to 2000, 74,941 Chinese women aged 40-70 years and residing in seven urban communities in Shanghai were recruited into the cohort study (response rate: 92.7%). Baseline data for all participants were collected by conducting in-person interviews and completing questionnaires. Interviewers measured participants’ body weight, standing and sitting height, and waist and hip circumferences and collected a blood sample from 75.8% of participants and a urine sample from 87.7% of participants. The blood sample was collected in an ethylenediaminetetraacetic acid (EDTA) vacutainer tube (Becton, Dickinson and Company). After collection, the samples were kept in a Styrofoam box with ice packs (0 - 4°C), and processed within 6 hours for long-term storage at −80°C. The cohort has been followed using a combination of biennial home visits and annual record linkage to cancer incidence and mortality data collected by the Shanghai Cancer Registry and death certificate data collected by the Shanghai Vital Statistic Unit. Nearly all cohort members were successfully followed (response rates for in-person follow-up surveys were over 96%). All possible incident cancer cases were verified by home visits. For cohort members who were diagnosed with cancer, medical charts were reviewed to verify the diagnosis and detailed information regarding pathologic characteristics of the cancer was obtained. The study protocol was approved by the Institutional Review Boards of Vanderbilt University and the Shanghai Cancer Institute, and all participants provided written informed consent before they were interviewed.
The current analysis is a part of broader study evaluating associations of leukocyte mtDNA copy number with multiple cancers, including CRC, breast cancer, endometrial cancer, ovarian cancer, and pancreatic cancer. The incidence-density sampling method was used to select one control for each participant diagnosed with breast cancer and two controls for each participant diagnosed with other types of cancer. Cases and controls were individually matched on age (≤ 730 days), date (≤ 30 days) and time (morning or afternoon) of sample collection, time interval since the last meal (≤ 2 hours), antibiotic use during the week before sample collection (yes/no), and menopausal status at the time of the sample collection. After excluding controls whose mtDNA copy number assays failed (n=7), the initial analysis of the nested case-control data included 444 incident CRC cases identified during follow-up through December 2009 and 881 individually matched controls. To maximize statistical power, we also conducted analyses including mtDNA data from all controls selected for all five types of cancer included in the broader study (n=1,423). The results from these two rounds of analyses were similar; thus, herein we report the results of the analyses that included data from all 1,423 controls.
Laboratory measurements
Genomic DNA was extracted from buffy coats using a QIAamp® DNA Blood Mini Kit (Qiagen, Valencia, CA), following the manufacturer's protocol. Based on a quantitative real-time polymerase chain reaction (RT-PCR) analyses, the relative mtDNA copy number was designated as a ratio of a mtDNA gene (MT-ND1 or ND1) to a nuclear gene (BRCA1). The sequences of ND1 gene chosen for the primers targeting mtDNA are single-copy in the mtDNA genome and not similar to any regions in the human genomic DNA sequences (23). A non-polymorphic region of the BRCA1 locus, which is present in only two copies in a diploid genome, was chosen for the primers’ targeting region. For ND1 gene fragment amplification, the primers were 5’-ACGCCATAAAACTCTTCACCAA-3’ (forward) and 5’-GGTTCGGTTGGTCTCTGCTA-3’ (reverse). The TaqMan® MGB probe was 5’-VICAGAACACCTCTGATTACTCMGBNFQ-3’ (23). For BRCA1 gene fragment amplification, the primers were 5’-AAACATGTTCCTCCTAAGGTGCTTT-3’ (forward) and 5’-ATGAAACCAGAAGTAAGTCCACCAGT-3’ (reverse). The TaqMan® MGB probe was 5’-6FAMACACAGCTAGGACGTMGBNFQ-3’. The total reaction volume was 10 μl and contained: 5 μl 2×TaqMan® Genotyping Master Mix (Life Technologies), 5 ng template DNA, 110 nmol/L primers and 100 nmol/L probe for ND1, and 440 nmol/L primers and 400 nmol/L probe for BRCA1 amplification. The quantitative RT-PCR for ND1 and BRCA1 measurement was run in the same well with a thermal cycling program as follows: 50°C for 2 min and 95°C for 10 min, followed by 40 cycles consisting of 95°C for 30 s and 60°C for 1 min. The RT-PCR was performed in an ABI Prism 7900HT Sequence Detection System (Life Technologies), generating amplicons of 437 bp in length for ND1 and 119 bp in length for BRCA1. Data analyses were based on measurement of the cycle threshold (Ct) by using SDS 2.3 software. Each sample was assayed in triplicate. The coefficient of variation (CV) was calculated from the ratios of ND1 to BRCA1 in each triplicate assay. The assays were repeated if the CV was over 15%. CEPH 1347-02 DNA was used as a reference and was serially diluted to make standard curves from 250 ng to 0.122 ng. The amount of ND1 and BRCA1 was calculated according to the standard curves, which were made in each assay plate. For all standard curves, the R2 was 0.99 or greater. In addition, each plate contained one negative control (water) and one or more quality controls (CEPH 1347-02, 5 ng), which were later used to normalize the different batches (plates) for statistical processing. Representative PCR amplification plots and standard curves are presented in the Supplementary Figure 1.
Statistical analysis
Means and frequency distributions for selected baseline characteristics were calculated for cases and controls, and differences were compared using t-tests (for continuous variables) or Chi-square tests (for categorical variables). mtDNA copy number for each sample was first normalized to the quality control DNA (CEPH 1347-02) to minimize batch (plate) effects. The standardized mtDNA copy number was then log-transformed to achieve normal distribution. We compared differences in the geometric means of mtDNA copy number between cases and controls by using ANOVA with adjustment for assay batch (plate) and age at blood collection.
To estimate associations of mtDNA copy number with CRC risk, cases were categorized according to mtDNA copy number into tertiles based on the distribution among controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by using logistic regression models with adjustment for assay batch (plate) and age at blood collection. Adjustment for matching variables (age, date and time of sample collection, antibiotic use, and menopausal status), exercise (ever/never), and total meat intake did not change the association results, and thus, we only presented the results without those adjustments in the manuscript. Tests for linear trend were estimated using the median value for each mtDNA copy number tertile. The linear association per standard deviation of decrease in mtDNA level was also estimated.
To evaluate non-linear associations between colorectal cancer risk and mtDNA copy number, the continuous, log-transformed mtDNA copy number and nonlinear terms created by using a restricted cubic spline function with 4 knots were included in the logistic regression model. The linear effect, non-linear effect, and overall effect of mtDNA copy number were assessed with the likelihood ratio test.
Analyses of the association of colorectal cancer risk with mtDNA copy number were stratified by the time interval between blood collection and cancer diagnosis to evaluate the consistency of the association over time. Associations of mtDNA copy number with colon cancer and rectal cancer were analyzed separately to evaluate possible heterogeneity of the association.
Results
The baseline demographic characteristics and major risk factors for CRC cases and controls are presented in Table 1. Because we included all available controls in the analysis, cases were older and more were postmenopausal compared with controls. Cases and controls were comparable in education level, body mass index (BMI), waist-to-hip ratio, fruit and vegetable intakes, and meat intake. Compared with controls, cases were more likely to participate in regular exercise. In addition, cases were more likely to have a family history of CRC or other cancer and to have a personal history of inflammatory bowel disease or diabetes. However, the differences were not statistically significant. Very few women in our cohort regularly smoked cigarettes, drank alcoholic beverages, or took aspirin or hormone replacement therapy.
Table 1.
Baseline characteristics of colorectal cancer cases and controls, the Shanghai Women's Health Study (1996-2000)
| Baseline characteristics | Cases (n=444) | Controls (n=1,423) | P value* |
|---|---|---|---|
| Age, years, mean (SD) | 58.6 (8.7) | 55.2 (9.1) | < 0.0001 |
| Postmenopausal, % | 77.0 | 61.4 | <0.0001 |
| Body mass index (BMI), mean (SD) | 24.6 (3.4) | 24.4 (3.5) | 0.2804 |
| Waist-to-hip ratio, mean (SD) | 0.820 (0.053) | 0.816 (0.054) | 0.1652 |
| Education ≥ high school, % | 32.0 | 36.8 | 0.0668 |
| Family history of colorectal cancer, % | 3.2 | 2.6 | 0.5326 |
| Family history of any cancer, % | 30.6 | 26.6 | 0.0939 |
| Personal history of inflammatory bowel disease, % | 0.9 | 0.6 | 0.5526 |
| Personal history of diabetes, % | 7.9 | 6.6 | 0.3543 |
| Ever smoked regularly, % | 2.5 | 3.2 | 0.4194 |
| Ever drank alcohol regularly, % | 3.2 | 2.7 | 0.6478 |
| Ever exercised regularly, % | 47.1 | 39.1 | 0.0030 |
| Ever used aspirin regularly, % | 2.5 | 2.5 | 0.9509 |
| Ever used hormone replacement therapy, %** | 3.8 | 4.2 | 0.7329 |
| Fruit and vegetable intake, g/d, mean (SD) | 558.0 (292.6) | 569.9 (296.4) | 0.4595 |
| Meat intake, g/d, mean (SD) | 48.9 (41.4) | 52.1 (50.2) | 0.1414 |
P values (except for age) were derived from regression models with adjustment for age at sample collection.
Among postmenopausal women only.
No associations were observed between mtDNA copy number and age or menopausal status (Table 2), nor were any associations observed for mtDNA copy number and major CRC risk factors (Table 2). There was a significant (p<0.0001) negative correlation (r = -0.123) between age at sample collection and mtDNA copy number.
Table 2.
mtDNA copy number by selected baseline characteristics of colorectal cancer cases and controls
| Cases (n=444) |
Controls (n=1,423) |
All Participants (n=1,867) |
||||
|---|---|---|---|---|---|---|
| Variable | No. | Geometric Mean (95%CI) | No. | Geometric Mean (95%CI) | No. | Geometric Mean (95%CI) |
| Age | ||||||
| ≤ 60 years | 194 | 0.268 (0.247-0.292) | 886 | 0.288 (0.280-0.297) | 1080 | 0.285 (0.278-0.293) |
| > 60 years | 250 | 0.265 (0.248-0.284) | 537 | 0.293 (0.281-0.305) | 787 | 0.287 (0.277-0.297) |
| P value | 0.8355 | 0.6276 | 0.8538 | |||
| Postmenopausal | ||||||
| No | 102 | 0.271 (0.245-0.300) | 549 | 0.295 (0.284-0.305) | 651 | 0.290 (0.280-0.300) |
| Yes | 342 | 0.265 (0.251-0.280) | 874 | 0.287 (0.280-0.295) | 1216 | 0.284 (0.277-0.290) |
| P value | 0.7239 | 0.3453 | 0.3730 | |||
| Body mass index (BMI) | ||||||
| < 25 | 263 | 0.268 (0.255-0.283) | 859 | 0.291 (0.285-0.297) | 1122 | 0.287 (0.282-0.293) |
| ≥ 25 | 180 | 0.263 (0.247-0.280) | 564 | 0.288 (0.281-0.295) | 744 | 0.284 (0.278-0.290) |
| P value | 0.5428 | 0.5078 | 0.4081 | |||
| Family history of colorectal cancer | ||||||
| No | 430 | 0.268 (0.255-0.280) | 1386 | 0.290 (0.286-0.295) | 1816 | 0.286 (0.282-0.291) |
| Yes | 14 | 0.238 (0.200-0.282) | 37 | 0.278 (0.254-0.304) | 51 | 0.268 (0.248-0.290) |
| P value | 0.1692 | 0.3477 | 0.1101 | |||
| Family history of any cancer | ||||||
| No | 308 | 0.269 (0.256-0.283) | 1045 | 0.290 (0.285-0.296) | 1353 | 0.287 (0.282-0.292) |
| Yes | 136 | 0.259 (0.243-0.277) | 378 | 0.289 (0.280-0.297) | 514 | 0.283 (0.276-0.290) |
| P value | 0.2572 | 0.7178 | 0.3121 | |||
| Personal history of inflammatory bowel disease | ||||||
| No | 440 | 0.266 (0.254-0.279) | 1414 | 0.290 (0.285-0.295) | 1854 | 0.286 (0.282-0.290) |
| Yes | 4 | 0.305 (0.223-0.415) | 9 | 0.272 (0.226-0.327) | 13 | 0.284 (0.242-0.332) |
| P value | 0.3927 | 0.4929 | 0.9174 | |||
| Personal history of diabetes | ||||||
| Yes | 35 | 0.258 (0.230-0.290) | 94 | 0.276 (0.260-0.293) | 129 | 0.273 (0.260-0.288) |
| No | 409 | 0.267 (0.255-0.280) | 1329 | 0.291 (0.286-0.296) | 1738 | 0.287 (0.283-0.291) |
| P value | 0.5607 | 0.0851 | 0.0696 | |||
| Ever smoked regularly | ||||||
| Yes | 11 | 0.255 (0.210-0.309) | 46 | 0.301 (0.277-0.326) | 57 | 0.291 (0.270-0.314) |
| No | 433 | 0.267 (0.254-0.280) | 1377 | 0.290 (0.285-0.294) | 1810 | 0.286 (0.282-0.290) |
| P value | 0.6371 | 0.3813 | 0.6148 | |||
| Ever drank alcohol regularly | ||||||
| Yes | 14 | 0.302 (0.254-0.359) | 39 | 0.297 (0.272-0.325) | 53 | 0.301 (0.278-0.326) |
| No | 430 | 0.265 (0.253-0.278) | 1384 | 0.290 (0.285-0.295) | 1814 | 0.285 (0.281-0.290) |
| P value | 0.1356 | 0.5817 | 0.1867 | |||
| Ever exercised regularly | ||||||
| Yes | 209 | 0.265 (0.249-0.280) | 557 | 0.289 (0.282-0.297) | 766 | 0.285 (0.279-0.292) |
| No | 235 | 0.268 (0.254-0.283) | 866 | 0.290 (0.285-0.296) | 1101 | 0.286 (0.281-0.292) |
| P value | 0.6738 | 0.7921 | 0.7800 | |||
| Ever used aspirin regularly | ||||||
| Yes | 11 | 0.283 (0.233-0.344) | 36 | 0.286 (0.260-0.314) | 47 | 0.288 (0.265-0.313) |
| No | 433 | 0.266 (0.254-0.279) | 1387 | 0.290 (0.285-0.295) | 1820 | 0.286 (0.282-0.290) |
| P value | 0.536 | 0.7610 | 0.8678 | |||
| Ever used hormone replacement therapy* | ||||||
| No | 32 | 0.268 (0.255-0.282) | 837 | 0.285 (0.278-0.291) | 1165 | 0.281 (0.275-0.288) |
| Yes | 9 | 0.311 (0.257-0.376) | 37 | 0.302 (0.275-0.331) | 50 | 0.304 (0.280-0.330) |
| P value | 13 | 0.1313 | 0.2144 | 0.0657 | ||
P values were derived from two-way ANOVA F test by using log-transformed data.
Among postmenopausal women.
Baseline mtDNA copy number was lower among women who subsequently developed CRC than among women who remained cancer free. When comparing the original mtDNA copy number (before standardization), the median (Q1, Q3) was 0.284 (0.225-0.341) for cases and 0.287 (0.238-0.347) for controls (P<0.0001). The geometric means (95% CIs) were 0.277 (0.269-0.285) for cases and 0.288 (0.284-0.293) for controls (P=0.0153) (Table 3).
Table 3.
Baseline mtDNA copy number for cases and controls and mtDNA copy number stratified by time since blood collection
| Cases | Controls | P value* | |
|---|---|---|---|
| Colorectal Cancer | |||
| Number | 444 | 1,423 | |
| Geometric mean (95% CI) | 0.277 (0.269-0.285) | 0.288 (0.284-0.293) | 0.0153 |
| Colon Cancer | |||
| Number | 276 | 1,382 | |
| Geometric mean (95% CI) | 0.276 (0.267-0.286) | 0.288 (0.284-0.293) | 0.0323 |
| Rectal Cancer | |||
| Number | 168 | 1,252 | |
| Geometric mean (95% CI) | 0.278 (0.266-0.291) | 0.287 (0.282-0.292) | 0.1971 |
| Time Since Blood Collection (Colorectal Cancer) | |||
| < 5 years | |||
| Number | 177 | 1,229 | |
| Geometric mean (95% CI) | 0.281 (0.269-0.293) | 0.289 (0.284-0.294) | 0.2313 |
| ≥ 5 years | |||
| Number | 267 | 1,372 | |
| Geometric mean (95% CI) | 0.277 (0.267-0.288) | 0.289 (0.285-0.294) | 0.0349 |
P values were derived from two-way (case-control status and assay batches/plates) ANOVA F test for the case-control difference by using log-transformed data.
The risk of developing CRC increased as mtDNA copy number decreased (Figure 1) and the association exhibited a linear dose-response pattern (P for trend=0.0200). Tests for nonlinearity were not significant (P=0.4266). The ORs were 1.26 (95% CI: 0.93-1.70) for the middle mtDNA copy number tertile and 1.44 (95% CI: 1.06-1.94) for the lower tertile compared with the upper tertile (highest number of copies of mtDNA) (Table 4). The observed association was slightly more evident for colon cancer than for rectal cancer (Table 4). The ORs per standard deviation decrease in mtDNA copy number were 1.15 (95% CI: 1.02-1.30; P=0.0204) for CRC, 1.16 (95% CI: 1.01-1.34; P=0.0430) for colon cancer, and 1.12 (95% CI: 0.94-1.34; P=0.2021) for rectal cancer (Table 4).
Figure 1. Association between baseline mitochondrial DNA (mtDNA) copy number and subsequent risk for colorectal cancer.
Solid line: ORs; Dotted line: 95% CI. P values for tests of overall association, linearity, and nonlinearity were 0.0683, 0.0200, and 0.4266, respectively.
Table 4.
Association of baseline mtDNA copy number and subsequent risk of colorectal cancer
| mtDNA copy number (tertiles) |
P for trend* | |||
|---|---|---|---|---|
| 3 (Highest) | 2 | 1 | ||
| Colorectal Cancer | ||||
| Number of case/control | 104/475 | 149/473 | 191/475 | |
| OR (95% CI) | 1.00 (reference) | 1.26 (0.93-1.70) | 1.44 (1.06-1.94) | |
| OR (95% CI)** | 1.15 (1.02-1.30) | 0.0204 | ||
| Colon Cancer | ||||
| Number of case/control | 63/459 | 93/454 | 120/469 | |
| OR (95% CI) | 1.00 (reference) | 1.27 (0.88-1.83) | 1.46 (1.02-2.10) | |
| OR (95% CI)** | 1.16 (1.01-1.34) | 0.0430 | ||
| Rectal Cancer | ||||
| Number of case/control | 41/404 | 56/416 | 71/432 | |
| OR (95% CI) | 1.00 (reference) | 1.24 (0.80-1.93) | 1.37 (0.88-2.13) | |
| OR (95% CI)** | 1.12 (0.94-1.34) | 0.2021 | ||
| Time Since Blood Collection (Colorectal Cancer) | ||||
| < 5 years | ||||
| Number of case/control | 40/410 | 61/412 | 76/407 | |
| OR (95% CI) | 1.00 (reference) | 1.39 (0.89-2.16) | 1.59 (1.02-2.48) | |
| OR (95% CI)** | 1.12 (0.94-1.33) | 0.2176 | ||
| ≥ 5 years | ||||
| Number of case/control | 64/464 | 88/449 | 115/459 | |
| OR (95% CI) | 1.00 (reference) | 1.18 (0.82-1.70) | 1.32 (0.92-1.90) | |
| OR (95% CI)** | 1.16 (1.00-1.34) | 0.0463 | ||
Conditional logistic regression models were used to derive P values for linear trends by modeling the log-transformed mtDNA content as a continuous variable.
OR per SD decrement.
Blood samples were collected from 1 to 65 months before CRC diagnosis, and additional analyses were conducted to evaluate CRC risk by the time interval between blood collection and cancer diagnosis. Cases were categorized into two strata: < 5 years and ≥ 5 years between blood collection and CRC diagnosis. The geometric mean was significantly lower among cases diagnosed ≥ 5 years after blood collection when compared with controls (P=0.0349) (Table 3). The geometric mean was also lower among CRC cases diagnosed < 5 years after blood collection, although the difference for this group was not statistically significant (P=0.2313) (Table 3). The ORs per each standard deviation decrease in mtDNA copy number were 1.16 (95% CI: 1.00-1.34; P=0.0463) for cases diagnosed ≥ 5 years after blood collection and 1.12 (95% CI: 0.94-1.33; P=0.2176) for cases diagnosed < 5 years after blood collection (Table 4).
We also evaluated whether mtDNA copy number is associated with CRC tumor stage among 351 CRC cases that have tumor stage information. No associations were observed between mtDNA copy number and TNM stage. The geometric means (95% CIs) were 0.286 (0.273-0.300), 0.262 (0.250-0.276), and 0.270 (0.244-0.299) for stages I/II, III, and IV, respectively (P=0.1508).
Discussion
In this prospective study, we found that pre-diagnostic mtDNA copy number in peripheral leukocytes was significantly and inversely associated with subsequent CRC risk among Han Chinese women in a dose-dependent manner. This association was observed for both colon cancer and rectal cancer, although the difference was not statically significant for rectal cancer, most likely due to the smaller sample size of this sub-group. The association varied little by the interval between blood collection and cancer diagnosis, suggesting that the observed association is unlikely a result of undiagnosed CRC present at the time of blood sample collection. Our study supports the hypothesis that mtDNA copy number is associated with CRC risk.
A previous case-control study conducted among 320 Chinese men and women with CRC and matched controls found that mtDNA copy number in peripheral leukocytes was higher among cases compared with controls (21). However, a case-control study cannot exclude the possibility that alterations in mtDNA copy number are actually a sign of CRC itself or a consequence of diagnostic processes or treatment for CRC. Recently, a prospective study including 422 colorectal cancer cases showed a U-shaped association between mtDNA copy number and risk for colorectal cancer (18). Among the 168 cases who provided a pre-diagnosis blood sample, when compared with the second mtDNA copy number quartile, women in the lowest quartile had a higher OR for colorectal cancer risk (OR, 4.83; 95% CI, 1.42-16.38) than did men in the lowest quartile (OR, 3.72; 95% CI, 1.02-13.54), whereas men in the highest quartile had a higher OR for colorectal cancer risk (OR, 8.74; 95% CI, 2.52-30.34) than did women in the highest quartile (OR, 2.69; 95% CI, 0.75-9.69) (18). The sample size for that analysis was small. In our study, women with lower mtDNA copy number had increased risk for colorectal cancer. Our finding is consistent with a report from Liao et al. (20) that decreased pre-diagnostic mtDNA copy number is associated with increased gastric cancer risk.
Lower mtDNA copy number has been reported in CRC tissue. Lee et al. (24) found lower mtDNA copy number in 28% (7/25) of CRC cases (24). Lin et al. (25) further showed that mtDNA depletion was correlated with decreased expression of mitochondrial transcription factor A (TFAM) and β-F1-ATPase among 153 CRC patients. Moreover, mtDNA copy number and TFAM and β-F1-ATPase expression were disease-stage dependent. Chang et al. (26) measured mtDNA copy number in 194 CRC patients and found low mtDNA copy number in tumor tissue was significantly associated with advanced tumor stage and higher p53 mutation. Lower mtDNA copy number was also correlated with microsatellite instability (MSI) based on data derived from 50 CRC biopsy samples (27). Taken together, the evidence suggests that reductions in mtDNA copy number in colorectal tissue may play a role in the development of CRC.
It has been shown that exercise and exposure to cold may lead to higher mtDNA copy number (28, 29). Exposure to cigarette smoke and alcohol may decrease expression of peroxisome proliferator activated receptor-gamma (PPAR-γ)-coactivator 1 alpha (PGC-1α), which is involved in mitochondrial biogenesis (30, 31). Compared with light smokers (< 30 pack-years), heavy smokers (≥30 pack-years) had lower mtDNA copy numbers in lung tissues (P<0.05) (32). Taken together, these findings indicate that environmental factors are involved in the regulation of mtDNA copy number. Xing et al. (17) have shown that mtDNA copy number in leukocytes is higher among smokers compared with never smokers and lower among men compared with women. However, Qu et al. (21) found no modulating effects for sex, age, BMI, smoking status, or alcohol consumption on mtDNA copy number in either CRC cases or controls (21). In our study, we also observed no association of mtDNA copy number with smoking status, alcohol consumption, aspirin intake, exercise, BMI, or other variables (Table 2). However, smoking, alcohol consumption, and aspirin use are all rare in our study population.
The potential mechanism by which lower mtDNA copy number leads to cancer development is unclear. mtDNA copy number in circulating blood cells and other tissues could be a biomarker of mitochondrial dysfunction (33). It has been suggested that reductions in mtDNA copy number may disrupt mitochondrial membrane potential and lead to mitochondrial dysfunction. Dysfunctional mitochondria could trigger retrograde signaling from the mitochondria to the nucleus, causing an adaptive response that results in alterations to the nuclear gene expression profile and to cell physiology and morphology (34). Further studies are needed to elucidate the potential molecular mechanisms by which lower mtDNA copy number is associated with increased CRC risk.
Our study has several strengths. First, participants were drawn from a large prospective cohort consisting of 74,941 women living in the same area and had a high response rate (92.7%). The study population is well-characterized, relatively homogeneous, and has low smoking and alcohol consumption rates (22), which reduces the effect of gender, lifestyle, and other confounding factors on mtDNA copy number. Second, the prospective study design reduces the possibility that lower mtDNA copy number is a consequence of CRC development or cancer treatments, such as chemotherapy or surgery, which have been reported to influence mitochondrial redox homeostasis and reduce mtDNA copy number (35-37). Third, we set up the quantitative RT-PCR for measurement of the mtDNA and reference gene in the same tube. Amplification of the mtDNA and reference nuclear gene under the same conditions should reduce assay variation. Different reference nuclear genes, such as hemoglobin beta (HGB) (38), 18s rDNA (18), and RBCA1 (our study) were used in the mtDNA copy number measurements, which should not affect the results of the association study. However, it would be difficult to compare the raw mtDNA copy number data among different studies. Our study also has some limitations. First, although our study is the largest prospective study conducted to date addressing the association of mtDNA copy number with colorectal cancer risk, the sample size for some analyses was relatively small. In addition, our study was conducted among Chinese women. Further research is warranted to evaluate the association in large prospective studies including both men and women and different racial/ethnic groups. Second, lifestyle changes over time could affect the association between mtDNA copy number and colorectal cancer risk and a single measurement may not reflect mtDNA copy number over a lifetime. However, Thyagarajan et al. (18) conducted a small study and found that mtDNA copy number has a relative small within-person variation over a two-month period. We also found no association between mtDNA copy number and major risk factors for CRC. Third, potential selection bias is another concern for epidemiological studies. However, the potential selection bias should be small in this study, because of the very high response rate (92.7%) at the baseline survey and the very low rate of loss to follow-up (<1%). Fourth, antioxidant capacity may play a role in mtDNA regulation. Further studies are needed to evaluate the joint effects of antioxidant capacity and mtDNA copy number in relation to CRC risk.
In summary, this prospective study indicates that lower mtDNA copy number in peripheral blood leukocytes is associated with subsequent development of colorectal cancer. mtDNA copy number measured in peripheral blood leukocytes may be a potential biomarker useful for CRC risk assessment. Further studies are needed to confirm our findings.
Supplementary Material
Acknowledgements
We thank Dr. Shimian Qu for his assistance in DNA preparation and Ms. Bethanie Rammer for her assistance in editing the manuscript for clarity, consistency, and English-language usage. Ms. Rammer is a salaried employee of Vanderbilt University Medical Center.
Grant Support
This study was supported by grant number R37 CA070867 (to W. Zheng) from the US National Institutes of Health (NIH). Sample preparation and mtDNA copy number assays were performed at the Survey and Biospecimen Shared Resource, which is supported in part by the Vanderbilt-Ingram Cancer Center and NIH grant number P30 CA068485 (to Q. Cai).
Abbreviations
- BMI
body mass index
- CI
confidence interval
- CRC
colorectal cancer
- CV
coefficient of variation
- mtDNA
mitochondrial DNA
- OR
odds ratio
- SWHS
Shanghai Women's Health Study
Footnotes
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
References
- 1.Ferlay J, Soerjomataram I, Ervik M, et al. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet] International Agency for Research on Cancer; Lyon, France: 2013. [May 13, 2014]. Available from: http://globocan.iarc.fr. [Google Scholar]
- 2.Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012;62:10–29. doi: 10.3322/caac.20138. [DOI] [PubMed] [Google Scholar]
- 3.Pourhoseingholi MA. Increased burden of colorectal cancer in Asia. World J Gastrointest Oncol. 2012;4:68–70. doi: 10.4251/wjgo.v4.i4.68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Anderson S, Bankier AT, Barrell BG, de Bruijn MH, Coulson AR, Drouin J, et al. Sequence and organization of the human mitochondrial genome. Nature. 1981;290:457–65. doi: 10.1038/290457a0. [DOI] [PubMed] [Google Scholar]
- 5.Wallace DC. Mitochondrial diseases in man and mouse. Science. 1999;283:1482–8. doi: 10.1126/science.283.5407.1482. [DOI] [PubMed] [Google Scholar]
- 6.Carew JS, Huang P. Mitochondrial defects in cancer. Mol Cancer. 2002;1:9. doi: 10.1186/1476-4598-1-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Trinei M, Berniakovich I, Pelicci PG, Giorgio M. Mitochondrial DNA copy number is regulated by cellular proliferation: a role for Ras and p66(Shc). Biochim Biophys Acta. 2006;1757:624–30. doi: 10.1016/j.bbabio.2006.05.029. [DOI] [PubMed] [Google Scholar]
- 8.Piko L, Matsumoto L. Number of mitochondria and some properties of mitochondrial DNA in the mouse egg. Dev Biol. 1976;49:1–10. doi: 10.1016/0012-1606(76)90253-0. [DOI] [PubMed] [Google Scholar]
- 9.Shay JW, Pierce DJ, Werbin H. Mitochondrial DNA copy number is proportional to total cell DNA under a variety of growth conditions. J Biol Chem. 1990;265:14802–7. [PubMed] [Google Scholar]
- 10.Wu Z, Puigserver P, Andersson U, Zhang C, Adelmant G, Mootha V, et al. Mechanisms controlling mitochondrial biogenesis and respiration through the thermogenic coactivator PGC-1. Cell. 1999;98:115–24. doi: 10.1016/S0092-8674(00)80611-X. [DOI] [PubMed] [Google Scholar]
- 11.Scarpulla RC. Transcriptional paradigms in mammalian mitochondrial biogenesis and function. Physiol Rev. 2008;88:611–38. doi: 10.1152/physrev.00025.2007. [DOI] [PubMed] [Google Scholar]
- 12.Higuchi M. Regulation of mitochondrial DNA content and cancer. Mitochondrion. 2007;7:53–7. doi: 10.1016/j.mito.2006.12.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Yu M. Generation, function and diagnostic value of mitochondrial DNA copy number alterations in human cancers. Life Sci. 2011;89:65–71. doi: 10.1016/j.lfs.2011.05.010. [DOI] [PubMed] [Google Scholar]
- 14.Rotig A, Poulton J. Genetic causes of mitochondrial DNA depletion in humans. Biochim Biophys Acta. 2009;1792:1103–8. doi: 10.1016/j.bbadis.2009.06.009. [DOI] [PubMed] [Google Scholar]
- 15.Hudson G, Chinnery PF. Mitochondrial DNA polymerase-gamma and human disease. Hum Mol Genet. 2006;15:R244–R252. doi: 10.1093/hmg/ddl233. [DOI] [PubMed] [Google Scholar]
- 16.Hosgood HD, III, Liu CS, Rothman N, Weinstein SJ, Bonner MR, Shen M, et al. Mitochondrial DNA copy number and lung cancer risk in a prospective cohort study. Carcinogenesis. 2010;31:847–9. doi: 10.1093/carcin/bgq045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Xing J, Chen M, Wood CG, Lin J, Spitz MR, Ma J, et al. Mitochondrial DNA content: its genetic heritability and association with renal cell carcinoma. J Natl Cancer Inst. 2008;100:1104–12. doi: 10.1093/jnci/djn213. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Thyagarajan B, Wang R, Barcelo H, Koh WP, Yuan JM. Mitochondrial copy number is associated with colorectal cancer risk. Cancer Epidemiol Biomarkers Prev. 2012;21:1574–81. doi: 10.1158/1055-9965.EPI-12-0138-T. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Purdue MP, Hofmann JN, Colt JS, Hoxha M, Ruterbusch JJ, Davis FG, et al. A case-control study of peripheral blood mitochondrial DNA copy number and risk of renal cell carcinoma. PLoS One. 2012;7:e43149. doi: 10.1371/journal.pone.0043149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Liao LM, Baccarelli A, Shu XO, Gao YT, Ji BT, Yang G, et al. Mitochondrial DNA copy number and risk of gastric cancer: a report from the Shanghai Women's Health Study. Cancer Epidemiol Biomarkers Prev. 2011;20:1944–9. doi: 10.1158/1055-9965.EPI-11-0379. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Qu F, Liu X, Zhou F, Yang H, Bao G, He X, et al. Association between mitochondrial DNA content in leukocytes and colorectal cancer risk: a case-control analysis. Cancer. 2011;117:3148–55. doi: 10.1002/cncr.25906. [DOI] [PubMed] [Google Scholar]
- 22.Zheng W, Chow WH, Yang G, Jin F, Rothman N, Blair A, et al. The Shanghai Women's Health Study: rationale, study design, and baseline characteristics. Am J Epidemiol. 2005;162:1123–31. doi: 10.1093/aje/kwi322. [DOI] [PubMed] [Google Scholar]
- 23.Ye C, Shu XO, Wen W, Pierce L, Courtney R, Gao YT, et al. Quantitative analysis of mitochondrial DNA 4977-bp deletion in sporadic breast cancer and benign breast diseases. Breast Cancer Res Treat. 2008;108:427–34. doi: 10.1007/s10549-007-9613-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Lee HC, Yin PH, Lin JC, Wu CC, Chen CY, Wu CW, et al. Mitochondrial genome instability and mtDNA depletion in human cancers. Ann N Y Acad Sci. 2005;1042:109–22. doi: 10.1196/annals.1338.011. [DOI] [PubMed] [Google Scholar]
- 25.Lin PC, Lin JK, Yang SH, Wang HS, Li AF, Chang SC. Expression of beta-F1-ATPase and mitochondrial transcription factor A and the change in mitochondrial DNA content in colorectal cancer: clinical data analysis and evidence from an in vitro study. Int J Colorectal Dis. 2008;23:1223–32. doi: 10.1007/s00384-008-0539-4. [DOI] [PubMed] [Google Scholar]
- 26.Chang SC, Lin PC, Yang SH, Wang HS, Liang WY, Lin JK. Mitochondrial D-loop mutation is a common event in colorectal cancers with p53 mutations. Int J Colorectal Dis. 2009;24:623–8. doi: 10.1007/s00384-009-0663-9. [DOI] [PubMed] [Google Scholar]
- 27.Huang P, Zhang YX, Zhao T. The relationship between copy number and microsatellite instability of mitochondrial DNA in colorectal cancer. Zhonghua Nei Ke Za Zhi. 2009;48:837–40. [PubMed] [Google Scholar]
- 28.Puigserver P, Wu Z, Park CW, Graves R, Wright M, Spiegelman BM. A cold-inducible coactivator of nuclear receptors linked to adaptive thermogenesis. Cell. 1998;92:829–39. doi: 10.1016/s0092-8674(00)81410-5. [DOI] [PubMed] [Google Scholar]
- 29.Menshikova EV, Ritov VB, Fairfull L, Ferrell RE, Kelley DE, Goodpaster BH. Effects of exercise on mitochondrial content and function in aging human skeletal muscle. J Gerontol A Biol Sci Med Sci. 2006;61:534–40. doi: 10.1093/gerona/61.6.534. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Tang K, Wagner PD, Breen EC. TNF-alpha-mediated reduction in PGC-1alpha may impair skeletal muscle function after cigarette smoke exposure. J Cell Physiol. 2010;222:320–7. doi: 10.1002/jcp.21955. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Chaung WW, Jacob A, Ji Y, Wang P. Suppression of PGC-1alpha by Ethanol: Implications of Its Role in Alcohol Induced Liver Injury. Int J Clin Exp Med. 2008;1:161–70. [PMC free article] [PubMed] [Google Scholar]
- 32.Lee HC, Lu CY, Fahn HJ, Wei YH. Aging-and smoking-associated alteration in the relative content of mitochondrial DNA in human lung. FEBS Lett. 1998;441:292–6. doi: 10.1016/s0014-5793(98)01564-6. [DOI] [PubMed] [Google Scholar]
- 33.Malik AN, Czajka A. Is mitochondrial DNA content a potential biomarker of mitochondrial dysfunction? Mitochondrion. 2013;13:481–92. doi: 10.1016/j.mito.2012.10.011. [DOI] [PubMed] [Google Scholar]
- 34.Guha M, Avadhani NG. Mitochondrial retrograde signaling at the crossroads of tumor bioenergetics, genetics and epigenetics. Mitochondrion. 2013;13:577–91. doi: 10.1016/j.mito.2013.08.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Ashley N, Poulton J. Mitochondrial DNA is a direct target of anti-cancer anthracycline drugs. Biochem Biophys Res Commun. 2009;378:450–5. doi: 10.1016/j.bbrc.2008.11.059. [DOI] [PubMed] [Google Scholar]
- 36.Potenza L, Calcabrini C, Bellis RD, Mancini U, Polidori E, Zeppa S, et al. Effect of surgical stress on nuclear and mitochondrial DNA from healthy sections of colon and rectum of patients with colorectal cancer. J Biosci. 2011;36:243–51. doi: 10.1007/s12038-011-9064-7. [DOI] [PubMed] [Google Scholar]
- 37.Potenza L, Calcabrini C, De BR, Guescini M, Mancini U, Cucchiarini L, et al. Effects of oxidative stress on mitochondrial content and integrity of human anastomotic colorectal dehiscence: a preliminary DNA study. Can J Gastroenterol. 2011;25:433–9. doi: 10.1155/2011/741073. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Liu CS, Tsai CS, Kuo CL, Chen HW, Lii CK, Ma YS, et al. Oxidative stress-related alteration of the copy number of mitochondrial DNA in human leukocytes. Free Radic Res. 2003;37:1307–17. doi: 10.1080/10715760310001621342. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.