BRCA1 mutation carriers face a high lifetime risk of developing breast cancer. Physical activity induces broad transcriptional changes, and multiple studies have documented its beneficial effects across cancers. Because haploinsufficiency predisposes to breast cancer in these women, factors that increase BRCA1 levels may mitigate the effect of the mutation. Whether physical activity modulates BRCA1 expression and whether lifestyle factors could benefit women with a mutation remain unclear. The objective of this study was to systematically evaluate whether physical activity or sedentary behavior affects BRCA1 mRNA expression. Activity levels were assessed in 50 female participants (14 BRCA1 mutation carriers and 36 noncarriers) using the GT3X Actigraph accelerometer, and BRCA1 mRNA expression was quantified from peripheral blood lymphocytes using the Nanostring nCounter Analysis System. There was a significant negative correlation between the longest sedentary bout and BRCA1 mRNA expression (ρ = –0.32; P = 0.02). Women below the median for the longest sedentary bout had significantly higher BRCA1 mRNA levels compared with women above the median (161 vs. 132 counts; P = 0.04; one-sided Mann–Whitney U test). There was no significant relationship between mean metabolic equivalents of task rate or mean sedentary time and BRCA1 mRNA expression (Spearman correlation P ≥ 0.75; P ≥ 0.14; Mann–Whitney U test). These findings suggest that prolonged periods of sedentary behavior are associated with significantly lower BRCA1 mRNA expression. Whether this translates into a potentially more harmful effect in BRCA1 mutation carriers warrants further investigation. Cancer Prev Res; 9(1); 83–88. ©2015 AACR.

Women with a BRCA1 mutation face an elevated risk of developing breast cancer, estimated at 60% by age 70 (1–3). Despite the well-established protective effects of preventive surgery, whether or not lifestyle factors influence the development and/or progression of BRCA-associated cancer remains unclear (4). Epidemiologic evidence supports a protective role of physical activity in the development of breast cancer among women at average risk (5–7). Of increasing importance is the independent adverse role of sedentary behavior (defined as sitting time) on chronic disease (8, 9). Only a few studies have evaluated whether such an association exists among women with a BRCA1 mutation (10–13). Cancer-protective mechanisms of physical activity have mainly focused on alterations in levels of sex hormones, inflammatory markers, and growth factors (14–16). It is plausible that such changes are due to physical activity–induced transcriptional changes, as physical activity has been shown to induce widespread transcriptional effects (17–20). This may be of particular relevance for BRCA1 mutation carriers given that their cancer predisposition is likely the result of haploinsufficiency associated with heterozygosity, thereby increasing genomic instability and accelerating the mutation rate of other critical genes (including the second copy of BRCA1; ref. 21). Of particular interest is an in vivo study showing that physical activity resulted in a significant increase in BRCA1 mRNA expression in the mammary glands of exercised compared with unexercised rats (22).

Thus, the overall goal of the current study was to systematically evaluate the relationship between measures of physical activity and sedentary behavior with BRCA1 mRNA expression. Factors that might increase the physiologic expression of the normal copy of BRCA1 and normalize protein levels may mitigate the effect of an inherited BRCA1 mutation, representing a plausible mechanism whereby these exposures may modify cancer risk in BRCA1 mutation carriers. To the best of our knowledge, ours is the first study evaluating such an association in BRCA1 mutation carriers.

Study design and population

Subjects included women who were at least 18 years old, had no personal history of cancer, and who were not pregnant or breastfeeding. The first group included women from the general population, recruited via posters, newsletters, or social media. The second group included women with a confirmed BRCA1 mutation, identified from the Women's College Research Institute (WCRI, Toronto, Canada). Two 30-minute appointments were scheduled for participants to provide study details. The study protocol was approved by the Research Ethics Board at the Women's College Hospital (# 2012-0055-B). All patients provided written-informed consent, and a total of 50 women enrolled in the study: 14 BRCA1 mutation carriers and 36 noncarriers.

Data and biologic sample collection

At the first visit, participants completed a research questionnaire to collect information on reproductive and medical history, lifestyle factors, and family history of cancer. Standardized procedures were used to collect anthropometric measurements [i.e., weight (kg), height (m), waist (inches), and hip circumference (inches)]. Measurements were taken in duplicate, and an average of the two measures was calculated. The four measurements were used to calculate body mass index (BMI; kg/m2), waist-to-hip-ratio (WHR), and the index of central obesity [waist circumference (inches)/height (inches)]. At the second study visit, a phlebotomist drew blood into two labeled EDTA containing tubes (∼8 mL) by venipuncture. The samples were placed on ice and delivered within 35 minutes to the Narod laboratory (Women's College Hospital) for RNA extraction.

Physical activity assessment

All participants were provided with a detailed explanation on how to use the ActiGraph GT3X accelerometer (ActiGraph). Participants were asked to wear the accelerometer on an elasticized belt for 7 consecutive days during waking hours except during bathing and water activities. The GT3X is a triaxial monitor that detects movement in vertical, horizontal, and anterior–posterior planes and converts these data into counts, which are used to calculate energy expenditure (23). Each monitor was programmed to record activity information for 1-second epochs. The GT3X monitor has been validated for use in an adult population (24). At the second study visit, participants returned the accelerometer and data from the GT3X were downloaded onto a secure server.

For the current analysis, a valid day was defined as at least 600 minutes of accelerometer wear. “Nonwear” time was defined as an interval of at least 10 consecutive minutes of zero activity counts. This resulted in all 50 participants having 6 or more valid days of monitor wear. “Wear-time” was validated using cut points from the Troiano and colleagues study (25). Raw accelerometer counts were used to determine the percentage of time each participant spent in various activity levels (i.e., sedentary, light, and moderate to vigorous) using the manufacturer's software (ActiLife ver. 6.8.2) in conjunction with previously described cut points (hours/day; ref. 25). The percentage time was calculated by adding the minutes within each activity level and then dividing by the total minutes of wear per day. The metabolic equivalents of task (MET) rate were calculated using the Swartz Adult Overground and Lifestyle cut points (26). METs are an estimation of the energy cost of physical activity defined as the ratio of work metabolic rate to a standard resting metabolic rate of 1.0 (4.184 kJ·kg−1·h−1). MET values range between 0 and 23, while a value of 1 MET represents resting metabolic rate during quiet sitting and 18 represents running at 16.1 kmph (27). The longest sedentary bout was defined as the length of longest inactivity time (seconds) within the 7 consecutive days of accelerometer wear. Mean sedentary time (hours/day) was defined as the average amount of time per day spent being sedentary.

RNA quantification

RNA isolation.

RNA was isolated from one of the two EDTA tubes using the LeukoLOCK Total RNA Isolation System (Ambion; ref. 28). In order to maximize RNA isolation yield, all samples were stabilized with RNA within 35 minutes of blood draw. Resulting extracted RNA was stored in several aliquots at −80°C.

RNA yield and quality.

Two aliquots per participant were used to assess the quality and quantity of RNA. The nucleic acid content was quantified using the Nanodrop spectrophotometer (ThermoScientific). Total RNA quality and quantity were then determined using the Agilent 2100 Bioanalyzer (The Centre for Applied Genomics, Toronto, Canada). The mean sample RNA concentration was 173.7 ng/μL (range, 85–368 ng/μL), which is sufficient for downstream analyses (29). The mean RNA integrity number (RIN) for the samples of interest was 8.7 (range, 7.9–9.3; refs. 30, 31).

Quantification of BRCA1 mRNA

The nCounter Human Cancer Reference Kit consisting of 230 cancer-related genes (Nanostring Technologies) was used to measure BRCA1 mRNA expression (expressed as counts) at the University Health Network Genomics Centre (Toronto, Canada; ref. 29). Briefly, the nCounter Analysis System probe library contains two sequence-specific probes, the capture probe and the reporter probe, for each gene of interest. Probe pairs are mixed with total RNA in one hybridization reaction, and then the structures are imaged with the use of fluorescent microscopy. Expression is measured by counting the number of unique colour tags within the gene-probe tripartite structures and is reported as “counts.”

Data analysis was performed using the nSolver software (Nanostring Technologies) and custom scripts in R (ver. 3.1.2). Positive and negative controls were used to check for background expression. Reference housekeeping gene normalization was then performed. As the aim of our study was to investigate the effects of physical and sedentary activity on BRCA1 expression, we strove to ensure that our set of reference genes was minimally affected by physical and sedentary activities. This was especially important for investigating the longest sedentary bouts, which were negatively correlated with the expression of 89% (211 of 236) of measured genes. To identify a reference gene set unaffected by longest sedentary bouts, we randomly sampled 5,000 sets of five genes; for each set, we calculated its geometric mean in each sample and then calculated the Spearman correlation between the means and longest sedentary bouts. We chose the set with the lowest correlation magnitude (closest to 0) as our reference set. This set comprised the following genes: CCND3 (cyclin D3), CDKN2B (cyclin-dependent kinase inhibitor 2B), COL1A1 (collagen, type I, alpha 1), MMP1 (matrix metallopeptidase 1), TNFSF10 [tumor necrosis factor (ligand) superfamily, member 10]. The geometric mean of these five genes was calculated for each lane, and the average of the geometric means was calculated across all lanes. A lane-specific normalization factor was calculated for each lane by dividing the average by the geometric mean of the lane. The endogenous counts in each lane were then multiplied by the lane's normalization factor (32).

Statistical analysis

The effects of physical activity and sedentary time on BRCA1 mRNA expression were assessed by Mann–Whitney U test, correlation, and linear regression. Dichotomous categories (high/low) of the longest sedentary bout (seconds), mean sedentary time (hours), and mean MET rate (kcal/kg/hour) were created, using the median to define high (above the median) versus low (at or below the median) levels for each of the exposures. The Mann–Whitney U test was used to evaluate differences in median BRCA1 mRNA levels among women in the high versus low exposure categories. Spearman correlation was calculated between each exposure and BRCA1 mRNA expression. Linear regression was used to examine the influence of physical activity and sedentary behavior on BRCA1 mRNA expression, adjusting for age (years), BRCA1 mutation status (carrier/noncarrier), and BMI (continuous).

Statistical significance was defined at the level of P ≤ 0.05, and all analyses were carried out using R Version 3.1.2.

Table 1 summarizes selected participant characteristics. The average age of the participants was 37.2 years, and thus, the majority of the women were premenopausal (74%) and nulliparous (60%). The mean BMI of the participants was 24.6, which is at the high end of the normal weight range defined as a BMI of 18.50 to 24.99.

The mean daily sedentary time was 8.60 hours per day, and 28% of the women spent more than 70% of their time in sedentary mode. The mean maximum length of a sedentary bout was 7,158 seconds (SD = 3854). There was a significant negative correlation between the longest sedentary bout (seconds) and BRCA1 mRNA expression (counts; ρ = −0.32; P = 0.02; Fig. 1). The relationship remained statistically significant (ρ = −0.28; P = 0.049) when adjusted for age, BRCA1 mutation status, and BMI (data not shown). Figure 2 illustrates that women below the median of the longest sedentary bout had significantly higher BRCA1 mRNA levels compared with women above the median (161 vs. 132 counts; P = 0.037; one-sided Mann–Whitney U test). There was no significant relationship between the mean MET rate (kcal/kg/hour) and BRCA1 mRNA expression (Supplementary Figs. S1 and S2). In an analysis stratified by BRCA1 mutation status, the negative correlation between the longest sedentary bout and BRCA1 mRNA expression was stronger for BRCA1 mutation carriers; however, this association did not achieve statistical significance likely due to the small sample size (ρ = −0.45; P = 0.11 vs. ρ = −0.21; P = 0.22 in noncarriers).

We also observed no significant correlation between mean sedentary time (hours/day) and BRCA1 mRNA expression (ρ = –0.045; P = 0.75; Supplementary Fig. S3). There was no significant difference in BRCA1 mRNA levels between women with mean sedentary time above compared with below the median (142 vs. 139 counts; P = 0.73; one-sided Mann–Whitney U test; Supplementary Fig. S4).

To systematically analyze what processes and signaling cascades involve genes significantly deregulated by sedentary activity (n = 24; Supplementary Table S1), we used pathDIP ver. 1 (33). pathDIP provides literature-driven and computationally predicted annotations for known membership and significant association of genes to molecular pathways. It integrates data that originate from multiple resources for molecular pathway annotations (34) and protein–protein interactions (35, 36). This analysis identified 968 significantly enriched pathways (P < 0.01; list in Supplementary Table S2). Significantly enriched pathways included diseases: cancer; processes: apoptosis, cell-cycle, and DNA damage response; and proteins and signaling cascades: MAPK, NF-κB, WNT, and PI3K.

The overall goal of this study was to evaluate whether accelerometer-derived measures of physical activity and sedentary behavior are associated with BRCA1 expression. We observed a significant negative correlation between the longest sedentary bout and BRCA1 mRNA levels but no relationship with MET rate. These findings point toward perhaps an even more important role of sedentary behavior per se rather than physical activity influencing gene expression. Importantly, because no significant relationship with mean sedentary time was observed, prolonged periods of inactivity may have a larger impact on BRCA1 rather than shorter periods of sedentary time with more frequent interruptions. These findings are in line with current literature outlining the detrimental and possibly additive effects of sedentary time, physical inactivity, and television watching on chronic disease (8, 37–39). Nonetheless, given the multiple comparisons and relatively small sample size, these findings should be interpreted with caution and require replication in a larger study population. However, considering current World Health Organization recommendations of 150 minutes of physical activity per week for adults, our study strongly suggests that being active may not be sufficient in the presence of prolonged sedentary bouts (40).

When identifying lifestyle interventions that may prevent breast cancer, it is important to consider that the mechanism underlying the predisposition is likely to be different in BRCA1 mutation carriers versus the general population (21). BRCA1 is an established mediator of genomic stability and regulator of the cellular response to DNA damage (41). In these roles, BRCA1 regulates processes pertinent to cell survival, proliferation, and differentiation (42). The consequences associated with inheritance of a single functional copy of the gene suggest a dosage effect of BRCA1. Therefore, titrating of BRCA1 mRNA and protein levels through nongenetic mechanisms may also affect the normal functions of the gene. More recently, our group demonstrated that oral supplementation with the phytochemical 3,3′-diindolylmethane was associated with an increase in BRCA1 mRNA expression in women with a BRCA1 mutation, providing important preliminary evidence that nongenetic exposures may affect gene expression (43). Whether this translates to a change in protein levels and subsequent cancer risk is not yet known.

The current study was prompted by two earlier publications reporting a relationship between physical activity and BRCA1 gene expression (22, 44). Using an in vivo model, Wang and colleagues found that prepubertal physical activity was associated with significantly higher mammary gland expression of BRCA1 mRNA, among other genes, compared with unexercised rats (22). In the second study, Magbanua and colleagues reported that vigorous activity significantly upregulated several gene sets, including homologous recombination repair and double-strand break repair in prostate tissue of men undergoing active surveillance (44). Although BRCA1 expression was not independently upregulated, both DNA repair pathways included BRCA1 (among other genes), suggesting that vigorous physical activity may modulate genomic stability by affecting the response of the cell to DNA damage in the prostate.

Three epidemiologic studies have evaluated the effect of physical activity on BRCA-associated cancer development (10). King and colleagues reported a significant delay in breast cancer onset among BRCA1/2 mutation carriers who were physically active as teenagers compared with those who were not (11), and Pijpe and colleagues reported a 42% reduction in risk with increasing levels of physical activity prior to, but not after, age 30 (HR, 0.58; 95% confidence interval, 0.35–0.94; highest vs. lowest tertile of mean MET hours/week before age 30; ref. 13). In contrast, a third study found no association between levels of physical activity (45). Although retrospective, these studies provide some evidence for a protective role of physical activity during adolescence or early adulthood among BRCA mutation carriers.

Prolonged sedentary behavior has been associated with increased cancer incidence and mortality, independent of physical activity levels, making this an important public health concern (8, 37, 46). Although the deleterious effects of increased sedentary time on cancer risk may be due to reduced physical activity along with the resultant metabolic and hormonal consequences (47), our data suggest that alterations in gene expression may also be involved. To our knowledge, this represents the first evaluation of physical activity/sedentary time and gene expression in women, including those with a hereditary predisposition. It has been suggested that characteristics of sedentary time, including how sedentary time is accrued throughout the day (i.e., long periods/few interruptions vs. short periods/more interruptions), are also important, and that breaking up sedentary time can beneficially affect markers of adiposity, insulin resistance, and inflammation (48–50). Our findings suggest that alterations in gene expression may also be involved, but the detailed mechanism, likely caused by the interplay of the above, will be the focus of future studies.

Although the mechanism linking prolonged sedentary bouts to lower BRCA1 expression remains unclear, it may involve BRCA1's role as a metabolic regulator. It has been shown that prolonged sedentary bouts may increase disease risk and that the underlying mechanism may involve lipoprotein lipase (48). During prolonged inactivity, reduced skeletal muscle contractions decrease lipoprotein lipase activity, resulting in reduced clearance of triglycerides and oral glucose from plasma (48). A similar energy balance mechanism may link prolonged sedentary bouts with reduced BRCA1 expression. BRCA1 is a key regulator of metabolism; by binding to the inactive form of Acetyl-CoA Carboxylase (ACCA), BRCA1 reduces the rate of long-chain fatty acid synthesis (51, 52). Thus, lipoprotein lipase and BRCA1 play complementary roles in response to increased energy needs: lipoprotein lipase breaks down fats and BRCA1 reduces their synthesis.

Strengths associated with the current study include the use of accelerometer-derived data to objectively quantify physical activity levels (53, 54). Although our results provide an exciting direction for prevention research, there are some limitations of our study, including the small sample size, relative homogeneity of our population in terms of physical activity/inactivity, and the quantification of gene expression in peripheral blood lymphocytes rather than the tissue of interest (i.e., breast; refs. 43, 55, 56). More participants representing wider extremes of physical activity and sedentary behavior levels may have yielded larger differences in the relationship with BRCA1 expression. Furthermore, although physical activity/inactivity differentially affects expression, there are also tissue-specific transcriptional changes that need to be assessed in detail. Although our study did not have sufficient samples to conduct stratified analysis by BRCA1 mutation status, we did not expect to detect significant differences in BRCA1 gene expression in women with and without a mutation for various reasons. First, there were five distinct BRCA1 mutations among the 13 study participants with a known BRCA1 mutation. Nine of the 13 mutation carriers shared one of two specific mutations, one in exon 20 (5382insC) and the other close to the translation initiation codon in exon 2 (185delAG), both of which are likely to escape nonsense-mediated mRNA decay. Thus, we would not expect to detect lower mRNA levels (57). Although the other four women shared three distinct germline mutations that would result in nonsense-mediated mRNA decay and likely lower expression, the relatively small sample size would likely not result in collectively lower levels compared with noncarriers. Future studies using RNA sequencing, which can discriminate between levels of the wild-type and mutant allele, are warranted. Also, a more comprehensive evaluation of lifestyle needs to be conducted, to ensure we identify all possible associations of (in)activity measures with disease-related gene expression.

In summary, results from this pilot study suggest that prolonged periods of sedentary behavior are inversely associated with BRCA1 mRNA expression. Whether this association also exists at the protein level, or importantly, translates to enhanced cancer risk remains unknown, but it is suggestive of a possible detrimental role of long periods of uninterrupted sedentary time on BRCA1 gene expression. Taken together, findings from this study provide important preliminary insight into how sedentary time and physical activity may mediate cancer risk in this high-risk population. This study offers the potential for a clinically important paradigm shift in the prevention strategies available for BRCA1 mutation carriers. The prospect of changing lifestyle for the purpose of preventing breast cancer in high-risk women, complemented by mechanistic evidence, warrants evaluation in large-scale prospective studies.

No potential conflicts of interest were disclosed.

Conception and design: S.A. Narod, M. Akbari, L. Salmena, J. Kotsopoulos

Development of methodology: R. Pettapiece-Phillips, L. Salmena, S.A. Narod, M. Akbari, I. Jurisica, J. Kotsopoulos

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): R. Pettapiece-Phillips, R. Chehade, S.A. Narod, M. Akbari, J. Kotsopoulos

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): M. Kotlyar, R. Chehade, L. Salmena, S.A. Narod, M. Akbari, I. Jurisica, J. Kotsopoulos

Writing, review, and/or revision of the manuscript: R. Pettapiece-Phillips, R. Chehade, M. Kotlyar, I. Jurisica, S.A. Narod, M. Akbari, L. Salmena, J. Kotsopoulos

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): R. Pettapiece-Phillips, J. Kotsopoulos

Study supervision: S.A. Narod, M. Akbari, L. Salmena, J. Kotsopoulos

J. Kotsopoulos is the recipient of a Cancer Care Ontario Research Chair in Population Studies. J. Kotsopoulos and M. Akbari are recipients of a Canadian Cancer Society Career Development Award in Prevention. L. Salmena is a recipient of a Tier 2 Canada Research Chair. S.A. Narod and I. Jurisica are recipients of a Tier I Canada Research Chair.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Antoniou
A
,
Pharoah
PD
,
Narod
S
,
Risch
HA
,
Eyfjord
JE
,
Hopper
JL
, et al
Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies
.
Am J Hum Genet
2003
;
72
:
1117
30
.
2.
Lubinski
J
,
Huzarski
T
,
Byrski
T
,
Lynch
HT
,
Cybulski
C
,
Ghadirian
P
, et al
The risk of breast cancer in women with a BRCA1 mutation from North America and Poland
.
Int J Cancer
2012
;
131
:
229
34
.
3.
Mavaddat
N
,
Peock
S
,
Frost
D
,
Ellis
S
,
Platte
R
,
Fineberg
E
, et al
Cancer risks for BRCA1 and BRCA2 mutation carriers: results from prospective analysis of EMBRACE
.
J Natl Cancer Inst
2013
;
105
:
812
22
.
4.
Kotsopoulos
J
,
Narod
SA
. 
Towards a dietary prevention of hereditary breast cancer
.
Cancer Causes Control
2005
;
16
:
125
38
.
5.
Friedenreich
CM
,
Cust
AE
. 
Physical activity and breast cancer risk: impact of timing, type and dose of activity and population subgroup effects
.
Br J Sports Med
2008
;
42
:
636
47
.
6.
Wu
Y
,
Zhang
D
,
Kang
S
. 
Physical activity and risk of breast cancer: a meta-analysis of prospective studies
.
Breast Cancer Res Treat
2013
;
137
:
869
82
.
7.
Lynch
BM
,
Neilson
HK
,
Friedenreich
CM
. 
Physical activity and breast cancer prevention
.
Recent Results Cancer Res
2011
;
186
:
13
42
.
8.
Biswas
A
,
Oh
PI
,
Faulkner
GE
,
Bajaj
RR
,
Silver
MA
,
Mitchell
MS
, et al
Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis
.
Ann Intern Med
2015
;
162
:
123
32
.
9.
Lynch
BM
,
Healy
GN
,
Dunstan
DW
,
Owen
N
. 
Sedentary versus inactive: distinctions for disease prevention
.
Nat Rev Cardiol
2010
;
7
.
10.
Pettapiece-Phillips
R
,
Narod
SA
,
Kotsopoulos
J
. 
The role of body size and physical activity on the risk of breast cancer in BRCA mutation carriers
.
Cancer Causes Control
2015
;
26
:
333
44
.
11.
King
MC
,
Marks
JH
,
Mandell
JB
. 
Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2
.
Science
2003
;
302
:
643
6
.
12.
Kotsopoulos
J
,
Olopado
OI
,
Ghadirian
P
,
Lubinski
J
,
Lynch
HT
,
Isaacs
C
, et al
Changes in body weight and the risk of breast cancer in BRCA1 and BRCA2 mutation carriers
.
Breast Cancer Res
2005
;
7
:
R833
43
.
13.
Pijpe
A
,
Manders
P
,
Brohet
RM
,
Collee
JM
,
Verhoef
S
,
Vasen
HF
, et al
Physical activity and the risk of breast cancer in BRCA1/2 mutation carriers
.
Breast Cancer Res Treat
2010
;
120
:
235
44
.
14.
Neilson
HK
,
Conroy
SM
,
Friedenreich
CM
. 
The influence of energetic factors on biomarkers of postmenopausal breast cancer risk
.
Curr Nutr Rep
2014
;
3
:
22
34
.
15.
Liu
D
,
Sartor
MA
,
Nader
GA
,
Gutmann
L
,
Treutelaar
MK
,
Pistilli
EE
, et al
Skeletal muscle gene expression in response to resistance exercise: sex specific regulation
.
BMC Genomics
2010
;
11
:
659
.
16.
Radom-Aizik
S
,
Zaldivar
F
 Jr
,
Leu
SY
,
Cooper
DM
. 
A brief bout of exercise alters gene expression and distinct gene pathways in peripheral blood mononuclear cells of early- and late-pubertal females
.
J Appl Physiol
2009
;
107
:
168
75
.
17.
Radom-Aizik
S
,
Zaldivar
F
 Jr.
,
Leu
SY
,
Galassetti
P
,
Cooper
DM
. 
Effects of 30 min of aerobic exercise on gene expression in human neutrophils
.
J Appl Physiol
2008
;
104
:
236
43
.
18.
Catoire
M
,
Mensink
M
,
Boekschoten
MV
,
Hangelbroek
R
,
Muller
M
,
Schrauwen
P
, et al
Pronounced effects of acute endurance exercise on gene expression in resting and exercising human skeletal muscle
.
PLoS One
2012
;
7
:
e51066
.
19.
Ronn
T
,
Volkov
P
,
Davegardh
C
,
Dayeh
T
,
Hall
E
,
Olsson
AH
, et al
A six months exercise intervention influences the genome-wide DNA methylation pattern in human adipose tissue
.
PLoS Genet
2013
;
9
:
e1003572
.
20.
Gjevestad
GO
,
Holven
KB
,
Ulven
SM
. 
Effects of exercise on gene expression of inflammatory markers in human peripheral blood cells: a systematic review
.
Curr Cardiovasc Risk Rep
2015
;
9
:
34
.
21.
Salmena
L
,
Narod
S
. 
BRCA1 haploinsufficiency: consequences for breast cancer
.
Women's Health
2012
;
8
:
127
9
.
22.
Wang
M
,
Yu
B
,
Westerlind
K
,
Strange
R
,
Khan
G
,
Patil
D
, et al
Prepubertal physical activity up-regulates estrogen receptor beta, BRCA1 and p53 mRNA expression in the rat mammary gland
.
Breast Cancer Res Treat
2009
;
115
:
213
20
.
23.
Corder
K
,
Brage
S
,
Ekelund
U
. 
Accelerometers and pedometers: methodology and clinical application
.
Curr Opin Clin Nutr Metab Care
2007
;
10
:
597
603
.
24.
Vanhelst
J
,
Mikulovic
J
,
Bui-Xuan
G
,
Dieu
O
,
Blondeau
T
,
Fardy
P
, et al
Comparison of two ActiGraph accelerometer generations in the assessment of physical activity in free living conditions
.
BMC Res Notes
2012
;
5
:
187
.
25.
Troiano
RP
,
Berrigan
D
,
Dodd
KW
,
Masse
LC
,
Tilert
T
,
McDowell
M
. 
Physical activity in the United States measured by accelerometer
.
Med Sci Sports Exerc
2008
;
40
:
181
8
.
26.
Swartz
AM
,
Strath
SJ
,
Bassett
DR
 Jr
,
O'Brien
WL
,
King
GA
,
Ainsworth
BE
. 
Estimation of energy expenditure using CSA accelerometers at hip and wrist sites
.
Med Sci Sports Exerc
2000
;
32
(
9 Suppl
):
S450
6
.
27.
Ainsworth
BE
,
Haskell
WL
,
Whitt
MC
,
Irwin
ML
,
Swartz
AM
,
Strath
SJ
, et al
Compendium of physical activities: an update of activity codes and MET intensities
.
Med Sci Sports Exerc
2000
;
32
(
9 Suppl
):
S498
504
.
28.
Bayatti
N
,
Cooper-Knock
J
,
Bury
JJ
,
Wyles
M
,
Heath
PR
,
Kirby
J
, et al
Comparison of blood RNA extraction methods used for gene expression profiling in amyotrophic lateral sclerosis
.
PLoS One
2014
;
9
:
e87508
.
29.
Geiss
GK
,
Bumgarner
RE
,
Birditt
B
,
Dahl
T
,
Dowidar
N
,
Dunaway
DL
, et al
Direct multiplexed measurement of gene expression with color-coded probe pairs
.
Nat Biotechnol
2008
;
26
:
317
25
.
30.
Schroeder
A
,
Mueller
O
,
Stocker
S
,
Salowsky
R
,
Leiber
M
,
Gassmann
M
, et al
The RIN: an RNA integrity number for assigning integrity values to RNA measurements
.
BMC Mol Biol
2006
;
7
:
3
.
31.
Fleige
S
,
Pfaffl
MW
. 
RNA integrity and the effect on the real-time qRT-PCR performance
.
Mol Aspects Med
2006
;
27
:
126
39
.
32.
Nanostring Technologies I
. 
nCounter expression data analysis guide
.
[cited 2014 May 20]. Available from
: http://www.nanostring.com/media/pdf/MAN_nCounter_Gene_Expression_Data_Analysis_Guidelines.pdf%3E, 
2012
.
33.
Rahmati
S
,
Abovsky
M
,
Pastrello
C
,
Chorlton
S
,
Sheahan
N
,
Kotlyar
M
et al 
pathDIP: A comprehensive resource for known and predicted human gene-pathway associations
. (http://ophid.utoronto.ca/navigator/).
34.
Kamburov
A
,
Stelzl
U
,
Lehrach
H
,
Herwig
R
. 
The ConsensusPathDB interaction database: 2013 update
.
Nucleic Acids Res
2013
;
41
(
Database issue
):
D793
800
.
35.
Brown
KR
,
Jurisica
I
. 
Unequal evolutionary conservation of human protein interactions in interologous networks
.
Genome Biol
2007
;
8
:
R95
.
36.
Kotlyar
M
,
Pastrello
C
,
Pivetta
F
,
Lo Sardo
A
,
Cumbaa
C
,
Li
H
, et al
In silico prediction of physical protein interactions and characterization of interactome orphans
.
Nat Methods
2015
;
12
:
79
84
.
37.
Shen
D
,
Mao
W
,
Liu
T
,
Lin
Q
,
Lu
X
,
Wang
Q
, et al
Sedentary behavior and incident cancer: a meta-analysis of prospective studies
.
PLoS One
2014
;
9
:
e105709
.
38.
Schmid
D
,
Leitzmann
MF
. 
Television viewing and time spent sedentary in relation to cancer risk: a meta-analysis
.
J Natl Cancer Inst
2014
;
106
.
39.
Janssen
I
. 
Health care costs of physical inactivity in Canadian adults
.
Appl Physiol Nutr Metab
2012
;
37
:
803
6
.
40.
World Health Organization
. 
Global Strategy on Diet, Physical Activity and Health. Physical Activity and Adults
.
Available from
: http://www.who.int/dietphysicalactivity/factsheet_adults/en/.
41.
Huen
MS
,
Sy
SM
,
Chen
J
. 
BRCA1 and its toolbox for the maintenance of genome integrity
.
Nat Rev Mol Cell Biol
2010
;
11
:
138
48
.
42.
Narod
SA
,
Foulkes
WD
. 
BRCA1 and BRCA2: 1994 and beyond
.
Nat Rev Cancer
2004
;
4
:
665
76
.
43.
Kotsopoulos
J
,
Zhang
S
,
Akbari
M
,
Salmena
L
,
Llacuachaqui
M
,
Zeligs
M
, et al
BRCA1 mRNA levels following a 4-6-week intervention with oral 3,3′-diindolylmethane
.
Br J Cancer
2014
;
111
:
1269
74
.
44.
Magbanua
MJ
,
Richman
EL
,
Sosa
EV
,
Jones
LW
,
Simko
J
,
Shinohara
K
, et al
Physical activity and prostate gene expression in men with low-risk prostate cancer
.
Cancer Causes Control
2014
;
25
:
515
23
.
45.
Nkondjock
A
,
Robidoux
A
,
Paredes
Y
,
Narod
SA
,
Ghadirian
P
. 
Diet, lifestyle and BRCA-related breast cancer risk among French-Canadians
.
Breast Cancer Res Treat
2006
;
98
:
285
94
.
46.
Kohl
HW
 3rd
,
Craig
CL
,
Lambert
EV
,
Inoue
S
,
Alkandari
JR
,
Leetongin
G
, et al
The pandemic of physical inactivity: global action for public health
.
Lancet
2012
;
380
:
294
305
.
47.
Calle
EE
,
Kaaks
R
. 
Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms
.
Nat Rev Cancer
2004
;
4
:
579
91
.
48.
Healy
GN
,
Dunstan
DW
,
Salmon
J
,
Cerin
E
,
Shaw
JE
,
Zimmet
PZ
, et al
Breaks in sedentary time: beneficial associations with metabolic risk
.
Diabetes Care
2008
;
31
:
661
6
.
49.
Healy
GN
,
Matthews
CE
,
Dunstan
DW
,
Winkler
EA
,
Owen
N
. 
Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 2003-06
.
Eur Heart J
2011
;
32
:
590
7
.
50.
Lynch
BM
,
Friedenreich
CM
,
Winkler
EA
,
Healy
GN
,
Vallance
JK
,
Eakin
EG
, et al
Associations of objectively assessed physical activity and sedentary time with biomarkers of breast cancer risk in postmenopausal women: findings from NHANES (2003–2006)
.
Breast Cancer Res Treat
2011
;
130
:
183
94
.
51.
Magnard
C
,
Bachelier
R
,
Vincent
A
,
Jaquinod
M
,
Kieffer
S
,
Lenoir
GM
, et al
BRCA1 interacts with acetyl-CoA carboxylase through its tandem of BRCT domains
.
Oncogene
2002
;
21
:
6729
39
.
52.
Jackson
KC
,
Gidlund
EK
,
Norrbom
J
,
Valencia
AP
,
Thomson
DM
,
Schuh
RA
, et al
BRCA1 is a novel regulator of metabolic function in skeletal muscle
.
J Lipid Res
2014
;
55
:
668
80
.
53.
Shephard
RJ
. 
Limits to the measurement of habitual physical activity by questionnaires
.
Br J Sports Med
2003
;
37
:
197
206
.
54.
Prince
SA
,
Adamo
KB
,
Hamel
ME
,
Hardt
J
,
Connor Gorber
S
,
Tremblay
M
. 
A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review
.
Int J Behav Nutr Phys Act
2008
;
5
:
56
.
55.
Whitney
AR
,
Diehn
M
,
Popper
SJ
,
Alizadeh
AA
,
Boldrick
JC
,
Relman
DA
, et al
Individuality and variation in gene expression patterns in human blood
.
Proc Natl Acad Sci U S A
2003
;
100
:
1896
901
.
56.
Cabanes
A
,
Wang
M
,
Olivo
S
,
DeAssis
S
,
Gustafsson
JA
,
Khan
G
, et al
Prepubertal estradiol and genistein exposures up-regulate BRCA1 mRNA and reduce mammary tumorigenesis
.
Carcinogenesis
2004
;
25
:
741
8
.
57.
Perrin-Vidoz
L
,
Sinilnikova
OM
,
Stoppa-Lyonnet
D
,
Lenoir
GM
,
Mazoyer
S
. 
The nonsense-mediated mRNA decay pathway triggers degradation of most BRCA1 mRNAs bearing premature termination codons
.
Hum Mol Genet
2002
;
11
:
2805
14
.