Association of HADHA expression with the risk of breast cancer: targeted subset analysis and meta-analysis of microarray data
© Mamtani et al; licensee BioMed Central Ltd. 2011
Received: 20 September 2011
Accepted: 12 January 2012
Published: 12 January 2012
The role of n-3 fatty acids in prevention of breast cancer is well recognized, but the underlying molecular mechanisms are still unclear. In view of the growing need for early detection of breast cancer, Graham et al. (2010) studied the microarray gene expression in histologically normal epithelium of subjects with or without breast cancer. We conducted a secondary analysis of this dataset with a focus on the genes (n = 47) involved in fat and lipid metabolism. We used stepwise multivariate logistic regression analyses, volcano plots and false discovery rates for association analyses. We also conducted meta-analyses of other microarray studies using random effects models for three outcomes--risk of breast cancer (380 breast cancer patients and 240 normal subjects), risk of metastasis (430 metastatic compared to 1104 non-metastatic breast cancers) and risk of recurrence (484 recurring versus 890 non-recurring breast cancers).
The HADHA gene [hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/enoyl-CoA hydratase (trifunctional protein), alpha subunit] was significantly under-expressed in breast cancer; more so in those with estrogen receptor-negative status. Our meta-analysis showed an 18.4%-26% reduction in HADHA expression in breast cancer. Also, there was an inconclusive but consistent under-expression of HADHA in subjects with metastatic and recurring breast cancers.
Involvement of mitochondria and the mitochondrial trifunctional protein (encoded by HADHA gene) in breast carcinogenesis is known. Our results lend additional support to the possibility of this involvement. Further, our results suggest that targeted subset analysis of large genome-based datasets can provide interesting association signals.
KeywordsBreast cancer HADHA gene Gene expression profiling Microarray Meta-analysis
Early detection of malignant breast neoplasms is critical to cancer prevention and treatment. Cancer chemoprevention (also called as treatment of carcinogenesis) is a primordial prevention step that is receiving considerable attention. In that context, the identification of an ideal biomarker for breast cancer has become increasingly important. In spite of the vast number of studies conducted in the past; a recent, comprehensive and elegant review argues that there is still a lack of clarity regarding the understanding of the process of breast carcinogenesis . Interestingly, it has been demonstrated that the mammary gland basal cells have features consistent with the progenitor stem cells and that they can differentiate into benign or malignant lesions intraductally . It has also been shown in murine models that differentiated intact mammary glands can exert a negative influence on the development of breast cancer . However, the search for an ideal breast cancer biomarker is still on .
A logical undertaking in this direction is the use of microarrays to study the differential gene expressions in breast cancer. Consistent with the spirit of research that encourages very early detection of carcinogenesis, Graham et al.  recently studied histologically normal epithelium from subjects with and without breast cancer and identified an 86-gene signature that indicated a genomic change prior to carcinogenesis. They found that many of these genes belonged to the family of growth factors, cytokines, oxidative stress modifiers, p38 MAP kinase pathway members, transcription regulators or determinants of nucleic acid stability .
Interestingly they did not find genes associated with fatty acid or lipid metabolism to be differentially expressed in histologically normal epithelium. Derangements of fatty acid and lipid metabolism have been implicated in oncogenesis in many studies, especially in the cancer of breast . It is generally believed that diets rich in n-6 polyunsaturated fatty acids (n-6 PUFA) and saturated fatty acids (SFA) increase the risk of tumorigenesis while diets rich in n-3 polyunsaturated fatty acids (n-3 PUFA) reduce the risk of cancer development [7–10]. Lipids have the ability to influence the process of neoplasia via their effects on hormone status, cell membrane integrity, signal transduction, immune modulation and regulation of gene expression [11, 12]. In this study, we specifically examined whether the genes related to fatty acid and lipid metabolism are also differentially associated with breast cancer status. For this, we used a targeted subset analysis of the microarray data from Graham et al.  and also conducted meta-analyses of other microarray datasets.
The primary dataset
The microarray dataset used in the present study is available for public use on the Gene Expression Omnibus website http://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS3716 of the National Institutes of Health, USA. Details of the study subjects on whom these microarray studies were conducted have been described previously . Briefly, the dataset comprises microarray data collected through Affymetrix Human Genome U133A platform that measures expression of 22,283 genes. The data were collected using histologically normal epithelium from four sets of subjects--those who underwent reduction mammoplasty (n = 18), those who underwent preventive mastectomy (n = 6), estrogen receptor positive (ER+) breast cancer patients (n = 9) and estrogen receptor negative (ER-) breast cancer patients (n = 9). The data were available in normalized format.
Targeted subset analysis
Genes included in the analyses
Affymetrix Probe Set Id
acetyl-Coenzyme A acyltransferase 1
acyl-Coenzyme A dehydrogenase, long chain
acyl-Coenzyme A dehydrogenase, C-4 to C-12 straight chain
acetyl-Coenzyme A acetyltransferase 1
acyl-CoA synthetase bubblegum family member 2
acyl-CoA synthetase long-chain family member 3
acyl-CoA synthetase long-chain family member 4
acyl-CoA synthetase long-chain family member 5
alcohol dehydrogenase 1B (class I), beta polypeptide; alcohol dehydrogenase 1A (class I), alpha polypeptide; alcohol dehydrogenase 1C (class I), gamma polypeptide
alcohol dehydrogenase 6 (class V)
adiponectin, C1Q and collagen domain containing
1-acylglycerol-3-phosphate O-acyltransferase 2 (lysophosphatidic acid acyltransferase, beta)
N-acylsphingosine amidohydrolase (acid ceramidase) 1
cholesteryl ester transfer protein, plasma
cytochrome P450, family 7, subfamily A, polypeptide 1
diacylglycerol O-acyltransferase homolog 1 (mouse)
enoyl-Coenzyme A, hydratase/3-hydroxyacyl Coenzyme A dehydrogenase
fatty acid binding protein 2, intestinal
fucosyltransferase 2 (secretor status included)
fucosyltransferase 4 (alpha (1,3) fucosyltransferase, myeloid-specific)
fucosyltransferase 5 (alpha (1,3) fucosyltransferase)
fucosyltransferase 9 (alpha (1,3) fucosyltransferase)
glutaryl-Coenzyme A dehydrogenase
glycerol kinase 2
hydroxyacyl-Coenzyme A dehydrogenase/3-ketoacyl-Coenzyme A thiolase/enoyl-Coenzyme A hydratase (trifunctional protein), alpha subunit
lymphotoxin alpha (TNF superfamily, member 1)
microsomal triglyceride transfer protein
NPC1 (Niemann-Pick disease, type C1, gene)-like 1
nuclear receptor subfamily 1, group H, member 3
oxidized low density lipoprotein (lectin-like) receptor 1
phosphoenolpyruvate carboxykinase 2 (mitochondrial)
phospholipid transfer protein
peroxisome proliferator-activated receptor gamma
retinoid × receptor, beta
retinoid × receptor, gamma
sphingomyelin synthase 1
ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 1
uncoupling protein 1 (mitochondrial, proton carrier)
uncoupling protein 3 (mitochondrial, proton carrier)
UDP-glucose ceramide glucosyltransferase
Replication of the results: meta-analyses
We also aimed to ensure that the results obtained from one microarray dataset were robust and could be replicated in other datasets. We queried the Oncomine database and retrieved microarray data from other relevant studies. We studied the association of gene expression with three outcomes--risk of breast cancer, risk of metastasis and risk of recurrence. We then combined these datasets meta-analytically using the random effects model of DerSimonian and Laird [13, 14]. For these analyses the effect size was measured and expressed as the standardized mean difference (SMD) and its 95% confidence intervals. The Oncomine website reports the results as means, medians, quartiles and minimum and maximum values. Since the random-effects model assumes normal distribution of the effect measures, we first estimated the mean and standard error for each group (for example, for subjects with breast cancer; subjects with a metastatic event or subjects with recurring breast cancer) using the method described by Hozo et al.  We then estimated the SMDs. To depict the potential variability in the HADHA expression based on the probes used by individual studies, we conducted the meta-analyses separately for each combination of the study and the probe used. Each comparison represented a specific combination of the included study and the reporter used in the study. The between-study heterogeneity in this meta-analysis was examined using the I2 statistic. Since expression data on all individual subjects was available for the outcome of risk of breast cancer, we also conducted individual patient data (IPD) meta-analysis . For this we used the clustered unconditional logistic regression analyses [16, 17] with disease status as a dichotomous dependent variable, comparison-specific z-scores as the predictor variable and comparison indicator as the clustering variable. Comparison-specific z-scores were estimated as the relative deviates (mean expression/standard deviation of expression) within each comparison group.
Other statistical analysis
To quantify and test differential gene expression, we used two-tailed Student's t tests for unpaired samples. The clinical and statistical significance of the findings were presented as volcano plots. To account for multiple testing, we estimated the false discovery rates (q values) using the QVALITY software program . Discriminant utility of each gene was assessed using non-parametric receiver operating characteristic (ROC) curve analysis. To group subjects based on their HADHA expression, we used a k-means clustering approach. All statistical analyses were conducted using Stata 10.0 software package (Stata Corp, College Station, Texas). We aimed for a type I error rate of 0.05 and a false discovery rate of 0.15.
Differential expression analyses
Does ER status influence the expression of HADHA?
To examine if this association could be influenced by the ER status, we conducted three sets of analyses. First, we studied whether HADHA expression was different based on the ER status. We found that the mean HADHA was not significantly differentially expressed by ER status (mean HADHA expression in subjects with ER + breast cancer = 6.00; in subjects with ER-breast cancer = 5.90; p = 0.462). Second, we adjusted the standard error estimates for the ER status using clustered logistic regression and observed that the statistical significance for the HADHA gene expression further increased (p = 0.0001) while that of the ARSA gene decreased (p = 0.082) indicating that the association of HADHA was unlikely to have been influenced by the ER status. Third, we constructed volcano plots and conducted stepwise logistic regression analyses by comparing the ER + and ER-subjects separately with subjects without cancer as the reference group. We observed (Figure 1C-F) that HADHA gene expression was the only consistent predictor across ER status but more so in the ER-subjects. Indeed, the q value for the HADHA gene was 0.15 for the cancer versus no cancer comparison, 0.13 for the ER-versus no cancer comparison but 0.88 for the ER + versus no cancer comparison. Two other genes (UCP3 and DGAT1) were retained in the final model of stepwise regression analyses when ER-subjects were compared to the no cancer group however this association was not observed when ER + subjects were compared to the same reference group.
Graded risk of breast cancer based on HADHA expression
We next considered whether the association of HADHA gene expression with risk of breast cancer exhibited a threshold effect or whether it was a graded dose-response. For this, we used two approaches. First, we normalized the gene expression in the no cancer group to 100%. We found (Figure 1G) that the HADHA expression had fallen to 73% (95% CI 64%-83%) in subjects with cancer; with a higher expression in ER + subjects (76% of the no cancer group, 95% CI 61%-91%) than in ER-subjects (70% of the no cancer group, 95% CI 55%-85%). Second, the k-means clusters (which explained 95.9% of the variability in HADHA expression) clearly demonstrated a dose-response association (Figure 1H) such that more severe down-regulation of HADHA was associated with a greater risk of being in the breast cancer group.
Meta-analyses of the differential expression of HADHA
Comparisons included in the meta-analysis of differential HADHA expression
Breast cancer histology
Invasive ductal carcinoma
Invasive lobular carcinoma
Invasive ductal carcinoma
Invasive mixed carcinoma
Ductal carcinoma in situ
Invasive ductal carcinoma
Invasive ductal carcinoma
Invasive ductal carcinoma
Invasive ductal carcinoma
Invasive ductal carcinoma
Invasive ductal carcinoma
Invasive lobular carcinoma
Invasive lobular carcinoma
Invasive lobular carcinoma
Invasive breast carcinoma
Invasive breast carcinoma
We observed that the invasive ductal carcinoma (p = 0.046) and unspecified invasive breast carcinoma (p = 0.005) showed a significant under-expression of HADHA gene but lobular carcinoma (p = 0.781), invasive lobular carcinoma (p = 0.780) or invasive mixed carcinoma (p = 0.717) did not show a significant alteration of HADHA gene expression. Alternatively, we conducted the IPD meta-analysis using logistic regression analyses. We found that the odds ratio for breast cancer was 0.74 (95% CI 0.60-0.92) after clustered analyses. Thus, there was a 26% reduction in the risk of breast cancer per unit increase in z-scores. These values show a striking resemblance with the findings observed in the Graham et al. dataset and demonstrate the replicability of our findings.
Our analyses of the microarray dataset based on the Graham et al.  study demonstrated a consistent, strong and significant association of the HADHA gene expression in histologically normal epithelium with the likelihood of breast cancer. Moreover, this observation was further substantiated by the meta-analysis of other published studies. Only one study has previously reported differential association of this gene with regard to BRCA1 positive, BRCA2 positive and sporadic malignant tumors of the breast . Our results further support the putative involvement of HADHA in breast cancer susceptibility.
Biological significance of our novel observations should be considered in the light of the following facts. First, the HADHA gene (chromosomal location 2p23) codes for the four alpha chains in the 8-meric mitochondrial trifunctional protein (TFP) . This enzyme performs three cardinal functions in the β-oxidation of long chain fatty acids by catalyzing the activities of the 2-enoyl-CoA hydratase (ECH), L-3-hydroxyacyl-CoA dehydrogenase (HACD) and 3-ketoacyl-CoA thiolase (KACT). Of these three, the first two enzymes (ECH and HACD) are specifically catalyzed by the alpha chains of TFP. Severe deficiency (< 50% of normal activity) of TFP is known to be associated with life-threatening manifestation of the long chain 3-hydroxyacyl-CoA dehydrogenase deficiency . However, the effects of a milder deficiency of TFP (for example, activity between 50%-80% of the normal) are currently unknown. Our results indicate that breast cancer patients had 18-30% decreased expression of HADHA gene. We therefore hypothesize that there may be a compromised metabolism of long chain fatty acids in breast cancer due to a relative deficiency of the alpha chains of TFP. In this context, it is noteworthy that a recent large genome-wide association study  found a strong association of breast cancer with a polymorphism in the gene encoding enoyl CoA hydratase domain containing 1 (ECHDC1), which also partakes in the integrity of the TFP.
Second, the efficacy of β-oxidation of n-3 and n-6 long chain fatty acids can be tissue- and location- specific. For example, in rat livers it has been shown that the n-3/n-6 ratio influences peroxisomal but not mitochondrial β-oxidation . In contrast, mitochondrial β-oxidation of long chain fatty acid has been implicated in breast cancer pathogenesis . We also could not demonstrate a significant association of the genes involved in the PPAR-γ pathway reinforcing the possibility that mitochondrial rather than peroxisomal β-oxidation of long chain fatty acids may be more critical in breast carcinogenesis. Third, HADHA occupies an important position in the network of genes that have been implicated in autophagy and apoptosis . Finally, triangulation of the following facts lends additional credence to our observations: i) intact epithelium of mammary glands has the ability to act as stem cells for carcinogenesis ; ii) n-3 long chain fatty acids have the ability to target such stem cells ; and iii) HADHA is involved in the mitochondrial β-oxidation of long chain fatty acids. Together these observations from published literature strongly support the biological plausibility of our finding that HADHA is differentially expressed in subjects with and without breast cancer.
Our study has all the limitations implicit in any microarray association study and meta-analyses. In addition, there are three more limitations. First, although there is a strong circumstantial evidence that favors an inference of HADHA expression-breast cancer association, it must be realized that robust functional studies are required before this association can be conclusively claimed. Our study does not have a component of functional assays that can help put these results in a biological perspective. Second, due to limitations imposed by the microarray platform used in the primary study, we could not evaluate the potential association of a large number of additional lipid and fat metabolism related genes with the risk of breast cancer. Inclusion of those genes may not only affect the q values associated with HADHA but also may provide a more comprehensive understanding of the role of fatty acids in breast cancer. Thirdly, although consistent, the observed differential expression of HADHA with cancer progression (as reflected by risk of metastasis and recurrence) is statistically inconclusive.
Our study has three important implications--biological, methodological and epidemiologic. Biologically, our study has identified a novel target gene that corroborates the existing knowledge about the role of long chain fatty acids in breast cancer and provides interesting directions for further research in this area. Also, our findings put the focus on the putative functional aspects of mitochondria and TFP in breast carcinogenesis.
From a methodological standpoint, our study shows that high dimensionality of omics-type datasets is fraught with the vexing problem of finding strong associations at the cost of potentially missing weaker but biologically meaningful associations. Literature addressing the issue of multiple comparisons in large volume datasets focuses primarily on the possibility of finding false positive associations . However, there exists a demonstrable probability that such high-volume datasets may also falsely mask true associations. It is likely that the Graham et al. study did not report a significant association of HADHA with the risk of breast cancer due to a large number of multiple comparisons. The fact that we discovered an association of HADHA with breast cancer shows that microarray dataset analysis (as well as analyses of other large datasets like genome-wide association studies, proteomics data or metabolomics datasets) may benefit by using targeted subset analyses based on functional annotation and conceptual understanding of the molecular mechanisms in disease. Finally, in an epidemiological context, our study shows that error in long chain fatty acid metabolism in the breast tissue might herald the onset of carcinogenesis and thus can be helpful for the primordial prevention of breast cancer.
- n-3 PUFA:
Omega--3 polyunsaturated fatty acids
- n-6 PUFA:
Omega--6 polyunsaturated fatty acids
Saturated fatty acids
Standardized mean difference
We sincerely acknowledge and deeply appreciate the Editorial assistance provided by Cindy Tumiel, Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, USA.
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