An indicator of cancer: downregulation of Monoamine Oxidase-A in multiple organs and species
© Rybaczyk et al; licensee BioMed Central Ltd. 2008
Received: 30 July 2007
Accepted: 20 March 2008
Published: 20 March 2008
Identifying consistent changes in cellular function that occur in multiple types of cancer could revolutionize the way cancer is treated. Previous work has produced promising results such as the identification of p53. Recently drugs that affect serotonin reuptake were shown to reduce the risk of colon cancer in man. Here, we analyze an ensemble of cancer datasets focusing on genes involved in the serotonergic pathway. Genechip datasets consisting of cancerous tissue from human, mouse, rat, or zebrafish were extracted from the GEO database. We first compared gene expression between cancerous tissues and normal tissues for each type of cancer and then identified changes that were common to a variety of cancer types.
Our analysis found that significant downregulation of MAO-A, the enzyme that metabolizes serotonin, occurred in multiple tissues from humans, rodents, and fish. MAO-A expression was decreased in 95.4% of human cancer patients and 94.2% of animal cancer cases compared to the non-cancerous controls.
These are the first findings that identify a single reliable change in so many different cancers. Future studies should investigate links between MAO-A suppression and the development of cancer to determine the extent that MAO-A suppression contributes to increased cancer risk.
One of the key goals in cancer research is to identify biological changes that distinguish normal tissue from cancerous tissue. A common approach to identifying oncogenes has been to assess gene expression in each type of cancer and compare it to non-cancerous tissue of the same organ. Comparisons within a single cancer type (e.g., breast cancer) or class (e.g., leukemia) have yielded potential oncogenic mechanisms that have been successfully used to develop therapeutic strategies for individual cancer types. For instance, real-time polymerase chain reaction (qPCR) research has found increased expression of oncogenes like c-myc  and decreased expression of tumor suppressors like Rb . Western blotting has been used to show overexpression of functional erb-B2 in breast cancers  and ovarian cancers . Unfortunately, comparing the data obtained from studies of individual types of cancer has resulted in only limited success at detecting consistent changes among different types of cancers. One such success is the identification of a mutation in p53, a protein responsible for repairing cellular DNA, which occurs in approximately 50% of all cancers . The discovery of similarities among various cancer tissues is the first step in identifying a common mechanism that contributes to the development of cancer. Once a change is identified, appropriate therapeutic targets can be developed to help physicians identify at-risk individuals and improve patient care. Indeed, novel therapeutic strategies have been developed as a result of the extensive study of p53 .
Although best known as a neurotransmitter, only 1% of the tryptophan (Trp) derivative, serotonin (5-HT), is found in the nervous system. The remaining serotonin is found in the periphery and is active in the immune, circulatory, reproductive, musculoskeletal, and gastrointestinal systems . Depending on receptor distributions, serotonin activity can promote or reduce apoptosis . Our previous work explored the activity of serotonin in an array of pathologies, particularly those in which epidemiological data suggests gender differences . We proposed that estrogenic effects on serotonergic function and receptor distribution could explain gender differences in pathologic incidence, as well as some of the effects of estrogen on breast cancer. Rather than being specific to breast cancer, the role of serotonin and its precursor Trp in cellular physiology suggests that the metabolic pathway of tryptophan and as a result serotonin metabolism may be involved in the promotion or progression of cancers in general. There is some literature supporting this hypothesis [9–17], but further research is needed to understand the exact relationship between Trp or its metabolites and cancer. Recently a mechanism was proposed by which catabolism of Trp by indoleamine 2,3-dioxygenase can be linked to immune evasion in tumor cells [13, 18]. Other studies suggest that decreased serum tryptophan levels are predictive of poorer prognosis and quality of life in cancer patients. For serotonin specifically, several studies have shown that the selective serotonin reuptake inhibitors (SSRIs) which prevent the reuptake of serotonin thus increase extracellular serotonin levels, have anti-cancer activity in cancer cell lines, decrease incidence of cancer in both animals  and humans , and can be used as a treatment for lymphoma/leukemia .
Using genechip technology to study multiple types of cancer simultaneously we can identify whether multiple cancers have similar gene expression changes . Here, we analyzed an ensemble of cancer genechip datasets focusing on genes involved in tryptophan metabolism, which include serotonergic genes among them. We first compare gene expression between cancerous tissues and normal tissues for each type of cancer and then identify changes that are common to a variety of cancer types.
Genes listed in the tryptophan pathway in KEGG.
HemK methyltransferase family member 1
Amiloride binding protein 1
acetyl-Coenzyme A acetyltransferase 1
indoleamine-pyrrole 2,3 dioxygenase
acetyl-Coenzyme A acetyltransferase 2
indoleamine-pyrrole 2,3 dioxygenase-like 1
Aminocarboxymuconate semialdehyde decarboxylase
Aldehyde dehydrogenase 1 family, member A
Aldehyde dehydrogenase 1 family, member B
Leucine carboxyl methyltransferase 1
Aldehyde dehydrogenase 2 family
Leucine carboxyl methyltransferase 2
Aldehyde dehydrogenase 3 family
ligand of numb-protein X 1
Aldehyde dehydrogenase 3 family, member A
monoamine oxidase A
Aldehyde dehydrogenase 7 family, member A
monoamine oxidase B
Aldehyde dehydrogenase 9 family, member A
methyltransferase like 2B
amine oxidase, copper containing 2
methyltransferase like 6
amine oxidase, copper containing 3
nuclear transcription factor, X-box binding
Aldehyde oxidase 1
Coactivator-associated arginine methyltransferase
Protein arginine methyltransferase
Protein arginine methyltransferase
Cytochrome P450, family 1, subfamily A
Protein arginine methyltransferase 5
Cytochrome P450, family 1, subfamily A
Protein arginine methyltransferase
cytochrome P450, family 1, subfamily B
Protein arginine methyltransferase 7
PRMT8, HRMT1L3, HRMT1L4
Protein arginine methyltransferase
enoyl Coenzyme A hydratase, short chain, 1,
TPH1, TPRH, TPH
Tryptophan hydroxylase 1
glutaryl-Coenzyme A dehydrogenase
Tryptophan hydroxylase 2
hydroxyacyl-Coenzyme A dehydrogenase
tryptophanyl tRNA synthetase 2, mitochondrial
hydroxyacyl-Coenzyme A dehydrogenase
Williams Beuren syndrome chromosome region
Descriptive information on datasets extracted from the GEO database used in this study.
Human Independent Samples (Case-control)
Cancer Type (N)
Mean Fold Difference in MAO-A
Percentage of cancer samples < lowest control
Cutaneous malignant melanoma
Malignant Melanoma (45)
Lung neuroendocrine tumor classification
Normal Lung (19)
Small Cell lung Carcinoma (15)
Human Malignant Pleural Mesothelioma
Normal Pleura (5)
Malignant pleural mesothelioma (40)
Breast tumor characterization
Normal Breast (10)
Basal-like Tumors (10)
Hypopharyngeal cancer at various stages of progression
Normal Uvula (3)
Early Stage (4)
Basal-like breast cancer tumors
Normal Breast (7)
Basal-like Cancer (18)
* Lung tissue and cell lines were excluded. Only primary tumor samples and normal pleura were analyzed.
GEO Series Number
Fold Difference in MAO-A
Human Squamous Cell Carcinoma of the lung
Clear Cell Carcinoma of the human kidney
Renal clear Cell Carcinoma (7)
Adjacent Normal (10)
Papillary thyroid cancer
Papillary thyroid Cancer (7)
Papillary thyroid cancer
Papillary thyroid Carcinoma (9)
**Paired data was extracted and unpaired samples were excluded.
GEO Series Number
Fold Difference in MAO-A
Urethane-induced lung tumor model of pulmonary adenocarcinoma
Adjacent Normal (10)
Liver cancer model
Normal Liver Tissue (10)
Liver Tumor (10)
LH overexpressing virgin mice (luteinizing hormone overexpression causes spontaneous mammary tumors)
Wild type Breast tissue (3)
LH-overexpressing Breast tissue (3)
Patched heterozygous model of medulloblastoma
Granule cell Precursor (4)
Tumor Cells from Heterozygotes (5)
C57/BL6 wild-type (6)
APC(Min/+) mutant (10)
N-methyl-N-nitrosourea-induced breast cancer model (R)
Mammary tumorigenesis in MMTV-neu model
Wild type Normal Breast (3)
Mammary tumors in MMTV-neu Model (7)
Within each dataset, between 67% to 100% of patients had MAO-A expression below the lowest control sample (see Table 2 for individual values). Examining data from individual patients among the paired data revealed a remarkable pattern of downregulation in cancerous tissue among paired samples analyzed. Only a subset of the datasets that compared cancerous tissue to normal tissue from the same patient failed to show significant downregulation. The three datasets that did not contain a significant shift in MAO-A expression after correcting for multiple t-tests were the two papillary thyroid cancer datasets and one gastric cancer dataset. However 69% of thyroid cancer patients and 100% of gastric cancer patients exhibited a decrease in expression compared to non-cancerous tissue from the same patient, this discordance suggests that a less pronounced downregulation of MAO-A occurs in patients with these two types of cancer.
To our knowledge there has been no previous report of any genetic mutation or expression change that occurs with such high frequency among patients in a variety of cancers. The regularity of this change suggests that low levels of MAO-A might serve as a biomarker for cancer. In addition, it is possible that the lack of significant differences in thyroid and gastric cancer could be a result of an overall decrease in MAO-A levels in both normal and cancerous tissue. Unfortunately, datasets that compared cancerous thyroid and gastric tissues to those of healthy individuals were not available at the time of this study.
In higher species there are two isoforms of Monoamine Oxidase (MAO): MAO-A and MAO-B. In humans MAO-A preferentially metabolizes serotonin and the dopamine derivatives epinephrine, and norepinephrine; specifically MAO-A is responsible for catalyzing the inactivation of serotonin by removal of a terminal amine group. MAO-B metabolizes phenethylamine, and both MAO-A and MAO-B metabolize dopamine. There is a significant amount of literature regarding the role of dopamine in carcinogenesis [10, 23, 24], and several studies have indicated that it may have an antiproliferative effect . The dopamine derivatives epinephrine and norepinephrine have also been implicated in carcinogenesis [26, 27]. These hormones are released in response to stress and are also metabolized by MOA-A. Both epinephrine and norepinephrine are capable of increasing cell growth in tumor cells  and drugs that prevent norepinephrine signaling can reduce cancer risk . In contrast the role of serotonin is less clear, while drugs that increase serotonin seem to have a protective effect in leukemias/lymphoms  as well as other cancers , increased serotonin appears to promote mitosis in vitro . However our findings of a consistent change in MAO-A, but not MAO-B, in conjunction with the literature provide some support for our premise that the critical role of MAO-A in cancer occurs via the serotonergic system rather than the dopaminergic or catecholamine system.
MAO-A is the target of monoamine oxidase inhibitors (MAOIs), a class of antidepressants. There have been several epidemiologic studies that examine the risk between antidepressants and cancer[15, 30–32]. While certain studies link the use of MAOIs with an increase risk for cancer[30, 32] the literature remains inconclusive about the exact risk [15, 30, 32, 33]. Given the downregulation of MAO-A observed in cancerous tissue here, one might expect that MAO-A-specific inhibitors may increase cancer risk. Indeed, MAO-AIs and other antidepressants increase cell proliferation in animal models [34–37]. If the same is true for humans, then MAO-AI's may be particularly likely to increase cancer risk.
SSRIs, another class of antidepressants, have recently been shown to be effective in vitro at inducing apoptosis in biopsy-like Burkitt's lymphoma cells, and have been shown in vivo to reduce the risk of colon cancer [9, 38]. Visual inspection of data from precancerous tissue in MMTV-Neu and Patched heterozygote transgenic mice suggests that there is more severe downregulation in MAO-A expression in cancer than in the pre-cancerous condition. This study adds to the evidence that SSRIs may be a safer alternative than other antidepressants for treatment of depression in patients with a precancerous condition (such as Atypical Ductal Hyperplasia or Barrett's Esophagus) or cancer .
Since genechips measure mRNA levels within a complex sample we were unable to discriminate between actively dividing cells and those at rest. It is possible the downregulation of MAO-A observed occurs in the resting cells. Although the presence of downregulation in many cancers is compelling, this evidence is not adequate to establish causation, and further clinical studies are needed to determine whether MAOIs and specifically MAO-AIs cause an increase in cancer risk.
Most current cancer research is focused on tissue-specific genetic mutations. Familial inheritance (e.g., APC in colon cancer), genetic mutation (e.g., p53), and overexpression of growth receptors (e.g., Her2-neu in breast cancer) can each lead to aberrant replication of a cell. Studies of these changes provide tremendous information about tissue-specific effects but are less informative about common changes that occur in multiple tissues. The similarity in the behavior of cancers from different organ systems and species indicates the potential for a universal change among cancers, regardless of the specific tissue or species. This study suggests that downregulation of MAO-A is such a change and could be an important indicator or even a factor in the development and spread of many types of cancers.
Interestingly, MAO-A inhibitors have been identified in tobacco . If decreased expression of MAO-A is demonstrated in clinical trials to be a risk factor for development of cancer, it may be particularly important for individuals with low levels of MAO-A to be advised against smoking. Future studies should investigate links between smoking, MAO-A suppression, and the development of cancer to determine whether MAO-A suppression might be a mechanism by which smoking contributes to increased cancer risk. There are already previously published reports showing that PET scans can detect decreased MAO-A levels in smokers . Our research shows that whole body PET scans might be a non-invasive way to identify MAO-A "cold spots", where localized downregulation indicates increased risk of cancer at that site. This type of a marker would permit more accurate diagnosis, leading to earlier treatment, and improved outcomes. Clinical studies are needed to determine whether changes in MAO-A can be used as a prognostic indicator of cancer risk in patients with a precancerous state.
Although the role of MAO-A in cancer has yet to be fully understood, examination of our results suggests its expression may act as a marker for the development of cancer. The presence of reduced expression of MAO-A in pre-cancerous states implies its levels indicate progression towards cancer, suggesting that MAO-A levels can be used to identify individuals that should receive increased surveillance and testing for the potential onset of cancer.
19 Genechip datasets consisting of cancerous tissue from 10 different organs derived from human, mouse, rat or zebrafish were extracted from the GEO profiles database. Datasets were first identified by using the search terms "cancer" or "metastasis". All datasets in the GEO profiles database as of March 31st 2007 were considered. The genechip data was selected such that both control and cancer samples were contained in the same dataset. Because of the differences in gene expression that are inherent to cell culture and fixation [41, 42], only data derived from non-processed primary biopsies was used. By definition cancer implies an invasive phenotype therefore all other tumor types were excluded such as adenomas, and carcinomas in situ. Among datasets that contained multiples types of cancer, the cancer with the closest number of samples to the control was used for analysis.
Table 2 describes the datasets used, which include 12 human cancer datasets, five mouse cancer datasets, one rat cancer dataset, and one dataset from zebrafish. All animal datasets were derived from cancers that were induced using viral, genetic, or by chemical means. No xenografts were included in this analysis. In total, 242 cancerous samples and 139 control samples were used. Prior to analysis, data that was logarithmic was transformed back to its original values, and all "null" values were excluded. Data points that were from more then one patient, also called pooled samples, were also excluded. Previous reports have shown that probe-sets can not be averaged  therefore all analyses were preformed on the probe-set with the highest mean expression value. Each genechip dataset was normalized (both intrachip and interchip) before being deposited in the GEO database. We independently validated the normalization in every dataset by inspecting the distribution of expression values. Datasets that were not appropriately normalized were excluded.
Monoamine Oxidase A
real time Polymerase Chain Reaction
5-hydroxytryptamine or Serotonin
Monoamine Oxidase B
National Center for Biotechnology Information Gene Expression Omnibus
Kyoto Encyclopedia of genes and genomes
Mouse Mammary Tumor Virus
Monoamine Oxidase Inhibitor
Positron Emission Tomography, Trp, Tryptophan
This work was partially supported by start-up funds provided by The Department of Biomedical Informatics at The Ohio State University. We thank Kelly Rybaczyk for her support during the research leading up to and the writing of this paper. In addition, we thank Fred Owens for helpful comments on an earlier draft of this manuscript. For their insights in the editing of this paper we thank Dr. Don Holzschu, Kristin Circle, and Dr. Jared Butcher. We are grateful to Lee Cooper for his help with generating the graphics in the manuscript. We also acknowledge Professor P.K. Pathak for his review of our statistical methods.
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