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 [7]. Depending on receptor distributions, serotonin activity can promote or reduce apoptosis [8]. Our previous work explored the activity of serotonin in an array of pathologies, particularly those in which epidemiological data suggests gender differences [7]. 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[14]. 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[17], decrease incidence of cancer in both animals [19] and humans [9], and can be used as a treatment for lymphoma/leukemia [20].

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 [25]. 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 [28] and drugs that prevent norepinephrine signaling can reduce cancer risk [29]. In contrast the role of serotonin is less clear, while drugs that increase serotonin seem to have a protective effect in leukemias/lymphoms [20] as well as other cancers [12], increased serotonin appears to promote mitosis in vitro [11]. 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 [16].

Interestingly, MAO-A inhibitors have been identified in tobacco [39]. 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 [40]. 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.

Table ​Table22 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 [43] 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.

As our interest was in tryptophan-related genes, we examined all human, mouse, and zebrafish tryptophan pathway genes (~60 genes depending on the species and gene chip) listed in Kyoto Encyclopedia of genes and genomes (KEGG) using BRB-array tools [44] developed by the National Cancer Institute's Biometrics Research Branch (NCI BRB). Table ​Table11 lists all the tryptophan genes in KEGG and Figure ​Figure22 is a graphical representation of the tryptophan genes included in this analysis, and the number of datasets in which these genes were differentially expressed. First, differences in the expression levels of the genes were examined within each of the individual cancer datasets. We performed either related samples or independent samples t-tests (as appropriate, see Table ​Table2)2) on the change in expression of the genes between control and cancer samples for each dataset. Since we focused on the behavior of individual genes across multiple types of cancer rather than groups of genes in individual datasets we only compensated for the multiple t-tests over the composite number of datasets. The probability that we would observe the same false positive from among approximately 60 genes, in at least 16 out of the 19 separate datasets, is at most of the order of 7.8 × 10^-17, an unlikely outcome. Therefore finding one specific gene (MAO-A) which is consistently down-regulated in 16 out of 19 datasets is an indication that the observed down-regulation of MAO-A in cancer tissues cannot be attributed to chance alone. Nonetheless, we did adjust for multiple t-tests among the 19 datasets by using the Bonferroni-Holm adjustment [45,46]. Specifically, we sorted the p-values for t-tests of expression of each gene from smallest to largest and compared the i-th p-value to the original alpha level (0.05) divided by the number of data sets+1-i. Thus we compared the smallest (first) p-value with 0.05/18 = 0.0028 and the largest (last) p-value with 0.05/1 = 0.05. For each dataset a list of tryptophan related genes whose expression level was significantly changed using these criteria was generated. Then the frequency of each gene appearing in all the lists was counted and the genes were sorted by the frequency from high to low. Only MAO-A was deregulated in the majority of the datasets analyzed.