Interplay between gut microbial composition and the melatonergic pathway: implications for hormonal receptor-positive breast cancer development

doi:10.21203/rs.3.rs-4363611/v1

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Interplay between gut microbial composition and the melatonergic pathway: implications for hormonal receptor-positive breast cancer development

https://doi.org/10.21203/rs.3.rs-4363611/v1

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Background

This study aimed to investigate the intricate relationship between the gut microbiota and serum melatonin levels in hormonal receptor-positive breast cancer (BC) patients, focusing on alterations in microbial composition, the melatonergic pathway, and their implications for BC development.

Methods

Serum and fecal samples were obtained from hormonal receptor-positive BC patients and healthy controls. Mass spectrometry was used to measure the serum levels of serotonin, N-acetylserotonin (NAS), and melatonin and the fecal levels of short-chain fatty acids (SCFAs). Beta-glucuronidase (βGD) activity was quantified using a fluorometric assay kit, while arylalkylamine N-acetyltransferase (AANAT), acetylserotonin-O-methyltransferase (ASMT), and zonulin were assessed via ELISA. The gut microbiota composition was evaluated using 16S rRNA sequencing.

Results

We identified significant alterations in the gut microbiota composition and melatonin production of BC patients compared to healthy controls. This dysbiosis is characterized by heightened serum serotonin, N-acetylserotonin (NAS), and fecal β-glucuronidase (βGD) activity, concomitant with diminished serum melatonin levels in BC patients. Moreover, increased fecal levels of isovaleric acid (IVA) and isobutyric acid (IBA), coupled with increased serum zonulin levels, highlight intestinal permeability alterations that could facilitate the translocation of gut bacteria and inflammatory compounds, predisposing individuals to cancer development. Notably, we observed reduced gut microbiota diversity and significant shifts in predominant bacterial taxa, with Bacteroides eggerthii enrichment and a reduction in beneficial Bifidobacterium longum positively associated with serum melatonin levels, suggesting potential roles in BC development. Dysregulation of the serotonin-NAS-melatonin axis, along with perturbed expression of enzymes involved in the melatonergic pathway, underscores their implications in BC. Finally, we propose the NAS/melatonin ratio as a potential diagnostic biomarker for discriminating hormonal receptor-positive BC patients from healthy individuals, offering promising avenues for clinical management strategies.

Conclusions

Overall, our findings shed valuable light on the contributions of the gut microbiota and the melatonergic pathway to the development of hormonal receptor-positive BC, warranting further research into potential therapeutic targets.

serotonin-NAS-melatonin axis

gut microbiota

breast cancer

dysbiosis

melatonergic pathway

circadian disruption

Breast cancer (BC) remains a significant health concern globally, and hormonal BC, including the HER2+/- subtypes, poses particular challenges in its diagnosis and treatment. Dysbiosis and circadian disruption have emerged as key factors influencing BC development, particularly through their impact on estrogen metabolism. The gut microbiome, which comprises a diverse array of microorganisms, plays a pivotal role in regulating steroid hormone metabolism, thereby influencing estrogen levels. Bacteria with beta-glucuronidase (βGD) activity can deconjugate estrogens, leading to their reabsorption and contributing to heightened estrogen exposure, a recognized risk factor for BC development. Conversely, melatonin, with its antiestrogenic properties, counteracts this effect by modulating the activity of estrogen metabolism enzymes [1, 2].

Emerging evidence suggests a bidirectional relationship between breast cancer, sleep disturbances, and alterations in gut microbiota composition [3]. Melatonin, often referred to as the "hormone of darkness," not only regulates sleep-wake cycles but also profoundly affects the gut microbiota composition. By favoring the growth of beneficial bacteria while suppressing pathogenic species, melatonin contributes to gut homeostasis and potentially mitigates BC risk [4, 5]. Studies have revealed that women with BC and poor sleep quality often have elevated Firmicutes levels, suggesting reduced melatonin [6]. Circadian disruption is correlated with increased abundance of Ruminococcus (a proinflammatory bacteria), suggesting that it has glucuronidase activity [5]. Melatonin boosts Lactobacillus crispatus and Bifidobacterium, reducing estrogen metabolism [4]. It also enhances gut health by increasing the abundance of beneficial bacteria such as Lactobacillus and Akkermansia muciniphila while reducing the abundance of specific pathogenic bacteria, including Escherichia coli, Helicobacter pylori, Clostridiales, Bacteroides massiliensis, and Erysipelotrichaceae [7, 8].

The synthesis of melatonin follows the tryptophan metabolism pathway, which involves several enzymatic steps [9]. Dysregulation of this pathway, coupled with alterations in tryptophan metabolism via the kynurenine pathway, has been implicated in BC development. Enzymes involved in melatonin synthesis, such as arylalkylamine N-acetyltransferase (AANAT) and acetylserotonin-O-methyltransferase (ASMT), play pivotal roles in regulating melatonin production, with disruptions in their activity potentially contributing to BC progression [10, 11].

The gut is a primary site for melatonin production, exceeding pineal gland levels significantly, especially after ingestion [12]. The gut microbiota contributes to melatonin production directly and indirectly by stimulating serotonin production, which is crucial for melatonin synthesis [13, 14]. Dysbiosis alters the gut microbiota composition, diverting tryptophan from the melatonergic pathway and reducing melatonin levels, thereby increasing BC risk. Reduced short-chain fatty acids (SCFAs), particularly butyrate, diminish melatonin production, elevating the NAS/melatonin ratio [3, 15]. These alterations in BC cell melatonergic pathways induce changes in microbiome composition and intestinal permeability. Dysbiosis exacerbates intestinal permeability, elevating proinflammatory cytokines and tryptophan catabolite levels and impacting melatonin production [15]. Melatonin locally modulates intestinal inflammation, reducing proinflammatory cytokines and enhancing intestinal barrier integrity [5]. It decreases Clostridiales abundance while increasing Lactobacillus abundance, which is associated with reduced intestinal permeability [7]. Thus, alterations in the gut microbiota influence the regulation of the melatonergic pathway, and changes in melatonin synthesis affect the microbiota, contributing to BC development and progression.

The objective of this study was to investigate the involvement of the gut microbiota, metabolites, and key enzymes in the melatonergic pathway in patients with hormonal receptor-positive BC (HER2+/-) compared to a control group of healthy subjects, with the aim of assessing BC development. Furthermore, we evaluated βGD activity, the levels of estrogens and SCFAs, and intestinal permeability (measured by zonulin) in these groups and elucidated any potential associations with the gut microbiota-melatonergic pathway axis.

Study design and participants

A cross-sectional study was performed on female patients diagnosed with primary BC and 16 healthy women aged 44–85 years. All subjects were recruited from the Intercenter Medical Oncology Service at the Virgen de la Victoria University Hospital of Málaga. The diagnosis of BC was established based on expert clinical assessments by pathologists. The exclusion criteria were as follows: triple negative BC; other cancer types or prior history of BC; synchronous tumors; metastatic disease; breast infection within 3 months prior to study inclusion; pregnancy, lactation, autoimmune, inflammatory, or gastrointestinal diseases; use of exogenous melatonin or psychotropic drugs; and use of antibiotics, probiotics, and/or prebiotics within the preceding 3 months before the study.

Both patients and controls were instructed to abstain from consuming foods rich in tryptophan and/or serotonin (e.g., chocolate, nuts, coffee, tea, and bananas) for 7 days preceding blood collection. Blood and fecal samples were collected in the morning between 8:00 a.m. and 10:00 a.m. at baseline before any treatment commenced.

The study obtained approval from the Medical Ethics Committee of the Virgen de la Victoria University Hospital of Málaga and adhered to Good Clinical Practice and the Declaration of Helsinki. Written informed consent was obtained from all participants before protocol initiation.

Fecal Sample Processing, DNA Extraction, and Gut Microbiota Sequencing

DNA extraction from 200 mg aliquots of fecal samples was performed using the QIAamp Fast DNA Stool Mini Kit according to the manufacturer’s instructions (Qiagen, Hilden, Germany). The concentration of DNA (A260) and its purity (A260/A280 ratio) were assessed using a Nanodrop spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA). For DNA amplification, an Ion 16S Metagenomics Kit (Thermo Fisher Scientific, Madrid, Spain) targeting multiple variable regions (V2-4-8 and V3-6, 7–9) of the 16S rRNA gene was used. Barcoded adapters were ligated to the generated amplicons using the Ion PlusTM Fragment Library Kit (Thermo Fisher Scientific, Madrid, Spain) to create barcoded libraries. These libraries were pooled and templated on an automated Ion Chef system (Thermo Fisher Scientific, Madrid, Spain) and subsequently sequenced on an Ion S5 platform (Thermo Fisher Scientific, Madrid, Spain).

Bioinformatics analysis

Analysis of 16S rRNA amplicons was performed using QIIME (version 2-2019.4). The q-dada2 plugin and DADA2 pipeline were utilized for quality filtering, denoising, dereplication, and chimera filtering of the raw sequence data. The obtained sequence variants were merged into a consolidated feature table using the q2-feature-table plugin. Clustering of all amplicon sequence variants into operational taxonomic units (OTUs) was carried out using the q2-vsearch plugin with 97% sequence similarity, employing the open reference clustering method against Greengenes version 13_8 and aligning at 97% similarity with OTUs reference sequences. The alignment of OTUs was performed using MAFFT (via q2-alignment) to construct a phylogenetic tree using fasttree2 (via q2-phylogeny). Taxonomy assignment to the OTUs was conducted using the q2-feature-classifier classify-sklearn naive Bayes taxonomy classifier. Alpha diversity metrics (Shannon and Chao1), beta diversity metrics (Bray–Curtis dissimilarity), and principal coordinate analysis (PCoA) were computed using the q2-diversity plugin after rarefying the samples to 994 sequences per sample. The significance of alpha diversity was determined using the Kruskal–Wallis test, while the significance of beta diversity was assessed using the nonparametric ANOSIM test.

Analysis of serum serotonin, N-acetylserotonin, and melatonin levels by high-performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS)

Serum levels of serotonin, NAS, and melatonin were quantified using HPLC-MS/MS at Chemical Sanitary Consulting (CQS Lab). Serotonin and melatonin standards were procured from Merck Life Science, while the NAS standard and deuterated internal standards were obtained from Cayman Chemical. Methanol and water were purchased from Supelco and VWR-BDH Chemicals, respectively, and ethyl acetate and formic acid were obtained from Merck Life Science. All reagents used were of high purity and compatible with HPLC-MS analysis.

For sample preparation, 10 µl of serum was diluted (1:20) with H2O:MeOH and spiked with a deuterated internal standard for serotonin. For NAS and melatonin determination, 390 µl of serum was spiked with a deuterated internal standard and subjected to double extraction with ethyl acetate (1 mL). After centrifugation, the organic phase was dried using nitrogen gas. The dry extract was reconstituted with 190 µL of H2O:MeOH.

Chromatographic separation was performed on a Phenomenex 00b-4251-B0 Luna C18 column with Solvents A (H2O with formic acid) and B (MeOH with formic acid) as the mobile phase. Analyses were conducted in positive ionization mode using a triple quadrupole liquid chromatography‒mass spectrometry system (Model 6460; Agilent Technologies, Santa Clara, CA, USA). The Agilent MassHunter Workstation software version B.06.00 controlled the HPLC‒MS system, while the Agilent MassHunter Quantitative Analysis software version B.06.00 was used for peak integration and quantitative calculations. The calibration curves spanned the ng/mL range for serotonin and the pg/mL range for melatonin and NAS.

Determination of the levels of serum enzymes involved in melatonin synthesis by enzyme-linked immunosorbent assay (ELISA)

Serum levels of AANAT, ASMT, and 14-3-3 protein were quantified using the Human AANAT ELISA Kit, Human ASMT ELISA Kit, and Human 14-3-3 Protein (14-3-3 Pro) ELISA Kit, respectively, which were obtained from MyBioSource, Inc. (San Diego, CA, USA), following the manufacturer's instructions. The mean values were utilized for data analysis. The intra- and interassay coefficients of variation were consistently less than 15% across all measurements. The detection limits were 0.094 ng/mL for AANAT, 0.1 ng/mL for ASMT, and 0.5 ng/mL for 14-3-3 Pro [16].

Measurement of fecal beta-glucuronidase activity

The quantitative determination of βGD activity in fecal samples was performed using the β-Glucuronidase Activity Assay Kit (Fluorometric) (BioVision, Inc., Abcam, Cambridge, MA, United States), with an adaptation of the protocol for fecal samples based on previous studies [17, 18]. Fluorescence was measured at an excitation wavelength of 330 nm and an emission wavelength of 450 nm using an FLS920 fluorimeter (Edinburgh Instruments). One unit represents the amount of βGD capable of cleaving 1 µmol of substrate per minute under the assay conditions.

Intestinal Permeability Analysis

Serum levels of zonulin were evaluated in duplicate using the commercial enzyme-linked immunosorbent assay Human Zonulin ELISA Kit 96T (Cusabio, Wuhan, China). The mean values were utilized for data analysis. The intra- and interassay coefficients of variation ranged from 3–10%, with a detection limit of 0.22 ng/mL.

Analysis of short-chain fatty acids (SCFAs) in fecal samples was conducted using gas chromatography coupled to triple quadrupole mass spectrometry (GC‒MS/MS)

The fecal concentrations of SCFAs were determined using GC‒MS/MS following previously described procedures [19] at the Eurecat Centre Tecnològic de Catalunya within the technological infrastructure of the Center for Omic Sciences (COS). Briefly, 20 mg of fecal sample was placed in a 2 mL Eppendorf tube and mixed with 200 µL of internal standard (IS) solution (acetic acid (AA-LAB) at 6000 µM, butyric acid (BA-LAB) at 1200 µM isobutyric acid (IBA-LAB), valeric acid (VA-LAB), isovaleric acid (IVA-LAB), and propionic acid (PA-LAB) at 600 µM) (Merk Life Science) along with 200 µL of PBS. The samples were vigorously mixed for 10 minutes and then centrifuged at 2500 rpm for 10 minutes at 4°C. The supernatants (50 µL) were acidified by adding 10 µL of 15% phosphoric acid, followed by SCFA extraction using 1000 µL of MTBE for 10 minutes. After centrifugation at 1500 rpm for 10 minutes at 4°C, the upper organic layer was transferred to a glass vial for analysis. Approximately 50 mg of the sample was placed in a 2 mL Eppendorf tube and subsequently freeze-dried to determine the sample mass on a dry basis.

The SCFAs were separated using a DB-FFAP chromatographic column (30 m × 0.25 mm × 0.25 µm). The temperature in the oven was programmed as follows: (i) initial temperature set at 40°C, (ii) linear increase at 12°C/min to 130°C (0 min), (iii) further linear increase at 30°C/min to 200°C (0 min), and (iv) a final step with a temperature ramp at 100°C/min to 250°C (4.5 min). The column flow was maintained at 1.5 mL/min using helium as the carrier gas. The injector temperature was set at 250°C, and the extracts were injected in splitless mode. Ionization was achieved by electronic impact (70 eV), and the mass analyzer was operated in multiple reaction monitoring (MRM) mode. The m/z values and their corresponding retention times are provided in Table 1.

Table 1

Details of the optimized MRM transition for each analyte.
Analyte	Retention time (min)	Quantitative transition (m/z)	Qualitative transition (m/z)	CE (V)
AA	6.62	60→45	60→43	10/5
PA	7.49	73→55	74→55	5/10
IBA	7.77	88→73	88→55	10/15
BA	8.27	60→42	73→55	10/5
IVA	8.56	60→42	60→45	10/10
VA	9.00	60→42	73→55	10/5

Statistical analysis

The Kruskal‒Wallis rank sum test was employed to compare bacterial abundance among the study groups, and false discovery rate (FDR) adjustment using the Benjamini–Hochberg method was applied to correct for significant p values (q < 0.05). The Kruskal‒Wallis rank sum test, followed by the Dunn post hoc test, was utilized to examine differences in serotonin metabolites and associated enzymes, SCFA levels, βGD activity, and zonulin levels among the study groups. Spearman correlation coefficients were computed to estimate correlations between bacterial taxa and the principal components of the serotonin-NAS-melatonin axis, as well as βGD. Statistical analyses were conducted using SPSS software version 26.0 (SPSS Inc., Chicago, IL, USA). P values less than 0.05 were considered to indicate statistical significance.

Clinical characteristics of the study groups

No significant differences in age were observed between BC patients and controls. The clinicopathological characteristics of the enrolled BC patients are detailed in Table 2. The mean age of the patients was 61 years, ranging from 44 to 86 years. Among the 76 primary breast tumors examined, 61 (80.26%) were invasive ductal carcinoma (IDC), 11 (14.47%) were invasive lobular carcinoma (ILC), two (2.63%) were papillary carcinomas, and two were mucinous carcinomas (2.64%). Of these, 32 tumors (42.11%) had diameters less than 2 cm, 25 tumors (46.05%) ranged between 2 and 5 cm, and 9 tumors (11.84%) were larger than 5 cm. Axillary lymph node metastasis (N1) was identified via core needle biopsy in 17 patients (22.37%), while axillary lymph node (N2) metastasis was found in 7 patients (9.21%). Fifty-two patients (68.42%) displayed no lymph node metastasis (N0).

Immunohistochemistry classified the tumors into subtypes as follows: 56 (73.68%) were hormone receptor-positive and HER2 negative (HR+/HER2-), and 20 (26.32%) were hormone receptor-positive and HER2 positive (HR+/HER2+). Regarding hormone receptor status, 73 (96.05%) BC patients were estrogen receptor (ER)-positive, 3 (3.95%) were ER-negative, 60 (78.95%) had ≥ 20% progesterone receptor (PR), 7 (9.21%) had < 20% PR, and 9 (11.84%) were PR-negative.

Most patients exhibited grade 2 tumors (43 patients, 56.58%), 26 patients (34.21%) had grade 3 tumors, and 7 patients (9.21%) were diagnosed with grade 1 tumors.

Table 2

Clinicopathological characteristics of the study subjects.
DIAGNOSTIC AGE (Years) Median (range)	64 (44–85)
	CONTROLS	BC PATIENTS
	n = 16	n = 76
ESTROGEN STATUS
Positive		73 (96.05%)
Negative		3 (3.95%)
PROGESTERONE STATUS
≥ 20%		60 (78.95%)
< 20%		7 (9.21%)
Negative		9 (11.84%)
HER2 STATUS
Positive		20 (26.32%)
Negative		56 (73.68%)
RESULTS CLASSIFICATION
HR + HER2-		56 (73.68%)
HR + HER2+		20 (26.32%)
AXILAR LYMPH NODE
N0		52 (68.42%)
N1		17 (22.37%)
N2		7 (9.21%)
TUMOR SIZE
< 2 cm		32 (42.11%)
2 to 5 cm		35 (46.05%)
> 5 cm		9 (11.84%)
TUMOR TYPE
IDC (invasive ductal carcinoma)		61 (80.26%)
ILC (invasive lobular carcinoma)		11 (14.47%)
Papilar carcinoma		2 (2.63%)
Mucinous carcinoma		2 (2.64%)
HISTOLOGICAL GRADE
Grade 1		7 (9.21%)
Grade 2		43 (56.58%)
Grade 3		26 (34.21%)

Differences in Fecal Microbiota Richness and Diversity between Breast Cancer Patients and Healthy Controls

At the genus level, different alpha diversity indices, including the Shannon (Fig. 1 (a)), Chao1 (Fig. 1 (b)) and ACE (Fig. 1 (c)) indices, were calculated to evaluate the alpha diversity within the fecal microbiota of the study groups. The only index that exhibited significant differences among our three study groups was the ACE index (Shannon p = 0.77; Chao 1 p = 0.12; ACE p = 0.016). The ACE index values for each group demonstrated a noteworthy reduction in diversity in the HR + HER2 + subgroup compared to the control group (p = 0.012) and the HR + HER2- subgroup (p = 0.013). However, no statistically significant differences were observed between the control and HR + HER2- groups (p = 0.438).

Furthermore, beta diversity, reflecting dissimilarities in microbiota communities among the study groups, was evaluated using the Bray–Curtis dissimilarity index. Principal component analysis plots revealed a notable separation in bacterial communities among the control, HR + HER2-, and HR + HER2 + groups (ANOSIM, p < 0.044) (Fig. 2).

Variations in the Taxonomic Composition of the Fecal Microbiota between Study Groups

The analysis of the fecal microbiota at the phylum level indicated that Bacteroides, Firmicutes, Proteobacteria, and Actinobacteria were the predominant phyla, with significant differences observed among all three study groups. Comparing the abundance of these phyla between the study groups, there was a notable increase in Actinobacteria in the control group compared to that in the HR + HER2- BC patients (q < 0.01), as well as increased levels of Bacteroidetes in the control subjects compared to those in the HR + HER2 + BC group (p < 0.01). Conversely, the HR + HER2- group displayed greater abundances of Bacteroidetes (p < 0.01), Firmicutes (p < 0.01), and Proteobacteria (p < 0.01) than did the HR + HER2 + group (Fig. 3).

At the genus level, significant differences in microbial composition were observed among the study groups. In the control group, we noted a significant increase in the abundance of Bifidobacterium (q < 0.01) compared to that in the HR + HER2- group and in the abundance of Bacteroides (q < 0.05), Blautia (q < 0.05), and Lawsonia (q < 0.05) compared to that in the HR + HER2 + group. When comparing the two BC groups, there was a significant increase in Bacteroides (q < 0.01), Blautia (q < 0.05), Lachnospira (q < 0.05), Lawsonia (q < 0.01), and Oscillospira (q < 0.01) in the HR + HER2- group compared to the HR + HER2 + group (Fig. 4).

Finally, at the species level, significant increases in the abundances of Bifidobacterium adolescentis (control vs HR + HER2-, q < 0.05; control vs HR + HER2+, q < 0.05) and Bifidobacterium longum (control vs HR + HER2-, q < 0.01; control vs HR + HER2+, q < 0.05) were detected in the control group compared to the BC group. The abundances of Bacteroides eggerthii (HR + HER2- vs control, q < 0.01; HR + HER2 + vs control, q < 0.05) and Bacteroides caccae (HR + HER2 + vs control, q < 0.04; HR + HER2- vs control, q < 0.05) were significantly greater in BC patients than in controls. Finally, Dorea formicigenerans was increased in HR + HER- patients compared to HR + HER2 + patients (q < 0.01) (Fig. 5).

Analysis of the Melatonergic Pathway in Breast Cancer Patients and Healthy Controls

Figure 6 (a) shows significantly greater serum serotonin levels in BC patients than in controls (means ± SDs: 109.83 ng/ml ± 44.34) (p < 0.01 in the case of HR + HER2- and p < 0.05 in the case of HR + HER2+). However, there were no significant differences in serotonin levels between the HR + HER2- (mean ± SD: 164.11 ng/ml ± 64.52) and HR + HER2+ (mean ± SD: 145.61 ng/ml ± 31.24) groups. Interestingly, a similar trend was observed for the NAS, which was significantly greater in both the HR + HER2- group (52.02 pg/ml ± 21.07) and the HR + HER2 + group (53.17 pg/ml ± 13.09) (p < 0.01) than in the control group (32.99 pg/ml ± 12.58) (Fig. 6 (b)). Furthermore, serum melatonin levels were significantly lower in individuals with BC than in controls (10.2 pg/ml ± 3.14) (p < 0.001). Specifically, HR + HER2 + patients exhibited the lowest level of this hormone (4.05 pg/ml ± 2.51), followed by HR + HER2- patients (6.27 pg/ml ± 2.14) (Fig. 6 (c)). Notably, significant differences were observed not only between the two groups and the controls but also between the two groups (p < 0.05).

When comparing the NAS/melatonin ratio between controls and BC patients, it became apparent that this ratio was significantly greater in BC patients. Furthermore, when examining variations in the NAS/melatonin ratio across different subgroups, significant differences were noted between the three study groups (p < 0.001), notably with a higher ratio observed in HR + HER2 + patients (11.76) followed by HR + HER2- patients (8.42) (Fig. 7).

As depicted in Fig. 8 (a), there was an increase in serum AANAT protein levels (responsible for converting serotonin into NAS) in HR + HER2- (means ± SDs: 8.59 ng/ml ± 3.05) and HR + HER2+ (means ± SDs: 7.34 ng/ml ± 1.39) BC patients compared to controls (means ± SDs: 4.96 ng/ml ± 1.94) (p < 0.001) and p < 0.05, respectively). However, there were no significant differences in AANAT levels between the BC subgroups. In contrast, a different trend was observed for ASMT levels (responsible for converting NAS into melatonin), which were significantly lower in HR + HER2- (3.04 ng/ml ± 1.05) and HR + HER2+ (2.74 ng/ml ± 1.30) BC patients than in controls (4.46 ng/ml ± 1.52) (p < 0.01) (Fig. 8 (b)). Finally, the analysis of 14-3-3 levels revealed no significant differences among any of the groups (controls: 2.65 ng/ml ± 0.80; HR + HER2-: 2.94 ng/ml ± 1.03; HR + HER2+: 2.72 ng/ml ± 0.82, p > 0.05) (Fig. 8 (c)).

Elevated Serum Estrogen Levels and Fecal Beta-Glucuronidase Activity in Breast Cancer Patients Compared to Healthy Controls

Significantly elevated serum estrogen levels were observed in both the HR + HER2- and HR + HER2 + groups compared to the control group (p < 0.01). However, no significant differences were identified between the HR+/HER2- and HR+/HER2 + groups (Fig. 9 (a)). Similarly, fecal βGD activity was significantly greater in both the HR + HER2- and HR + HER2 + groups than in the control group (p < 0.01), but no differences were found between the BC groups (Fig. 9 (b)).

Differences in the Fecal Levels of Short-Chain Fatty Acids (SCFAs) and Serum Zonulin Levels between Study Groups

A significant increase in acetic acid (AA) was observed in the HR + HER2 + subgroup compared to the control group (p < 0.05) and HR + HER2- group (p < 0.01). Similarly, isobutyric acid (IBA) exhibited a similar trend, with a significant increase in the HR + HER2 + and HR + HER2- groups compared to the control group (p < 0.001 and p < 0.05, respectively). No significant differences were found in the fecal levels of propionic acid (PA) (p = 0.17), butyric acid (BA) (p = 0.59) or valeric acid (VA) (p = 0.40) between the study groups. Finally, isovaleric acid (IVA) levels in the HR + HER2 + group were significantly greater than those in the control group (p < 0.05) (Fig. 10 (a)). In addition, serum zonulin levels were significantly lower in the control group (2.5 ng/ml ± 2.58) than in the other two groups of BC patients: HR + HER2- (5.35 ng/ml ± 4.22, p < 0.01) and HR + HER2+ (6.06 ng/ml ± 4.30, p < 0.01) (Fig. 10 (b)).

Associations between the Fecal Microbiota and the Serotonin-NAS-Melatonin Axis and Fecal Beta-Glucuronidase Activity

Correlation analyses were conducted to investigate relationships between different bacterial taxa exhibiting significant differences in abundance between the control group and hormonal receptor-positive BC patients (comprising both HER2- and HER2 + subtypes) and the primary components of the serotonin-NAS-melatonin axis. Additionally, correlations were explored with the activity of βGD in feces to establish possible connections. The serum levels of melatonin were significantly positively correlated with Actinobacteria (p = 0.034), Bifidobacterium (p = 0.043), and Bifidobacterium longum (p = 0.030) (Fig. 11). No significant associations were found between the serum levels of serotonin and NAS and the abundance of significant gut bacterial taxa (p > 0.05). Finally, fecal βGD activity correlated positively with Bacteroides eggerthii (p = 0.029) and negatively with Actinobacteria (p = 0.023), Bifidobacterium (p = 0.026), and Bifidobacterium longum (p = 0.001) (Fig. 11).

Serum Levels of Serotonin, N-Acetylserotonin, and Melatonin as Biomarkers for Breast Cancer Risk

The serum levels of serotonin, NAS, and melatonin were measured in BC patients and controls, and receiver operating characteristic (ROC) curve analysis was conducted to determine their discriminatory power. Serum serotonin and melatonin demonstrated significant discriminatory capacity for distinguishing BC patients from controls, with area under the curve (AUC) values of 0.855 (95% CI: 0.698–1.012) and 0.838 (95% CI: 0.700-0.976), respectively. The NAS exhibited the highest discriminatory power among the three biomarkers, with an AUC of 0.879 (95% CI: 0.754–1.004). Additionally, a composite score incorporating serotonin, the NAS, and melatonin was calculated, and ROC curve analysis was performed to evaluate its diagnostic performance. Combining serotonin, the NAS, and melatonin improved diagnostic performance, resulting in a greater AUC of 0.895 (95% CI: 0.765–1.026) than that of individual biomarkers. Furthermore, the NAS/melatonin ratio showed robust discriminatory ability (AUC: 0.995, 95% CI: 0.980–1.011) for distinguishing hormone receptor-positive BC patients from controls (Fig. 12).

Our study revealed that hormonal receptor-positive BC patients exhibit gut dysbiosis associated with increased serum levels of serotonin and NAS and increased fecal βGD activity, along with lower levels of serum melatonin, than healthy controls. Additionally, the BC patient groups displayed higher fecal SCFA levels, such as IBA and IVA, and serum zonulin levels. Elevated levels of zonulin have previously been associated with intestinal dysbiosis and increased intestinal permeability [20], which enhance the translocation of gut bacteria and compounds, such as lipopolysaccharides, that trigger inflammation and interact with the immune system to predispose patients to the development of cancer [21].

A bidirectional relationship exists between the gut microbiota, melatonin production and BC, wherein alterations in the intestinal microbiota influence the regulation of the melatonergic pathway, and changes in melatonin synthesis affect the microbiota composition [22]. Both factors may contribute to hormonal receptor-positive BC development. When comparing the gut microbiota composition among the three study groups, both BC patients exhibited decreased gut microbiota diversity compared to that of the controls. Furthermore, analysis of the Bray–Curtis PCoA plot for beta diversity revealed that the groups of BC patients clustered separately from those of healthy controls, indicating significant BC-mediated microbial changes. Regarding differences in predominant bacterial taxa, significant differences were found between the control and BC groups. At the species level, gut dysbiosis showed enrichment of Bacteroides eggerthii and Bacteroides caccae in HR + HER2 + and HR + HER2- BC patients, whereas Bifidobacterium adolescentis and Bifidobacterium longum were enriched in the control group. These findings align with previous research in colorectal cancer patients, where an increased abundance of Bacteroides eggerthii [23] and a reduced abundance of Bifidobacterium adolescentis were observed [24]. Notably, Bifidobacterium longum (isolated from breast milk and baby feces) has demonstrated anti-proliferative and antimicrobial activities in animal models of BC, suggesting a potential protective role not only in BC [25] but also in colorectal cancer [26].

Furthermore, our study revealed elevated serotonin and NAS levels and decreased melatonin levels in the serum of hormonal receptor-positive BC patients compared to controls. Similar to our study, previous research has demonstrated that human BC cells (MCF-7 and Bcap-37) and mammary epithelial cells (MCF-10A) are capable of synthesizing serotonin and melatonin, with higher serotonin expression observed in BC tissue and cell lines than in nontumorous mammary epithelial tissue [27, 28]. Dysregulation of serotonin signaling, including its receptors, contributes to BC development by affecting tumor cells and key processes such as cell cycle regulation, autophagy, and apoptosis, thereby promoting cell proliferation and resistance to apoptosis [29, 30]. Additionally, the expression of serotonin receptors has been linked to estrogen receptor and HER2 expression, suggesting that serotonin plays an important role in BC development and progression [31].

Our study presents a novel finding, revealing an increase in the intermediate metabolite NAS in the serum of hormonal receptor-positive BC patients for the first time. Notably, NAS plays specific biological roles by inducing the dimerization and autophosphorylation of TrkB, thereby activating pathways involved in BC cell proliferation and metastasis [11, 32]. Conversely, lower melatonin levels in BC patients have been consistently reported in previous studies [33–35]. Melatonin exerts antiestrogenic effects by influencing estrogen levels through various mechanisms, such as regulating aromatase and modulating estrogen sulfotransferase (EST) and sulfatase (STS) enzyme activity. This leads to an increase in biologically inactive, sulfoconjugated estrogens excreted through bile while simultaneously reducing the levels of biologically active estrogens [36]. In addition, our study revealed a significant difference in melatonin levels between the two BC study groups, with a notable increase in the HR + HER2- group compared to the HR + HER2 + group. Although this distinction has not been previously documented, several studies have linked melatonin with the HER2 + receptor in BC. For instance, melatonin supplementation has been shown to inhibit mammary tumor development and regulate the HER-2/neu oncogene in HER-2/neu transgenic mice [37]. Another study demonstrated that melatonin inhibits metastasis in HER2 + human BC cells by suppressing RSK2 expression [38].

Moreover, our study revealed changes in the serum levels of AANAT and ASMT, key enzymes in the melatonergic pathway. AANAT, which is responsible for converting serotonin to the NAS, showed elevated levels, while ASMT, which is responsible for converting the NAS to melatonin, exhibited decreased levels in BC patients compared to those in the control group. Tran et al. reported that higher ASMT correlated with improved relapse-free survival and longer metastasis-free survival, particularly following tamoxifen treatment [39]. Although previous studies have not linked increased AANAT levels to BC or other cancers, research has associated two single nucleotide polymorphisms (SNPs), rs4238989 and rs3760138, within regulatory regions of the AANAT gene with an increased risk of BC [40]. These findings suggest that dysregulated melatonin synthesis in hormonal receptor-positive BC patients may result from imbalances in AANAT and ASMT levels, leading to higher NAS levels relative to melatonin. The variations observed in the serum serotonin, NAS, melatonin, AANAT, and ASMT levels in hormonal receptor-positive BC patients may indicate alterations in the melatonergic pathway.

On the other hand, certain SCFAs produced by the gut microbiota indirectly stimulate melatonin production by promoting serotonin production. The interaction of SCFAs with enteroendocrine cell receptors induces the secretion of intestinal hormones, such as serotonin, potentially influencing tumor cell proliferation and apoptosis [28, 41]. Our study revealed significantly elevated fecal levels of IVA in the HR + HER2 + BC subgroup compared to the control subgroup. IVA has been shown to promote serotonin production by enhancing the expression of tryptophan hydroxylase 2 (Tph2), the rate-limiting enzyme in serotonin synthesis [42].

Furthermore, we analyzed the associations between the abundance of fecal microbiota significantly differing between study groups and the serotonin-NAS-melatonin axis, as well as fecal βGD activity. We found a positive association between the serum melatonin concentration and the abundance of fecal Bifidobacterium (specifically Bifidobacterium longum). Previous research investigating the correlation between melatonin concentration and ulcerative colitis severity revealed an increase in the presence of the probiotic Bifidobacterium and a decrease in the proportions of various pathogenic bacterial genera, including Desulfovibrio, Peptococcaceae, and Lachnospiraceae, following melatonin supplementation [43]. In contrast, in our study, Bifidobacterium longum was negatively correlated with fecal βGD activity, consistent with previous findings showing reduced βGD activity with the consumption of fermented milk containing Bifidobacterium spp. (such as Bifidobacterium longum SPM1207) [44]. Conversely, Bacteroides eggerthii showed a positive correlation with fecal βGD activity, in line with studies indicating that bacteria from the genus Bacteroides are major contributors to the intestinal pool of βGDs, encoding 120 of the 218 identified βGDs [45]. Moreover, in BC patients, an imbalance in both melatonin levels and the composition of intestinal bacteria with βGD activity may lead to dysbiosis. This dysbiosis could elevate circulating estrogen levels, thereby increasing the risk of BC.

Finally, our results suggest that the NAS/melatonin ratio is a potential biomarker for distinguishing between hormonal receptor-positive BC patients and healthy controls. The ratio was notably greater in BC patients than in controls. Moreover, disruptions in this ratio have been observed not only in BC but also in various other pathologies, including endometriosis, autism, glioblastoma, and depression, among others [15, 46–51].

However, the study has several limitations. First, the sample size was small, and this was a single-center clinical study, potentially introducing selection bias. Additionally, as a cross-sectional study, it is challenging to determine causal relationships. Another limitation is the assessment of metabolites solely from morning serum samples, as melatonin levels exhibit significant diurnal variations, peaking at night [52]. Nevertheless, since melatonin levels are uniformly low in the morning across all populations (both controls and BC patients) and all samples were collected simultaneously, they can be compared reliably, highlighting the lower levels of melatonin in BC patients. Furthermore, our study has several strengths, such as its meticulous design, age-matched cohorts of BC patients and healthy controls, and comprehensive definition of inclusion and exclusion criteria.

The findings of our study shed light on the intricate interplay between the gut microbiota and melatonin in hormonal receptor-positive BC. We observed alterations in the gut microbiota composition and melatonin production of BC patients compared to healthy controls. We revealed a pattern of gut dysbiosis characterized by elevated serum serotonin, NAS, and fecal βGD activity coupled with decreased serum melatonin levels in BC patients compared to healthy controls. This dysbiosis, marked by increased fecal levels of IVA and IBA, as well as elevated serum zonulin levels, underscore intestinal permeability alterations, facilitating the translocation of gut bacteria and inflammatory compounds and ultimately predisposing individuals to cancer development.

The bidirectional relationship between the gut microbiota, melatonin, and BC implicates microbiota-induced alterations in melatonin synthesis and, vice versa, shaping BC risk. Our investigation highlights distinct microbial compositions among BC patients and controls, with BC patients demonstrating reduced gut microbiota diversity and significant shifts in predominant bacterial taxa. Particularly noteworthy is the enrichment of Bacteroides eggerthii alongside a reduction in beneficial Bifidobacterium longum, which is positively associated with serum melatonin levels, suggesting potential roles in BC development. Interestingly, we observed a negative correlation between Bifidobacterium longum and fecal βGD activity, indicating a potential protective mechanism against BC development. Conversely, Bacteroides eggerthii exhibited a positive correlation with fecal βGD activity, indicating that it is involved in BC development through increased estrogen levels.

Furthermore, our study revealed disruptions in the serotonin-NAS-melatonin axis in BC patients characterized by elevated NAS levels and decreased melatonin synthesis. Dysregulation of this axis has profound implications for BC development, given the pivotal role of serotonin in promoting cell proliferation and conferring resistance to apoptosis. Moreover, the dysregulated expression of enzymes involved in the melatonergic pathway, AANAT and ASMT, may contribute to altered melatonin synthesis in BC patients, further implicating the melatonergic pathway in BC development.

Finally, our study identified the NAS/melatonin ratio as a potential diagnostic biomarker for discriminating hormonal receptor-positive BC patients from healthy individuals, providing a promising avenue for clinical management strategies. Overall, our findings provide valuable insights into how alterations in the gut microbiota and the melatonergic pathway may contribute to the risk of developing BC. Further research is warranted to elucidate the underlying mechanisms and potential therapeutic targets for hormonal receptor-positive BC prevention and treatment.

14-3-3 Pro: 14-3-3 Protein

AA: Acetic acid

AANAT: Arylalkylamine N-acetyltransferase

ASMT: Acetylserotonin-O-methyltransferase

AUC: Area under the curve

BA: Butyric acid

BC: Breast cancer

βGD: Beta-glucuronidase

COS: Center for Omic Sciences

CQS Lab: Chemical Sanitary Consulting

ELISA: Enzyme-linked immunosorbent assay

ER: Estrogen receptor

EST: Estrogen sulfotransferase

FDR: False discovery rate

GC-MS/MS: Gas chromatography coupled to triple quadrupole mass spectrometry

HPLC-MS/MS: High-performance liquid chromatography with tandem mass spectrometry

HR: Hormonal receptor

IBA: Isobutyric acid

IDC: Invasive ductal carcinoma

ILC: Invasive lobular carcinoma

IS: Internal standard

IVA: Isovaleric acid

MRM: Multiple reaction monitoring

NAS: N-acetylserotonin

OTUs: Operational taxonomic units:

PA: Propionic acid

PCoA: Principal coordinate analysis

PR: Progesterone receptor

ROC: Receiver operating characteristic

SCFAs: Short-chain fatty acids

SNPs: Single nucleotide polymorphisms

STS: Sulfatase

Tph2: Tryptophan hydroxylase 2

VA: Valeric acid

Ethics approval and consent to participate

This retrospective study adhered to the ethical guidelines outlined in the Declaration of Helsinki. The study design received approval from the Ethics Research Committee of Málaga Province on June 27, 2019.

Consent for publication

Not applicable

Availability of data and materials

Data will be made available on request.

Competing interests

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.

Funding

This work was funded in part by PE-0106-2019 from the Consejería de Salud de la Junta de Andalucía, C19047-2018 from Fundación Unicaja, UMA18-FEDERJA-042 from UMA-FEDER & ALIANZA MIXTA ANDALUCÍA-ROCHE and Instituto de Salud Carlos III project FIS21/00321 (“cofinanciado por la Unión Europea”). Alicia González González is a recipient of a postdoctoral grant, Sara Borrell (CD23/00110), from Instituto de Salud Carlos III. Aurora Laborda Illanes is a recipient of a predoctoral grant, PFIS-ISCIII (FI19-00112), cofunded by the Fondo Social Europeo (FSE). Lidia Sanchez Alcoholado is a recipient of a postdoctoral grant from Plan Propio de Investigación, Transferencia y Divulgación Científica de la Universidad de Málaga 2023. Lucía Aránega Martín is a recipient of a predoctoral grant, FPU22/01679, funded by a grant for University Teacher Training of the Ministry of Science, Innovation and Universities. Isaac Plaza Andrade is a recipient of a grant (PTA2022-022049-I), Personal Técnico de Apoyo de la Agencia Estatal de Investigación. Ministerio de Ciencias e Innovación.

Authors’ contributions

Conceptualization, A.G.G., E.A. and M.I.Q.O.; methodology, A.G.G., A.L.I., L.S.A., I.P.A., L.A.M., J.C.F.G., and J.P.L.; software, A.G.G., A.L.I., L.S.A., S.B., I.P.A., L.A.M., J.C.F.G., and J.P.L.; validation, A.G.G., A.L.I., L.S.A., and S.B.; formal analysis, A.G.G., A.L.I., L.S.A., and S.B.; investigation, A.G.G., A.L.I., L.S.A., S.B., I.P.A. and J.P.L.; resources, M.I.Q.O. and E.A.; data curation, A.G.G., A.L.I., L.S.A., J.C.F.G., and S.B.; writing—original draft preparation, M.I.Q.O., A.G.G. and E.A.; writing—review and editing, all authors; visualization, M.I.Q.O., A.G.G. and E.A.; supervision, M.I.Q.O., E.A. and A.G.G.; project administration, A.G.G., M.I.Q.O. and E.A.; funding acquisition, M.I.Q.O. and E.A. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

The authors are thankful to all the women who participated in the study. We thank Ian Johnstone for editing the manuscript.

Laborda-Illanes A, Sanchez-Alcoholado L, Dominguez-Recio ME, Jimenez-Rodriguez B, Lavado R, Comino-Méndez I, et al. Breast and Gut Microbiota Action Mechanisms in Breast Cancer Pathogenesis and Treatment. Cancers (Basel). 2020;12.
Parida S, Sharma D. The Microbiome–Estrogen Connection and Breast Cancer Risk. Cells. 2019;8:1642.
Laborda-Illanes A, Sánchez-Alcoholado L, Boutriq S, Plaza-Andrades I, Peralta-Linero J, Alba E, et al. A New Paradigm in the Relationship between Melatonin and Breast Cancer: Gut Microbiota Identified as a Potential Regulatory Agent. Cancers. 2021;13:3141.
Iesanu MI, Zahiu CDM, Dogaru I-A, Chitimus DM, Pircalabioru GG, Voiculescu SE, et al. Melatonin-Microbiome Two-Sided Interaction in Dysbiosis-Associated Conditions. Antioxidants (Basel). 2022;11:2244.
Mannino G, Caradonna F, Cruciata I, Lauria A, Perrone A, Gentile C. Melatonin reduces inflammatory response in human intestinal epithelial cells stimulated by interleukin-1β. J Pineal Res. 2019;67:e12598.
Yao Z-W, Zhao B-C, Yang X, Lei S-H, Jiang Y-M, Liu K-X. Relationships of sleep disturbance, intestinal microbiota, and postoperative pain in breast cancer patients: a prospective observational study. Sleep Breath. 2020;
Jing Y, Yang D, Bai F, Zhang C, Qin C, Li D, et al. Melatonin Treatment Alleviates Spinal Cord Injury-Induced Gut Dysbiosis in Mice. J Neurotrauma. 2019;36:2646–64.
Park YS, Kim SH, Park JW, Kho Y, Seok PR, Shin J-H, et al. Melatonin in the colon modulates intestinal microbiota in response to stress and sleep deprivation. Intest Res. 2020;18:325–36.
Cos S, Sánchez-Barceló EJ. Melatonin and mammary pathological growth. Front Neuroendocrinol. 2000;21:133–70.
Axelrod J, Weissbach H. Purification and properties of hydroxyindole-O-methyl transferase. J Biol Chem. 1961;236:211–3.
Jang S-W, Liu X, Pradoldej S, Tosini G, Chang Q, Iuvone PM, et al. N-acetylserotonin activates TrkB receptor in a circadian rhythm. PNAS. 2010;107:3876–81.
Mei Q, Diao L, Xu J, Liu X, Jin J. A protective effect of melatonin on intestinal permeability is induced by diclofenac via regulation of mitochondrial function in mice. Acta Pharmacol Sin. 2011;32:495–502.
Li Y, Hao Y, Fan F, Zhang B. The Role of Microbiome in Insomnia, Circadian Disturbance and Depression. Front Psychiatry [Internet]. 2018 [cited 2020 Oct 16];9. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6290721/
Reigstad CS, Salmonson CE, Iii JFR, Szurszewski JH, Linden DR, Sonnenburg JL, et al. Gut microbes promote colonic serotonin production through an effect of short-chain fatty acids on enterochromaffin cells. The FASEB Journal. 2015;29:1395–403.
Anderson G. Breast cancer: Occluded role of mitochondria N-acetylserotonin/melatonin ratio in co-ordinating pathophysiology. Biochem Pharmacol. 2019;168:259–68.
Slominski AT, Kim T-K, Kleszczyński K, Semak I, Janjetovic Z, Sweatman T, et al. Characterization of Serotonin and N-acetylserotonin Systems in the Human Epidermis and Skin Cells. Journal of pineal research. 2020;68:e12626.
Kaliannan K, Donnell SO, Murphy K, Stanton C, Kang C, Wang B, et al. Decreased Tissue Omega-6/Omega-3 Fatty Acid Ratio Prevents Chemotherapy-Induced Gastrointestinal Toxicity Associated with Alterations of Gut Microbiome. IJMS. 2022;23:5332.
Khan MH, Onyeaghala GC, Rashidi A, Holtan SG, Khoruts A, Israni A, et al. Fecal β-glucuronidase activity differs between hematopoietic cell and kidney transplantation and a possible mechanism for disparate dose requirements. Gut Microbes. 2022;14:2108279.
Lotti C, Rubert J, Fava F, Tuohy K, Mattivi F, Vrhovsek U. Development of a fast and cost-effective gas chromatography–mass spectrometry method for the quantification of short-chain and medium-chain fatty acids in human biofluids. Anal Bioanal Chem. 2017;409:5555–67.
Veres-Székely A, Szász C, Pap D, Szebeni B, Bokrossy P, Vannay Á. Zonulin as a Potential Therapeutic Target in Microbiota-Gut-Brain Axis Disorders: Encouraging Results and Emerging Questions. Int J Mol Sci. 2023;24:7548.
Dutta D, Lim SH. Bidirectional interaction between intestinal microbiome and cancer: opportunities for therapeutic interventions. Biomark Res. 2020;8:31.
Bonmati-Carrion M-A, Tomas-Loba A. Melatonin and Cancer: A Polyhedral Network Where the Source Matters. Antioxidants (Basel). 2021;10.
Senthakumaran T, Moen AEF, Tannæs TM, Endres A, Brackmann SA, Rounge TB, et al. Microbial dynamics with CRC progression: a study of the mucosal microbiota at multiple sites in cancers, adenomatous polyps, and healthy controls. European Journal of Clinical Microbiology & Infectious Diseases. 2023;42:305.
Chen S, Fan L, Lin Y, Qi Y, Xu C, Ge Q, et al. Bifidobacterium adolescentis orchestrates CD143 ⁺ cancer‐associated fibroblasts to suppress colorectal tumorigenesis by Wnt signaling‐regulated GAS1. Cancer Communications. 2023;43:1027–47.
Shimizu Y, Isoda K, Taira Y, Taira I, Kondoh M, Ishida I. Anti-tumor effect of a recombinant Bifidobacterium strain secreting a claudin-targeting molecule in a mouse breast cancer model. European Journal of Pharmacology. 2020;887:173596.
Parisa A, Roya G, Mahdi R, Shabnam R, Maryam E, Malihe T. Anti-cancer effects of Bifidobacterium species in colon cancer cells and a mouse model of carcinogenesis. Pizzo SV, editor. PLoS ONE. 2020;15:e0232930.
Karmakar S, Lal G. Role of serotonin receptor signaling in cancer cells and anti-tumor immunity. Theranostics. 2021;11:5296–312.
Xie Q-E, Du X, Wang M, Xie F, Zhang Z, Cao Y, et al. Identification of Serotonin as a Predictive Marker for Breast Cancer Patients. Int J Gen Med. 2021;14:1939–48.
Pai VP, Marshall AM, Hernandez LL, Buckley AR, Horseman ND. Altered serotonin physiology in human breast cancers favors paradoxical growth and cell survival. Breast Cancer Research. 2009;11:R81.
Jose J, Tavares CDJ, Ebelt ND, Lodi A, Edupuganti R, Xie X, et al. Serotonin Analogues as Inhibitors of Breast Cancer Cell Growth. ACS Med Chem Lett. 2017;8:1072–6.
Balakrishna P, George S, Hatoum H, Mukherjee S. Serotonin Pathway in Cancer. Int J Mol Sci. 2021;22:1268.
Shen J, Ghai K, Sompol P, Liu X, Cao X, Iuvone PM, et al. N-acetyl serotonin derivatives as potent neuroprotectants for retinas. Proceedings of the National Academy of Sciences. 2012;109:3540–5.
Gehlert S, Clanton M, On Behalf Of The Shift Work And Breast Cancer Strategic Advisory Group null. Shift Work and Breast Cancer. Int J Environ Res Public Health. 2020;17:9544.
Kubatka P, Zubor P, Busselberg D, Kwon TK, Adamek M, Petrovic D, et al. Melatonin and breast cancer: Evidences from preclinical and human studies. Crit Rev Oncol Hematol. 2018;122:133–43.
Srinivasan V, Spence DW, Pandi-Perumal SR, Trakht I, Esquifino AI, Cardinali DP, et al. Melatonin, environmental light, and breast cancer. Breast Cancer Res Treat. 2008;108:339–50.
Gonzalez A, Cos S, Martinez-Campa C, Alonso-Gonzalez C, Sanchez-Mateos S, Mediavilla MD, et al. Selective estrogen enzyme modulator actions of melatonin in human breast cancer cells. J Pineal Res. 2008;45:86–92.
Gurunathan S, Qasim M, Kang M-H, Kim J-H. Role and Therapeutic Potential of Melatonin in Various Type of Cancers. Onco Targets Ther. 2021;14:2019–52.
Mao L, Summers W, Xiang S, Yuan L, Dauchy RT, Reynolds A, et al. Melatonin Represses Metastasis in Her2 -Postive Human Breast Cancer Cells by Suppressing RSK2 Expression. Molecular Cancer Research. 2016;14:1159–69.
Tran QH, Than VT, Luu PL, Clarke D, Lam HN, Nguyen T-GT, et al. A novel signature predicts recurrence risk and therapeutic response in breast cancer patients. Int J Cancer. 2021;148:2848–56.
Zienolddiny S, Haugen A, Lie J-AS, Kjuus H, Anmarkrud KH, Kjærheim K. Analysis of polymorphisms in the circadian-related genes and breast cancer risk in Norwegian nurses working night shifts. Breast Cancer Res. 2013;15:R53.
Silva YP, Bernardi A, Frozza RL. The Role of Short-Chain Fatty Acids From Gut Microbiota in Gut-Brain Communication. Front Endocrinol (Lausanne). 2020;11:25.
Zhu P, Lu T, Chen Z, Liu B, Fan D, Li C, et al. 5-hydroxytryptamine produced by enteric serotonergic neurons initiates colorectal cancer stem cell self-renewal and tumorigenesis. Neuron. 2022;110:2268-2282.e4.
Zhao Z-X, Yuan X, Cui Y-Y, Liu J, Shen J, Jin B-Y, et al. Melatonin Mitigates Oxazolone-Induced Colitis in Microbiota-Dependent Manner. Front Immunol. 2021;12:783806.
Lee DK, Jang S, Baek EH, Kim MJ, Lee KS, Shin HS, et al. Lactic acid bacteria affect serum cholesterol levels, harmful fecal enzyme activity, and fecal water content. Lipids Health Dis. 2009;8:21.
Candeliere F, Raimondi S, Ranieri R, Musmeci E, Zambon A, Amaretti A, et al. β-Glucuronidase Pattern Predicted From Gut Metagenomes Indicates Potentially Diversified Pharmacomicrobiomics. Front Microbiol. 2022;13:826994.
Anderson G. Endometriosis Pathoetiology and Pathophysiology: Roles of Vitamin A, Estrogen, Immunity, Adipocytes, Gut Microbiome and Melatonergic Pathway on Mitochondria Regulation. Biomolecular Concepts. 2019;10:133–49.
Anderson G, Carbone A, Mazzoccoli G. Tryptophan Metabolites and Aryl Hydrocarbon Receptor in Severe Acute Respiratory Syndrome, Coronavirus-2 (SARS-CoV-2) Pathophysiology. Int J Mol Sci. 2021;22:1597.
Anderson G, Reiter RJ. Glioblastoma: Role of Mitochondria N-acetylserotonin/Melatonin Ratio in Mediating Effects of miR-451 and Aryl Hydrocarbon Receptor and in Coordinating Wider Biochemical Changes. Int J Tryptophan Res. 2019;12:1178646919855942.
Benabou M, Rolland T, Leblond CS, Millot GA, Huguet G, Delorme R, et al. Heritability of the melatonin synthesis variability in autism spectrum disorders. Sci Rep. 2017;7:17746.
Pagan C, Delorme R, Callebert J, Goubran-Botros H, Amsellem F, Drouot X, et al. The serotonin-N-acetylserotonin-melatonin pathway as a biomarker for autism spectrum disorders. Transl Psychiatry. 2014;4:e479.
Takada A, Shimizu F, Masuda J. Measurement of Plasma Tryptophan Metabolites: Clinical and Experimental Application for Depression and Stress States Assessment [Internet]. Melatonin - Molecular Biology, Clinical and Pharmaceutical Approaches. IntechOpen; 2018 [cited 2021 Sep 10]. Available from: https://www.intechopen.com/chapters/62436
Simonneaux V, Ribelayga C. Generation of the Melatonin Endocrine Message in Mammals: A Review of the Complex Regulation of Melatonin Synthesis by Norepinephrine, Peptides, and Other Pineal Transmitters. Pharmacol Rev. 2003;55:325–95.

No competing interests reported.

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Interplay between gut microbial composition and the melatonergic pathway: implications for hormonal receptor-positive breast cancer development

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Abstract

Background

Methods

Results

Conclusions

Figures

BACKGROUND

METHODS

Study design and participants

Fecal Sample Processing, DNA Extraction, and Gut Microbiota Sequencing

Bioinformatics analysis

Measurement of fecal beta-glucuronidase activity

Intestinal Permeability Analysis

Statistical analysis

RESULTS

Clinical characteristics of the study groups

Differences in Fecal Microbiota Richness and Diversity between Breast Cancer Patients and Healthy Controls

Variations in the Taxonomic Composition of the Fecal Microbiota between Study Groups

Analysis of the Melatonergic Pathway in Breast Cancer Patients and Healthy Controls

Associations between the Fecal Microbiota and the Serotonin-NAS-Melatonin Axis and Fecal Beta-Glucuronidase Activity

Serum Levels of Serotonin, N-Acetylserotonin, and Melatonin as Biomarkers for Breast Cancer Risk

DISCUSSION

CONCLUSIONS

LIST OF ABBREVIATIONS

DECLARATIONS

REFERENCES

Additional Declarations

Supplementary Files

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