Tumor Microbial Communities and Thyroid Cancer Development—The Protective Role of Antioxidant Nutrients: Application Strategies and Future Directions
Abstract
:1. Introduction
2. The Microbiota and Thyroid Axis
- Alteration of iodine uptake, the main rate-limiting step in thyroid hormonogenesis, affecting the activity of sodium iodide symporter (NIS) through two processes: (a) The binding of the Gram-negative bacterial endotoxin lipopolysaccharide (LPS), released by the gut microbiota, to the thyroid cell toll-like receptor 4 (TLR-4). TLR-4 in turn activates the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB), which subsequently promotes NIS transcription through paired box 8 (PAX8) [38]. (b) Alternatively, enhancement of NIS expression may also occur through histone deacetylase (HDAC) inhibition by an important metabolite of the gut microbiota, butyrate (which belongs to the class of short-chain fatty acids—SCFAs; see later in Section 6) [39,40];
- Modulation of activities of iodothyronine deiodinases, enzymes responsible for the conversion of thyroxine (T4) to its active form T3 by type 1 and type 2 deiodinases (D1, expressed mostly in the liver, kidney, thyroid, and pituitary, and D2, expressed primarily in the thyroid, central nervous system, pituitary, developing cochlea, brown adipose tissue, and skeletal muscle [41]) or to reverse T3, its inactive form, by type 3 deiodinase—D3 [42]. This occurs through a complex thyroid–gut axis pathway involving LPS capable of inducing the decrease in D1 activity in the liver [43] and, at the same time, activating D2 in the mediobasal hypothalamus, ultimately promoting the conversion of T4 to T3 [44];
- Modulation of T3 and T4 bioavailability through the deconjugation of sulfoconjugated and glucuroconjugated iodothyronines by bacterial sulfate esterase or β-glucuronidase, respectively, thus inducing the reabsorption of thyroid hormones in the enterohepatic circulation. In humans, a recycling mechanism has been described for steroids hormones, biliary acids, and vitamins, while as for thyroid hormones, direct proof has been only established in animal models [31];
- Regulation of the SCFAs-mediated balance between T helper 17 (Th17) cells and regulatory T cells (Treg), two subtypes of CD4+ lymphocytes exerting opposite effects (release of pro-inflammatory cytokines, i.e., interleukin—IL-17 or anti-inflammatory IL-10, respectively), in autoimmune inflammatory diseases and immune tolerance [35,45]. All these immune cells play a role in the pathogenesis of autoimmune thyroid disease (AITD), like Hashimoto disease (HD) and Graves’ disease. For instance, Prevotella is correlated with reduced proinflammatory Th17 polarization and increased differentiation of anti-inflammatory Treg; therefore, it has been speculated that the Th17/Treg homeostasis regulation might be a potential pathogenic pathway for Prevotella in HT patients [32,46];
- Involvement of the microbiota–gut–brain signaling in dopamine release, synthesis, and bioavailability. Certain species up-/downregulate the system dopamine transporter/dopamine binding efficiency, while others are positively or negatively correlated with the activity of tyrosine hydroxylase, an enzyme involved in dopamine synthesis [47,48]. Furthermore, butyrate’s intrinsic HDAC inhibitor activity influences neurotransmitter levels [47]. Since dopamine inhibits synthesis and secretion of the thyroid-stimulating hormone (TSH), thyroid function may be affected [35] (Figure 1).
3. The Association between Microbiota and Thyroid Cancer
Microbial Communities in Thyroid Cancer Tissues
4. Oxidative Stress in Thyroid Cancer
5. The Dual Relationship between the Microbiota and Cancer: The Biological Mechanisms
- DNA damage, both directly by inducing double-strand breaks and indirectly by eliciting high levels of ROS and RNS released from inflammatory cells, such as macrophages, as in colorectal cancer;
- β-Catenin signaling alterations, as observed in gastric cancer and colorectal adenomas and adenocarcinomas, leading to upregulation of genes involved in cellular proliferation, survival, and migration, as well as angiogenesis;
- Pro-inflammatory reactions induced by microorganism-associated molecular patterns that activate TLRs in several cell types, including macrophages, myofibroblasts, epithelial cells, and tumor cells, which, in loop, stimulate NF-κB, a master regulator of cancer-associated inflammation [136];
- Activation of the IL-23–IL-17 axis, which promotes tumor growth and progression [137], tumor necrosis factor–alpha (TNF-α)–TNF receptor signaling, and IL-11–IL-6 family member signaling, all activating the signal transducer and activator of transcription 3 (STAT3) (belonging to the family of STAT transcriptional factors that participate in the regulation of a variety of cellular process, including proliferation, differentiation, inflammation, and stemness) [138,139,140];
- Perturbations of the equilibrium between diet–gut microbiome–bile acid pool size/composition through the production of microbially generated secondary bile acids, like deoxycholic acid (DCA), responsible for activation of mitogenic and proinflammatory effects and subsequent promotion of liver cancer (see later in the text) [141,142] (Figure 2).
5.1. Gut Microorganisms: A Defense against Cancer
- Goblet cells, specialized epithelial cells, are essential to the formation of the mucus barriers [163];
- Paneth cells, secretory cells located in the crypts of Lieberkühn, produce antimicrobial peptides (AMPs) and proteins [164], whose activity is enhanced following signaling from local immune cells in response to the microbiota;
- Upon recognition of microbial peptidoglycan, nucleotide-binding oligomerization domain-containing protein 2 (NOD2) contributes to intestinal homeostasis by molecular signaling through the kinase receptor-interacting protein 2 and NF-κB, and inducing the production of AMPs and mucin [165];
- Microbial metabolites activate pathogen recognition receptors (PRRs), such as NOD-, leucine-rich repeat- and NOD-like receptor family pyrin domain-containing 6 (NLRP6) inflammasome (having a role in the maintenance of a stable microbial community in the intestine), which lead to the secretion of IL-18 and AMPs [165];
- Within adaptative mechanisms of microbial regulation, immunoglobulin A (IgA), secreted by plasma cells, terminally differentiated B cells residing in the intestinal lamina propria [168], acts primarily by inhibiting bacterial adherence to epithelial cells, also having direct effects on bacterial virulence [169];
- Pathogen-associated molecular patterns (e.g., LPS, flagellin) induce antigen-presenting cells, like dendritic cells (DCs); DCs then travel to mesentery, where they stimulate naïve T cells to differentiate into CD4+ Tregs and Th17 cells, which can migrate back to the gut mucosa or enter systemic circulation. While Tregs secrete the anti-inflammatory cytokine IL-10, Th17 cells, through the production of IL-17, can increase the Paneth cell production of antimicrobial peptides;
- SCFAs influence the immune response by a variety of signaling pathways, both in the innate immunity, e.g., activating NLRP3 that is responsible for IL-1β and IL-18 secretion [170]; inhibiting the production of cytokines, chemokines, and calprotectin produced by neutrophils [171]; inducing antimicrobial activity in the intestinal macrophage and increasing resistance to enteropathogens, and in the adaptative immunity, e.g., promoting the proliferation of Tregs [172]; and accelerating cellular metabolism and regulating gene expression to promote B cell differentiation into antibody-producing cells [173].
6. Diet: Enemy or Friend of the Gut Microbiota?
7. Antioxidant Nutrients at the Intersection of Gut Microbiota Health and Protection against Thyroid Cancer
7.1. Probiotics
7.2. Trace Elements
7.3. Vitamins
7.4. Polyphenols
8. Promoting Food Health: Should Citizens and Food Companies Meet Half Way?
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AITD | Autoimmune thyroid disease |
AMPs | Antimicrobial peptides |
ATC | Anaplastic thyroid cancer |
CAT | Catalase |
CRP | C-reactive protein |
D1,,/3 | Deiodinase type 1,2,3 |
DCs | Dendritic cells |
DCA | Deoxycholic acid |
DUOX | Dual oxidase |
ERK | Extracellular signal-regulated kinase |
FTC | Follicular thyroid cancer |
GABA | Gamma-aminobutyric acid |
FVPTC | Follicular variant of papillary thyroid cancer |
GPx | Glutathione peroxidase |
GPR | G protein-coupled receptors |
GSH | Glutathione |
H2O2 | Peroxide hydrogen |
HD | Hashimoto’s disease |
HDAC | Histone deacetylase |
HIF-1α | Hypoxia-inducible factor-1 alpha |
Hp | Helicobacter pylori |
IL | Interleukin |
LPS | Lipopolysaccharide |
MAPK | Mitogen-activated protein kinase |
NF-kB | Kappa-light-chain-enhancer of activated B cells |
NIS | Sodium iodide symporter |
NLRP3,6 | NOD-like receptor family pyrin domain-containing 3,6 |
NO | Nitric oxide |
NOCs | N-nitroso compounds |
NOD | Nucleotide-binding oligomerization domain-containing protein |
NOX | Nicotinamide adenine dinucleotide phosphate oxidase |
PAX8 | Paired box 8 |
PDTC | Poorly differentiated thyroid cancer |
PRRs | Pathogen recognition receptors |
PTC | Papillary thyroid cancer |
RNS | Reactive nitrogen species |
ROS | Reactive oxygen species |
rRNA | Ribosomal RNA |
SCFAs | Short-chain fatty acids |
Se | Selenium |
SOD | Superoxide dismutase |
STAT3 | Signal transducer and activator of transcription 3 |
T3 | Triiodothyronine |
T4 | Thyroxine |
TAMS | Tumor-associated macrophages |
TC | Thyroid cancer |
TCPTC | Tall cell papillary thyroid cancer |
TGF-β1 | Transforming growth factor-beta 1 |
Th | T helper cell |
TrxR | Thioredoxin reductase |
TLR | Toll-like receptor |
TNF-α | Tumor necrosis factor-alpha |
TPO | Thyroperoxidase |
TR | Thyroid receptor |
TSH | Thyroid-stimulating hormone |
Treg | Regulatory T cell |
Zn | Zinc |
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Study Design | Sample | Country | Microbial Indicators | Other Clinical Data | Reference |
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Cross-sectional | Blood sample from 77 patients with PTC (37 patients in distant metastasis group and 40 in ablation group) after total thyroidectomy and radioiodine treatment | China | - | 31 differentially abundant metabolites between the ablation and distant metastasis groups: 12 metabolites with higher concentrations in the distant metastasis group, while 19 metabolites showing lower concentrations. 31 metabolites mostly involved in “alanine, aspartate and glutamate metabolism” and “inositol phosphate metabolism”. | [71] |
Case-control | Peripheral blood and fecal samples from 30 preoperative patients diagnosed for TC and 35 healthy controls, 18 to 65 years of age | China | Chao index (richness) and Shannon index (richness and evenness) higher in the TC group. Β-diversity (Bray–Curtis method) significantly different between the two groups. Firmicutes and Proteobacteria increase and Bacteroidetes decrease in TC group. | TC-enriched genera, like Lactococcus, Ruminococcaceae_UCG_002, Intestinibacter, Clostridium_sensu_stricto_1, Christensenellaceae_ R-7_group, Lachnospiraceae_ND3007, and Turicibacter, positively correlated with Lpa titers. TC-enriched genera, including Ruminococcaceae_UCG-014 and Lactococcus, negatively correlated with the levels of ApoB. 72 significantly changed metabolites (mainly lipids) in TC group. Some TC-enriched genera, e.g., Klebsiella, Coprococcus_3, and [Eubacterium]_hallii_group, associated with lipid-related metabolites. The adipocytokine signaling, sphingolipid metabolism, cholesterol metabolism, and necroptosis pathways also significantly enriched in TC patients. | [20] |
Case-control | Fecal specimens from 74 subjects: 20 patients suffering from differentiated TC, 18 patients with thyroid nodules, and 36 healthy controls | China | 35 unique species, like Thiobacillus, Rhodobacter, Rheinheimera, Mycobacterium, and Anaerotruncus, found in TC group. Shannon index not significant. ACE index (microbial abundance) higher in TC and thyroid nodule groups than in control group. UniFrac distance (β-diversity) significantly different between thyroid disease and healthy groups. The gut microbiome of TC group characterized by a dominance of Prevotella, Roseburia, Coprococcus, Anaerostipes, Ruminococcus, Neisseria, Streptococcus, and Porphyromonas. | In TC group, relationships between 22 taxa and 7 clinical indices: significantly positive associations observed between TSH and the genus Porphyromonas and between FT3 and the genus Streptococcus, and significantly inverse associations between TRAb and the families Clostridiaceae and Lachnospiraceae and between TPOAb and the genus Ruminococcus. | [59] |
Cross-sectional | Stool samples collected prior to surgery from 90 patients with TC (88 cases with PTC and 2 cases with FTC; 56 cases of TCs with local lymph node metastasis—N1 group and 34 of TCs without local lymph node metastasis—N0 group) and 90 healthy controls | China | Reduced richness (Ace index) and diversity (Shannon index) in TC samples. Significant differences in β-diversity (UniFrac distance) between TC and healthy control groups. No significant difference in the Firmicutes/Bacteroidetes ratio between the two groups. Relatively higher abundance of Proteobacteria in TC patients. | No significant difference in Ace and Shannon indices between N0 and N1 subgroups. A four-genus microbial signature able to distinguish TC patients in N0 from those in N1 (Hungatella, Alistipes, Fusobacterium, and Phascolarctobacterium). Five categories related to the processing of genetic information (‘‘Aminoacyl-tRNA biosynthesis,” ‘‘Homologous recombination,” ‘‘Mismatch repair,” ‘‘DNA replication,” and ‘‘Nucleotide excision repair”) significantly increased in the healthy controls compared to TC patients. | [79] |
Study Design | Sample | Country | Microbial Indicators | Other Clinical Data | Reference |
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Cross-sectional | 25 patients with TC (19 malignant and 6 benign tumors), aged 25–66 years. Stool samples collected before thyroidectomy. Tissue samples removed during surgery and divided into tumor, paratumor, and normal tissues. | China | α-diversity (Chao1, Shannon, and Simpson indices) different in all types of samples. All fecal diversities higher than those in tissues. Diversity in malignant patients lower than that in benign patients, and decreasing with distance from cancerous tissues. Proteobacteria the most abundant in all three types of tissues, while Firmicutes dominant in stool samples. | The microbiome from thyroid completely different from that of the gut, with 18 of the 30 pathways significantly different. Two of the most abundant operational taxonomic units, Pseudomonas mucidolens and Escherichia fergusonii, associated with several processes. Several microbes negatively related with PTH, while Neisseria perflava positively related with TSH and T3. | [1] |
Retrospective | Tumor and peritumor tissues collected from 30 patients with PTC who underwent total thyroidectomy. | China | Chao1 index (richness) and Shannon index (richness and evenness) lower in tumor tissues than in peritumor tissues (the first index not significant). β-diversity (Bray–Curtis method) significantly different between the two groups. At the genus level, abundance of Sphingomonas and Aeromonas significantly increased in tumor tissues, whereas the abundance of Comamonas, Acinetobacter, Peptostreptococcus, and Proteus significantly increased in peritumor tissues. | Chao1 index significantly higher in N1 stage (presence of lymph node metastasis) than in N0 stage (absence of lymph node metastasis); Shannon index significantly lower in N1 stage than in N0 stage. Abundance of Sphingomonas significantly increased in N1 stage compared to N0 stage. No significant difference in Chao1 and Shannon indices between male and female patients. No difference in the diversity and composition between patients <55 and ≥55. | [12] |
Retrospective | Raw whole-transcriptome RNA-sequencing, Level 3 normalized mRNA expression read counts, and DNA methylation 450 k sequencing data for untreated, nonirradiated tumor, and adjacent normal tissue were downloaded from the Genomic Data Commons legacy archive for 563 TC patients (354 CPTC, 101 FVPTC, 35 TCPTC, 135 male, 366 female tumor samples: 505 tumor and 58 normal tissue samples). | United States | 45 microbes in CPTC, 34 in FVPTC, and 33 in TCPTC differentially abundant between tumor and normal tissue. 33 microbes in male samples and 49 microbes in female samples to be differentially abundant between tumor and normal tissue. Micrococcus luteus, Frankia sp., Anabaena sp. K119, and uncultured Gammaproteobacteria bacterium all similarly overabundant in normal tissue in CPTC, FVPTC, and TCPTC. In males, Synechococcus sp. CC9311 overabundant in the normal samples, while in females, overabundant in tumor samples. | Frankia sp. and uncultured Gammaproteobacteria bacterium Gammaproteobacteria bacterium, which are abundant in all PTC normal tissue samples, correlated with lower MACIS score). Bradyrhizobium sp. BTAi1, which is uniquely abundant in TCPTC normal tissue, correlated with higher MACIS score. Frankia sp. and Anabaena sp. K119, both of which are overabundant in normal tissue samples of all PTC subtypes, negatively correlated with pathologic M stage, while Stenotrophomonas maltophilia, dysregulated in only CPTC and FVPTC, positively correlated with pathologic M stage. Microbe abundance in FVPTC correlated with the greatest number of chromosomal alterations and mutations. Microbe abundance in males more frequently and strongly correlated with a greater number of CNV. Microbes dysregulated in TCPTC and male patients correlated with higher expression of the BRAFV600E mutation. The majority of most enriched pathways uniquely dysregulated in the different subtypes related to cell growth, in males belonging to the tumor suppression-related group, and in females, belonging to the DNA checkpoint and damage-related group. Lower microbe abundance correlated with greater extent of methylation at known tumor suppressor genes. | [97] |
Cross-sectional | Tumor samples from 80 patients with PTC. | China | α-diversity significantly lower in patients with T1/T2 PTC than in those with T3/T4 PTC (Shannon and Simpson indices). No significant differences in richness (Sobs index) between clinical stages. β-diversity (Bray–Curtis metric distances) significantly different with tumor progression, with differences in Psudomonas, Rhodococcus, and Sphingomas in abundance among various stages. Pseudomonas spp., presenting a higher abundance in tumors of patients with T1 and T2 PTC than in T3 or T4; Rhodococcus abundance significantly higher in T1 than in T3 PTC; and Sphingomonas more abundant in T1 and T2 than in T3. Eight-genera microbiome signature between T1_2 and T3_4 groups. α-diversity significantly lower in males compared to females, but no significant differences in β-diversity by sex. Rhodococcus, Ralstonia, Chryseobacterium, and Burkholderia-Caballeronia-Paraburkholderia more abundant in females than in males. | In T1_2 group, higher proportions of most metabolic pathways, while in T3_4 group, higher proportions of super pathways of purine nucleotide de novo biosynthesis and palmitate biosynthesis II. Positive associations between FT4 and Neisseria and norank_f__norank_o__Chloroplast, and FT3 and Treponema. Negative associations between FT4 and Klebsiella; T4 and Klebsiella and Escherichia-Shigella, T3 and Granulicatella; and TSH and norank_f__norank_o__Clostridia_UCG-014 and Prevotella. A negative correlation between anti-T SHR levels and Klebsiella and Burkholderia-Caballeronia-Paraburkholderia. Positive correlation of the anti-TG levels with Sphingomonas, Rhodococcus, Ralstonia, and Brevundimonas, but negative correlation with Anaerococcus and Akkermansia. Nine genera (UCG-002, Streptococcus, Parvimonas, Akkermansia, Bacteroides, Haemophilus, Selenomonas, Prevotella, and Bifidobacterium) negatively correlated with the anti-TPO levels. | [82] |
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Gorini, F.; Tonacci, A. Tumor Microbial Communities and Thyroid Cancer Development—The Protective Role of Antioxidant Nutrients: Application Strategies and Future Directions. Antioxidants 2023, 12, 1898. https://doi.org/10.3390/antiox12101898
Gorini F, Tonacci A. Tumor Microbial Communities and Thyroid Cancer Development—The Protective Role of Antioxidant Nutrients: Application Strategies and Future Directions. Antioxidants. 2023; 12(10):1898. https://doi.org/10.3390/antiox12101898
Chicago/Turabian StyleGorini, Francesca, and Alessandro Tonacci. 2023. "Tumor Microbial Communities and Thyroid Cancer Development—The Protective Role of Antioxidant Nutrients: Application Strategies and Future Directions" Antioxidants 12, no. 10: 1898. https://doi.org/10.3390/antiox12101898
APA StyleGorini, F., & Tonacci, A. (2023). Tumor Microbial Communities and Thyroid Cancer Development—The Protective Role of Antioxidant Nutrients: Application Strategies and Future Directions. Antioxidants, 12(10), 1898. https://doi.org/10.3390/antiox12101898