Microbiome and oral squamous cell carcinoma: a possible interplay on iron metabolism and its impact on tumor microenvironment

Abstract

There is increasing evidence showing positive association between changes in oral microbiome and the occurrence of oral squamous cell carcinoma (OSCC). Alcohol- and nicotine-related products can induce microbial changes but are still unknown if these changes are related to cancerous lesion sites. In an attempt to understand how these changes can influence the OSCC development and maintenance, the aim of this study was to investigate the oral microbiome linked with OSCC as well as to identify functional signatures and associate them with healthy or precancerous and cancerous sites. Our group used data of oral microbiomes available in public repositories. The analysis included data of oral microbiomes from electronic cigarette users, alcohol consumers, and precancerous and OSCC samples. An R-based pipeline was used for taxonomic and functional prediction analysis. The Streptococcus spp. genus was the main class identified in the healthy group. Haemophilus spp. predominated in precancerous lesions. OSCC samples revealed a higher relative abundance compared with the other groups, represented by an increased proportion of Fusobacterium spp., Prevotella spp., Haemophilus spp., and Campylobacter spp. Venn diagram analysis showed 52 genera exclusive of OSCC samples. Both precancerous and OSCC samples seemed to present a specific associated functional pattern. They were menaquinone-dependent protoporphyrinogen oxidase pattern enhanced in the former and both 3',5'-cyclic-nucleotide phosphodiesterase (purine metabolism) and iron(III) transport system ATP-binding protein enhanced in the latter. We conclude that although precancerous and OSCC samples present some differences on microbial profile, both microbiomes act as "iron chelators-like" potentially contributing to tumor growth.

Keywords: Alcohol; Cigarette; Functional pathways prediction; Iron(III) transport system; Microbiome; Oral squamous cell carcinoma.

Fig. 1

Stacked bar chart showing the relative abundance of the bacterial taxonomic hits at the genus level in buccal mucosa samples in the different studied groups: G1, electronic cigarette users; G2, alcohol consumers; G3, individuals diagnosed with precancerous lesions; and G4, individuals diagnosed with OSCC obtained by the MiSeq sequencing pipeline

Fig. 2

Representation of alpha- and beta-diversity on samples and community levels. a and b Alpha diversity analysis with Chao and Shannon metrics (Chao1 p=0.15125; Shannon p=0.476094), and boxplot graph analyzed by the same metrics (c and d); e principal components analysis (PCA; Bray-Curtis Index) with beta-diversity metric (PCA p<0.001), revealing an outlier group counting only cancer samples; f Venn diagram showing the occurrence and co-occurrence of the genera between the four groups of samples

Fig. 3

Pie chart and statistical analysis at genus level in OSCC samples according to tumor staging. a Pie chart showing the relative abundance at genus level in OSCC samples according to tumor staging (Union for International Cancer Control-American Joint Committee on Cancer—UICC-AJCC). b Statistical analysis at genus level in OSCC samples according to tumor staging. *p-value < 0.05, **p-value < 0.01, ***p-value < 0.0001 by Mann-Whitney test

Fig. 4

Heatmap clustering taxonomy abundance at genus level in OSCC samples according to gender, use or not of electronic cigarettes, consumption of alcohol, and cancer staging (T, tumor size according to the Union for International Cancer Control-American Joint Committee on Cancer—UICC-AJCC)

Fig. 5

Metabolic pathways predicted by the genera counts. The different pathways are regulated by bacteria in four different conditions, all the four groups are represented in the graphics in the following order: Cancer, precancer, alcohol and electronic cigarette users. The different molecules expressed are represented by the numbers: 1 hemG; menaquinone-dependent protoporphyrinogen oxidase [EC:1.3.5.3]; 2 sdhB, frdB; succinate dehydrogenase/fumarate reductase, iron-sulfur subunit [EC:1.3.5.1 1.3.5.4]; 3 pcnB; poly(A) polymerase [EC:2.7.7.19]; 4 E3.1.3.37; sedoheptulose-bisphosphatase [EC:3.1.3.37]; 5 cpdP; 3′,5′-cyclic-nucleotide phosphodiesterase [EC:3.1.4.17]; 6 E3.1.6.1, aslA; arylsulfatase [EC:3.1.6.1]; 7 afuC, fbpC; iron(III) transport system ATP-binding protein [EC:7.2.2.7]

Fig. 6

Heatmap indicating the number of molecules expressed per bacteria on two scenarios. a Shows the number of molecules expressed per species considering the four groups of samples; b(1) number of molecules expressed in electronic cigarette users; b(2) number of molecules expressed in alcohol consumers group; b(3) number of molecules expressed in precancerous group; b(4) number of molecules expressed in cancer. The top seven molecules expressed in the functional prediction more enriched pathways (Figure S4): 1 hemG; menaquinone-dependent protoporphyrinogen oxidase [EC:1.3.5.3]; 2 sdhB, frdB; succinate dehydrogenase/fumarate reductase, iron-sulfur subunit [EC:1.3.5.1 1.3.5.4]; 3 pcnB; poly(A) polymerase [EC:2.7.7.19]; 4 E3.1.3.37; sedoheptulose-bisphosphatase [EC:3.1.3.37]; 5 cpdP; 3′,5′-cyclic-nucleotide phosphodiesterase [EC:3.1.4.17]; 6 E3.1.6.1, aslA; arylsulfatase [EC:3.1.6.1]; 7 afuC, fbpC; iron(III) transport system ATP-binding protein [EC:7.2.2.7]. Molecules expressed exclusively in the precancerous group: b(3) 6 pulnA; pullulanase [EC:3.2.1.41], and cancerous group: b(4) 6 E3.1.6.1, aslA; arylsulfatase [EC:3.1.6.1]. Full species name: aap (Aggregatibacter aphrophilus), aaz (Aggregatibacter aphrophilus), abaa (Acinetobacter baumannii), ack (Acidovorax sp.), acq (Actinomyces sp.), aeu (Actinobacillus equuli), ajo (Acinetobacter johnsonii), amu (Akkermansia muciniphila), amy (Schaalia meyeri), aos (Actinomyces oris), apa (Actinobacillus pleuropneumoniae), blas (Blastomonas sp.), boa (Bacteroides ovatus), btra (Bibersteinia trehalosi), bvu (Bacteroides vulgatus), fhw (Fusobacterium hwasookii), fnc (Fusobacterium nucleatum), hia (Haemophilus influenzae), leo (Leptotrichia sp.), lim (Limnohabitans sp.), mbl (Moraxella bovoculi), nwe (Neisseria weaveri), pit (Prevotella intermedia), pje (Prevotella jejuni), pmic (Parvimonas micra), pmz (Prevotella melaninogenica), rdn (Rothia dentocariosa), rmu (Rothia mucilaginosa), scf (Streptococcus parasanguinis), smb (Streptococcus mitis), sor (Streptococcus oralis), ssa (Streptococcus sanguinis), ssah (Streptococcus salivarius), tde (Treponema denticola), vpr (Veillonella parvula)

Fig. 7

Correlation analysis between the bacterial community and the main bacteria that regulate the predicted molecules. a Network demonstrating positive and negative regulations between the different bacteria that make up the oral microbiome, b barplot showing different abundances taxa between the main bacteria that regulate molecules in cancerous and precancerous samples. *p-value < 0.05, **p-value < 0.01, ***p-value < 0.0001 by Mann-Whitney test