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Microbial and molecular differences according to the location of head and neck cancers

Cancer Cell International volume 22, Article number: 135 (2022) Cite this article

Abstract

Background

Microbiome has been shown to substantially contribute to some cancers. However, the diagnostic implications of microbiome in head and neck squamous cell carcinoma (HNSCC) remain unknown.

Methods

To identify the molecular difference in the microbiome of oral and non-oral HNSCC, primary data was downloaded from the Kraken-TCGA dataset. The molecular differences in the microbiome of oral and non-oral HNSCC were identified using the linear discriminant analysis effect size method.

Results

In the study, the common microbiomes in oral and non-oral cancers were Fusobacterium, Leptotrichia, Selenomonas and Treponema and Clostridium and Pseudoalteromonas, respectively. We found unique microbial signatures that positively correlated with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in oral cancer and positively and negatively correlated KEGG pathways in non-oral cancer. In oral cancer, positively correlated genes were mostly found in prion diseases, Alzheimer disease, Parkinson disease, Salmonella infection, and Pathogenic Escherichia coli infection. In non-oral cancer, positively correlated genes showed Herpes simplex virus 1 infection and Spliceosome and negatively correlated genes showed results from PI3K-Akt signaling pathway, Focal adhesion, Regulation of actin cytoskeleton, ECM-receptor interaction and Dilated cardiomyopathy.

Conclusions

These results could help in understanding the underlying biological mechanisms of the microbiome of oral and non-oral HNSCC. Microbiome-based oncology diagnostic tool warrants further exploration.

Introduction

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, with 890,000 new cases and 450,000 deaths in 2018 [1, 2]. HNSCC accounts for about 6% of all cancers and 1–2% of deaths due to neoplastic diseases [3,4,5]. HNSCC is a heterogeneous disease and tumours are distinguished based on location. HNSCC originates from the epithelial cells in the laryngeal and oropharynx, lips, mouth or larynx. Tobacco and alcohol consumption are the well-known and geographically most prevalent risk factors for HNSCC [6]. Heavy users of these carcinogens-containing products have a 35-fold higher risk of developing HNSCC than non-users [6, 7], and approximately three-quarters of HNSCC cases attributable to cigarette smoking and tobacco use [8]. In addition, betel nut chewing is independent risk factor for HNSCC in India, China or Taiwan [9, 10]. Especially, development of oropharyngeal cancers is strongly associated with HPV infection, which mainly occurs in Western Europe and the United States [6, 11].

Trillions of microbes have evolved and continue to live on and within human beings [12]. Numerous studies have suggested a link between the microbiota, which exist in various organs (e.g., gut and placenta) and pathological conditions such as neurologic diseases, metabolic disorders, and cancers [13,14,15,16]. With the development of omics technologies, such as metagenomics, transcriptomics, and proteomics, substantial evidence has been accumulated regarding the relationship of microorganisms and various diseases, including cancers [17].

The gut microbiome has been associated with various disorders, especially malignant tumours. The gut microbiome is involved in biological processes, including modulating the metabolic phenotype, regulating epithelial development, and influencing innate immunity [18]. Chronic diseases such as obesity, inflammatory bowel disease, diabetes mellitus, metabolic syndrome, atherosclerosis, alcoholic liver disease, non-alcoholic fatty liver disease, cirrhosis are associated with the human microbiome [19]. Several studies have demonstrated that gut microbiome dysbiosis is associated with tumourigenesis and/or tumour growth across cancer types, including colon, hepatocellular carcinoma, gastric, and breast [13, 18]. Moreover, the gut microbiome has been demonstrated to play a key role in the response to cancer therapy, such as chemotherapy, immune checkpoint blockade, and stem cell transplant [13]. For immune checkpoint blockade response, differential gut microbiome signatures exist in patients who respond to immune checkpoint blockade treatment [20,21,22].

Although intratumoral microbiota has not been studied as much as the gut microbiota, the importance of microbiota in tumours is increasing, with studies showing that it affects the response to cancer treatment [13, 23,24,25,26]. Intratumoral bacteria, which are metabolically active, can alter the chemical structure of anti-cancer drugs [27, 28]. In addition, Fusobacterium nucleatum in colorectal tumour promotes resistance to chemotherapy through modulation of autophagy [29]. HNSCC, especially oral squamous cell carcinoma (OSCC), is the most prevalent and commonly studied cancer associated with bacterial infection, and is the most common malignancy of the head and neck worldwide [30]. Two prominent oral pathogens, Porphyromonas gingivalis, and F. nucleatum have been reported to promote tumour progression in mice [31]. Periodontitis is an infectious disease causing chronic inflammation in the oral cavity [32, 33]. Periodontitis has been linked to various cancers, including oesophageal and oropharyngeal cancers [30]. Several studies have found that the risk of developing OSCC may increase with periodontal disease [34, 35], and periodontal disease increases the risk of oral cancer even after adjusting for significant risk factors [36, 37]. Herein, we investigated the underlying molecular differences of the microbiome of oral cancer and non-oral HNSCC.

Methods

Microbiome datasets & TCGA RNA-sequencing datasets

We downloaded Kraken-TCGA(The Cancer Genome Atlas) -Raw-Data (n = 17,625) from microbial count datasets [38] for this study. Primary tumours were selected from HNSCC of microbiome data, classified into RNA and WGS, and combined with TCGA clinical information to separate oral and non-oral subtype. RNA-expression sequencing and clinical data sets of HNSCC samples were downloaded from the Broad GDAC Firehose [39] on 20 Feb 2020. The samples were categorised based on the site of occurrence as either oral cancer (alveolar ridge, buccal mucosa, floor of the mouth, hard palate, lip, oral cavity, and oral tongue) or non-oral cancer (base of tongue, hypopharyngeal, larynx, oropharynx, and tonsil) (Supplementary Table). Preprocessing was used with the R program (version 4.0.3) [40].

Linear discriminant analysis effect size (LEfSe)

To identify significantly different bacteria (as biomarkers) between the two groups at the genus level, taxa summaries were reformatted and inputted into LEfSe via the Huttenhower Lab Galaxy Server [41]. The LDA values of oral and non-oral HNSCC microbiome data of RNA and DNA were obtained. We used the LDA method to estimate the effect size of the abundant genus level [41].

Then, we obtained common bacteria of RNA and DNA with the threshold on the logarithmic LDA score for discriminative features of 2.0108 (p < 0.0076). In the settings of LEfSe, the Kruskal–Wallis sum-rank test (α = 0.05) was used to detect taxa with significant differential abundance.

Phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) and ANOVA-like differential expression (ALDEx2)

The name of the common bacteria was changed to ID of Greengenes (97% taxonomy) (version 13.5) (http://greengenes.lbl.gov) and used as an input file. PICRUSt was performed using the Galaxy web application, which was used to predict bacterial metabolic contributions of oral rich and non-oral rich bacteria, respectively [42]. To filter the results of the PICRUSts, we merged results of oral rich and non-oral rich bacteria, and used the ALDEx2 [43] to obtain top five pathways with a p-value of 0.05 or less.

Correlation analysis

A correlation analysis was performed with respect to the RNA expression data and common bacteria data of oral and non-oral HNSCC. Using the Spearman correlation test, genes with oral/non-oral correlation coefficients r > 0.15 and r < − 0.15 were obtained. Significance levels were considered at P < 0.05.

Protein–protein interaction (PPI) analysis & Hub gene

PPI analysis of correlated genes was performed using the plug-in Search Tool for the Retrieval of Interacting Genes (STRING) app (version 1.5.1) [44]. The results of the analysis were imported into Cytoscape (version 3.8.2) [45] to establish a network model. The plug-in app cytohubba (version 0.1) [46] in Cytoscape was downloaded and installed. The top ten scores of the degree algorithm were taken as the criteria to screen out the hub genes with high connectivity in the gene expression network.

KEGG pathway and gene ontology (GO)

KEGG pathway and GO analysis were performed on the DAVID website [47] with the genes in the node table resulting from the PPI. Then, the genetic symbol was transferred to entrezID using the org.Hs.eg.db (version 3.12.0) package [48] with the same input file from the PPI for subsequent analysis. The results of enhanced GO entries and KEGG were visualised as path point plots using clusterProfiler (version 3.18.1), ggplot (version 3.3.5), and Enrichplot2 (version 1.10.2) packages. GO and KEGG analysed the used data with statistically significant false discovery rates < 0.05.

Results

Characterisation of unique microbial signatures of oral and non-oral HNSCC

To evaluate the unique microbial signatures of oral and non-oral HNSCC, we analysed Kraken-TCGA data sets using the linear discriminant analysis (LDA) method. We divided 691 HNSCC samples into 172 DNA whole genome sequencing (WGS) data and 519 RNA sequencing data (Fig. 1). Next, we analysed RNA sequencing as subtypes divided into 314 oral cancer and 205 non-oral cancer. DNA WGS data were also analysed as 115 oral and 57 non-oral subtypes. Clinical information related to these samples is described in Table 1. In both data, gender (P = 8.698E-05 (RNA)/2.372E-06 (DNA)) HPV status (P = 1.623E-09 (RNA)/5.201E-08 (DNA)), clinical stage (P = 3.998E-03 (RNA)/1.100E-03 (DNA)) and pathologic stage (P = 4.998E-04 (RNA)/2733E-05 (DNA)) were significantly different between patients with oral and non-oral cancers.

Fig. 1
figure 1

Pipeline flow chart throughout the study

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Table 1 Patient’s characteristics
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Investigation of the common microbiome of oral and non-oral HNSCC

The relatively enriched microbiome of oral and non-oral HNSCC are shown in Fig. 2a, b. The enriched microbiomes in oral HNSCC were Fusobacterium, Leptotrichia, Selenomonas and Treponema and the enriched microbiomes in non-oral HNSCC were Clostridium and Pseudoalteromonas, as determined by the linear discriminant analysis effect size (LEfSe) method (Fig. 2a, b). The distribution of count data for each microbiome subtypes is depicted in Fig. 2c–h.

Fig. 2
figure 2

Linear discriminant analysis effect size (LEfSe) analyses and distribution of the microbiome by subtype. LEfSe analysis of microbiome composition between oral and non-oral-associated cancers was performed on a bacterial DNA and b bacterial RNA, respectively. Bacteria species enriched in oral cancer had a positive linear discriminant analysis (LDA) score, while bacteria species enriched in non-oral cancer had a negative score. Microbiomes with higher levels of distribution in oral cancer were c Fusobacterium, d Leptotrichia, e Selenomonas, and f Treponema. Microbiomes with higher levels of distribution in non-oral cancer were f Clostridium and g Pseudoalteromonas

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Microbial Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein network of oral and non-oral HNSCC

We analysed the molecular mechanism of the microbiome of oral and non-oral HNSCC using KEGG pathway analysis and protein network analysis (Fig. 3, Tables 1 and 2). We found unique microbial signatures that positively correlated KEGG pathways in oral HNSCC, positively correlated KEGG pathways and negatively correlated KEGG pathways in non-oral HNSCC (Figs. 3 and 4). In oral HNSCC, positively correlated genes were mostly found in bacterial infection pathways, and the genes involved in neurodegenerative diseases (prion diseases, Alzheimer disease, and Parkinson disease). In non-oral cancer, positively correlated genes were found Herpes simplex virus 1 infection and Spliceosome and negatively correlated genes showed results from PI3K-Akt signaling pathway, focal adhesion and regulation of actin cytoskeleton and Dilated cardiomyopathy. In addition, we conducted a pathway and gene expression analysis using microbial data of subtypes from each oral and non-oral HNSCC. As a result of PICRUSt, rich microbiome within oral cancer was involved in germination, Huntington's disease, biosynthesis of siderophore group nonribosomal peptides, atrazine degradation and prion diseases. Rich microbiome within non-oral cancer was found to be associated with other glycan degradation, Lysosome, Glycosphingolipid biosynthesis—globo series, electron transfer carriers, and glycosaminoglycan degradation (Table 2 and Additional file 2: Table S1). Rich microbiome within non-oral cancer was found to be associated with biosynthesis and metabolism of glycan, transport, catabolism, and biosynthesis of other secondary metabolites. Rich microbiome within oral cancer was involved in the biodegradation and metabolism of xenobiotics, neurodegenerative diseases, and the circulatory system. We found significant pathways using correlated genes with microbiome. We identified the KEGG pathways by selecting only the nodded genes as a protein–protein interaction tool (Table 3). The results of the phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis are shown in Additional file 1: Fig. S1. ALDEx2 was performed by merging the KEGG pathways obtained after PICRUSt of each subtype. The result is the median expression value of the KEGG pathway, and is expressed as a dot on the graph (Additional file 2: Table S1).

Fig. 3
figure 3

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. a Significantly enriched KEGG pathways of the positively correlated genes in oral cancer. b Significantly enriched KEGG pathways of the positively correlated genes in non-oral cancer. c Significantly enriched KEGG pathways of the negatively correlated genes in non-oral cancer. The left Y-axis shows the KEGG pathway. The X-axis shows the gene ratio

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Fig. 4
figure 4

Graphical summary of this study

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Table 2 Results of PICRUSt KEGG pathway enrichment analysis
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Table 3 DAVID gene-annotation enrichment analysis of KEGG pathway
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Discussion

The microbiome plays an important role in the human host and participates in the development of a wide variety of diseases, such as cancer [12]. The tumor microbiome is associated with a chronic inflammatory state and modulates the initiation and development of various cancers, such as lung, breast, colon, gastric, pancreatic, cholangiocarcinoma, ovarian, and prostate cancers [13, 23,24,25,26, 49,50,51]. In colorectal cancer (CRC), transplant of stool containing the tumor microbiome from patients with CRC can induce polyp formation [52, 53]. Moreover, some bacterial species (F. nucleatum) can stimulate an inflammatory state that can promote carcinogenesis via increased production of reactive oxygen species [54], induction of proinflammatory toxins [55, 56], and suppression of anti-tumor immune functions [57, 58]. In this study, for the first time, we differentiated the microbiota of HNSCC into oral and non-oral cancers to identify differences in the abundance of the tumor microbiome. Then, we then attempted a molecular approach using the correlation between the microbiome and mRNA expression. We systematically selected six microbiomes as unique microbial signatures of oral and non-oral HNSCC. Microbiomes with higher levels of distribution in oral HNSCC were Selenomonas, Fusobacterium, Leptotrichia and Treponema, while microbiomes with higher levels of distribution in non-oral HNSC were Clostridium and Pseudoalteromonas.

The relationship between oral microbiota and human diseases has studied a lot. Especially, several bacteria including Porphyromonas gingivalis, Treponema denticola, Selenomonas sputigena and Fusobacterium nucleatum have been associated with cancer development [59,60,61]. In the current study, we observed the Fusobacterium, Treponema, Leptotrichia were enriched in oral cancer compared to non-oral cancer. In consistent with previous research, it may have a negative effect on cancer progression. Clostridium species, which are well-studied anaerobic bacterium, has high ability for colonization in the hypoxic and necrotic lesions in tumour [62]. Genetically modified Clostridium expressing tumour suppressive genes is one of the therapeutic strategies of cancers. Since the Clostridium is enriched in non-oral cancer, it may be used as therapeutic options for non-oral cancers.

The prevention and treatment of diseases by targeting the microbiome have been widely investigated [30]. Modulation of the microbiome may also contribute to the treatment of cancer [63]. Cancer therapy requires an intact commensal microbiome that mediates the therapy effects by modulating functions of myeloid-derived suppressor cells in the tumor microenvironment [24, 63, 64]. Some studies have shown the deleterious effects of antibiotics on the treatment of cancer [13, 65]. Patients with metastatic renal cell carcinoma or non-small-cell lung cancer had significantly worse survival outcomes if they received antibiotics just before or just after the initiation of treatment with immune checkpoint blockade [66]. In addition, patients who received anti-Gram-positive antibiotics along with cyclophosphamide for chronic lymphocytic leukemia or cisplatin for relapsed lymphoma had a lower overall response rate [55, 67]. These microbiomes may confer susceptibility to certain cancers, either through a direct effect by the local presence within the tumor microenvironment or via the systemic impact of the microbiome from a distant location, such as the gut and the skin [68].

There are several limitations in this study. The results were not validated in other cohorts or experimental procedures. We obtained the results by using Kraken pipeline, which obtains microbiome information from whole genome sequencing or RNA sequencing data. Therefore, it is necessary to verify it by microbiome sequencing and/or PCR analysis.

Taken together, stress conditions, such as diet, antigen exposure, medications, and stress are important factors that contributing to the state of health and also affect the microbiome [38]. This field is young, and we are left with many unanswered questions—especially regarding the mechanism of action as well as the group of bacterial species that are most important in mediating antitumor effects. Multifaceted strategies are needed to modulate precision medicine and treat disease. Efforts are currently underway to enhance therapeutic responses and/or abrogate treatment-associated toxicity chemotherapeutic agents via modulation of the microbiome.

Availability of data and materials

All the data were available on the manuscript or from the corresponding author on reasonable request.

Abbreviations

TCGA:

The Cancer Genome Atlas

ALDEx2:

ANOVA-like differential expression tool for high-throughput sequencing data

CRC:

Colorectal cancer

GO:

Gene Ontology

HNSCC:

Head and neck squamous cell carcinoma

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LDA:

Linear discriminant analysis

LEfSe:

Linear discriminant analysis effect size

OSCC:

Oral squamous cell carcinoma

PICRUSt:

Phylogenetic investigation of communities by reconstruction of unobserved states

PPI:

Protein–protein interaction

STRING:

Search Tool for the Retrieval of Interacting Genes/Proteins

TCGA:

The Cancer Genome Atlas

WGS:

Whole Genome equencing

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394.

    PubMed  Google Scholar 

  2. Ferlay J, Colombet M, Soerjomataram I, Mathers C, Parkin DM, Pineros M, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer. 2019;144:1941.

    CAS  PubMed  Google Scholar 

  3. Attar E, Dey S, Hablas A, Seifeldin IA, Ramadan M, Rozek LS, et al. Head and neck cancer in a developing country: a population-based perspective across 8 years. Oral Oncol. 2010;46:591.

    PubMed  PubMed Central  Google Scholar 

  4. Nandakumar A. Survival in head and neck cancers-results of a multi-institution study. Asian Pac J Cancer Prev. 2016;17:1745.

    PubMed  Google Scholar 

  5. Pai SI, Westra WH. Molecular pathology of head and neck cancer: implications for diagnosis, prognosis, and treatment. Annu Rev Pathol. 2009;4:49.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Johnson DE, Burtness B, Leemans CR, Lui VWY, Bauman JE, Grandis JR. Head and neck squamous cell carcinoma. Nat Rev Dis Prim. 2020. https://doi.org/10.1038/s41572-020-00224-3.

    Article  PubMed  Google Scholar 

  7. Hashibe M, Brennan P, Chuang S-C, Boccia S, Castellsague X, Chen C, et al. Interaction between tobacco and alcohol use and the risk of head and neck cancer: pooled analysis in the international head and neck cancer epidemiology consortium. Cancer Epidemiol Biomark Prev. 2009;18:541.

    CAS  Google Scholar 

  8. Blot WJ, McLaughlin JK, Winn DM, Austin DF, Greenberg RS, Preston-Martin S, et al. Smoking and drinking in relation to oral and pharyngeal cancer. Can Res. 1988;48:3282.

    CAS  Google Scholar 

  9. Lin YS, Jen YM, Wang BB, Lee JC, Kang BH. Epidemiology of oral cavity cancer in taiwan with emphasis on the role of betel nut chewing. ORL. 2005;67:230.

    PubMed  Google Scholar 

  10. Su YY, Chien CY, Luo SD, Huang TL, Lin WC, Fang FM, et al. Betel nut chewing history is an independent prognosticator for smoking patients with locally advanced stage IV head and neck squamous cell carcinoma receiving induction chemotherapy with docetaxel, cisplatin, and fluorouracil. World J Surg Oncol. 2016;14:86.

    PubMed  PubMed Central  Google Scholar 

  11. Wang C-P, Chen T-C, Chen H-H, Hsu W-L, Chang Y-L. Prevalence of current oral HPV infection among 100 betel nut chewers or cigarette smokers in Northern Taiwan. J Formos Med Assoc. 2019;118:203.

    PubMed  Google Scholar 

  12. Koh A, Bäckhed F. From association to causality: the role of the gut microbiota and its functional products on host metabolism. Mol Cell. 2020;78:584.

    CAS  PubMed  Google Scholar 

  13. Helmink BA, Khan MW, Hermann A, Gopalakrishnan V, Wargo JA. The microbiome, cancer, and cancer therapy. Nat Med. 2019;25:377.

    CAS  PubMed  Google Scholar 

  14. Czesnikiewicz-Guzik M, Müller DN. Scientists on the spot: salt, the microbiome, and cardiovascular diseases. Cardiovasc Res. 2018;114:e72.

    CAS  PubMed  Google Scholar 

  15. Hansen JJ, Sartor RB. Therapeutic manipulation of the microbiome in IBD: current results and future approaches. Curr Treat Options Gastro. 2015;13:105.

    Google Scholar 

  16. Kim D, Zeng MY, Núñez G. The interplay between host immune cells and gut microbiota in chronic inflammatory diseases. Exp Mol Med. 2017;49:e339.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Salihoğlu R, Önal-Süzek T. Tissue Microbiome Associated With Human Diseases by Whole Transcriptome Sequencing and 16S Metagenomics. Front Genet. 2021;12:281.

    Google Scholar 

  18. Papotto PH, Yilmaz B, Silva-Santos B. Crosstalk between γδ T cells and the microbiota. Nat Microbiol. 2021. https://doi.org/10.1038/s41564-021-00948-2.

    Article  PubMed  Google Scholar 

  19. Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Human gut microbes associated with obesity. Nature. 2006;444:1022.

    CAS  PubMed  Google Scholar 

  20. Routy B, Le Chatelier E, Derosa L, Duong CP, Alou MT, Daillère R, et al. Gut microbiome influences efficacy of PD-1–based immunotherapy against epithelial tumors. Science. 2018;359:91.

    CAS  PubMed  Google Scholar 

  21. Matson V, Fessler J, Bao R, Chongsuwat T, Zha Y, Alegre M-L, et al. The commensal microbiome is associated with anti–PD-1 efficacy in metastatic melanoma patients. Science. 2018;359:104.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Gopalakrishnan V, Spencer CN, Nezi L, Reuben A, Andrews M, Karpinets T, et al. Gut microbiome modulates response to anti–PD-1 immunotherapy in melanoma patients. Science. 2018;359:97.

    CAS  PubMed  Google Scholar 

  23. Ferreira RM, Pereira-Marques J, Pinto-Ribeiro I, Costa JL, Carneiro F, Machado JC, et al. Gastric microbial community profiling reveals a dysbiotic cancer-associated microbiota. Gut. 2018;67:226.

    CAS  PubMed  Google Scholar 

  24. Pushalkar S, Hundeyin M, Daley D, Zambirinis CP, Kurz E, Mishra A, et al. The pancreatic cancer microbiome promotes oncogenesis by induction of innate and adaptive immune suppression. Cancer Discov. 2018;8:403.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Sfanos KS, Sauvageot J, Fedor HL, Dick JD, De Marzo AM, Isaacs WB. A molecular analysis of prokaryotic and viral DNA sequences in prostate tissue from patients with prostate cancer indicates the presence of multiple and diverse microorganisms. Prostate. 2008;68:306.

    CAS  PubMed  Google Scholar 

  26. Urbaniak C, Gloor GB, Brackstone M, Scott L, Tangney M, Reid G. The microbiota of breast tissue and its association with breast cancer. Appl Environ Microbiol. 2016;82:5039.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Lehouritis P, Cummins J, Stanton M, Murphy CT, McCarthy FO, Reid G, et al. Local bacteria affect the efficacy of chemotherapeutic drugs. Sci Rep. 2015;5:1.

    Google Scholar 

  28. Panebianco C, Andriulli A, Pazienza V. Pharmacomicrobiomics: exploiting the drug-microbiota interactions in anticancer therapies. Microbiome. 2018;6:1.

    Google Scholar 

  29. Yu T, Guo F, Yu Y, Sun T, Ma D, Han J, et al. Fusobacterium nucleatum promotes chemoresistance to colorectal cancer by modulating autophagy. Cell. 2017;170:548.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Irfan M, Delgado RZR, Frias-Lopez J. The oral microbiome and cancer. Front Immunol. 2020. https://doi.org/10.3389/fimmu.2020.591088.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Gallimidi AB, Fischman S, Revach B, Bulvik R, Maliutina A, Rubinstein AM, et al. Periodontal pathogens Porphyromonas gingivalis and Fusobacterium nucleatum promote tumor progression in an oral-specific chemical carcinogenesis model. Oncotarget. 2015;6:22613.

    PubMed Central  Google Scholar 

  32. Hajishengallis G. Immunomicrobial pathogenesis of periodontitis: keystones, pathobionts, and host response. Trends Immunol. 2014;35:3.

    CAS  PubMed  Google Scholar 

  33. Hajishengallis G. Periodontitis: from microbial immune subversion to systemic inflammation. Nat Rev Immunol. 2015;15:30.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Galvão-Moreira LV, da Cruz MCFN. Oral microbiome, periodontitis and risk of head and neck cancer. Oral Oncol. 2016;53:17.

    PubMed  Google Scholar 

  35. Karmakar S, Kar A, Thakur S, Rao VU. Periodontitis and oral Cancer-A striking link. Oral Oncol. 2020;106:104630.

    PubMed  Google Scholar 

  36. Fitzpatrick SG, Katz J. The association between periodontal disease and cancer: a review of the literature. J Dent. 2010;38:83.

    PubMed  Google Scholar 

  37. Javed F, Warnakulasuriya S. Is there a relationship between periodontal disease and oral cancer? A systematic review of currently available evidence. Crit Rev Oncol Hematol. 2016;97:197.

    PubMed  Google Scholar 

  38. Poore GD, Kopylova E, Zhu Q, Carpenter C, Fraraccio S, Wandro S, et al. Microbiome analyses of blood and tissues suggest cancer diagnostic approach. Nature. 2020;579:567.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Deng M, Brägelmann J, Kryukov I, Saraiva-Agostinho N, Perner S. FirebrowseR: an R client to the Broad Institute’s Firehose Pipeline. Database. 2017. https://doi.org/10.1093/database/baw160.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Team RC: R: A language and environment for statistical computing ,Vienna. 2013. http://www.R-project.org/

  41. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.

    PubMed  PubMed Central  Google Scholar 

  42. Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31:814.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Fernandes AD, Reid JN, Macklaim JM, McMurrough TA, Edgell DR, Gloor GB. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome. 2014;2:15.

    PubMed  PubMed Central  Google Scholar 

  44. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607.

    CAS  PubMed  Google Scholar 

  45. Doncheva NT, Morris JH, Gorodkin J, Jensen LJ. Cytoscape StringApp: network analysis and visualization of proteomics data. J Proteome Res. 2019;18:623.

    CAS  PubMed  Google Scholar 

  46. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11.

    PubMed  PubMed Central  Google Scholar 

  47. Jiao X, Sherman BT, Huang DW, Stephens R, Baseler MW, Lane HC, et al. DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics. 2012;28:1805.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Banerjee S, Tian T, Wei Z, Shih N, Feldman MD, Alwine JC, et al. The ovarian cancer oncobiome. Oncotarget. 2017;8:36225.

    PubMed  PubMed Central  Google Scholar 

  50. Mao Q, Jiang F, Yin R, Wang J, Xia W, Dong G, et al. Interplay between the lung microbiome and lung cancer. Cancer Lett. 2018;415:40.

    CAS  PubMed  Google Scholar 

  51. Aviles-Jimenez F, Guitron A, Segura-Lopez F, Mendez-Tenorio A, Iwai S, Hernandez-Guerrero A, et al. Microbiota studies in the bile duct strongly suggest a role for Helicobacter pylori in extrahepatic cholangiocarcinoma. Clin Microbiol Infect. 2016;22:178.e11.

    CAS  Google Scholar 

  52. Nakatsu G, Li X, Zhou H, Sheng J, Wong SH, Wu WKK, et al. Gut mucosal microbiome across stages of colorectal carcinogenesis. Nat Commun. 2015;6:8727.

    CAS  PubMed  Google Scholar 

  53. Lu Y, Chen J, Zheng J, Hu G, Wang J, Huang C, et al. Mucosal adherent bacterial dysbiosis in patients with colorectal adenomas. Sci Rep. 2016;6:26337.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Mangerich A, Knutson CG, Parry NM, Muthupalani S, Ye W, Prestwich E, et al. Infection-induced colitis in mice causes dynamic and tissue-specific changes in stress response and DNA damage leading to colon cancer. Proc Natl Acad Sci. 2012;109:E1820.

    PubMed  PubMed Central  Google Scholar 

  55. Wu S, Rhee K-J, Albesiano E, Rabizadeh S, Wu X, Yen H-R, et al. A human colonic commensal promotes colon tumorigenesis via activation of T helper type 17 T cell responses. Nat Med. 2009;15:1016.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Boleij A, Hechenbleikner EM, Goodwin AC, Badani R, Stein EM, Lazarev MG, et al. The Bacteroides fragilis toxin gene is prevalent in the colon mucosa of colorectal cancer patients. Clin Infect Dis. 2015;60:208.

    CAS  PubMed  Google Scholar 

  57. Gur C, Ibrahim Y, Isaacson B, Yamin R, Abed J, Gamliel M, et al. Binding of the Fap2 protein of Fusobacterium nucleatum to human inhibitory receptor TIGIT protects tumors from immune cell attack. Immunity. 2015;42:344.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Arthur JC, Perez-Chanona E, Mühlbauer M, Tomkovich S, Uronis JM, Fan T-J, et al. Intestinal inflammation targets cancer-inducing activity of the microbiota. Science. 2012;338:120.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Tuominen H, Rautava J. Oral Microbiota and Cancer Development. Pathobiology. 2021;88:116.

    CAS  PubMed  Google Scholar 

  60. Nieminen MT, Listyarifah D, Hagstrom J, Haglund C, Grenier D, Nordstrom D, et al. Treponema denticola chymotrypsin-like proteinase may contribute to orodigestive carcinogenesis through immunomodulation. Br J Cancer. 2018;118:428.

    CAS  PubMed  Google Scholar 

  61. Rodriguez RM, Hernandez BY, Menor M, Deng Y, Khadka VS. The landscape of bacterial presence in tumor and adjacent normal tissue across 9 major cancer types using TCGA exome sequencing. Comput Struct Biotechnol J. 2020;18:631.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Theys J, Lambin P. Clostridium to treat cancer: dream or reality? Ann Transl Med. 2015;3:S21.

    PubMed  PubMed Central  Google Scholar 

  63. Iida N, Dzutsev A, Stewart CA, Smith L, Bouladoux N, Weingarten RA, et al. Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science. 2013;342:967.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Orberg ET, Fan H, Tam AJ, Dejea CM, Shields CD, Wu S, et al. The myeloid immune signature of enterotoxigenic Bacteroides fragilis-induced murine colon tumorigenesis. Mucosal Immunol. 2017;10:421.

    Google Scholar 

  65. Arthur JC, Gharaibeh RZ, Uronis JM, Perez-Chanona E, Sha W, Tomkovich S, et al. VSL# 3 probiotic modifies mucosal microbial composition but does not reduce colitis-associated colorectal cancer. Sci Rep. 2013;3:1.

    Google Scholar 

  66. Derosa L, Hellmann M, Spaziano M, Halpenny D, Fidelle M, Rizvi H, et al. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer. Ann Oncol. 2018;29:1437.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Pflug N, Kluth S, Vehreschild JJ, Bahlo J, Tacke D, Biehl L, et al. Efficacy of antineoplastic treatment is associated with the use of antibiotics that modulate intestinal microbiota. Oncoimmunology. 2016;5:e1150399.

    PubMed  PubMed Central  Google Scholar 

  68. Jacobsohn DA, Vogelsang GB. Acute graft versus host disease. Orphanet J Rare Dis. 2007;2:1.

    Google Scholar 

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Acknowledgements

Not applicable.

Funding

This work was supported by the National Research Foundation of Korea (NRF-2018R1A5A2023879, 2020R1A2C1005203, 2020R1C1C1003741, and 2021R1A2C4001466). This research was supported by a grant of the Medical data-driven hospital support project through the Korea Health Information Service (KHIS), funded by the Ministry of Health & Welfare, Republic of Korea. A portion of the data used for this study were obtained from the Genome-InfraNet (IDs: 1711020733, 1711032008, and 1711028992) of the Korea Bioinformation Center.

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Author notes

  1. Yun Kyeong Kim and Eun Jung Kwon contributed equally to this work

Authors and Affiliations

  1. Convergence Medical Sciences, Pusan National University, Yangsan, Republic of Korea

    Yun Kyeong Kim

  2. Interdisciplinary Program of Genomic Science, Pusan National University, Yangsan, Republic of Korea

    Eun Jung Kwon

  3. Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea

    Yeuni Yu

  4. Department of Convergence Medicine, School of Medicine, Pusan National University, 49 Busandaehak-ro, Yangsan, 50612, Republic of Korea

    Jayoung Kim, Soo-Yeon Woo, Hee-Sun Choi, Munju Kwon & Dongjun Lee

  5. Department of Anatomy and Cell Biology and Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea

    Keehoon Jung

  6. Department of Oral Pathology, School of Dentistry, Pusan National University, Yangsan, Republic of Korea

    Hyung-Sik Kim & Hae Ryoun Park

  7. Periodontal Disease Signaling Network Research Center, School of Dentistry, Pusan National University, Yangsan, Republic of Korea

    Hyung-Sik Kim, Hae Ryoun Park & Yun Hak Kim

  8. Department of Anatomy, School of Medicine, Pusan National University, 49 Busandaehak-ro, Yangsan, 50612, Republic of Korea

    Yun Hak Kim

  9. Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea

    Yun Hak Kim

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Contributions

DL and YHK initiated the study and guided the work. YKK and EJK collected, normalised, and interpreted the data. YY, JK, SYW, HSC, MK, KJ, HSK, and HRP analysed the experimental data. All authors wrote the manuscript with input from all co-authors. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Dongjun Lee or Yun Hak Kim.

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Supplementary Information

Additional file 1: Figure S1.

Output from ALDEx2 plot.

Additional file 2: Table S1.

The GO analysis results, hub genes, and tumour locations of included patients.

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Kim, Y.K., Kwon, E.J., Yu, Y. et al. Microbial and molecular differences according to the location of head and neck cancers. Cancer Cell Int 22, 135 (2022). https://doi.org/10.1186/s12935-022-02554-6

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Keywords

Cancer Cell International

ISSN: 1475-2867

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