Oral microbiota-based diagnosis of oral cancer

Abstract

The present invention identifies microbial groups that are significantly increased or decreased in the saliva of oral cancer patients compared to control groups, and relates to an oral cancer diagnostic composition containing a preparation for detecting the microbial groups, and a method for providing information for oral cancer diagnosis using these microorganisms. The oral cancer diagnostic composition and method for providing information for diagnosis based on the present invention can easily and accurately diagnose the presence or possibility of oral cancer at an early stage by analyzing and confirming the abundance levels of microorganisms that show significant changes compared to normal control groups.

Description

[Technical Field]

[0001] The present invention relates to oral cancer diagnosis based on oral microbiota, and to a composition for diagnosing oral cancer based on oral microbiota, a diagnostic kit for oral cancer including the same, and a method for providing information for diagnosing oral cancer. [Background technology]

[0002] The oral cavity, which is the gateway to the digestive system, harbors a complex bacterial community that is considered the second largest microbial community in the human body after the intestine. It is estimated that the human oral cavity contains 700 species of bacteria that play important roles in maintaining oral health.

[0003] Recently, some research groups have reported the presence of oral microorganisms in other parts of the human body and the association between these microorganisms and numerous systemic diseases. Recent studies have also revealed a strong correlation between the oral microbiota and various cancer sites, including colorectal, esophageal, and lung cancer. Among these studies, Porphyromonas gingivalis and Fusobacterium nucleatum are two oral bacteria that often exhibit strong carcinogenic potential, suggesting the value of the oral microbiome as a cancer biomarker.

[0004] Oral cancer is one of the most common malignancies worldwide, both in developed countries and in developing countries, where incidence and mortality rates are particularly high. The most common form (>90% of oral cancers), oral squamous cell carcinoma (OSCC), is often diagnosed at a late stage, and the complex management of the primary tumor results in poor quality of life and poor survival in most patients (<50%).

[0005] Therefore, in order to improve the quality of life of these patients and reduce medical costs, ongoing efforts are needed to diagnose oral cancer early and develop biomarkers. Summary of the Invention [Problem to be solved by the invention]

[0006] The present invention is directed to solving the above-mentioned problems and other related problems.

[0007] One exemplary object of the present invention is to provide a diagnostic composition that includes a formulation for detecting types of microorganisms that show significantly altered abundance in biological samples, particularly saliva, in oral cancer compared to control groups, and that can diagnose oral cancer non-invasively, accurately, and easily.

[0008] Another exemplary object of the present invention is to provide a kit for diagnosing oral cancer, which includes the composition for diagnosing oral cancer.

[0009] Another exemplary object of the present invention is to provide an informative method for diagnosing oral cancer, comprising determining the abundance of said microorganisms from a biological sample, in particular saliva, and comparing it with the abundance of the microorganisms in a control sample.

[0010] The technical problems to be achieved based on the technical ideas of the invention disclosed in this specification are not limited to the problems to solve the problems mentioned above, and other problems not mentioned can be clearly understood by those of ordinary skill in the art from the following description. [Means for solving the problem]

[0011] This will be explained in more detail as follows. Meanwhile, each description and embodiment disclosed in this application can also be applied to each of the other descriptions and embodiments. In other words, all combinations of various elements disclosed in this application belong to the category of this application. Furthermore, the specific descriptions described below cannot be considered to limit the category of this application.

[0012] In one aspect to achieve the above object, the present invention provides a composition for diagnosing oral cancer, comprising a preparation for detecting microorganisms whose abundance is significantly altered in oral cancer patients compared to a control group.

[0013] In the present invention, "oral cancer" refers collectively to cancers occurring in the oral cavity (tongue, floor of the mouth, buccal mucosa, gums, palate, and jawbone), lips, and oropharynx (the area behind the tongue that connects to the throat). Specific examples include salivary gland cancer occurring in the salivary glands, sarcoma occurring in the jawbone and facial muscles, malignant melanoma that forms black spots on the palate and gums, and oral squamous cell carcinoma derived from epithelial cells in the oral mucosa. Of these, approximately 90% of oral cancers are oral squamous cell carcinoma (OSCC). The oral cancer of the present invention may be, but is not limited to, oral squamous cell carcinoma.

[0014] The term "abundance" as used herein may refer to the relative abundance or relative ratio of a specific type of microorganism, and in particular, may refer to the relative abundance of a specific microorganism associated with the diagnosis of oral cancer among the microbiota in saliva, but is not limited thereto.

[0015] In the present invention, the "microorganism detection preparation" is not limited to a specific substance type, as long as it is a preparation capable of detecting the presence of one or more microorganisms selected from the group consisting of microorganisms that are differentially abundant in oral cancer patients compared to control groups for the purpose of predicting or diagnosing oral cancer. For example, the microorganism detection preparation may be, but is not limited to, an antisense oligonucleotide, a primer pair, a probe, a peptide, a polynucleotide, an oligonucleotide, an aptamer, or an antibody that specifically binds to the 16S rRNA of the microorganism.

[0016] Microorganism detection using the detection preparation can be carried out by an amplification reaction using one or more oligonucleotide primers that hybridize to a nucleic acid molecule encoding a microorganism-specific expression gene or the complement of the nucleic acid molecule. Detection of nucleic acids using primers can be carried out by amplifying the gene sequence using an amplification method such as PCR, and then confirming whether or not the gene has been amplified using methods known in the art.

[0017] The term "primer" refers to a polynucleotide or a variant thereof having a sequence of bases capable of binding complementarily to the end of a specific region of a gene, which is used to amplify the specific region corresponding to the target site of the gene using PCR. The primer does not need to be completely complementary to the end of the specific region, but can be used as long as it is complementary enough to hybridize to the end to form a double-stranded structure.

[0018] The term "probe" refers to a polynucleotide having a base sequence capable of binding complementarily to a target site of a gene, a variant thereof, or a polynucleotide and a labeled substance bound thereto.

[0019] The term "hybridization" refers to the formation of a duplex structure by pairing of complementary base sequences between two single-stranded nucleic acids. Hybridization can occur not only when the complementarity between the single-stranded nucleic acid sequences is perfect, but also when there are some mismatched bases.

[0020] The "antibody" (or immunoglobulin) refers to a substance that specifically binds to an antigen to cause an antigen-antibody reaction, and may be, for example, one or more selected from the group consisting of a polyclonal antibody, a monoclonal antibody, a recombinant antibody, or a combination thereof. Specifically, it may include not only polyclonal antibodies, monoclonal antibodies, recombinant antibodies, and intact forms having two full-length light chains and two full-length heavy chains, but also functional fragments of antibody molecules, such as Fab, F(ab'), F(ab')2, and Fv.

[0021] The "aptamer" is an oligonucleotide or peptide molecule, and the general content of aptamers is disclosed in detail in the literature [Bock LC et al., Nature 355(6360):5646(1992); Hoppe-Seyler F, Butz K "Peptide aptamers: powerful new tools for molecular medicine", J Mol Med. 78(8):42630(2000); Cohen BA, Colas P, Brent R. "An artificial cell cycle inhibitor isolated from a combinatorial library", Proc Natl Acad Sci USA. 95(24): 142727(1998)].

[0022] In addition, the diagnostic composition of the present invention may further include not only a preparation for measuring the presence or absence of the microorganism, but also a label that enables quantitative or qualitative measurement of the formation of an antigen-antibody complex, conventional tools and reagents used in immunological analysis, etc.

[0023] As used herein, the term "diagnosis" includes determining a subject's susceptibility to a particular disease or disorder, determining whether a subject currently has a particular disease or disorder, determining the prognosis of a subject with a particular disease or disorder (e.g., identifying pre-metastatic or metastatic cancer status, staging cancer, or determining cancer responsiveness to treatment), or therametrics (e.g., monitoring a subject's condition to provide information on the effectiveness of treatment). For purposes of this invention, diagnosis refers to determining or predicting the presence or absence or likelihood (risk) of developing oral cancer.

[0024] In the present invention, "microorganisms showing a significant change in abundance in oral cancer patients compared to a control group" refers to the types of microorganisms that are significantly increased or decreased in oral cancer patients compared to a healthy control group, and specifically refers to 18 genera (Abiotrophia, Actinomyces, Alloscardovia, Capnocytophaga, Dialister, Howardella, Leuconostoc, Mannheimia, Mogibacteria, and others). Mogibacterium, Olsenella, Paraburkholderia, Parvimonas, Pasteurellaceae, Porphyromonas, Prevotella, Saccharimonas, Sphingomonas, Streptococcus) and 16 species (AF385567_s, Actinomyces odontolyticus) odontolyticus, Alloprevotella tannerae, Capnocytophaga sputigena, Dialister pneumosintes, Fusobacterium periodonticum, Lactobacillus iners, Lactobacillus sakei, Leuconostoc gelidum, Neisseria elongate, Paraburkholderia graminis, Prevotella baroniae, Prevotella melaninogenicaThe five genera (Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus) and four species (Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, Streptococcus constellatus, Streptococcus pneumoniae) and four species (Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, Streptococcus constellatus, Streptococcus pneumoniae) showed high diagnostic accuracy (AUC value) and consistent results across all analyses. The microorganism may be, but is not limited to, one or more of the following microorganisms: Bacillus pallens, Streptococcus pneumoniae.

[0025] In the examples of the present invention, microorganisms with significantly different abundances were discovered between the oral microorganisms of normal control groups and oral cancer patients, and it was confirmed that these can be used as biomarkers for diagnosing oral cancer.

[0026] Based on the results of the present invention confirming the oral microbial community profiles of oral cancer patients and normal control groups, in the present invention, at the phylum level, Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria can be included as biomarkers for diagnosing oral cancer.

[0027] Specifically, the abundance of the Bacteroidetes, Fusobacteria, and Proteobacteria is significantly decreased in oral cancer compared to normal controls, while the abundance of Actinobacteria and Firmicutes is significantly increased in oral cancer.

[0028] In the present invention, at the genus level, Actinomyces, Streptococcus, Capnocytophaga, Granulicatella, Lautropia, Alloprevotella, Fusobacterium, Haemophilus, Porphyromonas, Prevotella, and Veillonella can be included as biomarkers for diagnosing oral cancer.

[0029] Specifically, the abundance of the above-mentioned Actinomyces, Streptococcus, Capnocytophaga, Granulicatella, and Lautropia is significantly increased in oral cancer compared to normal controls, while the abundance of Alloprevotella, Fusobacterium, Haemophilus, Porphyromonas, Prevotella, and Veillonella is significantly decreased in oral cancer compared to normal controls.

[0030] In the present invention, at the microbial species level, Fusobacterium nucleatum, Fusobacterium periodonticum, Granulicatella adiacens, Haemophilus parainfluenzae, KV831974_s, Lautropia mirabilis, Neisseria meningitidis, Neisseria subflava, PAC001345_s, Porphyromonas pasteri, Prevotella histicola, Prevotella melaninogenica, Prevotella Biomarkers for oral cancer diagnosis include Prevotella melaninogenica, Prevotella nanceiensis, Prevotella pallens, Prevotella salivae, Prevotella uc, Streptococcus pneumoniae, Streptococcus salivarius, Streptococcus sanguinis, Veillonella dispar, and Veillonella parvula.

[0031] Specifically, the abundance of Fusobacterium nucleatum, Granulicatella adiacens, Lautropia mirabilis, Neisseria meningitidis, Streptococcus pneumoniae, and Streptococcus sanguinis is significantly increased in oral cancer patients compared to normal controls, while the abundance of Fusobacterium peridonticum, Haemophilus parainfluenzae, KV831974_s, Neisseria subflava, PAC001345_s, Porphyromonas pasteri, Prevotella histicola, Prevotella melaninogenica, Prevotella nanceiensis, Prevotella pallens, Prevotella salivae, Prevotella_uc, Streptococcus salivarius, Veillonella dispar, and Veillonella parvula is significantly decreased in oral cancer patients compared to normal controls.

[0032] As shown in the results of linear discriminant analysis of LEfSE for microbiota imbalance analysis, the fold change and AUC values ​​for the relative abundance of these selected microorganisms support the feasibility of using these microorganisms as biomarkers.

[0033] Furthermore, through LASSO and RF analysis, at the genus level, Abiotrophia, Actinomyces, Alloscardovia, Capnocytophaga, Dialister, Howardella, Leuconostoc, Mannheimia, Mogibacterium, Olsenella, Paraburkholderia, Parvimonas, pasteurellaceae_uc, Porphyromonas, Prevotella, Saccharimonas, Sphingomonas, and Streptococcus can be included as biomarkers for diagnosing oral cancer. Here, the genera Prevotella and Streptococcus can be particularly significant biomarkers, with AUC values ​​of 0.7 or more.

[0034] Specifically, the abundance of the Abiotrophia, Actinomyces, Capnocytophaga, Dialister, Mogibacterium, Parvimonas, and Streptococcus is increased in oral cancer compared to normal controls, while the abundance of Mannheimia, Pasteurellaceae_uc, Pophyromonas, Prevotella, and Saccharimonas is significantly decreased in oral cancer compared to normal controls.

[0035] Furthermore, at the species level, AF385567_s, Actomyces odontolyticus, Alloprevotella tannerae, Capnocytophaga sputigena, Dialister pneumosintes, Fusobacterium periodonticum, Lactobacillus iners, Lactobacillus sakei, Leuconostoc gelidum, Neisseria elongate, Paraburkholderia graminis, Prevotella baroniae, Prevotella melaninogenica, Prevotella pallens, Streptococcus constellatus, and Streptococcus pneumoniae can be included as biomarkers for the diagnosis of oral cancer.

[0036] Specifically, Actinomyces odontolyticus, Alloprevotella tannerae, Capnocytophaga sputigena, Dialister pneumosintes, Neisseria elongate, Prevotella baroniae, Streptococcus constellatus, and Streptococcus pneumoniae were characterized by increased abundance in oral cancer compared to normal controls, while Fusobacterium periodonticum, Prevotella melaninogenica, and Prevotella pallens were characterized by significantly decreased abundance in oral cancer compared to normal controls.

[0037] One exemplary embodiment of the present invention relates to a composition for diagnosing oral cancer, comprising a preparation for detecting at least one microorganism selected from the group consisting of the genus Actinomyces, the genus Capnocytophaga, the genus Porphyromonas, the genus Prevotella, the genus Streptococcus, the species Fusobacterium periodonticum, the species Prevotella melaninogenica, the species Prevotella pallens, and the species Streptococcus pneumoniae.

[0038] In one exemplary embodiment of the present invention, the genus Granulicatella, the genus Lautropia, the genus Alloprevotella, the genus Fusobacterium, the genus Haemophilus, the genus Veillonella, the species Fusobacterium nucleatum, the species Granulicatella adiacens, the species Haemophilus parainfluenzae, the species KV831974_s, the species Lautropia mirabilis, the species Neisseria meningitidis, the species Neisseria subflava, the species Neisseria The composition may additionally contain a preparation that detects at least one microorganism selected from the group consisting of Porphyromonas pasteri, Prevotella histicola, Prevotella nanceiensis, Prevotella salivae, Prevotella uc, Streptococcus salivarius, Streptococcus sanguinis, Veillonella dispar, and Veillonella parvula.

[0039] In one exemplary embodiment of the present invention, the genus Dialister, the genus Mogibacterium, the genus Parvimonas, the genus Mannheimia, the genus Pasteurellaceae, the genus Saccharimonas, the genus Actinomyces odontolyticus, the genus Alloprevotella tannerae, the genus Capnocytophaga sputigena, the genus Dialister pneumosintes, the genus Neisseria elongate, the genus Prevotella baroniae, and the genus Streptococcus constellatus are selected from the group consisting of: The composition may additionally comprise a preparation for detecting at least one microorganism of the species Pseudomonas constellatus.

[0040] In one exemplary embodiment of the present invention, the preparation may further include a preparation for detecting at least one microorganism of the genera Howardella, Paraburkholderia, and Sphingomonas.

[0041] Furthermore, the preparation for detecting at least one microorganism selected from the genera Howardella, Paraburkholderia, Porphyromonas, Capnocytophaga, and Sphingomonas can be used to diagnose early-stage oral cancer.

[0042] In one exemplary embodiment of the present invention, the present invention may further include a preparation for detecting a gene encoding at least one of acyl-CoA dehydrogenase and dihydrofolate reductase, and the preparation for detecting the gene may be for diagnosing early-stage oral cancer.

[0043] In the present invention, the term "early-stage oral cancer" may refer to stages 1 and 2, which are divided into stages 1 to 4 based on the size of the primary cancer and the presence or absence of neck lymph node metastasis and distant metastasis. Specifically, stage 1 refers to a primary cancer measuring 2 cm or less and having no neck lymph node metastasis or distant metastasis; stage 2 refers to a primary cancer measuring 2 cm to 4 cm and having no neck lymph node metastasis or distant metastasis; stage 3 refers to a primary cancer measuring 4 cm or more and having one neck lymph node metastasis measuring 3 cm or less and no distant metastasis; and stage 4 refers to a primary cancer invading the bone, facial skin, or deep muscles of the tongue, having one lymph node metastasis measuring 3 to 6 cm near the primary site, having two or more lymph node metastases measuring 6 cm or less in the neck, having metastasis to lymph nodes on both sides or on the opposite side of the lesion but no distant metastasis, having lymph node metastasis measuring 6 cm or more in the neck, or having no distant metastasis or having distant metastasis when surgery on the primary site is impossible.

[0044] In another aspect to achieve the above object, the present invention provides an oral cancer diagnostic kit comprising an oral cancer diagnostic composition.

[0045] The term "oral cancer diagnostic kit" as used herein means a kit containing a diagnostic composition for oral cancer, and the kit may further contain, in addition to the diagnostic composition, instructions describing how to use the kit required for diagnosis.

[0046] As an example of the present invention, the kit detects increased abundance of Actinomyces, Capnocytophaga, Granulicatella, Lautropia, Leptotrichia, Rothia, and Streptococcus at the genus level, decreased abundance of Alloprevotella, Campylobacter, Fusobacterium, Haemophilus, Neisseria, Porphyromonas, Prevotella, and Veillonella at the species level, increased abundance of Fusobacterium nucleatum, Granulicatella adiacens, Lautropia mirabilis, Neisseria meningitidis, Streptococcus pneumoniae, and Streptococcus sanguinis, and increased abundance of Fusobacterium periodonticum, Haemophilus parainfluenzae, KV831974_s, and Neisseria at the species level, compared to normal human-derived samples. Decreased abundance of subflava, PAC001345_s, Porphyromonas pasteri, Prevotella histicola, Prevotella melaninogenica, Prevotella nanceiensis, Prevotella pallens, Prevotella salivae, Prevotella_uc, Streptococcus salivarius, Veillonella dispar, and Veillonella parvula may be diagnostic of oral cancer.

[0047] In another aspect of the present invention to achieve the above object, there is provided a method for providing information for diagnosing oral cancer.

[0048] The information providing method includes the steps of (a) identifying the abundance of microbial species whose abundance is significantly changed in oral cancer patients compared to a control group from a biological sample; and (b) comparing the abundance with the abundance of microorganisms in a control group sample.

[0049] As an example of the present invention, the "types of microorganisms whose abundances were significantly changed in oral cancer patients compared to the control group" include Actinomyces, Capnocytophaga, Granulicatella, Lautropia, Leptotrichia, Rothia, Streptococcus, Alloprevotella, Campylobacter, Fusobacterium, Haemophilus, Neisseria, Porphyromonas, Prevotella, and Veillonella at the genus level, and Fusobacterium nucleatum, Granulicatella adiacens, Lautropia mirabilis, Neisseria meningitidis, Streptococcus pneumoniae, Streptococcus sanguinis, Fusobacterium periodonticum, Haemophilus parainfluenzae, KV831974_s, Neisseria subflava, PAC001345_s, Porphyromonas pasteri, and Prevotella at the species level. The present invention may include one or more selected from the group consisting of Prevotella histicola, Prevotella melaninogenica, Prevotella nanceiensis, Prevotella pallens, Prevotella salivae, Prevotella uc, Streptococcus salivarius, Veillonella dispar, and Veillonella parvula.

[0050] In one example of the present invention, the "type of microorganism whose abundance was significantly changed in oral cancer patients compared to the control group" may include one or more selected from the group consisting of the genus Howardella, the genus Paraburkholderia, and the genus Sphingomonas.

[0051] The step of determining the abundance of a particular type of microorganism from the biological sample involves extracting DNA from the biological sample and analyzing the abundance of the microorganism.

[0052] As an example of the present invention, step (a) may be a step of analyzing the abundance of oral microorganisms using the microbial detection preparation according to an example of the present invention, for example, by extracting DNA from the microorganisms and using a PCR primer set for 16S rRNA, but is not limited thereto.

[0053] In one embodiment of the present invention, step (b) is a step of comparing the results of the abundance analysis in step (a) with a healthy normal control group, and it is found that at the genus level, the abundances of Actinomyces, Capnocytophaga, Granulicatella, Lautropia, Leptotrichia, Rothia, Streptococcus, Howardella, Dialister, Paraburkholderia, and Sphingomonas are increased, and the abundances of Alloprevotella, Campylobacter, Fusobacterium, Haemophilus, Neisseria, Porphyromonas, Prevotella, and Veillonella are decreased, and at the species level, the abundances of Fusobacterium nucleatum, Granulicatella adiacens, Lautropia mirabilis, Neisseria meningitidis, Streptococcus pneumoniae, and Streptococcus sanguinis are increased, and the abundances of Fusobacterium periodonticum and Haemophilus are decreased. Decreased abundance of parainfluenzae, KV831974_s, Neisseria subflava, PAC001345_s, Porphyromonas pasteri, Prevotella histicola, Prevotella melaninogenica, Prevotella nanceiensis, Prevotella pallens, Prevotella salivae, Prevotella_uc, Streptococcus salivarius, Veillonella dispar, and Veillonella parvula can be used to diagnose oral cancer or a risk of developing oral cancer.

[0054] As an example of the present invention, an increase in the abundance of the genera Howardella, Paraburkholderia, Capnocytophaga, and Sphingomonas, or a decrease in the abundance of the genus Porphyromonas, can be used to diagnose early-stage oral cancer.

[0055] The method for providing information may further include a step of checking the expression level of a gene encoding at least one of acyl-CoA dehydrogenase and dihydrofolate reductase. If the expression level of the gene encoding acyl-CoA dehydrogenase is increased, oral cancer can be diagnosed as being at an early stage, and if the expression level of the gene encoding dihydrofolate reductase is decreased, oral cancer can be diagnosed as being at an early stage.

[0056] As used herein, the term "biological sample" refers to any substance, biological fluid, tissue, or cell obtained from or derived from an individual, such as whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum, tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural effusion, etc. The biopsy material may include liquid biopsy material such as tissue, cells, blood, serum, plasma, saliva, sputum, or ascites collected from a subject for pathological examination. In one example of the present invention, the biopsy material may be saliva collected from a subject.

[0057] In another aspect to achieve the above object, the present invention provides a method for selecting a candidate drug for treating, ameliorating, or preventing oral cancer.

[0058] The method includes: (a) analyzing the abundance of at least one microorganism of the genus Actinomyces, the genus Capnocytophaga, the genus Porphyromonas, the genus Prevotella, the genus Streptococcus, the species Fusobacterium periodonticum, the species Prevotella melaninogenica, the species Prevotella pallens, and the species Streptococcus pneumoniae from a biological sample obtained from the subject before treatment with a candidate drug for treating, ameliorating, or preventing oral cancer;

[0059] (b) analyzing the abundance of at least one of the following microorganisms from the subject after treatment with the candidate drug: Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, and Streptococcus pneumoniae; and

[0060] (c) screening candidate drugs by comparing the abundance of the microorganisms analyzed in step (a) with the abundance of the microorganisms analyzed in step (b).

[0061] Here, the steps of analyzing the abundance of microorganisms in steps (a) and (b) are the same as those described in the method for providing information for diagnosing oral cancer.

[0062] In the present invention, the candidate drug is not limited in type, and may include, for example, natural products, compounds, biological substances, pharmaceuticals such as prebiotics, and functional food materials.

[0063] In another aspect of the present invention to achieve the above object, there is provided a method for predicting or monitoring response to treatment of oral cancer.

[0064] The method includes: (a) analyzing the abundance of at least one microorganism selected from the genus Actinomyces, the genus Capnocytophaga, the genus Porphyromonas, the genus Prevotella, the genus Streptococcus, the species Fusobacterium periodonticum, the species Prevotella melaninogenica, the species Prevotella pallens, and the species Streptococcus pneumoniae from a biological sample obtained from an oral cancer patient;

[0065] (b) analyzing the abundance of at least one of the following microorganisms from a biological sample obtained from a patient after treatment for oral cancer: Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, and Streptococcus pneumoniae; and

[0066] (c) predicting, assessing, or monitoring the therapeutic response by comparing the abundance of the microorganisms analyzed in step (a) with the abundance of the microorganisms analyzed in step (b).

[0067] It is important to present and evaluate appropriate treatments and establish treatment plans according to the onset and progression stage of oral cancer, and by analyzing the abundance of microorganisms in patient samples using the present invention, it is possible to predict and monitor the patient's treatment response with high accuracy.

[0068] Specifically, by analyzing and comparing the abundance of biomarker microorganisms in patient samples before and after treatment for the target oral cancer, it is possible to evaluate and monitor whether the oral cancer treatment results in a positive or negative therapeutic response, and it is also possible to predict future therapeutic responses.

[0069] In another aspect to achieve the above object, the present invention provides a probiotic composition for treating, ameliorating, or preventing oral cancer.

[0070] In the examples of the present invention, for microorganisms whose abundance was decreased in oral cancer patients compared to controls, oral cancer can be treated, improved, or prevented by increasing their abundance in the oral cavity. Therefore, the present invention provides a method for detecting and isolating the following species of bacteria: Porphyromonas genus, Prevotella genus, Fusobacterium periodonticum species, Prevotella melaninogenica species, Prevotella pallens species, Alloprevotella genus, Fusobacterium genus, Haemophilus genus, Veillonella genus, Haemophilus parainfluenzae species, KV831974_s species, Lautropia mirabilis species, Neisseria meningitidis, which are found to be decreased in abundance in oral cancer patients compared to normal subjects. meningitidis species, Neisseria subflava species, PAC001345_s species, Porphyromonas pasteri species, Prevotella histicola species, Prevotella nanceiensis species, Prevotella salivae species, Prevotella_uc species, Streptococcus salivarius species, Veillonella dispar species, Veillonella parvula The present invention provides a probiotic composition for treating, improving, or preventing oral cancer, which contains at least one microorganism selected from the group consisting of the species Parvula, the genus Mannheimia, the genus Pasteurellaceae_uc, and the genus Saccharimonas.

[0071] For the purposes of the present invention, "probiotics" are defined as live microorganisms that confer a health benefit.

[0072] The probiotic composition of the present invention can be provided in the form of a food or sample composition for humans or animals, or in the form of various quasi-drugs or pharmaceutical compositions for preventing, treating, or improving oral cancer.

[0073] In another aspect of the present invention, the present invention provides a method for diagnosing oral cancer.

[0074] In another aspect of the present invention to achieve the above object, there is provided a use of the above composition for diagnosing oral cancer.

[0075] In another aspect of the present invention to achieve the above object, there is provided a method for treating, ameliorating, or preventing oral cancer.

[0076] In another aspect to achieve the above object, the present invention provides use of the probiotic composition for the manufacture of a medicament for treating, ameliorating or preventing oral cancer. [Effects of the Invention]

[0077] The oral cancer diagnostic composition and the method for providing information for oral cancer diagnosis using the same according to the present invention can easily and accurately diagnose the presence or absence of oral cancer and the possibility of its onset at an early stage by analyzing the abundance of biomarker microorganisms from a biological sample and comparing it with a control group. [Brief explanation of the drawings]

[0078] [Figure 1] Figure 1 shows the CHAO index (A), Shannon index (B), beta diversity (C, D), and the degree of overlap at the species (E) and genus (F) levels between the healthy control and oral cancer patient groups. The results confirmed that oral cancer patients had significantly higher bacterial diversity in their oral microbial communities. [Figure 2]Figure 2 shows the differential oral microbial communities at the phylum (A), genus (B), and species (C) levels between healthy controls and oral cancer patients. [Figure 3] Figure 3 shows the results of the analysis of functional imbalance and co-occurrence networks of oral microorganisms. [Figure 4] Figure 4 shows the results of confirming the POD and AUC values ​​of the selected genus-based markers in healthy controls and oral cancer patients for the purpose of investigating and validating oral microbial genus-based markers for oral cancer. [Figure 5] Figure 5 shows the results of confirming the POD and AUC values ​​of selected species-based markers in healthy controls and oral cancer patients for the purpose of investigating and validating oral microbial species-based markers for oral cancer. [Figure 6] Figure 6 shows the results of examining the differences in abundance of five oral microbiomes according to the stage of oral cancer between control and oral cancer patients, in order to investigate and verify oral microbial genus-based markers for oral cancer. [Figure 7] Figure 7 shows the results of checking the AUC values ​​of the receiver operating characteristics (ROC) curves for five oral microbiomes to investigate and validate oral microbial genus-based markers for oral cancer. [Figure 8] FIG. 8 shows the results of confirming the difference in abundance of two orthologs between a control group and oral cancer patients according to the stage of oral cancer. DETAILED DESCRIPTION OF THE INVENTION

[0079] The present invention will be described in more detail with reference to the following examples. However, these examples are for illustrative purposes only and the scope of the present invention is not limited to these examples.

[0080] Example 1. Sample preparation and sequencing 1.1. DNA extraction within study population samples (1) Research subjects and sample collection The case-control study enrolled 167 men and women (aged 19 years or older) diagnosed with histologically confirmed oral squamous cell carcinoma (OSCC) of the tongue, upper and lower gums, buccal mucosa, retropalatal deltoid, hard and soft palate, floor of the mouth, and lower lip at the National Cancer Center (106 patients) and Seoul National University Dental Hospital (61 patients). A healthy control group of 569 cancer-free individuals at enrollment was recruited from the National Cancer Center's cancer screening cohort. Subjects who had undergone treatment or surgery or used immunosuppressants were excluded. All subjects signed written consent forms prior to enrollment and completed a detailed self-care health and lifestyle questionnaire, including questions about alcohol consumption behaviors. This study was approved by the National Cancer Center Institutional Ethics Committee (IRB No. NCC2018-0217). Saliva samples were collected in test tubes and subsequently frozen at -80°C until further analysis.

[0081] (2) DNA extraction and 16S rRNA gene amplicon sequencing Bacterial DNA extraction was performed using a Fast DNA Spin Extraction Kit (MP Biomedical, Santa Ana, CA, USA) according to the manufacturer's instructions. Data quality was confirmed by uploading the data to the EzBioCloud 16S-based MTP app (ChunLab, Inc., Seoul, South Korea), followed by DNA sequencing on an Illumina iSeq100 to generate single-end reads. Low-quality sequences with a read length of 2,000 bp and an average Q value of less than 25 were detected and filtered using the EzBioCloud software cloud app. DUDE-Seq software was used to denoise and extract non-redundant reads. The UCHIME algorithm was applied to the EzBioCloud 16S chimera-free database to confirm and remove chimeric sequences. The USEARCH program was used to perform taxonomic challenges and detect and calculate sequence similarity of query single-end reads to the EzBioCloud 16S database. EzBioCloud sequencing reads were clustered into OTUs at 97% sequence similarity using the UPARSE algorithm. Unidirectional reads for each sample were clustered into many OTUs using the UCLUST tool with the aforementioned cutoff values.

[0082] 1.2. Study population characteristics To examine the imbalance of microbial communities between groups, a total of 736 subjects (167 patients; 569 controls) were screened according to inclusion and exclusion criteria. Patients were additionally assigned to either the discovery set or the validation set based on their recruitment location, either the National Cancer Center (106 patients) or Seoul National University Hospital (61 patients). Controls were randomly divided into the discovery set (381 patients) and the validation set (188 patients). The demographic and clinical characteristics of oral cancer patients and healthy controls are summarized in Table 1.

[0083] [Table 1-1]

[0084] [Table 1-2]

[0085] The results of the variables are expressed in %. a P-values ​​were calculated using the Kruskal-Wallis test for continuous variables and the chi-square test for categorical variables. b Smoking status was categorized into two groups: current smoker / non-current smoker. c Drinking status was also categorized into two groups (current drinkers / current non-drinkers). d Alveolar ridge: maxillary gums, mandibular gums and buccal mucosa; e Others: maxillary sinus, soft palate, submandibular gland, and others. SD: standard error.

[0086] Example 2. Identification of microbial diversity in saliva of oral cancer patients compared to healthy controls 2.1. Analysis of sequencing results (1) Functional meta-genetic interpretation The EzBioCloud 16S-based MTP pipeline used the PICRUST algorithm to assess the functional profiles of microbial communities identified by 16S rRNA sequencing. Raw sequencing reads were calculated using the EzBioCloud 16S Microbiome Pipeline with basic parameters and collated identifiable reads against a reference database. Functional abundance profiles of oral microbial communities were predicted (annotated) based on bioinformatics analysis, specifically using the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology, module, and pathway databases, by multiplying the gene vector count for each OTU by the abundance of each OTU within each sample. The predicted metagenomic profiles were classified into KEGG orthology, KEGG module, and KEGG pathway clusters and compared across groups. The accuracy of each functional profile was determined based on the closest sequence taxon index.

[0087] (2) Statistical analysis Between-group differences in subject demographic and clinical characteristics were compared using Kruskal-Wallis and chi-square tests for continuous and categorical variables, respectively. Alpha diversity for the CHAO and Shannon indices was calculated for each group, and beta diversity was calculated by principal coordinate analysis (PCoA) based on weighted and unweighted UniFrac distances. Permutational multivariate analysis of variance (PERMANOVA) was performed to determine the significance of intergroup distances. Diversity and PERMANOVA results were analyzed using the "GUniFrac" package in R. Differential abundance analysis was performed using the Wilcoxon rank-sum test and LEfSe method for genus- and species-level taxa and functional data for KEGG pathways, KEGG modules, and KEGG-shaped taxa, with the default settings at https: / / huttenhower.sph.harvard.edu / galaxy / root. To exclude underrepresented taxa, only genera and species with at least one sequence within a minimum of 5% of all subjects were included. Heatmap correlation analysis was performed using the R package "heatmaply." Network models for correlations with a correlation coefficient of 0.4 or higher were displayed using Cytoscape version 3.9.0. Subsequently, classification and functional composition profiles were analyzed based on fold-change analysis, multivariate-adjusted conditional logistic regression, and area under the curve (AUC) values. Post hoc multiple comparisons using the Bonferroni method were performed to identify significant intergroup differences. Adjusted conditional logistic regression was performed to estimate ORs and their corresponding 95% CIs adjusted for sex, age, smoking, and alcohol consumption. AUCs of receiver operating characteristics (ROC) curves were calculated using five-fold cross-validation across the discovery, validation, and overall sets to characterize the diagnostic accuracy of biomarkers for oral cancer and healthy control status.All statistical tests were two-way with significance set at p < 0.05, and all other statistical analyses and visualizations were performed using the ggplot2 package in R version 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria).

[0088] 2.2. Confirmation of oral microbial community profile in oral cancer patients (1) Microbial community profiles showing differential abundance in oral cancer patients A total of 31 phyla, 116 classes, 244 orders, 492 families, 1,574 genera, and 3,936 species were identified in the study samples. Alpha diversity analysis revealed that both the CHAO and Shannon indices were significantly higher in oral cancer patients than in the control group (Figures 1A and 1B). Furthermore, beta diversity, which indicates the spatial distribution of microbial communities between groups, was analyzed by PCoA (Principal Coordinate Analysis) based on weighted and unweighted UniFrac distances. Permutation analysis of mutation tests also demonstrated significant differences in beta diversity between oral cancer patients and the control group (Figures 1C and 1D). Venn diagrams show that 664 genera and 1,455 species overlap between the two groups (Figures 1E and 1F).

[0089] We first analyzed the profiles of the top microbial communities with relative abundances of 1% or higher. At the phylum level, Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria were the five most abundant phyla, accounting for more than 90% of the bacterial community (Figure 2A). Bacteroidetes, Fusobacteria, and Proteobacteria were significantly reduced in oral cancer patients, whereas Actinobacteria and Firmicutes were significantly more abundant in oral cancer patients (Figure 2A). At the genus level, the top 15 genera accounted for more than 86% of the salivary microbial community (Figure 2B). Actinomyces, Streptococcus, Capnocytophaga, Granulicatella, and Lautropia were significantly higher in patients, whereas Alloprevotella, Fusobacterium, Haemophilus, Porphyromonas, Prevotella, and Veillonella were significantly lower in cancer patients (Figure 2B and Table 2). At the species level, all 21 species with relative abundances of 1% or higher showed significant differences in oral cancer (Table 2). Fusobacterium nucleatum, Granulicatella adiacens, Lautropia mirabilis, Neisseria meningitidis, Streptococcus pneumoniae, and Streptococcus sanguinis were significantly higher in patients, whereas other species, including six Prevotella spp., were significantly reduced (Figure 2C and Table 2).

[0090] [Table 2-1]

[0091] [Table 2-2]

[0092] aThe p-value was calculated by Kruskal-Wallis using the false discovery rate (FDR) developed by Benjamini-Hochberg. b The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was calculated using five-fold cross-validation on the entire dataset to characterize the general diagnostic accuracy of oral cancer versus healthy controls.

[0093] To investigate the imbalance of oral microbiota in cancer patients compared with healthy controls, we performed linear discriminant analysis of effect size (LEfSe) at the genus and species level, and included only genera and species with one or more sequences in at least 5% of all subjects to exclude underrepresented taxa.

[0094] Based on the LEfSe results, we identified 19 genera and 39 species that were not significantly regulated between groups. As shown in Table 2, except for one non-significant genus in the top dominant microbiome, all 11 species among the 15 genera and 21 species were repeatedly confirmed as significant biomarkers in the LEfSe results.

[0095] To further explore the association between oral cancer risk and the highly abundant genera and species in patients, we performed multiple conditional logistic regression adjusted for sex, age, smoking, and alcohol consumption status (Table 3, Association between the top 15 genera and 21 species and oral cancer risk in the entire study population). Each taxon was tested with continuous variables and categorical variables defined by tertile distribution among healthy controls. At the genus level, Actinomyces, Capnocytophaga, Lautropia, and Streptococcus were significantly associated with increased oral carcinogenesis risk, with higher risk identified in the highest tertile compared with the lowest tertile. In contrast, Fusobacterium, Haemophilus, Porphyromonas, Prevotella, and Veillonella showed a significant negative association with higher risk in the lowest tertile compared with the highest tertile.

[0096] [Table 3-1]

[0097] [Table 3-2]

[0098] [Table 3-3]

[0099] [Table 3-4]

[0100] [Table 3-5]

[0101] [Table 3-6]

[0102] [Table 3-7]

[0103] a Tertiles for each metabolite were separated based solely on distribution among the control groups. b Multiple conditional logistic regression (MCLR) was adjusted for sex, age, smoking and drinking status. c p-values ​​were calculated using the Kruskal-Wallis method developed by Benjamini-Hochberg using the false discovery rate (FDR). OR: odds ratio, 95% CI: confidence interval.

[0104] At the species level, in the up-adjusted group, Fusobacterium nucleatum, Lautropia mirabilis, Neisseria meningitidis, Streptococcus pneumoniae, and Streptococcus sanguinis were significantly associated with increased oral cancer risk, with higher risks identified in the highest tertile compared to the lowest.In the down-adjusted group, Fusobacterium periodonticum, Haemophilus parainfluenzae, KV831974_s, Neisseria subflava, PAC001345_s, Porphyromonas pasteri, Prevotella histicola, Prevotella melaninogenica, Prevotella nanceiensis, Prevotella pallens, Prevotella salivae, Prevotella_uc, Veillonella dispar, and Veillonella parvula were significantly negatively associated with higher risks identified in the lowest tertile compared to the highest.

[0105] (2) Functional imbalance and network analysis Using all samples from 736 participants, a total of 446 KEGG pathways were identified using the PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) built-in reference database. LEfSe analysis was then performed to study functional changes in the oral cancer microbiota. Results revealed that nine KEGG pathways were significantly different between groups (Figure 3A). Five pathways (lipopolysaccharide biosynthesis, porphyrin and chlorophyll metabolism, metabolic pathways, secondary metabolite biosynthesis, and ribosome) were enriched in the control group, while four pathways (quorum sensing, ABC transporter, two-component system, and phosphotransferase system (PTS)) were enriched in oral cancer patients.

[0106] As shown in Figure 3B, at the genus level, there are two co-abundance groups (Group 1 includes Actinomyces and Streptococcus, and Group 2 includes Prevotella and Porphyromonas). A stringent network excluding weak correlations (|r| < 0.4) also showed these association trends (Figure 3C). At the species level, Prevotella spp. are strongly positively correlated with each other, while Porphyromonas pasteri is strongly correlated with species from other phyla (Figures 3D and 3E).

[0107] Example 3. Identification of biomarkers based on genome analysis 3.1. Model building for biomarker investigation To discover biomarkers for oral cancer, we performed multi-stage feature selection for genera and species with at least one sequence in more than 5% of the total subjects.

[0108] Specifically, to reduce the number of features, we performed a model employing a two-stage machine learning approach: LASSO (Least Absolute Shrinkage and Selection Operator) and Random Forest (RF), using the R packages "glmnet" and "random forest," respectively.

[0109] First, LASSO adjusted the lambda parameter to efficiently select relevant taxa to improve model accuracy and analyzability. RF was then applied to a subset of key taxa selected by the LASSO algorithm to generate the final model. To obtain unbiased estimates of model performance, five-fold cross-validation (CV) was used to prevent overfitting bias. Within each iteration of the five-fold CV, the dataset was randomly divided into five parts, four of which were combined as the training set to generate a predictive model, and the remaining part was used as the test set to verify model performance. For LASSO, the number of features was reduced using a model with a λ smaller than one standard error of the minimum value. For RF, the model was trained on 40 to 400 sequences using a base threshold defined by the model, and an optimistic number of trees was selected. Taxa were then selected based on the average reduced accuracy model.

[0110] 3.2. Investigation and validation of oral cancer diagnostic biomarkers To identify genus- and species-based biomarkers for oral cancer, we used a machine learning model on the discovery set, consisting of a two-step procedure: LASSO (the least absolute shrinkage and selection operator) feature selection followed by random forest (RF). The same dataset used for the LEfSe analysis was used as input, and 10-fold cross-validation (CV) was applied to both LASSO and RF. At the genus level, 18 genera were selected as the optimal genus panel through machine learning (Table 4). In the discovery set (106 patients; 381 controls), the probability of disease (POD) index was significantly higher in cancer patients than in healthy controls (Figure 4A), and this POD index achieved an area under the curve (AUC) value of 0.92 (sensitivity 0.67; specificity 0.97) (Figure 4B). Similarly, in the validation set (61 patients; 188 controls), the POD was significantly higher in the patient group (Figure 4C), achieving an AUC value of 0.92 (sensitivity 0.80; specificity 0.98) (Figure 4D). To further confirm the diagnostic potential of this genera biomarker panel, the POD index and AUC were also calculated for the entire dataset (167 patients; 569 controls). The POD index was significantly higher in the oral cancer group with an AUC value of 0.93 (sensitivity 0.70; specificity 0.96) (Figures 4E and 4F). Simultaneously, AUC values ​​were calculated for single genera. Among the 18 genera, Prevotella and Streptococcus exhibited high AUC values ​​above 0.7, making them potential single biomarkers for oral cancer (Table 4). At the species level, 16 species were selected as the optimal biomarker set (Table 4). The POD index was significantly higher in the oral cancer group across all datasets (Figures 5A–5C). The AUC values ​​were 0.92 (sensitivity 0.66; specificity 0.96), 0.93 (sensitivity 0.77; specificity 0.98), and 0.93 (sensitivity 0.70; specificity 0.97) for the discovery, validation, and overall sets, respectively (Figures 5D-5F).Of the 16 species, six (AF385567_s, Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, Streptococcus constellatus, Streptococcus pneumoniae) could be potential single markers for oral cancer (AUC >= 0.7) (Table 4). Five genera (Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus) and four species (Fusobacterium periodonticum group, Prevotella melaninogenica, Prevotella pallens, Streptococcus pneumoniae group) were consistently found to be repeated in all analyses.

[0111] [Table 4-1]

[0112] [Table 4-2]

[0113] Example 4. Confirmation of diagnostic efficacy in early stage oral cancer We analyzed the oral microbiome, which is considered a potential single marker for the diagnosis of oral cancer, and two orthologs that showed significant expression differences between oral cancer patients and controls for early diagnosis of early-stage oral cancer. Data were analyzed after log2 normalization.

[0114] 4.1. Oral microbiome We analyzed the oral microbiome, namely strains of Howardella, Paraburkholderia, Porphyromonas, Capnocytophaga, and Sphingomonas, which are significant in the diagnosis of oral cancer, for early diagnosis of oral cancer. Oral cancer stages were classified into stages 1 to 4 according to the size (T) of the primary cancer, with stages 1 and 2 being early stage oral cancer and stages 3 and 4 being late stage oral cancer.

[0115] As a result, for Howardella strains, expression levels were relatively higher in step 4 compared to step 2 (p<0.001). However, no significant expression difference was observed between stage 1 and the other stages (Fig. 6A).

[0116] For Paraburkholderia strains, significant differences in expression were observed between the control group and oral cancer patients divided by stage, except for stage 3. Furthermore, the expression level of Paraburkholderia was relatively higher in stage 2 compared to stage 3 (p<0.01), and significant differences were also observed between stages 1 and 2, and between stages 3 and 4, respectively (Figure 6B).

[0117] For Porphyromonas strains, significant expression differences were observed between the control group and oral cancer patients grouped by stage, except for stage 3. Furthermore, the expression level was relatively higher in step 1 compared to step 3 (p<0.01) (Figure 6C).

[0118] Capnocytophaga strains showed a relatively higher detection rate in stage 2 compared to the control group and oral cancer patients divided by stage (p<0.01), and a relatively lower expression rate in stage 3 compared to stage 1 (p<0.01) (Figure 6D).

[0119] For Sphingomonas strains, significant differences in expression were observed between the control group and oral cancer patients divided by stage. Furthermore, the expression level of Sphingomonas was relatively lower in stage 4 compared to stage 2 (p < 0.001), supporting the efficacy of Sphingomonas for diagnosing early-stage oral cancer (Figure 6E).

[0120] Furthermore, receiver operating characteristics (ROC) curve analysis was performed to evaluate the performance of the five oral microbiomes as early oral cancer diagnostic biomarkers.

[0121] As a result, as shown in Figure 7, the five oral microbiomes demonstrated higher performance in diagnosing early-stage oral cancer, with an AUC of 0.834 for early-stage oral cancer, compared with an AUC of 0.794 for late-stage oral cancer.

[0122] 4.2.Orthology Orthologs are genes that have evolved through speciation while retaining their original function from a common ancestor; a gene with a given function in one species has the same function in other species.

[0123] To identify orthology-based biomarkers for early diagnosis of oral cancer, we analyzed orthologs that showed significant differences between oral cancer patients and healthy controls at early and late stages of oral cancer. Specifically, we examined the differences in expression between the control group and oral cancer patients, who were divided into stages 1 to 4 according to their stage of oral cancer.

[0124] As a result, of the two orthologs, acyl-CoA dehydrogenase was confirmed to have higher expression levels in stages 1 and 2 compared to the control group, while dihydrofolate reductase showed significant differences in all stages from 1 to 4, with expression levels particularly lower in stages 1 and 2 compared to the control group. Furthermore, dihydrofolate reductase levels were significantly lower in stage 2 compared to stage 3 (p<0.001). Therefore, we were able to confirm that acyl-CoA dehydrogenase (K06445) and dihydrofolate reductase (K18590) have potential as biomarkers for distinguishing early-stage oral cancer from late-stage oral cancer (Figure 8).

[0125] From the above description, those skilled in the art to which the present invention pertains will understand that the present invention can be embodied in other specific forms without changing the technical spirit or essential characteristics thereof. In this regard, it should be understood that the above-described embodiments are illustrative in all respects and are not limiting. The scope of the present invention should be interpreted as including all modifications and variations derived from the meaning and scope of the claims below, rather than the above detailed description, and equivalent concepts.

Claims

1. A biomarker composition for diagnosing oral cancer, comprising at least one microorganism selected from the group consisting of the genus Actinomyces, the genus Capnocytophaga, the genus Porphyromonas, the genus Prevotella, the genus Streptococcus, the species Fusobacterium periodonticum, the species Prevotella melaninogenica, the species Prevotella pallens, and the species Streptococcus pneumoniae.

2. Granulicatella genus, Lautropia genus, Alloprevotella genus, Fusobacterium genus, Haemophilus genus, Veillonella genus, Fusobacterium nucleatum species, Granulicatella adiacens species, Haemophilus parainfluenzae species, KV831974_s species, Lautropia mirabilis species, Neisseria meningitidis species, Neisseria subflava species 2. The biomarker composition for diagnosing oral cancer according to claim 1, further comprising at least one microorganism selected from the group consisting of Porphyromonas pasteri, Prevotella histicola, Prevotella nanceiensis, Prevotella salivae, Prevotella_uc, Streptococcus salivarius, Streptococcus sanguinis, Veillonella dispar, and Veillonella parvula.

3. Dialister spp., Mogibacterium spp., Parvimonas spp., Mannheimia spp., Pasteurellaceae spp., Saccharimonas spp., Actinomyces odontolyticus spp., Alloprevotella tannerae spp., Capnocytophaga sputigena spp., Dialister pneumosintes spp., Neisseria elongate spp., Prevotella baroniae spp., and Streptococcus constellatus spp.

10. The oral cancer diagnostic biomarker composition of claim 1, further comprising at least one microorganism of the species Pseudomonas constellatus.

4. The oral cancer diagnostic biomarker composition of claim 1 , wherein the composition comprises a formulation for detecting the microorganism.

5. 2. The biomarker composition for diagnosing oral cancer according to claim 1, further comprising at least one microorganism from the genera Howardella, Paraburkholderia, and Sphingomonas.

6. A biomarker composition for diagnosing oral cancer, comprising a preparation for detecting at least one microorganism of the genus Howardella, the genus Paraburkholderia, the genus Porphyromonas, the genus Capnocytophaga, and the genus Sphingomonas, The biomarker composition for diagnosing oral cancer according to claim 5, wherein the oral cancer is an early stage oral cancer.

7. The biomarker composition for diagnosing oral cancer further comprises a preparation for confirming the expression level of a gene encoding at least one of acyl-CoA dehydrogenase and dihydrofolate reductase, The oral cancer diagnostic biomarker composition according to claim 1, wherein the oral cancer is an early stage oral cancer.

8. An oral cancer diagnostic kit comprising the oral cancer diagnostic biomarker composition according to claim 1.

9. The kit for diagnosing oral cancer according to claim 8 , wherein the kit diagnoses oral cancer based on the abundance of microorganisms contained in a biological sample.

10. (a) analyzing the abundance of at least one of the following microorganisms from a biological sample: Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, and Streptococcus pneumoniae; and The method for providing information for diagnosing oral cancer according to claim 8, further comprising: (b) comparing the abundance of microorganisms analyzed in step (a) with the abundance of microorganisms in a control sample.

11. The method for providing information for diagnosing oral cancer according to claim 10, wherein the biological sample is saliva.

12. The method for providing information for diagnosing oral cancer according to claim 10, wherein DNA is extracted from the biological sample to confirm the abundance of microorganisms.

13. In the step (a), the sample is subjected to the following steps: to identify the genus Granulicatella, the genus Lautropia, the species Fusobacterium nucleatum, the species Granulicatella adiacens, the genus Alloprevotella, the genus Fusobacterium, the genus Haemophilus, the genus Veillonella, the species Haemophilus parainfluenzae, the species KV831974_s, the species Lautropia mirabilis, the species Neisseria meningitidis, the species Neisseria subflava, the species Pseudomonas aeruginosa ...

11. The method of claim 10, further comprising analyzing the abundance of at least one additional microorganism selected from the group consisting of Porphyromonas pasteri, Prevotella histicola, Prevotella nanceiensis, Prevotella salivae, Prevotella_uc, Streptococcus salivarius, Streptococcus sanguinis, Veillonella dispar, and Veillonella parvula.

14. In the step (a), the following bacteria are selected from the group consisting of the genus Dialister, the genus Mogibacterium, the genus Parvimonas, Actinomyces odontolyticus, Alloprevotella tannerae, Capnocytophaga sputigena, Dialister pneumosintes, Neisseria elongate, Prevotella baroniae, Streptococcus constellatus, and the like.

11. The method for providing information for diagnosing oral cancer according to claim 10, further comprising analyzing the abundance of at least one additional microorganism from the genera Mannheimia, Pasteurellaceae_uc, and Saccharimonas.

15. 11. The method for providing information for diagnosing oral cancer according to claim 10, wherein step (a) further comprises a step of analyzing the abundance of at least one additional microorganism of the genera Howardella, Paraburkholderia, and Sphingomonas in the sample.

16. 16. The method for providing information for diagnosing oral cancer according to claim 15, wherein the step of analyzing the abundance of at least one microorganism selected from the genera Howardella, Paraburkholderia, Porphyromonas, Capnocytophaga, and Sphingomonas in the sample is for diagnosing early-stage oral cancer.

17. The method for providing information for diagnosing oral cancer further comprises a step of determining an expression level of a gene encoding at least one of acyl-CoA dehydrogenase and dihydrofolate reductase in step (a), The method for providing information according to claim 10, wherein the oral cancer is an early stage oral cancer.

18. (a) analyzing the abundance of at least one of the following microorganisms from a biological sample obtained from a subject prior to treatment with a candidate drug for the treatment, amelioration, or prevention of oral cancer: Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, and Streptococcus pneumoniae; (b) analyzing the abundance of at least one of the following microorganisms from the subject's biological sample after treatment with the candidate drug: Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, and Streptococcus pneumoniae; and (c) screening candidate drugs by comparing the abundance of microorganisms analyzed in step (a) with the abundance of microorganisms analyzed in step (b).

19. (a) analyzing the abundance of at least one of the following microorganisms from a biological sample obtained from an oral cancer patient: Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, and Streptococcus pneumoniae; (b) analyzing the abundance of at least one of the following microorganisms from a biological sample obtained from a patient after treatment for oral cancer: Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, and Streptococcus pneumoniae; and (c) predicting or monitoring the response to treatment by comparing the abundance of microorganisms analyzed in step (a) with the abundance of microorganisms analyzed in step (b).

20. Porphyromonas genus, Prevotella genus, Fusobacterium periodonticum species, Prevotella melaninogenica species, Prevotella pallens species, Alloprevotella genus, Fusobacterium genus, Haemophilus genus, Veillonella genus, Haemophilus parainfluenzae species, KV831974_s species, Lautropia mirabilis species, Neisseria meningitidis species, Neisseria subflava species A probiotic composition for treating, ameliorating, or preventing oral cancer, comprising at least one microorganism selected from the group consisting of Porphyromonas pasteri species, Prevotella histicola species, Prevotella nanceiensis species, Prevotella salivae species, Prevotella_uc species, Streptococcus salivarius species, Veillonella dispar species, Veillonella parvula species, the genus Mannheimia, the genus Pasteurellaceae_uc, and the genus Saccharimonas.

21. (a) analyzing the abundance of at least one of the following microorganisms from a biological sample: Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, and Streptococcus pneumoniae; and (b) comparing the abundance of the microorganisms analyzed in step (a) with the abundance of microorganisms in a control sample.

22. Use of the composition according to any one of claims 1 to 7 for diagnosing oral cancer.

23. 21. A method for treating, ameliorating, or preventing oral cancer, comprising administering to an individual the composition of claim 20.

24. 21. Use of the composition of claim 20 for the manufacture of a medicament for the treatment, amelioration or prevention of oral cancer.

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