Diagnosis of oral cancer based on oral microbiota

Abstract

The present invention has ascertained a microbial flora that significantly increases or decreases in saliva of an oral cancer patient compared to a control group, and relates to a composition for diagnosing oral cancer comprising a preparation for detecting the microbial flora, and a method for providing information for diagnosing oral cancer using the same. According to the composition for diagnosing oral cancer and the method for providing information for diagnosis of the present invention, it is possible to simply and accurately perform early diagnosis of the onset of oral cancer or the likelihood of onset of oral cancer by analyzing and confirming the abundance level of microorganisms exhibiting a significant change compared to a normal control group.

Description

Technical Field

[0001] The present invention relates to the diagnosis of oral cancer based on the oral microbiota, and more particularly to a diagnostic composition for oral cancer based on the oral microbiota, an oral cancer diagnostic kit containing the diagnostic composition, and a method for providing information for diagnosing oral cancer. Background Art

[0002] The oral cavity is located at the position equivalent to the entrance of the digestive system and has a complex aggregate of bacteria that is considered the second largest microbiota in the human body after the gut. It is estimated that there are approximately 700 species of bacteria in the human oral cavity that play an important role in maintaining oral health.

[0003] Recently, some research teams have reported the presence of oral microbiota in other parts of the human body and the association between these microorganisms and numerous systemic diseases. In addition, recent studies have shown a strong correlation between the oral microbiota and various cancer sites such as colorectal cancer, esophageal cancer, and lung cancer. Among these studies, Porphyromonas gingivalis and Fusobacterium nucleatum are two oral bacterial species that often show a strong carcinogenic potential, which means they have the value as cancer biomarkers of the oral microbiome.

[0004] In addition, globally, oral cancer is one of the most common malignant tumors, whether in developed countries or developing countries with particularly high incidence and mortality rates. Oral squamous cell carcinoma (OSCC), as the most common form (accounting for more than 90% of oral cancers), is usually diagnosed at an advanced stage and will lead to a decline in the quality of life and survival rate (<50%) of most patients due to the complex management procedures of the primary tumor.

[0005] Therefore, in order to improve the quality of life of the above-mentioned patients and save medical costs, continuous efforts are needed to develop early diagnosis and biomarkers for oral cancer. Summary of the Invention

[0006] The object of the present invention is to solve the above-mentioned problems and other related problems.

[0007] One exemplary object of the present invention is to provide a biological sample, and in particular, a diagnostic composition that can accurately and simply diagnose oral cancer in a non-invasive manner as a composition containing a preparation for detecting microbial types showing significant abundance changes in oral cancer compared to a control group in saliva.

[0008] Another exemplary object of the present invention is to provide an oral cancer diagnostic kit containing the oral cancer diagnostic composition.

[0009] Another exemplary object of the present invention is to provide a method for providing information for diagnosing oral cancer, including the steps of confirming the abundance of the microorganism from a biological sample, especially from saliva, and comparing it with the abundance of the microorganism in a control group sample.

[0010] The technical problems to be achieved by the technical idea of the invention disclosed in this specification are not limited to the problems mentioned above. Those of ordinary skill in the art will clearly understand other problems not mentioned through the following description.

[0011] Next, its detailed lifespan will be described. In addition, each description and embodiment disclosed in this application can also be applied to other descriptions and embodiments respectively. That is, all combinations of various elements disclosed in this application are included within the scope of this application. In addition, it should not be considered that the scope of this application is limited by the following specific descriptions.

[0012] As one form for achieving the above object, the present invention provides an oral cancer diagnostic composition containing a preparation for detecting a microorganism showing a significant abundance change in oral cancer patients compared with a control group.

[0013] In the present invention, "oral cancer" is a general term for cancers occurring in the oral cavity (tongue, floor of the mouth, buccal mucosa, gingiva, hard palate, mandible) or lips, oropharynx (the part connected to the larynx behind the tongue). Specifically, it includes salivary gland cancer occurring in the salivary gland, sarcoma occurring in the mandible or facial muscles, etc., malignant melanoma forming black spots in the hard palate or gingiva, and oral squamous cell carcinoma derived from epithelial cells of the oral mucosa. Among them, about 90% of oral cancers are oral squamous cell carcinoma (Oral squamous cell carcinoma, OSCC). The oral cancer in the present invention can be oral squamous cell carcinoma, but is not limited thereto.

[0014] The term "abundance" in the present invention may refer to the relative abundance or relative proportion of a specific microorganism type, especially the abundance of the relative proportion of a specific microorganism related to the development of oral cancer in the oral microbiota in saliva. However, it is not limited thereto.

[0015] In the present invention, the "preparation for detecting microorganisms" refers to a preparation that can detect the presence of one or more selected from the group consisting of microorganisms having an abundance specific to oral cancer patients compared to a control group, and the type of the substance is not limited. For example, the microorganism detection preparation may be an antisense oligonucleotide, primer pair, probe, peptide, polynucleotide, oligonucleotide, aptamer or antibody that specifically binds to the 16S ribosomal ribonucleic acid (rRNA) of the microorganism, but is not limited thereto.

[0016] The detection of microorganisms using the detection preparation can be performed by an amplification reaction using one or more oligonucleotide primers that hybridize with a nucleic acid molecule encoding a microorganism-specifically expressed gene or a complement of the nucleic acid molecule, and the nucleic acid detection using the primer can be performed by confirming whether the gene is amplified or not using a method well known in the art after amplifying the gene sequence using an amplification method such as polymerase chain reaction (PCR).

[0017] The "primer" refers to a polynucleotide or its variant having a sequence base that can complementarily bind to the end of a specific region of a gene used to amplify a specific region of a target site equivalent to a gene using polymerase chain reaction (PCR). The primer does not require complete complementarity with the end of the specific region, and can be used as long as it can hybridize with the end and form a double-stranded structure to a complementary degree.

[0018] The "probe" refers to a polynucleotide, its variant having a sequence base that can complementarily bind to a target site of a gene, or a polynucleotide and a labeling substance bound thereto.

[0019] The "hybridization" refers to the formation of a double-stranded structure (duplex structure) by the pairing of two single-stranded nucleic acids through complementary base sequences, and includes not only the case of perfect match between single-stranded nucleic acid sequences, but also the case where hybridization can occur even in the presence of partially mismatched bases.

[0020] The "antibody" (or immunoglobulin) refers to a substance that can trigger an antigen-antibody reaction by specifically binding to an antigen, and can be, for example, one or more selected from the group consisting of polyclonal antibodies, monoclonal antibodies, recombinant antibodies or combinations thereof. Specifically, it includes not only polyclonal antibodies, monoclonal antibodies, recombinant antibodies and the complete form 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 oligonucleic acid or peptide molecule. General information related to aptamers has been described in detail in the literature [Bock LC et al., "Nature 355(6360):564 - 6(1992); Hoppe - Seyler F, Butz K, "Peptide aptamers: powerful new tools for molecular medicine". J Mol Med. 78(8):426 - 30(2000); Cohen BA, Colas P, Brent R. "An artificial cell - cycle inhibitor isolated from a combinatorial library". Proc Natl Acad Sci USA. 95(24):14272 - 7(1998)].

[0022] In addition, the diagnostic composition of the present invention may not only contain a preparation for determining the presence or absence of the microorganism, but may also include a marker for quantitatively or qualitatively determining the formation of an antigen - antibody complex, general tools and reagents used in immunological analysis, and the like.

[0023] The term "diagnosis" in the present invention includes determining the susceptibility of a subject to a particular disease or disorder, determining whether the 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 states, determining cancer stage or determining the responsiveness of cancer to treatment), or therapeutic monitoring (e.g., monitoring the condition of a subject to provide information related to the therapeutic effect). For the purposes of the present invention, the diagnosis is to distinguish or predict the onset or the likelihood of onset (risk) of oral cancer.

[0024] In the present invention, "microorganisms showing significant abundance changes in oral cancer patients compared with the control group" refers to microorganism types that are significantly increased or decreased in oral cancer patients compared with a healthy control group. Specifically, it can be one or more of 18 genera (Abiotrophia, Actinomyces, Alloscardovia, Capnocytophaga, Dialister, Howardella, Leuconostoc, Mannheimia, Mogibacterium, Olsenella, Paraburkholderia, Parvimonas, Pasteurellaceae_uc, Porphyromonas, Prevotella, Saccharimonas, Sphingomonas, and Streptococcus) and 16 species (AF385567_s, Actinomyces 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).In terms of higher diagnostic accuracy (area under the curve (AUC) value) and presenting consistent results in all analyses, it can be one or more microorganisms among 5 genera (Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, and Streptococcus) and 4 species (Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, and Streptococcus pneumoniae), but it is not limited thereto.

[0025] In the examples of the present invention, microorganisms with significant differences in abundance were found in the oral microbiota of the normal control group and oral cancer patients, and it was confirmed that they can be used as biomarkers for the diagnosis of oral cancer.

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

[0027] Specifically, it is characterized in that the abundances of Bacteroidetes, Fusobacteria, and Proteobacteria are significantly reduced in oral cancer compared with the normal control group, while the abundances of Actinobacteria and Firmicutes are significantly increased in oral cancer.

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

[0029] Specifically, it is characterized in that the abundances of Actinomyces, Streptococcus, Capnocytophaga, Granulicatella, and Lautropia are significantly increased in oral cancer compared with the normal control group, while the abundances of Alloprevotella, Fusobacterium, Haemophilus, Porphyromonas, Prevotella, and Veillonella are significantly decreased in oral cancer compared with the control group.

[0030] In the present invention, at the microbial species level, it may include 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 nanceiensis, Prevotella pallens, Prevotella salivae, Prevotella_uc, Streptococcus pneumoniae, Streptococcus salivarius, Streptococcus sanguinis, Veillonella dispar, and Veillonella parvula as biomarkers for diagnosing oral cancer.

[0031] Specifically, it is characterized in that the abundances of Fusobacterium nucleatum, Granulicatella adiacens, Lautropia mirabilis, Nisseria meningitidis, Streptococcus pneumoniae, and Streptococcus sanguinis are significantly increased in oral cancer compared with the control group, while the abundances of Fusobacterium periodonticum, 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 are significantly decreased in oral cancer compared with the normal control group.

[0032] As shown by the results of linear discriminant analysis of the linear discriminant analysis effect size (LEfSE) for microbiota imbalance analysis, the fold change and the area under the curve (AUC) value in the relative abundances of the screened microorganisms as described above both prove that these microorganisms can be used as biomarkers.

[0033] In addition, through LASSO (Least Absolute Shrinkage and Selection Operator) and random forest (RF) analyses, it can be confirmed that 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. Among them, in particular, the area under the curve (AUC) values of Prevotella and Streptococcus are above 0.7, and they can be particularly significant biomarkers.

[0034] Specifically, it is characterized in that the abundances of Abiotrophia, Actinomyces, Capnocytophaga, Dialister, Mogibacterium, Parvimonas, and Streptococcus in oral cancer are increased compared with the normal control group, while the abundances of Mannheimia, Pasteurellaceae_uc, Porphyromonas, Prevotella, and Saccharimonas in oral cancer are significantly decreased compared with the normal control group.

[0035] In addition, at the species level, AF385567-s, Actinomyces 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 diagnosing oral cancer.

[0036] Specifically, it is characterized in that the abundances of Actinomyces odontolyticus, Alloprevotella tannerae, Capnocytophaga sputigena, Dialister pneumosintes, Neisseria elongate, Prevotella baroniae, Streptococcus constellatus, and Streptococcus pneumoniae in oral cancer are increased compared with the normal control group, while the abundances of Fusobacterium periodonticum, Prevotella baroniae, and Prevotella pallens in oral cancer are significantly decreased compared with the normal control group.

[0037] As an exemplary implementation example of the present invention, it relates to an oral cancer diagnostic composition containing a preparation for detecting at least one microorganism among Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum species, Prevotella melaninogenica species, Prevotella pallens species, and Streptococcus pneumoniae species.

[0038] As an exemplary implementation example of the present invention, it may further additionally contain a preparation for detecting at least one microorganism among Granulicatella, Lautropia, Alloprevotella, Fusobacterium, Haemophilus, Veillonella, Fusobacterium nucleatum species, Granulicatella adiacens species, Haemophilus parainfluenzae species, KV831974_s species, Lautropia mirabilis species, Nisseria meningitidis species, Neisseria subflava species, PAC001345_s species, Porphyromonas pasteri species, Prevotella histicola species, Prevotella nananceiensis species, Prevotella salivae species, Prevotella_uc species, Streptococcus salivarius species, Streptococcus sanguinis species, Veillonella dispar species, and Veillonella parvula species.

[0039] As an exemplary implementation example of the present invention, it is also possible to additionally include a preparation for detecting at least one microorganism among Dialister, Mogibacterium, Parvimonas, Mannheimia, Pasteurellaceae_uc, Saccharimonas, Actinomyces odontolyticus species, Alloprevotella tannerae species, Capnocytophaga sputigena species, Dialister pneumosintes species, Neisseria elongate species, Prevotella baroniae species, and Streptococcus constellatus species.

[0040] As an exemplary implementation example of the present invention, it is also possible to additionally include a preparation for detecting at least one microorganism among Howardella, Paraburkholderia, and Sphingomonas.

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

[0042] As an exemplary implementation example of the present invention, it is also possible to additionally include a preparation for detecting a gene encoding at least one of acyl-CoA (CoA) dehydrogenase and dihydrofolate reductase, and the preparation for gene detection can be used to diagnose early-stage oral cancer.

[0043] In the present invention, the oral cancer in the early stage may refer to stage 1 and stage 2 when it is classified into stage 1 to stage 4 according to the size of the primary cancer, the metastasis of cervical lymph nodes, and the presence or absence of distant metastasis. Specifically, stage 1 means that the size of the primary cancer is 2 cm or less and there is no metastasis to cervical lymph nodes or distant metastasis. Stage 2 means that the size of the primary cancer is more than 2 cm and less than 4 cm and there is no metastasis to cervical lymph nodes or distant metastasis. Stage 3 means that the size of the primary cancer is 4 cm or more and there is a lymph node metastasis of 3 cm or less in the neck but no distant metastasis. And stage 4 means that the primary cancer invades the bone, facial skin, deep muscles of the tongue, or there is a lymph node metastasis of 3 to 6 cm on one side of the primary site, or there are two or more lymph node metastases of 6 cm or less in the neck, or metastases to both sides or the lymph nodes on the opposite side of the lesion but no distant metastasis, or there is a large lymph node metastasis of 6 cm or more in the neck, or the primary site cannot be surgically treated and there is no distant metastasis or there has been distant metastasis.

[0044] As another form for achieving the above object, the present invention provides an oral cancer cutting-off kit containing a composition for oral cancer diagnosis.

[0045] The term "oral cancer diagnosis kit" in the present invention refers to a kit containing a composition for oral cancer diagnosis. In addition to the diagnostic composition, the kit may further include an instruction manual describing the usage method of the kit required for diagnosis.

[0046] As an example of the present invention, the kit may have an increased abundance of Actinomyces, Capnocytophaga, Granulicatella, Lautropia, Leptotrichia, Rothia, and Streptococcus at the genus level, and a decreased abundance of Alloprevotella, Campylobacter, Fusobacterium, Haemophilus, Neisseria, Porphyromonas, Prevotella, and Veillonella compared to samples from normal individuals. At the species level, the abundances of Fusobacterium nucleatum, Granulicatella adiacens, Lautropia mirabilis, Neisseria meningitides, Streptococcus pneumoniae, and Streptococcus sanguinis are increased, while the abundances of Fusobacterium periodonticum, 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 are decreased, and it is diagnosed as oral cancer.

[0047] As another form for achieving the above object, the present invention provides a method for providing information for diagnosing oral cancer.

[0048] The information providing method includes: (a) a step of confirming the abundance of microbial types that have significantly changed in abundance in oral cancer patients compared to a control group from a biological sample; and (b) a step of comparing the abundance with the abundance of microorganisms in the control group sample.

[0049] As an example of the present invention, the "microbial types with significantly abundant changes in oral cancer patients compared to the control group" may include, at the genus level, one or more selected from the group consisting of Actinomyces, Capnocytophaga, Granulicatella, Lautropia, Leptotrichia, Rothia, Streptococcus, Alloprevotella, Campylobacter, Fusobacterium, Haemophilus, Neisseria, Porphyromonas, Prevotella, and Veillonella, and at the species level, one or more selected from the group consisting of an increase in the abundance of Fusobacterium nucleatum, Granulicatella adiacens, Lautropia mirabilis, Neisseria meningitides, Streptococcus pneumoniae, and Streptococcus sanguinis, Fusobacterium periodonticum, 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.

[0050] As an example of the present invention, the "microbial types with significantly changed abundance in oral cancer patients compared to the control group" may include one or more selected from the group consisting of Howardella, Paraburkholderia, and Sphingomonas.

[0051] In the step of confirming the abundance of a specific microbial type from a biological sample, the abundance of the microorganism is analyzed by extracting deoxyribonucleic acid (DNA) from the biological sample.

[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, ribonucleic acid (DNA) may be extracted from the microorganism and polymerase chain reaction (PCR) primers for 16S ribosomal ribonucleic acid (rRNA) may be used, but it is not limited thereto.

[0053] As an example of the present invention, the step (b) is a step of comparing the abundance analysis result in the step (a) with a healthy normal control group. The abundances of Actinomyces, Capnocytophaga, Granulicatella, Lautropia, Leptotrichia, Rothia, Streptococcus, Howardella, Dialister, Paraburkholderia, and Sphingomonas increase at the genus level, while the abundances of Alloprevotella, Campylobacter, Fusobacterium, Haemophilus, Neisseria, Porphyromonas, Prevotella, and Veillonella decrease. The abundances of Fusobacterium nucleatum, Granulicatella adiacens, Lautropia mirabilis, Neisseria meningitides, Streptococcus pneumoniae, and Streptococcus sanguinis increase at the species level.When the abundances of Fusobacterium periodonticum, 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 decrease, it is diagnosed as having oral cancer or being at risk of developing oral cancer.

[0054] As an example of the present invention, it can be diagnosed as early-stage oral cancer when the abundances of Howardella, Paraburkholderia, Capnocytophaga, and Sphingomonas increase, or when the abundance of Porphyromonas decreases.

[0055] The information providing method may further include the step of confirming the expression level of a gene encoding at least one of acyl-CoA dehydrogenase and dihydrofolate reductase. When the expression level of the gene encoding acyl-CoA dehydrogenase increases, it can be diagnosed as early-stage oral cancer, and when the expression level of the gene encoding dihydrofolate reductase decreases, it can be diagnosed as early-stage oral cancer.

[0056] The term "biological sample" in the present invention refers to any substance, biological fluid, tissue or cell obtained from or derived from an individual. For example, it may include 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 fluid, nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cells, cell extracts or cerebrospinal fluid, etc. Preferably, it may be a liquid biopsy sample collected from a diagnostic subject by a non-invasive method for pathological histological examination. For example, tissues, cells, blood, serum, plasma, saliva, sputum or ascites of the diagnostic subject, etc. As an example of the present invention, it may be saliva collected from a diagnostic subject.

[0057] As another form for achieving the above object, the present invention provides a method for selecting a candidate drug for treating, improving or preventing oral cancer.

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

[0059] (b) analyzing the abundance of at least one microorganism among Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum species, Prevotella melaninogenica species, Prevotella pallens species, and Streptococcus pneumoniae species in a biological sample obtained from the subject after treatment with the candidate drug; and,

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

[0061] Wherein, the step of analyzing the abundance of the microorganism in step (a) and step (b) is the same as that described previously in the method for providing information for diagnosing oral cancer.

[0062] In the present invention, the type of the candidate drug is not limited, and as an example, it may include all pharmaceutical and functional food raw materials such as natural products, compounds, biological substances, and probiotics.

[0063] As another form for achieving the above object, the present invention provides a method for predicting or monitoring the treatment response of oral cancer.

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

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

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

[0067] In view of the onset and progression stages of oral cancer, it is crucial to propose and evaluate appropriate treatment methods and formulate treatment plans. By analyzing the abundance of the microorganisms according to the present invention in patient samples, the treatment response of patients can be predicted and monitored with relatively high accuracy.

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

[0069] As another form for achieving the above object, the present invention provides a probiotic composition for treating, ameliorating or preventing oral cancer.

[0070] In an embodiment of the present invention, for the microorganisms whose abundance is reduced in the oral cancer patient group compared to the control group, oral cancer can be treated, improved or prevented by increasing their abundance in the oral cavity. Accordingly, the present invention provides a probiotic composition for treating, improving or preventing oral cancer, which comprises at least one microorganism selected from Porphyromonas, Prevotella, Fusobacterium periodonticum species, Prevotella melaninogenica species, Prevotella pallens species, Alloprevotella, Fusobacterium, Haemophilus, Veillonella, Haemophilus parainfluenzae species, KV831974_s species, Lautropia mirabilis species, Nisseriameningitidis 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 species, Mannheimia, Pasteurellaceae_uc, and Saccharimonas, whose abundance is reduced in the oral cancer patient group compared to the normal control group.

[0071] In the present invention, "probiotics" are defined as viable microorganisms that can provide benefits to health.

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

[0073] As another aspect for achieving the above object, the present invention provides a method for diagnosing oral cancer.

[0074] As another aspect for achieving the above object, the present invention provides the use of the composition for diagnosing oral cancer.

[0075] As another aspect for achieving the above object, the present invention provides a method for treating, ameliorating or preventing oral cancer.

[0076] As another aspect for achieving the above object, the present invention provides the use of the probiotic composition for manufacturing a medicament for treating, ameliorating or preventing oral cancer.

[0077] The composition for diagnosing oral cancer according to the present invention and the method for providing information for diagnosing oral cancer using the composition for diagnosing oral cancer can simply and accurately perform early diagnosis of the occurrence or the possibility of occurrence of oral cancer by analyzing the abundance of biomarkers from a biological sample and comparing it with a control group. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is the result of confirming the Chao index (A), Shannon index (B), β diversity (C, D), the degree of repetition at the species (E) and genus (F) levels in the healthy control group and the oral cancer patient group, and it can be confirmed that the bacterial diversity of the oral microbiota in oral cancer patients is significantly higher.

[0079] Figure 2 It is a schematic diagram illustrating the oral microbiota showing differences in the healthy control group and the oral cancer patient group at the phylum (A), genus (B) and species (C) levels.

[0080] Figure 3 It is the result of analyzing the functional imbalance and co-occurrence network of oral microorganisms.

[0081] Figure 4 It is the result of confirming the positive detection rate (POD) value and the area under the curve (AUC) value of the selected genus-based biomarkers in the healthy control group and oral cancer patients in order to identify and verify the genus-based biomarkers of oral cancer.

[0082] Figure 5 It is the result of confirming the positive detection rate (POD) value and the area under the curve (AUC) value of the selected species-based biomarkers in the healthy control group and oral cancer patients in order to identify and verify the species-based biomarkers of oral cancer.

[0083] Figure 6 This is the result of identifying and validating genus-based biomarkers for oral cancer by confirming the abundance differences of five oral microbiomes in the control group and at different stages of oral cancer in oral cancer patients.

[0084] Figure 7 This is the result of identifying and validating genus-based biomarkers for oral cancer by confirming the area under the curve (AUC) values of the receiver operating characteristics (ROC) curves of five oral microbiomes.

[0085] Figure 8 This is the result of confirming the abundance differences of two orthologs in the control group and at different stages of oral cancer in oral cancer patients. Detailed implementation mode

[0086] Next, the present invention will be described in more detail with reference to the following examples. However, these examples are only used for illustrative purposes of the present invention, and the scope of the present invention is not limited by these examples.

[0087] Example 1. Sample preparation and sequencing execution

[0088] 1.1. Extraction of deoxyribonucleic acid (DNA) from samples within the study population

[0089] (1) Study subjects and sample collection

[0090] In a patient-control study, 167 men and women (aged 19 and above) with histologically confirmed oral squamous cell carcinoma (OSCC) in the tongue, upper gingiva, lower gingiva, buccal mucosa, retromolar trigone, hard palate, soft palate, floor of the mouth, and lower lip were enrolled at the National Cancer Center of Korea (106 patients) and the Dental Hospital of Seoul National University (61 patients). 569 healthy controls without cancer at the time of enrollment were recruited from the cancer screening cohort of the National Cancer Center of Korea, and subjects who had received treatment or surgery or had used immunosuppressive agents were excluded at the time of enrollment. All subjects signed a written consent form before enrollment and filled out a detailed self-administered health and lifestyle questionnaire including alcohol consumption-related behavioral questions at the time of enrollment. This study was approved by the Institutional Review Board of the National Cancer Center of Korea (IRB number: NCC2018-0217). Saliva samples were collected into tubes and immediately frozen at -80 °C until further analysis.

[0091] (2) Deoxyribonucleic acid (DNA) extraction and 16S ribosomal ribonucleic acid (rRNA) gene amplicon sequencing

[0092] Bacterial deoxyribonucleic acid (DNA) was extracted using a Fast DNS Spin extraction kit (MP Biomedicals, Santa Ana, CA, USA) according to the manufacturer's operating guidelines. Data quality was confirmed by uploading to the EzBioCloud 16S-based MTP application (ChunLab, Inc., Seoul, Korea). Subsequently, single-end reads were generated by performing deoxyribonucleic acid (DNA) sequencing using an Illumina iSeq100. Read lengths (2,000 bp) and low-quality sequences with an average Q-value of less than 25 were detected and filtered using the cloud application of Ezbiocloud software. Denoising and extraction of non-redundant read sequences were performed using DUDE-Seq software. The UCHIME algorithm was applied to the Ezbiocloud 16S chimera-free database to identify and remove chimera sequences. Taxonomic assignments were performed using the USEARCH program to detect and calculate the sequence similarity of query single-end read sequences in the EzBioCloud 16S database. EzBioCloud sequencing read sequences were clustered into operational taxonomic units (OTUs) with 97% sequence similarity using the UPARSE algorithm. Single-end read sequences from each sample were clustered into a large number of operational taxonomic units (OTUs) using the above-mentioned cut-off values and the UCLUST tool.

[0093] 1.2. Characteristics of the study population

[0094] To investigate the microbiota imbalance between groups, a total of 736 subjects (167 patients; 569 controls) were selected according to inclusion and exclusion criteria. Patients were further assigned to the discovery or validation group according to the recruitment site, i.e., the National Cancer Center of Korea (106 patients) or Seoul National University Hospital (61 patients). Subsequently, 381 controls were randomly assigned to the discovery group and 188 controls were assigned to the validation group. Demographic and clinical characteristics of oral cancer patients and healthy controls are summarized in Table 1.

[0095]

Table 1

[0096]

[0097]

[0098]

[0099] The results of the variables are expressed as %. a The P-value was calculated using the Kruskal-Wallis test for continuous variables and the chi-square test for categorical variables. b Smoking status was classified into 2 groups (current smokers / non-smokers). c Alcohol drinking status was also classified into 2 groups (current drinkers / current non-drinkers). d Alveolar ridge: upper gingiva, lower gingiva, and buccal mucosa; e Others: maxillary sinus, soft palate, submental area, and others. SD: standard error.

[0100] Example 2. Confirmation of microbial diversity in saliva of oral cancer patients compared with healthy control group

[0101] 2.1. Analysis of sequencing results

[0102] (1) Functional metagenomic analysis

[0103] For the EzBioCloud 16S-based MTP analysis pipeline, the PICRUST algorithm was used to evaluate the functional profiles of microbial colonies identified by 16s ribosomal ribonucleic acid (rRNA) sequencing. Unprocessed sequencing read sequences were calculated using the EzBioCloud 16S microbiome analysis pipeline with default parameters and identifiable read sequences against the reference database. The functional abundance profiles of oral microbial colonies were predicted by multiplying the gene vector counts of each operational taxonomic unit (OUT) by the abundance of the operational taxonomic unit (OUT) within each sample, based on bioinformatics analysis, especially using the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology, modules, and pathway databases. The predicted metagenomic profiles were classified according to the clustering of Kyoto Encyclopedia of Genes and Genomes orthology (KEGG orthology), Kyoto Encyclopedia of Genes and Genomes (KEGG) modules, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and compared between groups. The accuracy of each functional profile was determined according to the closest sequence taxonomic unit index.

[0104] (2) Statistical analysis

[0105] The Kruskal-Wallis test and chi-square test were respectively used to compare the differences in the demographic and clinical characteristics of the subjects between groups for continuous variables and categorical variables. The alpha diversity of the Chao index and Shannon index was calculated for each group, while the 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 the distance between groups, and the "GUniFrac" package in R language was used to analyze the diversity and the results of PERMANOVA. According to the default settings of the website https: / huttenhower.sph.harvard.edu / galaxy / root, the Wilcoxon rank-sum test and linear discriminant analysis effect size (LEfSe) were used to perform differential abundance analysis on taxonomic units and functional data at the genus and species levels, namely Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, KEGG modules, and KEGG orthologous groups. To exclude a small number of taxonomic units, only genera and species with at least one or more sequences in at least 5% of all subjects were included. The heatmap-related analysis was performed using the R language package "heatmaply". The network model of the correlation relationship with a |correlation coefficient| of more than 0.4 was represented using Cytoscape version 3.9.0. Next, the classification and functional composition profiles were analyzed based on fold-change analysis, multivariable-adjusted conditional logistic regression, and the area under the curve (AUC) value. To identify significant differences between groups, post hoc multiple comparisons using the Bonferroni method were employed. Adjusted conditional logistic regression analysis was performed to estimate the odds ratio (OR) and the corresponding 95% confidence interval (CI) adjusted for gender, age, smoking, and alcohol consumption. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was calculated using 5-fold cross-validation in the discovery, validation, and overall datasets to characterize the diagnostic accuracy of biomarkers for oral cancer and healthy control group status.All statistical tests were two-way tests with a significance level 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).

[0106] 2.2. Confirmation of the oral microbiota profile in oral cancer patients

[0107] (1) Microbiota profiles with different abundances were presented in oral cancer patients

[0108] A total of 31 phyla, 116 classes, 244 orders, 492 families, 1574 genera, and 3936 species were observed in the study specimens. In the analysis of α-diversity, the Chao and Shannon indices were significantly higher in oral cancer patients compared to the control group ( Figure 1 A and 1B). In addition, β-diversity, which represents the microbial colony space between groups, was performed by principal coordinate analysis (PCoA) based on weighted and unweighted UniFrac distances. The results of the permutation analysis of the variance test also showed a significant difference in β-diversity between oral cancer patients and the control group ( Figure 1 C and Figure 1 D). The Venn diagram showed that 664 genera and 1,455 species were repeated between the two groups ( Figure 1 E and Figure 1 F).

[0109] First, the profiles of the major microbial colonies with a relative abundance of 1% or more were analyzed. At the phylum level, Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria were the five most abundant phyla that accounted for more than 90% of the bacterial colonies ( Figure 2 A). Bacteroidetes, Fusobacteria, and Proteobacteria were significantly reduced in oral cancer, while Actinobacteria and Firmicutes were significantly enriched in oral cancer ( Figure 2A). At the genus level, the top 15 genera accounted for more than 86% of the salivary microbiota ( Figure 2 B). Actinomyces, Streptococcus, Capnocytophaga, Granulicatella, and Lautropia were significantly higher in patients, while Alloprevotella, Fusobacterium, Haemophilus, Porphyromonas, Prevotella, and Veillonella were significantly lower in cancer patients ( Figure 2 B and Table 2). At the species level, 21 species with a relative abundance of more than 1% all showed significant differences in oral cancer (Table 2). Fusobacterium nucleatum species, Granulicatella adiacens species, Lautropia mirabilis species, Neisseria meningitidis species, Streptococcus pneumoniae species, and Streptococcus sanguinis species were significantly higher in patients, while 6 other species including Prevotella spp. were significantly reduced ( Figure 2 C and Table 2).

[0110]

Table 2

[0111]

[0112]

[0113]

[0114]

[0115] a The p-value was calculated using the Kruskal-Wallis test with false discovery rate (FDR) developed by Benjamini-Hochberg. bThe area under the receiver operating characteristics (ROC) curve (AUC) was calculated using 5-fold cross-validation in the overall dataset to characterize the general diagnostic accuracy of the healthy control group relative to oral cancer.

[0116] To investigate the oral microbiota imbalance in cancer patients compared to healthy control groups, linear discriminant analysis effect size (LEfSe) was performed at the genus and species levels, and only genera and species with more than one sequence in at least 5% of all subjects were included to exclude a small number of taxa.

[0117] Based on the results of linear discriminant analysis effect size (LEfSe), 19 genera and 39 species that were not significantly regulated between groups were identified. As shown in Table 2, except for 1 non-significant genus of the main dominant microbiota group, 11 out of 15 genera and 21 species were repeated in the linear discriminant analysis effect size (LEfSe) results, thus confirming their belonging to significant biomarkers.

[0118] To further investigate the association between the risk of oral cancer and genera and species with higher abundances in patients, logistic regression analysis was performed with multiple conditions adjusted for gender, age, smoking, and alcohol consumption status (Table 3, the association between the risk of oral cancer and the main 15 genera and 21 species in the overall study population). Each taxon was tested as a categorical variable defined by the tertiles distribution between continuous variables and healthy control groups. At the genus level, higher risks were observed in the highest tertile compared to the lowest tertile for Actinomyces, Capnocytophaga, Lautropia, and Streptococcus, and they were significantly associated with an increased risk of oral carcinogenesis. In contrast, Fusobacterium, Haemophilus, Porphyromonas, Prevotella, and Veillonella showed higher risks and significant negative associations in the lowest tertile compared to the highest tertile.

[0119]

Table 3

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] a The tertiles of each metabolite were divided based on the distribution among the control groups. b Multiple conditional logistic regression (MCLR) was adjusted for gender, age, smoking, and alcohol consumption status. c The p-value was calculated using the Kruskal-Wallis test with false discovery rate (FDR) developed by Benjamini-Hochberg. OR: odds ratio, 95% CI: confidence interval

[0129] At a certain level, in the upregulated group, higher risk levels were observed for *Fusobacterium nucleatum*, *Lautropia mirabilis*, *Nisseria meningitidis*, *Streptococcus pneumoniae*, and *Streptococcus sanguinis* in the highest tertile compared to the lowest tertile, and they were significantly associated with an increased risk of oral cancer. In the downregulated 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* showed higher risk levels and significant negative associations in the lowest tertile compared to the highest tertile.

[0130] (2) Functional imbalance and network analysis

[0131] In all samples of 736 participants, a total of 446 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified using the built-in reference database of Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt), and then the functional changes in the oral cancer microbiota were investigated by performing linear discriminant analysis effect size (LEfSe) analysis. As a result, 9 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were found to be significantly different between groups ( Figure 3A). Five pathways (lipopolysaccharide biosynthesis, porphyrin and chlorophyll metabolism, metabolic pathways, biosynthesis of secondary metabolites, and ribosome) were enriched in the control group, while four pathways (quorum sensing, ATP-binding cassette (ABC) transporters, two-component system, and phosphotransferase system_PTS) were enriched in oral cancer patients.

[0132] As Figure 3 shown in B, at the genus level, there were two co-abundance groups (Group 1 included Actinomyces and Streptococus, while Group 2 included Prevotella and Porphyromonas). Except for weak correlations (|r| < 0.4), the stringent network also showed the above-mentioned tendency of correlation Figure 3 C). At the species level, Prevotella spp. had strong positive correlations with each other, while Porphyromonas pasteri had strong correlations with species of other phyla Figure 3 D and Figure 3 D).

[0133] Example 3. Identification of Biomarkers Based on Genomic Analysis

[0134] 3.1. Model Construction for Biomarker Identification

[0135] To discover biomarkers for oral cancer, multi-stage feature screening was performed on genera and species with more than one sequence in more than 5% of the total subjects.

[0136] Specifically, to reduce features, a model of a machine-learning method consisting of two stages, namely the least absolute shrinkage and selection operator (LASSO) and random forest (RF) using the R language software packages "glmnet" and "randomforest" respectively, was performed.

[0137] First, in the least absolute shrinkage and selection operator (LASSO), relevant taxa were effectively selected by adjusting the λ parameter, thereby improving the model accuracy and interpretability. Next, a subset of important taxa selected by the LASSO algorithm was applied to a random forest (RF) to generate the final model. To obtain an unbiased estimate of the model performance, five-fold cross-validation (CV) was used to prevent over-fitting bias. In each iteration of the five-fold CV, a prediction model was generated by randomly dividing the dataset into five parts and combining four parts into a training set, while the remaining one part was used as a test set to validate the model performance. In the LASSO, the number of features was reduced by using a model with λ having a one standard error less than the minimum value. In the random forest (RF), the model was trained within a sequence of 40 to 400 using the default threshold defined by the model, thereby selecting an optimal number of decision trees. Next, taxa were selected based on the mean decrease in accuracy model.

[0138] 3.2. Identification and Validation of Oral Cancer Diagnostic Biomarkers

[0139] To identify genus- and species-based biomarkers for oral cancer, a machine learning model for the discovery set consisting of two-stage steps such as LASSO (least absolute shrinkage and selection operator) feature screening and random forest (RF) was used. In this process, the same dataset as that used in the linear discriminant analysis effect size (LEfSe) analysis was input and used, and 10-fold cross-validation (CV) was applied to both the LASSO and the random forest (RF). At the genus level, 18 genera were selected as the best genus combination by machine learning (Table 4). In the discovery set (106 patients; 381 controls), the disease probability (POD) index was significantly higher in cancer patients compared to healthy controls ( Figure 4 A), and the disease probability (POD) index achieved an area under the curve (AUC) value of 0.92 (sensitivity of 0.67; specificity of 0.97) ( Figure 4 B). Similarly, in the validation set (61 patients; 188 controls), the disease probability (POD) was also in the patient group achieving an area under the curve (AUC) value of 0.92 (sensitivity of 0.80; specificity of 0.98)Figure 4 significantly higher in (C) Figure 4 D). To further confirm the diagnostic potential of the genus biomarker combinations as described above, the disease probability (POD) index and the area under the curve (AUC) were also calculated for the overall dataset (167 patients; 569 controls). The disease probability (POD) index was significantly higher in the oral cancer group with an AUC value of 0.93 (sensitivity 0.70; specificity 0.96)( Figure 4 E and Figure 4 F). At the same time, the AUC values were also calculated for individual genera. Among the 18 genera, Prevotella and Streptococcus, which showed relatively high AUC values of over 0.7, could be potential single biomarkers for oral cancer in particular (Table 4). At the species level, 16 species were selected as the optimal biomarker set (Table 4). The disease probability (POD) index was significantly higher in the oral cancer group for all datasets( Figure 5 A to Figure 5 C). 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) in the discovery, validation, and overall cohorts, respectively( Figure 5 D to Figure 5F). Six out of 16 species (AF385567_s, Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, Streptococcus constellatus, and Streptococcus pneumoniae) could be potential single markers for oral cancer (area under the curve (AUC) ≥ 0.7) (Table 4). It was confirmed that in all analyses, five genera (Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, and Streptococcus) and four species (Fusobacterium periodonticum group, Prevotella melaninogenica, Prevotella pallens, and Streptococcus pneumoniae group) were consistently recurrent in all analyses.

[0140]

Table 4

[0141]

[0142]

[0143]

[0144]

[0145]

[0146] Example 4. Confirmation of Diagnostic Efficacy in Early-Stage Oral Cancer

[0147] Analyses for early diagnosis of early-stage oral cancer were performed on the oral microbiome, which was considered a potential single marker for oral cancer diagnosis, and two orthologs that showed significant expression differences between oral cancer patients and the control group. The data were analyzed after log2 normalization.

[0148] 4.1. Oral Microbiome

[0149] Analyses for early diagnosis of oral cancer were performed on oral microbiome strains that are significant in oral cancer diagnosis, namely Howardella, Paraburkholderia, Porphyromonas, Capnocytophaga, and Sphingomonas strains. The course of oral cancer was divided into stages 1 to 4 according to the size T of the primary cancer, where stages 1 and 2 were classified as early-stage oral cancer, and stages 3 and 4 were classified as late-stage oral cancer.

[0150] As a result, for Howardella strains, it was relatively higher in stage 4 compared to stage 2 (p < 0.001). However, no significant expression differences were shown between stage 1 and other stages ( Figure 6 A).

[0151] For Paraburkholderia strains, significant expression differences were shown between the control group and oral cancer patients grouped by stage, except for stage 3. In addition, the expression level of Paraburkholderia was relatively higher in stage 2 compared to stage 3 (p < 0.01), and significant expression differences were also shown between stage 1 and stage 2, and between stage 3 and stage 4, respectively ( Figure 6 B).

[0152] For Porphyromonas strains, significant expression differences were shown between the control group and oral cancer patients grouped by stage, except for stage 3. In addition, the expression level was relatively higher in stage 1 compared to stage 3 (p < 0.01) ( Figure 6 C).

[0153] For Capnocytophaga strains, a relatively higher expression ratio was shown in stage 2 compared to the control group between the control group and oral cancer patients grouped by stage (p < 0.01). In addition, a relatively lower expression was shown in stage 3 compared to stage 1 (p < 0.01) ( Figure 6 D).

[0154] For Sphingomonas strains, significant expression differences were presented between the control group and the oral cancer patients grouped by stage. In addition, the expression level of Sphingomonas was relatively lower in stage 4 compared to stage 2 (p<0.001), which can prove that Sphingomonas is effective in the diagnosis of early-stage oral cancer( Figure 6 E).

[0155] In addition, to evaluate the performance of the five oral microbiomes mentioned above as early oral cancer diagnostic biomarkers, receiver operating characteristics (ROC) curve analysis was performed.

[0156] As a result, as Figure 7 shown, the area under the curve (AUC) of the five oral microbiomes for late-stage oral cancer was 0.794, while the area under the curve (AUC) for early-stage oral cancer was 0.834, which can confirm that they show higher performance especially in the diagnosis of early-stage oral cancer.

[0157] 4.2. Orthology

[0158] Orthologs are genes that have evolved from the same ancestor through speciation while retaining their original functions. For a gene with a certain function in any species, it has the same function in other species.

[0159] To identify orthology-based biomarkers for early oral cancer diagnosis, orthologs that showed significant differences between oral cancer patients and healthy controls were analyzed in the early and late steps of oral cancer. Specifically, expression differences were observed between the control group and oral cancer patients in stages 1 to 4 divided according to oral cancer staging.

[0160] As a result, it was confirmed that among two orthologs, the expression levels of acyl-CoA dehydrogenase were higher in the first and second stages compared to the control group, while significant differences in dihydrofolate reductase were observed in the first to fourth stages, especially lower expression levels in the first and second stages compared to the control group. In addition, the level of dihydrofolate reductase was significantly lower in the second stage compared to the third stage (p<0.001). Thus, it was confirmed that (acyl-CoA dehydrogenase (K06445) and dihydrofolate reductase (K18590)) have the potential to be biomarkers for differentiating early-stage oral cancer and late-stage oral cancer ( Figure 8 ).

[0161] From the above description, those skilled in the art to which the present invention pertains should be able to understand that the present invention can be implemented in other specific forms without changing its technical idea or essential features. In this regard, the embodiments described above should be understood in all respects as exemplary rather than restrictive. The scope of the present invention is not limited to the above detailed description, and it should be interpreted that the meaning and scope of the appended claims and all changes or variations derived from their equivalent concepts are included within the scope of the present invention.

Claims

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

2. The biomarker composition for oral cancer diagnosis according to claim 1, It further additionally includes at least one microorganism selected from the group consisting of Granulicatella, Lautropia, Alloprevotella, Fusobacterium, Haemophilus, Veillonella, Fusobacterium nucleatum species, Granulicatella adiacens species, Haemophilus parainfluenzae species, KV831974_s species, Lautropia mirabilis species, Neisseria meningitidis species, Neisseria subflava species, PAC001345_s species, Porphyromonas pastoris species, Prevotella histicola species, Prevotella nanceiensis species, Prevotella salivae species, Prevotella_uc species, Streptococcus salivarius species, Streptococcus sanguinis species, Veillonella dispar species, and Veillonella parvula species.

3. The biomarker composition for oral cancer diagnosis according to claim 1, It further additionally includes at least one microorganism selected from the genus Dialister, the genus Mogibacterium, the genus Parvimonas, the genus Mannheimia, the genus Pasteurellaceae_uc, the genus Saccharimonas, the species Actinomyces odontolyticus, the species Alloprevotella tannerae, the species Capnocytophaga sputigena, the species Dialister pneumosintes, the species Neisseria elongate, the species Prevotella baroniae, and the species Streptococcus constellatus.

4. The biomarker composition for oral cancer diagnosis according to claim 1, The composition includes a preparation for detecting the microorganism.

5. The biomarker composition for oral cancer diagnosis according to claim 1, It further additionally includes at least one microorganism selected from the genus Howardella, the genus Paraburkholderia, and the genus Sphingomonas.

6. The biomarker composition for oral cancer diagnosis according to claim 5, As a biomarker composition for oral cancer diagnosis including a preparation for detecting at least one microorganism selected from the genus Howardella, the genus Paraburkholderia, the genus Porphyromonas, the genus Capnocytophaga, and the genus Sphingomonas, the oral cancer is in the early stage.

7. The biomarker composition for oral cancer diagnosis according to claim 1, As a biomarker composition for oral cancer diagnosis in which a preparation for confirming the expression level of a gene encoding at least one of acyl-CoA (CoA) dehydrogenase and dihydrofolate reductase is additionally included in the biomarker composition, the oral cancer is in the early stage.

8. A kit for oral cancer diagnosis, It includes the biomarker composition for oral cancer diagnosis according to claim 1.

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

10. An information providing method for diagnosing oral cancer, comprising: (a) analyzing the abundance of at least one microorganism among Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum species, Prevotella melaninogenica species, Prevotella pallens species, and Streptococcus pneumoniae species in a biological sample; and (b) comparing the abundance of the microorganism analyzed in step (a) with the abundance of the microorganism in a control group sample.

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

12. The information providing method for diagnosing oral cancer according to claim 10, wherein the abundance of the microorganism is confirmed by extracting deoxyribonucleic acid (DNA) from the biological sample.

13. The information providing method for diagnosing oral cancer according to claim 10, wherein in step (a), it further comprises: Step of analyzing the abundance of at least one additional microorganism among Granulicatella, Lautropia, Fusobacterium nucleatum species, Granulicatella adiacens species, Alloprevotella, Fusobacterium, Haemophilus, Veillonella, Haemophilus parainfluenzae species, KV831974_s species, Lautropia mirabilis species, Neisseria 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, Streptococcus sanguinis species, Veillonella dispar species, and Veillonella parvula species in the sample.

14. The method for providing information for diagnosing oral cancer according to claim 10, In the step (a), further comprising: A step of analyzing the abundance of at least one additional microorganism selected from the group consisting of Dialister, Mogibacterium, Parvimonas, Actinomyces odontolyticus species, Alloprevotella tannerae species, Capnocytophaga sputigena species, Dialister pneumosintes species, Neisseria elongate species, Prevotella baroniae species, Streptococcus constellatus species, Mannheimia, Pasteurellaceae_uc, and Saccharimonas in a sample.

15. The method for providing information for diagnosing oral cancer according to claim 10, In the step (a), further comprises: A step of analyzing the abundance of at least one additional microorganism selected from the group consisting of Howardella, Paraburkholderia, and Sphingomonas in a sample.

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

17. The method for providing information for diagnosing oral cancer according to claim 10, As in the step (a), further comprises: A step of confirming the expression level of a gene encoding one or more of acyl-CoA dehydrogenase and dihydrofolate reductase; The method for providing information for diagnosing oral cancer, wherein the oral cancer is early-stage oral cancer.

18. A method for selecting a candidate drug for treating, improving, or preventing oral cancer, comprises: (a) A step of analyzing the abundance of at least one microorganism among Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum species, Prevotella melaninogenica species, Prevotella pallens species, and Streptococcus pneumoniae species in a biological sample obtained from a subject before treatment with a candidate drug for treating, ameliorating, or preventing oral cancer; (b) A step of analyzing the abundance of at least one microorganism among Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum species, Prevotella melaninogenica species, Prevotella pallens species, and Streptococcus pneumoniae species in a biological sample obtained from the subject after treatment with the candidate drug; and, (c) A step of screening the candidate drug by comparing the abundance of the microorganism analyzed in step (a) with the abundance of the microorganism analyzed in step (b).

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

20. A probiotic composition for treating, improving, or preventing oral cancer, At least one microorganism selected from the group consisting of Porphyromonas, Prevotella, Fusobacterium periodonticum, Prevotella melaninogenica, Prevotella pallens, Alloprevotella, Fusobacterium, Haemophilus, Veillonella, Haemophilus parainfluenzae, KV831974_s, Lautropia mirabilis, Neisseria meningitidis, Neisseria subflava, PAC001345_s, Porphyromonas pasteuri, Prevotella histicola, Prevotella nanceiensis, Prevotella salivae, Prevotella_uc, Streptococcus salivarius, Veillonella dispar, Veillonella parvula, Mannheimia, Pasteurellaceae_uc, and Saccharimonas.

21. A method for diagnosing oral cancer, comprising: (a) A step of analyzing the abundance of at least one microorganism among Actinomyces, Capnocytophaga, Porphyromonas, Prevotella, Streptococcus, Fusobacterium periodonticum species, Prevotella melaninogenica species, Prevotella pallens species, and Streptococcus pneumoniae species in a biological sample; And, (b) A step of comparing the abundance of the microorganism analyzed in the step (a) with the abundance of the microorganism in a control group sample.

22. Use of a composition according to any one of claims 1 to 7, For diagnosing oral cancer.

23. A method for treating, improving or preventing oral cancer, Comprising: A step of administering the composition according to claim 20 to a subject.

24. Use of a composition according to claim 20, For manufacturing a medicament for treating, improving or preventing oral cancer.

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