CN113564224A - Biomarkers for malignant transformation of submucosal fibrosis in oral cavity - Google Patents
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- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
- C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
- C12Q1/06—Quantitative determination
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- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
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Abstract
A biomarker for malignant transformation of oral submucosa fibrosis, comprising an alteration in the salivary microbiome, wherein a characteristic bacterial species in the altered salivary microbiome comprises at least one selected from the group consisting of Porphyromonas cataniae, Prevotella multisaccharivorax, Prevotella sp hmt-300, Mitsuokella sp hmt-131, and Treponema sp hmt-927, or a combination thereof.
Description
Technical Field
The application relates to a biomarker of malignant transformation, in particular to a biomarker of malignant transformation of oral submucosal fibrosis.
Background
Oral cancer is a general term for malignant tumors occurring in the oral cavity, and most of them belong to Oral Squamous Cell Carcinoma (OSCC). Oral cancer has become a global burden, particularly in southeast asia. In taiwan, death due to oral cancer is also a social economic problem because oral cancer patients are abundant in young men compared to patients with other types of cancer. Oral Submucosa Fibrosis (OSF) is an unobvious and chronic precancerous oral stromal disease that can affect various parts of the oral cavity, sometimes the pharynx, such that the mucosal, epithelial and interstitial textures become fibrotic, cytopenic, and vascular insufficiency. According to global evidence, OSF lesions are associated with an atypical high malignant transformation rate. OSF patients usually have a habit of chewing betel nuts, and have a high malignant transformation rate, an odds ratio (odds ratio) of 23.3-27.5, and faster oral cancer formation compared to healthy people. According to studies reported in india and taiwan, patients with oral squamous cell carcinoma (OSCC-OSF) with OSF background were younger. Therefore, identifying high risk patients for OSCC among OSF patients is a considerable clinical challenge.
Invasive section examination is currently the standard method for diagnosing oral cancer. However, OSF patients often have limited oral ability, are often difficult to assess orally, and currently lack non-invasive tools for effectively screening large numbers of populations, particularly those with OSF. While past correlation studies have reported biomarkers predictive of OSF carcinogenesis, most biomarkers rely on collection of invasive test samples followed by immunochemical staining or tissue microarray analysis. Studies have demonstrated that saliva is one of the sources for detecting oral cancer biomarkers, but no effective saliva biomarker has been found for high risk OSF patients. Since saliva collection is a non-invasive test sample collection method, early intervention in this high risk group can be facilitated if biomarkers are detected in the saliva of OSF patients and the occurrence of oral cancer is effectively predicted.
There is increasing evidence that microbiota dysbiosis is closely related to human disease and its development. The oral cavity provides a complex habitat for the growth of diverse bacterial groups, such as the buccal region, mucosal surfaces of the tongue and gums, and the tooth surfaces. The bacterial community in the oral cavity is very complex, consisting of over 700 bacterial species, presenting a dynamically growing appearance in a specific oral microenvironment, and the oral microbiome (oral microbiome) provides various health benefits to the human host. Although the diversity, composition and function of the oral microbiome may change with the development of oral cancer, there is no data currently available for detecting the characteristics of OSCC salivary microbiome (salivary microbiome) in the high risk OSF population. Therefore, the present application is intended to compare the salivary microbiome of male OSF patients and OSCC-OSF patients who are accustomed to chewing areca catechu comprehensively to investigate the role of the microbiome in the development of OSF canceration.
Disclosure of Invention
It is an object of the present application to compare the salivary microbiome of OSF patients and OSCC-OSF patients and observe the alteration of the salivary microbiome during the malignant transformation of OSF into OSCC-OSF.
It is another object of the present application to provide biomarkers for malignant transformation of OSF to identify patients at high risk of OSCC among OSF patients for detection and treatment of the high risk patients.
To achieve the above objects, the present application provides a biomarker for malignant transformation of oral submucosa fibrosis, comprising an alteration of salivary microbiome, wherein a characteristic strain in the altered salivary microbiome comprises at least one selected from the group consisting of Porphyromonas cataniae, Prevotella multisaccharivorax, Prevotella sp.hmt-300, Mitsuokella sp.hmt-131, and Treponema sp.hmt-927, and a combination thereof.
In one embodiment, the species of the characterizer further comprises Diarister microrophilus and/or Mollicutes sp.HMT-504.
In one embodiment, the featured species further comprises at least one selected from the group consisting of Mycoplasma faucum, Prevotella denticola, Peptostreptococcus sp.HMT-369, Prevotella sp.HMT-315, Clostridiales sp.HMT-093, Eubacterium saphenous, Catonella sp.HMT-451, Treponema sp.HMT-237, Selenomonas sputigena, Haemophilus pittmania, Prevotella baronii, Actinomyces sp.HMT-169, Absidia (SR1) sp.HMT-874, Treponema sp.HMT-270, Mollicutes sp.HMT-906, Bacteroides sp.HMT-280, Treponema sp-238, and Treponema-258, and combinations thereof.
In one embodiment, the featured species further comprises at least one selected from the group consisting of Clostridium sp.HMT-876, Corynebacterium durum, Gracilobacteria sp.HMT-871, Megasphaera sp.HMT-123, Mobilucus mulieris, Negativiridites, Peptostreptococcus coccaceae and Selenomonas sp.HMT-478, and combinations thereof.
In one embodiment, the characteristic bacterial species is associated with oral health.
In one embodiment, the characteristic bacterial species is associated with smoking status.
In one embodiment, the alteration of the salivary microbiome comprises an alteration of relative abundance.
In one embodiment, the alteration of the salivary microbiome comprises an alteration of the microbial population.
To achieve the above objects, the present application also provides a biomarker of malignant transformation of oral submucosa fibrosis, comprising an alteration of a metabolic pathway, wherein the metabolic pathway comprises at least one selected from the group consisting of synthesis of S-adenosyl-L-methionine, synthesis of norspermine, synthesis of L-ornithine, synthesis of pyrimidine deoxyribonucleotides and formaldehyde metabolism, and a combination thereof.
In one embodiment, the metabolic pathway further comprises at least one selected from the group consisting of synthesis of L-histidine, synthesis of nicotinamide adenine dinucleotide and synthesis of 1, 4-dihydroxy-6-naphthoic acid, and combinations thereof.
Brief description of the drawings
FIG. 1 shows the probability distribution of null models of microbiome assembly for OSF and OSCC-OSF patients.
FIG. 2A shows the results of principal coordinate analysis of samples of the salivary microbiome from OSF and OSCC-OSF patients.
FIG. 2B shows the results of Adonis analysis of samples of the salivary microbiome from OSF and OSCC-OSF patients.
FIG. 3 shows the ratio of core species in saliva samples from OSF and OSCC-OSF patients and their Venturi (venn) plots.
FIG. 4 shows characteristic species identified by LEfSe analysis with significant differences in relative abundance in saliva samples from OSF and OSCC-OSF patients.
FIG. 5 shows that OSF and OSCC-OSF patients identified by Kruskal-Wallis test have a significant number of different characteristic species per ml of saliva sample.
FIG. 6 shows a Venturi plot of bacterial species associated with oral health and smoking status.
FIG. 7 shows the ROC curve for the random forest algorithm.
FIG. 8A shows the structure of the interaction network between salivary flora of OSF patients.
FIG. 8B shows the network structure of interaction between salivary flora of OSCC-OSF patients.
FIG. 9 shows metabolic pathways with significant differences in abundance in saliva samples from OSF and OSCC-OSF patients.
Detailed Description
Some embodiments which embody features and advantages of the present application will be described in detail in the following description. As will be realized, the application is capable of many variations in different forms without departing from the scope of the application, and the description and drawings are to be regarded as illustrative in nature, and not as restrictive.
The main purpose of the application is to systematically analyze the correlation between the microflora of saliva of a patient with oral submucosa fibrosis accompanied by oral cancer (OSCC-OSF) and the malignant transformation of the oral cancer, and understand the members and key strains of the salivary microorganism group (salivary microorganism) in promoting or relieving the malignant transformation of precancerous lesions, so as to develop a biomarker of microflora liquid biopsy as a strategy for judging the transformation of precancerous lesions of the oral cavity into accurate medical treatment of the oral cancer.
Clinical samples were collected first, wherein the exploratory group (clinical laboratories) was a saliva sample from 52 clinical samples of the adult hospital of taiwan province, including 18 OSF patients and 34 OSCC-OSF patients, and the validation group (clinical laboratories) was a clinical sample from the curiosity hospital of taiwan province, including 10 OSF patients and 11 OSCC-OSF patients. These participating patients were males with betel nuts chewed and were not treated with antibiotics for at least one month prior to saliva sample collection and were asked to refrain from eating, drinking or using oral hygiene products for at least 1 hour prior to saliva collection. Thereafter, the salivary microbiome was analyzed by amplicon sequencing and quantitative polymerase chain reaction (qPCR, also known as real-time PCR), statistical analysis of composition and microbial biomass data, and machine learning analysis.
Ecological mechanisms, including deterministic processes (e.g., host selection, immune response, etc.) and stochastic processes (e.g., environmental factors, stress, chemical exposure, etc.), can simultaneously affect bacterial community structure. To assess their relative advantages in controlling the salivary microbiome, the present application quantified the null-model-based probability of flight using the Bray-Curtis and weighted UniFrac (weighted UniFrac) distance-air model. FIG. 1 shows the probability-dependent distribution of the null models of the microbiome assembly of OSF and OSCC-OSF patients calculated from Bray-Curtis and weighted UniFrac distance functions, respectively. As shown in the figure, in both distance measurements, all the mean random probabilities in both OSF and OSCC-OSF groups (carbohydrates) exceed 50%, indicating that the stochastic process contributes more to the salivary microbiome assembly (salivary microbiome assembly) than to the host selection, i.e., the stochastic process dominates the formation of the salivary microbiome. It is noted that the mean over time probability for the OSCC-OSF group is higher than for the OSF group, which also indicates that the random advantage may be enhanced due to oral canceration.
It was further observed whether the oral health and smoking conditions caused changes in the composition of the microbiome (microbiome composition variation). The present application compares the microbiome structures of OSF and OSCC-OSF using four distance measurements, i.e., Jaccard distance, Bray-Curtis distance, unweighted UniFrac (unweighted UniFrac) distance, and weighted UniFrac (weighted UniFrac) distance, where unweighted and weighted UniFrac distances belong to phylogenetic distance measurements (phylogenetic distance measures), and where the weighting is based on the abundance of the microorganism. FIG. 2A shows the principal coordinate analysis of samples of the salivary microbiome from OSF and OSCC-OSF patients calculated according to Jaccard, Bray-Curtis, unweighed UniFrac and weighted UniFrac distance functions, respectively, and FIG. 2B shows the Adonis analysis of samples of the salivary microbiome from OSF and OSCC-OSF patients calculated according to Jaccard, Bray-Curtis, unweighed UniFrac and weighted UniFrac distance functions, respectively. In the principal coordinate analysis, as shown in FIG. 2A, the unweighted and weighted UniFrac distances are better able to distinguish OSFs from OSCC-OSFs in the reduced-dimensional coordinate space than the Jaccard and Bray-Curtis distances. The difference between the first two dominant coordinates (44.8%) was greater with the UniFrac distance than with the Jaccard and Bray-Curtis distances (< 26.1%). A distance-based Adonis analysis was then performed to test whether changes in microbiome composition were due to differences in the characteristics of the participating patients. As all patients in this application had chewed betel nuts, betel nut factors were excluded from the analysis. As shown in fig. 2B, the results of the tests using Bray-Curtis and weighted UniFrac distance showed that patient Age (Age), Smoking status (eating), drinking status (Alcohol), or oral health status, such as periodontal disease (periodontal condition) and OSF carcinogenesis (OSF carcinogenesis), were not significantly associated with changes in microbiome composition. However, based on the results of the tests using Jaccard and unweighted UniFrac distance, it was shown that OSF carcinogenesis (OSF vs. OSCC-OSF) and smoking status significantly affected changes in microbiome composition (P < 0.05). That is, oral health and smoking conditions do result in changes in the microbiome composition.
The present application also used high-throughput sequencing (HTS) of 16S rRNA gene amplicon V3-V4 region to analyze bacterial populations in saliva samples of 18 OSF patients and 34 OSCC-OSF patients and to obtain Amplicon Sequence Variants (ASV) to study microbial diversity. In HTS, 6160 unique ASVs were detected in total from 52 samples, and from analysis of ASV distribution, it can be seen that ASVs in the salivary microbiome differ significantly from individual to individual. These ASVs, which account for about 99.9% of the total readings, can be divided into 12 phyla of bacteria, including the following major taxa (> 1%): firmicutes (35.8 + -12.8%), bacteriodes (21.9 + -10.6%), Proteobacteria (21.0 + -14.0%), Saccharomyces (TM7) (7.3 + -9.5%), Actinobacillus (6.0 + -5.5%), Fusobacteria (5.5 + -4.3%) and Spirochaetes (1.6 + -2.6%). With regard to species identification (species identification), 408 and 470 taxa (taxa) were detected in the OSF and OSCC-OSF samples, respectively, wherein 51 (12.5%) and 28 (about 6%) taxa in the OSF and OSCC-OSF samples, respectively, had a prevalence (prediction) greater than 75% in patients, and thus were considered members of the core salivary microbiome of each cohort. These core species make up the majority of the salivary microbiome and represent 64.5% and 52.7% of the mean read abundance in OSF and OSCC-OSF, respectively. FIG. 3 shows the proportion of core species in saliva samples from OSF and OSCC-OSF patients and their Venturi (Venn) plots. Of the core species, 25 species were present in both groups, and 26 and 3 species were present in the OSF and OSCC-OSF groups, respectively, with the names of the species listed below the Venturi chart. In addition, the ratio of core species to total taxa was 12.5% and 6% in the OSF and OSCC-OSF samples, respectively, and it was also shown that the ratio of core species was decreased by the carcinogenesis of OSF. Therefore, the number and abundance of core species of OSCC-OSF were reduced compared to OSF, indicating that the carcinogenesis of OSF has a dispersing effect on the microbial composition.
The present application then uses different statistical analysis methods to identify unique microorganisms in OSF and OSCC-OSF groups that are associated with oral health status. First, analysis was performed by linear discriminant analysis effect (LDA) effect size (LEfSe), and fig. 4 shows that 42 species were identified as characteristic species having significant differences in relative abundance (relative abundance) among the saliva samples of OSF and OSCC-OSF patients identified by LEfSe analysis, and that the OSF and OSCC-OSF had a difference abundance (LDA score >2) between them. Of these 3 species, Haemophilus pittmaniae, Prevotella sp.HMT-309 and Treponema sp.HMT-270 are significantly more abundant in OSCC-OSF patients (P < 0.05). The prevalence rate of pittmaniae and Prevotella sp. HMT-309 in OSCC-OSF samples (Q2 prevalence rate (25-50%)) is significantly higher than the prevalence rate in OSF samples (Q1 prevalence rate (< 25%)). The other 39 species, classified as Prevotella (9), Treponema (6), Selenomonas (3) and the other 21 genera, one for each, were significantly more abundant in OSF patients (P <0.05) and the prevalence of these flora was significantly reduced in OSCC-OSF samples. Only 6 species, Mycoplasma faucum, Prevotella denticola, Clostridium sp. HMT-093, Prevotella baroniae, Prevotella outlorum and Selenomonas sputigena, were core species (belonging to the prevalence of Q4 (> 75%) and the prevalence in OSCC-OSF samples decreased maximally (up to 41.0% + -6.4%) compared to the transition flora (prevalence < 75%).
The relative abundance after correcting the copy number of 16S rRNA is multiplied by the qPCR quantitative cell number after correcting the copy number of 16S rRNA, so that the relative abundance of components is converted into microbial load (number of salivary bacteria per milliliter), and then the characteristic strains of OSF and OSCC-OSF are identified by absolute microbial load. FIG. 5 shows that the Kruskal-Wallis test identifies characteristic species that differ significantly in the amount of OSF and OSCC-OSF patients per ml of saliva sample. The data on absolute microbial biomass showed that there were statistical differences between the two groups between the 15 genera and 23 species (P)<0.05, effect amount
Prevalence of individual species in at least one group>33%). Furthermore, these 23 species can be mapped to the LEfSe discrimination results of FIG. 4, i.e., LEBoth the fSe analysis and Kruskal-Wallis test identified the 23 species consistently as characteristic species of OSF and OSCC-OSF.
According to the aforementioned fig. 2B, in addition to the oral health status, the smoking status also significantly affects the change in the microbiome composition. The bacterial species associated with oral health and smoking status can also be further analyzed according to the Kruskal-Wallis test of absolute microbial biomass data. FIG. 6 shows the Venturi plots of the strains associated with OSF carcinogenesis and smoking status, with the names of the respective strains listed below the Venturi plots. As can be seen from FIG. 6, 28 species were specifically associated with the patients' smoking habits, and 12 species out of 8 were also associated with OSF carcinogenesis. In addition, 11 species are particularly associated with OSF carcinogenesis. Wherein, the single-star marker is the core strain of OSF, the double-star marker is the characteristic strain identified by the statistical analysis method, wherein the double-star marker comprises 5, namely Mitsuokella sp.HMT-131, Porphyromonas cataniae, Prevotella multisaccharivorax, Prevotella sp.HMT-300 and Treponema sp.HMT-927, which belong to the characteristic strains related to smoking habits and OSF canceration.
The present application further uses machine learning (machine learning) to identify OSF and OSCC-OSF characteristic species, and the adopted machine learning feature selection algorithm (machine-learning feature-selection algorithm) detects that 15 species affect the discrimination efficiency, wherein 5 species, including Porphyromonas cataniae, Prevolla multisaccharvorax, Prevolla sp HMT-300, Mitsuokella sp HMT-131 and Treponema sp HMT-927, can correspond to characteristic species identified by using statistical analysis of Lefse and Kruskal-Wallis (FIG. 4 and FIG. 5), and also correspond to characteristic species related to both smoking habit and OSF canceration (FIG. 6). Another 2 species, namely Dialister micorophilus and Mollicutes sp. HMT-504, were also detected in the LEfSe analysis (fig. 4). The remaining 8 species were detected using the machine learned feature selection algorithm but were not detected by statistical analysis methods, including Clostridium sp. HMT-876, Corynebacterium durum, Gracilobacteria sp. HMT-871, Megasphaera sp. HMT-123, Mobilucus uliris, Negativicultures (not classified), Peptostreptococcus (not classified) and Selenomonas sp. HMT-478.
In other words, various statistical analyses and machine learning methods consistently identified 5 species, including Porphyromonas cataniae, Prevotella multisaccharivorax, Prevotella sp. HMT-300, Mitsuokella sp. HMT-131, and Treponema sp. HMT-927, and all of these 5 species were associated with the oral health and smoking status of OSF patients chewing Areca catechu and could be used as biomarkers for malignant transformation of OSF. Accordingly, the present application provides biomarkers for malignant transformation of OSF comprising alterations in the salivary microbiome, wherein the signature species in the altered salivary microbiome comprises at least one selected from the group consisting of Porphyromonas cataniae, Prevotella multisaccharivorax, Prevotella sp. HMT-300, Mitsuokella sp. HMT-131, and Treponema sp. HMT-927, and combinations thereof.
In one embodiment, the signature species may further comprise Diarister microophilus and/or Mollicutes sp.HMT-504, i.e., species consistently identified by statistical analysis of Lefse and machine learning.
In one embodiment, the featured species may further comprise at least one species selected from the group consisting of Mycoplasma faucum, Prevotella denticola, Peptosteptococcaceae sp. HMT-369, Prevotella sp. HMT-315, Clostridiales sp. HMT-093, Eubacterium saphenous, Catonella sp. HMT-451, Treponema sp. HMT-237, Selenomonas sputigena, Haemophilus pittmaniae, Prevotella baroniae, Actinomyces sp. HMT-169, Absidia (SR1) sp. HMT-874, Treponema sp. HMT-270, Mollicutes sp. HMT-906, Bacteroides sp. HMT-280, Treponema sp. HMT-238, and Wallace-258, and combinations thereof.
In one embodiment, the characterized species may further comprise at least one selected from the group consisting of Clostridium sp.HMT-876, Corynebacterium durum, Gracilobacteria sp.HMT-871, Megasphaera sp.HMT-123, Mobilucus mulieri, Negativicus, Peptostreptococcus and Selenomonas sp.HMT-478, and combinations thereof, i.e., species otherwise identified by machine learning.
For 23 characteristic strains consistently identified by using LEfSe and Kruskal-Wallis statistical analysis, a relative AUC (AUC) obtained by calculating the area under a Receiver Operating Characteristic (ROC) curve according to relative abundance and absolute microbial biomass can be usedrelative) And absolute AUC (AUC)absolute) Numerical values to evaluate the effectiveness of 23 characteristic species in differentiating OSF from OSCC-OSF. In addition to cross-validation of the probe panel dataset, the evaluation was also performed using the independent saliva microbiome dataset of the validation group, the results of which are shown in table 1 below. The mean AUC for the exploration and validation cohort data sets were 0.68 and 0.56, respectively. Among them, the characterized bacterial species Treponema sp.hmt-927, which is related to both oral health and smoking status, showed the highest resolvability in the exploratory group dataset (relative AUC 0.748, absolute AUC 0.758), and had high predictive performance in the validation group dataset (relative AUC 0.709, absolute AUC 0.682). The species with the best performance in the validation cohort data set was the absscondibactia (SR1) sp.hmt-874 (relative AUC 0.755, absolute AUC 0.809), a subgroup in the candidate phylum radiation group, also identified as the characteristic species associated with oral health (fig. 6). However, there was no statistical significance between the AUC obtained from the relative abundance and absolute microbial mass for either the exploratory dataset (H0.008, P0.923) or the validation dataset (H0.088, P0.767). This observation also shows that, whatever the quantitative method used in the study (relative abundance or absolute microbial biomass), the discriminatory properties of the characteristic species give similar results.
TABLE 1
By combining the signature species with host clinical and lifestyle characteristics, machine learning analysis also achieves high resolution efficiency. The two groups of OSF and OSCC-OSF can be distinguished most effectively by using the detected strains selected by characteristics, Faith's PD and life style (current smoking habit) data to train a random-forest algorithm. FIG. 7 shows the ROC curve (5-fold cross validation) of the random forest algorithm, which is plotted on the ordinate of sensitivity and on the abscissa of false positive rate (1-specificity), the more the ROC curve deviates from the diagonal (also called chance line), the larger the area under the ROC curve, and the higher the accuracy. As shown in fig. 7, the ROC curve obtained by cross-validating the trained random forest algorithm has an average 5-fold cross-validation accuracy (mean 5-fold cross-validation accuracy) of 85.1% and an AUC of 0.88, which shows that the trained random forest algorithm has a relatively high accuracy.
To assess the inter-species interactions in saliva, the present application further performed SparCC analysis using the microbiology panel dataset for OSF and OSCC-OSF groups. FIG. 8A shows the structure of the interaction network between salivary flora of OSF patients, and FIG. 8B shows the structure of the interaction network between salivary flora of OSCC-OSF patients. As can be seen in the figure, the microbial interactions of OSF patients (7173 lines connecting 362 nodes) are much more complex than those of OSCC-OSF patients (2695 lines connecting 351 nodes) and are nearly 3 times more complex. With respect to the network formed with the characterized species as the node and its highly relevant neighbors (SparCC correlation coefficient >0.6), fig. 8A and 8B show the completely different network structure between the two salivary flora ecosystem groups. As shown in fig. 8A, the OSF group had high connectivity and the positive relationship line (solid line, SparCC correlation coefficient >0.6) was greater than the negative relationship line (dotted line, SparCC correlation coefficient < -0.6), indicating the presence of a unique flora symbiosis pattern. In addition to Eubacterium saphenus, Mycoplasma faucum, Catonella sp.HMT451, and Clostridium sp.HMT-093, there are several characterizing species, including Treponema (HMT-927, HMT-258, HMT-237, and HMT-238) and Prevotella (P.dentiola and HMT-315), which are central hubs highly connected to the positive tie lines (solid lines) in the network. Two other central species, neissemia sp. (not classified) and oribacter sp. (not classified), are connected to other species, mostly with negative relationship lines (dashed lines). These observations highlight the importance of modulating bacterial interactions in the salivary microbiome of OSF patients. In contrast, as shown in fig. 8B, the absence of the corresponding flora symbiotic pattern in the OSCC-OSF patients indicates that the network structure between the flora has substantially changed, and the canceration process makes the original closely interactive structure between the flora of OSF disappear, confirming that the salivary microbiome indeed changes with malignant transformation.
To compare the biochemical functions of OSF and OSCC-OSF salivary microbiome, the present application further used LEfSe to discriminate quantitative differences in metabolic pathways predicted by PICRUSt2 from MetaCyc databases. A total of 85.77% of the denoised sequences had high to medium quality (nearest sequenced taxon indicator (NSTI)) scores < 0.15). To ensure proper prediction quality, the picrus 2 analysis was excluded from de-noising sequences with NSTI scores >2 (0.37% of the total de-noising sequence). Although many signature species were detected in the OSF and OSCC-OSF groups, only 9 metabolic pathways were quantitatively distinguishable. FIG. 9 shows metabolic pathways with significant differences in abundance in saliva samples from OSF and OSCC-OSF patients, of which 6 are closely related to OSF, including synthesis of L-ornithine (L-ornithine), L-histidine (L-histidine), pyrimidine deoxyribonucleotide (pyridine deoxyribose), and Nicotinamide Adenine Dinucleotide (NAD), and formaldehyde oxidation and assimilation (formaldehyde oxidation and assimilation). In OSCC-OSF, there are 3 more closely related metabolic pathways, i.e., the synthesis of 1, 4-dihydroxy-6-naphthoic acid (1,4-dihydroxy-6-naphthoate, an intermediate product of vitamin K2 synthesis), S-adenosyl-L-methionine (SAM), and norspermine (norspermidine). Among them, OSCC-OSF has high S-adenosyl-L-methionine and norspermine synthesis potential, but has low L-ornithine and pyrimidine deoxyribonucleotide synthesis and formaldehyde metabolism potential. These findings indicate that the salivary microbiome plays an important role in regulating microbial metabolism during oral canceration.
These 9 metabolic pathways can also be classified as 5 classes of metabolism. The synthesis of S-adenosyl-L-methionine, the synthesis of L-ornithine and the synthesis of L-histidine belong to the synthesis of amino acids (amino acid biosynthesis). The synthesis of pyrimidine deoxyribonucleotides belongs to the synthesis of nucleosides and nucleotides. Formaldehyde oxidation and assimilation belong to the utilization and assimilation of C1 compounds (C1 compound and assimilation). The synthesis of 1, 4-dihydroxy-6-naphthoic acid and the synthesis of NAD belong to cofactors, prosthetic group electron carriers and vitamin synthesis (cofactors). The synthesis of norspermine belongs to the synthesis of amines and polyamines (amine and polyamine biosyntheses). The mechanism of salivary microorganisms in regulating metabolic pathways remains to be further studied.
In summary, since changes in salivary microbiome are associated with Oral Squamous Cell Carcinoma (OSCC), and most of OSCC are caused by precancerous lesions, the malignant transformation rate of patients with Oral Submucosa Fibrosis (OSF) is high, but it is unclear how salivary microbiome changes with malignant transformation. Thus, the present application compares the salivary microbiome of patients with oral submucosal fibrosis accompanied by oral cancer (OSCC-OSF) and OSF patients, and observes changes in the salivary microbiome during malignant transformation of OSF into OSCC-OSF. According to the result of high-throughput sequencing on the V3-V4 region of the 16S rRNA gene of bacteria, the oral health condition and the smoking condition obviously influence the change of the salivary microbiome system composition, so that the strain abundance and the phylogenetic diversity are obviously reduced (P < 0.05). The malignant transformation of OSF increases the influence of randomness on the change of the composition of salivary microbiome, and completely changes the symbiotic network mode of strains. Various statistical analyses and machine learning methods consistently identified 5 species that varied significantly between OSF and OSCC-OSF, including Porphyromonas cataniae, Prevotella multisaccharivorax, Prevotella sp.HMT-300, Mitsuokella sp.HMT-131, and Treponema sp.HMT-927, and all 5 species were associated with oral health and smoking status. Therefore, these 5 species can be used as biomarkers for the malignant transformation of OSF, and these biomarkers are highly accurate for predicting the occurrence of oral cancer, whether in the exploration group data set or the validation group data set. In addition, OSCC-OSF has high S-adenosyl-L-methionine and norspermine synthesis potential, but low L-ornithine and pyrimidine deoxyribonucleotide synthesis and formaldehyde metabolism potential, which also indicates that the salivary microbiome plays an important role in regulating microbial metabolism during oral canceration. Thus, the present application provides biomarkers of malignant transformation of OSF, which are useful for the study of cancer development and treatment, and can identify patients at high risk of OSCC among OSF patients, thereby detecting and treating the patients at high risk.
While the present invention has been described in detail by the above embodiments and may be modified in various ways by anyone skilled in the art, all without departing from the scope of protection as claimed in the appended claims.
Claims (10)
1. A biomarker for malignant transformation of oral submucosa fibrosis, comprising an alteration in the salivary microbiome, wherein a characteristic bacterial species in the altered salivary microbiome comprises at least one selected from the group consisting of Porphyromonas cataniae, Prevotella multisaccharivorax, Prevotella sp hmt-300, Mitsuokella sp hmt-131, and Treponema sp hmt-927, or a combination thereof.
2. The biomarker of claim 1, wherein the signature species further comprises Dialister microrophilus and/or Mollicutes sp.
3. The biomarker of claim 1, wherein the characterizer further comprises at least one selected from the group consisting of Mycoplasma faucum, Prevotella denticola, Peptostreptococcus sp HMT-369, Prevotella sp HMT-315, Clostridiales sp HMT-093, Eubacterium saphenous, Catonella sp HMT-451, Treponema sp HMT-237, Selenomonas utogena, Haemophilus pittmania, Prevotella baroniae, Actinomyces sp HMT-169, Absidia diabacteria (SR1) sp HMT-874, Treponema sp HMT-270, Mollitessp HMT-906, Bacoiides sp HMT-280, Treponema sp-238, and Treponema sp-258.
4. The biomarker of claim 1, wherein the signature species further comprises at least one selected from the group consisting of clostridium sp.hmt-876, Corynebacterium durum, gracilibacter sp.hmt-871, Megasphaera sp.hmt-123, mobilus mulieris, negaviculis, Peptostreptococcaceae, and selenins sp.hmt-478, or a combination thereof.
5. The biomarker of claim 1, wherein the signature species is associated with oral health.
6. The biomarker of claim 1, wherein the signature species is associated with smoking status.
7. The biomarker of claim 1, wherein the alteration in the salivary microbiome comprises an alteration in relative abundance.
8. The biomarker of claim 1, wherein the alteration of the salivary microbiome comprises an alteration of microbial biomass.
9. A biomarker for malignant transformation of oral submucosa fibrosis, comprising an alteration in a metabolic pathway, wherein the metabolic pathway comprises at least one selected from the group consisting of synthesis of S-adenosyl-L-methionine, synthesis of norspermine, synthesis of L-ornithine, synthesis of pyrimidine deoxyribonucleotides, and formaldehyde metabolism, or a combination thereof.
10. The biomarker of claim 9, wherein the metabolic pathway further comprises at least one or a combination selected from the group consisting of synthesis of L-histidine, synthesis of nicotinamide adenine dinucleotide, and synthesis of 1, 4-dihydroxy-6-naphthoic acid.
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