TWI825404B - Use of signature species as biomarker of malignant transformation for oral submucous fibrosis - Google Patents

Abstract

A biomarker of malignant transformation for oral submucous fibrosis includes an alteration of salivary microbiome. A signature species in the altered salivary microbiome includes at least one selected from the group consisting of Porphyromonas catoniae, Prevotella multisaccharivorax, Prevotella sp. HMT-300, Mitsuokella sp. HMT-131, and Treponema sp. HMT-927, and the combination thereof.

Description

Use of characteristic bacterial species as biomarkers for progression of oral submucosal fibrosis

This case is about a biomarker for malignant progression, especially a biomarker for malignant progression of oral submucosal fibrosis.

Oral cancer is a general term for malignant tumors that occur in the oral cavity, and most of them are oral squamous cell carcinoma (OSCC). Oral cancer has become a global burden, especially in Southeast Asia. Deaths caused by oral cancer are also a socioeconomic problem in Taiwan because oral cancer patients are more likely to be younger men than patients with other types of cancer. Oral submucosal fibrosis (OSF) is an insignificant and chronic precancerous oral stromal disease (oral stromal premalignant disorder) that can affect all parts of the oral cavity and sometimes the pharynx, causing mucosal , the texture of the epithelium and stroma becomes fibrotic, hypocellular, and hypovascular. According to global evidence, OSF lesions are associated with an atypically high rate of malignant transformation. OSF patients usually have the habit of chewing betel nut. Compared with healthy people, OSF patients have a higher rate of malignant transformation, with an odds ratio of 23.3-27.5, and oral cancer forms faster. According to research reports from India and Taiwan, patients with oral squamous cell carcinoma (OSCC-OSF) with OSF background are younger. Therefore, identifying patients with high risk of OSCC among OSF patients is an important clinical challenge.

Invasive biopsy is the current standard method for diagnosing oral cancer. However, patients with OSF often have limited mouth opening abilities and are often difficult to perform oral assessment. Currently, there is a lack of methods for effectively screening large populations. (especially those suffering from OSF). Although relevant studies in the past have reported that OSF can be used as biomarkers to predict carcinogenesis, most biomarkers first relied on the collection of invasive specimens, followed by immunochemical staining or tissue microarray analysis. Studies have confirmed that saliva is one of the sources for detecting oral cancer biomarkers, but for high-risk OSF patients, effective salivary biomarkers have not yet been found. Since saliva collection is a non-invasive specimen collection method, if biomarkers are detected in the saliva of OSF patients and can effectively predict the occurrence of oral cancer, it can promote early intervention for this high-risk group.

Increasing evidence shows that microbiota dysbiosis is closely related to human diseases and their development. The oral cavity provides complex habitats for the growth of diverse bacterial populations, such as the buccal area, mucosal surfaces of the tongue and gums, and tooth surfaces. The bacterial community in the oral cavity is very complex, consisting of more than 700 bacterial species, showing dynamic growth in a specific oral microenvironment, and the oral microbiome provides various health benefits to the human host. Although the diversity, composition, and function of the oral microbiome change with the development of oral cancer, there are currently no data characterizing the OSCC salivary microbiome in high-risk OSF populations. Therefore, this case intends to conduct a comprehensive comparison of the salivary microorganisms of male OSF patients with betel nut chewing habits and OSCC-OSF patients to explore the role of microorganisms in the development of OSF cancer.

One of the purposes of this case is to compare the salivary microorganisms of OSF patients and OSCC-OSF patients, and observe the changes in salivary microorganisms during the malignant transformation of OSF into OSCC-OSF.

Another purpose of this case is to provide biomarkers of malignant transformation of OSF to identify high-risk OSCC patients among OSF patients, and then detect and treat high-risk patients.

To achieve the above purpose, this case provides a biomarker for the progression of oral submucosal fibrosis, which includes a change in salivary microorganisms, wherein a characteristic bacterial species in the changed salivary microorganisms includes Porphyromonas catoniae , Prevotella multisaccharivorax , At least one of the group consisting of Prevotella sp. HMT-300, Mitsuokella sp. HMT-131, and Treponema sp. HMT-927, and combinations thereof.

In one embodiment, the characteristic strains further include Dialister micraerophilus and/or Mollicutes sp. HMT-504.

In one embodiment, the characteristic bacterial species further include Mycoplasma faucium , Prevotella denticola , Peptostreptococcaceae sp.HMT-369, Prevotella sp.HMT-315, Clostridiales sp.HMT-093, Eubacterium saphenus , Catonella sp.HMT- 451. Treponema sp.HMT-237, Selenomonas sputigena , Haemophilus pittmaniae , Prevotella baroniae , Actinomyces sp.HMT-169, Absconditabacteria(SR1)sp.HMT-874, Treponema sp.HMT-270, Mollicutes sp.HMT-906, Bacteroidetes At least one of the group consisting of sp.HMT-280, Treponema sp.HMT-238, and Treponema sp.HMT-258.

In one embodiment, the characteristic bacterial species further include Clostridiales sp. HMT-876, Corynebacterium durum , Gracilibacteria sp. HMT-871, Megasphaera sp. HMT-123, Mobiluncus mulieris , Negativicutes , Peptostreptococcaceae , and Selenomonas sp. At least one of the groups composed of HMT-478 and combinations thereof.

In one embodiment, the characteristic bacterial species are related to oral health conditions.

In one embodiment, the characteristic bacterial species are related to smoking status.

In one embodiment, changes in salivary microorganisms include changes in relative abundance.

In one embodiment, changes in salivary microorganisms include changes in microbial biomass.

To achieve the above purpose, this case also provides a biomarker for the progression of oral submucosal fibrosis, which includes changes in a metabolic pathway. The metabolic pathway includes synthesis and removal of S-adenosyl-L-methionine. At least one of the group consisting of mesospermine synthesis, L-ornithine synthesis, pyrimidine deoxyribonucleotide synthesis, and formaldehyde metabolism, and combinations thereof.

In one embodiment, the metabolic pathway further includes a pathway selected from the group consisting of synthesis of L-histidine, synthesis of nicotinamide adenine dinucleotide, and synthesis of 1,4-dihydroxy-6-naphthoic acid. At least one of the groups and their combinations.

Figure 1 shows the null model random rate distribution of microbial assembly in OSF and OSCC-OSF patients.

Figure 2A shows the principal coordinate analysis results of salivary microbiome samples from OSF and OSCC-OSF patients.

Figure 2B shows the Adonis analysis results of salivary microbiome samples from OSF and OSCC-OSF patients.

Figure 3 shows the proportion of core bacterial species in saliva samples of OSF and OSCC-OSF patients and their text diagram.

Figure 4 shows the characteristic bacterial species with significant differences in relative abundance in saliva samples from OSF and OSCC-OSF patients identified by LEfSe analysis.

Figure 5 shows that the Kruskal-Wallis test identified significantly different numbers of characteristic bacterial species per milliliter of saliva samples from OSF and OSCC-OSF patients.

Figure 6 shows a text diagram of bacterial species associated with oral health status and smoking status.

Figure 7 shows the ROC curve of the random forest algorithm.

Figure 8A shows the interaction network structure between salivary flora of OSF patients.

Figure 8B shows the interaction network structure between salivary flora of OSCC-OSF patients.

Figure 9 shows metabolic pathways with significantly different abundances in saliva samples from OSF and OSCC-OSF patients.

Some embodiments embodying the features and advantages of the present invention will be described in detail in the following description. It should be understood that this case can have various changes in different aspects without departing from the scope of this case, and the descriptions and drawings are essentially for illustrative purposes rather than limiting this case.

The main purpose of this case is to systematically analyze the correlation between the salivary microbiota of patients with oral submucosal fibrosis and oral cancer (OSCC-OSF) and the malignant transformation of oral cancer, and to understand the role of salivary microbiome in promoting or alleviating cancer. Members and key bacterial species involved in the malignant transformation of precancerous lesions can be used to develop biomarkers in microbial-phase liquid biopsy as a strategy to determine the transformation of oral cancer precancerous lesions into oral cancer precision medicine.

First, clinical samples were collected. The exploratory cohorts were 52 clinical samples from National Cheng Kung University Hospital, including saliva samples from 18 OSF patients and 34 OSCC-OSF patients. The validation cohorts were from Chi Mei Hospital. The clinical samples included saliva samples from 10 OSF patients and 11 OSCC-OSF patients. The participating patients were all male betel quid chewers who had not received antibiotics for at least one month before saliva sample collection and were asked not to eat, drink, or use oral hygiene products for at least 1 hour before saliva collection. Then, the microorganisms in the saliva were analyzed through amplicon sequencing and quantitative polymerase chain reaction (qPCR, also known as real-time PCR), and statistical analysis of composition and microbial load data, as well as machine learning analysis, were performed.

Ecological mechanisms, including deterministic processes (such as host selection, immune response, etc.) and stochastic processes (such as environmental factors, stress, chemical exposure, etc.), will simultaneously affect bacterial community structure. To evaluate their relative advantages in controlling salivary microorganisms, null-model-based stochasticity was quantified using Bray-Curtis and Weighted UniFrac distances. Figure 1 shows the null model random rate distribution of microbial assembly in OSF and OSCC-OSF patients calculated according to Bray-Curtis and weighted UniFrac distance functions, respectively. As shown in the figure, in both distance measurement methods, all average random rates in the OSF and OSCC-OSF cohorts exceed 50%, indicating that random processes are important for salivary microbiome assembly. There is a greater contribution than host selection, that is, stochastic processes dominate the formation of salivary microorganisms. It is worth noting that the average randomization rate of the OSCC-OSF group was higher than that of the OSF group, which also means that the randomization advantage may be enhanced due to oral canceration.

To further observe whether oral health status and smoking status will cause microbiome composition variation. This case uses four distance measurement methods to compare the microbial structures of OSF and OSCC-OSF. The four distance measurement methods are 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 measures, and the weighting here is weighted according to the richness of microorganisms. Figure 2A shows the principal coordinate analysis results of salivary microbial samples from OSF and OSCC-OSF patients calculated according to Jaccard, Bray-Curtis, unweighted UniFrac, and weighted UniFrac distance functions, respectively. Figure 2B shows the results of principal coordinate analysis of salivary microbial samples from OSF and OSCC-OSF patients based on Jaccard, Bray-Curtis, and weighted UniFrac distance functions. Adonis analysis results of salivary microbiome samples from OSF and OSCC-OSF patients calculated by Curtis, unweighted UniFrac, and weighted UniFrac distance functions. In principal coordinate analysis, as shown in Figure 2A, unweighted and weighted UniFrac distances are better than Jaccard and Bray-Curti distances in distinguishing OSF and OSCC-OSF in the reduced-dimensional coordinate space. When using the UniFrac distance, the difference between the first two principal coordinates (44.8%) is greater than the difference when using the Jaccard and Bray-Curtis distances (<26.1%). A distance-based Adnois analysis was then performed to test whether changes in microbial composition were attributable to differences in participating patient characteristics. Since all patients in this case chewed betel nut, the betel nut factor was excluded from the analysis. As shown in Figure 2B, the test results using Bray-Curtis and weighted UniFrac distance show the patient's age (Age), smoking status (Smoking), drinking status (Alcohol), or oral health status, such as periodontal disease (periodontal). condition) and OSF carcinogenesis, are not significantly related to changes in microbial composition. However, according to the test results using Jaccard and unweighted UniFrac distance, it was shown that OSF canceration (OSF vs. OSCC-OSF) and smoking status significantly affected the changes in microbial composition (P<0.05). In other words, oral health and smoking status do cause changes in the composition of the microbiome.

In this case, tag-encoded high-throughput sequencing (HTS) of the V3-V4 region of the 16S rRNA gene amplicon was used to analyze the bacterial groups in the saliva samples of 18 OSF patients and 34 OSCC-OSF patients. And obtain amplicon sequence variants (ASV) to study microbial diversity. In HTS, a total of 6160 unique ASVs were detected from 52 samples, and analysis of ASV distribution revealed that ASVs in salivary microbiota differ significantly between individuals. These ASVs, which accounted for approximately 99.9% of the total reads, could be divided into 12 bacterial phyla, including the following major taxa (>1%): Firmicutes (35.8±12.8%), Bacteroidetes (21.9±10.6%), Proteobacteria (21.0±14.0% ), Saccharibacteria (TM7) (7.3±9.5%), Actinobacteria (6.0±5.5%), Fusobacteria (5.5±4.3%), and Spirochaetes (1.6±2.6%). Regarding species annotation, 408 and 470 taxa were detected in OSF and OSCC-OSF samples respectively, of which 51 (12.5%) were detected in OSF and OSCC-OSF samples respectively. and 28 (approximately 6%) taxa had a prevalence greater than 75% in patients and were therefore considered members of the core salivary microbiota for each group. These core species accounted for the majority of salivary microbiota and accounted for 64.5% and 52.7% of the average read richness in OSF and OSCC-OSF, respectively. Figure 3 shows the proportion of core bacterial species in saliva samples of OSF and OSCC-OSF patients and their Venn diagram. Among the core strains, 25 strains exist in both groups, and another 26 and 3 strains exist in the OSF and OSCC-OSF groups respectively. The names of each strain are listed below the text diagram. In addition, the proportion of core bacterial species in the total taxa in OSF and OSCC-OSF samples was 12.5% and 6% respectively, which also shows that canceration of OSF will reduce the proportion of core bacterial species. Therefore, compared with OSF, the number and richness of core bacterial species in OSCC-OSF are reduced, indicating that OSF canceration has a dispersing effect on the microbial composition.

We then used different statistical analysis methods to identify unique microorganisms associated with oral health status in the OSF and OSCC-OSF cohorts. First, linear discriminant analysis (LDA) effect size (LEfSe) was used to analyze. Figure 4 shows the relative abundance in saliva samples of OSF and OSCC-OSF patients identified by LEfSe analysis. Significantly different characteristic bacterial species, a total of 42 bacterial species were identified with differential abundance (LDA score > 2) between OSF and OSCC-OSF. Among them, three bacterial species, namely Haemophilus pittmaniae , Prevotella sp.HMT-309, and Treponema sp.HMT-270, were significantly more abundant in OSCC-OSF patients (P<0.05). The prevalence of H. pittmaniae and Prevotella sp. HMT-309 in OSCC-OSF samples (genus Q2 prevalence (25~50%)) is significantly higher than that in OSF samples (genus Q1 prevalence (<25%)) . Another 39 bacterial species, belonging to Prevotella (9 species), Treponema (6 species), Selenomonas (3 species) and 21 other genera (one species for each genus), were significantly more abundant in OSF patients (P<0.05). And the prevalence of these bacterial groups was significantly reduced in OSCC-OSF samples. Only 6 species, namely Mycoplasma faucium , Prevotella denticola , Clostridiales sp.HMT-093, Prevotella baroniae , Prevotella oulorum , and Selenomonas sputigena , are core species (genus Q4 prevalence (>75%)), and compared with transition Bacterial flora (prevalence rate <75%), its prevalence rate decreased the most in OSCC-OSF samples (up to 41.0%±6.4%).

>0.06，在至少一組的個別菌種盛行率>33%)。又，這23個菌種可對應到第4圖的LEfSe鑑別結果，亦即LEfSe分析及Kruskal-Wallis檢驗這兩種統計分析方法都一致地鑑別出這23個菌種為OSF及OSCC-OSF的特徵菌種。 In this case, the relative abundance of components corrected for 16S rRNA sets was multiplied by the qPCR quantitative cell number corrected for 16S rRNA sets to convert the relative abundance of components into microbial load, that is, the number of bacteria per milliliter of saliva. The characteristic bacterial species of OSF and OSCC-OSF were identified based on the absolute microbial count. Figure 5 shows that the Kruskal-Wallis test identified significantly different numbers of characteristic bacterial species per milliliter of saliva samples from OSF and OSCC-OSF patients. According to the absolute microbial biomass data, the numbers of 15 bacterial genera and 23 bacterial species were statistically different between the two groups (P<0.05, effect size

>0.06, prevalence of individual strains in at least one group >33%). In addition, these 23 bacterial species can correspond to the LEfSe identification results in Figure 4, that is, both statistical analysis methods, LEfSe analysis and Kruskal-Wallis test, consistently identify these 23 bacterial species as OSF and OSCC-OSF. Characteristic strains.

According to the aforementioned Figure 2B, in addition to oral health status, smoking status also significantly affects changes in microbial composition. Based on the Kruskal-Wallis test of absolute microbial load data, bacterial species related to oral health status and smoking status can also be further analyzed. Figure 6 shows a Venn diagram of bacterial species related to OSF canceration and smoking status. The names of relevant bacterial species are listed below the Venn diagram. As can be seen from Figure 6, 28 bacterial species are specifically related to the patient's smoking habits, and 12 species from 8 bacterial genera are also related to OSF canceration. In addition, 11 bacterial species are specifically related to OSF canceration. Among them, those marked with a single asterisk are the core strains of OSF, and those marked with a double asterisk are the characteristic strains identified by the aforementioned statistical analysis method. Among them, there are 5 marked with double asterisks, namely Mitsuokella sp.HMT-131, Porphyromonos catoniae , Prevotella multisaccharivorax , Prevotella sp.HMT-300, and Treponema sp.HMT-927 are all characteristic bacterial species related to smoking habits and OSF canceration.

This case further used machine learning to identify the characteristic strains of OSF and OSCC-OSF, and the machine-learning feature-selection algorithm used detected 15 strains that affected the discrimination. Efficiency, among which 5 strains, including Porphyromonas catoniae , Prevotella multisaccharivorax , Prevotella sp.HMT-300, Mitsuokella sp.HMT-131, and Treponema sp.HMT-927, can correspond to the identification using LEfSe and Kruskal-Wallis statistical analysis The characteristic bacterial species (Figures 4 and 5) also correspond to the characteristic bacterial species related to both smoking habits and OSF canceration (Figure 6). Two other bacterial species, namely Dialister micraerophilus and Mollicutes sp. HMT-504, were also detected in the LEfSe analysis (Figure 4). The remaining 8 bacterial species were detected using the machine learning feature selection algorithm but were not detected by the statistical analysis method, including Clostridiales sp.HMT-876, Corynebacterium durum , Gracilibacteria sp.HMT-871, Megasphaera sp.HMT-123, Mobiluncus mulieris , Negativicutes (unclassified), Peptostreptococcaceae (unclassified), and Selenomonas sp. HMT-478.

In other words, multiple statistical analyzes and machine learning methods consistently identified 5 bacterial species, including Porphyromonas catoniae , Prevotella multisaccharivorax , Prevotella sp.HMT-300, Mitsuokella sp.HMT-131, and Treponema sp.HMT-927, and these 5 species were Each bacterial species is related to the oral health status and smoking status of OSF patients who chew betel nut, and can be used as a biomarker for the progression of OSF. Therefore, this case provides biomarkers for the malignant transformation of OSF, including changes in salivary microorganisms, wherein the characteristic bacterial species in the changed salivary microorganisms include selected from the group consisting of Porphyromonas catoniae , Prevotella multisaccharivorax , Prevotella sp.HMT-300, Mitsuokella sp. At least one of the group consisting of HMT-131 and Treponema sp.HMT-927 and combinations thereof.

In one embodiment, the characteristic bacterial species may further include Dialister micraerophilus and/or Mollicutes sp. HMT-504, that is, the bacterial species consistently identified by LEfSe statistical analysis and machine learning methods.

In one embodiment, the characteristic bacterial species may further include Mycoplasma faucium , Prevotella denticola , Peptostreptococcaceae sp.HMT-369, Prevotella sp.HMT-315, Clostridiales sp.HMT-093, Eubacterium saphenus , Catonella sp. HMT-451, Treponema sp.HMT-237, Selenomonas sputigena , Haemophilus pittmaniae , Prevotella baroniae , Actinomyces sp.HMT-169, Absconditabacteria (SR1)sp.HMT-874, Treponema sp.HMT-270, Mollicutes sp.HMT-906 , Bacteroidetes sp.HMT-280, Treponema sp.HMT-238, and Treponema sp.HMT-258, and at least one of the groups and their combinations, that is, bacteria consistently identified by LEfSe and Kruskal-Wallis statistical analysis species.

In one embodiment, the characteristic bacterial species may further include Clostridiales sp. HMT-876, Corynebacterium durum , Gracilibacteria sp. HMT-871, Megasphaera sp. HMT-123, Mobiluncus mulieris , Negativicutes , Peptostreptococcaceae , and Selenomonas. At least one of the groups composed of sp.HMT-478 and their combinations, that is, the bacterial species additionally identified by the machine learning method.

For the 23 characteristic bacterial species consistently identified using LEfSe and Kruskal-Wallis statistical analysis, the area under the receiver operating characteristic (ROC) curve based on relative abundance and absolute microbial load can be calculated. ( _area under the curve, referred to as AUC) to evaluate the effectiveness _of 23 characteristic strains in distinguishing OSF and OSCC-OSF. In addition to cross-validation on the exploration group data set, the independent saliva microbiome data set of the validation group was also used for evaluation. The results are shown in Table 1 below. The average AUCs of the exploration and validation group data sets were 0.68 and 0.56 respectively. Among them, the characteristic strain Treponema sp.HMT-927, which is related to both oral health status and smoking status, showed the highest discriminability in the exploration group data set (relative AUC=0.748, absolute AUC=0.758), and in the validation group data The concentration has high prediction performance (relative AUC=0.709, absolute AUC=0.682). The best performing strain in the validation group data set is Absconditabacteria (SR1)sp.HMT-874 (relative AUC=0.755, absolute AUC=0.809), which is a classification in the candidate phyla radiation group group, are also identified as characteristic bacterial species related to oral health conditions (Figure 6). However, there was no statistical difference between the AUCs derived from relative richness and absolute microbial biomass, either for the exploration group data set ( H =0.008, P =0.923) or the validation group data set ( H =0.088, P =0.767). Statistical significance. This observation also shows that regardless of the quantitative method used in the study (relative richness or absolute microbial biomass), similar results were obtained for the resolvability of characteristic bacterial species.

By combining characteristic bacterial species with host clinical and lifestyle characteristics, machine learning analysis achieves high resolution efficiency. Training a random forest algorithm (random-forest algorithm) with data on bacterial species, Faith's phylogenetic diversity (Faith's PD) and lifestyle (current smoking habits) detected by feature selection can most effectively distinguish OSF and OSCC- OSF two groups. Figure 7 shows the ROC curve (5-fold cross-validation) of the random forest algorithm. It takes the sensitivity as the ordinate and the false positive rate (1-specificity) as the abscissa. The more the ROC curve deviates from the diagonal (also known as Opportunity line), the larger the area under the ROC curve, which also means the higher the accuracy. As shown in Figure 7, the ROC curve obtained by cross-validating the trained random forest algorithm has an average 5-fold cross-validation accuracy of 85.1% and an AUC of 0.88 , showing that the trained random forest algorithm has quite high accuracy.

In order to evaluate the interaction between bacterial species in saliva, this case further conducted SparCC analysis using the microbial data sets of the OSF and OSCC-OSF groups. Figure 8A shows the interaction network structure between the salivary flora of OSF patients, and Figure 8B shows the interaction network structure between the salivary flora of OSCC-OSF patients. As can be seen from 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 the complexity is nearly 3 times greater. Regarding the network formed with characteristic bacterial species as nodes and their highly correlated neighbors (SparCC correlation coefficient >0.6), Figures 8A and 8B show completely different network structures between the two salivary microbiota ecosystem groups. As shown in Figure 8A, the OSF group has higher connectivity, and there are more positive relationship lines (solid line, SparCC correlation coefficient >0.6) than negative relationship lines (dashed line, SparCC correlation coefficient <-0.6), indicating the existence of unique Microbiota symbiosis model. In addition to Eubacterium saphenus , Mycoplasma faucium , Catonella sp.HMT451, and Clostridiales sp.HMT-093, there are several characteristic strains, including Treponema (HMT-927, HMT-258, HMT-237, and HMT-238) and Prevotella ( P.denticola and HMT-315), is a central hub in the network that is highly connected to the positive relationship line (solid line). There are two other central bacterial species, namely Neisseria sp. (unclassified) and Oribacterium sp. (unclassified), which are mostly connected to other bacterial species by negative relationship lines (dashed lines). These observations highlight the importance of modulating bacterial-bacterial interactions in the salivary microbiota of patients with OSF. On the contrary, as shown in Figure 8B, there is no corresponding bacterial symbiosis pattern in OSCC-OSF patients, indicating that the network structure between bacterial groups has undergone substantial changes, and the cancerization process has made OSF originally have a structure of close interaction between bacterial groups. disappeared, confirming that salivary microorganisms do change with malignancy.

In order to compare the biochemical functions of OSF and OSCC-OSF salivary microorganisms, this case further used LEfSe to identify the quantitative differences in metabolic pathways predicted by PICRUSt2 from the MetaCyc database. A total of 85.77% of the denoised sequences were of high to moderate quality (nearest sequenced taxon indicator (NSTI) score <0.15). To ensure appropriate prediction quality, denoised sequences with NSTI scores >2 (0.37% of total denoised sequences) were excluded from the PICRUSt2 analysis. Although many characteristic bacterial species were detected in the OSF and OSCC-OSF cohorts, only nine metabolic pathways were quantitatively distinguishable. Figure 9 shows metabolic pathways with significantly different abundances in saliva samples of OSF and OSCC-OSF patients. Six of the metabolic pathways are closely related to OSF, including L-ornithine and L-histidine. (L-histidine), pyrimidine deoxyribonucleotide (pyrimidine deoxyribonucleotide), and nicotinamide adenine dinucleotide (NAD) synthesis, as well as formaldehyde oxidation and assimilation (formaldehyde oxidation and assimilation). In OSCC-OSF, there are three closely related metabolic pathways, namely 1,4-dihydroxy-6-naphthoate (1,4-dihydroxy-6-naphthoate, an intermediate product of vitamin K2 synthesis), S -Synthesis of S-adenosyl-L-methionine (SAM) and norspermidine. Among them, OSCC-OSF has higher S-adenosyl-L-methionine and norspermine synthesis potential, but lower L-ornithine and pyrimidine deoxyribonucleotide synthesis and formaldehyde metabolism. potential. These findings indicate that salivary microbiota play an important role in regulating microbial metabolism during oral carcinogenesis.

These 9 metabolic pathways can also be classified into 5 categories of metabolism. The synthesis of S-adenosyl-L-methionine, the synthesis of L-ornithine and the synthesis of L-histidine belong to amino acid biosynthesis. The synthesis of pyrimidine deoxyribonucleotides belongs to nucleoside and nucleotide biosynthesis. Formaldehyde oxidation and assimilation belong to C1 compound utilization and assimilation (C1 compound utilization and assimilation). The synthesis of 1,4-dihydroxy-6-naphthoic acid and the synthesis of NAD belong to cofactor, prosthetic group electron carrier and vitamin biosynthesis. The synthesis of norspermine belongs to the synthesis of amine and polyamine (amine and polyamine biosynthesis). The regulation mechanism of metabolic pathways by salivary microorganisms needs further study.

In summary, since changes in salivary microbiota are related to oral squamous cell carcinoma (OSCC), and most OSCC are caused by precancerous lesions, among which patients with oral submucosal fibrosis (OSF) have a high rate of malignant transformation. But how the salivary microbiome changes with malignancy is unclear. Therefore, this case compared the salivary microbiota of patients with oral submucosal fibrosis accompanied by oral cancer (OSCC-OSF) and OSF patients, and observed the changes in salivary microbiome during the malignant transformation of OSF into OSCC-OSF. According to the results of high-throughput sequencing of the V3-V4 region of bacterial 16S rRNA genes, it can be seen that oral health status and smoking status significantly affect changes in the composition of salivary microbial systems, resulting in a significant reduction in bacterial species richness and phylogenetic diversity ( P<0.05). The malignant transformation of OSF increases the impact of stochasticity on changes in the composition of salivary microorganisms and completely changes the bacterial symbiosis network pattern. Various statistical analyzes and machine learning methods consistently identified 5 bacterial species with significant changes between OSF and OSCC-OSF, including Porphyromonas catoniae , Prevotella multisaccharivorax , Prevotella sp.HMT-300, Mitsuokella sp.HMT-131, and Treponema sp.HMT-927, and these five bacterial species are all related to oral health and smoking status. Therefore, these five bacterial species can be used as biomarkers for the malignant transformation of OSF, and the accuracy of these biomarkers in predicting the occurrence of oral cancer is quite high whether in the exploratory 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 salivary microorganisms play an important role in regulating microbial metabolism during oral carcinogenesis. Therefore, this case provides a biomarker for the malignant transformation of OSF, which is helpful for research on cancer formation and treatment, and can identify high-risk OSCC patients among OSF patients, and then detect and treat high-risk patients.

Even though the present invention has been described in detail through the above embodiments, it can be modified in various ways as desired by those skilled in the art, without departing from the intended protection within the scope of the appended patent application.

Claims (3)

The use of a characteristic bacterial strain as a biomarker for the progression of oral submucosal fibrosis, wherein the abundance or microbial load of the characteristic strain before and after the progression of oral submucosal fibrosis is significantly different (P<0.05), and The characteristic bacterial strain is selected from Porphyromonas catoniae , Prevotella multisaccharivorax , Prevotella sp.HMT-300, Mitsuokella sp.HMT-131, Treponema sp.HMT-927, Mycoplasma faucium , Prevotella sp.HMT-315, Absconditabacteria (SR1 )sp.HMT-874, and Treponema sp.HMT-258. The use as described in claim 1, wherein the characteristic bacterial species is related to oral health conditions. The use as described in claim 1, wherein the characteristic bacterial species is related to smoking status.