Oral bacterial marker related to colorectal cancer prognosis and application thereof

Abstract

The invention belongs to the technical field of biological medicine, and discloses an oral bacterial marker related to colorectal cancer and application thereof. The invention provides an oral bacterial marker related to prognosis of colorectal cancer. The oral bacterial marker comprises at least one of Campylobacter gracilis, Neisseria Oracis and Treponema medium, and the oral bacterial marker comprises at least one of Campylobacter gracilis, Neisseria Oracis and Treponema medium, and the oral bacterial marker comprises at least one of Campylobacter gracilis, Neisseria Oracis and Treponema medium. The invention also provides an application of the oral bacterial marker as a prognosis prediction marker of colorectal cancer, and an application of a reagent for detecting oral bacteria in preparation of a product for predicting the prognosis condition of colorectal cancer. In-depth study finds that part of oral bacteria are significantly related to colorectal cancer progression, the progression and prognosis conditions of the colorectal cancer can be effectively predicted, a reference basis is provided for the prognosis conditions of the colorectal cancer, and a promising target and direction are provided for diagnosis and treatment of the colorectal cancer in the future.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and particularly relates to oral bacterial markers associated with colorectal cancer prognosis and applications thereof. Background Art

[0002] Colorectal cancer (CRC) is one of the malignant tumors with the highest mortality rate in the world. Although multidisciplinary comprehensive diagnosis and treatment techniques have made significant progress, the prognosis of colorectal cancer patients is still not optimistic due to the high rate of distant metastasis of colorectal cancer, the easy development of resistance to chemotherapy drugs, and the easy recurrence of cancer after treatment. Given the significant differences in postoperative outcomes and long-term prognosis among individuals, identifying reliable prognostic factors is crucial to optimize treatment strategies and improve the prognosis of colorectal cancer patients.

[0003] The human oral microbiome is a complex ecosystem that contains more than 770 species of oral microorganisms. Studies have found that oral microorganisms are associated with the development of head and neck tumors, esophageal cancer and other tumors. The relationship between changes in the oral microbiome and the progression and prognosis of colorectal cancer is still unclear. Summary of the invention

[0004] In order to overcome the above-mentioned shortcomings and deficiencies of the prior art, the primary purpose of the present invention is to provide oral bacterial markers associated with the prognosis of colorectal cancer.

[0005] The present invention obtains oral bacteria from the saliva of colorectal cancer patients and screens out microbial characteristics associated with the progression of colorectal cancer, thereby providing new oral bacterial markers associated with the prognosis of colorectal cancer.

[0006] Another object of the present invention is to provide the use of oral bacteria as a prognostic prediction marker for colorectal cancer.

[0007] Another object of the present invention is to provide the use of oral bacteria in the preparation of products for predicting the prognosis of colorectal cancer.

[0008] Another object of the present invention is to provide an application of a reagent for detecting oral bacteria in the preparation of a product for predicting the prognosis of colorectal cancer.

[0009] The purpose of the present invention is achieved through the following solutions:

[0010] In a first aspect, the present invention provides oral bacterial markers associated with the prognosis of colorectal cancer.

[0011] According to the in-depth research of the applicant of the present invention, oral bacteria were obtained from the saliva of colorectal cancer patients, and microbial characteristics associated with the progression of colorectal cancer were screened, thereby providing new oral bacterial markers associated with the prognosis of colorectal cancer.

[0012] The oral bacteria include at least one of Campylobacter gracilis, Neisseria oralis and Treponema medium.

[0013] In a second aspect, the present invention provides the use of the above-mentioned oral bacteria as a prognosis prediction marker for colorectal cancer.

[0014] To determine whether the oral microbiota of patients with colorectal cancer can predict their progression, patients with colorectal cancer were followed up and their saliva was sequenced by full-length 16S rRNA gene to obtain their oral microbiota data. After data mining analysis, the microbial signature oral bacteria associated with colorectal cancer progression were obtained, and based on this, a microbial risk score was provided to predict the progression risk of patients with colorectal cancer.

[0015] Furthermore, the oral microbiome risk score was combined with key clinical factors to construct a multifactorial prognostic model with higher predictive performance, providing a new prognostic assessment tool for patients with colorectal cancer.

[0016] In a third aspect, the present invention provides the use of a reagent for detecting oral bacteria in the preparation of a product for predicting the prognosis of colorectal cancer.

[0017] Furthermore, the products include test kits, test strips, chips or high-throughput sequencing platforms, etc.

[0018] Furthermore, the reagent for detecting oral bacteria can detect the abundance of oral bacteria.

[0019] Furthermore, the reagent for detecting oral bacteria can detect the abundance of oral bacteria by at least one method of metagenomic sequencing, 16S rRNA sequencing, and qPCR quantitative detection.

[0020] Furthermore, the reagents include at least one of a sample DNA extraction reagent, a metagenomic sequencing reagent, a 16S rRNA sequencing reagent, or a qPCR quantitative detection reagent.

[0021] Furthermore, the reagent for detecting oral bacteria includes at least one of a probe, a primer or an antibody for detecting oral bacteria.

[0022] Furthermore, the primers are primers for detecting 16S rRNA, an oral bacterial marker.

[0023] Based on the common knowledge in this field, the above-mentioned preferred conditions can be combined with each other to obtain a specific implementation method.

[0024] The present invention provides oral bacteria markers related to the prognosis of colorectal cancer and their applications through in-depth research. The results show that some oral bacteria are significantly correlated with the progression of colorectal cancer, thereby providing the application of oral bacteria as diagnostic or prognostic markers for colorectal cancer. Among them, Neisseria oralis and Campylobacter gracilis are significantly correlated with an increased risk of colorectal cancer progression, while Treponema medium is significantly correlated with a reduced risk of colorectal cancer progression. Therefore, the oral bacteria markers provided by the present invention can effectively predict the progression and prognosis of colorectal cancer, provide a reference basis for the prognosis of colorectal cancer, and provide a promising target and direction for the diagnosis and treatment of colorectal cancer in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 Comparison of the predictive performance of risk scores for different oral microbial combinations.

[0027] Figure 2 This is a graph showing the relationship between oral microbial risk score and colorectal cancer progression in population 1.

[0028] Figure 3 This is a curve chart showing the relationship between different clinical factors and colorectal progression.

[0029] Figure 4 To compare the prediction performance of different combinations of clinical factors.

[0030] Figure 5 This is a graph showing the relationship between oral microbiome risk score and colorectal cancer progression in population 2.

[0031] Figure 6 Functional pathways that significantly differed in oral microbial risk scores between low-risk and intermediate- and high-risk patients and their association with the three oral prognostic bacteria identified. DETAILED DESCRIPTION

[0032] The present invention is further described in detail below in conjunction with examples, but the embodiments of the present invention are not limited thereto. The materials involved in the following examples can be obtained from commercial channels unless otherwise specified. The methods described are conventional methods unless otherwise specified.

[0033] Example 1: Discovery of oral bacteria associated with colorectal cancer prognosis based on third-generation 16S rRNA amplicon sequencing and survival analysis

[0034] In this example, 176 patients with colorectal cancer were recruited, saliva samples were collected, clinical and basic information was collected, saliva DNA was extracted and 16S rRNA gene full-length amplicon sequencing (PacBio sequencing technology) was performed, and finally 156 cases of bacterial flora data that met the quality assessment were obtained, and then oral bacteria related to the prognosis of colorectal cancer were identified. The specific implementation plan is as follows:

[0035] 1.1 Research subjects inclusion and information collection

[0036] This study is a retrospective cohort study. The applicant's team recruited 176 patients diagnosed with stage I to IV colorectal cancer at the Sun Yat-sen University Cancer Center from December 2018 to April 2021. Professionally trained staff collected information, including basic personal information such as gender, age, marital status, personal life history such as smoking and drinking, and family history of cancer. Patients collected saliva before treatment, and Pacbio 16S rRNA gene full-length amplicon sequencing technology was used to explore the characteristics of colorectal cancer-related oral microbiota at the species level. Among them, 20 patients were excluded due to postoperative sampling; 156 colorectal cancer patients were finally included. This population is defined as population 1, and the basic information characteristics of the included population are shown in Table 1.

[0037] Table 1 Basic characteristics of population 1

[0038]

[0039]

[0040]

[0041] 1.2 Saliva sample collection and DNA extraction

[0042] Participants should not drink water or eat within half an hour before saliva collection. Saliva was collected using a sterile 50mL centrifuge tube. When the participants were in a natural state and waited for oral saliva to be secreted, they opened the cover of the collection tube and spit the oral saliva into the centrifuge tube. Spitting was prohibited. About 2-3mL of saliva was collected from each participant. After collection, the saliva was temporarily stored in an ice box and divided into EP tubes as soon as possible and frozen in a -80℃ ultra-low temperature refrigerator. DNA was extracted from saliva samples using the DNeasy PowerSoil Pro DNA extraction kit, and the extraction operation was performed according to the kit instructions throughout the process.

[0043] 1.3 Sample flora sequencing experimental process

[0044] The microbiome detection system of saliva DNA based on 27F / 1492R primers to amplify the full-length region of the 16S rRNA gene and combined with PacBio third-generation sequencing. The specific process is: the bacterial universal primers 27F (5'-AGRGTTYGATYMTGGCTCAG-3', Forward primer) and 1492R (5'-RGY TACCTTGTTACGACTT-3', Reverse primer) containing a 12bp barcode sequence are used to amplify the full-length gene of 16S rRNA. The configuration of the PCR amplification system is shown in Table 2; KAPA HiFi HotStart DNA polymerase is used to amplify the saliva sample DNA for 27 cycles.

[0045] Table 2

[0046]

[0047] The PCR reaction conditions were as follows: 95°C for 5 min, 1 cycle; 95°C for 5 min, 55°C for 20 sec, 72°C for 30 sec, and 4°C for 27 cycles.

[0048] The length of the amplified PCR product was confirmed by 1.2% agarose gel electrophoresis; the amplified product was purified using AgencourtAMPure XP magnetic beads, and the purified products of each sample were mixed at equimolar concentrations. The SMRTbell library was prepared from the purified amplicons by connecting adapters and sequenced using the PacBio Sequel platform (Pacific Biosciences).

[0049] 1.4 Analysis of 16S rRNA full-length region sequencing data

[0050] High-quality circular consensus sequences (CCS) were obtained from raw PacBio sequencing data using SMRT Link software (v9.0.0, Pacific Biosciences). Sequences were split into corresponding samples based on the 12 bp barcode sequence on the amplification primers during library construction using Lima (v2.0.0). The DADA2 (v1.22.0) workflow customized for PacBio full-length 16S rRNA gene sequencing data was used for quality control, denoising, and identification of amplicon sequence variants (ASVs) of CCS sequences. Based on the above process, the sequence quantity data for each sample in each ASV can be obtained.

[0051] The ASVs obtained from the above process were annotated with bacterial species using the silva_nr99_v138_train_set database and the silva_species_assignment_v138 sequence database. According to the "RIDE Checklist", a series of schemes were applied for sequence quality control and decontamination, specifically: (1) ASVs with annotation information of mitochondria or chloroplasts were removed; (2) ASVs that were not annotated at the bacterial phylum level were removed. In addition, the R package Decontam (v1.10.0) was used to identify and filter potential environmental contamination based on the sequence information obtained from the negative control samples designed during sample collection and processing and DNA library construction and sequencing. The sequencing depth of 2500 was used as the cutoff value, and samples with sequencing depths lower than this cutoff value were deleted, and finally the ASV abundance table after quality control was obtained.

[0052] 1.5 Statistical analysis

[0053] The present invention describes the demographic, socioeconomic and clinical characteristics of the patients, and uses the Wilcoxon rank sum test (for categorical variables) and one-way analysis of variance (ANOVA, for continuous variables) to compare the relationship between these characteristics and different clinical outcomes. All statistical analyses and visualization processes were performed using R software (version 4.4.0) and its designated R packages.

[0054] Progression-free survival (PFS) was defined as the time period from surgical resection to colorectal cancer recurrence or progression, or death from any cause. In order to explore the potential influencing factors of colorectal cancer progression, Kaplan-Meier survival analysis and Log-rank test were used to compare the relationship between various factors and colorectal cancer progression, and all variables were converted into categorical forms, such as gender, marital status, body mass index (BMI), family history of cancer, drinking history, smoking history, neoadjuvant chemoradiotherapy, tumor stage, histological grade, lymph node invasion, nerve bundle invasion, and lymph node metastasis. In addition, the Cox proportional hazard regression model was used to calculate the hazard ratio (HR) and its corresponding 95% confidence interval (CI) of different factors affecting colorectal cancer progression. The above analysis was performed using the "survival" and "survminer" packages of R software.

[0055] 1.6 Results: Oral bacteria are significantly associated with the prognosis of colorectal cancer patients

[0056] In this example, saliva samples were collected from 156 patients with colorectal cancer who were planned to undergo surgical treatment. The median follow-up period for patients with colorectal cancer progression was 18.4 months, during which 30 patients had progressive disease. Table 1 shows the basic demographic and clinical characteristics of the participants. There was no statistical difference in basic demographic characteristics such as age, gender, marital status, body mass index (BMI), family history of tumors, and drinking history between the non-progression group and the progression group. Compared with patients with better clinical outcomes, patients with progressive disease were more likely to be diagnosed with advanced clinical stages, and had a higher incidence of nerve tract invasion and lymph node metastasis (Table 1).

[0057] In this example, 16S rRNA gene full-length amplicon sequencing was performed on saliva samples of 156 patients with colorectal cancer to obtain comprehensive oral microbiome information. A total of 261 species with clear annotation information were detected. Species with a population detection rate greater than 10% and an average population relative abundance greater than 0.01% are defined as core species. They play a leading role in the function of microbial communities and have an important impact on microbial communities and functions. Among them, 89 detection rates were less than 10% and 60 average relative abundances were less than 0.001%. These non-core species with low detection rates or small numbers were not included in subsequent analysis. In the end, a total of 82 core species were included for subsequent analysis, which mainly belonged to the Bacteroidetes (28.4%), Firmicutes (28.4%) and Proteobacteria (24.7%).

[0058] In order to clarify whether oral bacteria are associated with the progression of colorectal cancer, the above 82 core species were further screened in two steps. First, a univariate Cox regression model was used for preliminary screening in the population, and it was found that 4 oral microorganisms were significantly associated with the progression of colorectal cancer, namely Streptococcus australis, Campylobacter gracilis, Neisseria oralis, and Treponema medium. The results of the univariate Cox regression analysis are shown in Table 3.

[0059] Table 3 Univariate Cox regression model analysis of the relationship between oral bacteria and colorectal cancer progression

[0060]

[0061] To further clarify the association between these bacteria and colorectal cancer progression, these univariately significant oral bacteria were subsequently included in the multivariate Cox regression model, and the oral bacteria independently associated with the prognosis of colorectal cancer were screened after adjusting for age, gender, tumor stage, and neoadjuvant chemoradiotherapy. The multivariate results showed that after adjusting for confounding factors, three of the bacteria were still significantly associated with the prognosis of colorectal cancer: Neisseria oralis and Campylobacter gracilis were significantly associated with an increased risk of colorectal cancer progression (Neisseria oralis: HR = 2.31, P = 0.0403; Campylobacter gracilis: HR = 3.57, P = 0.00164), while Treponema medium was significantly associated with a reduced risk of colorectal cancer progression (HR = 0.41, P = 0.0303). The results of the multivariate Cox regression analysis are shown in Table 4.

[0062] Table 4 Multivariate Cox regression model analysis of the relationship between oral bacteria and colorectal cancer progression

[0063]

[0064] Note: *Multivariate COX regression analysis was adjusted for age, gender, clinical stage, and neoadjuvant chemoradiotherapy.

[0065] Example 2: Construction of microbial risk score and prognosis prediction model

[0066] 2.1 Construction of oral microbial risk score

[0067] Based on the oral flora data in Example 1, three oral bacteria that were still significantly associated with the prognosis of colorectal cancer (CRC) were finally included to construct the oral microbial risk score (Microbial Riskscore, MRS). These bacteria include Campylobacter gracilis and Neisseria oralis, which are associated with an increased risk of colorectal cancer progression, and Treponema medium, which is associated with a reduced risk of colorectal cancer progression. Taking 0.04% abundance as the critical abundance, if the relative abundance of the above bacteria is lower than the critical value, it is defined as undetected; if it is greater than or equal to the critical value, it is defined as detectable. Among them, if Campylobacter gracilis (C. gracilis) or Neisseria oralis (N. oralis) with an increased risk of CRC progression is detected, it will be scored 1 point, and if it is not detected, it will be scored 0 points. If the intermediate Treponema (T. medium) with a reduced risk of colorectal cancer progression is not detected, it will be scored 1 point; if it is detected, it will be scored 0 points. Therefore, each patient can be assigned a risk score based on the detection of the above three bacteria in their oral cavity, and the calculation formula is as follows. Finally, each patient can obtain their oral microbial risk score (MRS), and the MRS range is 0 to 3.

[0068] MRS=S C.gracilis +S N.oralis +S T.medium

[0069] (S C.gracilis , S N.oralis , S T.medium are the corresponding bacterial fractions)

[0070] 2.2 Results

[0071] In order to evaluate the performance of the above-mentioned bacteria and their combinations, oral prognostic bacteria were gradually included in the population of Example 1, and the consistency index (C-index) was used as an indicator to evaluate the stability of the model. When only Treponema medium (T. medium) was added to the model, the consistency index was 0.58 (95% CI = 0.48-0.68); when only oral Neisseria (N. oralis) was included in the model, the consistency index was 0.60 (95% CI = 0.50-0.71); when only Campylobacter gracilis (C. gracilis) was included in the model, the consistency index was 0.64 (95% CI = 0.53-0.74). When the model included a combination of two oral bacteria, its consistency index ranged from 0.63 to 0.69. Among all combinations, the microbial risk score (MRS) containing three specific oral bacteria had the highest consistency index with colorectal cancer prognosis (C-index = 0.71, 95% CI = 0.62-0.80) ( Figure 1 ).

[0072] To further explore the association between MRS and the prognostic risk of colorectal cancer patients, patients were divided into three categories according to the oral microbial risk score: low risk (MRS score of 0 points), intermediate risk (MRS score of 1 or 2 points), and high risk (MRS score of 3 points). In population 1, only 5% of patients in the low-risk group experienced progression (1 in 20 patients); the proportion of patients in the intermediate-risk group who experienced progression was 19.6% (24 in 122 patients); and the proportion of patients in the high-risk group who experienced progression was as high as 35.7% (5 in 14 patients experienced postoperative disease progression). The results are shown in Table 1. Figure 2 (Log-rank test, P=0.00016). The above results all show that the microbial risk score (MRS) has good predictive performance for the progression of colorectal cancer.

[0073] Example 3: Effect of combining microbiome model with clinical factors on the prognosis prediction of colorectal cancer

[0074] In order to further illustrate the predictive performance of the oral microbial risk score and its comprehensive predictive ability in combination with clinical factors, this example constructed a comprehensive model based on population 1, incorporating the oral microbial risk score and the corresponding clinical factors.

[0075] 3.1 Statistical analysis

[0076] The univariate Log-rank test was used to explore the relationship between different clinical factors and the prognosis of colorectal cancer. When the P value was less than 0.05, the clinical factor was considered to be related to the prognosis of colorectal cancer. Subsequently, these statistically significant clinical factors were gradually incorporated into the prediction model, and the consistency index (C-index) was used as an indicator to evaluate the stability of the model. To further illustrate the predictive performance of the oral microbial risk score, a comprehensive model was constructed using COX proportional hazard regression. The oral microbial risk score and the corresponding three clinical factors (tumor stage, lymph node metastasis, and nerve tract invasion) were incorporated into the model to construct a comprehensive model. The Z-score test was used to compare whether the C-index difference between the clinical model and the comprehensive model was statistically significant.

[0077] 3.2 Results

[0078] The present invention identified clinical factors associated with colorectal cancer progression. Factors such as age, marital status, smoking history, drinking history, histological grade, and lymph node invasion were not associated with cancer progression. In line with previous studies, tumor stage, nerve tract invasion, and lymph node metastasis were significantly associated with colorectal cancer progression in the Log-rank test (P<0.05, see Figure 3). The present invention found that compared with the inclusion of one or any two clinical factors alone, the prediction effect was lower than the inclusion of all three clinical factors at the same time (see Figure 4 ). Therefore, the final clinical model simultaneously incorporates the above three significant clinical factors. The present invention gradually incorporates these clinical factors into the model, and finds that the C-index of the model including three clinical factors in independent population 1 is 0.75 (95% CI = 0.66-0.85) (see Table 5).

[0079] The present invention further combined these clinical factors with the oral microbial risk score to construct a comprehensive model (Cox proportional hazard regression model). The results showed that the C-index of the comprehensive model was increased to 0.83 (95% CI = 0.74-0.91). Compared with clinical factors, the comprehensive model had a significantly improved effect on the prediction of colorectal cancer progression (Z-score test, P = 1.40 × 10 -3 )(See Table 5).

[0080] Table 5 Predictive effects of clinical model, oral microbial risk scoring model and comprehensive model in population 1

[0081]

[0082] Note: # Pvalue is the result of the comparison between the clinical model and the comprehensive model calculated by the Z score test

[0083] Example 4: Independent population validation of oral microbial risk score and prediction model

[0084] To further verify the effect of the above-mentioned oral flora-based colorectal cancer prognosis prediction model (MRS), the present invention independently validated the model in another population, and systematically evaluated its effect on colorectal cancer prognosis prediction in combination with clinical factors. The detailed implementation is as follows:

[0085] 4.1 Research subjects inclusion and information collection

[0086] This cohort consisted of 185 patients diagnosed with stage I to IV colorectal cancer recruited from December 2018 to April 2021 at the Sun Yat-sen University Cancer Center. Information was collected by professionally trained staff, including basic personal information such as gender, age, marital status, personal life history such as smoking and drinking, and family history of cancer. Patients collected saliva before treatment, and their oral flora characteristics were detected using Pacbio 16S rRNA gene full-length amplicon sequencing technology. Among them, 22 were excluded because their preoperative saliva samples were not obtained, and 7 patients were excluded because the sequencing data did not meet the quality control standards. A total of 156 patients were included in the population. This population is defined as population 2, and its basic information characteristics are shown in Table 6.

[0087] Table 6 Basic characteristics of population 2

[0088]

[0089]

[0090] 4.2 Statistical analysis

[0091] In order to further verify the generalization ability of the oral flora risk score model, that is, the applicability of the model in different populations, this example externally validated the model in an independent cohort population. In this example, a new cohort population was recruited, a total of 156 colorectal cancer patients, saliva samples were collected and clinical and basic information was collected, saliva DNA was extracted and 16S rRNA gene full-length amplicon sequencing (PacBio sequencing technology) was performed, and the specific experimental operation method was the same as in Example 1.

[0092] 4.3 Results

[0093] There were no statistically significant differences in basic demographic characteristics such as age, sex, marital status, body mass index (BMI), family history of cancer, and alcohol consumption between the non-progression group and the progression group in population 2. Compared with patients with a good clinical outcome, patients with progressive disease were more likely to be diagnosed with advanced clinical disease and had a higher incidence of lymphatic infiltration, nerve tract invasion, and lymphatic metastasis.

[0094] First, the oral microbial risk score (MRS) model established in Example 2 was validated in an independent population of this example. The study found that the MRS model had a good validation effect in study population 2, with a C-index of 0.64 (95% CI 0.54-0.74), completing the independent validation of the flora model.

[0095] Similarly, the effectiveness of the oral microbial risk score (MRS) model for the prognosis of colorectal cancer patients was verified in an independent population 2. Using the MRS model and scoring rules constructed for population 1, population 2 was classified into low-, medium-, and high-risk groups with 18, 124, and 19 cases, respectively. The progression rates of the low- and medium-risk groups were 16.6% and 15.3%, respectively, while the high-risk group was as high as 50%. The results are shown in Figure 5 (log-rank test, P=0.0017).

[0096] In population 2, the comprehensive model of oral microbial risk score (MRS) and clinical factors also obtained good validation results, and the C-index of the comprehensive model was up to 0.81 (95% CI: 0.73-0.88). The C-index of the comprehensive model was higher than that of the clinical model and the difference was statistically significant (0.81 vs. 0.74, P = 1.84 × 10 -4 , Z-score test), the results are shown in Table 7.

[0097] Table 7 Validation results of clinical model, oral microbial risk scoring model and comprehensive model in population 2

[0098]

[0099] Note: # Pvalue is the result of the comparison between the clinical model and the comprehensive model calculated by the Z score test

[0100] The above results all show that the established oral microbial risk score (MRS) and its comprehensive model constructed in combination with clinical factors can be well validated in independent populations.

[0101] Example 5: Functional prediction of prognosis-related oral microorganisms

[0102] In order to explore the functions of oral microbiota in patients with different oral microbial risks, the present invention used the PICRUSt2 tool to explore the potential metabolic functions of oral microbiota based on the 16S rRNA gene sequence for the oral microbiota data of population 1 and population 2 in the above embodiment.

[0103] 5.1 Analysis Process

[0104] The PICRUSt2 tool was used to infer functional changes within the microbial community. Subsequently, the STAMP software was used to assess whether there were significant differences in metabolic functional pathways between the medium / high-risk MRS group and the low-risk MRS group, and the Bonferroni correction was applied, with the adjusted P value less than 0.05 considered to be statistically significant. The correlation between oral microorganisms and statistically significant differential pathways was analyzed by Spearman's rank correlation test.

[0105] 5.2 Results

[0106] Of the 344 KEGG pathways identified, pathways that appeared in less than 30% of patients and had an average relative abundance of less than 1% were excluded, and 282 metabolic pathways were finally included. Patients were divided into two groups: a low-risk oral microbiome group and an intermediate / high-risk oral microbiome group. Sixteen pathways were significantly different between the two groups, of which five pathways were significantly increased in the intermediate / high-risk group. These pathways were mainly related to promoting cancer cell proliferation, especially the polyamine biosynthesis superpathway II and polyamine biosynthesis, which are related to polyamine synthesis, which is often associated with rapid cancer cell proliferation. Among the 11 metabolic pathways significantly enriched in the low-risk MRS group, the one with the largest average abundance difference was the N-acetylglucosamine (GlcNAc) and N-acetylgalactosamine pathway. This pathway is related to the synthesis of GlcNAc and GalNAc, both of which can reduce inflammatory responses, regulate glycosylation, and inhibit cancer cell proliferation. Subsequently, the correlation between the differential metabolic pathways and the three identified oral microbes was analyzed. The results showed that oral bacteria associated with increased risk of colorectal cancer progression showed similar functional characteristics, but oral bacteria associated with reduced risk of colorectal cancer progression were significantly different. For example, Campylobacter tiliaceus and Neisseria oralis were positively correlated with increased KEGG pathways in the medium / high-risk MRS group and negatively correlated with decreased pathways. In contrast, Treponema intermedia, a bacterium enriched in the low-risk MRS group, showed the opposite trend (see results). Figure 6 The above results suggest the potential biological mechanism of the oral flora markers associated with colorectal cancer progression discovered by the present invention, and further suggest the reliability of the above markers in predicting colorectal cancer progression.

[0107] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. Oral bacterial markers associated with colorectal cancer prognosis, characterized by: The bacteria include at least one of Campylobacter gracilis, Neisseria oralis and Treponema medium.

2. Use of the oral bacterial marker according to claim 1 as a prognosis prediction marker for colorectal cancer.

3. Use of a reagent for detecting oral bacteria in the preparation of a product for predicting the prognosis of colorectal cancer, characterized in that: The bacteria include at least one of Campylobacter gracilis, Neisseria oralis and Treponema medium.

4. Application of oral bacteria detection reagents combined with clinical factor detection products in the preparation of products for predicting the prognosis of colorectal cancer.

5. The use according to claim 4, characterized in that: The clinical factors include at least one of tumor stage, nerve tract invasion, and lymph node metastasis.

6. The use according to claim 3, 4 or 5, characterized in that: The products include test kits, test strips, chips or high-throughput sequencing platforms.

7. The use according to claim 3, 4 or 5, characterized in that: The reagent for detecting oral bacteria detects the abundance of oral bacteria.

8. The use according to claim 3, 4 or 5, characterized in that: The reagent for detecting oral bacteria uses at least one method of metagenomic sequencing, 16S rRNA sequencing, and qPCR quantitative detection to detect the abundance of oral bacteria.

9. The use according to claim 3, 4 or 5, characterized in that: The reagents include at least one of a sample DNA extraction reagent, a metagenomic sequencing reagent, a 16S rRNA sequencing reagent, or a qPCR quantitative detection reagent.

10. The use according to claim 3, 4 or 5, characterized in that: The reagent for detecting oral bacteria includes at least one of a probe, a primer or an antibody for detecting oral bacteria.

11. The use according to claim 10, characterized in that: The primers are primers for detecting 16S rRNA of oral bacteria.

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