Oral bacterial markers associated with colorectal cancer prognosis and uses thereof

Abstract

This invention belongs to the field of biomedical technology and discloses oral bacterial biomarkers related to colorectal cancer and their applications. This invention provides oral bacterial biomarkers associated with the prognosis of colorectal cancer, wherein the bacteria include at least one of *Campylobacter gracilis*, *Neisseria oralis*, and *Treponema medium*. It also provides the application of the above-mentioned oral bacterial biomarkers as prognostic predictive markers for colorectal cancer, and the application of reagents for detecting oral bacteria in the preparation of products predicting the prognosis of colorectal cancer. Through in-depth research, this invention has discovered that some oral bacteria are significantly correlated with the progression of colorectal cancer and can effectively predict the progression and prognosis of colorectal cancer, providing a reference for the prognosis of colorectal cancer and offering a promising target and direction for the future diagnosis and treatment of colorectal cancer.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, and specifically relates to oral bacterial markers related to the prognosis of colorectal cancer and their applications. Background Technology

[0002] Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Despite significant advancements in multidisciplinary treatment, the prognosis for CRC patients remains challenging due to its high rate of distant metastasis, resistance to chemotherapy drugs, and frequent recurrence after treatment. Given the significant individual differences in postoperative outcomes and long-term prognosis, identifying reliable prognostic factors is crucial for optimizing treatment strategies and improving the outcomes for CRC patients.

[0003] The human oral microbiome is a complex ecosystem containing over 770 species of oral microorganisms. Studies have found a correlation between oral microbiota and the development of tumors such as head and neck cancer and esophageal cancer. However, the relationship between changes in the oral microbiome and the progression and prognosis of colorectal cancer remains unclear. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the prior art, the primary objective of this invention is to provide oral bacterial markers related to the prognosis of colorectal cancer.

[0005] This invention obtains oral bacteria from the saliva of colorectal cancer patients and screens for microbial features associated with the progression of colorectal cancer, thereby providing new oral bacterial biomarkers related to the prognosis of colorectal cancer.

[0006] Another object of the present invention is to provide the application of oral bacteria as a prognostic predictive biomarker 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 the use of reagents for detecting oral bacteria in the preparation of products for predicting the prognosis of colorectal cancer.

[0009] The objective of this invention is achieved through the following solution:

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

[0011] According to the applicant's in-depth research, oral bacteria were obtained from the saliva of colorectal cancer patients, and microbial features associated with the progression of colorectal cancer were screened, thereby providing new oral bacterial biomarkers related to the prognosis of colorectal cancer.

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

[0013] Secondly, the present invention provides the application of the above-mentioned oral bacteria as prognostic predictive biomarkers for colorectal cancer.

[0014] To determine whether the oral microbiota of colorectal cancer patients can predict their progression, colorectal cancer patients were followed up, and their saliva was subjected to full-length 16S rRNA sequencing to obtain oral microbiota data. Data mining analysis identified oral bacteria with microbial characteristics associated with colorectal cancer progression, and a microbial risk score was developed based on this to predict the progression risk of colorectal cancer patients.

[0015] Furthermore, by combining oral microbiome risk scores with key clinical factors, a multivariate prognostic model with higher predictive performance was constructed, providing a new prognostic assessment tool for colorectal cancer patients.

[0016] Thirdly, the present invention provides the application of reagents for detecting oral bacteria in the preparation of products for predicting the prognosis of colorectal cancer.

[0017] Furthermore, the products include reagent 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 be used to detect the abundance of oral bacteria using at least one method, such as metagenomic sequencing, 16S rRNA sequencing, or qPCR quantitative detection.

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

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

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

[0023] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined to obtain specific implementation methods.

[0024] This invention, through in-depth research, provides oral bacterial biomarkers related to the prognosis of colorectal cancer and their applications. Results showed that some oral bacteria are significantly associated with colorectal cancer progression, thus providing the application of oral bacteria as diagnostic or prognostic biomarkers for colorectal cancer. Specifically, *Neisseria oralis* and *Campylobacter gracilis* are significantly associated with an increased risk of colorectal cancer progression, while *Treponema medium* is significantly associated with a decreased risk. Therefore, the oral bacterial biomarkers provided by this invention can effectively predict the progression and prognosis of colorectal cancer, providing a reference for the prognosis of colorectal cancer and offering a promising target and direction for future diagnosis and treatment of colorectal cancer. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 Comparison of risk score prediction performance for different oral microbiome combinations.

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

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

[0029] Figure 4 Comparison of predictive performance for 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 led to significant differences in oral microbiome risk scores between low-risk and intermediate-to-high-risk patients and their relationship with three identified oral prognostic bacteria. Detailed Implementation

[0032] The present invention will be further described in detail below with reference to embodiments, but the implementation of the present invention is not limited thereto. Unless otherwise specified, all materials involved in the following embodiments are commercially available. Unless otherwise specified, all methods described are conventional methods.

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

[0034] In this embodiment, 176 colorectal cancer patients were recruited, and saliva samples were collected, along with clinical and basic information. Saliva DNA was extracted, and full-length 16S rRNA gene amplicon sequencing (PacBio sequencing technology) was performed. Ultimately, 156 cases of oral microbiota data met the quality assessment criteria. Oral bacteria associated with colorectal cancer prognosis were then identified. The specific implementation plan is as follows:

[0035] 1.1 Study Subject Inclusion and Information Collection

[0036] This retrospective cohort study included 176 patients diagnosed with stage I to IV colorectal cancer recruited by the applicant's team at Sun Yat-sen University Cancer Center from December 2018 to April 2021. Information was collected by professionally trained staff, including basic personal information such as gender, age, and marital status; personal lifestyle history such as smoking and alcohol consumption; and family history of cancer. Saliva samples were collected from patients before treatment, and the characteristics of the oral microbiota associated with colorectal cancer were explored at the species level using PacBio 16S rRNA amplicon sequencing technology. Twenty patients were excluded due to postoperative sampling; ultimately, 156 colorectal cancer patients were 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 refrained from drinking water and eating for half an hour prior to saliva collection. Saliva was collected using sterile 50mL centrifuge tubes. Participants were instructed to allow saliva to be produced naturally in their mouths, then open the tube cap and spit the saliva into the centrifuge tube; spitting was prohibited. Approximately 2-3mL of saliva was collected from each participant. After collection, the saliva was temporarily stored in an ice box and aliquoted into EP tubes as soon as possible, then frozen at -80°C. DNA was extracted from the saliva samples using the DNeasy PowerSoil Pro DNA Extraction Kit, following the kit's instructions throughout the extraction process.

[0043] 1.3 Sample microbial community sequencing experimental procedure

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

[0045] Table 2

[0046]

[0047] The PCR reaction conditions were: 95℃ for 5 minutes, 1 cycle; 95℃ for 5 minutes, 55℃ for 20 seconds, 72℃ for 30 seconds, 4℃ for maintenance, 27 cycles.

[0048] The length accuracy of the amplified PCR products was confirmed by 1.2% agarose gel electrophoresis. The amplified products were purified using AgencourtAMPure XP magnetic beads, and the purified products from each sample were mixed in equimolar concentrations. SMRTbell libraries were prepared from the purified amplicones by ligating adapters, and sequencing was performed using the PacBio Sequel platform (PacificBiosciences).

[0049] 1.416S rRNA full-length region sequencing data analysis

[0050] High-quality cycle-consistent sequences (CCS) were obtained from the raw PacBio sequencing data using SMRT Link software (v9.0.0, Pacific Biosciences). Lima (v2.0.0) was used to split the sequences into their corresponding samples based on the 12bp barcode sequence on the amplification primers used during library construction. The DADA2 (v1.22.0) workflow, customized for PacBio full-length 16S rRNA gene sequencing data, was used for CCS sequence quality control, noise reduction, and amplicon sequence variant (ASV) identification. Based on this workflow, the sequence count data for each ASV in each sample was obtained.

[0051] The ASVs obtained from the above process were classified and annotated using the silva_nr99_v138_train_set database in conjunction with the silva_species_assignment_v138 sequence database. Following the "RIDE checklist," a series of procedures were applied for sequence quality control and decontamination, specifically: (1) removing ASVs annotated as mitochondria or chloroplasts; (2) removing ASVs not annotated at the bacterial phylum level. Furthermore, combining the sequence information obtained from negative control samples designed during sample collection and processing, and DNA library construction and sequencing, the R package Decontam (v1.10.0) was used to identify and filter potential environmental contamination. Using a sequencing depth of 2500 as the cutoff value, samples with sequencing depths below this cutoff value were deleted, ultimately yielding a quality-controlled ASV abundance table.

[0052] 1.5 Statistical Analysis

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

[0054] Progression-free survival (PFS) is defined as the time from surgical resection to recurrence or progression of colorectal cancer, or death from any cause. To explore potential factors influencing colorectal cancer progression, Kaplan-Meier survival analysis and the Log-rank test were used to compare the relationships between various factors and colorectal cancer progression. All variables were converted to categorical forms, including sex, marital status, body mass index (BMI), family history of cancer, alcohol consumption history, smoking history, neoadjuvant chemoradiotherapy, tumor stage, histological grade, lymph node invasion, nerve bundle invasion, and lymph node metastasis. Furthermore, Cox proportional hazards regression models were used to calculate the hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) for different factors influencing colorectal cancer progression. All analyses were performed using the "survival" and "survminer" packages in R software.

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

[0056] This study collected saliva samples from 156 colorectal cancer patients scheduled for surgery. The median follow-up period for patients with progressive colorectal cancer was 18.4 months, during which 30 patients experienced disease progression. Table 1 shows the basic demographic and clinical characteristics of the participants. 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 and progression groups. Patients with disease progression were more likely to be diagnosed with advanced clinical disease, nerve tract invasion, and lymph node metastasis compared to patients with better clinical outcomes (Table 1).

[0057] In this study, 16S rRNA amplicon sequencing was performed on saliva samples from 156 colorectal cancer patients to obtain comprehensive oral microbiome information. A total of 261 species with clearly annotated information were detected. Species with a population detection rate greater than 10% and a population average relative abundance greater than 0.01% were defined as core species, playing a dominant role in microbial community function and having a significant impact on the microbial community and its function. Among them, 89 non-core species with a detection rate less than 10% and 60 species with an average relative abundance less than 0.001% were not included in subsequent analysis. Ultimately, 82 core species were included in subsequent analysis, mainly belonging to the phyla Bacteroidetes (28.4%), Firmicutes (28.4%), and Proteobacteria (24.7%).

[0058] To clarify whether oral bacteria are associated with colorectal cancer progression, a two-step screening was conducted on the aforementioned 82 core species. First, a preliminary screening was performed in the population using a univariate Cox regression model, which revealed that four oral microorganisms were significantly associated with colorectal cancer progression: *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 a multivariate Cox regression model. After adjusting for age, sex, tumor stage, and neoadjuvant chemoradiotherapy, oral bacteria independently associated with colorectal cancer prognosis were screened. Multivariate results showed that, after adjusting for confounding factors, three bacteria remained significantly associated with colorectal cancer prognosis: *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 decreased 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 adjusted for age, sex, clinical stage, and neoadjuvant chemoradiotherapy.

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

[0066] 2.1 Construction of Oral Microbiome Risk Score

[0067] Based on the oral microbiota data in Example 1, three oral bacteria that remained significantly associated with colorectal cancer (CRC) prognosis in multivariate analysis were ultimately included to construct the Microbial Risk Score (MRS). These bacteria included *Campylobacter gracilis* and *Neisseria oralis*, which were associated with an increased risk of CRC progression, and *Treponema medium*, which was associated with a decreased risk of CRC progression. A cutoff abundance of 0.04% was used; if the relative abundance of the above bacteria was below this cutoff value, it was defined as undetectable; if it was greater than or equal to this cutoff value, it was defined as detectable. Specifically, *Campylobacter gracilis* or *Neisseria oralis*, which are associated with an increased risk of CRC progression, were scored as follows: 1 point for detection, 0 points for undetected. *Treponema medium*, which is associated with a decreased risk of CRC progression, was scored as follows: 1 point for undetected, 0 points for detected. Therefore, each patient can have a risk score calculated based on the detection of the above three bacteria in their oral cavity, as shown in the following formula. Ultimately, each patient will receive an Oral Microbial Risk Score (MRS), ranging from 0 to 3.

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

[0069] (S C.gracilis S N.oralis S T.medium (These are the scores of the corresponding bacteria)

[0070] 2.2 Results

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

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

[0073] Example 3: The effect of combining gut microbiota models with clinical factors on the prognostic prediction of colorectal cancer

[0074] To further illustrate the predictive performance of the oral microbiome risk score and its comprehensive predictive ability in combination with clinical factors, this embodiment constructs a comprehensive model based on population 1, incorporating the oral microbiome risk score and corresponding clinical factors.

[0075] 3.1 Statistical Analysis

[0076] A univariate log-rank test was used to explore the relationship between different clinical factors and the prognosis of colorectal cancer. A p-value less than 0.05 was considered to indicate a correlation between the clinical factor and colorectal cancer prognosis. Subsequently, these statistically significant clinical factors were progressively incorporated into the predictive model, and the C-index was used to assess model stability. To further illustrate the predictive performance of the oral microbiome risk score, a comprehensive model was constructed using Cox proportional hazards regression. This model incorporated the oral microbiome risk score and three corresponding clinical factors (tumor stage, lymph node metastasis, and nerve bundle invasion). The Z-score test was used to compare whether the difference in C-index between the clinical model and the comprehensive model was statistically significant.

[0077] 3.2 Results

[0078] This invention identified clinical factors associated with colorectal cancer progression. Age, marital status, smoking history, alcohol consumption history, histological grade, and lymph node invasion were not associated with cancer progression. Consistent with previous studies, tumor stage, nerve bundle invasion, and lymph node metastasis were significantly associated with colorectal cancer progression in the Log-rank test (P<0.05, see...). Figure 3This invention found that, compared to including one or any two clinical factors individually, the predictive effect was lower than that of including all three clinical factors simultaneously (see...). Figure 4 Therefore, the final clinical model incorporates all three significant clinical factors mentioned above. This invention progressively incorporates these clinical factors into the model and found that the C-index of the model containing the three clinical factors in independent population 1 was 0.75 (95% CI = 0.66-0.85) (see Table 5).

[0079] Furthermore, by combining these clinical factors with oral microbiome risk scores, this invention constructed a comprehensive model (Cox proportional hazards regression model). The results showed that the C-index of this comprehensive model increased to 0.83 (95% CI = 0.74–0.91), and compared to clinical factors, the comprehensive model significantly improved the predictive effect of colorectal cancer progression (Z-score test, P = 1.40 × 10⁻⁶). -3 (See Table 5).

[0080] Table 5. Predictive Performance of the Clinical Model, Oral Microbiome Risk Scoring Model, and Integrated Model in Population 1

[0081]

[0082] Note: # P-value is calculated using the Z-score test to compare the clinical model with the comprehensive model.

[0083] Example 4: Independent population validation of oral microbiome risk scoring and prediction model

[0084] To further validate the effectiveness of the oral microbiota-based colorectal cancer prognostic prediction model (MRS) established above, this invention conducted independent validation of the model in another population and a systematic review of its prognostic predictive effect on colorectal cancer in combination with clinical factors. Detailed implementation is as follows:

[0085] 4.1 Study Subject Inclusion and Information Collection

[0086] This cohort comprised 185 patients diagnosed with stage I to IV colorectal cancer recruited at Sun Yat-sen University Cancer Center between December 2018 and April 2021. Information was collected by professionally trained staff, including basic personal information such as gender, age, and marital status; personal lifestyle history such as smoking and alcohol consumption; and family history of cancer. Saliva samples were collected from patients before treatment, and their oral microbiota characteristics were analyzed using PacBio 16S rRNA amplicon sequencing technology. Twenty-two patients were excluded because preoperative saliva samples were not available, and seven patients were excluded because their sequencing data did not meet quality control standards, resulting in a final cohort of 156 patients. This cohort was 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] To further validate the generalization ability of the oral microbiota risk scoring model, i.e., its applicability in different populations, this embodiment conducted external validation of the model in an independent cohort. In this embodiment, a new cohort of 156 colorectal cancer patients was recruited. Saliva samples were collected, and clinical and basic information was gathered. Saliva DNA was extracted and subjected to full-length 16S rRNA amplicon sequencing (PacBio sequencing technology). The specific experimental procedures were the same as in Example 1.

[0092] 4.3 Results

[0093] In population 2, 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 and progression groups. Compared with patients with better clinical outcomes, patients with disease progression were more likely to be diagnosed with advanced clinical disease and had a higher incidence of lymph node infiltration, nerve tract invasion, and lymph node metastasis.

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

[0095] Similarly, the effectiveness of the oral microbiome risk score (MRS) model for predicting the prognosis of colorectal cancer patients was validated in an independent population 2. Using the MRS model and scoring rules constructed in population 1, 18, 124, and 19 patients in population 2 were identified as low-, intermediate-, and high-risk groups, respectively. The progression rates in the low- and intermediate-risk groups were 16.6% and 15.3%, respectively, while the rate in the high-risk group was as high as 50%. Results are shown below. Figure 5 (log-rank test, P = 0.0017).

[0096] In population 2, the integrated model combining oral microbiome risk score (MRS) and clinical factors also showed good validation results, with a C-index of 0.81 (95% CI: 0.73–0.88). The C-index of the integrated model was higher than that of the clinical model, and the difference was statistically significant (0.81 vs. 0.74, P = 1.84 × 10⁻⁶). -4 (Z-score test), the results are shown in Table 7.

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

[0098]

[0099] Note: # P-value is calculated using the Z-score test to compare the clinical model with the comprehensive model.

[0100] All of the above results show that the established oral microbiome risk score (MRS) and its integrated model constructed in conjunction with clinical factors can be well validated in independent populations.

[0101] Example 5: Functional prediction of prognostic oral microbiota

[0102] To investigate the function of oral microbiota in patients with different oral microbial risks, this invention uses the PICRUSt2 tool to explore the potential metabolic functions of oral microbiota based on the 16S rRNA gene sequence of oral microbiota data from populations 1 and 2 in the above embodiments.

[0103] 5.1 Analysis Process

[0104] The PICRUSt2 tool was used to infer functional changes within the oral microbiome. Subsequently, the STAMP software was used to assess whether there were significant differences in metabolic pathways between the medium / high-risk and low-risk MRS groups, with Bonferroni correction applied. A p-value less than 0.05 after correction was considered statistically significant. The correlation between oral microbiota and statistically significant differential pathways was analyzed using the Spearman rank correlation test.

[0105] 5.2 Results

[0106] Of the 344 identified KEGG pathways, those present in less than 30% of patients and with a mean relative abundance of less than 1% were excluded, resulting in 282 metabolic pathways. Patients were divided into two groups: a low-risk oral microbiome group and a medium / high-risk oral microbiome group. Significant differences were found in 16 pathways between the two groups, with 5 pathways significantly increased in the medium / high-risk group. These pathways were primarily associated with promoting cancer cell proliferation, particularly the polyamine biosynthesis superpathway II and polyamine biosynthesis, which are involved in polyamine synthesis—a process typically associated with rapid cancer cell proliferation. Among the 11 significantly enriched metabolic pathways in the low-risk MRS group, the N-acetylglucosamine (GlcNAc) and N-acetylgalactosamine pathways showed the greatest difference in mean abundance. This pathway is associated with the synthesis of GlcNAc and GalNAc, both of which can reduce inflammatory responses, regulate glycosylation, and inhibit cancer cell proliferation. Subsequently, the correlation between differentially enriched metabolic pathways and three identified oral microbiota was analyzed. The results showed that oral bacteria associated with an increased risk of colorectal cancer progression exhibited similar functional characteristics, but those associated with a decreased risk of colorectal cancer progression differed significantly. For example, Campylobacter fibrosus and Neisseria oralis were positively correlated with increased KEGG pathway activity in the intermediate / high-risk MRS group and negatively correlated with decreased KEGG pathway activity. In contrast, the bacteria enriched in the low-risk MRS group—Treponema intermedia—showed the opposite trend (see results). Figure 6 The above results suggest the potential biological mechanism of the oral microbiota biomarkers associated with colorectal cancer progression discovered in this invention, and further suggest the reliability of the above biomarkers in predicting colorectal cancer progression.

[0107] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. The application of a reagent for detecting the abundance of oral bacterial markers associated with colorectal cancer prognosis in the preparation of products predicting colorectal cancer prognosis, characterized in that: The oral bacterial markers are composed of Campylobacter gracilis , Neisseria oralis and Treponema medium The reagent is used to detect saliva samples from patients with colorectal cancer.

2. The application of reagents for detecting the abundance of oral bacterial markers in conjunction with products for detecting clinical factors in colorectal cancer patients in the preparation of products predicting the prognosis of colorectal cancer, wherein the oral bacterial markers are derived from... Campylobacter gracilis , Neisseria oralis and Treponema medium The reagent is used to detect saliva samples from patients with colorectal cancer, and the clinical factors are tumor stage, nerve bundle invasion, and lymph node metastasis.

3. The application according to claim 1 or 2, characterized in that: Products used to predict the prognosis of colorectal cancer include reagent kits, test strips, chips, or high-throughput sequencing platforms.

4. The application according to claim 1 or 2, characterized in that: The reagents were used to detect the abundance of oral bacterial markers using at least one of metagenomic sequencing, 16S rRNA sequencing, and qPCR quantitative detection.

5. The application according to claim 1 or 2, characterized in that: The reagents include at least one of the following: reagents for sample DNA extraction, reagents for metagenomic sequencing, reagents for 16S rRNA sequencing, or reagents for qPCR quantitative detection.

6. The application according to claim 1 or 2, characterized in that: The reagent includes at least one of a probe and a primer for detecting the oral bacterial markers.

7. The application according to claim 6, characterized in that: The primers are 16S rRNA primers for detecting the oral bacterial markers.

Images