Esophageal cancer early screening model based on oral microorganisms and construction method thereof

Abstract

The invention belongs to the technical field of biological medicine and analysis, and provides an esophageal cancer early screening model based on oral microorganisms and a construction method thereof. The model is an oral cavity microbial flora as follows: Prevotella, Granulicalala, Rothia, Prevotella, Blauria, Abiotrophia, Peptostreptococcus, Actinomycetes, Burkholderia, Akkermansia, Fudania, Kineothrix, Bacillus, Duncaniella, and Eisenbergiella, and the oral cavity microbial flora is selected from the group consisting of the following microbial flora: the oral cavity microbial flora, the oral cavity microbial flora, the oral cavity microbial flora, the oral cavity microbial flora, the oral cavity microbial flora, the oral cavity microbial flora, the oral cavity microbial flora, the oral cavity microbial flora, the oral cavity microbial flora, the oral cavity microbial flora, the oral cavity microbial flora and the oral cavity microbial flora. By identifying non-invasive biomarkers, early-stage asymptomatic esophageal cancer patients can be found.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine and analysis technology, and specifically relates to an early screening model for esophageal cancer based on oral microorganisms and a construction method thereof. Background Art

[0002] Esophageal cancer is a malignant tumor of the upper gastrointestinal tract that develops from atypical hyperplasia to precancerous lesions, carcinoma in situ, and metastatic cancer. It ranks ninth among the most common cancers in the world and sixth among the causes of cancer deaths. Epidemiological research reports show that in 2020, there were 604,100 new cases of esophageal cancer (6.3 / 100,000) and 544,076 deaths (5.6 / 100,000) worldwide. By 2040, this number will increase to 957,000 cases (6.3 / 100,000) and 880,000 cases (5.6 / 100,000), with more than 50% of patients occurring in China [1-4]. However, the cause of such a high incidence of esophageal cancer is still unclear, resulting in a lack of primary etiological prevention. Therefore, secondary prevention of "screening" and "early diagnosis and early treatment" has become the main way to prevent esophageal cancer [5]. Early screening is one of the most effective ways to detect cancer and save lives. Studies have shown that if detected in the early stages, more than 90% of cancer patients can be successfully treated [5]. Unfortunately, the development of early screening strategies for esophageal cancer in my country has been limited over the past few decades, and early screening methods based on upper gastrointestinal endoscopy are still the only option at present [1-3]. During upper gastrointestinal endoscopy, a thin and flexible tube with a small camera at the end (called an endoscope) is passed down the patient's throat into the esophagus. This is an invasive examination method with poor compliance among subjects and is not easy to popularize, so esophageal cancer cannot be detected in time. In addition, due to the lack of specific symptoms in the early stage, most patients are diagnosed in the middle and late stages and quickly progress to the late stage with limited treatment options and no hope of cure, resulting in high mortality [6,7]. The current status of clinical treatment of esophageal cancer, coupled with the lack of primary prevention targets, urgently need a non-invasive method that is easy to screen, diagnose and treat early to control the progression of esophageal cancer.

[0003] As the second largest bacterial reservoir in the human body[8], the oral microbiome is considered to be a potential biomarker for predicting head and neck cancer[9], lung cancer

[10] , and colorectal cancer

[11] . Although previous studies have shown that patients with esophageal cancer also have oral flora imbalance[12,13] and poor oral hygiene is associated with an increased risk of esophageal cancer, the predictive value of oral flora for esophageal cancer is still unclear due to the small sample size and the lack of periodontitis patients as another control group besides healthy controls[14,15]. Since periodontitis is one of the risk factors for esophageal cancer, it can also cause oral flora imbalance

[16] . Some pathogenic bacteria of periodontitis (such as Porphyromonas gingivalis and Fusobacterium nucleatum) not only exist in cancer lesions, but also promote cancer progression through multiple mechanisms, which is a key issue affecting the accuracy of diagnostic / screening models[17-19]. It is crucial to determine whether the oral microbiome of cancer patients is different from that of periodontitis patients. If the oral flora of the two groups is basically the same, existing biomarkers may mistakenly diagnose periodontitis patients as cancer patients. Therefore, it is necessary to recruit not only healthy people and cancer patients, but also periodontitis patients to build a cancer screening model based on oral flora. This is the main limitation of previous studies and the most important factor affecting sensitivity and accuracy. Summary of the invention

[0004] In order to solve the problems that the early screening methods for esophageal cancer patients in my country are currently limited, the subjects' compliance is poor, and it is difficult to popularize it widely, the present invention provides an early screening model for esophageal cancer based on oral microorganisms and a construction method thereof.

[0005] The present invention is implemented by the following technical solution: an early screening model for esophageal cancer based on oral microorganisms, wherein the model uses the following oral microbial flora as markers: Prevotella , Granulicatella , Rothia , Prevotellamassilia , Blautia , Abiotrophia , Peptostreptococcus , Actinomyces , Burkholderia , Akkermansia , Fudania , Kineothrix , Bacillus , Duncaniella , Eisenbergiella .

[0006] Furthermore, the relative abundance information of the oral microbial flora was used as the independent variable of the model, and whether the patient was an esophageal cancer patient was used as the dependent variable. The Logistic regression model was used for modeling. The formula of the model constructed by Logistic regression was: y = 1 / (1 + e^(2.89285631363524 + -0.130555223035498 *Prevotella +-0.234624415130977 * Granulicatella +-0.123421428328489 * Rothia +0.0305284360696491 * Prevotellamassilia + 2.72774911990433 * Blautia +-0.671714432121879 * Abiotrophia +-0.643868902165489 * Peptostreptococcus +1.81604325065481 * Actinomyces +9.28516953416389 * Burkholderia +-3.52739890519049 * Akkermansia +9.46455532514789 * Fudania +-7.93770666127306 * Kineothrix +-127.099900821647 * Bacillus +-93.3296419351167 * Duncaniella +-37.9213542816937 * Eisenbergiella )); The name of the bacterial community in the formula refers to the relative abundance information of the bacterial community.

[0007] The present invention also provides a method for constructing the oral microbiome-based esophageal cancer early screening model, comprising the following steps: (1) Collect oral swab samples from healthy patients, oral swab samples from patients with periodontitis, and oral swab samples from patients with esophageal cancer as analysis samples; (2) Extract DNA from the analysis samples and build libraries. Use Qubit3.0 Fluorometer and FEMTO Pulsesystem to test library quality. Qualified libraries are sequenced using Pacbio Sequel platform. (3) Analysis of the composition and structure of oral flora in qualified libraries: using Bray-Curtis sample distance (4) Constructing an early screening model (initial model): The "Rand" function in EXCEL was used to divide the cohort into a discovery cohort and a validation cohort. First, based on the discovery cohort, the random forest method randomforest V4.6.12 was used, with parameters: randomForest obs ~ ., data = data, importance = T to screen species that distinguish patients with esophageal cancer. The Logistic regression algorithm R-3.6.3 function glm parameters: glm (obs ~ ., family = binomial(), data) was used to construct the model. The area under the curve (AUC), specificities, and sensitivity in the receiver operating characteristic curve (ROC) were used to determine the classification effect of the initial model.

[0008] (5) Model simplification: The effect of eliminating a single species on the efficacy indicators of the esophageal cancer early screening model (initial model) was investigated by the “one-by-one elimination” method. Finally, four species with low Mean Decrease Gini index that had little effect on the model efficacy indicators were eliminated. The early screening model was reconstructed using the Logistic algorithm as the ultimate simplified model, and the ultimate simplified model was verified in the validation cohort. Similarly, the area under the curve (AUC), accuracy (Specificities), and sensitivity (Sensitivities) in the receiver operating characteristic curve (ROC) were used as the model efficacy indicators.

[0009] The model has a good esophageal screening effect. The specific parameters are as follows: the accuracy of the model in the discovery cohort is 0.839, the sensitivity is 0.911, the area under the receiver operating curve is 0.932, and the 95% confidence interval is 0.900-0.965. After evaluation by the receiver operating curve, the accuracy of the model in the validation cohort is 0.768, the sensitivity is 0.820, the area under the curve is 0.856, and the 95% confidence interval is 0.806-0.906.

[0010] The method of the present invention only requires the collection of oral swab (saliva) samples from patients, and the subsequent sequencing, analysis and diagnosis process is completed by professionals to achieve esophageal cancer screening. The method of the present invention has the advantages of being non-invasive, convenient and easy to popularize. The subjects can complete the sample collection work at home by themselves, which can achieve early detection and early treatment of esophageal cancer.

[0011] The present invention develops an efficient, specific and sensitive early screening method for esophageal cancer by clarifying the differences in oral flora between esophageal cancer patients and healthy people and periodontitis patients, and then identifying the specific flora characteristics of esophageal cancer patients. By recruiting 109 age-matched healthy volunteers, 138 periodontitis patients and 224 esophageal cancer patients in Shanxi Province, a high-incidence area of ​​esophageal cancer in the country, oral swab samples of the subjects were collected, and 16S-rDNA sequencing and flora characteristics of the samples were analyzed. Subsequently, an early screening model based on oral flora was constructed by the relative abundance of the indicator flora, and the receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic effect of the indicator flora, so that the early screening model can successfully screen out esophageal cancer patients from healthy people and periodontitis patients. The present invention provides a non-invasive screening method for the screening and early diagnosis of esophageal cancer, thereby providing patients with the opportunity for early treatment. The present invention identifies non-invasive biomarkers to discover early asymptomatic esophageal cancer patients, thereby improving their poor prognosis and saving lives. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a quality control flow chart for oral swab flora sequencing data; Figure 2 The principal coordinates plot (PCoA plot) of the diversity differences in the composition and structure of oral flora between patients with esophageal cancer and healthy controls and patients with periodontitis; Figure 3 Schematic diagram of using the “Rand” function to randomly divide the study cohort into a discovery cohort and a validation cohort; Figure 4 To discover 20 genus-level species in the cohort that can be used to build an esophageal cancer early screening model through random forest screening; Figure 5 In order to screen out 19 genus-level species as indicator species for constructing an esophageal cancer early screening model (initial model) based on the characteristics of multiple evaluation indicators of the receiver operating curve (area under the curve AUC, sensitivity, accuracy); Figure 6 This is the receiver operating characteristic curve of the esophageal cancer early screening model (initial model) in the discovery cohort. The AUC marked in the figure is the area under the curve of the receiver operating characteristic curve; Figure 7 The effect of eliminating a single species on the efficacy indicators of the esophageal cancer early screening model (initial model) was investigated by the "one by one elimination" method. The species marked in red indicated the species that had a greater impact on the model efficacy indicators after elimination, and the species marked in blue indicated the species that had an impact on the model efficacy indicators after elimination. Solobacterium , Catonella , Filifactor , Bergeyella , the remaining 15 Genus species were used for modeling; Figure 8 This is the receiver operating characteristic curve of the esophageal cancer early screening model (ultimate simplified model) constructed using 15 Genus species in the discovery cohort. The AUC marked in the figure is the area under the curve of the receiver operating characteristic curve; Figure 9 This is the receiver operating characteristic curve of the esophageal cancer early screening model (ultimate simplified model) applied to the validation cohort. The AUC marked in the figure is the area under the curve of the receiver operating characteristic curve. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0014] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, and the disclosure and materials cited therein are hereby incorporated by reference.

[0015] Technical equivalents to the specific embodiments described that are apparent to those skilled in the art using no more than routine experimentation are intended to be encompassed by this application.

[0016] The experimental methods in the following examples are all conventional methods unless otherwise specified. The instruments and equipment used in the following examples are all conventional laboratory instruments and equipment unless otherwise specified; the experimental materials used in the following examples are all purchased from conventional biochemical reagent stores unless otherwise specified.

[0017] The reagents and consumables used in this example are shown in Table 1, and the instruments are shown in Table 2.

[0018] Table 1: Reagents and consumables Table 2: Instruments used 1. Experimental Methods 1. Recruitment of research subjects: This cohort is a case-control study design. In 2023-2024, 109 healthy people (H) were recruited from the Physical Examination Center of the First Hospital of Shanxi Medical University, 138 periodontitis patients (P) were recruited from the Shanxi Provincial Stomatological Hospital, and 224 esophageal cancer patients (EC) were recruited from the Shanxi Provincial Cancer Hospital. Oral swab samples were collected from the enrolled population, and the staff collected information including gender, age, medical history, smoking and drinking history, and brushing frequency.

[0019] Inclusion criteria for healthy people: people over 50 years old who come to the hospital for physical examination; exclusion criteria: patients with obvious inflammation and periodontitis in the oral cavity; bacterial or viral infection of tonsils, salivary glands or throat within 1 month before sampling; periodontal treatment within 3 months before sampling; general anesthesia surgery, radiotherapy and chemotherapy in the past six months; patients with oral squamous cell carcinoma, head and neck squamous cell carcinoma, colorectal cancer; vulnerable groups such as pregnant women.

[0020] Inclusion criteria for patients with periodontitis: patients diagnosed with periodontitis on admission (over 50 years old); exclusion criteria: oral mucosal lesions; bacterial or viral infection of tonsils, salivary glands or throat within 1 month before sampling; periodontal treatment within three months before sampling; surgery, radiotherapy or chemotherapy within the past six months; patients with oral squamous cell carcinoma, head and neck squamous cell carcinoma, colorectal cancer; vulnerable groups such as pregnant women.

[0021] Inclusion criteria for patients with esophageal cancer: patients diagnosed with esophageal cancer upon admission; Exclusion criteria: patients with obvious inflammation in the oral cavity; bacterial or viral infection of the tonsils, salivary glands or throat within 1 month before sampling; periodontal treatment within 3 months before sampling; minors, pregnant women and other vulnerable groups who underwent general anesthesia surgery in the past six months.

[0022] The information of all subjects is shown in Table 3.

[0023] Table 3: Information of the subjects 2. Oral swab collection method: Before sampling, drink about 50 ml of clean water to fully rinse the mouth for about 10 seconds, and spit it out. Insert the swab into the mouth, and the swab head fully contacts the inside of the cheek and the upper and lower gum mucosa. Rub it up and down with the force of brushing teeth, and rotate the swab at the same time to make the swab head fully contact the oral mucosa. Repeat this action for 1 minute, put the swab back into the sterile collection tube, and store it at low temperature.

[0024] 3. DNA extraction and library construction A. DNA extraction (use Hipure Universal DNA kits for extraction, refer to the instructions for specific experimental methods, the following is a brief process): 1) Transfer the swab sample to a 2 ml centrifuge tube, immediately add 500 ul buffer ATL (provided in the kit) and 20 ul proteinase K (provided in the kit), and incubate at 56°C with shaking for 1 hour; 2) Add 500ul buffer AL (provided in the kit) and 500ul anhydrous ethanol and vortex for 10s; 3) Place Hipure DNA Mini Colum I (provided in the kit) in a 2 ml collection tube, transfer the mixture (no more than 750 μl) to the column, and centrifuge at 10,000 g for 1 min. 4) Discard the waste liquid at the bottom, put the column back into the collection tube, transfer the remaining mixed liquid to the column, centrifuge at 10,000 g for 1 min, and discard the filtrate and collection tube; 5) Discard the waste liquid at the bottom, place the column in a new collection tube, add 500ul buffer GW1 (diluted with ethanol) to the column, and centrifuge at 10000g for 1min; 6) Discard the waste liquid at the bottom, put the column back into the collection tube, add 650ul Buffer GW2 to the column (diluted with ethanol), and centrifuge at 10000g for 1min; 9) Discard the waste liquid at the bottom, put the column back into the collection tube, centrifuge at 10000g for 3 minutes, and spin dry the column; 10) Place the column in a 1.5 ml centrifuge tube, add 50-100 μl of Buffer AE (provided in the kit) preheated to 70°C to the center of the column membrane, place at room temperature for 3 minutes, and centrifuge at 10,000 g for 1 minute; 11) Discard the DNA binding column and use the DNA for subsequent experiments or freeze it at -20℃.

[0025] B. PCR amplification: Template: extracted DNA, 16S V1-V9 primers (with barcode): 27F—AGRGTTYGATYMTGGCTCAG; 1492R—RGYTACCTTGTTACGACTT.

[0026] Amplification system: 50ul TransGen High-Fidelity PCR SupermMix mixture, including 0.2uM upstream and downstream primers, 5ng DNA template. The amplification program is shown in Table 4.

[0027] Table 4: Amplification Procedure C. Purification and recovery with AMPure® PB Beads: If the sample concentration or purity does not meet the standard, AMPure® PB Beads can be used to purify and recover the amplified product. Take 1-2ug of the amplified product into a new 1.5ml centrifuge tube.

[0028] Take out AMP PB Bead from 2-8℃ 30min in advance and let it stand to equilibrate to room temperature; mix AMP PB Bead thoroughly, pipette an equal volume and add it to the amplified product sample after screening; at room temperature, slowly pipette and mix 15 times with a wide pipette tip, do not flick the tube, and incubate at room temperature for 5min; after the incubation, centrifuge instantly with a handheld centrifuge; place the sample on a magnetic rack, and carefully remove the supernatant after the solution is clarified; Keep the sample on the magnetic stand, add 200ul of freshly prepared 80% ethanol to rinse the magnetic beads, incubate at room temperature for 30s, and remove the supernatant; repeat the previous step; remove the tube and place it in a handheld centrifuge for instant centrifugation for 3s; place the sample on the magnetic stand for about 10s, and carefully remove the ethanol; keep the sample on the magnetic stand, open the lid and dry the magnetic beads at room temperature for about 5-10min; remove the sample from the magnetic stand, add 45.5ul Elution Buffer, use a wide-mouth pipette tip to gently blow and mix, let it stand at room temperature for 15min, then place it on the magnetic stand to wait for the solution to clarify; carefully pipette 25ul of supernatant into a new nuclease-free EP tube.

[0029] D. Damage repair: The reaction system is as follows: Amplification product 25.0ul; DNA Damage Repair Mix v2, 2.0ul; Total Volume, 27.0ul; incubate at 37℃ for 30min, cool to 4℃ for storage, and proceed to the next step.

[0030] E. End repair and addition of A: The reaction system is as follows: amplification product 27.0ul, End Prep Mix, 3.0ul; Total Volume, 30.0ul; incubate at 20℃ for 10min, incubate at 65℃ for 30min, store at 4℃, and proceed to the next step.

[0031] F. Linker: The reaction system is as shown in Table 5. Incubate at 20°C for 60 min, store at 4°C, and proceed to the next step.

[0032] Table 5: Reaction system G. AMPure® PB Bead purification: Add 3ul Elution Buffer to the reaction system in the previous step; mix thoroughly, pipette 45ul and add it to the ligation product; at room temperature, slowly pipette and mix 15 times with a wide-mouth pipette tip, do not flick the tube, and incubate at room temperature for 5min; after the incubation, centrifuge instantly; place the sample on the magnetic rack, and carefully remove the supernatant after the solution is clarified; keep the sample on the magnetic rack, add 200ul of freshly prepared 80% ethanol to rinse the magnetic beads, incubate at room temperature for 30s, and carefully remove the supernatant; repeat the previous step; remove the tube and centrifuge instantly for 3s using a handheld centrifuge.

[0033] The library quality was tested using Qubit3.0 Fluorometer and FEMTO Pulse system, and qualified libraries were sequenced using Pacbio Sequel platform.

[0034] 4. Analysis of the differences in the composition and structure of oral flora between esophageal cancer patients, healthy people and periodontitis patients: β diversity is an indicator of the degree of difference in species composition and structural diversity between different ecosystems. In this implementation plan, the Bray-Curtis sample distance calculation method ( ) to perform β-diversity statistics, and finally present the differences in microecological composition and structure between different cohorts using PCOA diagrams (ref. Figure 2 ). Mainly uses the Vegan package of R language for memory operations and statistics.

[0035] 5. Construction of the esophageal cancer early screening model (initial model): The "Rand" function in EXCEL was used to divide the cohort into a discovery cohort and a validation cohort. First, based on the discovery cohort, the random forest method (randomforest (V4.6.12), parameters: randomForest (obs ~ ., data = data, importance = T)) was used to screen species that can distinguish esophageal cancer patients, and the Logistic regression algorithm (R-3.6.3 function glm parameters: glm (obs ~ ., family =binomial(), data)) was used to construct the early screening model (initial model) based on the discovery cohort, and the corresponding model algorithm was generated. The model classification effect was determined by the receiver operating characteristic curve (ROC).

[0036] 6. Simplification of the initial model: The effect of eliminating a single species on the efficacy indicators of the esophageal cancer early screening model (initial model) was investigated through the "one by one elimination" method. Finally, after trying to eliminate the four species with a low Mean Decrease Gini index and little effect on the model efficacy indicators after elimination, the early screening model was reconstructed through the Logistic algorithm as the ultimate simplified model, and the ultimate simplified model was verified in the verification cohort. Similarly, the area under the curve (AUC), accuracy (Specificities), and sensitivity (Sensitivities) in the receiver operating characteristic curve (ROC) were used as the model efficacy indicators.

[0037] 2. Experimental results: 1. There are significant differences in the oral flora structure between esophageal cancer patients and healthy people and patients with periodontitis: Through the analysis of the microecological β diversity of the enrolled population, it was found that the oral microecological structure of esophageal cancer patients was significantly different from that of healthy people and patients with periodontitis. In the PCOA analysis based on Bray-curtis distance, the sample distribution of esophageal cancer patients was significantly different from that of healthy people and patients with periodontitis, and after statistical analysis, P=0.001 (see Figure 2 ). This indicates that the oral flora of patients with esophageal cancer is different from that of patients without esophageal cancer (including healthy people and patients with periodontitis) in terms of overall composition and structure.

[0038] 2. Random allocation into discovery cohort and validation cohort: The healthy subjects, periodontitis patients, and esophageal cancer patients were randomly divided into discovery cohort and validation cohort by using the “Rand” function in the EXCEL worksheet (see Figure 3 ), where the discovery cohort is used to build an early screening model, and the validation cohort is used to re-validate the diagnostic effect of the constructed screening model.

[0039] 3. Use the random forest model to screen for marker species (genus level) that can be used for early screening and diagnosis of esophageal cancer: First, in the discovery cohort, healthy people and periodontitis patients were mixed as the non-esophageal cancer patient cohort, and the random forest model was further used to screen the best genus-level species that can distinguish esophageal cancer patients from non-esophageal cancer patients. The Mean Decrease Gini index obtained by random forest analysis was sorted in descending order, and the top 20 genus-level species ranked by the Mean Decrease Gini index (respectively Prevotella , Gardnerella , Granulicatella , Rothia , Prevotellamassilia , Blautia , Abiotrophia , Peptostreptococcus , Actinomyces , Filifactor , Solobacterium, Bergeyella , Burkholderia , Akkermansia , Catonella , Fudania , Kineothrix , Bacillus , Duncaniella , Eisenbergiella ) as a marker species for constructing early screening models ( Figure 4 ).

[0040] 4. Use receiver operating characteristic curves to evaluate the diagnostic / screening / classification effects of the above 20 genus-level marker species for esophageal cancer patients: the area under the curve (AUC) is an indicator to measure the quality of a species for diagnosis / screening / classification of esophageal cancer, generally between 0.5-1.0. The closer to 1.0, the better the effect of the species for esophageal cancer diagnosis. In addition, accuracy (Specificities) and sensitivity (Sensitivities) can respectively reflect the reliability and sensitivity of a species in the diagnosis / screening / classification of esophageal cancer. The higher the accuracy, the lower the false positive rate, and the higher the sensitivity, the lower the false negative rate. Figure 5 According to the results, only Gardnerella The AUC of the species was 0.305, and the AUCs of the other species were all greater than 0.5, indicating that Gardnerella It does not have a good ability to distinguish esophageal cancer patients, so in the subsequent Logistic modeling analysis, Gardnerella , and the remaining 19 genus-level species ( Prevotella , Granulicatella , Rothia , Prevotellamassilia , Blautia , Abiotrophia , Peptostreptococcus , Actinomyces , Filifactor , Solobacterium , Bergeyella , Burkholderia , Akkermansia , Catonella , Fudania , Kineothrix , Bacillus , Duncaniella , Eisenbergiella ) to build an esophageal cancer early screening model (initial model).

[0041] 5. Modeling of esophageal cancer early screening model (initial model): First, in the discovery cohort, the 19 species screened by the random forest model were modeled using the Logistic regression model. The independent variable of the model is the relative abundance information of each bacterial community, and the dependent variable is the disease group (whether the patient is esophageal cancer). After logistic regression analysis and modeling, the receiver operating curve was used to evaluate the prediction effect of the model. It was found that the early screening model (initial model) based on these 19 species had high accuracy (0.927) and sensitivity (0.839) in the discovery cohort, and the area under the curve reached 0.934 ( Figure 6 ), indicating that the early screening model constructed based on 19 genus-level species has excellent diagnosis / screening / classification capabilities for esophageal cancer in the discovery cohort.

[0042] 6. Simplification of the esophageal cancer early screening model: Through the method of "eliminating one by one", the effect of eliminating a single species on the evaluation index of the esophageal cancer early screening model (initial model) was examined. The species with a greater impact on the model evaluation index after elimination were marked in red, and the species with an impact on the model evaluation index after elimination were marked in blue ( Figure 7 ), and finally determine the elimination based on the level of Mean DecreaseGini index Solobacterium , Catonella , Filifactor , Bergeyella , the remaining 15 Genus species were used to model the esophageal cancer early screening model (final simplified version).

[0043] 7. Modeling of esophageal cancer early screening model (ultimate simplified model): In the discovery cohort, the 15 species screened above were modeled using logistic regression analysis. The independent variable of the model was the relative abundance information of each bacterial community, and the dependent variable was the disease group (whether the patient was esophageal cancer). After logistic regression analysis and modeling, the receiver operating curve was used to evaluate the prediction effect of the model. It was found that the early screening model (ultimate simplified model) based on these 15 species had high accuracy (0.839) and sensitivity (0.911) in the discovery cohort, and the area under the curve reached 0.932 ( Figure 8 ). Compared with the initial model before simplification, the area below the line is almost the same, the sensitivity is improved, and the accuracy is reduced. Overall, the esophageal cancer early screening model (ultimate simplified model) still maintains a good ability to diagnose / screen / classify esophageal cancer.

[0044] The formula of the model constructed by Logistic regression is: y = 1 / (1 + e^(2.89285631363524 + -0.130555223035498 * Prevotella +-0.234624415130977 *Granulicatella +-0.123421428328489 * Rothia + 0.0305284360696491 * Prevotellamassilia +2.72774911990433 * Blautia +-0.671714432121879 * Abiotrophia +-0.643868902165489 * 消化链球菌属 + 1.81604325065481 * 放线菌属 +9.28516953416389 * 伯克霍尔德菌属 +-3.52739890519049 * 阿克曼氏菌属 +9.46455532514789 * 富达尼亚属 +-7.93770666127306 * 动胶菌属 +-127.099900821647 * 芽孢杆菌属 +-93.3296419351167 * 邓卡尼氏菌属 +-37.9213542816937 * 艾森伯格菌属 )); The name of the bacterial community in the formula refers to the relative abundance information of the bacterial community.

[0045] 8. Evaluation of the classification effect of the esophageal cancer early screening model (ultimate simplified model) in the validation cohort: The early screening model constructed above was further applied to the validation cohort and evaluated through the receiver operating curve ( Figure 9 ), and found that the accuracy of the model was 0.768, the sensitivity was 0.820, and the area under the curve was 0.856, indicating that the model still had a good effect in diagnosing and screening esophageal cancer in an independent validation cohort.

[0046] References: 1. Bray, F., et al., 《2018年全球癌症统计数据:全球癌症负担(GLOBOCAN)对185个国家36种癌症的发病率和死亡率估计》 185个国家36种癌症的全球发病率和死亡率估计。 CACancer J Clin, 2018.68(6): p. 394-424. 2. Zheng, R., et al., 中国的癌症发病率和死亡率 2016. Journal of the National Cancer Center, 2022.2(1): p. 1-9. 3. Chen, R., et al., 中国食管癌发病率的模式和趋势 基于癌症登记数据的中国食管癌发病率和死亡率分析。Journal ofthe National Cancer Center, 2023.3(1): p. 21-27. 4、Morgan, E., et al., 2020年食管鳞状细胞癌和食管腺癌发病率及死亡率的全球格局 以及到2040年的预测:来自GLOBOCAN 2020的新估计。 中国食管鳞状细胞癌的精准筛查 Gastroenterology,2022.163(3): p. 649-658.e2. 5、He, Z. and Y. Ke, 2003 - 2015年中国癌症生存率的变化: 17个人口癌症登记处的汇总分析。 Chin J Cancer Res, 2020.32(6): p. 673-682. 6、Zeng, H., et al., 食管癌 人类口腔微生物群 Lancet GlobHealth, 2018.6(5): p. e555-e567. 7、Rustgi, AK and HB El-Serag, 口腔微生物群与头颈部鳞状细胞癌发病风险的关联 N Engl J Med,2014.371(26): p. 2499-509. 8、Dewhirst, FE, et al., 口腔微生物群与肺癌风险:三项前瞻性队列研究的分析 J Bacteriol,2010.192(19): p. 5002-17. 9、Hayes, RB, et al., 口腔微生物群作为新型生物标志物用于 ​ JAMA Oncol, 2018.4(3): p. 358-365. 10、Vogtmann, E., et al., ​ ​ J Natl Cancer Inst, 2022.114(11): p.1501-1510. 11、Rezasoltani, S., et al., ​ Colorectal Cancer Screening. Cancers (Basel), 2022.15(1). 12、Peters, BA, et al., Oral Microbiome Composition Reflects Prospective Risk for Esophageal Cancers. Cancer Res, 2017.77(23): p. 6777-6787. 13、Chen, X., et al., Oral Microbiota and Risk for Esophageal Squamous Cell Carcinoma in a High-Risk Area of China. PLoS One, 2015.10(12): p.e0143603. 14. Liu, F., et al., Oral microbiome and risk of malignant esophageal lesions in a high-risk area of China: A nested case-control study. Chin JCancer Res, 2020.32(6): p. 742-754. 15. Li, Z., et al., Characterization of the Oral and Esophageal Microbiota in Esophageal Precancerous Lesions and Squamous Cell Carcinoma. Front Cell Infect Microbiol, 2021.11: p. 714162. 16. Wen, BW, et al., Cancer risk among gingivitis and periodontitis patients: a nationwide cohort study. Qjm, 2014.107(4): p. 283-90. 17. Ding, N., et al., Fusobacterium nucleatum Infection Induces Malignant Proliferation of Esophageal Squamous Cell Carcinoma Cell by Putrescine Production. Microbiol Spectr, 2023.11(2): p. e0275922. 18、Nomoto, D., et al., Fusobacterium nucleatum promotes esophageal squamous cell carcinoma progression via the NOD1 / RIPK2 / NF-kappaB pathway. Cancer Lett, 2022.530: p. 59-67. 19、Chen, MF, et al., Porphyromonas gingivalis promotes tumor progression in esophageal squamous cell carcinoma. Cell Oncol (Dordr), 2021.44(2): p. 373-384.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An early screening model for esophageal cancer based on oral microorganisms, characterized by: The model uses the following oral microbial flora as markers: Prevotella , Granulicatella , Rothia , Prevotellamassilia , Blautia , Abiotrophia , Peptostreptococcus , Actinomyces , Burkholderia , Akkermansia , Fudania , Kineothrix , Bacillus , Duncaniella , Eisenbergiella .

2. The early screening model for esophageal cancer based on oral microorganisms according to claim 1, characterized in that: The relative abundance information of the oral microbial flora was used as the independent variable of the model, and whether the patient was an esophageal cancer patient was used as the dependent variable. The Logistic regression model was used for modeling. The formula of the model constructed by Logistic regression was: y = 1 / (1 + e^(2.89285631363524 + -0.130555223035498 * Prevotella +-0.234624415130977 * Granulicatella +-0.123421428328489 * Rothia + 0.0305284360696491 * Prevotellamassilia + 2.72774911990433 * Blautia +-0.671714432121879 * Abiotrophia +-0.643868902165489 * Peptostreptococcus + 1.81604325065481 * Actinomyces +9.28516953416389 * Burkholderia +-3.52739890519049 * Akkermansia +9.46455532514789 * Fudania +-7.93770666127306 * Kineothrix +-127.099900821647 * Bacillus +-93.3296419351167 * Duncaniella +-37.9213542816937 * Eisenbergiella )); The name of the bacterial community in the formula refers to the relative abundance information of the bacterial community.

3. The method for constructing an early screening model for esophageal cancer based on oral microorganisms according to claim 1 or 2, characterized in that: The following steps are involved: (1) Collect oral swab samples from healthy patients, oral swab samples from patients with periodontitis, and oral swab samples from patients with esophageal cancer as analysis samples; (2) Extract DNA from the analysis samples and build libraries. Use Qubit3.0 Fluorometer and FEMTO Pulse system to test library quality. Qualified libraries are sequenced using Pacbio Sequel platform. (3) For qualified libraries, the composition and structure of oral flora were analyzed: the Bray-Curtis sample distance calculation method was used to reflect the differences between samples and between communities; (4) Constructing an early screening model, i.e., the initial model: Use the "Rand" function in EXCEL to divide the cohort into a discovery cohort and a validation cohort; First, based on the discovery cohort, use the random forest method randomforest V4.6.12 with parameters: randomForestobs ~ ., data = data, importance = T to screen species that distinguish patients with esophageal cancer, and use the Logistic regression algorithm R-3.6.3 function glm with parameters: glm (obs ~ ., family = binomial(), data) to construct a model. Use the area under the curve (AUC), accuracy (Specificities), and sensitivity (Sensitivities) in the receiver operating characteristic curve (ROC) to determine the classification effect of the initial model; (5) Model simplification: The effect of eliminating a single species on the efficacy indicators of the esophageal cancer early screening model, i.e., the initial model, was investigated by the "one-by-one elimination" method. Finally, four species with low Mean Decrease Gini index and small impact on the model efficacy indicators were eliminated. The early screening model was constructed again using the Logistic algorithm as the ultimate simplified model. The ultimate simplified model was then verified in the validation cohort. Similarly, the area under the curve (AUC), specificities, and sensitivities in the receiver operating characteristic curve (ROC) were used as the efficacy indicators of the model.

4. The construction method according to claim 3, characterized in that: The specific parameters are as follows: the accuracy of the model in the discovery cohort was 0.839, the sensitivity was 0.911, the area under the receiver operating curve was 0.932, and the 95% confidence interval was 0.900-0.965; after receiver operating curve evaluation, the accuracy of the model in the validation cohort was 0.768, the sensitivity was 0.820, the area under the curve was 0.856, and the 95% confidence interval was 0.806-0.906.

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