Oral microbial marker and application thereof in diagnosis and prognosis of gastric cancer

Abstract

The invention relates to the technical field of microorganisms and the technical field of medicines, in particular to an oral microbial marker and application thereof in diagnosis and prognosis of gastric cancer. The oral microbial marker comprises at least one of the following components: Aggregate bacter (Aggregate bacter), Filifactor (Lactobacillus genus), Moryella (Moralea genus), and Leptotricha (cilia genus). The oral microbial marker is characterized in that the oral microbial marker comprises at least one component selected from the group consisting of the Aggregate bacter (Aggregate bacter), the Filifactor (Lactobacillus genus), the Moryella (Moralea genus) and the Leptotricha (Lactobacillus genus). The level change of the human oral flora in different prognosis outcomes of the gastric cancer is found for the first time, and the prognosis lifetime of the gastric cancer patient can be predicted by detecting the change of the flora. When being used for diagnosis, the oral microbial marker has the advantages of high specificity and high sensitivity. The clinical correlation of oral microbiota in gastric cancer prognosis is proved, and a noninvasive and highly sensitive tool is provided for identifying patients with bad outcome risks before treatment.

Description

Technical Field

[0001] The present invention relates to the fields of microbial technology and medical technology, and in particular to an oral microbial marker and application thereof in diagnosing the prognosis of gastric cancer. Background Art

[0002] Gastric cancer is the fifth most common malignancy in the world, with about half of cases occurring in China. Its pathophysiology involves a complex interaction of multiple genes and factors, including genetic, environmental, individual, and immune factors. Although current staging methods, such as the tumor-node-metastasis (TNM) staging system, remain the cornerstone of gastric cancer diagnosis, prognosis assessment, and optimized treatment, they have inherent limitations. Therefore, non-invasive, accurate, and personalized patient staging methods are essential to improve the prognosis and quality of life of gastric cancer patients.

[0003] In recent years, the association between oral microbiota and a variety of gastrointestinal diseases, including GC, has attracted widespread attention. Studies have shown that the presence of a large number of oral bacteria in the gastrointestinal tract increases the host's susceptibility to various diseases, including gastric cancer, colorectal cancer, cardiovascular disease, and neuropsychiatric diseases. With the advancement of detection technology, it has become feasible to use oral microbiota as a biomarker for disease diagnosis and prognosis assessment. In addition, artificial intelligence (AI), especially deep neural networks (DNN), has shown great potential in the medical field. AI technology can integrate new biomarkers and improve the accuracy of disease diagnosis. Therefore, establishing a diagnostic solution with high sensitivity and specificity that is convenient and economical is one of the most critical links in clinical practice. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an oral microbial marker that can be used to evaluate / assist in the diagnosis of gastric cancer prognosis.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] In a first aspect, the present invention provides an oral microbial marker comprising at least one of the orders Aggregatibacter, Filifactor, Moryella, and Leptotrichia.

[0007] The present invention has found through a large number of studies that Aggregatibacter, Filifactor, Moryella and Leptotrichia are closely related to the prognosis and survival of gastric cancer. Among them, Aggregatibacter and Leptotrichia are higher in the oral cavity of gastric cancer patients with poor prognosis (short survival, SS), and Filifactor and Moryella are higher in the oral cavity of gastric cancer patients with good prognosis (long survival, LS).

[0008] In the present invention, the term "microbial marker" refers to microorganisms with significant differences in abundance or grouping contributions in the oral microbiome of patients with different prognostic survival periods obtained by Boruta algorithm and LEfSe algorithm analysis.

[0009] In a second aspect, the present invention provides the use of a substance for detecting the above-mentioned oral microbial markers in the preparation of a product for evaluating / assisting diagnosis of gastric cancer prognosis.

[0010] Preferably, the substance includes a reagent for detecting the content or abundance of the oral microbial marker; the method for detecting the abundance of the oral microbial marker includes at least one of metagenomic sequencing, 16S rRNA amplicon sequencing, droplet digital PCR, and qPCR quantitative detection; the reagent includes at least one of a primer, a probe, an antisense oligonucleotide, an aptamer, and an antibody that is specific to the oral microbial marker.

[0011] Preferably, in the present invention, the substance for detecting the oral microbial marker can be various detection products suitable for detecting the oral microbial marker in the art, for example, it can be a PCR kit, a FISH kit, etc. The objects targeted by the detection product can be dental plaque, saliva, sputum, and tongue coating samples, so as to better reflect the oral flora. The preferred detection technology is the full length of the bacterial 16S rRNA gene or the V3-V4 region, so as to obtain the information of the oral microbial marker in the sample.

[0012] In another specific embodiment of the present invention, the substance used to detect Aggregatibacter, Filifactor, Moryella and Leptotrichia can be a substance used to detect the enrichment degree (abundance) of Aggregatibacter, Filifactor, Moryella and Leptotrichia, and the enrichment degree can be the content of Aggregatibacter, Filifactor, Moryella and Leptotrichia in a certain amount of sample (for example, unit volume, unit mass, etc.), the percentage in the flora, etc.

[0013] In another specific embodiment of the present invention, the method for using the material and / or kit for detecting Aggregatibacter, Filifactor, Moryella and Leptotrichia may include the following steps: obtaining the total DNA of the oral flora in the sample, amplifying and sequencing the bacterial 16S rRNA full-length gene or partial variable region characteristic tag sequence, and obtaining the enrichment information of Aggregatibacter, Filifactor, Moryella and Leptotrichia in the sample according to the sequencing results. For the classification of bacterial species, the amplicon sequence variant (ASV) method can be used.

[0014] In the present invention, the kit evaluates the therapeutic effect of gastric cancer and / or judges the prognosis survival period and probability of gastric cancer according to the enrichment degree of Aggregatibacter, Filifactor, Moryella and Leptotrichia in the sample, and the sample targeted is preferably a saliva sample. Generally speaking, the higher the enrichment degree of Filifactor and Moryella in the test results, and the lower the enrichment degree of Aggregatibacter and Leptotrichia, the better the therapeutic effect and / or the prognosis long survival period. The evaluation of the therapeutic effect of gastric cancer and / or the judgment of the prognosis of gastric cancer specifically refers to the evaluation of the long-term survival outcome of gastric cancer patients. The long-term survival can be predicted by a deep neural network model (DNN). For example, if the evaluator judges that the survival probability of 18 months (median survival period) is high (>50%), it indicates that the treatment result is good. If the survival probability of 18 months is low (≤50%), it indicates that the prognosis is poor.

[0015] In the present invention, the term "primer" refers to a 7 to 50 nucleic acid sequence that can form a base pair complementary to the template chain and acts as a starting point for replicating the template chain. Primers are usually synthesized, but naturally occurring nucleic acids can also be used. The sequence of the primer does not necessarily need to be exactly the same as the sequence of the template, as long as it is sufficiently complementary and can hybridize with the template. Additional features that do not change the basic properties of the primer can be mixed in. Examples of additional features that can be mixed in include methylation, capping, replacement of more than one nucleic acid by a homolog, and modification between nucleic acids, but are not limited to these. In the present application, the term "16s rRNA" refers to the rRNA that constitutes the 30S small subunit of the prokaryotic ribosome, which, on the one hand, has a large portion of its base sequence highly preserved, and on the other hand, some regions show high base sequence diversity (V1-V9 variable regions). In particular, there is almost no diversity between the same species, but diversity is shown between different species, so by comparing the full-length sequence or part of the variable region of 16S rRNA, prokaryotes can be effectively identified.

[0016] As an embodiment, in the present invention, the above primers can be used to amplify the 16SrRNA and other gene sequences that can identify the microorganisms retained in the corresponding microorganisms. After the sequence is amplified, the presence of the microorganism can be detected or the level of the microorganism can be measured by the generation of the target product. The sequence amplification method using primers can use a variety of methods known in the art. For example, polymerase chain reaction (PCR), reverse transcription-polymerase chain reaction (RT-PCR), multiplex PCR, touchdown PCR, hot start PCR, nested PCR, booster PCR, ENJ (real-time) PCR, Differential Display PCR (DD-PCR), Rapid Amplification of cDNA ends (RACE), inverse polymerase chain reaction, vector-mediated PCR, thermal asymmetric interlaced PCR, ligase chain reaction, repair chain reaction, transcription-mediated amplification, self-sustained sequence replication, and selective amplification reaction of target base sequences can be used, but the scope of the present invention is not limited thereto.

[0017] In the present invention, the term "probe" refers to a molecule that can bind to a specific sequence or subsequence or other part of another molecule. Unless otherwise indicated, the term "probe" generally refers to a polynucleotide probe that can bind to another polynucleotide (often referred to as a "target polynucleotide") through complementary base pairing. Depending on the stringency of the hybridization conditions, the probe can bind to a target polynucleotide that lacks complete sequence complementarity with the probe. The probe can be directly or indirectly labeled, including primers. Hybridization methods include, but are not limited to: solution phase, solid phase, mixed phase or in situ hybridization assays.

[0018] Exemplary probes in the present invention include PCR primers and gene-specific DNA oligonucleotide probes, such as microarray probes immobilized on microarray substrates, quantitative nuclease protection assay probes, probes linked to molecular barcodes, and probes immobilized on beads.

[0019] The probe has a base sequence complementary to a specific base sequence of the target gene. Here, the so-called "complementary" only needs to be hybridized, and may not be completely complementary. These polynucleotides usually have a homology of more than 80%, preferably more than 90%, more preferably more than 95%, and particularly preferably 100% with respect to the specific base sequence. These probes can be DNA or RNA, and can also be polynucleotides in which nucleotides in part or all of them are replaced by artificial nucleic acids such as PNA (Polyamide Nucleic Acid), LNA (locked Nucleic Acid, Bridged Nucleic Acid), ENA (2'-0,4'-C-Ethylene-bridged nucleic acid), GNA (Glycerol Nucleic Acid), TNA (Threose Nucleic Acid), etc.

[0020] Preferably, the product includes a kit, a chip, a system, or a device.

[0021] Preferably, the kit comprises a test tube or other suitable container, a reaction buffer, deoxynucleotide triphosphates (dNTPs), Taq-polymerase reverse transcriptase, SYBR Green fluorescent dye, DEPC-water and the like.

[0022] The method for using the kit comprises the following steps:

[0023] (1) Provide a saliva sample from the subject;

[0024] (2) extracting bacterial DNA from the saliva sample;

[0025] (3) Using specific identification reagents for oral flora to perform PCR amplification and sequencing to obtain sequencing data:

[0026] (4) ASV (amplicon sequence variant) analysis and species annotation were performed on the sequencing data to obtain the abundance data of Aggregatibacter, Filifactor, Moryella and Leptotrichia, which were analyzed using a neural network algorithm.

[0027] The largest ASV sequences of Aggregatibacter, Filifactor, Moryella, and Leptotrichia used in the analysis are as follows:

[0028] Aggregatibacter:

[0029] GGAATATTGCGCAATGGGGGCAACCCTGACGCAGCCATGCCGCGTGAATG

[0030] AAAGAAGGCCTTCGGGTTGTAAAGTTCTTTCGGTGACGAGGAAGGCGTGAT

[0031] GTTTAATAGGCATCACGATTGACGTTAATCACAGAAGAAGCACCGGCTAAC

[0032] TCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCGAGCGTTAATCGGAAT

[0033] AACTGGGCGTAAAGGGCACGCAGGCGGCTATTTAAGTGAGGTGTGAAATC

[0034] CCCGGGCTTAACCTGGGAATTGCATTTCAGACTGGGTAGCTAGAGTACTTT

[0035] AGGGAGGGGTAGAATTCCACGTGTAGCGGTGAAATGCGTAGAGATGTGGA

[0036] GGAATACCGAAGGCGAAGGCAGCCCCTTGGGAATGTACTGACGCTCATGTGCGAAAGCGTGGGGAGCAAACAGG;

[0037] Filifactor:

[0038] GTGGGGAATATTGCACAATGGGGGGAACCCTGATGCAGCAACGCCGCGTGAGTGAAGAAGGCATTCGTGTCGTAAAACTCTGTAGTAGGGGAAGAAAGAAATGACAGTACCCTAAAAGAAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGAATAACTGGGCGTAAAGGGTGCGCAGGTGGTTTAACAAGTTAGTGGTGAAAGGCATAGGCTCAACCAATGTAAGCCATTAAAACTGTTTAACTTGAGTGCAGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAATACCAGTGGCGAAGGCGACTTTCTGGACTGCAACTGACACTGAGGCACGAAAGCGTGGGGAGCAAACAGGATTA;

[0039] Moryella:

[0040] GTGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGCGTG

[0041] AGTGAAGAAGTACTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATG

[0042] ACGGTACCTGAGTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGT

[0043] AATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCG

[0044] CAGACGGTTGCGCAAGTCTGAAGTGAAATCCCGAGGCTTAACCACGGGAC

[0045] TGCTTTGGAAACTGTGCGACTTGAGTATCGGAGGGGCAGGCGGAATTCCT

[0046] AGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCGGTGGCGAAGG

[0047] CGGCCTGCTGGACGAAAACTGACGTTGAGGCTCGAAGGCGTGGGGAGCAAACAGGATTA;

[0048] Leptotrichia:

[0049] GTGGGGAATATTGGACAATGGGGGCAACCCTGATCCAGCAATTCTGTGTGC

[0050] ACGATGAAGGTTTTCGGATTGTAAAGTGCTTTCAGCAGGGAAGAAAAAAA

[0051] TGACGGTACCTGCAGAAGAAGCGACGGCTAAATACGTGCCAGCAGCCGCG

[0052] GTAATACGTATGTCGCAAGCGTTATCCGGAATTATTGGGCATAAAGGGCATC

[0053] TAGGCGGCCAGATAAGTCTGGGGTGAAAACTTGCGGCTCAACCGCAAGCC

[0054] TGCCCTGGAAACTATGTGGCTAGAGTACTGGAGAGGTGGACGGAACTGCA

[0055] CGAGTAGAGGTGAAATTCGTAGATATGTGCAGGAATGCCGATGATGAAGAT

[0056] AGTTCACTGGACGGTAACTGACGCTGAAGTGCGAAAGCTAGGGGAGCAAACAGGATTA;

[0057] (6) Predicting whether the subject has a high risk of prognosis for gastric cancer (i.e., short survival period).

[0058] In the present invention, the kit can be used to evaluate the therapeutic effect of various conventional treatment methods for gastric cancer and / or to judge the prognosis of gastric cancer. The treatment methods can be palliative surgery, radiotherapy, radiofrequency ablation, intraperitoneal perfusion and arterial interventional embolization perfusion, chemotherapy and molecular targeted drugs and other treatment methods for gastric diseases.

[0059] The kit is used to evaluate the therapeutic effect of gastric cancer and / or determine the prognosis survival period and probability of gastric cancer. The kit evaluates the prognosis survival period of gastric cancer based on the enrichment degree of Aggregatibacter, Filifactor and Leptotrichia in the sample. Generally speaking, the higher the enrichment degree of Filifactor in the sample of the test object and the lower the enrichment degree of Aggregatibacter and Leptotrichia, the better the treatment effect and / or prognosis survival period.

[0060] In a third aspect, the present invention provides a product for gastric cancer prognosis diagnosis, comprising a substance for detecting the above-mentioned oral microbial markers.

[0061] Preferably, the substance includes a reagent for detecting the content or abundance of the oral microbial marker; the method for detecting the abundance of the oral microbial marker includes at least one of metagenomic sequencing, 16S rRNA amplicon sequencing, droplet digital PCR, and qPCR quantitative detection; the reagent includes at least one of a primer, a probe, an antisense oligonucleotide, an aptamer, and an antibody that is specific to the oral microbial marker.

[0062] In a fourth aspect, the present invention provides a method for constructing a model for gastric cancer prognosis diagnosis, the method comprising constructing a model using the above-mentioned oral microbial markers.

[0063] Preferably, the input variable of the model is the relative abundance of the oral microbial marker; the method for determining the relative abundance of the oral microbial marker includes at least one of metagenomic sequencing, 16S rRNA amplicon sequencing, droplet digital PCR, and qPCR quantitative detection.

[0064] The present study found that the AI ​​model based on oral microbiota showed high sensitivity and accuracy in the diagnosis of gastric cancer, providing new possibilities for the prognostic risk assessment of gastric cancer.

[0065] In a fifth aspect, the present invention provides a system for gastric cancer prognosis diagnosis, the system comprising a computing device for judging the prognosis risk of gastric cancer based on the detection results of the oral microbial markers; and also comprising at least one of a detection device, a detection result collection device, a diagnosis result output device, and a diagnosis result sending device.

[0066] Preferably, the test result is obtained by testing a test sample of the subject to be tested; the test sample includes a saliva sample or a gastric mucosal sample of the subject to be tested.

[0067] Preferably, the "sample" includes cells, tissues, organs, body fluids (blood, lymph, etc.), digestive fluids, sputum, alveolar bronchial washings, urine, feces, etc.

[0068] Preferably, the sample is tissue or blood.

[0069] Preferably, the sample is an oral sample.

[0070] In a specific embodiment of the present invention, the sample is a saliva or gastric mucosa sample.

[0071] The beneficial effects of the present invention are:

[0072] The present invention has discovered for the first time the differences in the levels of oral flora in different prognostic manifestations of gastric cancer. By detecting the changes in the flora, the risk and prognosis of gastric cancer can be evaluated and predicted. The oral microbial markers of the present invention are used for diagnosis with high specificity and strong sensitivity; the collection and processing of the test samples of the test subjects is simple, non-invasive and low-cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 To screen oral microorganisms related to gastric cancer prognosis and survival, and to construct and optimize a gastric cancer prognosis neural network model based on oral microbiota; Figure 1 A is the contribution of Leptotrichia obtained by Boruta algorithm to the classification of gastric cancer prognosis patients with long-term survival (LS) and short-term survival (SS); Figure 1 B, LEfSe analysis showed that the relative abundance of Aggregatibacter was lower, while the relative abundance of Filifactor and Moryella was higher in gastric cancer patients in the LS group; Figure 1 C is a schematic diagram of DNN model construction;

[0074] Figure 1 D is a deep neural network model (DNN) constructed based on the relative abundance of Aggregatibacter, Filifactor, Moryella and Leptotrichia in the oral microbiome of 99 gastric cancer patients to predict the long-term and short-term survival of gastric cancer.

[0075] Figure 2 To verify the accuracy of oral microbial markers for gastric cancer prognosis with external data, DNN based on oral microbial markers was used in ( Figure 2 A) Training set (n = 77) and ( Figure 2 B) The 3-year and 5-year survival of 111 gastric cancer patients were predicted on the test set (n=34). The DNN based on oral microbial markers was used in the training set ( Figure 2 C) and the test set ( Figure 2 D) Kaplan-Meier survival curve; Figure 2 Figure EF shows the decision curve analysis and comparison of DNN and TNM staging based on oral microbial markers in the training set and test set. DETAILED DESCRIPTION

[0076] In order to better illustrate the purpose, technical solutions and advantages of the present invention, the present invention will be further described below in conjunction with specific embodiments.

[0077] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0078] Before further describing the specific embodiments of the present invention, it should be understood that the scope of protection of the present invention is not limited to the specific specific embodiments described below; it should also be understood that the terms used in the examples of the present invention are for describing the specific specific embodiments rather than for limiting the scope of protection of the present invention; in the present specification and claims, unless otherwise expressly stated herein, the singular forms "a", "-" and "the" include plural forms.

[0079] When the embodiments give numerical ranges, it should be understood that, unless otherwise specified in the present invention, both endpoints of each numerical range and any numerical value between the two endpoints can be selected. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those generally understood by those skilled in the art. In addition to the specific methods, equipment, and materials used in the embodiments, according to the grasp of the prior art by those skilled in the art and the record of the present invention, any methods, equipment, and materials of the prior art similar or equivalent to the methods, equipment, and materials described in the embodiments of the present invention can also be used to realize the present invention.

[0080] Unless otherwise stated, the experimental methods, detection methods, and preparation methods disclosed in the present invention all adopt conventional techniques in the field of molecular biology, biochemistry, chromatin structure and analysis, analytical chemistry, cell culture, recombinant DNA technology, and related fields.

[0081] In addition, molecular immunological methods widely used in the art can be used for detecting microorganisms or measuring microorganism levels in the present invention.

[0082] Example 1: Screening oral microorganisms associated with gastric cancer prognosis and survival, and constructing and optimizing a gastric cancer prognosis neural network model based on oral microbiota

[0083] 1. Sample collection

[0084] From June 2019 to June 2020, saliva from gastric cancer patients was collected from the General Hospital of the People's Liberation Army and the Civil Aviation General Hospital and stored in a -80°C refrigerator.

[0085] Inclusion criteria were: (1) aged between 18 and 80 years, (2) patients with pathological diagnosis of gastric cancer TNM stage II / III and receiving treatment; (3) no antibiotics or probiotic products were used 60 days before sample collection. The details of the entire experimental process and the subsequent data release were agreed to by the patients themselves, and they signed an informed consent form. After tracking and following up the survival of the enrolled patients, 99 patients were finally selected for intestinal microecological analysis, of which 44 patients had a survival period of ≥ 3 years.

[0086] 2. 16S rRNA gene sequencing

[0087] (2.1) Bacterial genomic DNA was extracted using the CTAB method.

[0088] Add 1000μL CTAB lysis buffer to a 2.0mL EP tube, add lysozyme, add the saliva swab sample to the lysis buffer, and place in a 65℃ water bath. Invert and mix several times during the process to fully lyse the sample. Centrifuge to obtain the supernatant, add the mixed solution (phenol (pH=8.0): chloroform: isoamyl alcohol=25:24:1), invert and mix, and centrifuge at 12000rpm for 10min. Take the supernatant, add chloroform: isoamyl alcohol (24:1), invert and mix, and centrifuge at 12000rpm for 10min. Pipette the supernatant into a 1.5mL centrifuge tube, add isopropanol, shake up and down, and precipitate at -20℃. Centrifuge at 12000rpm for 10min, pour out the liquid, and be careful not to pour out the precipitate. Wash twice with 1mL 75% ethanol. The remaining small amount of liquid can be collected by centrifugation again, and then sucked out with a gun tip. Blow dry on the clean bench or dry at room temperature (DNA samples should not be too dry, otherwise they will be difficult to dissolve). Add ddH 2 Dissolve the DNA sample in 50% HO and incubate at 55-60°C for 10 min to aid dissolution if necessary. Add 1 μL of RNase A to digest RNA and place at 37°C for 15 min.

[0089] (2.2) DNA sample purity and concentration determination

[0090] DNA concentration was determined by spectrophotometer and DNA integrity was checked by electrophoresis. 2 O was diluted to 10 ng / μL DNA for construction of 16S amplification library.

[0091] (2.3) 16S rRNA primer construction

[0092] In order to obtain relatively accurate phylogenetic information, the present invention uses the following primers to perform PCR amplification on the bacterial 16S rDNA V3-V4 variable region:

[0093] 515F: 5'-GTGYCAGCMGCCGCGGTAA-3',

[0094] 805R: 5'-GGACTACHVGGGTWTCTAAT-3'.

[0095] (2.4) PCR amplification, product purification and library construction

[0096] Prepare 12 μL PCR reaction system: 6 μL SYBR Green, 0.5 μL each of upstream and downstream primers and 5 μL genomic DNA, mix well, and react on a fluorescent real-time quantitative PCR instrument.

[0097] The reaction conditions were set as follows: 95°C pre-denaturation for 2 min, 94°C denaturation for 30 s, 56°C annealing for 25 s, 72°C extension for 25 s, 30 cycles, and finally 72°C extension for 5 min. The PCR amplification products were detected by 2% agarose gel electrophoresis, and the target fragments were recovered using the AxyPrep PCR Cleanup kit. The purified PCR products were quantified using the Quant-iT PicoGreen dsDNA Assay Kit on the Qbit fluorescence quantitative system, and the qualified library concentration should be above 2 nM.

[0098] (2.5) Illumina sequencing

[0099] After gradient dilution of each qualified sequencing library (Index sequence cannot be repeated), mix them in the corresponding proportion according to the required sequencing amount, denature them into single strands by NaOH for sequencing, and use Illumina sequencer for 2×250bp double-end sequencing.

[0100] 3. Data Analysis

[0101] (3.1) Data preprocessing

[0102] QIIME2 sequencing data was used for assembly, quality control and filtering. A naive Bayes classifier was used to train ASV classification, and the GreenGenes2 database was used as a reference library to annotate ASV species.

[0103] (3.2) Characteristic microbial analysis of oral flora

[0104] The results of the marker analysis using oral flora data from 99 gastric cancer patients with different prognoses are as follows: Figure 1 As shown. LEfSe analysis and Boruta algorithm (version 8.0.0) were used to identify microbial biomarkers that significantly contributed to the classification of gastric cancer patients with long survival (LS, ≥3 years) and short survival (SS, <3 years). Leptotrichia obtained by Boruta algorithm significantly contributed to the classification of gastric cancer patients with long survival (LS) and short survival (SS) prognosis (Boruta parameters: pValue = 0.05, mcAdj = TRUE, maxRuns = 100, doTrace = 0); LEfSe analysis showed that the relative abundance of Aggregatibacter was lower in gastric cancer patients in the LS group, while the relative abundance of Filifactor and Moryella was higher (LEfSe threshold: P < 0.05, LDA score > 3).

[0105] (3.3) Deep neural network model construction

[0106] A total of 99 gastric cancer patients were randomly divided into a training set (n=69) and a test set (n=30) in a ratio of 7:3. There were no differences in age, gender, overall survival and TNM stage between the training set and the test set. Leptotrichia, Aggregatibacter, Filifactor and Moryella were used as features to construct a deep neural network model using the neuralnet (version 1.44.2) toolkit (key parameters: algorithm = "rprop-", hidden = c(20,20,20), stepmax = 1e+09, rep = 1, act.fct = "logistic", err.fct = "ce") for predicting the prognosis and survival of gastric cancer patients.

[0107] The deep neural network model showed extremely high classification performance, with classification AUCs of 0.998 (95% CI, 0.995-1.000) and 0.986 (95% CI, 0.972-1.000) for the short-term survival (SS) group and the long-term survival (LS) group, respectively.

[0108] Example 2: Verification of the ability of the oral microbiome-based deep neural network model to assess gastric cancer prognosis

[0109] This paper analyzes the data of 111 gastric cancer patients from the TCMA database. The microbial community in gastric tumor tissue was analyzed, and it was found that the gastric cancer prognosis characteristic microorganisms Aggregatibacter, Filifactor and Leptotrichia from the oral cavity were also present in the gastric mucosal tissue of gastric cancer patients; and the relative abundance of these three oral microorganisms in the gastric microecology was used to construct a DNN model, which can also accurately predict the survival risk of gastric cancer patients.

[0110] The results showed that the microorganisms showed significant predictive performance in the 3-year and 5-year survival rates of gastric cancer patients: in the training data set (n = 77), the AUCs obtained by the DNN model were: 3-year survival = 0.978 (95% CI, 0.935-1.000), 5-year survival = 1.000 (95% CI, 1.000-1.000) ( Figure 2 A); in the validation set (n = 34), AUCs were: 3-year survival = 0.838 (95% CI, 0.621-1.000), 5-year survival = 0.951 (95% CI, 0.856-1.000) ( Figure 2 B), and can more accurately predict the prognosis and survival rate of cancer patients ( Figure 2 C-2D). The survival curve comparison analysis showed that DNN performed better than TNM staging in both the training and test datasets ( Figure 2 E-2F), demonstrating that the DNN model has superior clinical applicability in predicting high-risk prognosis and low-risk of gastric cancer.

[0111] Example 3: Preparation of Gastric Cancer Prognosis Risk Assessment Kit

[0112] According to the correlation between Aggregatibacter, Filifactor and Leptotrichia and gastric cancer prognosis, the gastric cancer prognosis survival period can be evaluated by detecting the abundance of Aggregatibacter, Filifactor and Leptotrichia in oral microbial samples. The present invention provides a kit for evaluating gastric cancer prognosis survival based on detecting the abundance of Aggregatibacter, Filifactor and Leptotrichia.

[0113] The kit comprises the following components: a DNA extraction reagent, a primer pair for specifically detecting Aggregatibacter, Filifactor and Leptotrichia, a reaction buffer, deoxynucleotide triphosphates (dNTPs), Taq-polymerase reverse transcriptase, DEPC-water, SYBRGreen fluorescent dye and the like.

[0114] The primer pairs for specific detection of Aggregatibacter, Filifactor and Leptotrichia are:

[0115] Aggregatibacter:

[0116] F: 5'-CTAGGTATTGCGAAACAATTTG;

[0117] R: CCTGAAATTAAGCTGGTAATC-3';

[0118] Filifacto:

[0119] F: 5'-CAGGTGGTTTAACAAGTTAGTGG-3';

[0120] R: 5'CTAAGTTGTCCTTAGCTGTCTCG-3';

[0121] Leptotrichia:

[0122] F: 5'-TATCGGAGAGGTGGACGGAA;

[0123] R: TCGCACTTCAGCGTCAGTTA-3'.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. An oral microbial marker, characterized in that: The invention comprises at least one of the orders Aggregatibacter, Filifactor, Moryella and Leptotrichia.

2. Use of a substance for detecting the oral microbial marker as claimed in claim 1 in the preparation of a product for evaluating / assisting diagnosis of gastric cancer prognosis.

3. The use according to claim 2, characterized in that The substance includes a reagent for detecting the content or abundance of the oral microbial marker; the method for detecting the abundance of the oral microbial marker includes at least one of metagenomic sequencing, 16S rRNA amplicon sequencing, droplet digital PCR, and qPCR quantitative detection; the reagent includes at least one of a primer, a probe, an antisense oligonucleotide, an aptamer, and an antibody that is specific to the oral microbial marker.

4. The use according to claim 2, characterized in that The products include test kits, chips, systems, and devices.

5. A product for gastric cancer prognosis diagnosis, characterized in that: Comprising a substance for detecting the oral microbial marker as claimed in claim 1.

6. The product for gastric cancer prognosis diagnosis according to claim 5, characterized in that: The substance includes a reagent for detecting the content or abundance of the oral microbial marker; the method for detecting the abundance of the oral microbial marker includes at least one of metagenomic sequencing, 16S rRNA amplicon sequencing, droplet digital PCR, and qPCR quantitative detection; the reagent includes at least one of a primer, a probe, an antisense oligonucleotide, an aptamer, and an antibody that is specific to the oral microbial marker.

7. A method for constructing a model for gastric cancer prognosis diagnosis, characterized in that: The construction method comprises using the oral microbial markers described in claim 1 to construct a model.

8. The method for constructing a model for gastric cancer prognosis diagnosis according to claim 7, characterized in that: The input variable of the model is the relative abundance of the oral microbial marker as claimed in claim 1; the method for determining the relative abundance of the oral microbial marker includes at least one of metagenomic sequencing, 16S rRNA amplicon sequencing, droplet digital PCR, and qPCR quantitative detection.

9. A system for gastric cancer prognosis diagnosis, characterized in that: The system comprises a computing device for judging the prognosis risk of gastric cancer based on the detection results of oral microbial markers according to claim 1; and also comprises at least one of a detection device, a detection result collection device, a diagnosis result output device, and a diagnosis result sending device.

10. The system for gastric cancer prognosis diagnosis according to claim 9, characterized in that: The test result is obtained by testing a test sample of the test subject; the test sample includes a saliva sample and a gastric mucosa sample of the test subject.

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