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

Abstract

The present application relates to the technical field of microorganism and the technical field of medicine, and particularly relates to an oral microorganism marker and application thereof in diagnosis of prognosis of gastric cancer. The oral microorganism marker comprises at least one of Aggregatibacter, Filifactor, Moryella and Leptotrichia. The present application firstly discovers that the level of human oral flora changes in different prognosis outcomes of gastric cancer, and by detecting the change of the flora, the prognosis survival period of the patient with gastric cancer can be predicted. The oral microorganism marker has the advantages of high specificity and strong sensitivity in diagnosis. The present application proves the clinical correlation of the oral microorganism flora in the prognosis of gastric cancer, and provides a non-invasive and highly sensitive tool for identifying the patient at risk of poor outcome before treatment.

Description

Technical Field

[0001] This invention relates to the fields of microbiology and pharmaceutical technology, and in particular to an oral microbial biomarker and its application in the diagnosis of gastric cancer prognosis. Background Technology

[0002] Gastric cancer is the fifth most common malignant tumor worldwide, with approximately half of all cases occurring in China. Its pathophysiology involves complex interactions of multiple genes and factors, including genetic, environmental, individual, and immune factors. While current staging methods, such as the tumor-nodule-metastasis (TNM) staging system, remain the cornerstone of gastric cancer diagnosis, prognostic assessment, and optimized treatment, they have inherent limitations. Therefore, non-invasive, accurate, and personalized patient staging methods are crucial for improving the prognosis and quality of life of gastric cancer patients.

[0003] In recent years, the association between oral microbiota and various gastrointestinal diseases, including gastrointestinal tract infections (GC), has attracted widespread attention. Studies have demonstrated that the presence of a large number of oral bacteria in the gastrointestinal tract increases host susceptibility to various diseases, including gastric cancer, colorectal cancer, cardiovascular disease, and neuropsychiatric disorders. With advancements in detection technologies, utilizing oral microbiota as biomarkers for disease diagnosis and prognostic assessment has become feasible. Furthermore, artificial intelligence (AI), especially deep neural networks (DNNs), has shown great potential in the medical field. AI technology can integrate novel biomarkers to improve the accuracy of disease diagnosis. Therefore, establishing highly sensitive, specific, convenient, and economical diagnostic protocols is one of the most crucial aspects of clinical practice. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and provide an oral microbial biomarker that can be used to assess / assist in the diagnosis of gastric cancer prognosis.

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

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

[0007] Extensive research has revealed a close relationship between Aggregatibacter, Filifactor, Moryella, and Leptotrichia and the prognosis and survival of gastric cancer. Specifically, Aggregatibacter and Leptotrichia were found at higher levels in the oral cavity of gastric cancer patients with poor prognosis (short survival, SS), while Filifactor and Moryella were found at higher levels in the oral cavity of gastric cancer patients with good prognosis (long survival, LS).

[0008] In this invention, the term "microbial biomarker" refers to microorganisms whose abundance or group contribution is significantly different in the oral microbiome of patients with different prognostic survival periods, as obtained by Boruta algorithm and LEfSe algorithm analysis.

[0009] Secondly, the present invention provides the application of substances that detect the above-mentioned oral microbial markers in the preparation of products for assessing / aiding the 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 primers, probes, antisense oligonucleotides, aptamers, and antibodies that are specific to the oral microbial marker.

[0011] Preferably, in this invention, the substance used to detect the oral microbial markers can be any detection product suitable for detecting the oral microbial markers in the art, such as a PCR kit, a FISH kit, etc. The detection product can be used to detect samples such as dental plaque, saliva, sputum, and tongue coating, which can better reflect the oral flora. The preferred detection technology is the full-length bacterial 16S rRNA gene or the V3-V4 region, thereby obtaining information about the oral microbial markers 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. The enrichment degree can be the content of Aggregatibacter, Filifactor, Moryella, and Leptotrichia in a certain amount of sample (e.g., per unit volume, per unit mass, etc.), the percentage of them in the bacterial community, etc.

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

[0014] In this invention, the kit is used to assess the treatment efficacy and / or predict the prognosis and survival probability of gastric cancer based on the enrichment levels of Aggregatibacter, Filifactor, Moryella, and Leptorichia in a sample. The preferred sample is a saliva sample. Generally, higher enrichment levels of Filifactor and Moryella, and lower enrichment levels of Aggregatibacter and Leptorichia, correspond to better treatment efficacy and / or longer prognosis and survival. Specifically, assessing the treatment efficacy and / or predicting the prognosis of gastric cancer refers to evaluating the long-term survival outcome of gastric cancer patients. Long-term survival can be predicted using a deep neural network (DNN) model. For example, if the assessor determines a high probability of survival (>50%) at 18 months (median survival), it indicates a good treatment outcome; if the 18-month survival probability is low (≤50%), it indicates a poor prognosis.

[0015] In this invention, the term "primer" refers to a sequence of 7 to 50 nucleic acid sequences capable of forming a base pair complementary to the template strand and serving as a starting point for template strand replication. Primers are typically synthesized, but naturally occurring nucleic acids can also be used. The primer sequence does not necessarily need to be identical to the template sequence, as long as it is sufficiently complementary to hybridize with the template. Additional features that do not alter the basic properties of the primer can be incorporated. Examples of such additional features include methylation, capping, substitution of more than one nucleic acid with a homologue, and modifications between nucleic acids, but are not limited to these. In this application, the term "16S rRNA" refers to the 30S subunit of the prokaryotic ribosome, which, on the one hand, has a largely preserved base sequence, and on the other hand, exhibits high base sequence diversity in certain regions (V1-V9 variable regions). In particular, there is almost no diversity between species but diversity between species, thus prokaryotes can be effectively identified by comparing the full-length sequence or some variable regions of 16S rRNA.

[0016] As one implementation method, in this invention, the primers described above can be used to amplify the 16S rRNA and other gene sequences that can identify the microorganisms retained in the corresponding microorganisms. After amplification, the presence of the microorganisms or the level of microorganisms can be detected by whether or not the target product is generated. A variety of methods known in the art can be used for sequence amplification using primers. For example, polymerase chain reaction (PCR), reverse transcription-polymerase chain reaction (RT-PCR), multiplex PCR, touchdown PCR, hotstart 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 of target base sequences can be used, but the scope of the present invention is not limited thereto.

[0017] In this invention, the term "probe" refers to a molecule capable of binding to a specific sequence, subsequence, or other portion of another molecule. Unless otherwise specified, the term "probe" generally refers to a polynucleotide probe capable of binding 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. Probes can be labeled directly or indirectly, 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 this invention include PCR primers and gene-specific DNA oligonucleotide probes, such as microarray probes immobilized on a microarray substrate, 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, "complementary" means any hybridization, and does not have to be perfectly complementary. These polynucleotides typically have 80% or more, preferably 90% or more, more preferably 95% or more, and particularly preferably 100% homology with respect to the specific base sequence. These probes can be DNA or RNA, or they can be polynucleotides obtained by replacing some or all of their nucleotides with PNA (Polyamide Nucleic Acid), LNA (Locked Nucleic Acid, Bridged Nucleic Acid), ENA (2'-O,4'-C-Ethylene-bridged nucleic acids), GNA (Glycerol Nucleic Acid), TNA (Threose Nucleic Acid), etc.

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

[0021] Preferably, the kit includes test tubes or other suitable containers, reaction buffer, deoxyribonucleotide triphosphates (dNTPs), Taq polymerase reverse transcriptase, SYBR Green fluorescent dye, DEPC-water, etc.

[0022] The method of using the kit includes the following steps:

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

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

[0025] (3) PCR amplification and sequencing were performed using oral microbiota-specific recognition reagents to obtain sequencing data:

[0026] (4) Perform ASV (amplicon sequence variant) analysis and species annotation on the sequencing data to obtain abundance data of Aggregatibacter, Filifactor, Moryella and Leptotrichia, and analyze them 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) Predict whether the subject is at high risk of gastric cancer (i.e., short survival).

[0058] In this invention, the kit can be used to evaluate the therapeutic effects of various conventional treatments for gastric cancer and / or to determine the prognosis of gastric cancer. These treatments may include palliative surgery, radiotherapy, radiofrequency ablation, intraperitoneal perfusion and arterial interventional embolization, chemotherapy, and molecularly targeted drugs, among other treatments for gastric diseases.

[0059] The kit is used to evaluate the efficacy of gastric cancer treatment and / or to determine the prognostic survival and probability of gastric cancer. The kit assesses prognostic survival of gastric cancer based on the enrichment levels of Aggregatibacter, Filifactor, and Leptorichia in the sample. Generally, higher enrichment levels of Filifactor and lower enrichment levels of Aggregatibacter and Leptorichia in the sample correspond to better treatment efficacy and / or longer prognostic survival.

[0060] Thirdly, the present invention provides a product for the prognostic diagnosis of gastric cancer, 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 primers, probes, antisense oligonucleotides, aptamers, and antibodies that are specific to the oral microbial marker.

[0062] Fourthly, the present invention provides a method for constructing a model for the prognostic diagnosis of gastric cancer, the method comprising using the above-mentioned oral microbial markers for model construction.

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

[0064] This invention has found that AI models based on oral microbiota demonstrate high sensitivity and accuracy in the diagnosis of gastric cancer, providing new possibilities for prognostic risk assessment of gastric cancer.

[0065] Fifthly, the present invention provides a system for the prognostic diagnosis of gastric cancer, the system comprising a calculation device for determining the prognostic risk of gastric cancer based on the detection results of the oral microbial markers; and further comprising at least one of a detection device, a detection result collection device, a diagnostic result output device, and a diagnostic result transmission device.

[0066] Preferably, the detection result is obtained by detecting a test sample of the subject; the test sample includes a saliva sample and a gastric mucosa sample of the subject.

[0067] Preferably, the "sample" includes cells, tissues, organs, body fluids (blood, lymph, etc.), digestive juices, sputum, bronchial lavage fluid, 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 this invention are as follows:

[0072] This invention is the first to discover the differences in oral microbiota levels across different prognostic manifestations of gastric cancer. By detecting changes in the microbiota, the risk and prognosis of gastric cancer can be assessed and predicted. Diagnosis using the oral microbial biomarkers of this invention exhibits high specificity and sensitivity; the collection and processing of test samples is simple, non-invasive, and low-cost. Attached Figure Description

[0073] Figure 1 To screen oral microbiota associated with gastric cancer prognosis and survival, a gastric cancer prognostic neural network model based on oral microbiota was constructed and optimized. Figure 1 A represents the contribution of Leptotrichia obtained by the Boruta algorithm to the classification of patients with long-term survival (LS) and short-term survival (SS) gastric cancer prognosis. Figure 1 B-type LEfSe analysis showed that the relative abundance of Aggregatibacter was lower in the LS group of gastric cancer patients, while the relative abundance of Filifactor and Moryella was higher. 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 Leptorichia in the oral microbiome of 99 gastric cancer patients to predict the long-term and short-term survival of gastric cancer prognosis.

[0075] Figure 2 To validate the accuracy of oral microbial biomarkers in assessing the prognosis of gastric cancer using external data, a DNN based on oral microbial biomarkers was developed. Figure 2 A) Training set (n=77) and ( Figure 2 B) On the test set (n=34) data, the 3-year and 5-year survival of 111 gastric cancer patients were predicted; DNN based on oral microbial biomarkers was used on the training set (Figure 2 C) and test set ( Figure 2 Kaplan-Meier survival curves in D); Figure 2 The EF plot shows a comparison of decision curves for DNN and TNM staging based on oral microbial biomarkers on the training and test sets. Detailed Implementation

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

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

[0078] Before further describing 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 embodiments described below; it should also be understood that the terminology used in the embodiments of the present invention is for describing specific embodiments and not for limiting the scope of protection of the present invention; in the specification and claims of the present invention, unless otherwise expressly stated in the text, the singular forms "a", "—" and "this" include the plural forms.

[0079] When numerical ranges are given in the embodiments, it should be understood that, unless otherwise stated in the present invention, both endpoints of each numerical range and any value between the two endpoints may be selected. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. In addition to the specific methods, apparatus, and materials used in the embodiments, based on the knowledge of the prior art possessed by one of ordinary skill in the art and the description of this invention, any prior art methods, apparatus, and materials similar to or equivalent to those described, apparatus, and materials in the embodiments of this invention may be used to implement the present invention.

[0080] Unless otherwise stated, the experimental methods, detection methods, and preparation methods disclosed in this invention all employ conventional techniques in 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 field can be used for the detection of microorganisms or the determination of microbial levels in this invention.

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

[0083] 1. Sample collection

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

[0085] Inclusion criteria were: (1) age between 18 and 80 years, (2) pathological diagnosis of gastric cancer stage II / III (TNM stage) and receiving treatment, and (3) no use of antibiotics or probiotic products in the 60 days prior to sample collection. All details of the experimental process and subsequent data release were agreed upon by the patients, and informed consent forms were signed. After follow-up on the survival of enrolled patients, 99 patients were ultimately selected for gut microbiota analysis, of whom 44 had a survival of ≥3 years.

[0086] 2. 16S rRNA gene sequencing

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

[0088] Add 1000 μL of CTAB lysis buffer to a 2.0 mL EP tube, add lysozyme, and add the saliva swab sample to the lysis buffer. Incubate at 65°C, inverting several times during incubation to ensure complete lysis. Centrifuge and collect the supernatant. Add a mixture (phenol (pH=8.0):chloroform:isoamyl alcohol = 25:24:1), invert and mix, centrifuge at 12000 rpm for 10 min. Collect the supernatant, add chloroform:isoamyl alcohol (24:1), invert and mix, centrifuge at 12000 rpm for 10 min. Transfer the supernatant to a 1.5 mL centrifuge tube, add isopropanol, shake, and incubate at -20°C to precipitate. Centrifuge at 12000 rpm for 10 min, discard the liquid, being careful not to discard the precipitate. Wash twice with 1 mL of 75% ethanol. The remaining small amount of liquid can be collected by centrifugation again and then aspirated with a pipette tip. Dry in a clean bench or air dry at room temperature (DNA samples should not be too dry, otherwise they will be difficult to dissolve). Add ddH2O to dissolve the DNA sample; if necessary, incubate at 55-60℃ for 10 min to aid dissolution. Add 1 μL of RNase A to digest the RNA and incubate at 37℃ for 15 min.

[0089] (2.2) Determination of DNA sample purity and concentration

[0090] DNA concentration was determined using a spectrophotometer, and DNA integrity was detected by electrophoresis. DNA was diluted to 10 ng / μL using ddH2O for constructing 16S amplification libraries.

[0091] (2.3) Construction of 16S rRNA primers

[0092] To obtain relatively accurate phylogenetic information, this invention uses the following primers to perform PCR amplification of 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 a 12 μL PCR reaction system: mix 6 μL of SYBR Green, 0.5 μL each of forward and reverse primers, and 5 μL of genomic DNA thoroughly, and perform the reaction on a real-time quantitative PCR instrument.

[0097] The reaction conditions were set as follows: 95℃ pre-denaturation for 2 min, 94℃ denaturation for 30 s, annealing at 56℃ for 25 s, extension at 72℃ for 25 s, for a total of 30 cycles, with a final extension at 72℃ for 5 min. PCR amplification products were detected by 2% agarose gel electrophoresis, and the target fragment was recovered using the AxyPrep PCR Cleanup kit. The purified PCR products were quantified using the Quant-iT PicoGreen dsDNA Assay Kit on a Qbit real-time quantitative PCR system. A qualified library concentration should be above 2 nM.

[0098] (2.5) Illumina sequencing

[0099] After gradient dilution of each qualified sequencing library (index sequences are non-reproducible), they are mixed in the appropriate proportions according to the required sequencing volume, and denatured into single strands with NaOH before sequencing. 2×250bp paired-end sequencing is performed using an Illumina sequencer.

[0100] 3. Data Analysis

[0101] (3.1) Data Preprocessing

[0102] QIIME2 sequencing data were used for assembly, quality control, and filtering. A Naive Bayes classifier was used for ASV classification training, and the GreenGenes2 database was used as a reference library for ASV species annotation.

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

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

[0105] (3.3) Construction of Deep Neural Network Model

[0106] Ninety-nine gastric cancer patients were randomly assigned to a training set (n=69) and a test set (n=30) at a ratio of approximately 7:3. There were no differences between the training and test sets in terms of age, sex, overall survival, and TNM stage. Using Leptorichia, Aggregatibacter, Filifactor, and Moryella as features, a deep neural network model was constructed 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") to predict the prognostic survival of gastric cancer patients.

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

[0108] Example 2: Validation of the ability of a deep neural network model based on oral microbiota for prognostic assessment of gastric cancer

[0109] This invention analyzed data from 111 gastric cancer patients in the TCMA database. Analysis of the microbial community in gastric tumor tissue revealed that Aggregatibacter, Filifactor, and Leptotrichia, prognostic microorganisms originating from the oral cavity, were also present in the gastric mucosa of gastric cancer patients. Furthermore, a DNN model constructed using the relative abundance of these three oral microorganisms in the gastric microecology can accurately predict the survival risk of gastric cancer patients.

[0110] The results showed that the microorganisms exhibited significant predictive performance on the 3-year and 5-year survival rates of gastric cancer patients: in the training dataset (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), the 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 predict the prognostic survival rate of cancer patients relatively accurately. Figure 2 C-2D). Survival curve comparison analysis revealed that DNN outperformed TNM staging in both the training and test datasets. Figure 2 E-2F) demonstrates that the DNN model has superior clinical applicability in predicting high-risk and low-risk prognoses for gastric cancer.

[0111] Example 3: Preparation of a gastric cancer prognostic risk assessment kit

[0112] Based on the correlation between Aggregatibacter, Filifactor, and Leptorichia and gastric cancer prognosis, the prognostic survival of gastric cancer can be assessed by detecting the abundance of Aggregatibacter, Filifactor, and Leptorichia in oral microbial samples. This invention provides a kit for assessing gastric cancer prognostic survival based on the detection of Aggregatibacter, Filifactor, and Leptorichia abundance.

[0113] The kit includes the following components: DNA extraction reagent, primer pairs for specific detection of Aggregatibacter, Filifactor and Leptorichia, reaction buffer, deoxyribonucleotide triphosphates (dNTPs), Taq polymerase reverse transcriptase, DEPC-water, SYBR Green fluorescent dye, etc.

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

[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 solutions of the present invention and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. The application of reagents for detecting the abundance of oral microbial markers in the preparation of products for assessing / aiding the diagnosis of gastric cancer prognosis; characterized in that, The oral microbial markers include bacteria. Aggregatibacter , Filifactor and Leptotrichia The reagent includes at least one of primers, probes, and antisense oligonucleotides that are specific to the oral microbial markers.

2. The application as described in claim 1, characterized in that, The methods for detecting the abundance of oral microbial biomarkers include at least one of metagenomic sequencing, 16S rRNA amplicon sequencing, droplet digital PCR, and qPCR quantitative detection.

3. The application as described in claim 1, characterized in that, The products include reagent kits, chips, systems, and devices.

4. A method for constructing a model for prognostic diagnosis of gastric cancer, characterized in that, The construction method includes model construction using oral microbial biomarkers; the oral microbial biomarkers include the order Mycota. Aggregatibacter , Filifactor and Leptotrichia .

5. The method for constructing a model for prognostic diagnosis of gastric cancer as described in claim 4, characterized in that, The input variable of the model is the relative abundance of the oral microbial markers; the method for determining the relative abundance of the oral microbial markers includes at least one of metagenomic sequencing, 16S rRNA amplicon sequencing, droplet digital PCR, and qPCR quantitative detection.

6. A system for prognostic diagnosis of gastric cancer, characterized in that, The system includes a calculation device for determining the prognostic risk of gastric cancer based on the detection results of oral microbial markers; it also includes at least one of a detection device, a detection result collection device, a diagnostic result output device, and a diagnostic result transmission device; the oral microbial markers include the order Mycota. Aggregatibacter , Filifactor and Leptotrichia .

7. The system for prognostic diagnosis of gastric cancer as described in claim 6, characterized in that, The test results are obtained by testing the test samples of the test subjects; the test samples include saliva samples and gastric mucosa samples of the test subjects.

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