Pancreatic cancer biomarker based on intestinal flora and application thereof

Abstract

The invention provides a pancreatic cancer biomarker based on intestinal flora and application of the pancreatic cancer biomarker, and belongs to the technical field of disease detection and biological medicine. 20 intestinal flora biomarkers related to pancreatic cancer diagnosis are found for the first time, the abundance of the intestinal flora biomarkers shows significant difference in pancreatic cancer patients and healthy people, further, a pancreatic cancer diagnosis classifier model is constructed through a random forest machine learning algorithm, and tests verify that the pancreatic cancer diagnosis classifier model has a significant effect on pancreatic cancer diagnosis. The kit has high accuracy, specificity and sensitivity, can be used for diagnosing pancreatic cancer, and thus has good practical application value.

Description

Technical Field

[0001] The present invention belongs to the field of disease detection and biomedicine technology, and specifically relates to a pancreatic cancer biomarker based on intestinal flora and its application. Background Art

[0002] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention, and should not necessarily be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art.

[0003] Pancreatic cancer is one of the leading causes of cancer-related deaths worldwide, with an extremely high mortality rate. Due to the lack of effective early detection methods and the lack of obvious pancreatic cancer-specific symptoms, most pancreatic cancer patients are already in the late stage when diagnosed, and the prognosis is extremely poor. Currently, the diagnosis of pancreatic cancer relies on high-resolution imaging techniques, such as contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI), which can accurately classify and evaluate pancreatic cancer and be combined with ultrasound or endoscopically guided biopsy for confirmation. However, early screening applications are limited by the high cost and risks of invasive examinations.

[0004] In existing early screening, the biomarker widely used clinically is serum carbohydrate antigen 19-9 (CA19-9). However, due to the limited sensitivity and specificity of CA19-9 in detection, its application is limited. The main reasons include the negative expression of Lewis antigen in some patients, which leads to no increase in CA19-9 levels, and other factors such as obstructive jaundice leading to false positives. Studies have reported that the median sensitivity of CA19-9 for diagnosing pancreatic cancer is 79% (70%-90%), and the median specificity is 82% (68%-91%). Therefore, considering the importance of early detection of pancreatic cancer and the shortcomings of existing diagnostic methods, there is an urgent need for a feasible, sensitive and non-invasive diagnostic method to improve the early identification rate and timeliness of treatment of pancreatic cancer.

[0005] In recent years, the use of intestinal flora information as a diagnostic tool has shown great potential in disease detection. The intestinal microbiota plays an important role in maintaining human health. Studies have shown that intestinal flora imbalance is closely related to a variety of diseases, including intestinal inflammation, gastric cancer, liver cancer, colorectal cancer, and pancreatic cancer. Since the digestive tract is the habitat of most gastrointestinal microorganisms, changes in microbial ecology are closely related to the occurrence and development of a variety of diseases. On this basis, it is practical to use microbiome technology to identify one or more microbial targets to diagnose pancreatic cancer. However, there are currently few reports on the construction of intestinal flora diagnostic models for pancreatic cancer based on microbiome data. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides a pancreatic cancer biomarker based on intestinal flora and its application. Specifically, the present invention discovered 20 intestinal flora biomarkers related to the diagnosis of pancreatic cancer through comparative analysis of the intestinal flora of pancreatic cancer patients and healthy volunteers, and constructed a pancreatic cancer diagnosis classifier model based on the intestinal flora biomarkers through a random forest machine learning algorithm. By detecting the abundance of the biomarkers in the intestinal flora of the subjects, pancreatic cancer and healthy people can be effectively distinguished, which has good diagnostic value. Based on the above research results, the present invention is completed.

[0007] In order to achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0008] The first aspect of the present invention provides a pancreatic cancer biomarker, wherein the pancreatic cancer biomarker is selected from at least one of the following:

[0009] Methyloversatilis, Ralstonia, Candidatus_Saccharimonas, Arthrobacter, Enterorhabdus, Campylobacter, Candidatus_Planktophila, Klebsiella, Merdimonas, Ligilactoba cillus, Anaerotignum, hgcI_clade, Lachnospiraceae_NK4A136_group, Ruminococcus, Trueperella, Pseudoalteromonas, Candidatus_Megaira, Akkermansia, CHKCI002 and Vibrio.

[0010] Experiments have shown that when the above-mentioned intestinal microbial flora are used in combination, the sensitivity and specificity of diagnosing pancreatic cancer are significantly improved.

[0011] The second aspect of the present invention provides the use of a reagent for detecting the above biomarkers in the preparation of a product for diagnosing whether a subject has pancreatic cancer or predicting the risk of a subject having pancreatic cancer.

[0012] The third aspect of the present invention provides a product, which includes a reagent for detecting the above-mentioned biomarker, and the product is used to diagnose whether a subject has pancreatic cancer or predict the risk of whether a subject has pancreatic cancer.

[0013] A fourth aspect of the present invention provides a system for diagnosing whether a subject has pancreatic cancer or predicting the risk of a subject having pancreatic cancer, the system comprising:

[0014] i) an analysis module, the analysis module comprising: a detection substance for determining the relative abundance information of the biomarkers selected from the above in a sample to be tested by the subject, and;

[0015] ii) an evaluation module, the evaluation module comprising: diagnosing whether the subject has pancreatic cancer or predicting the risk of the subject having pancreatic cancer based on the relative abundance information of the biomarkers determined in i).

[0016] In a fifth aspect of the present invention, there is provided the use of a biomarker as a target for screening drugs for preventing or treating pancreatic cancer. In another specific embodiment of the present invention, the biomarker is the above-mentioned biomarker.

[0017] Beneficial technical effects of the above technical solution:

[0018] The above technical solution reported for the first time 20 intestinal flora biomarkers related to the diagnosis of pancreatic cancer, whose abundance showed significant differences between pancreatic cancer patients and healthy people. Furthermore, the above technical solution also constructed a pancreatic cancer diagnosis classifier model through a random forest machine learning algorithm. It has been verified by experiments that it has high accuracy, specificity and sensitivity, and can be used for the diagnosis of pancreatic cancer, so it has good practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0020] Figure 1 It is a pancreatic cancer intestinal flora biomarker under the random forest algorithm in an embodiment of the present invention.

[0021] Figure 2 : is a ROC curve diagram of the intestinal flora diagnostic model for the training set in an embodiment of the present invention.

[0022] Figure 3 It is a ROC curve diagram of the intestinal flora diagnostic model of the internal validation set in the embodiment of the present invention.

[0023] Figure 4 It is a ROC curve diagram of the intestinal flora diagnostic model of the external validation set in the embodiment of the present invention. DETAILED DESCRIPTION

[0024] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0025] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof. 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 terms used in the embodiments of the present invention are intended to describe specific embodiments, rather than to limit the scope of protection of the present invention.

[0026] The terms used in the present invention have the meanings commonly understood by those of ordinary skill in the relevant art. However, in order to better understand the present invention, some definitions and related terms are explained as follows:

[0027] "Biomarker" refers to "a characteristic that can be objectively detected and evaluated and can serve as an indicator of normal biological processes, pathological processes or pharmacological responses to therapeutic interventions". For example, nucleic acid markers, protein markers, cytokine markers, chemokine markers, carbohydrate markers, antigen markers, antibody markers, species markers and functional markers, etc. In the present invention, "biomarkers" refer to intestinal microbial markers, which can also be represented by "intestinal microorganisms" or "intestinal flora", because the pancreatic cancer-related microbial markers used in the present invention are all derived from fecal samples after intestinal metabolism of the subjects.

[0028] The biomarkers can be analyzed in batches using high-throughput sequencing for stool samples of healthy people and pancreatic cancer patients. Based on the high-throughput sequencing data, the healthy people and the pancreatic cancer patient group are compared to determine the specific nucleic acid sequences associated with the pancreatic cancer patient group.

[0029] In short, the steps are as follows:

[0030] Sample collection and processing: Fecal samples were collected from healthy people and pancreatic cancer patients, and DNA was extracted using a kit to obtain nucleic acid samples;

[0031] Library construction and sequencing: DNA library construction and sequencing were performed using high-throughput sequencing to obtain the nucleic acid sequences of intestinal microorganisms contained in fecal samples;

[0032] By the analysis method of bioinformatics, the specific intestinal microorganism nucleic acid sequence associated with pancreatic cancer patients is determined. First, the sequencing sequence (reads) is compared with the reference gene set (also referred to as the reference gene set, which can be a newly constructed gene set or a database of any known sequence, for example, using a known human intestinal microbial community non-redundant gene set). Next, based on the comparison results, the relative abundance of each gene in the nucleic acid sample from the healthy population and the pancreatic cancer patient group fecal sample is determined respectively. By comparing the sequencing sequence with the reference gene set, the sequencing sequence can be established with the gene in the reference gene set. Corresponding relationship, so that for a specific gene in the nucleic acid sample, the number of sequencing sequences corresponding thereto can effectively reflect the relative abundance of the gene. Thus, the relative abundance of genes in the nucleic acid sample can be determined by the comparison results, according to conventional mathematical statistical analysis methods. Finally, after determining the relative abundance of each gene in the nucleic acid sample, a statistical test is performed on the relative abundance of each gene in the nucleic acid samples from the feces of healthy people and pancreatic cancer patients. This can determine whether there are genes with significant differences in relative abundance between the healthy population and the pancreatic cancer patient population. If there are genes with significant differences, the gene is regarded as a biomarker of abnormal status, that is, a nucleic acid marker.

[0033] In addition, for known or newly constructed reference gene sets, they usually contain gene species information and functional annotations. Therefore, on the basis of determining the relative abundance of genes, the species information and functional annotations of genes can be further classified to determine the species relative abundance and functional relative abundance of each microorganism in the intestinal flora, and thus the species markers and functional markers of abnormal states can be further determined.

[0034] In a typical embodiment of the present invention, a pancreatic cancer biomarker is provided, wherein the pancreatic cancer biomarker is selected from at least one of the following:

[0035] Methyloversatilis, Ralstonia, Candidatus_Saccharimonas, Arthrobacter, Enterorhabdus, Campylobacter, Candidatus_Planktophila, Klebsiella, Merdimonas, Ligilactoba cillus, Anaerotignum, hgcI_clade, Lachnospiraceae_NK4A136_group, Ruminococcus, Trueperella, Pseudoalteromonas, Candidatus_Megaira, Akkermansia, CHKCI002 and Vibrio.

[0036] In another specific embodiment of the present invention, the pancreatic cancer biomarker is a group consisting of all the above pancreatic cancer biomarkers. Experiments have shown that when the above intestinal microbial flora are used in combination, the sensitivity and specificity of diagnosing pancreatic cancer are significantly improved.

[0037] The above-mentioned microbial flora is selected from a sample to be tested of a subject, and the sample to be tested may be a feces sample of the subject.

[0038] In the present invention, alignment similarity, which may also be referred to as alignment similarity, refers to the proportion of identical bases or amino acid residue sequences between a target sequence (sequence to be determined) and a reference sequence (known sequence) during sequence alignment.

[0039] In another specific embodiment of the present invention, there is provided use of a reagent for detecting the above biomarkers in the preparation of a product for diagnosing whether a subject has pancreatic cancer or predicting the risk of a subject having pancreatic cancer.

[0040] According to an embodiment of the present invention, the diagnosis or prediction comprises the following steps:

[0041] 1) Collecting samples from the subject to be tested;

[0042] 2) determining the relative abundance information of the biomarker in the sample obtained in step 1), wherein the biomarker is the biomarker according to the first aspect of the present invention;

[0043] 3) Comparing the relative abundance information described in step 2) with a reference data set or reference value.

[0044] According to the kit, the relative abundance of these markers in the intestinal flora can be determined, and thus, the obtained relative abundance values ​​can be used to determine whether the subject suffers from or is susceptible to pancreatic cancer, and to monitor the efficiency of the treatment effect of pancreatic cancer patients.

[0045] In another specific embodiment of the present invention, a product is provided, which includes a reagent for detecting the above-mentioned biomarker, and the product is used to diagnose whether a subject has pancreatic cancer or predict the risk of whether a subject has pancreatic cancer.

[0046] The product includes but is not limited to a kit, a device and / or an apparatus for detecting the (relative) abundance and / or quantity (number of OTUs) of a biomarker in a sample to be tested.

[0047] The relative abundance information of the biomarker is obtained using a sequencing method, which further includes: isolating a nucleic acid sample from a sample (feces) of a subject, constructing a DNA library based on the obtained nucleic acid sample, sequencing the DNA library to obtain sequencing results, and based on the sequencing results, comparing the sequencing results with a reference gene set to determine the relative abundance information of the biomarker.

[0048] In another specific embodiment of the present invention, the product may be a kit.

[0049] In another specific embodiment of the present invention, a system for diagnosing whether a subject has pancreatic cancer or predicting the risk of a subject having pancreatic cancer is provided, the system comprising:

[0050] i) an analysis module, the analysis module comprising: a detection substance for determining the relative abundance information of a selected biomarker in a sample to be tested by a subject, and;

[0051] ii) an evaluation module, the evaluation module comprising: diagnosing whether the subject has pancreatic cancer or predicting the risk of the subject having pancreatic cancer based on the relative abundance information of the biomarkers determined in i).

[0052] Wherein, in the i) analysis module, the biomarker is selected from at least one of the following:

[0053] Methyloversatilis, Ralstonia, Candidatus_Saccharimonas, Arthrobacter, Enterorhabdus, Campylobacter, Candidatus_Planktophila, Klebsiella, Merdimonas, Ligilactoba cillus, Anaerotignum, hgcI_clade, Lachnospiraceae_NK4A136_group, Ruminococcus, Trueperella, Pseudoalteromonas, Candidatus_Megaira, Akkermansia, CHKCI002 and Vibrio.

[0054] In another specific embodiment of the present invention, the biomarker is a group consisting of all the above biomarkers.

[0055] The relative abundance information of the biomarker is obtained by using a sequencing method, including: isolating a nucleic acid sample from the sample of the object, constructing a DNA library based on the obtained nucleic acid sample, sequencing the DNA library to obtain sequencing results, and based on the sequencing results, comparing the sequencing results with a reference gene set to determine the relative abundance information of the biomarker.

[0056] In another specific embodiment of the present invention, the reference gene set includes performing metagenomic sequencing on samples from multiple pancreatic cancer patients and multiple healthy controls to obtain a non-redundant gene set, and then merging the non-redundant gene set with the intestinal microbial gene set to obtain the reference gene set. The reference gene set in the present invention can be an existing gene set, such as an existing publicly available intestinal microbial reference gene set; or it can be performing metagenomic sequencing on samples from multiple pancreatic cancer patients and multiple healthy controls to obtain a non-redundant gene set, and then merging the non-redundant gene set with the intestinal microbial gene set to obtain the reference gene set, so that the reference gene set information obtained is more comprehensive and the detection results are more reliable.

[0057] The non-redundant gene set described in the present invention is explained as commonly understood by those skilled in the art, and is simply a set of genes remaining after removing redundant genes. Redundant genes generally refer to multiple copies of a gene appearing on a chromosome.

[0058] In another specific embodiment of the present invention, the sample is a stool sample.

[0059] In another specific embodiment of the present invention, the sequencing method is performed by a second generation sequencing method or a third generation sequencing method. The means of sequencing is not particularly limited, and sequencing by a second generation or third generation sequencing method can achieve fast and efficient sequencing.

[0060] In another specific embodiment of the present invention, in the above use, the sequencing method is performed by at least one selected from HiSeq2000, NovaSeq 6000, SOLiD, 454 and single molecule sequencing devices. Thus, the high throughput and deep sequencing characteristics of these sequencing devices can be utilized, which is beneficial to the analysis of subsequent sequencing data, especially the precision and accuracy when performing statistical tests.

[0061] As well as known to those skilled in the art, the step of associating the (relative) abundance of biomarkers with a certain possibility or risk can be implemented and realized in different ways. In the present invention, the (relative) abundance information of biomarkers is mathematically associated with the diagnostic problem. The determination of the (relative) abundance information of biomarkers can be achieved by any suitable prior art mathematical method, including but not limited to support vector machine, random forest, neural network, Bayesian network and Boosting, wherein the random forest algorithm is a method based on decision tree integration, with strong anti-overfitting ability and efficient feature processing performance. It improves the accuracy and robustness of the model by constructing multiple decision trees and voting or averaging the results. In addition, the random forest model can automatically identify and evaluate the importance of features, help screen out key biomarkers related to the disease, thereby accelerating the construction of the diagnostic model. Therefore, combining microbiome technology with machine learning methods can not only more quickly and accurately identify effective and reliable diagnostic markers, but also build a robust diagnostic model by cross-validation means, providing new solutions for early detection of diseases and precision medicine. Therefore, in a specific embodiment of the present invention, it is preferably a random forest.

[0062] In another embodiment of the present invention, the use of the above biomarkers as targets for screening drugs for preventing or treating pancreatic cancer is provided. In another embodiment of the present invention, the biomarkers are based on the above biomarkers. In another embodiment of the present invention, the effects of candidate drugs on these biomarkers before and after use can be used to determine whether the candidate drugs can be used to prevent or treat pancreatic cancer.

[0063] The present invention is further explained by the following examples, but they are not intended to limit the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0064] Example

[0065] 1. Screening method for intestinal flora biomarkers, steps:

[0066] 1.1 Pancreatic cancer patients and healthy subjects were recruited as two groups of subjects; a total of 187 subjects, including 97 in the pancreatic cancer group and 90 in the healthy control group, and stool samples were collected from the subjects.

[0067] 1.2 The pancreatic cancer patients described in the present invention refer to outpatients or inpatients at Peking Union Medical College Hospital from November 2020 to May 2024. The pancreatic cancer patients are all patients with pancreatic malignant tumors confirmed by puncture or postoperative pathology.

[0068] 1.3 The pancreatic cancer patients and healthy subjects included in this invention are excluded from (1) those who currently or previously suffered from other malignant tumors, infectious diseases, oral or gastrointestinal diseases, and mental or neurodegenerative diseases; (2) those who received specific medical procedures or interventions within a specified time, including: (a) use of antibiotics, hormone therapy or immunosuppressants in the past three months, (b) gastrointestinal reconstructive surgery in the past three months, (c) frequent use of laxatives, antidiarrheal drugs or therapeutic doses of probiotics in the past month, (d) oral or gastrointestinal examinations in the past three days

[0069] 1.4 The subjects were stored in sterile stool test tubes immediately after defecation and frozen at -80℃ within 2 hours;

[0070] 1.5 The 16s rRNA gene V3-V4 region amplicon was sequenced, and the primers for V3-V4 region sequencing were 341F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′). PCR amplification, PCR product purification, library preparation and quality inspection were then performed in sequence, and 2×250bp double-end sequencing was performed using the NovaSeq 6000 sequencer to obtain the raw data.

[0071] 1.6 Preprocess the raw data to obtain valid data, perform clustering and DADA2 denoising based on the valid data, and obtain ASV feature sequences and ASV abundance tables.

[0072] 1.7 Based on the results of clustering and denoising, the SILVA database and NT-16s database were used for species annotation to obtain the corresponding intestinal flora species information and species-based abundance distribution.

[0073] 1.8 The dataset was randomly stratified into a training set and a validation set with a ratio of 7:3. The training set samples were 130 cases and the validation set samples were 57 cases. The training set was used to screen pancreatic cancer diagnostic biomarkers and construct a diagnostic classifier. The validation set was used as an independent internal validation set to evaluate the performance of the classifier.

[0074] Selection of diagnostic markers: In the training set, the random forest algorithm was used for analysis and the top 20 bacterial genera were selected as intestinal flora biomarkers for distinguishing pancreatic cancer patients from healthy volunteers by sorting by Mean DecreaseAccuracy value. The bacterial genera were Methyloversatilis, Ralstonia, Candidatus_Saccharimonas, Arthrobacter, Enterorhabdus, Campylobacter, Candidatus_Planktophila, Klebsiella, Merdimonas, Ligilactobacillus, Anaerotignum, hgcI_clade, Lachnospiraceae_NK4A136_group, Ruminococcus, Trueperella, Pseudoalteromonas, Candidatus_Megaira, Akkermansia, CHKCI002, Vibrio( Figure 1 ).

[0075] 2. Construction and evaluation of the intestinal flora pancreatic cancer classifier model: Based on the 20 intestinal flora diagnostic markers selected above, the intestinal flora diagnostic model was constructed using the random forest algorithm, and the model was optimized and evaluated in the training set based on 5 repeated 10-fold cross-validation, and the model was evaluated in the validation set.

[0076] 2.1 Cross-validation: Using the training function in the caret package in R language, a 5-fold 10-fold cross-validation method was used. Cross-validation method: In the 5-fold 10-fold cross-validation process, the training set was further divided into 10 mutually exclusive subsets (folds). In each iteration, one of the 10 subsets was selected as the test set, and the remaining 9 subsets were used as the training set. This process was repeated 10 times to ensure that each subset was used as a test set.

[0077] 2.2 Use of test set: In each iteration, the constructed model is trained using the training set, and the test set is used for preliminary evaluation of the model performance. The cross-validation process will change the test set multiple times to fully evaluate the stability and generalization ability of the model.

[0078] 2.3 Evaluation indicators: The diagnostic performance indicators of the model are calculated on each test set, including ROC curve and AUC value. The final model performance is measured by the average indicator of all test sets to ensure that the model will not be overfitted or underfitted due to specific data partitioning.

[0079] 2.4 Internal validation: In an internal independent validation set, the diagnostic performance of the classifier was evaluated and the stability and accuracy of the classifier were determined.

[0080] 2.5 Diagnostic performance evaluation: The classifier was comprehensively evaluated in the training set and internal independent validation set through ROC curve, AUC value and other indicators to ensure the efficiency of the classifier. The AUC values ​​were 0.912 and 0.890 respectively. Figure 2 , Figure 3 ).

[0081] 3. External Validation:

[0082] 3.1 External validation data set: The inventors of the present invention obtained an external Chinese population pancreatic cancer intestinal flora data set based on the database PRJNA595787 published in the NCBI SRA database. Based on the published original data and the amplicon sequence of the 16srRNA gene V3-V4 region, the original data was obtained and the bacterial genus abundance table was compiled.

[0083] 3.2 External validation of the model: In the external validation set, the diagnostic efficacy of the fecal flora pancreatic cancer diagnostic classifier was evaluated by ROC curve, AUC value and other indicators. The AUC value in the external validation set can reach 0.878 ( Figure 4 ).

[0084] It should be noted that the above examples are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention is described in detail with reference to the given examples, those skilled in the art may modify or replace the technical solution of the present invention as needed without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A pancreatic cancer biomarker, characterized in that: The pancreatic cancer biomarker is selected from at least one of the following: Methyloversatilis, Ralstonia, Candidatus_Saccharimonas, Arthrobacter, Enterorhabdus, Campylobacter, Candidatus_Planktophila, Klebsiella, Merdimonas, Ligilactoba cillus, Anaerotignum, hgcI_clade, Lachnospiraceae_NK4A136_group, Ruminococcus, Trueperella, Pseudoalteromonas, Candidatus_Megaira, Akkermansia, CHKCI002 and Vibrio.

2. The pancreatic cancer biomarker according to claim 1, characterized in that The pancreatic cancer biomarkers are a group consisting of all pancreatic cancer biomarkers.

3. Use of a reagent for detecting the pancreatic cancer biomarker according to any one of claims 1 to 2 in the preparation of a product for diagnosing whether a subject has pancreatic cancer or predicting the risk of a subject having pancreatic cancer.

4. A product, characterized in that The product comprises a reagent for detecting the pancreatic cancer biomarker according to claim 1 or 2, and the product is used to diagnose whether a subject has pancreatic cancer or predict the risk of whether a subject has pancreatic cancer.

5. The product according to claim 4, characterized in that: The product includes a kit, a device and / or an apparatus for detecting the (relative) abundance and / or quantity (number of OTUs) of a biomarker in a sample to be tested; Furthermore, the product is a test kit.

6. A system for diagnosing whether a subject has pancreatic cancer or predicting the risk of a subject having pancreatic cancer, characterized in that: The system comprises: i) an analysis module, the analysis module comprising: a detection substance for determining the relative abundance information of the pancreatic cancer biomarker selected from claim 1 or 2 in a test sample of a subject, and; ii) an evaluation module, the evaluation module comprising: diagnosing whether the subject has pancreatic cancer or predicting the risk of the subject having pancreatic cancer based on the relative abundance information of the biomarkers determined in i).

7. The system according to claim 5, characterized in that: The sample to be tested is a stool sample.

8. The system according to claim 5, characterized in that: The relative abundance information of the biomarkers is obtained using a sequencing method; Furthermore, the sequencing method is performed by a second-generation sequencing method or a third-generation sequencing method.

9. The system according to claim 5, characterized in that: In the system, any one of support vector machine, random forest, neural network, Bayesian network and Boosting is selected, preferably random forest.

10. Use of the pancreatic cancer biomarker according to claim 1 or 2 as a target for screening drugs for preventing or treating pancreatic cancer.

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