Microbial marker group, tumor prognosis prediction model establishment method and application thereof

Abstract

The application provides a microorganism marker group, a tumor prognosis prediction model establishment method and application thereof, and relates to the technical field of biomedicine. The microorganism marker group is related to a preset total survival time. Based on the microorganism marker group obtained from tumor tissues and paracancer normal tissues, the prognosis prediction accuracy and sensitivity of a digestive tract tumor can be effectively improved.

Description

Technical Field

[0001] This application relates to the field of biomedical technology, specifically to microbial biomarker groups, methods for establishing tumor prognostic prediction models, and their applications. Background Technology

[0002] Gut microbiota can be used for prognostic prediction of gastrointestinal tumors. However, there is currently no widely accepted microbial biomarker for predicting the prognosis of gastrointestinal tumors that can be applied in clinical practice. Prognostic prediction of gastrointestinal tumors (such as colorectal cancer, esophageal cancer, and gastric cancer) based on characteristic species identified from gut microbiota is severely limited in accuracy due to the lack of close interaction with host tissue cells and the inability to accompany and effectively reflect molecular changes occurring in patient tissues during the disease. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in the related art. Therefore, one objective of this application is to provide a tissue-based microbial biomarker set for improving the accuracy of prognostic prediction of gastrointestinal tumors (such as primary colorectal cancer).

[0004] Another objective of this application is to propose a method for constructing tumor prognostic prediction models, which can be used to quickly construct tumor prognostic prediction models based on tissue microbiome.

[0005] Another objective of this application is to propose a predictive model for the efficient and accurate prediction of tumor prognosis based on tissue biomarkers, providing a reference for clinical treatment and diagnosis.

[0006] Firstly, this application proposes a set of microbial biomarkers. According to embodiments of this application, the set of microbial biomarkers is associated with a preset total survival time and includes: Lachnospiraceae_CAG-81.sp900066785, Alphaproteobacteria_CAG-495, Erysipelatoclostridium.sp000752095, Eubacterium.ramulus, Gemmiger, Lachnospiraceae_CAG-81, Gemmiger.sp003476825, Lachnospiraceae_00259, and Faecalicatena. p900066545, Lachnospira.sp900316325, Lachnospiraceae_GCA-900066135, Dorea_02992, Anaerostipes.hadrus, Lachnospiraceae_02075, D orea, Agathobacter, Faecalibacterium_02040, Clostridia_CAG-245, Eggerthella, Blautia.sp900066205, Lachnospiraceae_KLE1615, Chris tensenellales_UBA11524.sp000437595, Lachnospiraceae_COE1, Oscillospiraceae_ER4, Lachnospiraceae_UBA7182, Lachnospiraceae_001 41. Faecalicatena.lactaris, Faecalibacterium_03899, Lachnospiraceae_UBA7160, Blautia.wexlerae, Blautia.massiliensis, Agathobacu lum, Blautia.sp900066335, Clostridium_01246, Oscillospiraceae_ER4.sp003522105, Lachnospiraceae_CAG-81.sp900066055, Christense nellales_UBA11524, Ruminococcus, Clostridium.sp000431375, Lachnospiraceae_00137, Faecalicatena_03316, Lachnospiraceae_CAG-127.sp900319515、Lachnospiraceae_CAG-95、Lachnospiraceae_02304、Oscillospiraceae_ER4.sp000765235、Faecalibacterium_04336、Anaerovor acaceae_00668、Faecalibacterium.sp003449675、Faecalibacterium_03675、Agathobacter_02670、Lachnospiraceae_03335、Faecalibacterium _03291、Hungatella_00278、Ruminococcaceae_CAG-115.sp003531585、Bifidobacterium.adolescentis、Lachnospiraceae_GCA-900066135.sp9 00066135、Eubacterium.hallii、Lachnospiraceae_00194、Eggerthella.lenta、Lachnospiraceae_04140、Clostridium.sp003024715、Alphaprot eobacteria_CAG-495.sp001917125, Faecalibacterium_02651, Eubacterium, Agathobacter.faecis, Erysipelatoclostridium_00670, Clostridium.bolteae, Lawsonibacter_03903, Klebsiella, Faecalibacterium_00589, Anaerostipes, Anaerotruncus.sp900199635, Blautia.sp0002858 55, Lachnospiraceae_CAG-81.sp900066535, Faecalibacterium_02223, Blautia.sp900120195, Clostridia_CAG-269, Clostridia_CAG-245.sp000435175, Erysipelatoclostridium.ramosum, Lawsonibacter_00090, Robinsoniella, Lachnospiraceae_01178, Dorea.scindens, Ruminococcus.clever、Roseburia_00245、Blautia_00954、Clostridium.symbiosum、Acetatifactor.sp900066365、Blautia.obeum、Anaerovoracaceae_01577、 Eubacterium.ventriosum、Oscillospirales_CAG-272、Lachnospiraceae_GCA-900066575、Blautia.sp000436615、Lachnobacterium、Acetatifact or、Lachnospiraceae_UBA9502、Faecalibacterium_00512、Dorea.formicigenerans、Ruminococcus.bromii、Oscillibacter_00837、Acetatifacto r.sp003447295、Lachnospira.sp003537285、Oscillospiraceae_ER4_04276、Blautia、Faecalibacterium_02610、Gemmiger_01627、Oscillospirace ae_02034, Lachnospiraceae_GCA-900066575.sp900066385, Erysipelatoclostridium.sp003024675, Eisenbergiella.massiliensis, Bifidobacterium.infantis, Clostridium.clostridioforme, Ruminococcus.sp000437095, Absiella, Anaerotruncus, Emergencia, Faecalibacterium, Fusicatenibacter, Lachnoclostridium, Lachnospira, Lachnospiraceae_CAG-56, Lachnospiraceae_TF01-11, Roseburia, Ruthenibacterium, Absiella.innocuum, Acutalibacter.timonensis, Agathobaculum.butyriciproducens, Akkermansia.muciniphila, Clostridium.lavalense, Clostridium.leptum, Dorea.longicatena, Faecalibacterium.prausnitzii, Faecalibacterium_03166, Faecalicatena.faecis, Faecalicatena_02772, Fusicatenibacter.saccharivorans, Fusicatenibacter_03676, Hungatella.effluvii, Lachnospira. sp000437735, Lachnospiraceae_CAG-56.sp900066615, Lachnospiraceae_KLE1615.sp900066985, Porphyromonas.somera e. At least one of Roseburia.inulinivorans, Ruminococcus.sp003011855, Streptococcus.sp000187445 and Streptococcus_02891. In some examples of this application, the aforementioned microbial biomarker set obtained from tumor tissue and adjacent normal tissue can effectively improve the accuracy and sensitivity of prognostic prediction for gastrointestinal tumors.

[0007] Secondly, this application discloses the use of reagents for detecting a group of microbial biomarkers in the preparation of kits for predicting the prognosis of gastrointestinal tumors in target samples, wherein the microbial biomarker group is as described in the first aspect. In some examples of this application, kits prepared based on reagents for detecting the aforementioned microbial biomarkers are efficient and portable for predicting the prognosis of gastrointestinal tumors.

[0008] Thirdly, this application provides a reagent kit. According to embodiments of this application, the reagent kit includes reagents for detecting the microbial biomarker group described in the first aspect. In some examples of this application, the aforementioned reagent kit is efficiently and portable for prognostic prediction of gastrointestinal tumors.

[0009] Fourthly, this application proposes a method for establishing a tumor prognostic prediction model. According to embodiments of this application, the method includes: acquiring microbial sequencing data of training samples; filtering and analyzing the microbial sequencing data to determine microbial biomarker group information in the training samples; based on a survival analysis model, determining microbial biomarker group information related to a preset survival time, including: microbial biomarker group information related to outside the preset survival time and microbial biomarker group information related to within the preset survival time; constructing the tumor prognostic prediction model based on the differences between the microbial biomarker group information related to outside the preset survival time and the microbial biomarker group information related to within the preset survival time; wherein, the microbial biomarker group is as shown in the first aspect. In some examples of this application, the aforementioned method can quickly establish a tumor prognostic prediction model based on microbial biomarker groups.

[0010] Fifthly, this application proposes a method for predicting tumor prognosis. According to embodiments of this application, the method includes: acquiring microbial biomarker information of a target sample, the microbial biomarker information including: the total number of relevant microbial taxa outside a preset survival time, the number of relevant microorganisms outside a preset survival time, the relative abundance of relevant microorganisms outside a preset survival time, the total number of relevant microbial taxa within a preset survival time, the number of relevant microorganisms within a preset survival time, and the relative abundance of relevant microorganisms within a preset survival time; inputting the microbial biomarker information into a tumor prognosis prediction model to determine an MRS score; and determining the prognosis of the target sample based on the difference between the MRS score and a predetermined MRS score threshold; wherein the microbial biomarker group is as shown in the first aspect; and the tumor prognosis prediction model is constructed using the method described in the fourth aspect. In some examples of this application, tumor prognosis prediction based on the aforementioned method can effectively improve the accuracy and sensitivity of the prediction, providing important reference value for clinical treatment and diagnosis.

[0011] Sixthly, this application proposes a device for establishing a tumor prognostic prediction model. According to embodiments of this application, the device includes: a sequencing data acquisition unit for acquiring microbial sequencing data of training samples; a microbial biomarker information determination unit for filtering and analyzing the microbial sequencing data to determine microbial biomarker information in the training samples; a classification unit for determining, based on a survival analysis model, microbial biomarker information related to a preset survival time, including: microbial biomarker information related to outside the preset survival time and microbial biomarker information related to within the preset survival time; and a model construction unit for constructing the tumor prognostic prediction model based on the differences between the microbial biomarker information related to outside the preset survival time and the microbial biomarker information related to within the preset survival time; wherein the microbial biomarker group is as shown in the first aspect. In some examples of this application, the aforementioned device can be used to efficiently construct a tumor prognostic prediction model.

[0012] Seventhly, this application proposes a tumor prognosis prediction system. According to embodiments of this application, the system includes: a target sample information acquisition module, used to acquire microbial biomarker group information of the target sample, the microbial biomarker group information including: the total number of relevant microbial taxa outside a preset survival time, the number of relevant microorganisms outside a preset survival time, the relative abundance of relevant microorganisms outside a preset survival time, the total number of relevant microbial taxa within a preset survival time, the number of relevant microorganisms within a preset survival time, and the relative abundance of relevant microorganisms within a preset survival time; an MRS score determination module, used to input the microbial biomarker group information into a tumor prognosis prediction model to determine an MRS score; and a judgment module, used to determine the prognosis of the target sample based on the difference between the MRS score and a predetermined MRS score threshold; wherein the microbial biomarker group is as shown in the first aspect; and the tumor prognosis prediction model is constructed using the apparatus described in the sixth aspect of claim. In some examples of this application, the aforementioned system can effectively improve the accuracy and sensitivity of prediction, providing important reference value for clinical treatment and diagnosis.

[0013] Eighthly, this application proposes a computer program product. According to embodiments of this application, the computer program product includes computer instructions that, when some or all of the computer instructions are executed on a computer, cause the tumor prognosis prediction model establishment method as described in the fourth aspect or the tumor prognosis prediction method as described in the fifth aspect of this application to be executed.

[0014] Ninthly, this application proposes a computing device. According to an embodiment of this application, the device includes: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the computer program to implement the tumor prognosis prediction model establishment method as described in the fourth aspect or the tumor prognosis prediction method as described in the fifth aspect of this application.

[0015] Tenthly, this application provides a computer-readable storage medium. According to embodiments of this application, the storage medium includes computer instructions that, when executed by a computer, cause the computer to implement the tumor prognosis prediction model establishment method as described in the fourth aspect or the tumor prognosis prediction method as described in the fifth aspect of this application.

[0016] In some examples of this application, the aforementioned computer program product, computing device, and computer-readable storage medium achieve highly efficient automation of the tumor prognosis prediction model establishment method or tumor prognosis prediction method through the automatic execution of computer instructions. Furthermore, real-time performance is achieved, making it suitable for application scenarios requiring timely results. By embedding it into a computer program, computer resources are effectively utilized, reducing the time and labor costs required for manual operation.

[0017] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the tumor prognosis prediction model establishment method provided in the embodiments of this application;

[0020] Figure 2 This is a schematic diagram of the tumor prognosis prediction method provided in the embodiments of this application;

[0021] Figure 3 This is a schematic diagram of the structure of the tumor prognosis prediction model establishment device provided in the embodiments of this application;

[0022] Figure 4 This is a schematic diagram of the tumor prognosis prediction system provided in the embodiments of this application;

[0023] Figure 5 This is a schematic diagram of an electronic device provided in an embodiment of this application;

[0024] Figure 6 This is a schematic diagram of the Kaplan-Meier curve results of MRS-T and MRS-N provided in the embodiments of this application;

[0025] Figure 7 This is a schematic diagram illustrating the prognostic performance of the microbial risk score (MRS-T and NATs (MRS-N) from tumors) on 5-year overall survival (OS) and recurrence-free survival (RFS) provided in the embodiments of this application.

[0026] Figure 8 This is a schematic diagram of the consistency index (C-index) results of the combination of traditional risk factors and microbial scores provided in the embodiments of this application. The box indicates the increase in C-index after adding MRS-T and MRS-N to the baseline model. The baseline model includes age, sex, anatomical location, tumor stage, mutation status, CMS subtype and microsatellite instability (MSI) status. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein. In this application, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0029] This application discloses a microbial biomarker set, the use of reagents for detecting the microbial biomarker set in the preparation of a reagent kit, a reagent kit, a method and apparatus for establishing a tumor prognosis prediction model, a tumor prognosis prediction method and system, a computer program product, a computing device, and a computer-readable storage medium. These are described below:

[0030] Microbial biomarkers

[0031] In a first aspect, this application proposes a set of microbial biomarkers associated with a predetermined total survival time, including: Lachnospiraceae_CAG-81.sp900066785, Alphaproteobacteria_CAG-495, Erysipelatoclostridium.sp000752095, Eubacterium.ramulus, Gemmiger, Lachnospiraceae_CAG-81, Gemmiger.sp003476825, Lachnospiraceae_00259, Fae calicatena.sp900066545, Lachnospira.sp900316325, Lachnospiraceae_GCA-900066135, Dorea_02992, Anaerostipes.hadrus, Lachnospirace ae_02075, Dorea, Agathobacter, Faecalibacterium_02040, Clostridia_CAG-245, Eggerthella, Blautia.sp900066205, Lachnospiraceae_KLE16 15. Christensenellales_UBA11524.sp000437595, Lachnospiraceae_COE1, Oscillospiraceae_ER4, Lachnospiraceae_UBA7182, Lachnospirace ae_00141, Faecalicatena.lactaris, Faecalibacterium_03899, Lachnospiraceae_UBA7160, Blautia.wexlerae, Blautia.massiliensis, Agatho baculum, Blautia.sp900066335, Clostridium_01246, Oscillospiraceae_ER4.sp003522105, Lachnospiraceae_CAG-81.sp900066055, Christen senellales_UBA11524, Ruminococcus, Clostridium.sp000431375, Lachnospiraceae_00137, Faecalicatena_03316, Lachnospiraceae_CAG-127.sp900319515、Lachnospiraceae_CAG-95、Lachnospiraceae_02304、Oscillospiraceae_ER4.sp000765235、Faecalibacterium_04336、Anaerovor acaceae_00668、Faecalibacterium.sp003449675、Faecalibacterium_03675、Agathobacter_02670、Lachnospiraceae_03335、Faecalibacterium _03291、Hungatella_00278、Ruminococcaceae_CAG-115.sp003531585、Bifidobacterium.adolescentis、Lachnospiraceae_GCA-900066135.sp9 00066135、Eubacterium.hallii、Lachnospiraceae_00194、Eggerthella.lenta、Lachnospiraceae_04140、Clostridium.sp003024715、Alphaprot eobacteria_CAG-495.sp001917125, Faecalibacterium_02651, Eubacterium, Agathobacter.faecis, Erysipelatoclostridium_00670, Clostridium.bolteae, Lawsonibacter_03903, Klebsiella, Faecalibacterium_00589, Anaerostipes, Anaerotruncus.sp900199635, Blautia.sp0002858 55, Lachnospiraceae_CAG-81.sp900066535, Faecalibacterium_02223, Blautia.sp900120195, Clostridia_CAG-269, Clostridia_CAG-245.sp000435175, Erysipelatoclostridium.ramosum, Lawsonibacter_00090, Robinsoniella, Lachnospiraceae_01178, Dorea.scindens, Ruminococcus.clever、Roseburia_00245、Blautia_00954、Clostridium.symbiosum、Acetatifactor.sp900066365、Blautia.obeum、Anaerovoracaceae_01577、 Eubacterium.ventriosum、Oscillospirales_CAG-272、Lachnospiraceae_GCA-900066575、Blautia.sp000436615、Lachnobacterium、Acetatifact or、Lachnospiraceae_UBA9502、Faecalibacterium_00512、Dorea.formicigenerans、Ruminococcus.bromii、Oscillibacter_00837、Acetatifacto r.sp003447295、Lachnospira.sp003537285、Oscillospiraceae_ER4_04276、Blautia、Faecalibacterium_02610、Gemmiger_01627、Oscillospirace ae_02034, Lachnospiraceae_GCA-900066575.sp900066385, Erysipelatoclostridium.sp003024675, Eisenbergiella.massiliensis, Bifidobacterium.infantis, Clostridium.clostridioforme, Ruminococcus.sp000437095, Absiella, Anaerotruncus, Emergencia, Faecalibacterium, Fusicatenibacter, Lachnoclostridium, Lachnospira, Lachnospiraceae_CAG-56, Lachnospiraceae_TF01-11, Roseburia, Ruthenibacterium, Absiella.innocuum, Acutalibacter.timonensis, Agathobaculum.butyriciproducens, Akkermansia.muciniphila, Clostridium.lavalense, Clostridium.leptum, Dorea.longicatena, Faecalibacterium.prausnitzii, Faecalibacterium_03166, Faecalicatena.faecis, Faecalicatena_02772, Fusicatenibacter.saccharivorans, Fusicatenibacter_03676, Hungatella.effluvii, Lachnospira. sp000437735, Lachnospiraceae_CAG-56.sp900066615, Lachnospiraceae_KLE1615.sp900066985, Porphyromonas.somera e. At least one of Roseburia.inulinivorans, Ruminococcus.sp003011855, Streptococcus.sp000187445 and Streptococcus_02891. Microbial biomarkers obtained from non-tissue samples (such as feces) have low accuracy and sensitivity in predicting the prognosis of gastrointestinal tumors. Through extensive research, the inventors discovered that a group of microbial biomarkers based on tissue samples (such as tumor tissue or adjacent normal tissue) significantly improves the accuracy and sensitivity in predicting the prognosis of gastrointestinal tumors.

[0032] In some examples of this application, the aforementioned biomarker set may also include at least one of the following additional technical features:

[0033] The isolation of a strain of the strain of Fusicatenibacter_03676 from Dorea.longicatena Fusicatenibacter, Lachnospiraceae_CAG-81.sp900066785, Fusicaten ibacter.saccharivorans、Alphaproteobacteria_CAG-495、Faecalibacterium.prausnitzii、Faecalibacterium、Lachnospira.sp900316325、Ery sipelatoclostridium.sp000752095, Phaecalicatena.faecis, Lachnospiraceae_CAG-56.sp900066615, Lachnospiraceae_TF01-11, Ruminococcac eae_CAG-115.sp003531585, Clostridia_CAG-245, Christensenellales_UBA11524.sp000437595, Lachnospiraceae_02075, Eubacterium.ramulus. Phaecalicatena_02772, Lachnospiraceae_CAG-127.sp900319515, Alphaproteobacteria_CAG-495.sp001917125, Lachnospiraceae_CAG-56, Rumin ococcus.sp003011855, Lachnospiraceae_00259, Faecalicatena.sp900066545, Dorea_02992, Christensenellales_UBA11524, Treasurer, Lachnosp iraceae_CAG-81, Treasurer.sp003476825, Agathobaculum.butyricproducens, Faecalibacterium_03166, Roseburia.inulinivorans, Lachnospi raceae_GCA-900066135, Lachnospiraceae_00141, Anaerotypes.hadrus, Faecalibacterium_02040, Clostridia_CAG-245.sp000435175, Blautia.sp900066205、Lachnospiraceae_COE1、Lachnospiraceae_00141、Faecalicatena.lactaris、Anaerovoracaceae_00668、Bifidobacterium.adolesc entis、Agathobacter、Lachnospiraceae_KLE1615.sp900066985、Lachnospiraceae_UBA7182、Blautia.sp900066335、Agathobacter_02670、Erysip elatoclostridium_00670、Lachnospiraceae_KLE1615、Faecalibacterium_03899、Clostridium_01246、Oscillospiraceae_ER4.sp003522105、Lac hnospiraceae_CAG-81.sp900066055、Clostridium.sp000431375、Faecalibacterium_03675、Lachnospiraceae_03335、Faecalibacterium_03291、 Lachnospiraceae_00194、Lachnospiraceae_04140、Blautia.wexlerae、Lachnospiraceae_02304、Lachnospiraceae_01178、Lawsonibacter_03903 、Ruminococcus.callidus、Lachnospiraceae_UBA7160、Lachnospiraceae_00137、Lachnospiraceae_CAG-95、Faecalibacterium_04336、Faecaliba cterium.sp003449675, Lachnospiraceae_GCA-900066135.sp900066135, Oscillospiraceae_ER4, Blautia.massiliensis, Ruminococcus, Oscillospiraceae_ER4.sp000765235, Hungatella_00278, Faecalibacterium_02651, Eubacterium.ventriosum, Oscillospirales_CAG-272, Clostridium.sp003024715, Agathobacter.faecis, Faecalibacterium_00589, Roseburia_00245, Anaerovoracaceae_01577, Agathobaculum, Eubacterium.hallii, Lachnospira, Lachnobacterium, Erysipelatoclostridium.sp003024675, Roseburia, Anaerostipes, Blautia.sp000285855, Lachnospiraceae_C AG-81.sp900066535、Faecalibacterium_02223、Blautia.sp900120195、Clostridia_CAG-269、Blautia_00954、Ruminococcus.bromii、Lachnospira .sp000437735、Oscillospiraceae_02034、Lawsonibacter_00090、Dorea、Blautia.sp000436615、Oscillibacter_00837、Lachnospira.sp003537285 , Oscillospiraceae_ER4_04276, Acetatifactor.sp900066365, Blautia.obeum, Lachnospiraceae_GCA-900066575, Lachnospiraceae_UBA9502, Faecalibacterium_00512, Acetatifactor.sp003447295, Blautia, Faecalibacterium_02610, Gemmiger_01627, Bifidobacterium.infantis, Ruminococcus.sp000437095, Eubacterium, Acetatifactor, Dorea.formicigenerans, Lachnospiraceae_GCA-900066575.sp900066385, Eisenbergiella.massiliensis, Clostridium.clostridioforme, Absiella, Robinsoniella, Dorea.scindens, Anaerotruncus.sp900199635, Erysipelatoclostridium.At least one of *Ramosum*, *Clostridium symbiosum*, *Klebsiella*, *Clostridium lavalense*, *Clostridium bolteae*, *Eggerthella lenta*, *Faecalicatena_03316*, and *Eggerthella*. In some examples of this application, the aforementioned microbial biomarker set is obtained from screening tumor tissue and is used for prognostic prediction of gastrointestinal tumors, which can significantly improve accuracy and sensitivity.

[0034] The aforementioned set of microbial biomarkers obtained from screening tumor tissue includes: a set of microbial biomarkers related to within a preset survival time and a set of microbial biomarkers related to outside the preset survival time.

[0035] It should be noted that, unless otherwise specified, the term "predicted survival time" in this article refers to the follow-up period, which is the time interval from the start of the study (usually the time when the patient participates in the study or begins treatment) to the end of the study (usually the time when the patient's last follow-up, withdrawal from the study, death, disease relapse, etc.), such as 3 years, 4 years, or 5 years. Within the predicted survival time (same as "shorter overall survival") indicates an adverse outcome during the follow-up period; outside the predicted survival time (same as "longer overall survival") indicates no adverse outcome during the follow-up period.

[0036] Among them, the group of microbial biomarkers in tumor tissue that were associated with survival beyond the pre-specified time included: Fusicatenibacter_03676, Dorea.longicatena, Fusicatenibacter, Lachnospiraceae_CAG-81.sp900066785, Fusicatenibacter.saccharivorans, Alphaproteobacteria_CAG-495, Faecalibacterium.prausnitzii, Faecalibacterium, and Lachnospira.sp9 00316325, Erysipelatoclostridium.sp000752095, Faecalicatena.faecis, Lachnospiraceae_CAG-56.sp900066615, Lachnospiraceae_TF01-11 , Ruminococcaceae_CAG-115.sp003531585, Clostridia_CAG-245, Christensenellales_UBA11524.sp000437595, Lachnospiraceae_02075, Eubac terium.ramulus, Faecalicatena_02772, Lachnospiraceae_CAG-127.sp900319515, Alphaproteobacteria_CAG-495.sp001917125, Lachnospirac eae_CAG-56, Ruminococcus.sp003011855, Lachnospiraceae_00259, Faecalicatena.sp900066545, Dorea_02992, Christensenellales_UBA11524 , Gemmiger, Lachnospiraceae_CAG-81, Gemmiger.sp003476825, Agathobaculum.butyriciproducens, Faecalibacterium_03166, Roseburia.inul inivorans, Lachnospiraceae_GCA-900066135, Lachnospiraceae_00141, Anaerostipes.hadrus, Faecalibacterium_02040, Clostridia_CAG-245.sp000435175、Blautia.sp900066205、Lachnospiraceae_COE1、Lachnospiraceae_00141、Faecalicatena.lactaris、Anaerovoracaceae_00668 、Bifidobacterium.adolescentis、Agathobacter、Lachnospiraceae_KLE1615.sp900066985、Lachnospiraceae_UBA7182、Blautia.sp90006633 5、Agathobacter_02670、Erysipelatoclostridium_00670、Lachnospiraceae_KLE1615、Faecalibacterium_03899、Clostridium_01246、Oscill ospiraceae_ER4.sp003522105、Lachnospiraceae_CAG-81.sp900066055、Clostridium.sp000431375、Faecalibacterium_03675、Lachnospirac eae_03335、Faecalibacterium_03291、Lachnospiraceae_00194、Lachnospiraceae_04140、Blautia.wexlerae、Lachnospiraceae_02304、Lach nospiraceae_01178、Lawsonibacter_03903、Ruminococcus.callidus、Lachnospiraceae_UBA7160、Lachnospiraceae_00137、Lachnospiraceae _CAG-95, Faecalibacterium_04336, Faecalibacterium.sp003449675, Lachnospiraceae_GCA-900066135.sp900066135, Oscillospiraceae_ER4, Blautia.massiliensis, Ruminococcus, Oscillospiraceae_ER4.sp000765235, Hungatella_00278, Faecalibacterium_02651, Eubacterium.ventriosum, Oscillospirales_CAG-272, Clostridium.sp003024715, Agathobacter.faecis, Faecalibacterium_00589, Roseburia_0 0245,Anaerovoracaceae_01577,Agathobaculum,Eubacterium.hallii,Lachnospira,Lachnobacterium,Erysipelatoclostridium.sp 003024675, Roseburia, Anaerostipes, Blautia.sp000285855, Lachnospiraceae_CAG-81.sp900066535, Faecalibacterium_02223, Bla utia.sp900120195, Clostridia_CAG-269, Blautia_00954, Ruminococcus.bromii, Lachnospira.sp000437735, Oscillospiraceae_020 34. Lawsonibacter_00090, Dorea, Blautia.sp000436615, Oscillibacter_00837, Lachnospira.sp003537285, Oscillospiraceae_ER4 _04276, Acetatifactor.sp900066365, Blautia.obeum, Lachnospiraceae_GCA-900066575, Lachnospiraceae_UBA9502, Faecalibacter At least one of the following: ium_00512, Acetatifactor.sp003447295, Blautia, Faecalibacterium_02610, Gemmiger_01627, Bifidobacterium.infantis, Ruminococcus.sp000437095, Eubacterium, Acetatifactor, Dorea.formicigenerans, and Lachnospiraceae_GCA-900066575.sp900066385. Based on the aforementioned microbial biomarker set, microorganisms that negatively impact the prognosis of gastrointestinal tumors can be identified.

[0037] In tumor tissue, the microbial biomarker group associated with a pre-defined survival time includes at least one of the following: *Eisenbergiella massiliensis*, *Clostridium clostridioforme*, *Absiella*, *Robinsoniella*, *Dorea scindens*, *Anaerotruncus sp900199635*, *Erysipelatoclostridium ramosum*, *Clostridium symbiosum*, *Klebsiella*, *Clostridium lavalense*, *Clostridium bolteae*, *Eggerthella lenta*, *Faecalicatena_03316*, and *Eggerthella*. Based on the aforementioned microbial biomarker group, microorganisms that have a positive impact on the prognosis of gastrointestinal tumors can be identified.

[0038] In some examples of this application, the aforementioned biomarker group includes: Lachnospiraceae_CAG-56.sp900066615, Faecalicatena.faecis, Fusicatenibacter, Fusicatenibacter.saccharivorans, Lachnospiraceae_TF01-11, Fusicatenibacter_03676, Faecalicatena_02772, Lachnospiraceae_CAG-56, Faecalibacterium_03166, Dorea.longicatena, Roseburia.inulinivorans, Lachnospira.sp000437735, Ruminococcus.sp003011855, Faecalibacterium, Roseburia, Faecalibacterium.prausnitzii The microbial biomarkers are selected from at least one of the following: *Lachnospira*, *Agathobaculum.butyriciproducens*, *Lachnospiraceae_KLE1615.sp90006698*, *Streptococcus.sp000187445*, *Hungatella.effluvii*, *Streptococcus_02891*, *Clostridium.leptum*, *Lachnoclostridium*, *Absiella*, *Porphyromonas.somerae*, *Acutalibacter.timonensis*, *Clostridium.bolteae*, *Akkermansia.muciniphila*, *Dorea.scindens*, *Ruthenibacterium*, *Absiella.innocuum*, *Clostridium.lavalense*, *Emergencia*, and *Anaerotruncus*. In some examples of this application, the aforementioned microbial biomarkers are obtained from screening adjacent normal tissues and are used for prognostic prediction of gastrointestinal tumors, which can significantly improve accuracy and sensitivity.

[0039] The aforementioned set of microbial biomarkers obtained from screening adjacent normal tissues includes: a set of microbial biomarkers related to overall survival (OS) and a set of microbial biomarkers related to beyond the preset survival time.

[0040] The explanations for the terms "preset lifetime," "within the preset lifetime," and "outside the preset lifetime" are the same as above and will not be repeated here.

[0041] Among the microbial biomarkers in adjacent normal tissues associated with a pre-defined survival time, at least one of the following is included: Streptococcus sp000187445, Hungatella effluvii, Streptococcus 02891, Clostridium leptum, Lachnoclostridium, Absiella, Porphyromonas somerae, Acutalibacter timonensis, Clostridium bolteae, Akkermansia muciniphila, Dorea scindens, Ruthenibacterium, Absiella innocuum, Clostridium lavalense, Emergencia, and Anaerorotruncus. Based on the aforementioned microbial biomarkers, microorganisms that negatively impact the prognosis of gastrointestinal tumors can be identified.

[0042] In adjacent normal tissues, the group of microbial biomarkers associated with survival beyond the pre-specified time included: Lachnospiraceae_CAG-56.sp900066615, Faecalicatena.faecis, Fusicatenibacter, Fusicatenibacter.saccharivorans, Lachnospiraceae_TF01-11, Fusicatenibacter_03676, Faecalicatena_02772, Lachnospiraceae_CAG-56, and Faecalibacteri. At least one of the following: um_03166, Dorea.longicatena, Roseburia.inulinivorans, Lachnospira.sp000437735, Ruminococcus.sp003011855, Faecalibacterium, Roseburia, Faecalibacterium.prausnitzii, Lachnospira, Agathobaculum.butyriciproducens, and Lachnospiraceae_KLE1615.sp90006698. Based on the aforementioned microbial biomarker set, microorganisms with a positive impact on the prognosis of gastrointestinal tumors can be identified.

[0043] Use of reagents for detecting microbial biomarkers in kit preparation

[0044] In a second aspect of this application, the use of reagents for detecting a group of microbial biomarkers in the preparation of a kit for predicting the prognosis of gastrointestinal tumors in a target sample, wherein the microbial biomarkers are as illustrated in any of the foregoing examples. The kit prepared based on the aforementioned reagents for detecting microorganisms can rapidly and accurately detect the prognostic effects of gastrointestinal tumors in target samples.

[0045] In some examples of this application, the foregoing uses may also include at least one of the following additional technical features:

[0046] In some examples of this application, the aforementioned digestive tract tumor includes at least one of colorectal cancer, esophageal cancer, gastric cancer, and pancreatic cancer. In a preferred example of this application, the aforementioned digestive tract tumor is colorectal cancer, more preferably primary colorectal cancer.

[0047] In some examples of this application, the aforementioned target samples include tumor tissue samples or adjacent normal tissue samples. Predicting the prognosis of gastrointestinal tumors based on a microbial biomarker set obtained from tissue samples significantly improves accuracy and sensitivity.

[0048] Reagent test kit

[0049] In a third aspect, this application provides a kit comprising reagents for detecting a group of microbial biomarkers of any of the foregoing examples. In some examples of this application, the accuracy and sensitivity of prognostic prediction of gastrointestinal tumors using the aforementioned kit are significantly improved.

[0050] In some examples of this application, the aforementioned kit may also include at least one of the following additional technical features:

[0051] In some examples of this application, the kit includes reagents for detecting a group of microbial biomarkers in tumor tissue. These reagents further include reagents for detecting microbial biomarkers relevant within a predetermined time period and microbial biomarkers relevant outside the predetermined time period.

[0052] The aforementioned tumor tissue microbial biomarker group, the microbial biomarker group related within a preset time period, and the microbial biomarker group related outside the preset time period, as described in the first aspect, will not be repeated here due to space limitations.

[0053] In some examples of this application, the kit includes reagents for detecting a group of microbial biomarkers in adjacent normal tissue. These reagents for detecting the group of microbial biomarkers in adjacent normal tissue further include reagents for detecting microbial biomarkers relevant within a preset time period and microbial biomarkers relevant outside the preset time period.

[0054] The aforementioned microbial biomarkers in adjacent normal tissue, microbial biomarkers associated with the preset survival time, and microbial biomarkers associated with the preset survival time, as described in the first aspect, will not be repeated here due to space limitations.

[0055] In some examples of this application, information on the detection of microbial biomarkers in adjacent normal tissue or tumor tissue, such as the total number of relevant microbial taxa outside the preset survival time, the number of relevant microorganisms outside the preset survival time, the relative abundance of relevant microorganisms outside the preset survival time, the total number of relevant microbial taxa within the preset survival time, the number of relevant microorganisms within the preset survival time, and the relative abundance of relevant microorganisms within the preset survival time, are substituted into the following formula to obtain the MRS score.

[0056]

[0057] Where MRS represents the microbial risk score; |N+| represents the total number of taxa of relevant microorganisms within the preset survival time; |N-| represents the total number of taxa of relevant microorganisms outside the preset survival time; R N+ Indicates the number of relevant microorganisms within a preset survival time; R N- This indicates the number of relevant microorganisms beyond the preset survival time; p i p represents the relative abundance of relevant microorganisms within the preset survival time of each sample. j This indicates the relative abundance of relevant microorganisms outside the preset survival time for each sample.

[0058] The obtained MRS score is compared with a predetermined MRS score threshold to determine the tumor prognosis prediction result of the target sample. For example, if the MRS score is higher than the predetermined MRS score threshold, it indicates a poor prognosis for the target sample; if the MRS score is lower than the predetermined MRS score threshold, it indicates a good prognosis for the target sample.

[0059] In some examples of this application, the aforementioned predetermined MRS score threshold is determined based on the median of the MRS scores of the training samples. Those skilled in the art can set the aforementioned predetermined MRS score threshold according to actual needs. The median is only an exemplary method of determination, and it can also be determined by other methods, such as quartiles.

[0060] Methods for establishing tumor prognostic prediction models

[0061] In a fourth aspect of this application, a method for establishing a tumor prognosis prediction model is proposed, referring to... Figure 1 The method includes:

[0062] S1, Obtain microbial sequencing data of the training samples.

[0063] In some examples of this application, the aforementioned tumor is selected from digestive tract tumors. In some preferred examples of this application, the aforementioned digestive tract tumor includes at least one of colorectal cancer, esophageal cancer, gastric cancer, and pancreatic cancer. In some more preferred examples of this application, the aforementioned digestive tract tumor is primary colorectal cancer.

[0064] In some examples of this application, the aforementioned training samples are selected from gastrointestinal tumor tissues or adjacent normal tissues. Compared to microbial biomarkers obtained from non-tissue samples, the accuracy and sensitivity of the gastrointestinal tumor prognosis prediction based on microbial biomarkers obtained from tissue samples in this application are significantly improved.

[0065] In some examples of this application, the aforementioned training samples are no less than 400, preferably no less than 500.

[0066] In some examples of this application, routine sequencing is performed on gastrointestinal tumor tissue or adjacent normal tissue. Sequences aligned to the human genome are removed, as are low-quality sequences and microbial sequences introduced by environmental or reagent contamination. Finally, prokaryotic microbial sequences are retained. The retained prokaryotic microbial sequences are aligned to a human gut microbiome reference genome set, and the relative abundance of different microorganisms within the tissue is quantified to obtain microbial biomarker information.

[0067] Those skilled in the art will understand that the aforementioned "removal of sequencing sequences aligned to the human genome, removal of low-quality sequencing sequences, removal of microbial sequencing sequences introduced by environmental or reagent contamination, and finally retention of prokaryotic microbial sequences" is selected from conventional bioinformatics analysis procedures in the field, and will not be described in detail here due to space limitations.

[0068] S2, filter and analyze the microbial sequencing data to determine the microbial biomarker group information in the training sample.

[0069] In some examples of this application, the aforementioned filtering analysis includes: removing contaminating microbial genera from the microbial sequencing data. The criteria for identifying the contaminating microbial genera need to satisfy any one of 1)-3):

[0070] 1) Occurrence rate in blood samples ≥20%;

[0071] 2) It appears more frequently in blood than in tissue samples;

[0072] 3) Microbial genera in the negative blank control.

[0073] It should be noted that "higher than" in condition 2) above can be determined by chi-square test (P < 0.05) or similar methods.

[0074] In some examples of this application, the aforementioned filtering analysis further includes removing microbial genera with an occurrence rate of less than 20% in all samples to improve the stability and reliability of the data.

[0075] After filtering and analyzing the microbial sequencing data, the microbial biomarker group information in the training samples can be obtained. The aforementioned microbial biomarker group, such as the biomarker group in any example of the first aspect, will not be listed here again due to space limitations.

[0076] S3. Based on the survival analysis model, determine the microbial biomarker group information related to the preset survival time in the microbial biomarker group information, including: microbial biomarker group information related to outside the preset survival time and microbial biomarker group information related to within the preset survival time.

[0077] The Cox proportional hazards model is a survival analysis method used to assess the impact of multiple variables on the timing of events such as death or relapse. This model does not require specific distributional assumptions about survival time data; instead, it is based on the "proportional hazards assumption," which states that the hazard ratios between different variables remain constant over time. The Cox model quantifies the relative impact of each variable on the risk of the event by calculating the hazard ratio (HR) (HR>1 indicates increased risk, HR<1 indicates decreased risk). In some examples of this application, the Cox proportional hazards model is used as a survival analysis model to obtain microbiota associated with the patient's pre-defined survival time.

[0078] Regarding the explanations of the terms "preset lifetime," "within the preset lifetime," and "outside the preset lifetime," the first aspect will not be repeated here. In some examples of this application, the aforementioned "preset lifetime" is 5 years.

[0079] In some examples of this application, the aforementioned microbial biomarker group information related to beyond the preset survival time includes: the total number of taxa of microorganisms related to beyond the preset survival time, the number of microorganisms related to beyond the preset survival time, and the relative abundance of microorganisms related to beyond the preset survival time.

[0080] In some examples of this application, the aforementioned microbial biomarker group information related to the preset survival time includes: the total number of taxa of related microorganisms within the preset survival time, the number of related microorganisms within the preset survival time, and the relative abundance of related microorganisms within the preset survival time.

[0081] S4. Based on the difference between the microbial biomarker information outside the preset survival time and the microbial biomarker information within the preset survival time, construct the tumor prognosis prediction model.

[0082] In some examples of this application, the aforementioned tumor prognostic prediction model has the following formula:

[0083]

[0084] Where MRS represents the microbial risk score; |N+| represents the total number of taxa of relevant microorganisms within the preset survival time; |N-| represents the total number of taxa of relevant microorganisms outside the preset survival time; R N+ Indicates the number of relevant microorganisms within a preset survival time; R N- This indicates the number of relevant microorganisms beyond the preset survival time; p i p represents the relative abundance of relevant microorganisms within the preset survival time of each sample. j This indicates the relative abundance of relevant microorganisms outside the preset survival time for each sample.

[0085] It should be noted that in this model, the term "each sample" is selected from tumor tissue samples or adjacent normal tissue samples.

[0086] The above methods can efficiently and quickly establish tumor prognosis prediction models.

[0087] Tumor prognosis prediction methods

[0088] In a fifth aspect of this application, a method for predicting tumor prognosis is proposed, referring to... Figure 2 The method includes:

[0089] S01, Obtain the microbial biomarker group information of the target sample, wherein the microbial biomarker group is as shown in any example of the first aspect. The microbial biomarker group information includes: the total number of taxa of relevant microorganisms outside the preset survival time, the number of relevant microorganisms outside the preset survival time, the relative abundance of relevant microorganisms outside the preset survival time, the total number of taxa of relevant microorganisms within the preset survival time, the number of relevant microorganisms within the preset survival time, and the relative abundance of relevant microorganisms within the preset survival time.

[0090] In some examples of this application, the aforementioned target sample is selected from digestive tract tumor samples or adjacent normal tissue samples.

[0091] Obtaining microbial biomarker information of the target sample includes: sequencing the target sample to obtain sequencing data; further performing comparative analysis, quality control processing, and removing human sequences on the sequencing data to obtain microbial sequencing data; and obtaining microbial biomarker information of any example of the first aspect in the target sample through the microbial sequencing data.

[0092] S02, the microbial biomarker group information is input into the tumor prognostic prediction model to determine the MRS score. The tumor prognostic prediction model is constructed using the method described in any example of the fourth aspect.

[0093] S03, Based on the difference between the MRS score and the predetermined MRS score threshold, determine the prognosis of the target sample.

[0094] In some examples of this application, the predetermined MRS score threshold is determined based on the median of the MRS scores of the training samples. Those skilled in the art can set the aforementioned predetermined MRS score threshold according to actual needs. The median is only one exemplary method of determination; it can also be determined in other ways, such as by the quartiles.

[0095] In some examples of this application, if the MRS score is higher than a predetermined MRS score threshold, it indicates a poor prognosis for the target sample; if the MRS score is lower than the predetermined MRS score threshold, it indicates a good prognosis for the target sample.

[0096] The above methods improve the accuracy and sensitivity of prognostic prediction for gastrointestinal tumors, which helps to achieve early intervention and improve patients' survival rate and quality of life.

[0097] Tumor prognosis prediction model establishment device

[0098] In a sixth aspect of this application, a device for establishing a tumor prognosis prediction model is proposed, with reference to... Figure 3 The device includes: a sequencing data acquisition unit 100, a microbial biomarker information determination unit 200, a classification unit 300, and a model construction unit 400. Among them,

[0099] 100 units, used to acquire microbial sequencing data from training samples.

[0100] In some examples of this application, the tumor is selected from gastrointestinal tumors. These gastrointestinal tumors include at least one of colorectal cancer, esophageal cancer, gastric cancer, and pancreatic cancer.

[0101] In some examples of this application, the training samples are selected from digestive tract tumor tissue or adjacent normal tissue.

[0102] 200 units are used to filter and analyze the microbial sequencing data to determine the microbial biomarker group information in the training samples.

[0103] In some examples of this application, the filtering analysis includes removing contaminating microbial genera from the microbial sequencing data.

[0104] In some examples of this application, the following conditions 1)-3) are considered to be met to be the contaminating microbial genus: 1) the occurrence rate in blood samples is ≥20%; 2) the occurrence rate in blood is higher than that in tissue samples; 3) the microbial genus in the negative blank control.

[0105] In some examples of this application, the survival analysis model is selected from the Cox proportional hazards model.

[0106] Unit 300 is used to determine, based on a survival analysis model, the microbial biomarker group information related to a preset survival time, including: microbial biomarker group information related to outside the preset survival time and microbial biomarker group information related to within the preset survival time.

[0107] In some examples of this application, the survival analysis model is selected from the Cox proportional hazards model.

[0108] In some examples of this application, the microbial biomarker group information related to the time beyond the preset survival time includes: the total number of taxa of microorganisms related to the time beyond the preset survival time, the number of microorganisms related to the time beyond the preset survival time, and the relative abundance of microorganisms related to the time beyond the preset survival time.

[0109] In some examples of this application, the microbial biomarker group information related to the preset survival time includes: the total number of taxa of related microorganisms within the preset survival time, the number of related microorganisms within the preset survival time, and the relative abundance of related microorganisms within the preset survival time.

[0110] 400 units are used to construct the tumor prognosis prediction model based on the difference between the microbial biomarker group information related outside the preset survival time and the microbial biomarker group information related within the preset survival time; wherein, the microbial biomarker group is as shown in the first aspect.

[0111] In some examples of this application, the tumor prognosis prediction model has the following formula:

[0112]

[0113] Where MRS represents the microbial risk score; |N+| represents the total number of taxa of relevant microorganisms within the preset survival time; |N-| represents the total number of taxa of relevant microorganisms outside the preset survival time; R N+ Indicates the number of relevant microorganisms within a preset survival time; R N- This indicates the number of relevant microorganisms beyond the preset survival time; p i p represents the relative abundance of relevant microorganisms within the preset survival time of each sample. j This indicates the relative abundance of relevant microorganisms outside the preset survival time for each sample.

[0114] In some examples of this application, the aforementioned units can be connected via hardware or via a network.

[0115] It should be understood that this device embodiment corresponds to the aforementioned tumor prognosis prediction model establishment method embodiment, and similar descriptions can be found in the method embodiment. To avoid repetition, further details are omitted here. Specifically, Figure 3 The apparatus shown can execute the above-described method for establishing a tumor prognosis prediction model, and the operations and / or functions performed by each unit in the apparatus correspond to those in the method embodiment. For the sake of brevity, they will not be described in detail here.

[0116] It should be understood that the features and beneficial effects described above for the tumor prognosis prediction model establishment method also apply to this device, and will not be repeated here.

[0117] Tumor prognosis prediction system

[0118] In its seventh aspect, this application proposes a tumor prognosis prediction system, referring to... Figure 4 The system includes: a target sample information acquisition module 01, an MRS score determination module 02, and a judgment module 03. Among them,

[0119] Module 01 is used to acquire microbial biomarker group information of the target sample. The microbial biomarker group information includes: the total number of taxa of related microorganisms outside the preset survival time, the number of related microorganisms outside the preset survival time, the relative abundance of related microorganisms outside the preset survival time, the total number of taxa of related microorganisms within the preset survival time, the number of related microorganisms within the preset survival time, and the relative abundance of related microorganisms within the preset survival time.

[0120] Module 02 is used to input the microbial biomarker group information into the tumor prognosis prediction model to determine the MRS score.

[0121] Module 03 is used to determine the prognostic outcome of the target sample based on the difference between the MRS score and a predetermined MRS score threshold; wherein the microbial biomarker group is as shown in the first aspect; and the tumor prognostic prediction model is constructed using the apparatus of claim 6.

[0122] In some examples of this application, the predetermined MRS score threshold is determined based on the median of the MRS scores of the training samples.

[0123] In some examples of this application, if the MRS score is higher than a predetermined MRS score threshold, it is an indication of a poor prognosis for the target sample.

[0124] In some examples of this application, if the MRS score is lower than a predetermined MRS score threshold, it is an indication that the target sample has a good prognosis.

[0125] In some examples of this application, the aforementioned modules can be connected via hardware or via a network.

[0126] It should be understood that the system implementation embodiment and the aforementioned tumor prognosis prediction model establishment method implementation embodiment can correspond to each other, and similar descriptions can be referred to the method implementation embodiment. To avoid repetition, they will not be repeated here. Specifically, Figure 4 The system shown can execute the above-described method for establishing a tumor prognosis prediction model, and the operations and / or functions performed by each unit in the system correspond to those in the method embodiment. For the sake of brevity, they will not be described in detail here.

[0127] It should be understood that the features and beneficial effects described above for tumor prognosis prediction methods also apply to this system, and will not be repeated here.

[0128] Computer program products, computing devices and computer-readable storage media

[0129] In other aspects of this application, a computer program product, a computing device, and a computer-readable storage medium are provided. Based on the aforementioned computer program product, computing device, or computer-readable storage medium, the aforementioned method for establishing a tumor prognosis prediction model or a tumor prognosis prediction method can be executed.

[0130] Descriptions of computer program products, computing devices, or computer-readable storage media may be referenced interchangeably. This document uses electronic devices as examples for detailed description. The term "electronic device" is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Computing devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0131] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 502 or a computer program loaded from storage unit 508 into RAM (Random Access Memory) 503. The RAM 503 can also store various programs and data required for the operation of the device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An I / O (Input / Output) interface 505 is also connected to the bus 504.

[0132] Multiple components in device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0133] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as methods for establishing tumor prognostic prediction models or methods for predicting tumor prognostics. For example, in some embodiments, the methods for establishing tumor prognostic prediction models or methods for predicting tumor prognostics can be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 501 may be configured in any other suitable manner (e.g., by means of firmware) to perform the aforementioned tumor prognosis prediction model building method or tumor prognosis prediction method.

[0134] In this application, the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, the computer-readable medium can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or, if necessary, processing in other suitable ways, and then stored in a computer memory. The various computer-readable storage media described in this invention can represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" can include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0135] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0136] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The aforementioned program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0137] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0138] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0139] It should be noted that the features and technical effects described in this article for different aspects can be mutually referenced, and will not be elaborated further here.

[0140] Embodiments of this application will now be described in more detail, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. Where specific techniques or conditions are not specified in the embodiments, they shall be performed in accordance with the techniques or conditions described in the literature in the art or in accordance with the product specification.

[0141] Example 1: Construction and Evaluation of a Prognostic Prediction Model for Gastrointestinal Tumors

[0142] This embodiment is suitable for the construction and evaluation of prognostic models for gastrointestinal tumors (such as colorectal cancer, esophageal cancer, gastric cancer, and pancreatic cancer). The following detailed explanation uses colorectal cancer as an example.

[0143] 1.1 Queue Construction

[0144] The samples in this study were obtained from colorectal cancer patients diagnosed in Cohort 1 or Cohort 2 between 2004 and 2019. Sample collection and analysis were conducted with appropriate ethical permission. Clinicopathological data and follow-up information were primarily extracted from the Swedish National Colorectal Cancer Quality Registry (SCRCR), supplemented by medical records. Patients who received chemotherapy or radiotherapy prior to sampling or surgery were not included in this study; a total of 937 treatment-naïve patients (Cohort 1: 775 cases, Cohort 2: 162 cases) were ultimately included in the analysis. As of June 14, 2023, the shortest follow-up time for surviving patients was 3.9 years, and the median follow-up time was 8 years, with 870 patients (93%) completing 5 years of follow-up (Table 1).

[0145] In addition, to assess the reliability of our findings, we introduced an external cohort, AC-ICAM (Atlas and Compass of Immune-Colon cancer-Microbiome interactions), and collected 5-year follow-up data of patients included in AC-ICAM (Table 1).

[0146] Table 1 Sample Statistics

[0147]

[0148] 1.2 Sample collection and nucleic acid extraction

[0149] Tissue samples from Cohort 1 were embedded and stored at -70°C on the day of collection. Tissue samples from Cohort 2 were directly frozen into segments and stored in a similar manner. Patient-matched normal DNA samples were obtained from tumor-adjacent normal tissue (NAT) (Cohort 1 = 313; Cohort 2 = 162). For Cohort 1 samples, tissue DNA was extracted using the NucleoSpin Tissue Kit. For Cohort 2 tissue samples, DNA was extracted using the AllPrep DNA Kit.

[0150] 1.3 Whole Genome Sequencing and Data Processing

[0151] Library preparation was performed using MGIEAsy FSDNA, constructing whole-genome sequencing (WGS) libraries from 937 primary colorectal cancer (CRC) tumor samples and their paired control samples. These libraries were sequenced on the DNBSEQ platform (MGI, Shenzhen, China), with all samples sequenced using 100-bp paired ends to achieve ≥60× read coverage. During WGS data preprocessing, SOAPnuke (version 2.0.7) was used to remove low-quality reads and adapter sequences. SentieonGenomics software (v.sentieon-genomics-202010; https: / / www.sentieon.com / ) was used for alignment and processing of high-quality sequences for downstream analysis, including aligning each tumor and adjacent normal sample to the human genome reference sequence hg38 using the parameter '-MK 100000000' in the alt-aware mapping model of BWA-MEM (version 0.7.17-r1188).

[0152] 1.4 Identification, decontamination, and quantification of microorganisms

[0153] Extracting non-human paired sequencing sequences from whole-genome sequencing (WGS) data (SAMtools version 1.9, parameter view-f 12) and performing the following quality control: 1) Using fastp (version 0.20.1, parameters: --cut_front --cut_right --cut_front_window_size 4 --cut_front_mean_quality 20)

[0154] --cut_tail_window_size 4 --cut_tail_mean_quality 20 --length_required 51) Remove low-quality sequencing sequences; 2) Identify and remove human sequences again by aligning with the GRCh38 genome (Bowtie2 version 2.4.2, parameter -very-sensitive). After quality control, an average of 1.54 ± 5.68 million paired high-quality non-human DNA sequences were obtained from the tissue samples.

[0155] High-quality non-human sequences were preserved and aligned to the human gut reference genome set (Kraken2). 1272 genera of microorganisms were detected in tissue samples from cohort 1, and 1257 genera were detected in tissue samples from cohort 2 (within ≥10 aligned sequences). Genera were then considered potentially contaminating and removed in subsequent analyses if they met any of the following criteria: 1) presence ≥20% in blood samples; 2) significantly higher presence in blood than in tissue samples; or 3) were genera present in the negative control. Finally, genera with a presence less than 20% in all samples were removed, leaving 292 genera used for downstream analysis. All genera were normalized in individual samples to obtain a relative abundance matrix.

[0156] At the species level, coverage of all 596 microbial species under 292 genera was calculated using bedtools (version 2.30.0, default parameters). Species with at least 5% coverage in at least 5% of samples were retained for downstream analysis. All microbial species were normalized in individual samples to obtain a relative abundance matrix.

[0157] 1.5 Identification of microorganisms in tumors and adjacent normal tissues that are significantly associated with host prognosis

[0158] The relationship between 608 common microorganisms (occurrence rate ≥50%, including 188 genera and 420 species) and host 5-year survival was analyzed using a multivariate Cox proportional hazards model (adjusted for five host factors and total prokaryotic sequence genera, R version 3.5-5, function coxph). In cohort 1 tumors (N=689), 136 microbial taxa (36 genera and 100 species, Table 2) were identified as prognostic biomarkers (BH-corrected P<0.05); in adjacent normal tissues of cohort 1 tumors (N=288), 35 microbial taxa (11 genera and 24 species, Table 3) were identified as prognostic biomarkers (P<0.01).

[0159] Table 2. Microbial groups associated with prognosis of primary colorectal tumor tissue.

[0160]

[0161]

[0162]

[0163]

[0164] Table 3. Microbial groups associated with prognosis of primary colorectal cancer in adjacent normal tissue.

[0165]

[0166]

[0167] 1.6 Construction and application of tumor tissue microbial risk score and adjacent normal tissue microbial risk score

[0168] Microbes identified in tumors and adjacent normal tissues that are associated with the prognosis of colorectal cancer were used to construct the tumor microbial risk score (MRS-T) and the tumor-adjacent normal tissue microbial risk score (MRS-N), respectively, as shown in the following formula:

[0169]

[0170] Where |N+| and |N-| represent the total number of prognostic microbial taxa associated with shorter overall survival and longer overall survival (OS), respectively. R N+ and R N- This indicates the number of microorganisms associated with shorter and longer OS. pi and pj represent the relative abundance of microorganisms associated with shorter and longer OS in each sample;

[0171] Then, the Cox proportional hazards model was used to estimate the independent hazard ratios (adjusted age, tumor stage, tumor location, treatment status, and total number of prokaryotic sequences) of MRS-T and MRS-N for stage I-III patients in cohort 1 (tumor = 689, NAT = 288) and cohort 2 (tumor = 150, NAT = 150) in terms of 5-year overall survival (OS) and 5-year recurrence-free survival (RFS). The results showed that MRS-T and MRS-N were independently associated with poorer survival in cohort 1 patients, and the adjusted survival hazard ratio (HR) of the MSR-T score was [not specified in the original text]. OS The adjusted relapse-free survival hazard ratio (HR) was 1.65. RFS The adjusted HR was 1.51; OS The adjusted relapse-free survival hazard ratio (HR) was 2.03. RFS The value was 1.82, and these associations were validated in queue 2 and the AC-ICAM queue. Figure 6 ).

[0172] Finally, the prognostic accuracy of MRS-T and MRS-N was assessed using the concordance index (C-index, dynpredR package, version 0.1.2). Specifically, the predictive power of MRS-T, MRS-N, and conventional risk factors alone in 5-year overall survival was evaluated in cohort 1, cohort 2, and AC-ICAM patients. The results showed that both MRS-T and MRS-N scores improved the accuracy of prognostic models based on individual host phenotypes (including age, sex, body mass index, tumor location, tumor grade, clinical stage, mutation status, microsatellite instability (MSI) status, and tumor microenvironment (CMS) subtype). Figure 7 Overall, MRS-T and MRS-N improved the C-index of 5-year overall survival prediction by 0.049 and 0.077 respectively in cohort 1, reaching 0.722 and 0.750; and improved the C-index of 5-year relapse-free event prediction by 0.048 and 0.071 respectively, reaching 0.681 and 0.704. Figure 8 The improved accuracy of MRS-T and MRS-N prognostic models compared to clinical host phenotypes was also validated in cohort 2 and the AC-ICAM external cohorts. Figure 7 , Figure 8 In validation cohort 2, MRS-T and MRS-N improved the C-index of the conventional model for 5-year overall survival prediction by 0.013 and 0.014, respectively, reaching 0.790 and 0.790; and improved the C-index of the conventional model for 5-year relapse-free event prediction by 0.050 and 0.042, reaching 0.795 and 0.788. Figure 8MRS-T improved the C-index of the conventional model for 5-year overall survival prediction by 0.012, reaching 0.724, and improved the C-index for 5-year relapse-free event prediction by 0.026, reaching 0.787. Figure 8 ).

[0173] Summarizing all the analysis results, we found that the MRS-T and MRS-N constructed based on this application were associated with overall survival and recurrence events in patients with primary colorectal cancer, demonstrating the importance of applying microbiome analysis in predicting the risk of death and recurrence in colorectal cancer, providing microbial targets for monitoring colorectal cancer, and providing evidence to support intervention strategies.

[0174] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0175] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application without departing from the principles and spirit of this application.

Claims

1. A set of microbial biomarkers, characterized in that, The microbial biomarker set is correlated with a preset total survival time and includes: Lachnospiraceae_CAG-81.sp900066785、Alphaproteobacteria_CAG-495、Erysipelatoclostridium.sp000752095、Eubacterium.ramulu s、Gemmiger、Lachnospiraceae_CAG-81、Gemmiger.sp003476825、Lachnospiraceae_00259、Faecalicatena.sp900066545、Lachnospira.s p900316325, Lachnospiraceae_GCA-900066135, Dorea_02992, Anaerostipes.hadrus, Lachnospiraceae_02075, Dorea, Agathobacter, Faecalibacterium_02040, Clostridia_CAG-245, Eggerthella, Blautia.sp900066205, Lachnospiraceae_KLE1615, Christensenellales_UB A11524.sp000437595、Lachnospiraceae_COE1、Oscillospiraceae_ER4、Lachnospiraceae_UBA7182、Lachnospiraceae_00141、Faecalicatena.lactaris、Faecalibacterium_03899、Lachnospiraceae_UBA7160、Blautia.wexlerae、Blautia.massiliensis、Agathobaculum、Blau tia.sp900066335、Clostridium_01246、Oscillospiraceae_ER4.sp003522105、Lachnospiraceae_CAG-81.sp900066055、Christensenell ales_UBA11524、Ruminococcus、Clostridium.sp000431375、Lachnospiraceae_00137、Faecalicatena_03316、Lachnospiraceae_CAG-127.sp900319515、Lachnospiraceae_CAG-95、Lachnospiraceae_02304、Oscillospiraceae_ER4.sp000765235、Faecalibacterium_04336、Anaerovor acaceae_00668、Faecalibacterium.sp003449675、Faecalibacterium_03675、Agathobacter_02670、Lachnospiraceae_03335、Faecalibacterium _03291、Hungatella_00278、Ruminococcaceae_CAG-115.sp003531585、Bifidobacterium.adolescentis、Lachnospiraceae_GCA-900066135.sp9 00066135、Eubacterium.hallii、Lachnospiraceae_00194、Eggerthella.lenta、Lachnospiraceae_04140、Clostridium.sp003024715、Alphaprot eobacteria_CAG-495.sp001917125, Faecalibacterium_02651, Eubacterium, Agathobacter.faecis, Erysipelatoclostridium_00670, Clostridium.bolteae, Lawsonibacter_03903, Klebsiella, Faecalibacterium_00589, Anaerostipes, Anaerotruncus.sp900199635, Blautia.sp0002858 55, Lachnospiraceae_CAG-81.sp900066535, Faecalibacterium_02223, Blautia.sp900120195, Clostridia_CAG-269, Clostridia_CAG-245.sp000435175, Erysipelatoclostridium.ramosum, Lawsonibacter_00090, Robinsoniella, Lachnospiraceae_01178, Dorea.scindens, Ruminococcus.clever、Roseburia_00245、Blautia_00954、Clostridium.symbiosum、Acetatifactor.sp900066365、Blautia.obeum、Anaerovoracaceae_01577、 Eubacterium.ventriosum、Oscillospirales_CAG-272、Lachnospiraceae_GCA-900066575、Blautia.sp000436615、Lachnobacterium、Acetatifact or、Lachnospiraceae_UBA9502、Faecalibacterium_00512、Dorea.formicigenerans、Ruminococcus.bromii、Oscillibacter_00837、Acetatifacto r.sp003447295、Lachnospira.sp003537285、Oscillospiraceae_ER4_04276、Blautia、Faecalibacterium_02610、Gemmiger_01627、Oscillospirace ae_02034, Lachnospiraceae_GCA-900066575.sp900066385, Erysipelatoclostridium.sp003024675, Eisenbergiella.massiliensis, Bifidobacterium.infantis, Clostridium.clostridioforme, Ruminococcus.sp000437095, Absiella, Anaerotruncus, Emergencia, Faecalibacterium, Fusicatenibacter, Lachnoclostridium, Lachnospira, Lachnospiraceae_CAG-56, Lachnospiraceae_TF01-11, Roseburia, Ruthenibacterium, Absiella.innocuum, Acutalibacter.timonensis, Agathobaculum.butyriciproducens, Akkermansia.muciniphila, Clostridium.lavalense, Clostridium.At least one of leptum, Dorea.longicatena, Faecalibacterium.prausnitzii, Faecalibacterium_03166, Faecalicatena.faecis, Faecalicatena_02772, Fusicatenibacter.saccharivorans, Fusicatenibacter_03676, Hungatella.effluvii, Lachnospira.sp000437735, Lachnospiraceae_CAG-56.sp900066615, Lachnospiraceae_KLE1615.sp900066985, Porphyromonas.somerae, Roseburia.inulinivorans, Ruminococcus.sp003011855, Streptococcus.sp000187445, and Streptococcus_02891.

2. The microbial biomarker group according to claim 1, characterized in that, include: Fusicatenibacter_03676、Dorea.longicatena、Fusicatenibacter、Lachnospiraceae_CAG-81.sp900066785、Fusicatenibacter.saccharivor ans、Alphaproteobacteria_CAG-495、Faecalibacterium.prausnitzii、Faecalibacterium、Lachnospira.sp900316325、Erysipelatoclostridia um.sp000752095、Faecalicatena.faecis、Lachnospiraceae_CAG-56.sp900066615、Lachnospiraceae_TF01-11、Ruminococcaceae_CAG-115.sp 003531585、Clostridia_CAG-245、Christensenellales_UBA11524.sp000437595、Lachnospiraceae_02075、Eubacterium.ramulus、Faecalicate na_02772、Lachnospiraceae_CAG-127.sp900319515、Alphaproteobacteria_CAG-495.sp001917125、Lachnospiraceae_CAG-56、Ruminococcus. sp003011855、Lachnospiraceae_00259、Faecalicatena.sp900066545、Dorea_02992、Christensenellales_UBA11524、Gemmiger、Lachnospirace ae_CAG-81、Gemmiger.sp003476825、Agathobaculum.butyriciproducens、Faecalibacterium_03166、Roseburia.inulinivorans、Lachnospirac eae_GCA-900066135、Lachnospiraceae_00141、Anaerostipes.hadrus、Faecalibacterium_02040、Clostridia_CAG-245.sp000435175、Blautia.sp900066205、Lachnospiraceae_COE1、Lachnospiraceae_00141、Faecalicatena.lactaris、Anaerovoracaceae_00668、Bifidobacterium.adolesc entis、Agathobacter、Lachnospiraceae_KLE1615.sp900066985、Lachnospiraceae_UBA7182、Blautia.sp900066335、Agathobacter_02670、Erysip elatoclostridium_00670、Lachnospiraceae_KLE1615、Faecalibacterium_03899、Clostridium_01246、Oscillospiraceae_ER4.sp003522105、Lac hnospiraceae_CAG-81.sp900066055、Clostridium.sp000431375、Faecalibacterium_03675、Lachnospiraceae_03335、Faecalibacterium_03291、 Lachnospiraceae_00194、Lachnospiraceae_04140、Blautia.wexlerae、Lachnospiraceae_02304、Lachnospiraceae_01178、Lawsonibacter_03903 、Ruminococcus.callidus、Lachnospiraceae_UBA7160、Lachnospiraceae_00137、Lachnospiraceae_CAG-95、Faecalibacterium_04336、Faecaliba cterium.sp003449675, Lachnospiraceae_GCA-900066135.sp900066135, Oscillospiraceae_ER4, Blautia.massiliensis, Ruminococcus, Oscillospiraceae_ER4.sp000765235, Hungatella_00278, Faecalibacterium_02651, Eubacterium.ventriosum, Oscillospirales_CAG-272, Clostridium.sp003024715, Agathobacter.faecis, Faecalibacterium_00589, Roseburia_00245, Anaerovoracaceae_01577, Agathobaculum, Eubacterium.hallii, Lachnospira, Lachnobacterium, Erysipelatoclostridium.sp003024675, Roseburia, Anaerostipes, Blautia.sp000285855, Lachnospiraceae_C AG-81.sp900066535、Faecalibacterium_02223、Blautia.sp900120195、Clostridia_CAG-269、Blautia_00954、Ruminococcus.bromii、Lachnospira .sp000437735、Oscillospiraceae_02034、Lawsonibacter_00090、Dorea、Blautia.sp000436615、Oscillibacter_00837、Lachnospira.sp003537285 , Oscillospiraceae_ER4_04276, Acetatifactor.sp900066365, Blautia.obeum, Lachnospiraceae_GCA-900066575, Lachnospiraceae_UBA9502, Faecalibacterium_00512, Acetatifactor.sp003447295, Blautia, Faecalibacterium_02610, Gemmiger_01627, Bifidobacterium.infantis, Ruminococcus.sp000437095, Eubacterium, Acetatifactor, Dorea.formicigenerans, Lachnospiraceae_GCA-900066575.sp900066385, Eisenbergiella.massiliensis, Clostridium.clostridioforme, Absiella, Robinsoniella, Dorea.scindens, Anaerotruncus.sp900199635, Erysipelatoclostridium.At least one of the following: ramosum, Clostridium symbiosum, Klebsiella, Clostridium lavalense, Clostridium bolteae, Eggerthella lenta, Faecalicatena_03316, and Eggerthella.

3. The microbial biomarker group according to claim 2, characterized in that, include: Fusicatenibacter_03676、Dorea.longicatena、Fusicatenibacter、Lachnospiraceae_CAG-81.sp900066785、Fusicatenibacter.saccharivor ans、Alphaproteobacteria_CAG-495、Faecalibacterium.prausnitzii、Faecalibacterium、Lachnospira.sp900316325、Erysipelatoclostridia um.sp000752095、Faecalicatena.faecis、Lachnospiraceae_CAG-56.sp900066615、Lachnospiraceae_TF01-11、Ruminococcaceae_CAG-115.sp 003531585、Clostridia_CAG-245、Christensenellales_UBA11524.sp000437595、Lachnospiraceae_02075、Eubacterium.ramulus、Faecalicate na_02772、Lachnospiraceae_CAG-127.sp900319515、Alphaproteobacteria_CAG-495.sp001917125、Lachnospiraceae_CAG-56、Ruminococcus. sp003011855、Lachnospiraceae_00259、Faecalicatena.sp900066545、Dorea_02992、Christensenellales_UBA11524、Gemmiger、Lachnospirace ae_CAG-81、Gemmiger.sp003476825、Agathobaculum.butyriciproducens、Faecalibacterium_03166、Roseburia.inulinivorans、Lachnospirac eae_GCA-900066135、Lachnospiraceae_00141、Anaerostipes.hadrus、Faecalibacterium_02040、Clostridia_CAG-245.sp000435175、Blautia.sp900066205、Lachnospiraceae_COE1、Lachnospiraceae_00141、Faecalicatena.lactaris、Anaerovoracaceae_00668、Bifidobacterium.adolesc entis、Agathobacter、Lachnospiraceae_KLE1615.sp900066985、Lachnospiraceae_UBA7182、Blautia.sp900066335、Agathobacter_02670、Erysip elatoclostridium_00670、Lachnospiraceae_KLE1615、Faecalibacterium_03899、Clostridium_01246、Oscillospiraceae_ER4.sp003522105、Lac hnospiraceae_CAG-81.sp900066055、Clostridium.sp000431375、Faecalibacterium_03675、Lachnospiraceae_03335、Faecalibacterium_03291、 Lachnospiraceae_00194、Lachnospiraceae_04140、Blautia.wexlerae、Lachnospiraceae_02304、Lachnospiraceae_01178、Lawsonibacter_03903 、Ruminococcus.callidus、Lachnospiraceae_UBA7160、Lachnospiraceae_00137、Lachnospiraceae_CAG-95、Faecalibacterium_04336、Faecaliba cterium.sp003449675, Lachnospiraceae_GCA-900066135.sp900066135, Oscillospiraceae_ER4, Blautia.massiliensis, Ruminococcus, Oscillospiraceae_ER4.sp000765235, Hungatella_00278, Faecalibacterium_02651, Eubacterium.ventriosum, Oscillospirales_CAG-272, Clostridium.At least one of sp003024715, Agathobacter.faecis, Faecalibacterium_00589, Roseburia_00245, Anaerovoracaceae_01577, Agathobaculum, Eubacterium.hallii, Lachnospira, Lachnobacterium, Erysipelatoclostridium.sp003024675, Roseburia, Anaerostipes, Blautia.sp000285855, Lachnospiraceae_CAG-81.sp900066535, Faecalibacterium_02223, Blautia.sp900120195, Clostridia_CAG-269, Blautia_00954, Ruminococcus.bromii, Lachnospira.sp000437735, Oscillospiraceae_02034, Lawsonibacter_00090, Dorea, Blautia.sp000436615, Oscillibacter_00837, Lachnospira.sp003537285, Oscillospiraceae_ER4_04276, Acetatifactor.sp900066365, Blautia.obeum, Lachnospiraceae_GCA-900066575, Lachnospiraceae_UBA9502, Faecalibacterium_00512, Acetatifactor.sp003447295, Blautia, Faecalibacterium_02610, Gemmiger_01627, Bifidobacterium.infantis, Ruminococcus.sp000437095, Eubacterium, Acetatifactor, Dorea.formicigenerans and Lachnospiraceae_GCA-900066575.sp900066385.

4. The microbial biomarker group according to claim 2, characterized in that, include: Eisenbergiella.massiliensis, Clostridium.clostridioforme, Absiella, Robinsoniella, Dorea.scindens, Anaerotruncus.sp900199635, Erysipelatoclostridiu At least one of m.ramosum, Clostridium.symbiosum, Klebsiella, Clostridium.lavalense, Clostridium.bolteae, Eggerthella.lenta, Faecalicatena_03316 and Eggerthella.

5. The microbial biomarker group according to claim 1, characterized in that, include: Lachnospiraceae_CAG-56.sp900066615, Faecalicatena.faecis, Fusicatenibacter, Fusicatenibacter.saccharivorans, Lachnospiraceae_TF01-11, Fusicatenibacter_03676, Faecalicatena_02772, Lachnospiraceae_CAG-56, Faecalibacterium_03166, Dorea.longicatena, Roseburia.inulinivorans, Lachnospira.sp000437735, Ruminococcus.sp003011855, Faecalibacterium, Roseburia, Faecalibacterium.prausnitzii, Lachnospir a, Agathobaculum.butyriciproducens, Lachnospiraceae_KLE1615.sp90006698, Streptococcus.sp000187445, Hungatella.effluvii, Streptococcus_02891, Clostridium.leptum, Lachnoclostridium, Absiella, Porphyromonas.somerae, Acutalibacter.timonensis, Clostridium.bolteae, Akkermansia.muciniphila, Dorea.scindens, Ruthenibacterium, Absiella.innocuum, Clostridium.lavalense, Emergencia and Anaerotruncus at least one.

6. The microbial biomarker group according to claim 5, characterized in that, including: Streptococcus.sp000187445, Hungatella.effluvii, Streptococcus_02891, Clostridium.leptum, Lachnoclostridium, Absiella, Porphyromonas.somerae, Acutalibacter.timon ensis, Clostridium.bolteae, Akkermansia.muciniphila, Dorea.scindens, Ruthenibacterium, Absiella.innocuum, Clostridium.lavalense, Emergencia and Anaerotruncus.

7. The microbial biomarker group according to claim 5, characterized in that, include: Lachnospiraceae_CAG-56.sp900066615, Faecalicatena.faecis, Fusicatenibacter, Fusicatenibacter.saccharivorans, Lachnos piraceae_TF01-11, Fusicatenibacter_03676, Faecalicatena_02772, Lachnospiraceae_CAG-56, Faecalibacterium_03166, Dorea.l At least one of the following: ongicatena, Roseburia.inulinivorans, Lachnospira.sp000437735, Ruminococcus.sp003011855, Faecalibacterium, Roseburia, Faecalibacterium.prausnitzii, Lachnospira, Agathobaculum.butyriciproducens, and Lachnospiraceae_KLE1615.sp90006698.

8. Use of reagents for detecting a group of microbial biomarkers in the preparation of a kit for predicting the prognosis of gastrointestinal tumors in a target sample, wherein the group of microbial biomarkers is as described in any one of claims 1-7.

9. The use according to claim 8, characterized in that, The gastrointestinal tumors include at least one of colorectal cancer, esophageal cancer, gastric cancer, and pancreatic cancer; Optionally, the target sample includes: a tumor tissue sample or a normal tissue sample adjacent to the cancer.

10. A reagent kit, characterized in that, include: Reagents for detecting the microbial biomarker group according to any one of claims 1-7.

11. A method for establishing a tumor prognosis prediction model, characterized in that, include: Obtain microbial sequencing data from the training samples; The microbial sequencing data is filtered and analyzed to determine the microbial biomarker group information in the training samples; Based on the survival analysis model, the microbial biomarker group information related to the preset survival time is determined, including: microbial biomarker group information related to the time outside the preset survival time and microbial biomarker group information related to the time within the preset survival time; The tumor prognosis prediction model is constructed based on the differences between the microbial biomarker information outside the preset survival time and the microbial biomarker information within the preset survival time. The microbial biomarker group is as described in any one of claims 1-7.

12. The method according to claim 11, characterized in that, The tumor was selected from gastrointestinal tumors; Optionally, the gastrointestinal tumor includes at least one of colorectal cancer, esophageal cancer, gastric cancer, and pancreatic cancer; Optionally, the training samples are selected from gastrointestinal tumor tissue or adjacent normal tissue.

13. The method according to claim 12, characterized in that, The filtering analysis includes: removing contaminating microbial genera from the microbial sequencing data; Optionally, a species is considered to be the genus of contaminating microorganisms if any of the following conditions 1)-3) are met: 1) Occurrence rate in blood samples ≥20%; 2) It appears more frequently in blood than in tissue samples; 3) Microbial genera in the negative control; Optionally, the filtering analysis further includes: removing microbial genera that occur in less than 20% of all samples; Optionally, the survival analysis model is selected from the Cox proportional hazards model; Optionally, the microbial biomarker group information related to the time beyond the preset survival time includes: the total number of taxa of microorganisms related to the time beyond the preset survival time, the number of microorganisms related to the time beyond the preset survival time, and the relative abundance of microorganisms related to the time beyond the preset survival time; Optionally, the microbial biomarker group information related to the preset survival time includes: the total number of taxa of related microorganisms within the preset survival time, the number of related microorganisms within the preset survival time, and the relative abundance of related microorganisms within the preset survival time.

14. The method according to any one of claims 11-13, characterized in that, The tumor prognosis prediction model has the following formula: Where MRS represents the microbial risk score; |N+| represents the total number of taxa of relevant microorganisms within the preset survival time; |N-| represents the total number of taxa of relevant microorganisms outside the preset survival time; R N+ Indicates the number of relevant microorganisms within a preset survival time; R N- This indicates the number of relevant microorganisms beyond the preset survival time; p i p represents the relative abundance of relevant microorganisms within the preset survival time of each sample. j This indicates the relative abundance of relevant microorganisms beyond the preset survival time for each sample.

15. A method for predicting tumor prognosis, characterized in that, include: Obtain the microbial biomarker group information of the target sample. The microbial biomarker group information includes: the total number of taxa of related microorganisms outside the preset survival time, the number of related microorganisms outside the preset survival time, the relative abundance of related microorganisms outside the preset survival time, the total number of taxa of related microorganisms within the preset survival time, the number of related microorganisms within the preset survival time, and the relative abundance of related microorganisms within the preset survival time. The microbial biomarker group information is input into the tumor prognosis prediction model to determine the MRS score; The prognostic outcome of the target sample is determined based on the difference between the MRS score and a predetermined MRS score threshold. Wherein, the microbial biomarker group is as described in any one of claims 1-7; the tumor prognosis prediction model is constructed by the method described in any one of claims 11-14.

16. The method according to claim 15, characterized in that, The predetermined MRS score threshold is determined based on the median MRS score of the training samples; Optionally, if the MRS score is higher than a predetermined MRS score threshold, it is an indication of a poor prognosis for the target sample. Optionally, if the MRS score is lower than a predetermined MRS score threshold, it is an indication that the target sample has a better prognosis.

17. A computing device, characterized in that, include: Processor and memory; The memory is used to store computer programs; The processor is configured to execute the computer program to implement the tumor prognosis prediction model establishment method as described in any one of claims 11-14 or the tumor prognosis prediction method as described in any one of claims 15-16.

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