Application of flora metabolite in esophageal cancer prognosis

Abstract

The invention belongs to the technical field of molecular diagnosis, and particularly relates to application of flora metabolites in esophageal cancer prognosis. The invention provides a flora metabolite composition. The flora metabolite composition is prepared from any one or more of the following items: beta-Guandinopropic acid, beta-glucosidase, beta-glucosidase, beta-glucosidase, beta-glucosidase, beta-glucosidase and beta-glucosidase. The preparation method comprises the following steps: preparing 2-Amini-butyric acid; the preparation method comprises the following steps of: obtaining N, N-(1-Deoxy-1-ductosyl) thread; the preparation method comprises the following steps: preparing gamma-Glutamylproline; (2S)-1-pyrolidine-2-carboxylic Acid, and (2S)-1-pyrolidine-2-carboxylic Acid; the preparation method comprises the following steps: adding 2, 2, 6, 6-Tetrametyl-4-piperidinone into a reaction kettle; the preparation method comprises the following steps: (1) carrying out a reaction on 3, 3 alpha-Hydroxy-5beta-chola-7, 9 (11)-dien-24-oic acid in the presence of a catalyst; the invention further provides application of the flora metabolite composition in preparing a product for predicting the CD8 + T cell infiltration degree, a product for predicting the immunotherapy effect or an esophageal cancer prognosis product. The application can be used for predicting the CD8 + T cell infiltration degree and the immunotherapy effect of the esophageal cancer patient and performing prognosis evaluation on the esophageal cancer patient, and is high in accuracy and good in sensitivity and specificity.

Description

Technical Field

[0001] This application belongs to the field of molecular diagnostic technologies, and specifically relates to the application of microbial metabolites in the prognosis of esophageal cancer. Background Art

[0002] Esophageal cancer is a very common malignant tumor. Worldwide, the mortality rate of esophageal cancer ranks sixth (accounting for 5.3% of the total number of cancer deaths). The main subtype of esophageal cancer is esophageal squamous cell carcinoma, and approximately 90% of esophageal cancers are esophageal squamous cell carcinomas. The diagnosis and prognosis assessment of esophageal cancer still need to be improved. How to accurately evaluate the molecular characteristics of esophageal cancer is of great significance for the precise treatment of patients, prognosis assessment, and reduction of social burden.

[0003] The occurrence of esophageal cancer is significantly correlated with nutritional status, eating habits, etc., and the number of different types of gut microbiota is also affected by the above factors. The human body can provide an ecosystem for 10 - 100 trillion microorganisms, and each individual has 500 - 1000 unique microbial species. Microorganisms produce a large number of metabolites that accumulate in esophageal tissues. In esophageal squamous cell carcinoma lesions, these accumulated bacterial products have a significant impact on the number of infiltrating CD8+ T cells through complex biochemical reactions and immune reactions. Therefore, specific esophageal microbiota metabolites and the infiltration number of CD8+ T cells in an individual can be measured through metabolomics and single-cell sequencing analysis.

[0004] The disturbance of the ecosystem of esophageal microbiota will affect human health, and the differences in the metabolites of the microbiota may have a significant impact on the strength of the tumor immune response. By analyzing the metabolites of an individual's esophageal microbiota, the infiltration number of its CD8+ T cells can be predicted. By analyzing the infiltration number of CD8+ T cells, the sensitivity and prognosis of esophageal cancer patients to immunotherapy can be predicted.

[0005] However, there is still a lack of detection methods with both accuracy and application value that can be used to predict the sensitivity and prognosis of esophageal cancer patients to immunotherapy. Summary of the Invention

[0006] To solve the defects of the prior art, this application uses LASSO regression (lasso regression) and binary logistic regression to screen the microbiota metabolites related to the infiltration number of CD8+ T, and constructs a score Score. The cut-off value corresponding to the score in esophageal cancer is obtained through ROC curve analysis, which can be used to infer the infiltration number of relevant CD8+ T in esophageal cancer patients, infer the effect of immunotherapy, and prognose esophageal cancer patients. After verification, it has the advantages of high accuracy and good specificity, and has good application prospects.

[0007] The first aspect of the present application provides a composition of microbial metabolites, including any one or more of the following: β-Guanidinopropionic acid; 2-Amino-butanoic acid; N-(1-Deoxy-1-fructosyl)threonine; γ-Glutamylproline; (2S)-1-pyrrolidine-2-carboxylic Acid; 2,2,6,6-Tetramethyl-4-piperidinone; 3alpha-Hydroxy-5beta-chola-7,9(11)-dien-24-oic Acid.

[0008] The second aspect of the present application provides the use of microbial metabolites and / or reagents for detecting microbial metabolites in the preparation of a product, wherein the microbial metabolites are the composition of microbial metabolites in the first aspect above, and the product is selected from any one or more of the following:

[0009] 1) A product for predicting the degree of CD8+ T cell infiltration;

[0010] 2) A product for predicting the efficacy of immunotherapy for esophageal cancer;

[0011] 3) A prognostic product for esophageal cancer.

[0012] The third aspect of the present application provides a kit for predicting the degree of CD8+ T cell infiltration, and / or predicting the efficacy of immunotherapy for esophageal cancer, and / or the prognosis of esophageal cancer, including microbial metabolites and / or reagents for detecting microbial metabolites, wherein the microbial metabolites are the composition of microbial metabolites in the first aspect above.

[0013] The fourth aspect of the present application provides a device for predicting the degree of CD8+ T cell infiltration, and / or predicting the efficacy of immunotherapy for esophageal cancer, and / or the prognosis of esophageal cancer, the device comprising:

[0014] A data module for obtaining the deposition amount of each item in the composition of microbial metabolites in the first aspect above of the sample;

[0015] An analysis module for calculating a score through a mathematical model based on the deposition amount of each item in the composition of microbial metabolites in the first aspect above of the sample, and judging the degree of CD8+ T cell infiltration, and / or the efficacy of immunotherapy, and / or the prognosis of esophageal cancer patients.

[0016] The fifth aspect of the present application provides a computer-readable storage medium containing a computer program, characterized in that the computer program, when run by a processor, executes a method comprising the following steps:

[0017] Obtain the deposition amount of each item in the microbial metabolite composition of the sample as described in the first aspect above, calculate a score through a mathematical model, and judge the infiltration degree of CD8+ T cells, and / or the immunotherapy effect, and / or the prognosis of esophageal cancer patients.

[0018] The sixth aspect of the present application provides an electronic terminal, comprising a memory and a processor, wherein the memory contains a computer program, and is characterized in that when the computer program is run by the processor, it executes a method comprising the following steps:

[0019] Obtain the deposition amount of each item in the microbial metabolite composition of the sample as described in the first aspect above, calculate a score through a mathematical model, and judge the infiltration degree of CD8+ T cells, and / or the immunotherapy effect, and / or the prognosis of esophageal cancer patients.

[0020] The seventh aspect of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it realizes a method comprising the following steps:

[0021] Obtain the deposition amount of each item in the microbial metabolite composition of the sample as described in the first aspect above, calculate a score through a mathematical model, and judge the infiltration degree of CD8+ T cells, and / or the immunotherapy effect, and / or the prognosis of esophageal cancer patients.

[0022] The eighth aspect of the present application provides a method for predicting the prognosis of esophageal cancer patients for non-disease diagnosis and treatment purposes, including obtaining the deposition amount of each item in the microbial metabolite composition of the sample as described in the first aspect above, calculating a score through a mathematical model, and judging the infiltration degree of CD8+ T cells, and / or the immunotherapy effect, and / or the prognosis of esophageal cancer patients.

[0023] The construction method of the mathematical model is: constructing by using binary Logistic regression for the deposition amount of each item in the microbial metabolite composition.

[0024] Preferably, the mathematical model is:

[0025] Score = (β-Guanidinopropionic acid × 0.0241) + (2-Amino-butanoic acid × 0.0357) + (N-(1-Deoxy-1-fructosyl)threonine × 0.0414) + (γ-Glutamylproline × 0.0138) + ((2S)-1-pyrrolidine-2-carboxylic Acid × 0.0412) + (2,2,6,6-Tetramethyl-4-piperidinone × 0.0703691) + (3α-Hydroxy-5beta-chola-7,9(11)-dien-24-oic Acid × 0.0253849).

[0026] The method for judging the infiltration degree of CD8+ T cells, and / or the effect of immunotherapy, and / or the prognosis of esophageal cancer patients is as follows: when the score is higher than the cut-off value, it is judged that the infiltration degree of CD8+ T cells is low, and / or the effect of immunotherapy is poor, and / or the prognosis is poor; when the score is lower than the cut-off value, it is judged that the infiltration degree is high, and / or the effect of immunotherapy is significant, and / or the prognosis is good.

[0027] The beneficial effects of the present application are as follows:

[0028] 1. The present application first proposes a metabolite composition for predicting the infiltration degree of CD8+ T cells in esophageal cancer patients, which can evaluate the infiltration degree of CD8+ T cells in esophageal cancer patients, so as to accurately predict the prognosis of esophageal cancer patients, effectively guide the individualized treatment of esophageal cancer patients, improve clinical benefits, and at the same time avoid unnecessary waste of medical resources.

[0029] 2. At present, since detecting the local flora metabolites of esophageal cancer patients to infer CD8+ is an unconventional detection item in clinical practice, and the cost of detecting the above genes alone is relatively high. The prediction model constructed based on metabolomics and single-cell sequencing technology in the present application can predict the infiltration degree of CD8+ T cells in esophageal cancer patients and evaluate the prognosis of esophageal cancer patients, with the advantages of being economical, having high accuracy, good sensitivity and specificity. Description of the Drawings

[0030] Figure 1 The infiltration degree of CD8+ T cells in a small amount of flora metabolite deposition (left figure) and a large amount of flora metabolite deposition (right figure).

[0031] Figure 2 The deposition amount of each item in the flora metabolite composition of the present application in the high infiltration group.

[0032] Figure 3The deposition amounts of various components in the microbial metabolite composition of the present application for the low infiltration group.

[0033] Figure 4 It is the curve of the mean square error with respect to log(λ) in the LASSO regression.

[0034] Figure 5 It is the receiver operating characteristic curve (ROC) of the prediction model of the present application in clinical subjects.

[0035] Figure 6 It is a schematic diagram of the device of the present application for predicting the infiltration degree of CD8+ T cells or the prognosis of esophageal cancer.

[0036] Figure 7 It is a schematic diagram of the electronic terminal of the present application.

[0037] Figure 8 It is a schematic diagram of the method of the present application for predicting the infiltration degree of CD8+ T cells or the prognosis of esophageal cancer. Detailed implementation manners

[0038] The following uses specific specific examples to illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation manners, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application.

[0039] Before further describing the specific implementation manners of the present application, it should be understood that the protection scope of the present application is not limited to the specific specific implementation manners described below; it should also be understood that the terms used in the embodiments of the present application are for describing specific specific implementation manners, rather than for limiting the protection scope of the present application; in the specification and claims of the present application, unless otherwise clearly indicated in the text, the singular forms "a", "an" and "the" include the plural forms.

[0040] When the embodiments give a numerical range, it should be understood that unless otherwise specified in the present application, any value between the two endpoints of each numerical range and any value between the two endpoints can be selected. Unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as commonly understood by those skilled in the art of this technology. In addition to the specific methods, devices, and materials used in the embodiments, according to the knowledge of those skilled in the art of this technology and the description of the present application, any methods, devices, and materials of the prior art similar to or equivalent to the methods, devices, and materials described in the embodiments of the present application can also be used to implement the present application.

[0041] The present application first provides a composition of microbial metabolites, which includes any one or more of the following: β-Guanidinopropionic acid; 2-Amino-butanoic acid; N-(1-Deoxy-1-fructosyl)threonine; gamma-Glutamylproline; (2S)-1-pyrrolidine-2-carboxylic Acid; 2,2,6,6-Tetramethyl-4-piperidinone; 3alpha-Hydroxy-5beta-chola-7,9(11)-dien-24-oic Acid.

[0042] In a specific embodiment of the present application, the composition of microbial metabolites includes all of the following: β-Guanidinopropionic acid; 2-Amino-butanoic acid; N-(1-Deoxy-1-fructosyl)threonine; γ-Glutamylproline; (2S)-1-pyrrolidine-2-carboxylic Acid; 2,2,6,6-Tetramethyl-4-piperidinone; 3alpha-Hydroxy-5beta-chola-7,9(11)-dien-24-oic Acid.

[0043] β-Guanidinopropionic acid has a molecular formula of C4H9N3O2, a molecular weight of 131.13, and a PubChem CID of 67701. The structural formula is as follows.

[0044]

[0045] 2-Amino-butanoic acid has a molecular formula of C4H9NO2, a molecular weight of 103.120, and a PubChem CID of 80283. The structural formula is as follows:

[0046]

[0047] N-(1-Deoxy-1-fructosyl)threonine has a molecular formula of C 10 H 19 NO8, a molecular weight of 281.26, and a PubChem CID of 131752246. The structural formula is as follows:

[0048]

[0049] γ-Glutamylproline, with the molecular formula C 10 H 16 N2O5, with a molecular weight of 244.24, PubChem CID is 9856242, and the structural formula is as follows:

[0050]

[0051] (2S)-1-pyrrolidine-2-carboxylic acid, with the molecular formula C5H9NO2, a molecular weight of 115.13, PubChem CID is 145742, and the structural formula is as follows:

[0052]

[0053] 2,2,6,6-Tetramethyl-4-piperidinone, with the molecular formula C9H 17 NO, with a molecular weight of 155.24, PubChem CID is 13220, and the structural formula is as follows:

[0054]

[0055] 3alpha-Hydroxy-5beta-chola-7,9(11)-dien-24-oic Acid, with the molecular formula C 24 H 36 O3, with a molecular weight of 372.5, PubChem CID is 5284077, and the structural formula is as follows:

[0056]

[0057] This application also provides the use of the microbial metabolites and / or reagents for detecting microbial metabolites in the preparation of a product, wherein the microbial metabolites are the above-mentioned microbial metabolite composition, and the product is selected from any one or more of the following:

[0058] 1) A product for predicting the degree of CD8+ T cell infiltration;

[0059] 2) A product for predicting the efficacy of immunotherapy for esophageal cancer;

[0060] 3) A product for the prognosis of esophageal cancer.

[0061] The degree of CD8+ T cell infiltration refers to the degree of infiltration and the number of CD8+ T cells (cytotoxic T cells) in tumor tissue or other diseased tissues, and the infiltration refers to the process of diffusion, penetration or invasion of cells, tissues or fluids in tissues or organs.

[0062] In esophageal cancer, the efficacy of immunotherapy can be classified as follows:

[0063] (1) iCR (immune complete remission): All target lesions disappear.

[0064] (2) iPR (immune partial remission): The sum of the greatest diameters of the target lesions is reduced by at least 30% and maintained for more than 4 weeks.

[0065] (3) iSD (immune stable disease): The condition does not change significantly and the symptoms remain unchanged.

[0066] (4) iCPD (immune confirmed progressive disease): The sum of the greatest diameters of the target lesions is increased by at least 20% or new lesions appear.

[0067] The esophageal cancer may include esophageal squamous cell carcinoma and esophageal adenocarcinoma.

[0068] The prognosis refers to the prediction and judgment of the possible development trend, outcome, and the future health status of the patient for a disease or health problem.

[0069] The prognosis may include predicting the sensitivity of esophageal cancer patients to immunotherapy, predicting the pathological remission of esophageal cancer patients, and / or the survival period.

[0070] In a specific embodiment of the present application, the above product may be one or more of a reagent, a kit, a prototype, or a chip.

[0071] In a specific embodiment of the present application, the reagent for detecting the metabolites of the flora is a reagent for detecting the deposition amount of the metabolites of the flora.

[0072] The deposition amount of the metabolites of the flora refers to the accumulation or storage level of specific metabolites in the tissues, cells, or body fluids of an organism. In a specific embodiment of the present application, the deposition amount of the metabolites of the flora may be the level, content, or abundance of the metabolites of the flora in the tissue, such as the relative content or absolute content. In a certain embodiment of the present application, the deposition amount is the relative content of the metabolites of the flora.

[0073] In a specific embodiment of the present application, the deposition amount of the metabolites of the flora can be detected by targeted metabolomics or non-targeted metabolomics. For example, liquid chromatography-mass spectrometry can be used for detection, which is applicable to non-volatile and thermally unstable compounds (such as short-chain fatty acids, bile acids, amino acids, etc.). Gas chromatography-mass spectrometry can also be used for detection, which is applicable to volatile metabolites or derivatized compounds (such as organic acids, sugars). Nuclear magnetic resonance can also be used for detection, which is applicable to high-abundance metabolites and structural analysis.

[0074] In the specific embodiments of the present application, the reagent for detecting the deposition amount of intestinal flora metabolites can be a reagent required for detecting the deposition amount of intestinal flora metabolites by using metabolomics methods. For example, more specifically, the reagent for detecting the deposition amount of intestinal flora metabolites can be any one or a combination of reagents required for detecting the deposition amount of flora metabolites by using liquid chromatography-mass spectrometry, reagents required for detecting the deposition amount of flora metabolites by using gas chromatography-mass spectrometry, and reagents required for detecting the deposition amount of flora metabolites by using nuclear magnetic resonance.

[0075] As known to those skilled in the art, the reagents required for detecting the deposition amount of flora metabolites by using liquid chromatography-mass spectrometry refer to the reagents that must be used when detecting by using liquid chromatography-mass spectrometry, such as including but not limited to suitable internal standards, liquid chromatography columns, mobile phases, and buffers.

[0076] As known to those skilled in the art, the reagents required for detecting the deposition amount of flora metabolites by using gas chromatography-mass spectrometry refer to the reagents that must be used when detecting by using gas chromatography-mass spectrometry, such as including but not limited to suitable internal standards, derivatization reagents, and gas chromatography columns.

[0077] As known to those skilled in the art, the reagents required for detecting the deposition amount of flora metabolites by using nuclear magnetic resonance refer to the reagents that must be used when detecting by using nuclear magnetic resonance, such as including but not limited to suitable deuterated reagents, internal standards, and buffers.

[0078] In the specific embodiments of the present application, the reagent for detecting the deposition amount of metabolites of the flora may further include reagents required for pretreatment and reagents required for metabolite extraction, which are used for pretreating or extracting the samples of patients to obtain samples that can be used for liquid chromatography-mass spectrometry, or gas chromatography-mass spectrometry, or nuclear magnetic resonance detection, such as including but not limited to organic solvents such as methanol or acetonitrile, derivatization reagents, internal standards, deionized water, PBS buffer, etc.

[0079] The present application also provides a kit for detecting the infiltration degree of CD8+ T cells, and / or predicting the immunotherapy effect of esophageal cancer, and / or the prognosis of esophageal cancer, including flora metabolites and / or reagents for detecting flora metabolites. The flora metabolites are the flora metabolite composition as described above and can be used as a standard product or a positive control in the kit. The reagents for detecting flora metabolites are as described above and will not be elaborated here.

[0080] The kit may further include test tubes or other suitable containers, instructions for use, etc.

[0081] The present application also provides a device for detecting the infiltration degree of CD8+ T cells, and / or predicting the immunotherapy effect of esophageal cancer, and / or the prognosis of esophageal cancer, such asFigure 6 As shown, the device includes:

[0082] A data module 11, configured to obtain the deposition amount of each item in the microbial metabolite composition of the sample as described above;

[0083] An analysis module 12, configured to calculate a score based on the deposition amount of each item in the microbial metabolite composition of the sample as described above, and to judge the infiltration degree of CD8+ T cells, and / or the immunotherapy effect, and / or the prognosis of esophageal cancer patients.

[0084] It should be noted that in the specific embodiments of the present application, the division of each module of the device is only a logical function division. In actual implementation, it can be fully or partially integrated into a physical entity, or physically separated. These modules can all be implemented in the form of software called by a processing element; they can also all be implemented in the form of hardware; or some modules can be implemented in the form of software called by a processing element, and some modules can be implemented in the form of hardware. For example, the detection module can be a separately established processing element, or can be integrated in a certain chip. In addition, it can also be stored in the memory in the form of program code and called and executed by a certain processing element to perform the functions of the above database acquisition module. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together or can be independently implemented. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed by the hardware integrated logic circuit or software-form instructions in the processor element.

[0085] For example, these modules in the embodiments of the present application can be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or, one or more microprocessors (digital singnal processors, DSPs for short), or, one or more Field Programmable Gate Arrays (FPGAs) or Graphics Processing Units (GPUs for short), etc. Again, when a certain module above is implemented in the form of a processing element scheduling program code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processors that can call program code. Again, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0086] The present application also provides a computer-readable storage medium containing a computer program, characterized in that when the computer program is run by a processor, it executes a method comprising the following steps:

[0087] Obtain the deposition amount of each item in the microbial metabolite composition of the sample as described above, calculate a score through a mathematical model, and judge the infiltration degree of CD8+ T cells, and / or the immunotherapy effect, and / or the prognosis of esophageal cancer patients.

[0088] In some embodiments of the present application, the computer-readable storage medium may include a read-only memory, a random access memory, an EEPROM, a CD-ROM or other optical disc storage device, a magnetic disc storage device or other magnetic storage device, a flash memory, a USB flash drive, a portable hard drive, or any other medium that can be used to store the desired program code in the form of instructions or data structures and can be accessed by a computer. Additionally, any connection can be appropriately referred to as a computer-readable storage medium. For example, if the instructions are sent from a website, server or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wireless technologies such as infrared, radio and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL or wireless technologies such as infrared, radio and microwave are included in the definition of the medium. However, it should be understood that the computer-readable storage medium does not include carrier waves, signals or other transient media, but is intended for non-transient, tangible storage media.

[0089] The present application also provides an electronic terminal, as Figure 7 shown, comprising a memory and a processor, wherein the memory contains a computer program, characterized in that when the computer program is run by the processor, it executes a method comprising the following steps:

[0090] Obtain the deposition amount of each item in the microbial metabolite composition of the sample as described above, calculate a score through a mathematical model, and judge the infiltration degree of CD8+ T cells, and / or the immunotherapy effect, and / or the prognosis of esophageal cancer patients.

[0091] The schematic diagram of the electronic terminal is as Figure 7 shown, the electronic terminal includes: a processor 21, a memory 22, a communicator 23, a communication interface 24 and a system bus 25; the memory 22 and the communication interface 24 are connected to the processor 21 and the communicator 23 through the system bus 25 and complete communication with each other, the memory 22 is used to store a computer program, the communication interface 24 is used to communicate with other devices, and the processor 21 and the communicator 23 are used to run the computer program so that the terminal executes the above method.

[0092] The system bus 25 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0093] The communication interface 24 is used to implement communication between the database access device and other devices (such as clients, read-write libraries, and read-only libraries).

[0094] The memory 22 may include a Random Access Memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0095] The processor 21 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0096] The electronic terminal may be a mobile phone, a computer device, a tablet device, a personal digital processing device, a factory background processing device, etc.

[0097] This application also provides a computer program product, including a computer program, and the method for implementing the following steps when the computer program is executed by a processor:

[0098] Obtain the deposition amount of each item in the microbial metabolite composition of the sample as described above, calculate a score through a mathematical model, and judge the infiltration degree of CD8+ T cells, and / or the immunotherapy effect, and / or the prognosis of esophageal cancer patients.

[0099] This application also provides a method for the prognosis of esophageal cancer patients for non-disease diagnosis and treatment purposes, such as Figure 8As shown, it includes obtaining the deposition amount of each item in the microbial metabolite composition such as the above-mentioned sample, calculating a score through a mathematical model, and judging the infiltration degree of CD8+ T cells, and / or the immunotherapy effect, and / or the prognosis of esophageal cancer patients.

[0100] In the present application, in the above-mentioned device, computer-readable storage medium, electronic terminal, computer program product, method, the construction method of the mathematical model is: constructing a binary Logistic regression for the deposition amount of each item in the microbial metabolite composition.

[0101] In some embodiments of the present application, in the above-mentioned device, computer-readable storage medium, electronic terminal, computer program product, method, the mathematical model is:

[0102] Score = (β-Guanidinopropionic acid × 0.0241) + (2-Amino-butanoic acid × 0.0357) + (N-(1-Deoxy-1-fructosyl)threonine × 0.0414) + (γ-Glutamylproline × 0.0138) + ((2S)-1-pyrrolidine-2-carboxylic Acid × 0.0412) + (2,2,6,6-Tetramethyl-4-piperidinone × 0.0703691) + (3α-Hydroxy-5beta-chola-7,9(11)-dien-24-oic Acid × 0.0253849);

[0103] Among them, β-Guanidinopropionic acid; 2-Amino-butanoic acid; N-(1-Deoxy-1-fructosyl)threonine; γ-Glutamylproline; (2S)-1-pyrrolidine-2-carboxylic Acid; 2,2,6,6-Tetramethyl-4-piperidinone; 3α-Hydroxy-5beta-chola-7,9(11)-dien-24-oic Acid are their deposition amounts.

[0104] In this application, in the above-mentioned device, computer-readable storage medium, electronic terminal, computer program product, and method, the method for judging the infiltration degree and / or prognosis of CD8+ T cells in esophageal cancer patients is as follows: when the score is higher than the cut-off value, it is judged that the infiltration degree of CD8+ T cells is low, and / or the immunotherapy effect is poor, and / or the prognosis is poor; when the score is lower than the cut-off value, it is judged that the infiltration degree of CD8+ T cells is high, and / or the immunotherapy effect is significant, and / or the prognosis is good.

[0105] In some embodiments of this application, a low infiltration degree of CD8+ T cells may mean that the infiltration number of T cells is less than 30,000, and a high infiltration degree of CD8+ T cells may mean that the infiltration number of T cells is greater than or equal to 30,000, which can be obtained, for example, by performing single-cell sequencing on the tumor microenvironment or tissue samples.

[0106] In some embodiments of this application, a significant immunotherapy effect may refer to iCR (immune complete remission) and iPR (immune partial remission), and a poor immunotherapy effect may refer to iSD (immune stable) and iCPD (immune confirmed progression).

[0107] As known to those skilled in the art, in esophageal cancer, the infiltration number of CD8+ T cells is correlated with the prognosis of esophageal cancer, and the prognosis of esophageal cancer patients can be judged by detecting the infiltration number of CD8+ T cells. In esophageal cancer, a good prognosis may, for example, mean that the tumor is confined within the esophagus, has not spread to lymph nodes or distant organs, the 5-year survival rate of early-stage patients can reach 80%-90%, the swallowing function recovers well after treatment, the patient can eat normally, and there are no complications; a poor prognosis may, for example, mean that the tumor has spread to lymph nodes or distant organs, recurs or metastasizes within a short period (such as within 1-2 years) after treatment, and the patient has serious complications (such as esophageal perforation, massive hemorrhage, etc.).

[0108] The cut-off value, which can be replaced by threshold, Cut off value, critical value, boundary value, etc., refers to the critical point between two different states of a thing, the boundary at which the state of a thing undergoes a jump change, that is, the lowest value or the highest value at which an effect can be produced. It can be a numerical value, a numerical range, or a formula for calculating a numerical value or a numerical range. In the ROC curve, the cut-off value is the coordinate point corresponding to the point on the ROC curve that is closest to the upper left corner (0, 1.0).

[0109] The cut-off value can be obtained based on the Youden index, which is also known as the correct index. The correct index refers to the overall ability of a test method to detect positive and negative cases. The correct index = sensitivity + specificity - 1. Different values are used as the basis for judging negative and positive cases, and the negative and positive cases of the sample are judged. According to the true negative and positive situations in the sample, the sensitivity and specificity of each value are calculated. The value with the largest correct index according to the above formula is the threshold of the detection method.

[0110] In some embodiments of the present application, the cut-off value can be 0.44749. At this time, the sensitivity is 0.80 and the specificity is 1. That is, in the above-mentioned device, computer-readable storage medium, electronic terminal, computer program product, and method, the method for judging the CD8+ T cell infiltration degree and / or prognosis of esophageal cancer patients is as follows: when the score is higher than 0.44749, it is judged that the CD8+ T cell infiltration degree is low, and / or the immunotherapy effect is poor, and / or the prognosis is poor; when the score is lower than 0.44749, it is judged that the infiltration degree is high, and / or the immunotherapy effect is significant, and / or the prognosis is good.

[0111] In the present application, the sample for detection is the esophageal cancer tissue of the patient.

[0112] In the present application, "patient" can also be used interchangeably with "subject", referring to the group of patients with esophageal cancer.

[0113] In the present application, the sample for detection can be derived from mammals, such as but not limited to humans, primates, livestock (such as sheep, cattle, horses, donkeys, pigs), pets (such as dogs, cats), laboratory test animals (such as mice, rabbits, rats, guinea pigs, hamsters), or captured wild animals (such as foxes, deer). Preferably, the sample for detection is derived from primates, and more preferably, the sample for detection is derived from humans.

[0114] The present application will be described below through specific embodiments. Unless otherwise specified, the experimental methods, detection methods, and preparation methods disclosed in the present invention all adopt the conventional techniques in the fields of molecular biology, biochemistry, chromatin structure and analysis, analytical chemistry, cell culture, recombinant DNA technology, and related fields in the art. These techniques have been well described in the existing literature. For details, see Sambrook et al., MOLECULAR CLONING: A LABORATORY MANUAL, Second edition, Cold Spring Harbor Laboratory Press, 1989 and Third edition, 2001; Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, John Wiley & Sons, New York, 1987 and periodic updates; the series METHODS IN ENZYMOLOGY, Academic Press, San Diego; Wolffe, CHROMATIN STRUCTURE AND FUNCTION, Third edition, Academic Press, San Diego, 1998; METHODS IN ENZYMOLOGY, Vol. 304, Chromatin (P.M. Wassarman and A.P. Wolffe, eds.), Academic Press, San Diego, 1999; and METHODS IN MOLECULAR BIOLOGY, Vol. 119, Chromatin Protocols (P.B. Becker, ed.) Humana Press, Totowa, 1999, etc. Unless otherwise specified, the instruments, reagents, and materials used in the examples can be obtained through conventional channels.

[0115] Example 1 Construction of the Scoring Model

[0116] This example is for the construction of a scoring model for predicting the proportion of metabolites of specific intestinal flora (Bacteroidetes, Firmicutes, Actinobacteria) in esophageal cancer patients.

[0117] First, clinical subjects were divided into a significant effect group and a poor effect group according to the significant degree of their immunotherapy effect, according to the internationally unified standard related to esophageal cancer:

[0118] iCR (immune complete response): All target lesions disappeared.

[0119] iPR (Immune Partial Response): The sum of the greatest diameters of target lesions is reduced by at least 30% and maintained for more than 4 weeks.

[0120] iSD (Immune Stable Disease): The condition shows no obvious change and the symptoms remain unchanged.

[0121] iCPD (Immune Clinically Progressive Disease): The sum of the greatest diameters of target lesions is increased by at least 20% or new lesions appear.

[0122] The groups with significant effects are iCR (Immune Complete Response) and iPR (Immune Partial Response); the groups with poor effects are iSD (Immune Stable Disease) and iCPD (Immune Clinically Progressive Disease).

[0123] Then, the number of CD8+ T cell infiltrations in these samples and the composition and deposition amount of the microbiota metabolites in esophageal cancer tissues were obtained by single-cell sequencing and metabolomics analysis. The metabolites produced by microbiota (such as gut microbiota, environmental microbiota) were qualitatively or quantitatively analyzed by metabolomics techniques (such as LC-MS, GC-MS, NMR, etc.) to reveal their metabolic characteristics and functions. The deposition amount of metabolites was obtained by dividing the absolute content by the average value of metabolites in all samples to obtain the relative content.

[0124] Group the esophageal cancer samples: significant effect group vs. poor effect group, sample size: at least 6 - 10 biological replicates in each group (adjusted according to statistical requirements). Collect samples quickly to avoid metabolite degradation (for example, esophageal cancer tissues need to be frozen within 30 minutes). Remove impurities (such as peritumoral adipose tissue and vascular connective tissue). Weigh an appropriate amount of samples and add pre-cooled extraction solvent (such as methanol:water = 4:1, containing internal standard). Vortex oscillation (5 min), ultrasonic disruption (ice bath, 10 min), grinding (ball milling after freezing in liquid nitrogen). Centrifuge at low temperature (4°C, 12,000×g, 15 min), collect the supernatant, repeat the extraction 1 - 2 times, and combine the supernatants. Concentrate by vacuum centrifugation or dry by nitrogen blowing, and redissolve in a solvent suitable for the analysis platform (such as acetonitrile / water or methanol / water).

[0125] Select an analysis platform, and there are the following multiple options: 1. LC-MS (Liquid Chromatography-Mass Spectrometry): Suitable for non-volatile and thermally unstable compounds (such as short-chain fatty acids, bile acids, amino acids, etc.). 2. GC-MS (Gas Chromatography-Mass Spectrometry): Suitable for volatile metabolites or compounds after derivatization treatment (such as organic acids, sugars). 3. NMR (Nuclear Magnetic Resonance): Suitable for high-abundance metabolites and structural analysis.

[0126] More specifically, the experimental method of metabolomics sequencing (LC-MS / GC-MS combined platform):

[0127] 1. Experimental design

[0128] (1) Research type differentiation:

[0129] Targeted metabolomics: Focus on specific metabolic pathways (such as TCA cycle, glutathione metabolism)

[0130] Untargeted metabolomics: Adopt a full-spectrum scanning strategy (m / z 50 - 1500)

[0131] Dynamic metabolic flux analysis (requires stable isotope labeling, such as 13C6 - glucose tracer)

[0132] (2) Sample grouping and replication

[0133] Biological replicates: ≥6 independent samples per group (clinical samples need to match age / sex / BMI)

[0134] Technical replicates: 10% of the samples are randomly injected for replication

[0135] Blank control: Parallel treatment with extraction solvent (methanol:water = 4:1)

[0136] 2. Sample preparation

[0137] (1) Pretreatment:

[0138] Tissue samples:

[0139] Quick-freeze in liquid nitrogen and grind to a particle size <50μm

[0140] Use the modified Bligh - Dyer method for phase separation extraction of lipid / water-soluble metabolites

[0141] Add isotope internal standards (such as d4 - succinic acid, 13C3 - lactic acid);

[0142] (2) If using GC - MS, its derivatization treatment is:

[0143] Oximation with methoxyamine hydrochloride (20mg / mL pyridine solution), 37°C for 90min

[0144] Derivatization with MSTFA silanization reagent (1% TMCS), 70°C for 60min

[0145] 3. Data acquisition

[0146] (1) LC - MS parameters:

[0147] Chromatographic column: HILIC column (2.1×100mm, 1.7μm)

[0148] Mobile phase: Phase A is 5mM ammonium formate (containing 0.1% formic acid); Phase B is acetonitrile (containing 0.1% formic acid)

[0149] Gradient program: 95% B (0 - 2 min) → 50% B (15 min) → 5% B (18 - 20 min)

[0150] Mass spectrometry mode: ESI+ in positive ion mode, spray voltage 3.5 kV; ESI- in negative ion mode, spray voltage 2.8 kV; full scan resolution 70,000 @ m / z 200

[0151] (2) GC-MS parameters:

[0152] Chromatographic column: DB-5MS (30 m × 0.25 mm × 0.25 μm)

[0153] Temperature programming: 70 °C (2 min) → 10 °C / min → 310 °C (5 min)

[0154] Ion source: EI 70 eV, scan range m / z 50 - 600

[0155] (3) Quality control system:

[0156] QC samples: Prepared by mixing equal amounts of all samples, inserted once every 10 injections

[0157] System stability: Retention time drift < 0.1 min, peak intensity RSD < 15%

[0158] 4. Data processing

[0159] (1) Raw data preprocessing:

[0160] Software: Progenesis QI / XCMS Online

[0161] Peak extraction: Noise threshold S / N > 3, mass error < 5 ppm

[0162] Peak alignment: RT window ± 0.2 min, m / z window ± 0.01 Da

[0163] (2) Metabolite annotation:

[0164] Databases: HMDB, METLIN, MassBank

[0165] Confirmation strategy:

[0166] Level 1: Standard retention time matching (± 0.1 min)

[0167] Level 2: MS / MS spectrum matching (Dot product > 0.8)

[0168] Level 3: Molecular formula prediction (verified by Seven Golden Rules)

[0169] (3) Statistical analysis:

[0170] Multivariate analysis: PCA (unsupervised) → PLS-DA (supervised) → VIP>1.5

[0171] Univariate analysis: nonparametric test (Mann-Whitney U, FDR correction), metabolite fold change (FC) > 2.0 and p < 0.05

[0172] 4. Innovative analysis module

[0173] (1) Metabolic network reconstruction:

[0174] Using MetaboAnalyst 5.0 to build a KEGG pathway topology model

[0175] Constraint-based algorithms (such as Flux Balance Analysis) predict metabolic flux directions

[0176] (2) Multi-omics integration:

[0177] Correlation of transcriptome-metabolome modules by WGCNA algorithm

[0178] Constructing Gaussian graphical model to reveal key regulatory nodes

[0179] (3) Machine Learning Modeling:

[0180] Random forest algorithm to screen characteristic metabolites (OOB error < 0.2)

[0181] Support vector machine (SVM) diagnostic model (ROC AUC>0.85)

[0182] 5. Method Validation

[0183] (1) Repeatability evaluation:

[0184] Technical repeat peak intensity CV < 15%

[0185] Biological replicate intragroup correlation r>0.8 (Pearson)

[0186] (2) Linear range verification:

[0187] Standard concentration gradient (0.1-100μM)

[0188] R 2 >0.99, LOD<0.05μM

[0189] (3) Recovery rate test:

[0190] Spiked recovery rate: 85 - 115%

[0191] Matrix effect coefficient: 0.8 - 1.2

[0192] In the treatment of esophageal cancer, CD8+ T cells indicate good immunotherapy effect. In this example, to test accuracy and ensure randomness, in the case of unknown immunotherapy effect, the number of CD8+ T cell infiltrations was clustered into two groups by R language, namely the high infiltration group and the low infiltration group. When the number of T cell infiltrations was greater than or equal to 30,000, it was classified into the high infiltration group; when the number of T cell infiltrations was less than 30,000, it was classified into the low infiltration group. Thus, it was further verified that: esophageal cancer patients with significant immunotherapy effect had a high number of T cell infiltrations, that is, the group with significant immunotherapy effect was matched with the high infiltration group; esophageal cancer patients with poor immunotherapy effect had a low number of T cell infiltrations, that is, the group with poor immunotherapy effect was matched with the low infiltration group.

[0193] LASSO regression analysis was performed on the number of CD8+ T cell infiltrations, the composition and deposition amount data of esophageal tissue flora metabolites in 100 esophageal cancer patient esophageal tissue samples in the form of [log2(FPKM + 1)], as Figure 1 shown, and the flora metabolites related to the number of CD8+ T cell infiltrations were obtained. Finally, 7 metabolites were selected: β-Guanidinopropionic acid, 2-Amino-butanoic acid, N-(1-Deoxy-1-fructosyl)threonine, γ-Glutamylproline, (2S)-1-pyrrolidine-2-carboxylic Acid, 2,2,6,6-Tetramethyl-4-piperidinone, 3alpha-Hydroxy-5beta-chola-7,9(11)-dien-24-oicAcid.

[0194] Among them, the deposition amounts of six compounds, namely β-Guanidinopropionic acid, 2-Amino-butanoic acid, N-(1-Deoxy-1-fructosyl)threonine, (2S)-1-pyrrolidine-2-carboxylic, gamma-GlutamylprolinAcid, and 3alpha-Hydroxy-5beta-chola-7,9(11)-dien-24-oic Acid, are negatively correlated with the infiltration degree of CD8+ T cells in esophageal cancer patients. The more their deposition amounts are, the lower the infiltration degree of CD8+ T cells is, and the worse the effect of immunotherapy is. 2,2,6,6-Tetramethyl-4-piperidinone is positively correlated with the infiltration number of CD8+ T cells. The more its deposition amount is, the higher the infiltration degree of CD8+ T cells is, and the more significant the effect of immunotherapy is. As Figure 1 shown.

[0195] In the second step, a scoring model was constructed by binary Logistic regression, and the following scoring model was obtained:

[0196] Score = (β-Guanidinopropionic acid × 0.0241) + (2-Amino-butanoic acid × 0.0357) + (N-(1-Deoxy-1-fructosyl)threonine × 0.0414) + (γ-Glutamylproline × 0.0138) + ((2S)-1-pyrrolidine-2-carboxylic Acid × 0.0412) + (2,2,6,6-Tetramethyl-4-piperidinone × 0.0703691) + (3alpha-Hydroxy-5beta-chola-7,9(11)-dien-24-oicAcid × 0.0253849).

[0197] Finally, based on this scoring model, the scores of each sample were calculated, and a receiver operating characteristic curve (ROC curve) was established. As Figure 1 shown, the optimal threshold point, that is, the cut-off value, was obtained through the ROC curve. The cut-off value was 0.44749. According to the cut-off value, the clinical samples were divided into two groups. Samples with scores higher than this cut-off value were judged to belong to the low infiltration group, predicting poor immunotherapy effect (iSD, iCPD) and poor prognosis. Samples with scores lower than this cut-off value were judged to belong to the high infiltration group, predicting significant treatment effect (iCR, iPR) and good prognosis.

[0198] The experimental results show that the sensitivity of the prediction model is 0.80, the specificity is 1, and the AUC is 0.88.

[0199] Verification of the scoring model in Example 2

[0200] Collect esophageal cancer tissue samples from another 25 esophageal cancer patients, detect the metabolite deposition in the esophageal cancer tissue samples according to the metabolomics detection method of Example 1, substitute them into the scoring model obtained in Example 1 to get Score. According to Score and the cut-off value obtained in Example 1, the 25 samples are classified as follows: those with a score higher than the cut-off value are judged to belong to the low-infiltration group, predicting poor immunotherapy effect and poor prognosis; those with a score lower than the cut-off value are judged to belong to the high-infiltration group, predicting significant immunotherapy effect and good prognosis, and are divided into the low-infiltration group (poor treatment effect, poor prognosis) and the high-infiltration group (significant treatment effect, good prognosis) as the prediction results. Then compare the prediction results with the actual CD8+ T cell infiltration numbers of the samples, and the results are shown in Table 1 below.

[0201] Table 1 Situation of the validation cohort

[0202]

[0203] In the validation cohort, when using the scoring model and cut-off value of Example 1 for prediction, its specificity is 0.89 and the sensitivity is 0.75, proving that the scoring model constructed in this application has high specificity and sensitivity.

[0204] The above embodiments are only illustrative of the principles and effects of this application, and are not used to limit this application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed in this application should still be covered by the claims of this application.

Claims

1. A composition of microbial metabolites, comprising any one or more of the following: β-Guanidinopropionic acid; 2-Amino--butanoic acid; N-(1-Deoxy-1-fructosyl)threonine; γ-Glutamylproline; (2S)-1-pyrrolidine-2-carboxylic Acid; 2,2,6,6-Tetramethyl-4-piperidinone; 3alpha-Hydroxy-5beta-chola-7,9(11)-dien-24-oic Acid.

2. Use of microbial metabolites and / or reagents for detecting microbial metabolites in the preparation of a product, wherein the microbial metabolites are the composition of microbial metabolites as described in claim 1, and the product is selected from any one or more of the following: 1) A product for predicting the degree of CD8+ T cell infiltration; 2) A product for predicting the efficacy of immunotherapy for esophageal cancer; 3) A prognostic product for esophageal cancer.

3. The use according to claim 2, characterized in that, The reagent for detecting microbial metabolites is a reagent for detecting the deposition amount of microbial metabolites; and / or, the product is selected from one or more of reagents, kits, test strips and chips.

4. The use according to claim 3, characterized in that, The reagent for detecting the deposition amount of microbial metabolites is a reagent required for detecting the deposition amount of microbial metabolites by using metabolomics methods; preferably, the reagent for detecting the deposition amount of microbial metabolites is selected from any one or more of the following: a reagent required for detecting the deposition amount of microbial metabolites by liquid chromatography-mass spectrometry, a reagent required for detecting the deposition amount of microbial metabolites by gas chromatography-mass spectrometry, and a reagent required for detecting the deposition amount of microbial metabolites by nuclear magnetic resonance.

5. A kit for predicting the degree of CD8+ T cell infiltration, and / or predicting the efficacy of immunotherapy for esophageal cancer, and / or prognosticating esophageal cancer, comprising microbial metabolites and / or reagents for detecting microbial metabolites, wherein the microbial metabolites are the composition of microbial metabolites as described in claim 1.

6. A device for predicting the degree of CD8+ T cell infiltration, and / or predicting the efficacy of immunotherapy for esophageal cancer, and / or prognosticating esophageal cancer, the device comprising: A data module for obtaining the deposition amount of each item in the composition of microbial metabolites as described in claim 1 of the sample; An analysis module for calculating a score through a mathematical model based on the deposition amount of each item in the composition of microbial metabolites as described in claim 1 of the sample, and judging the degree of CD8+ T cell infiltration, and / or the efficacy of immunotherapy, and / or the prognosis of esophageal cancer patients.

7. A computer-readable storage medium, comprising a computer program, characterized in that, The computer program, when run by a processor, executes a method comprising the following steps: Obtaining the deposition amount of each item in the composition of microbial metabolites as described in claim 1 of the sample, calculating a score through a mathematical model, and judging the degree of CD8+ T cell infiltration, and / or the efficacy of immunotherapy, and / or the prognosis of esophageal cancer patients.

8. An electronic terminal, comprising a memory and a processor, wherein the memory contains a computer program, characterized in that The computer program, when run by a processor, executes a method comprising the following steps: Obtain the deposition amount of each item in the microbial metabolite composition as described in claim 1 of the sample, calculate the score through a mathematical model, and judge the CD8+ T cell infiltration degree, and / or immunotherapy effect, and / or prognosis of esophageal cancer patients.

9. A computer program product, comprising a computer program, the method of implementing the following steps when the computer program is executed by a processor: Obtain the deposition amount of each item in the microbial metabolite composition as described in claim 1 of the sample, calculate the score through a mathematical model, and judge the CD8+ T cell infiltration degree, and / or immunotherapy effect, and / or prognosis of esophageal cancer patients.

10. A method for predicting the CD8+ T cell infiltration degree and / or prognosis of esophageal cancer patients for non-disease diagnosis and treatment purposes, comprising obtaining the deposition amount of each item in the microbial metabolite composition as described in claim 1 of the sample, calculating the score through a mathematical model, and judging the CD8+ T cell infiltration degree, and / or immunotherapy effect, and / or prognosis of esophageal cancer patients.

11. The device according to claim 6, or the computer-readable storage medium according to claim 7, or the electronic terminal according to claim 8, or the computer program product according to claim 9, or the method according to claim 10, characterized in that The construction method of the mathematical model is: constructing by binary logistic regression for the deposition amount of each item in the microbial metabolite composition; Preferably, the mathematical model is: Score = (β - Guanidinopropionic acid × 0.0241) + (2 - Amino - butanoic acid × 0.0357) + (N-(1 - Deoxy - 1 - fructosyl)threonine × 0.0414) + (γ - Glutamylproline × 0.0138) + ((2S)-1 - pyrrolidine - 2 - carboxylic Acid × 0.0412) + (2,2,6,6 - Tetramethyl - 4 - piperidinone × 0.0703691) + (3α - Hydroxy - 5beta - chola - 7,9(11)-dien - 24 - oic Acid × 0.0253849); And / or, the method for judging the CD8+ T cell infiltration degree, and / or immunotherapy effect, and / or prognosis of esophageal cancer patients is: when the score is higher than the cut - off value, it is judged that the CD8+ T cell infiltration degree is low, and / or the immunotherapy effect is poor, and / or the prognosis is poor; when the score is lower than the cut - off value, it is judged that the infiltration degree is high, and / or the immunotherapy effect is significant, and / or the prognosis is good.

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