A device and system for diagnosing and typing colorectal cancer

Abstract

The present invention discloses a device and system for diagnosing and typing colorectal cancer. The present invention provides a device and system for diagnosing and typing colorectal cancer and a computer-readable storage medium. The device can effectively screen markers, diagnose colorectal cancer and type colorectal cancer, providing a new idea for the early diagnosis and subsequent effective treatment of colorectal cancer, and has broad application prospects.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and specifically relates to a device and system for diagnosing and typing colorectal cancer. Background Art

[0002] The increasing number of cases and deaths of colorectal cancer (CRC) has brought tremendous pressure to clinical treatment. Due to the heterogeneity of colorectal cancer, in order to improve the treatment effect of colorectal cancer, individualized precision treatment is needed for different patients. To this end, clinically, it is necessary to divide patients into different subtypes based on clinical pathological characteristics or molecular characteristics and predict survival time, so as to make appropriate treatment plans.

[0003] Therefore, it is of vital importance to find biomarkers that can specifically diagnose and classify colorectal cancer. Summary of the invention

[0004] To remedy the deficiencies of the prior art, the present invention provides a device and system for diagnosing and typing colorectal cancer.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] A first aspect of the present invention provides a device for diagnosing and typing colorectal cancer, the device comprising:

[0007] Memory: The memory is used to store program instructions;

[0008] Processor: The processor is used to call program instructions. When the program instructions are executed, it is used to perform the following colorectal cancer diagnosis and classification method steps:

[0009] Obtaining NFE2L3 gene expression data in the sample to be tested;

[0010] The gene expression data is input into a colorectal cancer diagnosis and typing model to obtain a colorectal cancer diagnosis and typing result of the sample to be tested. The method for constructing the colorectal cancer diagnosis and typing model includes:

[0011] Obtain colorectal cancer exosome data and colorectal cancer tissue data;

[0012] Obtain candidate panels of exosome-derived targeted genes and tissue-derived targeted genes;

[0013] The candidate gene NFE2L3 was obtained by taking the intersection of the candidate panel of exosome-derived targeted genes and the candidate panel of tissue-derived targeted genes.

[0014] Furthermore, the colorectal cancer exosome data were obtained from the BBCancer database and the exoRBase database.

[0015] Furthermore, the colorectal cancer tissue data were obtained from NCBI and SRA.

[0016] Furthermore, the BBCancer database includes lncRNA, mirna, and mrna differential expression data.

[0017] Furthermore, the screening criteria for the lncRNA, mirna, and mrna differential expression data are:

[0018] lncRNA:adj-pvalue<0.01&Fold Change>2

[0019] mirna:adj-pvalue<0.05&Fold Change>1

[0020] mrna:adj-pvalue<0.01&Fold Change>2.

[0021] Furthermore, the exoRBase database includes lncRNA and mrna gene TPM differential expression data.

[0022] Furthermore, the screening criteria for the lncRNA and mrna gene TPM differential expression data were adj-pvalue<0.01&Fold Change>2.

[0023] Furthermore, the method for constructing the colorectal cancer diagnosis and classification model also includes merging the data obtained from the BBCancer database and the exoRBase database respectively.

[0024] Next, use the rbind function to merge.

[0025] Furthermore, the intersect function was used to obtain the intersection of the exosome-derived targeted gene candidate panel and the tissue-derived targeted gene candidate panel.

[0026] Furthermore, the classification includes normal tissue, polyp tissue, cancer TNM1, cancer TNM2, cancer TNM3, and cancer TNM4.

[0027] A second aspect of the present invention provides a system for diagnosing and typing colorectal cancer, the system comprising:

[0028] Acquisition unit: used to acquire NFE2L3 gene expression data in the sample to be tested;

[0029] Processing unit: used for inputting the gene expression data into the colorectal cancer diagnosis and typing model to obtain the colorectal cancer diagnosis and typing results of the sample to be tested;

[0030] The colorectal cancer diagnosis and typing model is constructed by the following method:

[0031] Obtain colorectal cancer exosome data and colorectal cancer tissue data;

[0032] Obtain candidate panels of exosome-derived targeted genes and tissue-derived targeted genes;

[0033] The candidate gene NFE2L3 was obtained by taking the intersection of the candidate panel of exosome-derived targeted genes and the candidate panel of tissue-derived targeted genes.

[0034] Furthermore, the colorectal cancer exosome data were obtained from the BBCancer database and the exoRBase database.

[0035] Furthermore, the colorectal cancer tissue data were obtained from NCBI and SRA.

[0036] Furthermore, the BBCancer database includes lncRNA, mirna, and mrna differential expression data.

[0037] Furthermore, the exoRBase database includes lncRNA and mrna gene TPM differential expression data.

[0038] Furthermore, the method also includes merging the data obtained from the BBCancer database and the exoRBase database respectively.

[0039] Next, use the rbind function to merge.

[0040] Furthermore, the intersect function was used to obtain the intersection of the exosome-derived targeted gene candidate panel and the tissue-derived targeted gene candidate panel.

[0041] Furthermore, the classification includes normal tissue, polyp tissue, cancer TNM1, cancer TNM2, cancer TNM3, and cancer TNM4.

[0042] The third aspect of the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, implements the method for constructing a cancer prognosis prediction model involved in the device described in the first aspect of the present invention.

[0043] The fourth aspect of the present invention provides the use of NFE2L3 in constructing a system for diagnosing and typing colorectal cancer.

[0044] Furthermore, the classification includes normal tissue, polyp tissue, cancer TNM1, cancer TNM2, cancer TNM3, and cancer TNM4.

[0045] The fifth aspect of the present invention provides the use of NFE2L3 in constructing a device for diagnosing and typing colorectal cancer.

[0046] Furthermore, the classification includes normal tissue, polyp tissue, cancer TNM1, cancer TNM2, cancer TNM3, and cancer TNM4.

[0047] A sixth aspect of the present invention provides a product for diagnosing and typing colorectal cancer, the product comprising a reagent for detecting the expression level of NFE2L3.

[0048] Furthermore, the reagents include primers and probes targeting NFE2L3.

[0049] Furthermore, the reagent also includes a detectable label.

[0050] Furthermore, the detectable labels include isotopes, enzymes, fluorescent molecules, and magnetic particles.

[0051] Furthermore, the product includes a test kit, a test paper or a nucleic acid membrane strip.

[0052] Advantages and beneficial effects of the present invention:

[0053] The present invention provides a device, system and computer-readable storage medium for diagnosing and typing colorectal cancer. It can effectively screen markers, diagnose colorectal cancer and type colorectal cancer, providing new ideas for early diagnosis and subsequent effective treatment of colorectal cancer, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of the screening method;

[0055] Figure 2 This is the NFE2L3 expression trend chart for 6 types of cancer;

[0056] Figure 3 is a graph showing the trend of NFE2L3 expression in cancer and normal tissues. DETAILED DESCRIPTION

[0057] The definitions of some terms used in this specification are provided below. Unless otherwise defined, all technical and scientific terms used herein generally have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0058] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in the present invention are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0060] Figure 1 is a flow chart of a screening method, the method comprising the steps of:

[0061] 101: Obtain colorectal cancer exosome data and colorectal cancer tissue data.

[0062] 102: Based on colorectal cancer exosome data, screen for candidate panels of genes that can be targeted as exosome sources.

[0063] 103: Based on colorectal cancer tissue data, screen for candidate panels of tissue-derived targeted genes.

[0064] 104: The candidate gene NFE2L3 is obtained by taking the intersection of the candidate panel of exosome-derived targeted genes and the candidate panel of tissue-derived targeted genes.

[0065] In one embodiment, NFE2L3 includes wild-type, mutant, or fragments thereof. The term encompasses full-length, unprocessed NFE2L3, any form of NFE2L3 derived from processing in cells, and naturally occurring variants of NFE2L3 (e.g., splice variants or allelic variants). The term encompasses, for example, human NFE2L3 as well as NFE2L3 from any other vertebrate source, including mammals, such as primates and rodents (e.g., mice and rats), Gene ID: 9603.

[0066] In one embodiment, colorectal cancer exosome data can be obtained from any database. In a specific embodiment, the exosome data is obtained from the BBCancer database and the exoRBase database.

[0067] In one embodiment, the colorectal cancer tissue data can be obtained from any database. In a specific embodiment, the colorectal cancer tissue data is obtained from NCBI (TCGA) and SRA.

[0068] In one embodiment, the data in the BBCancer database may include one or more gene differential expression data of lncRNA, mirna, mrna or other data. In a specific embodiment, the BBCancer database includes lncRNA, mirna, and mrna differential expression data.

[0069] In one embodiment, the exoRBase database may include one or more gene differential expression data of lncRNA, mRNA or other data. In a specific embodiment, the xoRBase database includes lncRNA, mRNA gene TPM expression data.

[0070] Among them, TPM (Transcripts Per Million) represents the proportion of a transcript per million reads.

[0071] In one embodiment, the data on differential expression of lncRNA, mirna, and mRNA in the BBCancer database are screened by adj-pvalue and FoldChange, and the screening criteria are: lncRNA: adj-pvalue<0.01&FoldChange>2; mirna: adj-pvalue<0.05&FoldChange>1; mrna: adj-pvalue<0.01&FoldChange>2.

[0072] In one embodiment, the TPM differential expression data of lncRNA and mRNA genes in the xoRBase database are screened by adj-pvalue and FoldChange, and the screening criteria are pvalue<0.01&FoldChange>2.

[0073] In one embodiment, the method further comprises merging the data obtained from the BBCancer database and the exoRBase database, respectively. In a specific embodiment, the rbind function is used for merging.

[0074] The present invention provides a device for diagnosing and typing colorectal cancer, the device comprising:

[0075] Memory: The memory is used to store program instructions;

[0076] Processor: The processor is used to call program instructions. When the program instructions are executed, it is used to perform the following colorectal cancer diagnosis and classification method steps:

[0077] Obtaining NFE2L3 gene expression data in the sample to be tested;

[0078] The gene expression data is input into a colorectal cancer diagnosis and typing model to obtain a colorectal cancer diagnosis and typing result of the sample to be tested. The method for constructing the colorectal cancer diagnosis and typing model includes:

[0079] Obtain colorectal cancer exosome data and colorectal cancer tissue data;

[0080] Obtain candidate panels of exosome-derived targeted genes and tissue-derived targeted genes;

[0081] The candidate gene NFE2L3 was obtained by taking the intersection of the candidate panel of exosome-derived targeted genes and the candidate panel of tissue-derived targeted genes.

[0082] In one embodiment, the device is a computer device, which may be a terminal, including a processor and a memory connected via a system bus; and also including a network interface, a display screen and an input device. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0083] The above-mentioned devices are only partial structures related to the scheme of the present application and do not constitute a limitation on the computer devices to which the scheme of the present application is applied. The specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0084] The present invention provides a system for diagnosing and typing colorectal cancer, the system comprising:

[0085] Acquisition unit: used to acquire NFE2L3 gene expression data in the sample to be tested;

[0086] Processing unit: used for inputting the gene expression data into the colorectal cancer diagnosis and typing model to obtain the colorectal cancer diagnosis and typing results of the sample to be tested.

[0087] In one embodiment, the system includes a processor, which may be a single-core or multi-core processor or more than one processor for parallel processing. The system also includes a memory (e.g., random access memory, read-only memory, flash memory), an electronic storage unit (e.g., a hard disk), a communication interface (e.g., a network adapter) and peripheral devices for communicating with one or more other systems, such as a cache memory, other memories, data storage and / or an electronic display adapter. The memory, electronic storage unit, communication interface and peripheral devices communicate with the processor via a communication bus (solid line), such as a motherboard. The storage unit may be a data storage unit (or data repository) for storing data. The system may be operably coupled to a computer network by means of a communication interface. The network may be the Internet, an intranet and / or an extranet, or an intranet and / or an extranet communicating with the Internet. In some cases, the network is a communication and / or data network. The network may include one or more computer servers, which may support distributed computing, such as cloud computing. In some cases, with the aid of the system, the network may implement a peer-to-peer network, which may enable the device coupled to the system to operate as a client or server.

[0088] In one embodiment, the processor may execute a series of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as a memory. The instructions may be directed to the processor, which may then program or otherwise configure the processor to implement the method of the present invention. Examples of operations performed by the processor may include reading, decoding, executing, and writing back.

[0089] In one embodiment, the processor may be part of a circuit such as an integrated circuit, and one or more other components of the system may be included in the circuit, which in some cases is an application specific integrated circuit.

[0090] In one embodiment, the electronic storage unit can store files, such as drivers, libraries, and saved programs. The electronic storage unit can store user data, such as user preferences and user programs. In some cases, the system can include one or more additional data storage units that are external to the computer system, such as located on a remote server that communicates with the system via an intranet or the Internet.

[0091] In one embodiment, the system can communicate with one or more remote computer systems via a network. For example, the system can communicate with a remote computer system of a user (e.g., a physician). Examples of remote computer systems include personal computing, tablet or tablet PCs, phones, smart phones, or personal digital assistants. Users can access the system via a network.

[0092] In one embodiment, the method described in the present application can be implemented by means of a machine (e.g., a computer processor) executable code, which is stored in an electronic storage location of the system, such as a memory or an electronic storage unit. The machine executable code or machine readable code can be provided in the form of software. During use, the code can be executed by the processor. In some cases, the code can be retrieved from the electronic storage unit and stored in the memory for immediate access by the processor. In some cases, the electronic storage unit can be excluded and the machine executable instructions can be stored in the memory.

[0093] The code may be precompiled and configured for use with a machine having a processor suitable for executing the code, or may be compiled during runtime. The code may be provided in a programming language that may be selected so that the code can be executed in a precompiled or compiled-as-is manner.

[0094] The present invention provides a computer-readable storage medium having executable instructions stored thereon. When the executable instructions are executed by a processor, the method for constructing a cancer prognosis prediction model involved in the above-mentioned device is implemented.

[0095] In one embodiment, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface storage, optical disk, or CD-ROM; or various devices including one or any combination of the above memories.

[0096] In one embodiment, executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0097] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0098] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0099] In one embodiment, the executable instructions can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the process of the embodiments of the above methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0100] The present invention provides a kit for diagnosing and typing colorectal cancer, wherein the kit comprises a reagent for detecting the expression level of NFE2L3.

[0101] The reagents include primers and probes targeting NFE2L3.

[0102] In one embodiment, a probe refers to a molecule that can bind to a specific sequence or subsequence or other portion of another molecule. Unless otherwise indicated, a probe generally refers to a polynucleotide probe that can bind to another polynucleotide (often referred to as a target polynucleotide) through complementary base pairing. Depending on the stringency of the hybridization conditions, the probe can bind to a target polynucleotide that lacks complete sequence complementarity with the probe. The probe can be directly or indirectly labeled, including primers. Hybridization methods include, but are not limited to, solution phase, solid phase, mixed phase, or in situ hybridization assays.

[0103] In one embodiment, a primer refers to a short nucleic acid sequence, as a nucleic acid sequence having a short free 3' terminal hydroxyl group (free 3' hydroxyl group), which can form a base pair with a complementary template and serve as a starting point for replicating the template.

[0104] The reagent also includes a detectable label.

[0105] In one embodiment, label refers to the composition that can produce the detectable signal indicating the presence of target polynucleotide in the assay sample. Suitable label includes but is not limited to radioisotope, nucleotide chromophore, enzyme, substrate, fluorescent molecule, chemiluminescent part, magnetic particle, bioluminescent part. Therefore, label is any composition that can be detected by device or method, including but not limited to spectrum, photochemistry, biochemistry, immunochemistry, electricity, optics, chemical detection device or any other suitable device. In some embodiments, label can be detected visually without the aid of device. Label is used to refer to any chemical group or part with detectable physical property or can cause chemical group or part to show any compound of detectable physical property, such as catalytic substrate conversion into detectable product enzyme. Label also encompasses the compound that suppresses the performance of specific physical property. Label can also be a compound as a member of a binding pair, and another member thereof has a detectable physical property.

[0106] Among them, radioactive isotopes include but are not limited to 3 H. 14 C. 35 S. 125 I. 131 I.

[0107] Enzymes include, but are not limited to, horseradish peroxidase, beta-galactosidase, luciferase, alkaline phosphatase, acetylcholinesterase.

[0108] Fluorescent molecules include, but are not limited to, FITC, rhodamine, and lanthanide phosphors.

[0109] In one embodiment, a kit refers to a set of components provided in the context of a system for sequencing nucleotides and / or isolating nucleotide sequences and / or diagnosing a subject with a disease or infection based on the presence, absence and / or amount of expressed nucleotide sequences from a sample or cell.

[0110] The kit includes a gene detection kit and a protein detection kit. The gene detection kit includes a reagent or a chip for detecting the transcription level of the NFE2L3 gene, and the protein detection kit includes a reagent or a chip for detecting the expression level of the NFE2L3 protein.

[0111] In one embodiment, the kit further comprises one or more substances from the following group: a container, a positive control substance, a negative control substance, a buffer, a preservative, and a protein stabilizer.

[0112] The kit may also be accompanied by an instruction manual for use of the kit, which describes how to use the kit for testing, and how to use the test results to judge tumor development and select a treatment plan.

[0113] The components of the test kit can be packaged in the form of an aqueous medium or in a lyophilized form. Suitable containers in the test kit usually include at least a vial, test tube, flask, PET bottle, syringe or other container, in which a component can be placed, and preferably, can be appropriately divided. When there is more than one component in the test kit, the test kit will usually also include a second, third or other additional container, in which additional components are placed separately. However, the components of different combinations can be contained in a vial. The test kit of the present invention also usually includes a container for accommodating reactants, which is sealed for commercial sale. This container can include a plastic container of injection molding or blow molding, in which the required vial can be retained.

[0114] The invention will be further described below in conjunction with specific examples. It should be understood that the specific embodiments described herein are presented by way of example and are not intended to limit the invention. The main features of the invention may be used in a variety of embodiments without departing from the scope of the invention.

[0115] Example

[0116] 2 Analysis process and results

[0117] 2.1BBCancer exosome database: download lncRNA, mirna, mrna differential expression data, data screening criteria:

[0118] lncRNA:adj-pvalue<0.01&Fold Change>2

[0119] mirna:adj-pvalue<0.05&Fold Change>1

[0120] mrna:adj-pvalue<0.01&Fold Change>2

[0121] Data operation process: Taking lncRNA data as an example: Use data.table::fread function to read the pre-downloaded differential expression file Colorectal.cancer-EVs.lncRNA.txt, save it as variable bbc.lnc, and perform data combination column screening on variable bbc.lnc, select the following columns: "gene", "FoldChange", "baseMean", "p-value", "adj-pvalue", and add gene type class column, and fill the content with lncRNA. According to the above lncRNA differential expression gene screening criteria, the adj-pvalue column and FoldChange column are numerically filtered. Perform the same above operations on mirna and mrna. The results obtained from the above lncrna, mirna and mrna are merged using rbind function to obtain the exosome candidate target panel based on BBCancer database.

[0122] 2.2exoRBase exosome database: download lncRNA, mRNA gene TPM expression data, and perform differential expression analysis:

[0123] The differential expression analysis was performed using the limma module of the R language. The screening criteria for differentially expressed genes were: adj-pvalue<0.01&Fold Change>2

[0124] Data operation process: Taking lncRNA data as an example: Use the read.table function to read the pre-downloaded gene expression files CRC_lncRNA_TPM.txt and Normal_lncRNA_TPM.txt, save them as variables case and normal, use the log2(x+1) function to perform logarithmic transformation on the variables case and normal, use the intersect function to take the intersection of the case and normal variable row names to obtain the common gene features, and cbind to merge the common gene features of the case and normal variables to obtain the variable mat.data. Use the lmFit function of the limma module to perform differential expression analysis on mat.data. The screening criteria are as mentioned above. The data columns of the limma output results are screened, and the following columns are selected: "gene", "FoldChange", "baseMean", "p-value", "adj-pvalue", and the gene type class column is added, and the content is filled with lncrna. Perform the same above operations on mrna. The results obtained from the above lncRNA and mRNA were merged using the rbind function to obtain a panel of exosome candidate targets based on the exoRBase database.

[0125] 2.3 Exosome candidate target panel based on BBCancer database and exosome candidate target panel based on exoRBase database. Use the unique function to extract the unique value of the "gene" column in the collection, and save the result as the unique.lm variable. The unique.lm variable can be used as the exosome-derived targeting gene candidate panel.

[0126] 2.4 Processing of colorectal cancer tissue data

[0127] The sequencing data of the PRJEB27536 dataset were downloaded from SRA, and the colorectal cancer gene expression data were downloaded from TCGA in NCBI. The sample size was normal tissue: polyp tissue: colorectal cancer TNM1: TNM2: TNM3: TNM4 = 50:21:52:123:89:44.

[0128] Data processing

[0129] The raw data from the SRA database was processed and quality controlled using fastq software, and the clean data was aligned to the human reference gene group using ICGC software, version (GRCh38.d1.vd1), and the bam file was quantified using htseq software.

[0130] Quantitative mode: intersection-nonempty;

[0131] Quantitative standard type: gene_id;

[0132] Chain Specificity: Nonspecific.

[0133] The expression files were merged according to geneID to construct an M*N gene expression matrix. The value of the i-th row and j-th column in the gene expression matrix represents the expression count value of the i-th gene corresponding to the j-th sample, where 1≤i≤M, 1≤j≤N; M represents the number of detected genes, and N represents the number of analyzed samples. The expression matrix was saved as a .Rdata object file.

[0134] The processed count expression matrix and TCGA expression matrix were merged, and the merging standard was gene ID.

[0135] Obtain tissue-derived targeted gene candidate panels.

[0136] 2.5 Exosome-derived targeted gene candidate panel and tissue-derived targeted gene candidate panel. The intersect function was used to take the intersection and the candidate gene NFE2L3 (ENSG00000050344) was obtained. The candidate gene only appeared in the BBCancer exosome database. The differential expression of NFE2L3 in the BBCancer exosome database was extracted. The details are shown in Table 1.

[0137] Table 1 NFE2L3 expression

[0138] gene FoldChange baseMean p-value adj-pvalue class id NFE2L3 4.7 9.37 7.27E-06 2.26E-05 mRNA ENSG00000050344.8

[0139] The expression of NFE2L3 in tissue sample data was extracted, and the boxplot function was used to perform statistics and visualization of the expression trends of six types of normal tissue, polyp tissue, and cancer TNMI1-TNM4. The results are as follows: Figure 1 shown.

[0140] 2.6 Electronic verification of genes in the GEO database

[0141] The dataset GSE127069 was used to validate NFE2L3:

[0142] https: / / www.ncbi.nlm.nih.gov / geo / query / acc.cgi?acc=GSE127069

[0143] Data operation process: data.table::fread function reads the pre-downloaded GSE127069_series_matrix.txt file and saves it as a dataframe variable expr, reads the pre-downloaded chip annotation file GPL17077-17467.txt and saves it as a dataframe variable panel, uses the panel's "ID", "GENE_SYMBOL" information to annotate the ID_REF of expr, and uses the aggregate function to aggregate and average the expr of the annotated gene ID. The limma module lmFit function of the R language was used to perform differential expression analysis between colorectal cancer and normal tissues. The screening criteria for differentially expressed genes were: adj-pvalue<0.05&FoldChange>1. The obtained differentially expressed gene list was compared with the above-mentioned candidate target genes, and the results are shown in Table 2.

[0144] Table 2 NFE2L3 gene verification

[0145] logFC AveExpr t P.Value adj.P.Val B NFE2L3 2.813082 10.38564 5.970689 7.72E-05 0.016853 1.917799

[0146] The expression trend of NFE2L3 gene in cancer (CRC) and normal tissue (Normal) was statistically analyzed and visualized using ggboxplot. The results are as follows: Figure 2 shown.

[0147] The description of the above embodiments is only used to understand the method and core idea of ​​the present invention. It should be pointed out that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principle of the present invention, and these improvements and modifications will also fall within the scope of protection of the claims of the present invention.

Claims

1. A device for colorectal cancer typing, characterized in that: The device comprises: Memory: The memory is used to store program instructions; Processor: The processor is used to call program instructions. When the program instructions are executed, the processor is used to perform the following colorectal cancer typing method steps: Obtaining NFE2L3 gene expression data in the sample to be tested; The gene expression data is input into a colorectal cancer typing model to obtain a colorectal cancer typing result of the sample to be tested. The method for constructing the colorectal cancer typing model includes: Obtain colorectal cancer exosome data and colorectal cancer tissue data; Obtain candidate panels of exosome-derived targeted genes and tissue-derived targeted genes; The candidate gene NFE2L3 was obtained by taking the intersection of the candidate panel of exosome-derived targeted genes and the candidate panel of tissue-derived targeted genes. The colorectal cancer exosome data were obtained from the BBCancer database and the exoRBase database; The colorectal cancer tissue data were obtained from NCBI and SRA; The BBCancer database includes lncRNA, mirna, and mrna differential expression data; The screening criteria for the lncRNA, mirna, and mrna differential expression data are: lncRNA: adj-pvalue<0.01 & Fold Change > 2 mirna:adj-pvalue<0.05 & Fold Change > 1 mrna: adj-pvalue<0.01 & Fold Change > 2; The exoRBase database includes lncRNA and mrna gene TPM differential expression data; The screening criteria for the TPM differential expression data of the lncRNA and mrna genes were adj-pvalue < 0.01 & FoldChange > 2; The method for constructing the colorectal cancer typing model further includes merging the data obtained from the BBCancer database and the exoRBase database respectively; Use rbind function to merge; Use the intersect function to obtain the intersection of the exosome-derived targeted gene candidate panel and the tissue-derived targeted gene candidate panel; The classification includes normal tissue, polyp tissue, cancer TNM1, cancer TNM2, cancer TNM3, and cancer TNM4.

2. A system for colorectal cancer classification, characterized in that: The system comprises: Acquisition unit: used to acquire NFE2L3 gene expression data in the sample to be tested; Processing unit: used for inputting the gene expression data into the colorectal cancer typing model to obtain the colorectal cancer typing result of the sample to be tested; The colorectal cancer typing model is constructed by the following method: Obtain colorectal cancer exosome data and colorectal cancer tissue data; Obtain candidate panels of exosome-derived targeted genes and tissue-derived targeted genes; The candidate gene NFE2L3 was obtained by taking the intersection of the candidate panels of exosome-derived targeted genes and the candidate panels of tissue-derived targeted genes. The colorectal cancer exosome data were obtained from the BBCancer database and the exoRBase database; The colorectal cancer tissue data were obtained from NCBI and SRA; The BBCancer database includes lncRNA, mirna, and mrna differential expression data; The exoRBase database includes lncRNA and mrna gene TPM differential expression data; The method further comprises merging the data obtained from the BBCancer database and the exoRBase database respectively; Use rbind function to merge; Use the intersect function to obtain the intersection of the exosome-derived targeted gene candidate panel and the tissue-derived targeted gene candidate panel; The classification includes normal tissue, polyp tissue, cancer TNM1, cancer TNM2, cancer TNM3, and cancer TNM4.

3. A computer-readable storage medium having executable instructions stored thereon, characterized in that: When the executable instructions are executed by the processor, the method for constructing the cancer prognosis prediction model involved in the device of claim 1 is implemented.

4. Application of NFE2L3 in constructing a system for colorectal cancer classification; The classification includes normal tissue, polyp tissue, cancer TNM1, cancer TNM2, cancer TNM3, and cancer TNM4.

5. Application of NFE2L3 in constructing equipment for colorectal cancer classification; The classification includes normal tissue, polyp tissue, cancer TNM1, cancer TNM2, cancer TNM3, and cancer TNM4.

6. A product for colorectal cancer typing, characterized in that: The product includes a reagent for detecting the expression level of NFE2L3.

7. The product according to claim 6, characterized in that The reagents include primers and probes targeting NFE2L3.

8. The product according to claim 6, characterized in that The reagent also includes a detectable label.

9. The product according to claim 8, characterized in that The detectable labels include isotopes, enzymes, fluorescent molecules, and magnetic particles.

10. The product according to claim 6, characterized in that The products include test kits, test strips or nucleic acid membrane strips.

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