Biomarkers for the diagnosis and staging of colorectal cancer

Abstract

The present invention relates to the field of oncology. In particular, the present invention relates to biomarkers for colorectal cancer and their use for diagnosing or staging colorectal cancer. The biomarkers of the present invention allow for the sensitive and accurate diagnosis of colorectal cancer and the differentiation between non-metastatic and metastatic colorectal cancer.

Description

Technical Field

[0001] This invention relates to the field of oncology. Specifically, this invention relates to biomarkers for colorectal cancer and their use in the diagnosis or staging of colorectal cancer. Background Technology

[0002] Colorectal cancer (CRC) ranks third among all malignant tumors globally, with over 1.35 million new cases annually. In my country, the incidence of CRC rose continuously from 2000 to 2011, with 376,000 new cases and 191,000 deaths in 2015. Current screening methods for CRC include fecal occult blood testing, colonoscopy, and CT imaging, but some methods are unavailable in areas with limited economic and medical resources. Colonoscopy is a highly sensitive method, but it is dependent on the surgeon's skill, expensive, and carries certain risks for patients. CRC typically metastasizes to multiple organs; at diagnosis, over 25% of patients have liver metastases (simultaneous metastasis), and the remaining 25-30% develop liver metastases within the next 2-3 years (metachronous metastasis). The survival of CRC patients is primarily determined by the progression of liver metastases, not the primary tumor. Without treatment, the survival time for patients with liver metastases from CRC is less than one year. Liver tumors can cause pain in the right upper quadrant or the entire abdomen, as well as weight loss and exacerbate metabolic disorders. As the disease progresses, symptoms such as ascites, jaundice, and portal hypertension may appear, indicating a poor prognosis. Currently, the diagnosis of colorectal cancer liver metastases mainly relies on liver ultrasound and CT scans. However, these two methods are expensive for long-term monitoring of colorectal cancer liver metastases and require frequent hospital visits.

[0003] In summary, there is currently a lack of effective biomarkers for monitoring colorectal cancer and liver metastasis of colon cancer in clinical practice. Summary of the Invention

[0004] Through extensive experimentation and repeated exploration, the inventors of this application unexpectedly discovered a biomarker for the diagnosis and staging of colorectal cancer. This biomarker can sensitively and accurately diagnose colorectal cancer and distinguish between non-metastatic and metastatic colorectal cancer, thereby completing this invention.

[0005] Biomarkers for the diagnosis of colorectal cancer

[0006] A first aspect of the present invention provides a method for determining whether a subject has or is at risk of having colorectal cancer (CRC), comprising:

[0007] (1) Detect the level of a biomarker in a liquid sample from the subject, the biomarker being selected from one or more of CORO1C, APRC5, RAD23B, BHMT, DCP1B, TPM3, CALD1, ATP5F1A (e.g., 1, 2, 3, 4, 5, 6, 7 or all 8).

[0008] (2) Compare the level with the reference value.

[0009] In some embodiments, the biomarker comprises one or more (e.g., one, two, or all three) selected from CORO1C, APRC5, and RAD23B.

[0010] In some implementations, the biomarkers include CORO1C, APRC5, and RAD23B.

[0011] In some embodiments, the biomarker may also include one or more selected from BHMT, DCP1B, TPM3, CALD1, ATP5F1A (e.g., 1, 2, 3, 4, or all 5).

[0012] It should be understood that the “level” mentioned in steps (1) and (2) includes the absolute amount, relative amount or concentration of the biomarker and any value or parameter associated with or derived therefrom.

[0013] It should be understood that the “comparison” mentioned in step (2) generally refers to a comparison of corresponding parameters or values, such as comparing an absolute amount with an absolute reference amount, or a concentration with a reference concentration, or comparing the intensity signal of a biomarker obtained from a sample with the same type of intensity signal obtained from a reference sample. Comparisons can be performed manually or with computer assistance. The measured or detected levels of biomarkers in samples obtained from individuals or patients and the values ​​of reference levels can be compared, for example, with each other, and such comparisons can be performed automatically by a computer program that executes an algorithm for the comparison.

[0014] It should be understood that the “reference value” mentioned in step (2) refers to a value that allows differentiation between subjects at risk of having colorectal cancer and those not at such risk, such as a value that allows differentiation between healthy controls and patients with colorectal cancer. The reference value may be pre-determined and set to meet routine requirements in terms of, for example, specificity and / or sensitivity.

[0015] Based on the teachings given in this invention, obtaining such reference values ​​is within the capabilities of those skilled in the art. For example, the levels of the biomarkers described in this invention can be determined in a representative population, and reference values ​​can be calculated using suitable statistical methods (e.g., median, mean, quantiles, PLS-DA, logistic regression, random forest classification, or other methods that provide thresholds). The thresholds should take into account desired clinical settings for the sensitivity and specificity of diagnostic and prognostic testing. In one embodiment, the reference values ​​can be determined in one or more reference samples from patients with colorectal cancer. In another embodiment, the reference values ​​can be determined in one or more reference samples from subjects not at risk of colorectal cancer (e.g., healthy controls).

[0016] In some embodiments, the reference value represents the level of the biomarker in a body fluid sample from a healthy individual who has not had colorectal cancer. In some embodiments, if the level measured in step (1) is higher than the reference value, the subject is determined to have colorectal cancer or be at risk of having colorectal cancer.

[0017] In some embodiments, the level of the biomarker is determined by an immunological assay. In some embodiments, the immunological assay is selected from ELISA, Elispot, dot blot, or Western blot.

[0018] In some implementations, the immunological assay includes the use of a primary antibody capable of specifically binding to the biomarker.

[0019] In some implementations, the primary antibody carries a detectable marker.

[0020] In some embodiments, the immunological assay further includes the use of a secondary antibody specific to the primary antibody, the secondary antibody carrying a detectable label.

[0021] In this document, the secondary antibody is specific to antibodies from the species from which the primary antibody originates (e.g., mouse, rat, sheep, rabbit, etc.). In some embodiments, the secondary antibody is selected from anti-immunoglobulin antibodies, such as anti-IgG antibodies, anti-IgM antibodies, or anti-IgA antibodies.

[0022] In some embodiments, the detectable marker is selected from enzymes (e.g., horseradish peroxidase or alkaline phosphatase), chemiluminescent reagents (e.g., acridine esters), fluorescent dyes, or biotin.

[0023] In other embodiments, the levels of the biomarker are determined by mass spectrometry.

[0024] In some embodiments, the colorectal cancer includes non-metastatic colorectal cancer or metastatic colorectal cancer. In some embodiments, the metastatic colorectal cancer includes lymph node metastasis. In some embodiments, the metastatic colorectal cancer includes distant metastasis, such as liver metastasis.

[0025] In some embodiments, the body fluid sample is selected from blood, serum, plasma, urine, and saliva. In some embodiments, the body fluid sample is urine.

[0026] In some implementations, the subject is a human being.

[0027] Another aspect of the invention relates to the use of reagents capable of detecting biomarkers in the preparation of kits for determining whether a subject has or is at risk of having colorectal cancer (CRC), said biomarkers as defined in any embodiment of the first aspect.

[0028] In some embodiments, the reagent measures the level of the biomarker by an immunological assay. In some embodiments, the immunological assay is selected from ELISA, Elispot, dot blot, or Western blot.

[0029] In some embodiments, the reagent comprises a primary antibody capable of specifically binding to the biomarker.

[0030] In some implementations, the primary antibody carries a detectable marker.

[0031] In some embodiments, the reagent further comprises a secondary antibody specific to the primary antibody, the secondary antibody being labeled with a detectable marker.

[0032] In some embodiments, the detectable marker is selected from enzymes (e.g., horseradish peroxidase or alkaline phosphatase), chemiluminescent reagents (e.g., acridine esters), fluorescent dyes, or biotin.

[0033] In some embodiments, the reagent is used to determine the level of the biomarker by mass spectrometry.

[0034] In some embodiments, the kit further includes one or more reagents or devices selected from: (i) a device for collecting or storing bodily fluid samples (e.g., urine) from a subject; and (ii) other reagents (e.g., buffers, diluents, eluents, blocking solutions, and / or standards) required to perform the assay (e.g., immunological detection or mass spectrometry).

[0035] In some implementations, the kit determines whether a subject has or is at risk of having colorectal cancer (CRC) using the method described in the first aspect.

[0036] Another aspect of the present invention provides a biomarker combination comprising at least two selected from CORO1C, APRC5, and RAD23B. In some embodiments, the biomarker combination comprises CORO1C, APRC5, and RAD23B. In some embodiments, the biomarker combination may further comprise one or more (e.g., one, two, three, four, or all five) selected from BHMT, DCP1B, TPM3, CALD1, and ATP5F1A.

[0037] Another aspect of the present invention provides a kit comprising a combination of biomarkers as described above, and / or reagents capable of detecting each biomarker in the combination of biomarkers.

[0038] In some embodiments, the reagent is used to determine the level of the biomarker via immunological detection.

[0039] In some embodiments, the reagent comprises reagents for mass spectrometry detection of the biomarker. In such embodiments, the biomarker combination can serve as a control sample in mass spectrometry.

[0040] In some embodiments, the kit further includes one or more reagents or devices selected from: (i) a device for collecting or storing bodily fluid samples (e.g., urine) from a subject; and (ii) other reagents (e.g., buffers, diluents, eluents, blocking solutions, and / or standards) required to perform the assay.

[0041] In some embodiments, the reagents described in (ii) are selected from other reagents required for performing immunological assays (e.g., buffers, diluents, elution buffers, blocking solutions, and / or standards).

[0042] In other embodiments, the reagents described in (ii) are selected from other reagents required for performing mass spectrometry (e.g., buffers, diluents, eluents, blocking solutions, and / or standards).

[0043] Biomarkers for staging colorectal cancer

[0044] A second aspect of the present invention provides a method for differentiating colorectal cancer status in subjects, comprising:

[0045] (1) Detect the level of biomarkers in liquid samples from subjects, the biomarkers being selected from one or more of CORO1C, RAD23B, GSPT2, NDN, KRT7, PROZ, PRDX1, PAM, APRC5 (e.g., 1, 2, 3, 4, 5, 6, 7, 8 or all 9).

[0046] (2) Compare the level with the reference value.

[0047] In some embodiments, the method is used to differentiate between non-metastatic colorectal cancer and metastatic colorectal cancer (e.g., lymph node metastasis and / or distant metastasis (e.g., liver metastasis)). In such embodiments, the biomarker preferably comprises one or more selected from CORO1C, RAD23B, GSPT2, and NDN (e.g., 1, 2, 3, or all 4). In some embodiments, the biomarker comprises CORO1C, RAD23B, GSPT2, and NDN. In some embodiments, the biomarker may also comprise one or more selected from KRT7, PROZ, PRDX1, PAM, and APRC5 (e.g., 1, 2, 3, 4, or all 5).

[0048] In some embodiments, the method is used for staging colorectal cancer. In such embodiments, the biomarkers preferably comprise one or more (e.g., one, two, three, four, or all five) selected from CORO1C, RAD23B, GSPT2, NDN, and APRC5. The term "staging colorectal cancer" preferably refers to differentiating multiple stages of colorectal cancer. General classifications of colorectal cancer stages are well known to those skilled in the art. In some embodiments, the staging is TNM staging, such as stages I, II, III, or IV. In some embodiments, the staging is M staging, such as M0 or M1.

[0049] It should be understood that the “level” mentioned in steps (1) and (2) includes the absolute amount, relative amount or concentration of the biomarker and any value or parameter associated with or derived therefrom.

[0050] It should be understood that the “comparison” mentioned in step (2) generally refers to a comparison of corresponding parameters or values, such as comparing an absolute amount with an absolute reference amount, or a concentration with a reference concentration, or comparing the intensity signal of a biomarker obtained from a sample with the same type of intensity signal obtained from a reference sample. Comparisons can be performed manually or with computer assistance. The measured or detected levels of biomarkers in samples obtained from individuals or patients and the values ​​of reference levels can be compared, for example, with each other, and such comparisons can be performed automatically by a computer program that executes an algorithm for the comparison.

[0051] It should be understood that the “reference value” mentioned in step (2) refers to a value that allows for the determination of colorectal cancer status (e.g., distinguishing between non-metastatic and metastatic colorectal cancer, or different stages of colorectal cancer) in subjects with colorectal cancer. The reference value may be pre-determined and set to meet routine requirements in terms of, for example, specificity and / or sensitivity.

[0052] Based on the teachings given in this invention, obtaining such reference values ​​is within the capabilities of those skilled in the art. For example, the levels of the biomarkers described in this invention can be determined in a representative population, and reference values ​​can be calculated using suitable statistical methods (e.g., median, mean, quantiles, PLS-DA, logistic regression, random forest classification, or other methods that provide thresholds). The thresholds should take into account desired clinical settings for the sensitivity and specificity of diagnostic and prognostic testing.

[0053] Reference values ​​can be derived from samples from subjects with colorectal cancer at different stages. For example, to differentiate between non-metastatic and metastatic colorectal cancer, reference values ​​could be derived from samples from (i) subjects with non-metastatic colorectal cancer and / or (ii) subjects with metastatic colorectal cancer. Similarly, to differentiate between different TNM stages of colorectal cancer, reference values ​​could be derived from samples from (i) subjects in stage I, (ii) subjects in stage II, (iii) subjects in stage III, and / or (iv) subjects in stage IV.

[0054] In some embodiments, when the method is used to differentiate between non-metastatic and metastatic colorectal cancer, the reference value represents the level of the biomarker in a body fluid sample of a subject with non-metastatic colorectal cancer. In some embodiments, if the level is higher than the reference value, the subject is determined to have metastatic colorectal cancer or be at risk of having metastatic colorectal cancer.

[0055] In some implementations, when the method is used to differentiate between different stages of colorectal cancer, the reference value may represent the level of the biomarker in body fluid samples from subjects with different stages of colorectal cancer.

[0056] In some embodiments, the level of the biomarker is determined by an immunological assay. In some embodiments, the immunological assay is selected from ELISA, Elispot, dot blot, or Western blot.

[0057] In some implementations, the immunological assay includes the use of a primary antibody capable of specifically binding to the biomarker.

[0058] In some implementations, the primary antibody carries a detectable marker.

[0059] In some embodiments, the immunological assay further includes the use of a secondary antibody specific to the primary antibody, the secondary antibody carrying a detectable label.

[0060] In this document, the secondary antibody is specific to antibodies from the species from which the primary antibody originates (e.g., mouse, rat, sheep, rabbit, etc.). In some embodiments, the secondary antibody is selected from anti-immunoglobulin antibodies, such as anti-IgG antibodies, anti-IgM antibodies, or anti-IgA antibodies.

[0061] In some embodiments, the detectable marker is selected from enzymes (e.g., horseradish peroxidase or alkaline phosphatase), chemiluminescent reagents (e.g., acridine esters), fluorescent dyes, or biotin.

[0062] In other embodiments, the levels of the biomarker are determined by mass spectrometry.

[0063] In some embodiments, the body fluid sample is selected from blood, serum, plasma, urine, and saliva. In some embodiments, the body fluid sample is urine.

[0064] In some embodiments, the subject is a human being. In some embodiments, the subject has or has been diagnosed with colorectal cancer.

[0065] Another aspect of the invention relates to the use of reagents capable of detecting biomarkers in the preparation of kits for differentiating colorectal cancer status in subjects, said biomarkers being defined as in any embodiment of the second aspect.

[0066] In some embodiments, the reagent measures the level of the biomarker by an immunological assay. In some embodiments, the immunological assay is selected from ELISA, Elispot, dot blot, or Western blot.

[0067] In some embodiments, the reagent comprises a primary antibody capable of specifically binding to the biomarker.

[0068] In some implementations, the primary antibody carries a detectable marker.

[0069] In some embodiments, the reagent further comprises a secondary antibody specific to the primary antibody, the secondary antibody being labeled with a detectable marker.

[0070] In some embodiments, the detectable marker is selected from enzymes (e.g., horseradish peroxidase or alkaline phosphatase), chemiluminescent reagents (e.g., acridine esters), fluorescent dyes, or biotin.

[0071] In some embodiments, the reagent is used to determine the level of the biomarker by mass spectrometry.

[0072] In some embodiments, the kit further includes one or more reagents or devices selected from: (i) a device for collecting or storing bodily fluid samples (e.g., urine) from a subject; and (ii) other reagents (e.g., buffers, diluents, eluents, blocking solutions, and / or standards) required to perform the assay (e.g., immunological detection or mass spectrometry).

[0073] In some implementations, the kit distinguishes colorectal cancer status in subjects using the method described in the second aspect.

[0074] Another aspect of the present invention relates to a biomarker combination comprising at least two (e.g., two, three, or all four) selected from CORO1C, RAD23B, GSPT2, and NDN. In some embodiments, the biomarker combination comprises CORO1C, RAD23B, GSPT2, and NDN. In some embodiments, the biomarker may further comprise one or more (e.g., one, two, three, four, or all five) selected from KRT7, PROZ, PRDX1, PAM, and APRC5.

[0075] Another aspect of the present invention relates to a biomarker combination comprising at least two (e.g., two, three, four, or all five) selected from CORO1C, RAD23B, GSPT2, NDN, and APRC5. In some embodiments, the biomarker combination comprises CORO1C, RAD23B, GSPT2, NDN, and APRC5.

[0076] Another aspect of the present invention relates to a kit comprising a combination of biomarkers as described above, and / or reagents capable of detecting each biomarker in the combination of biomarkers.

[0077] In some embodiments, the reagent is used to determine the level of the biomarker via immunological detection.

[0078] In some embodiments, the reagent comprises reagents for mass spectrometry detection of the biomarker. In such embodiments, the biomarker combination can serve as a control sample in mass spectrometry.

[0079] In some embodiments, the kit further includes one or more reagents or devices selected from: (i) a device for collecting or storing bodily fluid samples (e.g., urine) from a subject; and (ii) other reagents (e.g., buffers, diluents, eluents, blocking solutions, and / or standards) required to perform the assay.

[0080] In some embodiments, the reagents described in (ii) are selected from other reagents required for performing immunological assays (e.g., buffers, diluents, elution buffers, blocking solutions, and / or standards).

[0081] In other embodiments, the reagents described in (ii) are selected from other reagents required for performing mass spectrometry (e.g., buffers, diluents, eluents, blocking solutions, and / or standards).

[0082] Terminology Definition

[0083] In this invention, unless otherwise stated, the scientific and technical terms used herein have the meanings commonly understood by those skilled in the art. Furthermore, the operational procedures used herein, such as those related to oncology, molecular genetics, nucleic acid chemistry, cell culture, biochemistry, and cell biology, are all conventional procedures widely used in their respective fields. To better understand this invention, definitions and explanations of relevant terms are provided below.

[0084] As used herein, the term "CORO1C" refers to human coronain 1C, also known as Coronin 3. The sequence of CORO1C is known in the art, for example, see GenBank: NM_014325.

[0085] As used herein, the term "ARPC5" refers to Actin-related protein 2 / 3 complex subunit 5. The sequence of ARPC5 is known in the art, for example, see GenBank: NM_005717.

[0086] As used herein, the term "GSPT2 (G1 to S phase transition 2)" is also known as the eukaryotic peptide chain releasing factor GTP-binding subunit (eRF3b). The sequence of GSPT2 is known in the art, for example, see GenBank: NM_018094.

[0087] As used herein, the term "RAD23B" refers to homology B of the ultraviolet excision repair protein RAD23. The sequence of RAD23B is known in the art, for example, see GenBank: NM_002874.

[0088] As used herein, the term “NDN” is also referred to as NECD (Necdin). Sequences of NDNs are known in the art, see, for example, GenBank: NM_002487.

[0089] As used herein, the term "BHMT" refers to betaine homocysteine ​​methyltransferase. The sequence of BHMT is known in the art, for example, see GenBank: NM_001713.

[0090] As used herein, the term "DCP1B" is a core component of the mRNA uncapping complex. The sequence of DCP1B is known in the art, for example, see GenBank: NM_152640.

[0091] As used herein, the term "TPM3" refers to human tropomyosin-3. The sequence of TPM3 is known in the art, for example, see GenBank: NM_152263.

[0092] As used herein, the term “CALD1” refers to Caldesmon-1. The sequence of CALD1 is known in the art, see, for example, GenBank: NM_033138.

[0093] As used herein, the term "ATP5F1A" refers to the F1 subunit α of ATP synthase. The sequence of ATP5F1A is known in the art, for example, see GenBank: NM_004046.

[0094] As used herein, the term "KRT7" refers to Keratin 7. The sequence of KRT7 is known in the art, see, for example, GenBank: NM_005556.

[0095] As used herein, the term "PROZ" refers to protein Z. The sequence of PROZ is known in the art, for example, see GenBank: NM_003891.

[0096] As used herein, the term "PRDX1" refers to Peroxiredoxin 1. The sequence of PRDX1 is known in the art, see, for example, GenBank: NM_002574.

[0097] As used herein, the term "PAM" refers to peptidyl glycine α-amidyl monooxygenase. The sequence of PAM is known in the art, for example, see GenBank: NM_000919.

[0098] As used herein, the term "detectable label" can refer to any substance that can be detected by fluorescent, spectroscopic, photochemical, biochemical, immunological, electrical, optical, or chemical means. Particularly preferred are such labels that are suitable for immunological assays (e.g., enzyme-linked immunosorbent assays, radioimmunoassays, fluorescence immunoassays, chemiluminescent immunoassays, etc.). Such labels are well known in the art and include, but are not limited to, enzymes (e.g., horseradish peroxidase, alkaline phosphatase, β-galactosidase, urease, glucose oxidase, etc.) and radionuclides (e.g., 3 H, 125 I, 35 S, 14 C or 32 P), fluorescent dyes (e.g., fluorescein isothiocyanate (FITC), fluorescein, tetramethylrhodamine isothiocyanate (TRITC), phycoerythrin (PE), Texas Red, rhodamine, quantum dots or cyanine dye derivatives (e.g., Cy7, Alexa 750)), luminescent materials (e.g., chemiluminescent materials, such as acridine esters), magnetic beads (e.g., ), thermal markers such as colloidal gold or colored glass or plastic (e.g., polystyrene, polypropylene, latex, etc.) beads, and biotin for binding avidin (e.g., streptavidin) modified with the above markers.

[0099] As used herein, the term "specific binding" refers to a non-random binding reaction between two molecules (i.e., a binding molecule and a target molecule), such as the reaction between an antibody and its target antigen. The binding affinity between two molecules can be measured using Kx. D Value description. K D The value refers to the dissociation constant obtained by the ratio of kd (the dissociation rate of a specific binding molecule-target molecule interaction; also known as koff) to ka (the association rate of a specific binding molecule-target molecule interaction; also known as kon), or kd / ka expressed as molar concentration (M). D The smaller the value, the tighter the binding between the two molecules, and the higher the affinity. In some embodiments, an antibody that specifically binds to a certain antigen (or an antibody that is specific to a certain antigen) refers to an antibody with a binding affinity of less than approximately 10. -5 M, for example, less than approximately 10 -6 M, 10 -7 M, 10 -8 M, 10 -9 M or 10 -10 M or lower affinity (K) D ) binds to this antigen. K D The value can be determined by methods well known in the art, such as using surface plasmon resonance (SPR) in a BIACORE instrument.

[0100] As used herein, the term "immunologic assay" or "immunologic method assay" refers to an assay that utilizes the specific interaction / binding affinity between an antigen and antibody, and is generally used to detect the presence or level of a specific antigen or antibody in a sample. Such immunological assays are well known to those skilled in the art and include, but are not limited to, ELISA assays, Elispot assays, Western blotting, and surface plasmon resonance assays. For a detailed description of immunological assays, see, for example, Fundamental Immunology, Ch. 7, Paul W., ed., 2nd edition, Raven Press, NY (1989).

[0101] As used herein, the term "metastasis" refers to the spread of cancer cells from their primary site to another part of the body. The term "metastasis" includes "distant metastasis," which refers to metastasis away from the primary tumor and the local lymph node system. The cells in a metastatic tumor are the same as those in the primary tumor. This means, for example, if colorectal cancer metastasizes to the liver, the metastatic tumor is composed of abnormal colorectal cells (rather than abnormal liver cells). In this case, the tumor in the liver is called metastatic colorectal cancer, not liver cancer. According to the present invention, metastatic colorectal cancer can include cancer in lymph nodes, cancer in the liver, cancer in the lungs, cancer in the bone, and cancer in the brain.

[0102] As used herein, the term "subject" can refer to any mammal, preferably a human, regardless of age or sex. In some embodiments, the subject has or has been diagnosed with colorectal cancer, and how to assess whether a subject has colorectal cancer is known in the art.

[0103] Beneficial effects of the invention

[0104] This invention is the first to discover a urinary protein biomarker for the diagnosis and staging of colorectal cancer (CRC). This biomarker can sensitively and accurately diagnose colorectal cancer and differentiate between non-metastatic and metastatic colorectal cancer, which has significant clinical value.

[0105] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings and examples. However, those skilled in the art will understand that the following drawings and examples are for illustrative purposes only and are not intended to limit the scope of the invention. Various objects and advantages of the present invention will become apparent to those skilled in the art from the following detailed description of the drawings and preferred embodiments. Attached Figure Description

[0106] Figure 1Venn diagram analysis revealed differences in urinary proteinomes between healthy individuals and colorectal cancer patients. CRC: Colorectal cancer without metastasis; CRC-LNM: Colorectal cancer with lymph node metastasis; CRC-DM: Colorectal cancer with liver metastasis; HC: Healthy controls.

[0107] Figure 2A IPA analysis results of differentially expressed urinary proteins in tumor-related pathways between CRC patients and healthy controls. The color of each node in the group represents the -log10 (p-value) of that pathway, and the size of each node represents the number of differentially expressed proteins in that pathway / disease / function. Interactions between paired pathways are represented by curves.

[0108] Figure 2B IPA analysis results of CRC metastasis-related differentially expressed urinary proteins in tumor progression-related pathways. The color of each node in the group represents the -log10 (p-value) of that pathway, and the size of each node represents the number of differentially expressed proteins in that pathway / disease / function. Interactions between paired pathways are represented by curves.

[0109] Figure 3A Unsupervised clustering analysis results of 41 differentially expressed proteins in healthy controls and CRC patients based on PRM data.

[0110] Figure 3B The variable importance plot, generated by the random forest algorithm based on PRM data, uses the mean decline in accuracy (MDA) as a metric. The most important predictor variables have the highest MDA values. The left plot shows the diagnostic model (distinguishing between CRC patients and healthy controls); the right plot shows the metastasis model (distinguishing between CRC patients with and without metastasis).

[0111] Figure 3C The results of ROC curve area under the curve (AUC) analysis of single proteins in PRM data distinguishing CRC patients from healthy controls (left) and CRC patients with and without metastasis (right).

[0112] Figure 3D A matrix plot of AUC for two variables in a diagnostic model based on PRM data. Proteins exhibiting excellent discriminative power and complementarity are marked in red.

[0113] Figure 3E A matrix plot of AUC for two variables in a transfer model based on PRM data. Proteins exhibiting superior discriminative power and complementarity are marked in red.

[0114] Figure 4A ROC curves of individual proteins and their combinations in a diagnostic model based on PRM data.

[0115] Figure 4B ROC curves of individual proteins and their combinations used to distinguish HC from CRC-NM, CRC-LNM, or CRC-DM in diagnostic models based on PRM data.

[0116] Figure 5A ROC curves of individual proteins and their combinations in a transfer model based on PRM data.

[0117] Figure 5B ROC curves of individual proteins and their combinations in a transfer model based on PRM data, used to distinguish between CRC-NM and CRC-LNM or CRC-DM, respectively.

[0118] Figure 6A Representative Dot blot results and scatter plots of urine levels for CORO1C, ARPC5, RAD23B, GSPT2, and NDN in healthy controls (HC), non-metastatic colorectal cancer (NM), lymph node metastasis (LNM), and liver metastasis (DM) groups, respectively. Std, standard; *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001; ns, not significant.

[0119] Figure 6B Box plots showing the correlation between the abundance of CORO1C, ARPC5, RAD23B, GSPT2, and NDN and TNM and M stages based on Dot blot data. TNM, TNM stage; M0, no distant metastasis; M1, distant metastasis present; *, P<0.05; **, P<0.01; ***, P<0.001; ***, P<0.0001; ns, not significant.

[0120] Figure 7A The ROC curves for the diagnostic model training set based on Dot blot data, consisting of CORO1C, APRC5, and RAD23B, as well as the combination of the panel and serum CEA, were used to distinguish between healthy controls and CRC patients. Panel: Diagnostic group consisting of CORO1C, APRC5, and RAD23B.

[0121] Figure 7B The validation set of the diagnostic model based on Dot blot data includes a panel composed of CORO1C, APRC5, and RAD23B, as well as ROC curves of the panel combined with serum CEA and CA19-9 used to distinguish between healthy controls and CRC patients. Panel: Diagnostic group composed of CORO1C, APRC5, and RAD23B.

[0122] Figure 8The positive rate of individual panel detection for CEA-misidentified individuals in the training and validation sets of the diagnostic model based on Dot blot data.

[0123] Figure 9 The panel consisting of CORO1C, ARPC5, and RAD23B in the diagnostic model based on Dot blot data, and its combination with CEA, are used to distinguish the ROC curves of HC and CRC-DM, CRC-LNM, and CRC-NM, respectively.

[0124] Figure 10A The ROC curves used in the training set of the transfer model based on Dot blot data are CORO1C, GSPT2, NDN, RAD23B, a combination of the four (Panel), and a combination of Panel and CEA to distinguish between non-transferable and transferable CRC.

[0125] Figure 10B The ROC curves used in the transfer model validation set based on Dot blot data are CORO1C, GSPT2, NDN, RAD23B, a combination of the four (Panel), and the combination of Panel and CEA to distinguish between non-transferable and transferable CRC.

[0126] Figure 11 The positive rate of transfer prediction for individuals misidentified by CEA using individual panels in the training and validation sets of the transfer model based on Dot blot data.

[0127] Figure 12 The combination (Panel) of CORO1C, GSPT2, NDN, and RAD23B in the transfer model based on Dot blot data, and its combination with CEA, are used to distinguish the ROC curves of untransferred CRC CRC-DM and CRC-LNM or CRC-NM, respectively. Detailed Implementation

[0128] The invention will now be described with reference to the following embodiments, which are intended to illustrate the invention (and not limit it).

[0129] Unless otherwise specified, the experiments and methods described in the examples are generally performed according to conventional methods well known in the art and described in various references. Where specific conditions are not specified in the examples, conventional conditions or conditions recommended by the manufacturer are followed. Reagents or instruments whose manufacturers are not specified are all commercially available conventional products. Those skilled in the art will understand that the examples describe the invention by way of illustration and are not intended to limit the scope of protection claimed by the invention. All disclosures and other references mentioned herein are incorporated herein by reference in their entirety.

[0130] The sources of the main reagents involved in the following examples are as follows:

[0131]

[0132] Example 1: Preliminary screening and validation of urinary protein markers

[0133] 1.1 Preliminary screening based on TMT-based quantitative proteomics analysis

[0134] Urine samples were collected from subjects, and urinary proteins were enriched and eluted. Protein concentration was determined using the Bradford method, and the samples were stored at -80°C. CRC urine samples were obtained from inpatients in the Department of Colorectal Surgery and Interventional Therapy at the Cancer Hospital of the Chinese Academy of Medical Sciences. Sample collection and use were approved by the hospital's ethics committee, and all patients were diagnosed by at least two senior pathologists as having CRC-NM (non-metastasis), CRC-LNM (lymph node metastasis), or CRC-DM (liver metastasis). Healthy control (HC) urine samples were collected partly from volunteers at the Cancer Hospital of the Chinese Academy of Medical Sciences and partly from the Central Laboratory of the Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences.

[0135] Quantitative proteomics analysis using TMT (Tandem Mass Tag) was performed on all samples. In short, after enzymatic digestion, urinary protein samples were labeled with TMT using the TMT-10plex kit, and TMT peptides were separated by offline high-pH reversed-phase high-performance liquid chromatography (HPLC). Proteomic identification was then performed using LC-MS / MS. Raw data were retrieved from the Proteome Discover database and compared with the Swissprot human proteome database. Results are as follows: Figure 1 As shown, the number of single-peptide non-redundant proteins identified in the HC, CRC-NM, CRC-LNM and CRC-DM groups were 1976, 2151, 2635 and 2772, respectively, with a total of 995 proteins in the four groups.

[0136] Unsupervised clustering analysis was performed on the proteins in each group, with a difference greater than 1.5-fold as the criterion. Compared with the HC group, the three CRC groups (NM, LNM, DM) showed 273, 337, and 355 differentially expressed proteins, respectively. Ingenuity Pathway Analysis (IPA) was then performed on the tumor-related differentially expressed proteins, and the results are as follows: Figure 2A As shown. Furthermore, under the same criteria, 93, 69, and 114 differentially expressed proteins were found among the three CRC groups (NM vs. LNM, DM vs. NM, and LNM vs. DM), respectively. Ingenuity Pathway Analysis (IPA) was performed on tumor-related differentially expressed proteins, and the results are shown below. Figure 2BAs shown. The obtained differentially expressed proteins will be used as candidate biomarkers for PRM validation.

[0137] 1.2 Preliminary validation of the targeted quantitative proteomics method based on parallel reaction monitoring (PRM)

[0138] During the PRM validation phase, 82 independent samples were recollected (HC: 25, NM: 21, LNM: 13, DM: 23). CRC urine samples were collected from inpatients in the Department of Colorectal Surgery, Hepatobiliary Surgery, and Laboratory Medicine at the Cancer Hospital of the Chinese Academy of Medical Sciences. Sample collection and use were approved by the hospital's ethics committee; CRC metastasis grouping was interpreted by senior pathology experts. Some HC urine samples were collected from the Laboratory Medicine Department of the National Cancer Center.

[0139] Urinary proteins were enriched using nitrocellulose membranes and then eluted from the membranes. The Bradford method was used for quantification of urinary proteins, preparing for PRM-targeted quantification validation. After enzymatic digestion, urinary proteins were analyzed and identified using LC-MS / MS. Skyline 3.6 software was used to analyze the data generated by PRM. Results were imported into Skyline software, the correct peaks were manually selected, and all peptide results from all samples were output. The total ionic intensity (TIC) of +2 to +5 charge was extracted for each sample using Progenesis software. The mass spectrometry signal intensity of each peptide in each sample was normalized using the total ionic intensity of the sample to correct for errors in sample loading and mass spectrometry signal. The results for each peptide were quantitatively analyzed, differentially expressed proteins between different groups were screened, and compared with TMT results.

[0140] The results showed that PRM-targeted mass spectrometry identified 66 peptides from 41 differentially expressed proteins, and these proteins exhibited trends consistent with the TMT results. Unsupervised cluster analysis was performed on these 41 differentially expressed proteins, and the results are as follows: Figure 3A As shown, 18 proteins were significantly downregulated in the CRC group, while 23 proteins were upregulated, showing a gradual increasing trend with disease progression.

[0141] Further analysis of the 23 upregulated proteins excluded proteins showing moderate correlations with more than five other proteins (Spearman rank correlation coefficient ≥ 0.6). The remaining 14 candidate proteins were then evaluated using a random forest algorithm, and the feature importance ranking results are as follows: Figure 3B As shown.

[0142] Furthermore, ROC analysis was used to examine the classification performance of the aforementioned candidate proteins in CRC diagnostic prediction (HC vs CRC) and CRC metastasis prediction (LNM / DM vs NM). The content of candidate proteins in urine was used as the test variable, and the known diagnostic results were used as the state variable. ROC curves were plotted, and the area under the curve (AUC) was calculated. The results are as follows: Figure 3C As shown. (Summary) Figure 3B and 3C According to the ranking results, the proteins that performed well in the CRC diagnostic model (distinguishing between healthy controls and CRC patients) include: RAD23B, CORO1C, ARPC5, BHMT, DCP1B, TPM3, CALD1, and ATP5F1A. The proteins that performed well in the CRC metastasis model (distinguishing between non-metastatic and metastatic CRC) include: NDN, CORO1C, RAD23B, GSPT2, KRT7, PROZ, PRDX1, and PAM.

[0143] The complementarity of any two proteins was further evaluated by comparing the AUC values ​​of the combined detection. The results of the CRC diagnostic model and the metastasis model are as follows: Figure 3D and Figure 3E As shown, CORO1C, APRC5, and RAD23B exhibit superior discriminative power and complementarity in the diagnostic model, while CORO1C, RAD23B, GSPT2, and NDN demonstrate superior discriminative power and complementarity in the transfer model. Therefore, a diagnostic model composed of three urinary proteins (CORO1C, APRC5, and RAD23B) and a transfer prediction model composed of four urinary proteins (CORO1C, RAD23B, GSPT2, and NDN) were established.

[0144] ROC curves were plotted for CORO1C, APRC5, and RAD23B, individually or in combination (i.e., a diagnostic panel composed of CORO1C, APRC5, and RAD23B), to differentiate between HC and CRC samples. The results are as follows: Figure 4A As shown, the AUC of this diagnostic panel is 0.887, the specificity is 80.0%, and the sensitivity is 86.0%. Furthermore, Figure 4B The performance of this diagnostic panel in differentiating HC from CRC-NM, CRC-LNM, or CRC-DM is shown, with AUCs of 0.800, 0.948, and 0.935, respectively.

[0145] ROC curves were plotted for CORO1C, RAD23B, GSPT2, and NDN, individually or in combination (i.e., a metastasis panel composed of CORO1C, RAD23B, GSPT2, and NDN) to differentiate between CRC patients with and without metastasis. The results are as follows: Figure 5AAs shown, the transfer panel had an AUC of 0.784, a specificity of 70.0%, and a sensitivity of 81.1%. Furthermore, Figure 5B The performance of this transfer panel in distinguishing between CRC-NM and CRC-LNM or CRC-DM is shown, with AUCs of 0.723 and 0.827, respectively.

[0146] Example 2: Validation of quantitative Dot blot analysis of urinary protein markers

[0147] 2.1 Quantitative analysis method of urinary protein using Dot blot

[0148] Cut filter paper (13 x 10 cm) and PVDF membrane (12 x 9 cm) to appropriate sizes. Activate the PVDF membrane by soaking it in methanol solution for 20 minutes in a clean container, then soak it in 1x PBS solution for 10 minutes. Soak the filter paper 2-3 minutes before assembling the filter assembly (if using a nitrocellulose membrane, soak it directly in PBS for 10 minutes; methanol activation is not required).

[0149] Sample preparation: Prepare 500 μL of liquid per well with PBS to ensure uniform dot matrix. Obtain different concentration gradients of standards using a serial dilution method. Shake to mix the sample, then place it on ice.

[0150] Instrument assembly: Assemble the filter device according to the instruction manual, ensuring that there are no air bubbles between the two layers of filter paper and between the filter paper and the PVDF membrane.

[0151] Sample loading: Add 500 μL of sample to each well, avoiding air bubbles. The total sample loading time should not exceed 5 minutes. Simultaneously connect the vacuum pump and apply a low-speed vacuum (suction rate of 100 μL / min, vacuum pump pressure of 7 kPa). After all sample wells have been filtered, add 200 μL of 2% BSA to each well and let it stand for 5 minutes. Apply a low-speed vacuum to seal the unbound protein portions of the wells (this step mainly pre-seales the sample loading wells).

[0152] Sealing: Remove the nitrocellulose membrane or PVDF membrane and place it in a clean container. Seal with 10% skim milk powder at room temperature for 4-5 hours or overnight at 4°C (note that the front side should be facing up. Since the sample well part of the membrane will bulge after vacuum filtration, add a little more sealing solution).

[0153] Incubate primary antibody: Dilute the target primary antibody with 3% skim milk powder at a certain ratio and incubate overnight at 4°C or for 4 hours at room temperature.

[0154] Wash the membrane: Use 1x TBST to wash the membrane for 5-8 minutes each time, and wash 3-4 times.

[0155] Secondary antibody incubation: Dilute the secondary antibody with 3% skim milk powder at an appropriate ratio and incubate at room temperature for about 1 hour. Wash the membrane with 1x TBST for 5-8 minutes each time, 3-4 times.

[0156] Exposure: Mix the two colorimetric substrates in a 1:1 ratio, and evenly cover the membrane surface with the mixed liquid. At the same time, cover the membrane surface with a layer of transparent cellophane (the function of cellophane is: 1. to use the tension between cellophane and liquid to ensure that the luminescent liquid is evenly covered in each sample well; 2. to avoid local evaporation and drying of liquid on the membrane surface, which would affect the experimental results). Place the membrane in an exposure apparatus for exposure and acquire images.

[0157] Grayscale scan:

[0158] Dot blot results were obtained using ECL developer and a LAS4000. The results were then subjected to grayscale scanning, following these steps: First, the image was converted to grayscale; second, the background was removed, and quantitative parameters and units were set; then, the image was converted to a bright band, and dots were selected using Freehand Selection. The IntDen grayscale value was obtained by clicking 'm'. The same sample was used to calibrate the grayscale values ​​between experiments.

[0159] Quantitative and statistical analysis of the results:

[0160] Gray-scale analysis of 96 spots on the entire membrane was performed using Scion Image software. The vertical axis is y = , and the horizontal axis is the concentration of the standard. A standard curve was plotted using Microsoft Office Excel 2016 software, and the protein content in each urine sample was calculated. Precision analysis: Ten parallel analyses of the same urine sample were performed using the method established in this study, and the CV value of the tested samples was calculated.

[0161] 2.2 Protein content in urine creatinine-corrected samples

[0162] The urinary creatinine content was detected by ELISA. By dividing the content of the candidate protein in each sample obtained by the above method by the urinary creatinine, the content of the candidate protein under the same dilution in different samples can be obtained.

[0163] 2.3 Statistical Analysis

[0164] Statistical analysis was performed using the Mann-Whitney test (nonparametric test) in Graphpad Prism 6 software. Receiver operating characteristic (ROC) curves were generated using SPSS 16.0 software, with P < 0.05 considered statistically significant.

[0165] 2.4 Clinical Samples

[0166] A total of 434 independent samples were collected again (HC: 255, NM: 46, LNM: 75, DM: 58). CRC urine samples were obtained from inpatients in the Department of Colorectal Surgery, Department of Hepatobiliary Surgery, and Department of Laboratory Medicine at the Cancer Hospital of the Chinese Academy of Medical Sciences. Sample collection and use were approved by the hospital's ethics committee; CRC metastasis grouping was interpreted by senior pathology experts. Some HC urine samples were collected from the Department of Laboratory Medicine at the National Cancer Center.

[0167] 2.5 Test Results

[0168] The urinary protein markers in the samples were hybridized by Dot blot. The raw values ​​obtained by grayscale scanning were used to calculate the biomarker content in each sample using a standard curve. After correction by the urinary creatinine value of the same sample, the final content of the biomarkers in the tested samples was obtained.

[0169] The Dot blot results for CORO1C, APRC5, RAD23B, GSPT2, and NDN are as follows: Figure 6A As shown, the concentrations of CORO1C, APRC5, RAD23B, GSPT2, and NDN in the urine of CRC patients were significantly higher than those in the urine of healthy controls (P<0.0001). In all three CRC groups, the levels of these five urinary proteins showed a gradient increasing trend with disease progression. The concentrations of urinary RAD23B and GSPT2 in the LNM group were significantly higher than those in the NM group (P<0.05). Furthermore, in patients with distant metastases, the levels of these five proteins in their urine were significantly higher than those in CRC patients without metastases (P<0.001), and also higher than those in patients with only lymph node metastases (P<0.01). In addition, the abundance of these five proteins in urine was significantly positively correlated with TNM stage and M stage (P<0.01). Figure 6B ).

[0170] Example 3: Efficacy evaluation of urinary protein markers and their combinations in distinguishing healthy controls from CRC patients

[0171] Using the Dot blot quantitative analysis method described in Example 2, the levels of CORO1C, APRC5, and RAD23B in urine samples from the training and validation sets were measured to examine the ability to distinguish between healthy controls and CRC patients (hereinafter referred to as the diagnostic model). The training set contained 103 HC samples and 105 CRC samples, while the validation set contained 51 HC samples and 53 CRC samples. All these samples had corresponding serum CEA test results.

[0172] ROC curves were plotted for CORO1C, APRC5, and RAD23B, individually or in combination (a diagnostic panel consisting of all three), to distinguish between HC and CRC samples. The training set results are as follows... Figure 7A As shown, the area under the ROC curve for the combined detection of CORO1C, ARPC5, and RAD23B is 0.828. In the validation set, the AUC for the combined detection of CORO1C, ARPC5, and RAD23B can reach 0.875. Figure 7B The above results indicate that these biomarkers and their combinations can be used to distinguish between healthy controls and CRC patients.

[0173] Currently, serum carcinoembryonic antigen (CEA) is a commonly used biomarker for CRC in clinical practice. Therefore, this study further investigated the diagnostic ability of combining the aforementioned urinary protein markers with serum CEA. ROC curves were plotted based on the matched serum CEA test results. The results for the training and validation sets are shown below. Figures 7A-7B As shown, CORO1C, APRC5, and RAD23B combined with serum CEA achieved excellent diagnostic predictive ability, with AUC values ​​of 0.927 and 0.892 in the training and validation groups, respectively. Specifically, the diagnostic panel composed of CORO1C, APRC5, and RAD23B can supplement the discrimination results of serum CEA (≥5 ng / ml is positive). In both the training and validation groups, for patients diagnosed as CEA negative (CEA <5 ng / ml), the above diagnostic panel correctly diagnosed 61.2% (30 / 49) of patients in the training set and 56.3% (18 / 32) of patients in the validation group. Figure 8 Furthermore, the diagnostic panel used in conjunction with CEA demonstrated superior diagnostic predictive ability in differentiating HC from the three types of CRC (CRC-NM, CRC-LNM, and CRC-DM), with AUCs of 0.825, 0.929, and 0.965, respectively. Figure 9 ).

[0174] Example 4: Evaluation of the efficacy of urinary protein markers and their combinations in differentiating between non-metastatic and metastatic CRC

[0175] Using the Dot blot analysis method described in Example 2, the levels of CORO1C, AD23B, GSPT2, and NDN in urine samples from the training and validation sets were measured to examine the ability to distinguish between non-metastatic and metastatic CRC (hereinafter referred to as the metastasis model). The training set included 27 CRC-NM samples (repeated twice, for a total of 81 CRC-NM samples) and 78 metastatic CRC samples, while the validation set included 14 CRC-NM samples and 39 metastatic CRC samples. All these samples had corresponding serum CEA test results.

[0176] ROC curves were plotted individually or in combination (using a transfer panel comprised of all four) for CORO1C, AD23B, GSPT2, and NDN to distinguish between non-transferred and transferable CRC samples. The results for the training and validation sets are shown below. Figures 10A-10B As shown, the areas under the ROC curves used in combination were 0.773 and 0.676, respectively.

[0177] The diagnostic ability of combining the above-mentioned urinary protein markers with serum CEA was further investigated. ROC curves were plotted based on the matched serum CEA test results. The results for the training and validation sets are as follows: Figures 10A-10B As shown, the metastasis panel combining serum CEA had good predictive power of 0.854 and 0.736 in the training and validation groups, respectively. Furthermore, for CEA-negative CRC patients (CEA < 5 ng / ml), 57.6% (19 / 33) and 52.6% (13 / 19) of patients in the training and validation sets, respectively, were accurately diagnosed by the metastasis panel composed of CORO1C, AD23B, GSPT2, and NDN. Figure 11 The transfer panel combined with CEA showed significantly better predictive ability in distinguishing between CRC-NM and CRC-DM, with an AUC of 0.906, higher than the 0.784 for distinguishing between CRC-NM and CRC-LNM. Figure 12 ).

[0178] Although specific embodiments of the invention have been described in detail, those skilled in the art will understand that various modifications and variations can be made to the details based on all the published teachings, and all such changes are within the scope of protection of the invention. The entire scope of the invention is given by the appended claims and any equivalents thereof.

Claims

1. The use of reagents capable of detecting biomarkers in the preparation of kits for determining whether a subject has or is at risk of having colorectal cancer (CRC), wherein, The biomarkers include CORO1C and RAD23B, and the reagent is used to detect the levels of the biomarker proteins.

2. The use as described in claim 1, wherein, The biomarkers include CORO1C, APRC5, and RAD23B.

3. The use as described in claim 1 or 2, wherein, The biomarkers also include one, two, three, four, or all five selected from BHMT, DCP1B, TPM3, CALD1, and ATP5F1A.

4. The use as described in claim 1 or 2, wherein, The reagent is used to determine the level of the biomarker through immunological detection.

5. The use as described in claim 4, wherein, The immunological assays are selected from ELISA, Elispot, dot blot, chemiluminescence, or Western blot.

6. The use as described in claim 4, wherein, The reagent contains a primary antibody capable of specifically binding to the biomarker.

7. The use as described in claim 6, wherein, The primary antibody carries a detectable marker.

8. The use as described in claim 7, wherein, The detectable marker is selected from enzymes, chemiluminescent reagents, fluorescent dyes, or biotin.

9. The use as described in claim 8, wherein, The enzyme is horseradish peroxidase or alkaline phosphatase.

10. The use as described in claim 8, wherein, The chemiluminescent reagent is an acridine ester compound.

11. The use as described in claim 6, wherein, The reagent also contains a secondary antibody specific to the primary antibody, the secondary antibody being labeled with a detectable marker.

12. The use as described in claim 11, wherein, The detectable marker is selected from enzymes, chemiluminescent reagents, fluorescent dyes, or biotin.

13. The use as described in claim 12, wherein, The enzyme is horseradish peroxidase or alkaline phosphatase.

14. The use as described in claim 12, wherein, The chemiluminescent reagent is an acridine ester compound.

15. The use as described in claim 1 or 2, wherein, The reagents are used to determine the levels of the biomarkers by mass spectrometry.

16. The use as described in claim 1 or 2, wherein, The kit determines whether a subject has or is at risk of having colorectal cancer (CRC) by means of the following steps: (1) measuring the level of the biomarker in a body fluid sample from the subject; and, (2) comparing the level with a reference value.

17. The use as described in claim 16, wherein, The body fluid samples were selected from blood, serum, plasma, urine, and saliva.

18. The use as described in claim 17, wherein, The body fluid sample was urine.

19. The use as claimed in claim 16, wherein, The subjects were human.

20. The use as described in claim 16, wherein, The reference value represents the level of this biomarker in body fluid samples from healthy individuals.

21. The use as described in claim 20, wherein, If the level is higher than the reference value, the subject is judged to have colorectal cancer or be at risk of having colorectal cancer.

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