Metabolic markers of colorectal cancer and their application in the diagnosis of colorectal cancer

Abstract

The present invention discloses colorectal cancer metabolic markers and their application in diagnosing colorectal cancer. The present invention collects samples from colorectal cancer patients and healthy controls, performs metabolomics analysis, and discovers key metabolic markers related to colorectal cancer and important metabolic pathways related thereto. The present invention discovers for the first time that the above-mentioned metabolic markers show significant differences between colorectal cancer patients and healthy people. Model validation shows that the above-mentioned metabolic markers have high AUC values ​​for diagnosis, and have good predictive effects on early colorectal cancer patients and colorectal precancerous lesions, indicating that the above-mentioned metabolic markers have good clinical use and promotion value.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and specifically, relates to a metabolic marker for colorectal cancer, and more specifically, relates to a metabolic marker for colorectal cancer and an application thereof in diagnosing colorectal cancer. Background Art

[0002] Colorectal cancer is the most common malignant tumor of the gastrointestinal tract. Its prevalence ranks third among tumors worldwide, and its mortality rate has risen to the second place in the cause of cancer death worldwide. Moreover, its incidence is showing a trend of younger patients. The early clinical symptoms of colorectal cancer are not obvious. By the time obvious symptoms appear and the diagnosis is confirmed, the disease has often developed to the middle and late stages, when the efficacy is limited and the cost is high. Early screening of colorectal cancer is conducive to early diagnosis and treatment, improving patient survival rate and reducing medical costs. It is the most important prevention and treatment strategy for colorectal cancer. Currently, common screening methods used in clinical practice for colorectal cancer include colon gas barium double contrast, CT imaging, colonoscopy, serum tumor markers, fecal occult blood test (FOBT), fecal immunochemical test (FIT), etc. There is radiation damage in gastrointestinal contrast and CT imaging; colonoscopy is an invasive operation and there are risks of gastrointestinal perforation, bleeding, mesenteric tear, etc.; serum tumor markers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) are not sensitive and specific for diagnosing colorectal cancer. Metabolic reprogramming is one of the important characteristics of malignant tumors, and the characteristic metabolic phenotype it causes provides a biochemical basis for the screening and diagnosis of malignant tumors. Metabolomics is a technology for qualitative and quantitative analysis of metabolites.

[0003] Proton Nuclear Magnetic Resonance (1H-NMR) provides an efficient, simple, stable and low-cost tool for detecting the metabolic fingerprint of the body. In the early stage, we used nuclear magnetic resonance to detect the feces, serum and urine of patients with colorectal cancer, and found that the metabolism of the three groups of body fluids can reflect the metabolic disorders of cancer tissues, which provides a basis for the screening of body fluid metabolism for colorectal cancer. However, any single body fluid metabolism can only partially present the metabolic state of the body. If a predictive model of multi-source body fluid metabolism can be constructed, it is expected to more accurately warn of early colorectal cancer. This study adopted a research strategy of tissue and body fluid metabolism, using a high-resolution nuclear magnetic resonance system to analyze the metabolism of urine, feces and cancer tissues of colorectal cancer, using cancer tissue metabolism as an anchor to mine body fluid metabolism feature labels, and then using regression methods to construct a multi-source body fluid metabolism visualization model for early warning of colorectal cancer.

[0004] At present, the main metabolomics technologies include proton nuclear magnetic resonance spectroscopy (PNR). 1H-NMR) and mass spectrometry (MS). MS has high detection sensitivity, but is limited to detecting easily ionized compounds, and the detection is time-consuming, the standard reagents are expensive, and the sample pretreatment is complicated. 1 H-NMR is based on radio frequency pulses irradiating samples to excite the observed nuclei and cause resonance, so as to quantitatively detect the small metabolite changes that occur in the body under pathological and physiological conditions. 1 H-NMR has unique advantages: simple sample pretreatment and cheap reagents; highly stable detection, short time, high accuracy, good repeatability, and non-destructiveness; a single test can obtain rich information on hydrogen-containing metabolites; a variety of spectrum editing technologies make metabolite detection flexible and efficient; advances in magnetic shielding and cryogenic technology make nuclear magnetic resonance instruments more sophisticated and cheaper. Practice has proved that 1 H-NMR is suitable for clinical multi-center large sample and longitudinal research, whether it is tens of thousands of samples or multi-center samples spanning many years. 1 H-NMR detection is stable and non-diffusive, and is the preferred technology for tumor metabolomics screening research. Summary of the invention

[0005] In view of this, the purpose of the present invention is to screen metabolite markers with good diagnostic efficacy by conducting metabolomics analysis on colorectal cancer samples. The present invention uses colorectal cancer tissue metabolite markers as anchors to achieve non-invasive, non-label-based diagnosis of early colorectal cancer. 1 The construction, verification and prediction of the "optimal early warning model" of H-NMR multivariate body fluid metabolic fingerprint spectrum provides a metabolic marker with good diagnostic efficacy for the early diagnosis or prediction of colorectal cancer, which has great scientific significance and clinical application value.

[0006] The above-mentioned object of the present invention is achieved through the following technical solutions:

[0007] The first aspect of the present invention provides the use of a metabolic marker in preparing a kit for diagnosing and / or early predicting colorectal cancer.

[0008] Furthermore, the metabolic markers are Inositol, Glutamate, Very Low Density Lipoprotein, N-Acetyl-glycoprotein, Low Density Lipoprotein, Kynurenine and / or alpha-Ketoleucine.

[0009] Furthermore, the kit includes a reagent for detecting the concentration or content of the metabolic marker in the sample to be tested;

[0010] Preferably, the reagent includes a reagent for detecting the concentration or content of the metabolic marker in the sample to be tested by targeted or non-targeted nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry or a combination thereof;

[0011] Preferably, the sample is blood, serum and / or plasma.

[0012] In some embodiments, the metabolic markers include any one or more combinations of Inositol, Glutamate, Very Low Density Lipoprotein, N-Acetyl-glycoprotein, Low Density Lipoprotein, Kynurenine, and alpha-Ketoleucine.

[0013] In some embodiments, the chromatography includes, but is not limited to, high performance liquid chromatography, thin layer chromatography, gas chromatography, and / or any combination thereof.

[0014] In some embodiments, the spectroscopy includes, but is not limited to, refractive index spectroscopy, ultraviolet spectroscopy, near infrared spectroscopy, and / or any combination thereof.

[0015] In some embodiments, the mass spectrometry includes but is not limited to: matrix-assisted laser desorption ionization (MALDI)-time of flight (TOF) mass spectrometry, MALDI-TOF-TOF mass spectrometry, MALDI quadrupole-time of flight (Q-TOF) mass spectrometry, electrospray ionization (ESI)-TOF mass spectrometry, tandem mass spectrometry, ESI-Q-TOF, ESI-TOF-TOF, ESI-ion trap mass spectrometry, ESI triple quadrupole mass spectrometry, ESI Fourier transform mass spectrometry (FTMS).

[0016] In a specific embodiment of the present invention, the present invention has experimentally demonstrated that the above-mentioned metabolic markers have good diagnostic efficacy for early diagnosis or prediction of colorectal cancer.

[0017] In a preferred embodiment, the metabolic marker is a combination of Inositol, Glutamate, Very Low Density Lipoprotein, N-Acetyl-glycoprotein, Low Density Lipoprotein, Kynurenine and alpha-Ketoleucine.

[0018] The second aspect of the present invention provides the use of a metabolic marker in the preparation of a kit for diagnosing and / or early predicting colorectal cancer.

[0019] Furthermore, the metabolic marker is Creatinine, tran-Aconitate, Asparagine, Glutamate, Creatine, Isoleucine, Valine, O-Acetylgycoprotein, Phosphoglycerolcholine, N-Acetylglycoprotein and / or Homogentisate.

[0020] Furthermore, the kit includes a reagent for detecting the concentration or content of the metabolic marker in the sample to be tested;

[0021] Preferably, the reagent includes a reagent for detecting the concentration or content of the metabolic marker in the sample to be tested by targeted or non-targeted nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry or a combination thereof;

[0022] Preferably, the sample is urine.

[0023] In some embodiments, the metabolic markers include any one or more combinations of Creatinine, tran-Aconitate, Asparagine, Glutamate, Creatine, Isoleucine, Valine, O-Acetylgycoprotein, Phosphoglycerol choline, N-Acetylglycoprotein, and Homogentisate.

[0024] In a specific embodiment of the present invention, the present invention has experimentally demonstrated that the above-mentioned metabolic markers have good diagnostic efficacy for early diagnosis or prediction of colorectal cancer.

[0025] In a preferred embodiment, the metabolic marker is a combination of Creatinine, tran-Aconitate, Asparagine, Glutamate, Creatine, Isoleucine, Valine, O-Acetylgycoprotein, Phosphoglycerol choline, N-Acetylglycoprotein and Homogentisate.

[0026] The third aspect of the present invention provides a reagent for detecting the concentration or content of a metabolic marker in a sample to be tested.

[0027] Further, the metabolite marker is the metabolite marker described in the first aspect of the present invention or the metabolite marker described in the second aspect of the present invention;

[0028] Preferably, the reagent includes a reagent for detecting the concentration or content of the metabolite marker in the sample to be tested by targeted or non-targeted nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry or a combination thereof.

[0029] In some embodiments, the reagent determines the concentration or amount of the metabolite marker in the sample to be tested by the following methods: nuclear magnetic resonance spectroscopy (NMR), mass spectrometry, chromatography, spectroscopy, radiochemical analysis, electrophoresis, fluorescence measurement, ultraviolet spectroscopy (UV), fluorescence analysis, immunoaffinity, immunohybridization, immunochemistry, near infrared spectroscopy (near IR), light scattering analysis (LS), turbidimetry.

[0030] A fourth aspect of the present invention provides a kit for diagnosing and / or early predicting colorectal cancer.

[0031] Further, the kit includes a reagent for detecting the concentration or content of the metabolic marker described in the third aspect of the present invention in the sample to be tested;

[0032] Preferably, the kit further comprises a pretreatment reagent for pretreating the sample to be tested.

[0033] In the present invention, the kit can be used for diagnosing and / or early predicting colorectal cancer, improving the convenience of diagnosis and / or prediction, and promoting the standardization of diagnosis and / or prediction methods.

[0034] The fifth aspect of the present invention provides the use of the metabolite marker described in the first aspect of the present invention or the metabolite marker described in the second aspect of the present invention in constructing a colorectal cancer early warning model.

[0035] Furthermore, the colorectal cancer early warning model uses the concentration value or content value of the metabolic marker in the sample to be tested as an input variable.

[0036] The sixth aspect of the present invention provides a colorectal cancer early warning device based on metabolomics data.

[0037] Furthermore, the colorectal cancer early warning device comprises:

[0038] A model loading module, used to load the colorectal cancer early warning model constructed by the application described in the fifth aspect of the present invention;

[0039] An index value acquisition module, used for acquiring the index value of each metabolite marker corresponding to the subject to be diagnosed according to the colorectal cancer early warning model, wherein the metabolite marker is the metabolite marker described in the fifth aspect of the present invention;

[0040] An index value detection module is used to determine whether the index value of each metabolic marker corresponding to the subject to be diagnosed exceeds a preset normal value range;

[0041] The colorectal cancer early warning module is used to output colorectal cancer early warning information of the subject to be diagnosed based on the judgment result.

[0042] A seventh aspect of the present invention provides a system for diagnosing and / or early predicting colorectal cancer using metabolic markers.

[0043] Further, the metabolite marker is the metabolite marker described in the first aspect of the present invention or the metabolite marker described in the second aspect of the present invention, and the system comprises:

[0044] A detection device is used to detect the level of the metabolic marker in the samples of each reference person and the subject to be tested in the reference group; the reference group is composed of colorectal cancer patients and non-colorectal cancer healthy people;

[0045] A reference device, used for receiving the information of the levels of the metabolite markers in all the reference group samples output by the detection device, and setting the information of the levels of the metabolite markers in all the reference person samples in the reference group as the known grouping information;

[0046] The comparison device is used to receive the information on the level of the metabolite marker in the sample of the subject to be tested output by the detection device and the known grouping information output by the reference device, set the information on the level of the metabolite marker in the sample of the subject to be tested as unknown information, compare the unknown information with the known grouping information data set, and determine whether the subject to be tested belongs to the colorectal cancer group or the non-colorectal cancer group.

[0047] In some embodiments, the device or system of the present invention is a method for distinguishing different components, elements, parts, parts or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions. It is well known to those skilled in the art that the present invention can be implemented as an apparatus, method or computer program product. Therefore, the content disclosed in the present invention can be specifically implemented in the following forms, that is, it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), and it can also be a combination of hardware and software. In addition, in some specific embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains a computer-readable program code.

[0048] The eighth aspect of the present invention provides the use of the metabolite marker described in the first aspect of the present invention or the metabolite marker described in the second aspect of the present invention in constructing a computational model for diagnosis and / or early prediction of colorectal cancer.

[0049] In addition, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the device described in the sixth aspect of the present invention or the system described in the seventh aspect of the present invention.

[0050] In some embodiments, the computer-readable medium of the present invention may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0051] Further, more specific examples of computer-readable storage media include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk-read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0052] The present invention has the beneficial effects:

[0053] The present invention conducts metabolomics analysis on colorectal cancer samples and finds for the first time that the metabolite markers "Inositol, Glutamate, Very Low Density Lipoprotein, N-Acetyl-glycoprotein, Low Density Lipoprotein, Kynurenine and / or alpha-Ketoleucine" or the metabolite markers "Creatinine, tran-Aconitate, Asparagine, Glutamate, Creatine, Isoleucine, Valine, O-Acetylgycoprotein, Phosphoglycerol choline, N-Acetylglycoprotein and / or Homogentisate" can be used for the diagnosis and / or early prediction of colorectal cancer, and further finds that the combination of the metabolite markers has extremely high diagnostic value, and can diagnose and / or early predict whether the subject to be tested has colorectal cancer or the risk of colorectal cancer with high sensitivity, high accuracy and high specificity, and has great scientific significance and clinical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1The results of pattern recognition analysis of colorectal cancer serum, urine, feces, and tissues are shown in Figure A, where Figure B: colorectal cancer serum, Figure C: colorectal cancer feces, and Figure D: colorectal cancer tissues.

[0055] Figure 2 The results of the importance analysis of metabolic pathways in colorectal cancer tissues and body fluids are shown in Figure A: metabolic pathway diagram of colorectal cancer tissues; Figure B: metabolic pathway diagram of colorectal cancer serum; Figure C: metabolic pathway diagram of colorectal cancer urine;

[0056] Figure 3 This is a graph showing the expression difference and diagnostic efficacy of the serum metabolic marker Inositol;

[0057] Figure 4 This is a graph showing the expression difference and diagnostic efficacy of the serum metabolic marker Glutamate;

[0058] Figure 5 This is a graph showing the expression difference and diagnostic efficacy of the serum metabolic marker Very Low Density Lipoprotein;

[0059] Figure 6 This is a graph showing the expression difference and diagnostic efficacy of the serum metabolic marker N-Acetyl-glycoprotein;

[0060] Figure 7 This is a graph showing the expression difference and diagnostic efficacy of the serum metabolic marker Low Density Lipoprotein;

[0061] Figure 8 This is a graph showing the expression difference and diagnostic efficacy of the serum metabolic marker Kynurenine;

[0062] Fig. 9 This is a graph showing the expression difference and diagnostic efficacy of the serum metabolic marker alpha-Ketoleucine;

[0063] Fig.10 The efficacy of serum metabolic markers Inositol, Glutamate, Very Low Density Lipoprotein, N-Acetyl-glycoprotein, Low Density Lipoprotein, Kynurenine, and alpha-Ketoleucine in the combined diagnosis of colorectal cancer;

[0064] Fig.11 This is a graph showing the expression difference and diagnostic efficacy of the urine metabolic marker Creatinine;

[0065] Fig.12 This is a graph showing the expression difference and diagnostic efficacy of the urine metabolic marker tran-Aconitate;

[0066] Fig.13 This is a graph showing the expression difference and diagnostic efficacy of the urine metabolic marker Asparagine;

[0067] Fig.14 This is a graph showing the expression difference and diagnostic efficacy of the urine metabolic marker Glutamate;

[0068] Fig.15 This is a graph showing the expression difference and diagnostic efficacy of the urine metabolic marker Creatine;

[0069] Fig.16 This is a graph showing the expression difference and diagnostic efficacy of the urine metabolic marker Isoleucine;

[0070] Fig.17 This is a graph showing the expression difference and diagnostic efficacy of the urine metabolic marker Valine;

[0071] Fig.18 This is a graph showing the expression difference and diagnostic efficacy of the urine metabolic marker O-Acetylgycoprotein;

[0072] Fig.19 This is a graph showing the expression difference and diagnostic efficacy of the urine metabolic marker Phosphoglycerol choline;

[0073] Fig. 20 This is a graph showing the expression difference and diagnostic efficacy of the urine metabolic marker N-Acetylglycoprotein;

[0074] Fig.21 This is a graph showing the expression difference and diagnostic efficacy of the urine metabolic marker Homogentisate;

[0075] Fig. 22 The efficacy of urine metabolic markers Creatinine, tran-Aconitate, Asparagine, Glutamate, Creatine, Isoleucine, Valine, O-Acetylgycoprotein, Phosphoglycerol choline, N-Acetylglycoprotein, and Homogentisate in the combined diagnosis of colorectal cancer;

[0076] Fig.23The efficacy of serum metabolic markers Inositol, Glutamate, Very Low Density Lipoprotein, N-Acetyl-glycoprotein, Low Density Lipoprotein, Kynurenine, and alpha-Ketoleucine in the combined diagnosis of colorectal cancer;

[0077] Fig.24 The efficacy of serum (precancerous lesion serum) metabolic markers Inositol, Glutamate, Very Low Density Lipoprotein, N-Acetyl-glycoprotein, Low Density Lipoprotein, Kynurenine, and alpha-Ketoleucine in the combined diagnosis of colorectal cancer;

[0078] Fig.25 The efficacy of the combined diagnosis of colorectal cancer by urine metabolic markers Creatinine, tran-Aconitate, Asparagine, Glutamate, Creatine, Isoleucine, Valine, O-Acetylgycoprotein, Phosphoglycerol choline, N-Acetylglycoprotein, and Homogentisate. DETAILED DESCRIPTION

[0079] The inventors of the present invention have conducted extensive and in-depth research, and through a large number of screenings, tests and verifications, have provided metabolic markers that can be used for accurate diagnosis and / or early prediction of colorectal cancer. The inventors of the present invention have found that the metabolic markers have good diagnostic efficacy for colorectal cancer. The combined application of the metabolic markers in the diagnosis of colorectal cancer has extremely high diagnostic value, and can diagnose and / or early predict whether a subject to be tested has colorectal cancer or the risk of colorectal cancer with high sensitivity, high accuracy and high specificity. The present invention was completed on this basis.

[0080] In order to enable those skilled in the art to better understand the technical solution of the present invention, some of the terms involved in the present invention are explained as follows. Unless otherwise specified, the following terms involved in the application documents of the present invention refer to the following contents that are well known in the art.

[0081] As used herein, the terms "comprising" or "including" refer to compositions, methods, and their corresponding components, which are present in a given embodiment, but are also open ended and include unspecified elements.

[0082] As used herein, the term "metabolic marker" refers to a compound suitable as an indicator of the presence and state of a disease or patient, which is a metabolite or metabolic compound that occurs during the metabolic process in a mammal. In the present invention, the term "metabolic marker" refers to a compound suitable as an indicator of the presence and state of colorectal cancer, which is a metabolite or metabolic compound that occurs during the metabolic process in a mammal.

[0083] In a specific embodiment of the present invention, the term "metabolic marker" refers to "Inositol, Glutamate, VeryLow Density Lipoprotein, N-Acetyl-glycoprotein, Low Density Lipoprotein, Kynurenine and / or alpha-Ketoleucine", or "Creatinine, tran-Aconitate, Asparagine, Glutamate, Creatine, Isoleucine, Valine, O-Acetylgycoprotein, Phosphoglycerolcholine, N-Acetylglycoprotein and / or Homogentisate".

[0084] In some embodiments, the metabolic markers provided by the present invention can be used alone or in combination for diagnosis and / or early prediction to evaluate the subject's colorectal cancer status, including the presence or absence of colorectal cancer, the risk of colorectal cancer, and the detection of colorectal cancer disease progression. In addition, based on the subject's colorectal cancer status, additional procedures can be indicated, including, for example, additional diagnostic tests or treatment procedures.

[0085] As used herein, the terms "diagnosis", "early diagnosis" or "making a diagnosis" refer to the discovery, judgment or recognition of an individual's health status or condition based on one or more symptoms, data or other information related to the individual. The individual's health status can be diagnosed as healthy / normal (i.e., the absence of disease or illness), or can be diagnosed as unhealthy / abnormal (i.e., the presence of disease or illness).

[0086] In some embodiments, the terms "diagnosis", "early diagnosis" or "making a diagnosis" and variations of these terms include early detection of a disease / condition associated with a particular disease or disorder; the characterization or classification of a disease; the discovery of the progression, cure or recurrence of a disease; the discovery of an individual's response to the disease after treatment or therapy. In the present invention, the diagnosis of colorectal cancer includes distinguishing between individuals who do not have colorectal cancer and individuals who have colorectal cancer.

[0087] As used herein, the term "prediction" or "early prediction" refers to assessing whether a subject is at or not at risk of colorectal cancer. In a preferred embodiment, it should be assessed whether the subject's risk is at an increased risk or a reduced risk compared to the average risk of the subject population. The term "prediction of colorectal cancer" or "early prediction of colorectal cancer" means herein that the subject is analyzed by the method of the present invention to be divided into a group of subjects at risk of colorectal cancer, or into a group of subjects not at risk of colorectal cancer.

[0088] According to the present invention, having a risk of colorectal cancer preferably means an increased risk of colorectal cancer (preferably falling within the prediction window). According to the present invention, a subject who is not at risk of colorectal cancer preferably has a reduced risk of colorectal cancer (preferably falling within the prediction window). A subject who is at risk of colorectal cancer preferably has a risk of colorectal cancer of 10-20% or more, more preferably 20% or more. A subject who is not at risk of colorectal cancer preferably has a risk of colorectal cancer of less than 10%, more preferably less than 5% or less.

[0089] As used herein, the term "sample" refers to a composition obtained or derived from a subject of interest that contains cellular entities and / or other molecular entities to be characterized and / or identified, for example, based on physical, biochemical, chemical and / or physiological characteristics.

[0090] In some embodiments, the term "sample" can be obtained from the subject's blood and other fluid samples and tissue samples of biological origin, such as biopsy tissue samples or tissue cultures or cells derived therefrom. The source of the tissue sample can be solid tissue, such as from fresh, frozen and / or preserved organ or tissue samples, biopsy tissues or aspirates; blood or any blood component; body fluid; cells from any time of individual pregnancy or development; or plasma. The sample includes biological samples that have been processed in any way after it is obtained, such as reagent treatment, stabilization, or enrichment for certain components (such as proteins or polynucleotides), or embedded in a semi-solid or solid matrix for sectioning purposes. The samples described in the present invention include, but are not limited to: blood, tissue, blood-derived cells, serum, plasma, lymph, synovial fluid, cell extracts, and combinations thereof.

[0091] As used herein, the term "subject" refers to any animal, including humans and non-human animals. The non-human animals include all vertebrates, for example, mammals, such as non-human primates (particularly higher primates), sheep, dogs, rodents (such as mice or rats), guinea pigs, goats, pigs, cats, rabbits, cattle, and any livestock or pets; and non-mammals, such as chickens, amphibians, reptiles, etc. In a specific embodiment of the present invention, the subject is preferably a human.

[0092] As used herein, the term "AUC" or "area under the receiver operating characteristic (ROC) curve" refers to the area under the curve of the receiver operating characteristic (ROC) curve, both of which are well known in the art. The AUC measurement is useful for comparing the accuracy of classifiers across the entire data range. A classifiers with higher AUCs have a higher ability to correctly classify unknowns between two or more target groups. The ROC curve is useful for describing the performance of a specific feature (e.g., any metabolic marker described herein and / or any entry of additional biomedical information) when distinguishing between two populations. Typically, feature data is selected across the entire population in ascending order based on the value of a single feature. Then, for each value of the feature, the true positive rate and false positive rate of the data are calculated. The true positive rate is determined by counting the number of cases with a value higher than the feature and divided by the total number of cases. The false positive rate is determined by counting the number of controls with a value higher than the feature and divided by the total number of controls. ROC curves can be generated with respect to individual features, and can be generated with respect to other individual outputs, for example, a combination of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide a single sum value, and the single sum value can be plotted in the ROC curve. In addition, any combination of multiple features where the combination is derived from a single output value can be plotted in the ROC curve. These combinations of features can include tests. ROC curves can be used to analyze the accuracy of a diagnosis.

[0093] The present invention is further described below in conjunction with specific embodiments, which are only used to explain the present invention and cannot be understood as limiting the present invention. It can be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and purpose of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0094] The reagents and raw materials used in the present invention are easily obtained by ordinary technicians in the field and can be obtained from commercial channels unless otherwise specified. The experimental methods in the present invention that do not specify specific conditions are usually carried out under conventional conditions or under conditions recommended by the manufacturer. In particular, the following examples are only used to illustrate the present invention and should not limit the scope of the present invention in any way.

[0095] Example Screening and efficacy assessment of metabolites related to colorectal cancer diagnosis and prediction

[0096] 1. Study subjects and study design

[0097] Tissues of colorectal cancer patients and the accompanying 5 cm adjacent normal tissues, colorectal cancer serum, urine, and stool samples, and normal control group serum, urine, and stool samples were collected from the Second Affiliated Hospital of Shantou University Medical College, totaling 835 cases (106 cases of cancer tissues and 5 cm adjacent normal tissues, 164, 137, and 102 cases of colorectal cancer serum, precancerous lesion serum, and control group samples, respectively, 86, 15, and 43 cases of colorectal cancer urine, precancerous lesion urine, and control group samples, respectively, and 88, 16, and 78 cases of colorectal cancer feces, precancerous lesion feces, and control group samples, respectively). After sample collection, they were pre-processed, packaged, and quickly frozen in liquid nitrogen, and then stored in a -80°C refrigerator, with dry ice used as the cold chain during transportation.

[0098] Inclusion criteria:

[0099] 1) Colorectal cancer is confirmed by pathological biopsy; no obvious lower gastrointestinal symptoms and organic lesions of the stomach and small intestine; no other major systemic diseases; no radiotherapy / chemotherapy within one year, and no use of antibiotics.

[0100] 2) Healthy subjects (matched age, gender, BMI and other baseline data) with normal physical examination indicators and no abnormalities found in endoscopic examination; no history of any systemic disease or drug treatment in the 3 months before participating in the experiment.

[0101] 3) The use of clinical samples involved in this project has been reviewed by the Ethics Committee, and the consent of the subjects has been obtained and they have signed an informed consent form before sampling.

[0102] 2. Preparation of tissue, serum and urine samples and NMR spectroscopy

[0103] 2.1 Metabolite extraction

[0104] 1) Preparation of PBS / D2O phosphate buffer solution:

[0105] pH 7.4, K2HPO4 and NaH2PO4 molar ratio is 4:1. Serum PBS 90 mM, containing 0.9% NaCl; urine PBS 1.5 M, containing 0.05% TSP (sodium 3-trimethylsilyl propionate-D4); tissue PBS 150 mM, containing 0.05% TSP.

[0106] 2) Serum preparation:

[0107] 400 μL of serum + 200 μL of serum PBS were vortexed to mix; centrifuged at 10,000 rpm at 4°C for 10 min, 550 μL of the supernatant was taken and placed in a 5 mm nuclear magnetic resonance tube for sampling.

[0108] 3) Urine preparation:

[0109] 500 μL of urine + 50 μL of urine PBS were vortexed to mix; centrifuged at 10,000 rpm for 10 min at 4°C, 500 μL of the supernatant was taken and placed in a 5 mm NMR tube for sampling.

[0110] 2.2 Metabolites 1 H-NMR detection

[0111] The tissue was collected using a Bruker 600 MHz spectrometer. 1 H-NMR spectra. Serum and urine were collected using a Bruker 600MHz spectrometer. 1 H-NMR spectrum. Tissue extracts and urine were sampled using the NOESYGPPR1D spin echo pulse sequence: [RD-90°-t1-90°-tm-90°-ACQ], and serum was sampled using the CPMGPR1D sequence: [RD-90°-(τ-180°-τ)n-ACQ]. A standard pre-saturation pulse sequence was used to suppress the water peak signal and obtain a free decay signal (FID signal). The FID signal was converted into a one-dimensional NMR spectrum by Fourier transformation, and the chemical shift value was adjusted using the TSP signal peak or lactic acid peak as the internal standard 0 point to obtain the corresponding 1 H-NMR spectrum.

[0112] 2.3 1 H-NMR data preprocessing and spectrum analysis

[0113] Due to the large signal and complex noise of the original NMR spectrum, the original data were preprocessed using MestReNova NMR spectrum processing software (version 14.0), including Fourier transform, phase correction, baseline correction, frequency calibration and peak attribution. All spectra were multiplied by an exponential window function with a broadening factor of 1 Hz during Fourier transform to improve the signal-to-noise ratio. The chemical shift of the metabolites was determined by the internal standard TSP (δ 0.00 ppm) or the lactic acid peak (δ 1.33 ppm). The spectrum in the range of δ 0-9 ppm was segmented and integrated every 0.002 ppm for data dimension reduction. Before integration, the peak intensity of 4.6-5.2 ppm was set to 0 to eliminate the influence of the residual water peak on the surrounding peaks, and then the spectrum was normalized for the entire spectrum.

[0114] 3. Screening of potential biomarkers

[0115] 3.1 Multivariate Statistical Analysis

[0116] Each group of data in the data matrix was normalized according to the total integrated area and then imported into SIMCA 14.1 software for Pareto scaling to standardize the data and eliminate the dimensional relationship between variables. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to filter out signals that were irrelevant to the model classification and orthogonal signals to obtain the OPLS-DA model. The quality of the model was further cross-validated (CV) and permutation test (Permutation Test) to verify the effectiveness of the model.

[0117] 3.2 Metabolic marker screening

[0118] Potential markers were screened based on the variable importance projection value (VIP value) of the model and the statistical difference of metabolites between groups (corrected P value < 0.05). SPSS 26.0 was used for nonparametric tests and receiver operating characteristic (ROC) analysis, and pattern recognition and machine learning methods were combined to extract metabolites that contributed to the classification, determine their sensitivity and specificity, construct a diagnostic model, and test its accuracy in predicting early colorectal cancer.

[0119] 4. Key metabolic pathways and diagnostic metabolic markers (groups) for colorectal cancer

[0120] 4.1 Evaluation of the diagnostic and predictive performance of metabolic markers (panels)

[0121] 1) The area under the receiver operating curve (AUC) of differential metabolites in tissues and body fluids (serum and urine) was calculated respectively, and multiple alternative diagnostic models were obtained by combining algorithms such as support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA).

[0122] 2) Starting from the tissues, we select the differential metabolites with the best AUC values ​​that can be reflected in body fluids as diagnostic markers.

[0123] 5. Experimental Results

[0124] 5.1 Results of pattern recognition analysis of colorectal cancer serum (A), urine (B), feces (C), and tissue (D)

[0125] Figure 1 AC(a) is the orthogonal partial least squares discriminant analysis (OPLS-DA) score plot shown, Figure 1A(a) is the orthogonal partial least squares discriminant analysis (OPLS-DA) score diagram showing that the pattern recognition analysis results of colorectal cancer tissue (CCT) and adjacent paracancerous tissue (DNT) show that the samples are clearly distinguished in the first principal component. Although there are some sample overlaps, the R2Y(cum) and Q2(cum) values ​​of tissue sample pattern recognition are 0.748 and 0.504, respectively, indicating that the constructed model can explain 74.8% of the variation of the original data, and the prediction ability reaches 50.4%. In serum (B) and urine (C) samples, colorectal cancer (CRC) and healthy control group (HC) samples are clearly distinguished in the first principal component, and colorectal cancer and precancer serum samples are not clearly distinguished in the second principal component, but there is a clear trend of separation. The R2Y(cum) and Q2(cum) values ​​of serum sample pattern recognition are 0.610 and 0.526, respectively, indicating that the constructed model can explain 61.0% of the variation of the original data, and the prediction ability reaches 52.6%. The R2Y(cum) and Q2(cum) values ​​of urine sample pattern recognition were 0.783 and 0.533, respectively, indicating that the constructed model can explain 78.3% of the variation of the original data and the prediction ability is 53.3%. The R2Y(cum) and Q2(cum) values ​​of fecal sample pattern recognition were 0.672 and 0.510, respectively, indicating that the constructed model can explain 67.2% of the variation of the original data and the prediction ability is 51.0%. Figure 1 AC (b) is the permutation test of the corresponding model, indicating that the model classification results are reliable and there is no overfitting. According to the conditions of the model VIP value greater than 1, the confidence interval of the VIP value, and the non-parametric test p < 0.05, the characteristic metabolic markers in colorectal cancer tissue, serum, and urine that contribute to the distinction between colorectal cancer, precancerous lesions, and normal control groups were screened out.

[0126] 5.2 Analysis of the Importance of Metabolic Pathways in Colorectal Cancer Tissues and Body Fluids

[0127] Based on the characteristic markers in colorectal cancer tissues and body fluids that have been screened, MetaboAnalyst 6.0 software was used to perform KEGG metabolic pathway enrichment analysis on the disorders of different metabolic pathways in which they are involved, in order to clarify the changes in the occurrence and development of colorectal cancer at the tissue and body fluid metabolic levels. Figure 2 A is the metabolic pathway diagram of colorectal cancer tissue: the main metabolic disorder pathways with higher weights involved in the tissue include "arginine and proline metabolism", "alanine, aspartate and glutamate metabolism", "glycine, serine and threonine metabolism", "histidine metabolism", "D-glutamate and D-glutamine metabolism" and other pathways. Figure 2B is the metabolic pathway diagram of colorectal cancer serum: the main metabolic disorder pathways with higher weights involved in serum include "arginine and proline metabolism", "alanine, aspartate and glutamate metabolism", "glycine, serine and threonine metabolism", "glutathione metabolism", "histidine metabolism", "D-glutamate and D-glutamine metabolism" and other pathways. Figure 2 C is a diagram of metabolic pathways in colorectal cancer urine: the main metabolic disorder pathways with higher weights involved in urine include "biosynthesis of phenylalanine, tyrosine, and tryptophan", "histidine metabolism", "tyrosine metabolism", "glutathione metabolism", "alanine, aspartate, and glutamate metabolism", "glycine, serine, and threonine metabolism", and "D-glutamate and D-glutamine metabolism". Combining the importance analysis of metabolic pathways in colorectal cancer tissue, serum, and urine, we found that the "alanine, aspartate, and glutamate metabolism", "glycine, serine, and threonine metabolism", "histidine metabolism", and "D-glutamine and D-glutamate metabolism" pathways showed significant changes in colorectal cancer tissues and various body fluids.

[0128] 5.3 Compare the key metabolites that have changed in tissues (the origin of colorectal cancer), screen out the differential metabolites with the best AUC values ​​that can be reflected in body fluids as diagnostic markers, and construct serum and urine diagnostic models respectively

[0129] 1) Serum diagnostic model metabolite markers include one or a combination of Inositol, Glutamate, Very Low Density Lipoprotein, N-Acetyl-glycoprotein, Low Density Lipoprotein, Kynurenine, alpha-Ketoleucine, or metabolite pairwise ratios. The expression difference and diagnostic efficacy data of each metabolite are as follows: Figure 3-9 As shown in Figure 2, the combined diagnostic efficacy of the serum metabolite panel for colorectal cancer is Fig.10 As shown, the results show that the serum metabolic markers screened by the present invention have high diagnostic efficacy for colorectal cancer, and the efficacy of combined diagnosis is the best, with an AUC as high as 1.000.

[0130] 2) Metabolite markers of urine diagnostic model include Creatinine, tran-Aconitate, Asparagine, Glutamate, Creatine, Isoleucine, Valine, O-Acetylgycoprotein, Phosphoglycerolcholine, N-Acetylglycoprotein, Homogentisate, one or a combination of several, or metabolite pairwise ratios. The expression difference and diagnostic efficacy data of each metabolite are as follows: Figure 11-21 The combined diagnostic efficacy of the urine metabolite panel for colorectal cancer is Fig. 22 As shown, the results show that the urine metabolic markers screened by the present invention have high diagnostic efficacy for colorectal cancer, and the efficacy of combined diagnosis is the best, with an AUC as high as 0.929.

[0131] 5.4 Serum data from 35 patients with early colorectal cancer, serum data from 116 patients with colorectal cancer precancerous lesions, and serum data from 35 healthy controls, urine data from 35 patients with colorectal cancer, and urine data from 20 healthy controls were collected and validated using the serum and urine marker panel constructed by the present invention.

[0132] The diagnostic and predictive efficiencies of the metabolite markers screened by the present invention are as follows: Fig.23 (serum), Fig.24 (Precancerous lesion serum), Fig.25 (urine) as shown, the diagnostic performance of each group was analyzed as follows:

[0133] 1) Serum marker group: good diagnostic efficacy. The AUC of serum marker group combined for diagnosis of early colorectal cancer can reach 1.000 ( Fig.23 );

[0134] 2) Serum marker group: good diagnostic efficacy. The AUC of serum marker group combined with diagnosis of colorectal precancerous lesions can reach 0.870 ( Fig.24 );

[0135] 3) Urine marker group: The combined diagnosis of urine metabolic markers has better efficacy, and its advantage is that sample collection is more convenient and can complement the advantages of serum. The AUC of urine marker group combined diagnosis of early colorectal cancer can reach 0.915 ( Fig.25 ).

[0136] 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. Use of a reagent for detecting the concentration or content of a metabolic marker in a sample to be tested in the preparation of a kit for diagnosing and / or early prediction of colorectal cancer, characterized in that: The metabolic markers are Inositol, Glutamate, Very Low Density Lipoprotein, N-Acetyl-glycoprotein, Low Density Lipoprotein, Kynurenine and alpha-Ketoleucine, and the sample is serum.

2. The use according to claim 1, characterized in that: The reagents include reagents for detecting the concentration or content of the metabolic marker in the sample to be tested by targeted or non-targeted nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry or a combination thereof.

3. A reagent for detecting the concentration or content of a metabolic marker in a sample to be tested, characterized in that: The metabolite marker is the metabolite marker described in claim 1, and the sample is serum.

4. The reagent according to claim 3, characterized in that The reagents include reagents for detecting the concentration or content of the metabolic marker in the sample to be tested by targeted or non-targeted nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry or a combination thereof.

5. A kit for diagnosing and / or early predicting colorectal cancer, characterized in that: The kit comprises a reagent for detecting the concentration or content of the metabolic marker described in claim 1 in a sample to be tested, and the sample is serum.

6. The kit according to claim 5, characterized in that The kit also includes a pretreatment reagent for pretreating the sample to be tested.

7. The use of the metabolic markers described in claim 1 in constructing a colorectal cancer early warning model, characterized in that: The colorectal cancer early warning model uses the concentration value or content value of the metabolic marker in the sample to be tested as an input variable, and the sample is serum.

8. A colorectal cancer early warning device based on metabolomics data, characterized in that: The colorectal cancer early warning device comprises: A model loading module, used to load the colorectal cancer early warning model constructed by the application of claim 7; An index value acquisition module, used for acquiring the index value of each metabolite marker corresponding to the subject to be diagnosed according to the colorectal cancer early warning model, wherein the metabolite marker is the metabolite marker described in claim 1; An index value detection module is used to determine whether the index value of each metabolic marker corresponding to the subject to be diagnosed exceeds a preset normal value range; The colorectal cancer early warning module is used to output colorectal cancer early warning information of the subject to be diagnosed based on the judgment result.

9. A system for diagnosing and / or early predicting colorectal cancer using metabolic markers, characterized in that: The metabolite marker is the metabolite marker described in claim 1, and the system comprises: A detection device is used to detect the level of the metabolic marker in the samples of each reference person and the subject to be tested in the reference group; the reference group is composed of colorectal cancer patients and non-colorectal cancer healthy people; A reference device, used for receiving the information of the levels of the metabolite markers in all the reference group samples output by the detection device, and setting the information of the levels of the metabolite markers in all the reference person samples in the reference group as the known grouping information; A comparison device, used for receiving the information of the level of the metabolite marker in the sample of the test subject output by the detection device and the known grouping information output by the reference device, setting the information of the level of the metabolite marker in the sample of the test subject as unknown information, comparing the unknown information with the known grouping information data set, and determining whether the test subject belongs to the colorectal cancer group or the non-colorectal cancer group; The sample is serum.

10. Use of the metabolic markers described in claim 1 in constructing a computational model for diagnosis and / or early prediction of colorectal cancer.

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