WO2023185709A1 - 结直肠进展期肿瘤诊断标志物组合及其应用 - Google Patents

结直肠进展期肿瘤诊断标志物组合及其应用 Download PDF

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WO2023185709A1
WO2023185709A1 PCT/CN2023/084000 CN2023084000W WO2023185709A1 WO 2023185709 A1 WO2023185709 A1 WO 2023185709A1 CN 2023084000 W CN2023084000 W CN 2023084000W WO 2023185709 A1 WO2023185709 A1 WO 2023185709A1
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acid
advanced
patients
colorectal
colorectal cancer
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PCT/CN2023/084000
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English (en)
French (fr)
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唐堂
张明亮
张卫琴
彭浩文
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武汉迈特维尔医学科技有限公司
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Priority claimed from CN202210317138.9A external-priority patent/CN114924073B/zh
Priority claimed from CN202310192816.8A external-priority patent/CN116430047A/zh
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids

Definitions

  • the present invention relates to the field of detection technology, and in particular to a combination of diagnostic markers for advanced colorectal tumors and its application.
  • Colorectal Cancer also known as large intestine cancer
  • CRC Colorectal Cancer
  • the development of colorectal cancer is a slow process. It is usually asymptomatic and difficult to detect in the early stage. The vast majority of patients only go to the hospital after experiencing symptoms such as blood in the stool and abdominal pain, losing the best opportunity for surgical treatment. Even if it is performed With surgical treatment, the patient's 5-year survival rate is also greatly reduced.
  • AA Advanced adenoma
  • AA is a precancerous lesion that is ⁇ 1 cm in size or has a villous component of ⁇ 25% of any size or high-grade dysplasia that can easily develop into colorectal cancer over time.
  • colorectal examination is the gold standard for colorectal cancer diagnosis and the most effective screening method for precancerous lesions and onset.
  • the sensitivity of the above-mentioned examination technology for advanced adenoma is even lower, less than 40%, which is far lower than the detection sensitivity of colorectal cancer.
  • This metabolic marker combination can be used in the preparation of diagnostics or monitoring advanced adenomas and colorectal cancer (referred to as advanced colorectal tumors). reagents (boxes) for application.
  • Plasma analysis is a commonly used clinical disease diagnosis method and is widely used because of its advantages of being simple, fast, economical, and relatively non-invasive. No one has yet used plasma metabolite levels to diagnose colorectal cancer and/or advanced adenomas. The application of plasma targeted metabolomics to search for metabolic markers to diagnose colorectal cancer and/or advanced adenomas is of great significance for the rapid early clinical diagnosis of colorectal cancer.
  • This application conducts plasma-targeted quantitative metabolomics research by constructing a plasma-specific metabolome database for advanced colorectal tumors, obtaining a large number of disease-related specific metabolites, and analyzing metabolic markers using samples from multiple medical centers. Verification was conducted to find plasma metabolic markers with high sensitivity, good specificity, stable and reliable for the diagnosis of advanced colorectal tumors.
  • the present invention provides a set of metabolic markers for diagnosis, monitoring or risk assessment of advanced adenoma and/or colorectal cancer.
  • the metabolic markers are any one or more combinations of the following metabolic markers: 3 ⁇ -deoxychol. acid, lysophosphatidylethanolamine (P-18:0), lithocholic acid, DL-2-aminocaprylic acid, 3 ⁇ -hyodeoxycholic acid, lysophosphatidylcholine (14:0) (also known as “1-ten Tetraacyl-2-hydroxylecithin”), inositol, glutamic acid, pseudouridine, propionyl-L-carnitine hydrochloride, 4-aminobutyric acid, hydroxydecanoic acid (also known as "2-hydroxydecanoic acid ”), 20-carboxyarachidonate, L-pyroglutamic acid, cis-4-hydroxy-L-proline, symmetric N,N-dimethylarginine, S-adenos
  • the metabolic marker combination at least contains any two, any three, any four, any five, any six, any seven, any eight, any nine, any ten or more metabolites .
  • the metabolic marker is at least selected from the group consisting of 3 ⁇ -deoxycholic acid, lithocholic acid, lysophosphatidylcholine (14:0), DL-2-aminocaprylic acid, 3 ⁇ -hyodeoxycholic acid, Inositol, glutamic acid, pseudouridine, propionyl-L-carnitine hydrochloride, cis-4-hydroxy-L-proline, symmetric N,N-dimethylarginine, S-adenosine isoform
  • At least one of cysteine, ⁇ -linolenic acid, and hippuric acid is used to differentiate and diagnose colorectal cancer patients and healthy people.
  • the metabolic marker is at least selected from the group consisting of lysophosphatidylethanolamine (P-18:0), inositol, 4-aminobutyric acid, L-pyroglutamic acid, S-adenosyl homocysteine At least one of amino acid, asymmetric dimethylarginine, and taurolithocholic acid-3-sulfate is used to differentiate and diagnose advanced adenomas and non-colorectal advanced tumors.
  • P-18:0 lysophosphatidylethanolamine
  • 4-aminobutyric acid 4-aminobutyric acid
  • L-pyroglutamic acid L-pyroglutamic acid
  • S-adenosyl homocysteine At least one of amino acid, asymmetric dimethylarginine, and taurolithocholic acid-3-sulfate is used to differentiate and diagnose advanced adenomas and non-colorectal advanced tumors.
  • the metabolic marker is at least selected from the group consisting of lithocholic acid, 3 ⁇ -hyodeoxycholic acid, inositol, pseudouridine, hydroxydecanoic acid, 20-carboxyarachidonic acid, and L-pyroglutamine. At least one of acid, hippuric acid, 12-hydroxyeicosatetraenoic acid, and chenodeoxycholic acid is used to differentially diagnose colorectal advanced tumors and non-colorectal advanced tumors.
  • the metabolic marker is at least selected from the group consisting of 3 ⁇ -deoxycholic acid, lithocholic acid, 3 ⁇ -hyodeoxycholic acid, myo-inositol, hydroxydecanoic acid, 20-carboxyarachidonic acid, L-pyro At least one of glutamic acid, 12-hydroxyeicosatetraenoic acid, L-valine, asymmetric dimethylarginine, DL-beta-phenyllactic acid, and chenodeoxycholic acid, used for differential diagnosis Colorectal advanced tumors and healthy humans.
  • the metabolic marker is at least selected from the group consisting of lysophosphatidylcholine (14:0), hippuric acid, 2-hydroxydecanoic acid, ursodeoxycholic acid, DL-2-aminocaprylic acid, 3 ⁇ - Ursodeoxycholic acid, 3-hydroxybutyric acid, 12-hydroxyeicosatetraenoic acid, 20-carboxyarachidonic acid, 2-azahexanone, glycolithocholic acid, 7-methylxanthine, bile At least one of acid, S-adenosylhomocysteine, hexadecanoic acid, trans-3-hydroxycotinine, and piperine.
  • the metabolic marker combination used for risk assessment of colorectal cancer patients and healthy people is at least selected from:
  • Component A at least one or more selected from the group consisting of hexadecanedioic acid, 3 ⁇ -ursodeoxycholic acid, 1-tetradecanoyl-2-hydroxylecithin, and 2-azacyclone; and
  • Component B at least one selected from 3-hydroxybutyric acid, 2-hydroxydecanoic acid, hippuric acid, and DL-2-aminooctanoic acid.
  • the combination of the above 8 metabolic markers is preferred, and the overall detection effect is more accurate.
  • the metabolic marker is at least selected from the group consisting of 1-tetradecanoyl-2-hydroxylecithin, hippuric acid, 2-hydroxydecanoic acid, DL-2-aminocaprylic acid, 3 ⁇ -ursodeoxycholic acid, At least one of 2-azacycline, cholic acid, S-(5'-adenosyl)-L-homocysteine, hexadecanoic acid, piperine, and trans-3-hydroxycotinine (preferably a combination of five or more) for risk assessment of patients with colorectal cancer and non-colorectal advanced tumors.
  • the metabolic marker is selected from the group consisting of 1-tetradecanoyl-2-hydroxylecithin, hippuric acid, DL-2-aminocaprylic acid, deoxycholic acid, and 12-hydroxyeicosatetraenoic acid. , 7-methylxanthine, S-(5'-adenosyl)-L-homocysteine and various combinations of piperine (preferably four or more combinations), used for the treatment of colorectal progression Risk assessment in patients with adenomas and non-colorectal advanced tumors.
  • the metabolic marker is at least selected from the group consisting of 1-tetradecanoyl-2-hydroxylecithin, hippuric acid, DL-2-Aminocaprylic acid, deoxycholic acid, chenodeoxycholic acid, 12-hydroxyeicosatetraenoic acid, 7-methylxanthine, cholic acid, S-(5'-adenosine)-L-
  • Various combinations of homocysteine, piperine and 1-methylxanthine are used for risk assessment in patients with advanced colorectal adenoma and healthy people.
  • the metabolic marker is at least selected from the group consisting of 1-tetradecanoyl-2-hydroxylecithin, hippuric acid, 2-hydroxydecanoic acid, ursodeoxycholic acid, DL-2-aminocaprylic acid, 3 ⁇ -Usodeoxycholic acid, 3-hydroxybutyric acid, 12-hydroxyeicosatetraenoic acid, 20-carboxyarachidonic acid, 2-azahexanone, glycinolithocholic acid, 7-methylxanthine, Various combinations of S-(5'-adenosyl)-L-homocysteine, hexadecanoic acid, piperine, 1-methylxanthine and trans-3-hydroxycotinine (preferred Combinations of five or more), used for risk assessment of patients with advanced colorectal tumors and non-colorectal advanced tumors.
  • the metabolic marker is at least selected from the group consisting of 1-tetradecanoyl-2-hydroxylecithin, hippuric acid, ursodeoxycholic acid, DL-2-aminocaprylic acid, 3-hydroxybutyric acid, 12 -Hydroxyeicosatetraenoic acid, 7-methylxanthine, cholic acid, S-(5'-adenosyl)-L-homocysteine, hexadecanedioic acid and trans-3-hydroxycan
  • tinine preferably five or more combinations
  • the object of the present invention is to provide a reagent product containing the above-mentioned diagnostic markers suitable for the diagnosis of colorectal cancer and/or advanced adenoma, including standards of the metabolic markers and solvents for extracting and enriching the metabolic markers. , can be used for the diagnosis of colorectal cancer and/or advanced adenomas.
  • the kit provided by the invention can be used to diagnose patients with colorectal cancer, and can also be used to diagnose advanced adenomas, improve the convenience of diagnosis, and provide early diagnosis and treatment of colorectal cancer.
  • the plasma metabolites provided by the invention can effectively diagnose, monitor and risk assess colorectal cancer patients and advanced adenoma patients with high sensitivity.
  • the present invention constructs a colorectal cancer plasma-specific metabolome database, and the diagnostic metabolic markers are more representative.
  • the present invention can achieve diagnosis only by taking blood for detection without collecting additional tissue samples, and can well replace or assist existing colonoscopy detection.
  • the invention is simple, fast, relatively non-invasive, beneficial to early screening of colorectal cancer and/or advanced adenoma, and has good clinical use and promotion value.
  • This embodiment provides a method for constructing a plasma-specific metabolite ion pair database for advanced colorectal tumors, including the following steps:
  • this study collected healthy controls, non-progressive adenoma patients, progressive adenoma patients, Peripheral venous blood plasma from 20 samples of colorectal cancer patients.
  • the healthy controls were from people without intestinal diseases after physical examination; the disease groups were all confirmed by colorectal examination and postoperative diagnosis. All samples had no history of any other malignant tumors, other major systemic diseases, or chronic medical history of long-term medication. Samples were matched for age and sex between groups, and the non-colorectal cancer group included healthy controls and patients with non-progressive adenomas. Blood collection times were all in the early morning on an empty stomach.
  • Liquid chromatography tandem mass spectrometry realizes the entire process from substance separation using chromatography to substance identification using mass spectrometry.
  • Maiwei Metabolism uses the above-mentioned mixed detection solution to establish a colorectal cancer plasma-specific metabolite ion pair database.
  • Metabolite ion pairs mainly come from the following four sources: MIM-EPI collection, TOF collection, Maiwei Metabolism Standards database and colorectal cancer literature metabolites.
  • This embodiment provides a method for screening metabolic markers of advanced colorectal tumors, including the following steps:
  • peripheral veins of 795 healthy controls, 393 patients with non-progressive adenomas, 193 patients with advanced adenomas, and 494 patients with colorectal cancer were collected from 3 independent clinical medical centers.
  • blood plasma The healthy controls were from people without intestinal diseases after physical examination; the disease groups were all confirmed by colorectal examination and postoperative diagnosis. All samples had no history of any other malignant tumors, other major systemic diseases, or chronic medical history of long-term medication. Samples were matched for age and sex between groups, and the non-colorectal cancer group included healthy controls and patients with non-progressive adenomas. Blood collection times were all in the early morning on an empty stomach. All plasma samples were centrifuged and stored in a -80°C refrigerator. During the study, the plasma samples were taken out and thawed for subsequent analysis.
  • step S1 Take out the sample collected in step S1 from the -80°C refrigerator and thaw it on ice until there is no ice in the sample (all subsequent operations must be done on ice); after the sample is thawed, vortex for 10 seconds to mix, and add 50 ⁇ L of the sample to the corresponding In the numbered centrifuge tube; add 300 ⁇ L of pure methanol internal standard extraction solution (containing 100 ppm concentration of L-2-phenylalanine, [2H3]-L-carnitine-d3 hydrochloride, 4-fluoro-L-2- Phenylglycine, L-phenylalanine, [2H5]-hippuric acid, [2H5]-kynuric acid, [2H5]-phenoxyacetic acid internal standard); vortex for 5 minutes, let stand for 24 hours, and then incubate at 12000r/ min, centrifuge for 10 min at 4°C; absorb 270 ⁇ L of the supernatant and concentrate for 24 h
  • Chromatographic column Waters ACQUITY UPLC HSS T3 C18 1.8 ⁇ m, 2.1mm*100mm; column temperature is 40°C; injection volume is 2 ⁇ L.
  • Phase A is an aqueous solution containing 0.1% acetic acid
  • phase B is an acetonitrile solution containing 0.1% acetic acid.
  • the elution gradient program is: 0 min, the volume ratio of phase A to phase B is 95:5; 11.0 min, the volume ratio of phase A to phase B is 10:90; 12.0 min, the volume ratio of phase A to phase B is 10 :90; 12.1min, the volume ratio of phase A to phase B is 95:5; 14.0min, the volume ratio of phase A to phase B is 95:5V/V.
  • the flow rate is 0.4mL/min.
  • Electrospray ionization (ESI) temperature is 500°C
  • mass spectrum voltage is 5500V (positive) or -4500V (negative)
  • ion source gas I GS I
  • gas II GS II
  • curtain gas curtain gas
  • CUR collision-activated dissociation
  • each ion pair is scanned in MRM mode based on the optimized declustering potential (DP) and collision energy (CE).
  • DP declustering potential
  • CE collision energy
  • Metabolites of different molecular weights can be separated by liquid chromatography.
  • the characteristic ions of each substance are screened out through the multiple reaction monitoring mode (MRM) of the triple quadrupole, and the signal intensity (CPS) of the characteristic ions is obtained in the detector.
  • MRM multiple reaction monitoring mode
  • CPS signal intensity
  • Use MultiQuant 3.0.3 software to open the sample off-machine mass spectrum file and perform integration and correction of the chromatographic peaks.
  • the peak area (Area) of each chromatographic peak represents the relative content of the corresponding substance. Set S/N>5 and retain the time offset. Peaks that do not exceed 0.2 minutes are retained, and finally all chromatographic peak area integration data are exported and saved.
  • the CV value is the ratio of the standard deviation of the original data to the mean of the original data, which can reflect the degree of data dispersion.
  • the frequency of occurrence of substance CVs smaller than the reference value can be analyzed using the Empirical Cumulative Distribution Function (ECDF).
  • the proportion of substances with a CV value of 0.5 is higher than 85%, indicating that the experimental data is relatively stable; the proportion of substances with a QC sample CV value less than 0.3 is higher than 75%, indicating that the experimental data is very stable.
  • the changes in all internal standard CV values during the detection process were monitored. The change in internal standard CV values was less than 20%, indicating that the instrument was stable during the detection process.
  • the peak area integration data was used to conduct differential metabolite analysis between the two groups, and Pvalue ⁇ 0.05 was set as the significance standard for difference.
  • the differential metabolites were screened as candidate metabolic markers for the diagnosis of colorectal cancer.
  • the machine learning random forest (RF) algorithm was used to analyze the metabolite integral data between the two groups, and the above colorectal cancer 2/3 of the patient samples and non-colorectal cancer patient plasma sample data were used as the training set, and 1/3 was used as the test set.
  • Decision tree modeling is performed on the training set, and then the predictions of multiple decision trees are combined to obtain the final prediction result through voting. This metabolite model can effectively diagnose colorectal cancer patients.
  • the set of metabolic markers screened by the above differential analysis and random forest model are used to predict the molecular mass and molecular formula of the markers based on their retention time, primary and secondary levels, and compare them with the spectrum information in the metabolite spectrum database, thereby Qualitative identification of metabolites.
  • Example 3 Construction of plasma-targeted metabolome colorectal advanced tumor diagnosis model
  • peripheral veins of 311 healthy controls 100 patients with non-progressive adenomas, 100 patients with advanced adenomas and 355 patients with colorectal cancer were collected from 3 independent clinical medical centers.
  • blood plasma The healthy controls were from people without intestinal diseases after physical examination; the disease groups were all diagnosed by colorectal examination. All samples had no history of any other malignant tumors, no other major systemic diseases, and no chronic history of long-term medication. Samples in each group were matched for age and gender.
  • the non-colorectal cancer group included healthy controls and non-progressive adenoma patients, and the malignant tumor group included patients with advanced adenoma and colorectal cancer. Blood collection times were all in the early morning on an empty stomach. All plasma samples were centrifuged and stored in a -80°C refrigerator. During the study, the plasma samples were taken out and thawed for subsequent analysis.
  • targeted quantitative detection uses two methods, T3 column and Amide column, to separate metabolites to ensure the accuracy of metabolite quantification.
  • Chromatographic column Waters ACQUITY UPLC HSS T3 C18 1.8 ⁇ m, 2.1mm*100mm; column temperature 40°C; injection volume 2 ⁇ L.
  • Phase A is an acetonitrile solution containing 0.04% acetic acid
  • phase B is an acetonitrile solution containing 0.04% acetic acid
  • elution gradient program 0 min, the volume ratio of phase A to phase B is 95:5; 11.0 min, phase A and phase B are 95:5.
  • the volume ratio of phase B is 10:90; 12.0min, the volume ratio of phase A to phase B is 10:90; 12.1min, the volume ratio of phase A to phase B is 95:5; 14.0min, the volume ratio of phase A to phase B
  • the volume ratio is 95:5V/V.
  • the flow rate is 0.4mL/min.
  • Chromatographic column Waters ACQUITY UPLC BEH Amide 1.7 ⁇ m, 2.1mm*100mm; column temperature 40°C; injection volume 2 ⁇ L.
  • Phase A is ultrapure water (10mM ammonium acetate + 0.3% ammonia + 1 mg methylene diphosphoric acid), phase B is 90% acetonitrile water (containing 1 mg methylene diphosphoric acid); elution gradient program: 0 min, The volume ratio of phase A to phase B is 10:90; 9.0min, the volume ratio of phase A to phase B is 40:60; 10.0min, the volume ratio of phase A to phase B is 60:40; 11.0min, phase A The volume ratio of phase A to phase B is 60:40; at 11.1min, the volume ratio of phase A to phase B is 10:90; at 15.0min, the volume ratio of phase A to phase B is 10:90.
  • the flow rate is 0.4mL/min.
  • T3 column and Amide column mass spectrum acquisition conditions are the same, mainly including: electrospray ionization (ESI) temperature 500°C, mass spectrum voltage 5500V (positive), -4500V (negative), ion source gas I (GS I) 55psi, Gas II (GS II) is 60 psi, curtain gas (CUR) is 25 psi, and collision-activated dissociation (CAD) parameters are set to high.
  • ESI electrospray ionization
  • GS I mass spectrum voltage 55psi
  • Gas II GS II
  • CUR curtain gas
  • CAD collision-activated dissociation
  • each ion pair is determined according to the optimized declustering potential (DP) and collision energy ( CE) perform MRM mode scanning detection.
  • MultiQuant 3.0.3 software is used to process the mass spectrum data, refer to the retention time and peak shape information of the standard, and perform integral correction on the mass spectrum peaks detected in different samples of the analyte to ensure accurate qualitative and quantitative results.
  • the dilution factor in MultiQuant 3.0.3 is set to 3.
  • the integrated peak area ratio in the final sample is substituted into the concentration obtained by the standard curve.
  • the value (ng/mL) is the content data of the substance in the actual sample.
  • the CV value is the coefficient of variation (Coefficient of Variation). It is the ratio of the standard deviation of the original data to the mean of the original data, which can reflect the degree of data dispersion.
  • the frequency of occurrence of substance CVs smaller than the reference value can be analyzed using the Empirical Cumulative Distribution Function (ECDF).
  • the CV value is less than 0.3, indicating that the experimental data is relatively stable; the proportion of QC samples with CV values less than 0.2 is higher than 90, indicating that the experimental data is very stable.
  • the changes in the CV value of the isotope internal standard were monitored during the detection process. The change in the CV value of the internal standard was less than 20%, indicating that the instrument had good stability during the detection process.
  • the significance of the difference in metabolite concentrations between the colorectal cancer group and the non-colorectal cancer group was analyzed, and Pvalue ⁇ 0.05 was set as the significance standard.
  • the screened differential metabolism uses a binary logistic regression algorithm to build a classification model to obtain a diagnostic model for colorectal cancer.
  • the fold change results of individual metabolic markers are shown in the table below:
  • This diagnostic model contains the following 29 metabolites: 3 ⁇ -deoxycholic acid, lysophosphatidylethanolamine (P-18:0), lithocholic acid, DL-2-aminooctanoic acid, 3 ⁇ -hyodeoxycholic acid, lysophosphatidylcholate Base (14:0), inositol, glutamic acid, pseudouridine, propionyl-L-carnitine hydrochloride, 4-aminobutyric acid, hydroxydecanoic acid, 20-carboxyarachidonic acid, L-pyroglutamine Acid, cis-4-hydroxy-L-proline, symmetric N,N-dimethylarginine, S-adenosylhomocysteine, ⁇ -linolenic acid, hippuric acid, glycyl-L -Leucine, 12-hydroxyeicosatetraenoic acid, L-valine, succinic acid, asymmetric dimethylarginine,
  • Each of these 29 differential metabolites has a strong ability to diagnose and distinguish advanced colorectal tumors from non-progressive tumors of the colorectum, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance; these 29 differential metabolites When used in combination for diagnosis, the AUC was further improved, and the AUC of 29 combinations for diagnosing advanced colorectal tumors reached 0.991.
  • the results of single metabolic markers used in the diagnosis of advanced colorectal tumors are shown in the table below:
  • Example 4 Construction of a diagnostic model for advanced colorectal tumors using two plasma metabolic markers
  • Example 3 The research objects and detection and analysis methods of this embodiment are the same as those of Example 3. Only any two of the above plasma metabolism markers are used in the binary logistic regression modeling in step (6).
  • the AUC of the combination of 3 ⁇ -deoxycholic acid and lysophosphatidylethanolamine (P-18:0) in the diagnosis of colorectal cancer was 0.862.
  • the AUC of L-pyroglutamic acid and cis-4-hydroxy-L-proline combined for the diagnosis of colorectal cancer was 0.751.
  • the AUC of DL- ⁇ -phenyllactic acid and chenodeoxycholic acid combined in the diagnosis of colorectal cancer was 0.716.
  • Example 5 Construction of a diagnostic model for advanced colorectal tumors using 5 plasma metabolic markers
  • Example 3 The research objects and detection and analysis methods of this embodiment are the same as those of Example 3. Only the above-mentioned five plasma metabolism markers are used in the binary logistic regression modeling in step (6).
  • the AUC of the combination of 3 ⁇ -deoxycholic acid, lysophosphatidylethanolamine (P-18:0), lithocholic acid, DL-2-aminocaprylic acid and 3 ⁇ -hyodeoxycholic acid for the diagnosis of advanced colorectal tumors was 0.892.
  • Tauolithocholic acid-3-sulfate, glycinolithocholic acid, gamma-murinecholic acid, DL-beta-phenyllactic acid and chenodeoxycholic acid are used in combination
  • the AUC for diagnosing advanced colorectal tumors was 0.738.
  • Example 6 Construction of a diagnostic model for advanced colorectal tumors using 9 plasma metabolic markers
  • Example 3 The research objects and detection and analysis methods of this embodiment are the same as those of Example 3. Only any of the above-mentioned 9 plasma metabolism markers are used in the binary logistic regression modeling in step (6).
  • Example 7 Construction of a diagnostic model for advanced colorectal tumors using 12 plasma metabolic markers
  • Example 3 The research objects and detection and analysis methods of this embodiment are the same as those of Example 3. Only the above-mentioned 12 plasma metabolism markers are used in the binary logistic regression modeling in step (6).
  • Hippuric acid glycyl-L-leucine, 12-hydroxyeicosatetraenoic acid, L-valine, succinic acid, asymmetric dimethylarginine, taurolithocholic acid-3-sulfate
  • AUC of salt, glycolithocholic acid, ⁇ -murinecholic acid, DL- ⁇ -phenyllactic acid, and chenodeoxycholic acid in the diagnosis of advanced colorectal tumors was 0.776.
  • Example 8 Construction of a diagnostic model for advanced colorectal tumors using 15 plasma metabolic markers
  • Example 3 The research objects and detection and analysis methods of this embodiment are the same as those of Example 3, except that any of the above-mentioned 15 plasma metabolism markers are used only in the binary logistic regression modeling in step (6).
  • 3 ⁇ -deoxycholic acid lysophosphatidylethanolamine (P-18:0), lithocholic acid, DL-2-aminocaprylic acid, 3 ⁇ -hyodeoxycholic acid, lysophosphatidylcholine (14:0), inositol, Glutamic acid, pseudouridine, propionyl-L-carnitine hydrochloride, 4-aminobutyric acid, hydroxydecanoic acid, 20-carboxyarachidonic acid, L-pyroglutamic acid and cis-4-hydroxy-L-
  • the AUC of proline combination for the diagnosis of advanced colorectal tumors is 0.952.
  • Cis-4-hydroxy-L-proline symmetric N,N-dimethylarginine, S-adenosylhomocysteine, ⁇ -linolenic acid, hippuric acid, glycyl-L-leucine Amino acid, 12-hydroxyeicosatetraenoic acid, L-valine, succinic acid, asymmetric dimethylarginine, taurolithocholic acid-3-sulfate, glycinolithocholic acid, ⁇ -
  • the AUC of the combination of murine cholic acid, DL- ⁇ -phenyllactic acid and chenodeoxycholic acid in the diagnosis of advanced colorectal tumors was 0.788.
  • Example 9 Construction of a diagnostic model for advanced colorectal tumors using 19 plasma metabolic markers
  • Example 3 The research objects and detection and analysis methods of this embodiment are the same as those of Example 3, except that any of the above-mentioned 19 plasma metabolism markers are used only in the binary logistic regression modeling in step (6).
  • 3 ⁇ -deoxycholic acid lysophosphatidylethanolamine (P-18:0), lithocholic acid, DL-2-aminocaprylic acid, 3 ⁇ -hyodeoxycholic acid, lysophosphatidylcholine (14:0), inositol, Glutamic acid, pseudouridine, propionyl-L-carnitine hydrochloride, 4-aminobutyric acid, hydroxydecanoic acid, 20-carboxyarachidonic acid, L-pyroglutamic acid, cis-4-hydroxy-L -The AUC of proline, symmetric N,N-dimethylarginine, S-adenosylhomocysteine, ⁇ -linolenic acid and hippuric acid in the diagnosis of advanced colorectal tumors was 0.966.
  • Example 10 Construction of a diagnostic model for advanced colorectal tumors using 24 plasma metabolic markers
  • Example 3 The research objects and detection and analysis methods of this embodiment are the same as those of Example 3. Only the above-mentioned 24 plasma metabolism markers are used in the binary logistic regression modeling in step (6).
  • Example 11 Construction of a diagnostic model for plasma-targeted metabolome targeted diagnosis to distinguish colorectal cancer from healthy people
  • the samples in this example are from Example 3, including 355 colorectal cancer patients and 311 healthy people.
  • the metabolite detection and analysis methods were the same as in Example 3, and the above 29 metabolites were quantitatively detected.
  • Further preferred combinations of metabolic markers are: 3 ⁇ -deoxycholic acid, lithocholic acid, lysophosphatidylcholine (14:0), DL-2-aminocaprylic acid, 3 ⁇ -hyodeoxycholic acid, inositol, glutamic acid, Pseudouridine, propionyl-L-carnitine hydrochloride, cis-4-hydroxy-L-proline, symmetric N,N-dimethylarginine, S-adenosylhomocysteine, ⁇ - Linolenic acid, hippuric acid.
  • These metabolites undergo significant changes in patients with colorectal cancer. The specific changes are shown in the table below:
  • Example 12 Construction of a diagnostic model for plasma-targeted metabolome targeted diagnosis to distinguish patients with advanced adenomas from patients with advanced non-colorectal tumors
  • the samples in this example are from Example 3, 100 patients with advanced adenoma. There were 200 patients with non-colorectal advanced tumors, including 100 healthy patients and 100 patients with non-advanced adenomas.
  • the metabolite detection and analysis methods were the same as in Example 3, and the above 29 metabolites were quantitatively detected.
  • metabolic markers are lysophosphatidylethanolamine (P-18:0), myo-inositol, 4-aminobutyric acid, L-pyroglutamic acid, S-adenosylhomocysteine, and asymmetric dimethylarginine Acid, taurolithocholic acid-3-sulfate. These metabolites undergo significant changes in patients with advanced adenomas. The specific changes are shown in the table below:
  • Example 13 Construction of a diagnostic model for plasma-targeted metabolome diagnosis to differentiate between advanced adenoma patients and healthy people
  • metabolic markers are lysophosphatidylethanolamine (P-18:0), hippuric acid, glycyl-L-leucine, 12-hydroxyeicosatetraenoic acid, succinic acid, and asymmetric dimethylarginine Acid, glycinolithocholic acid, gamma-murinecholic acid. These metabolites undergo significant changes in patients with advanced adenomas. The specific changes are shown in the table below:
  • Example 14 Construction of a diagnostic model for plasma-targeted metabolome diagnosis to distinguish patients with advanced colorectal tumors from patients with non-progressive colorectal tumors
  • the samples of this example are derived from Example 3. There are 455 patients with advanced colorectal tumors, including 100 patients with advanced adenomas and 355 patients with colorectal cancer. There are 411 patients with non-colorectal advanced tumors, including 331 patients. Healthy subjects and 100 cases of non-progressive adenomas.
  • the metabolite detection and analysis methods were the same as in Example 3, and the above 29 metabolites were quantitatively detected.
  • Example 15 Construction of a diagnostic model for plasma-targeted metabolome diagnosis to distinguish patients with advanced colorectal tumors from healthy people
  • the samples in this example were derived from Example 3, including 455 patients with advanced colorectal tumors, including 100 patients with advanced adenomas and 355 patients with colorectal cancer; and 100 healthy people.
  • the metabolite detection and analysis methods were the same as in Example 3, and the above 29 metabolites were quantitatively detected.
  • metabolic markers are 3 ⁇ -deoxycholic acid, lithocholic acid, 3 ⁇ -hyodeoxycholic acid, inositol, hydroxydecanoic acid, 20-carboxyarachidonic acid, L-pyroglutamic acid, and 12-hydroxyeicosanoids.
  • Example 16 Constructing a colorectal cancer diagnostic model using tissue samples
  • the metabolite detection and analysis methods in this embodiment are the same as those in Example 3, and the above 29 metabolites are quantitatively detected.
  • the results of the use of individual metabolic markers in tissues for the diagnosis of colorectal cancer are shown in the table below:
  • Example 17 Constructing a colorectal cancer diagnostic model using stool samples
  • the metabolite detection and analysis methods in this embodiment are the same as those in Example 3, and the above 29 metabolites are quantitatively detected.
  • the results of single metabolic markers in stool for colorectal cancer diagnosis are shown in the table below:
  • Example 2 provides a method for screening differential metabolic markers of advanced colorectal adenomas and colorectal cancers. , the difference lies in the sample collection in step S1:
  • Example 3 Referring to the method steps of Example 3 in the patent document CN114924073A which discloses "Diagnostic Marker Combination and Application of Advanced Colorectal Tumors", the differences in the test steps of this example are:
  • the training set includes peripheral venous blood plasma of 341 healthy people, 108 non-progressive adenoma patients, 110 progressive adenoma patients and 383 colorectal cancer patients.
  • the validation set included peripheral venous blood plasma from 417 healthy subjects, 100 non-progressive adenoma patients, 209 progressive adenoma patients, and 313 colorectal cancer patients.
  • the healthy people were from people who had no intestinal diseases after physical examination; the disease groups were all diagnosed by colorectal examination. All samples had no history of any other malignant tumors, no other major systemic diseases, and no chronic history of long-term medication.
  • the samples in each group were matched for age and gender.
  • the non-colorectal cancer group included healthy people and patients with non-progressive adenoma
  • the advanced colorectal tumor group included patients with advanced adenoma and colorectal cancer.
  • Blood collection times were all in the early morning on an empty stomach. All plasma samples were centrifuged and stored in a -80°C refrigerator. During the study, the plasma samples were taken out and thawed for subsequent analysis.
  • the logistic regression model was used to analyze the 48 preliminary differential metabolites obtained from the above screening, and 30 metabolites were found: 3-hydroxybutyric acid, hexadecanoic acid, 2-hydroxydecanoic acid, 3 ⁇ -ursodeoxycholic acid, 1-tetradecanoyl-2-hydroxylecithin, 2-azahexanone, hippuric acid, DL-2-aminocaprylic acid, ursodeoxycholic acid, deoxycholic acid, glycinolithocholic acid, 20-carboxyarachidic acid Tetraenoic acid, piperine, 1-methylxanthine, 7-methylxanthine, pseudouridine, trans-3-hydroxycotinine, 12-hydroxyeicosatetraenoic acid, chenodeoxycholic acid , cholic acid, 1,7-dimethylxanthine, S-(5'-adenosyl)-L-homocysteine, theobromine, propofol glucuronide, acet
  • Example 20 Construction of colorectal cancer diagnostic model using 2 plasma metabolic markers
  • Example 19 The research objects and detection and analysis methods of this embodiment are the same as those of Example 19. Only any two of the above plasma metabolism markers are used in the binary logistic regression modeling in step (6).
  • the AUC of the training set of 1-tetradecanoyl-2-hydroxylecithin and 2-hydroxyhippuric acid in the diagnosis of colorectal cancer was 0.911, and the AUC of the validation set was 0.900.
  • the AUC of the training set of deoxycholic acid and chenodeoxycholic acid combined to diagnose colorectal cancer was 0.791, and the AUC of the validation set was 0.782.
  • the AUC of the training set of 1-methylxanthine and trans-3-hydroxycotinine in the diagnosis of colorectal cancer was 0.726, and the AUC of the validation set was 0.668.
  • Example 21 Construction of colorectal cancer diagnostic model using 5 plasma metabolic markers
  • Example 19 The research objects and detection and analysis methods of this embodiment are the same as those of Example 19. Only the above-mentioned any five plasma metabolism markers are used in the binary logistic regression modeling in step (6).
  • the AUC of the training set of the combination of 1-tetradecanoyl-2-hydroxylecithin, 2-hydroxyhippuric acid, hippuric acid, 3-(3-hydroxyphenyl)propionic acid and acetaminophen for the diagnosis of colorectal cancer is 0.938
  • the AUC of the validation set is 0.917.
  • the AUC of the training set of 3 ⁇ -ursodeoxycholic acid, 3-hydroxybutyric acid, deoxycholic acid, chenodeoxycholic acid and 12-hydroxyeicosatetraenoic acid in the diagnosis of colorectal cancer was 0.808, and the AUC of the validation set was 0.808.
  • the AUC is 0.810.
  • the AUC of the combination of theobromine, pseudouridine, piperine, 1-methylxanthine and trans-3-hydroxycotinine for the diagnosis of colorectal cancer was 0.748 for the training set and 0.705 for the validation set.
  • Example 22 Construction of colorectal cancer diagnostic model using 8 plasma metabolic markers
  • Example 19 The research objects and detection and analysis methods of this embodiment are the same as those of Example 19. Only the above-mentioned 8 plasma metabolism markers are used in the binary logistic regression modeling in step (6).
  • the AUC of the training set was 0.821
  • the AUC of the validation set was 0.818.
  • Example 19 The research objects and detection and analysis methods of this embodiment are the same as those of Example 19. Only the above-mentioned 12 plasma metabolism markers are used in the binary logistic regression modeling in step (6).
  • 1-tetradecanoyl-2-hydroxylecithin 2-hydroxyhippuric acid, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, acetaminophen, propofol glucuronide, 3-hydroxyhippuric acid , phenylacetylglutamine, 3-indolepropionic acid, 2-hydroxydecanoic acid, ursodeoxycholic acid and DL-2-aminoctanoic acid
  • the AUC of the training set for the diagnosis of colorectal cancer was 0.951
  • the AUC of the validation set The AUC is 0.938.
  • Example 19 The research objects and detection and analysis methods of this embodiment are the same as those of Example 19, except that any of the above-mentioned 15 plasma metabolism markers are used only in the binary logistic regression modeling in step (6).
  • 1-tetradecanoyl-2-hydroxylecithin 2-hydroxyhippuric acid, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, acetaminophen, propofol glucuronide, 3-hydroxyhippuric acid , phenylacetylglutamine, 3-indolepropionic acid, 2-hydroxydecanoic acid, ursodeoxycholic acid, DL-2-aminocaprylic acid, 3 ⁇ -ursodeoxycholic acid, 3-hydroxybutyric acid and deoxycholate
  • the AUC of acid combination for diagnosing colorectal cancer was 0.972 for the training set and 0.961 for the validation set.
  • Example 25 Construction of colorectal cancer diagnostic model using 20 plasma metabolic markers
  • Example 19 The research objects and detection and analysis methods of this embodiment are the same as those of Example 19. Only the above-mentioned 20 plasma metabolism markers are used in the binary logistic regression modeling in step (6).
  • 1-tetradecanoyl-2-hydroxylecithin 2-hydroxyhippuric acid, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, acetaminophen, propofol glucuronide, 3-hydroxyhippuric acid , Phenylacetylglutamine, 3-indolepropionic acid, 2-hydroxydecanoic acid, ursodeoxycholic acid, DL-2-aminocaprylic acid, 3 ⁇ -ursodeoxycholic acid, 3-hydroxybutyric acid, deoxycholate Acid, chenodeoxycholic acid, 12-
  • the AUC of the training set of the combination of hydroxyeicosatetraenoic acid, 1,7-dimethylxanthine, 20-carboxyarachidonic acid and 2-azahexanone in diagnosing colorectal cancer is 0.983, and the AUC of the validation set is 0.968.
  • the AUC of the training set for the combination of homocysteine, hexadecanoic acid, theobromine, pseudouridine, piperine, 1-methylxanthine and trans-3-hydroxycotinine for the diagnosis of colorectal cancer is 0.811, and the AUC of the validation set is 0.786.
  • Example 26 Construction of colorectal cancer diagnostic model using 25 plasma metabolic markers
  • Example 19 The research objects and detection and analysis methods of this embodiment are the same as those of Example 19. Only the above-mentioned 25 plasma metabolism markers are used in the binary logistic regression modeling in step (6).
  • 1-tetradecanoyl-2-hydroxylecithin 2-hydroxyhippuric acid, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, acetaminophen, propofol glucuronide, 3-hydroxyhippuric acid , Phenylacetylglutamine, 3-indolepropionic acid, 2-hydroxydecanoic acid, ursodeoxycholic acid, DL-2-aminocaprylic acid, 3 ⁇ -ursodeoxycholic acid, 3-hydroxybutyric acid, deoxycholate Acid, chenodeoxycholic acid, 12-hydroxyeicosatetraenoic acid, 1,7-dimethylxanthine, 20-carboxyarachidonic acid, 2-azahexanone, glycinolithocholic acid, 7 -Methylxanthine, cholic acid, S-(5'-adenosyl)-L-homocysteine and hexadecanoic acid combined to diagnose colore
  • Propofol glucuronide 3-hydroxyhippuric acid, phenylacetyl glutamine, 3-indolepropionic acid, 2-hydroxydecanoic acid, ursodeoxycholic acid, DL-2-aminocaprylic acid, 3 ⁇ -ursodeoxycholic acid Cholic acid, 3-hydroxybutyric acid, deoxycholic acid, chenodeoxycholic acid, 12-hydroxyeicosatetraenoic acid, 1,7-dimethylxanthine, 20-carboxyarachidonic acid, 2- Azacyclidone, glycolithocholic acid, 7-methylxanthine, cholic acid, S-(5'-adenosyl)-L-homocysteine, hexadecanoic acid, theobromine, pseudouria
  • the AUC of the combination of glycosides, piperine, 1-methylxanthine and trans-3-hydroxycotinine in the diagnosis of colorectal cancer reached 0.878 in the training set, and the A
  • Example 27 Construction of a diagnostic model for plasma-targeted metabolome targeted diagnosis to distinguish colorectal cancer from healthy people
  • the samples of this embodiment are from Example 19. There are 383 colorectal cancer patients and 341 healthy people in the training set; 313 colorectal cancer patients and 417 healthy people in the validation set.
  • the metabolite detection and analysis methods were the same as in Example 3, and the above 30 metabolites were quantitatively detected.
  • a further preferred combination of metabolic markers is: 1-tetradecanoyl-2-hydroxylecithin, hippuric acid, 2-hydroxydecanoic acid, DL-2-aminocaprylic acid, 3 ⁇ -ursodeoxycholic acid, 3-hydroxybutyric acid, 2 -Azacyclone and hexadecanoic acid. These metabolites change significantly in patients with colorectal cancer. The specific changes in the training set are shown in the table below:
  • Example 28 Construction of a diagnostic model for plasma-targeted metabolome targeted diagnosis to distinguish colorectal cancer and non-progressive tumor patients
  • the samples in this example are from Example 19.
  • the training set includes 383 colorectal cancer patients and 449 non-colorectal advanced tumor patients, including 341 healthy patients and 108 non-advanced adenoma patients.
  • the validation set included 313 colorectal cancer patients and 517 non-colorectal advanced tumor patients, including 417 healthy patients and 100 non-advanced adenoma patients.
  • the metabolite detection and analysis methods were the same as in Example 3, and the above 30 metabolites were quantitatively detected.
  • metabolic markers are: 1-tetradecanoyl-2-hydroxylecithin, hippuric acid, 2-hydroxydecanoic acid, DL-2-aminooctanoic acid, 3 ⁇ -ursdeoxycholic acid, and 2-azahexanone bile acid, S-(5'-adenosyl)-L-homocysteine, hexadecanoic acid, piperine, and trans-3-hydroxycotinine. These metabolites change significantly in patients with colorectal cancer. The specific changes in the training set are shown in the table below:
  • Example 29 Construction of a diagnostic model for plasma-targeted metabolome targeted diagnosis to distinguish patients with advanced adenomas from patients with advanced non-colorectal tumors
  • the samples of this example are derived from Example 19.
  • the training set contains 110 patients with advanced adenomas and 449 patients with non-colorectal advanced tumors, including 341 healthy patients and 108 patients with non-progressive adenomas; the validation set has advanced patients.
  • the metabolite detection and analysis methods were the same as in Example 3, and the above 30 metabolites were quantitatively detected.
  • metabolic markers 1-tetradecanoyl-2-hydroxylecithin, hippuric acid, DL-2-aminocaprylic acid, deoxycholic acid, 12-hydroxyeicosatetraenoic acid, 7-methylxanthine, S-(5'-adenosyl)-L-homocysteine and piperine. These metabolites change significantly in patients with advanced adenomas.
  • the specific changes in the training set are shown in the table below:
  • Example 30 Construction of a diagnostic model for plasma-targeted metabolome diagnosis to differentiate between advanced adenoma patients and healthy people
  • the samples in this example are from Example 19.
  • the training set includes 110 patients with advanced adenomas and 341 healthy people.
  • the validation set includes 209 patients with advanced adenomas and 417 healthy people.
  • the metabolite detection and analysis methods were the same as in Example 3, and the above 30 metabolites were quantitatively detected.
  • metabolic markers 1-tetradecanoyl-2-hydroxylecithin, hippuric acid, DL-2-aminocaprylic acid, deoxycholic acid, chenodeoxycholic acid, 12-hydroxyeicosatetraenoic acid, 7 -Methylxanthine, cholic acid, S-(5'-adenosyl)-L-homocysteine, piperine and 1-methylxanthine.
  • the AUC of the training set ranges from 0.710 to 0.933
  • the AUC of the validation set ranges from 0.698 to 0.915.
  • Example 31 Construction of a diagnostic model for plasma-targeted metabolome diagnosis to distinguish patients with advanced colorectal tumors from patients with non-progressive colorectal tumors
  • the samples of this example are derived from Example 19.
  • the training set includes 493 patients with advanced colorectal tumors, including 110 patients with advanced adenomas and 383 patients with colorectal cancer; 449 patients with non-colorectal advanced tumors, including 341 healthy patients and 108 patients with non-progressive adenomas.
  • the validation set included 522 patients with advanced colorectal tumors, including 209 patients with advanced adenomas and 313 patients with colorectal cancer; 517 patients with non-colorectal advanced tumors, including 417 healthy individuals and 100 patients with non-advanced adenomas. patient.
  • the metabolite detection and analysis methods were the same as in Example 19, and the above 30 metabolites were quantitatively detected.
  • metabolic markers 1-tetradecanoyl-2-hydroxylecithin, hippuric acid, 2-hydroxydecanoic acid, ursodeoxycholic acid, DL-2-aminocaprylic acid, 3 ⁇ -ursdeoxycholic acid, 3-hydroxy Butyric acid, 12-hydroxyeicosatetraenoic acid, 20-carboxyarachidic acid Tetraenoic acid, 2-azahexanone, glycolithocholic acid, 7-methylxanthine, S-(5'-adenosyl)-L-homocysteine, hexadecanoic acid, piperine , 1-methylxanthine and trans-3-hydroxycotinine.
  • These metabolites change significantly in patients with advanced colorectal tumors.
  • the specific changes in the training set are shown in the table below:
  • Example 32 Construction of a diagnostic model for plasma-targeted metabolome diagnosis to distinguish patients with advanced colorectal tumors from healthy people
  • the samples in this example are from Example 19.
  • the training set includes 493 patients in the colorectal advanced tumor group, including 110 advanced adenoma patients and 383 colorectal cancer patients; and 341 healthy people.
  • the validation set included 522 patients with advanced colorectal tumors, including 209 patients with advanced adenomas and 313 patients with colorectal cancer; and 417 healthy subjects.
  • the metabolite detection and analysis methods were the same as those in Example 20, and the above 30 metabolites were quantitatively detected.
  • metabolic markers 1-tetradecanoyl-2-hydroxylecithin, hippuric acid, ursodeoxycholic acid, DL-2-aminocaprylic acid, 3-hydroxybutyric acid, 12-hydroxyeicosatetraenoic acid, 7-methylxanthine, cholic acid, S-(5'-adenosyl)-L-homocysteine, hexadecanoic acid, and trans-3-hydroxycotinine.
  • These metabolites change significantly in patients with colorectal cancer.
  • the specific changes in the training set are shown in the table below:
  • the AUC of the healthy people training set is 0.722 ⁇ 0.938
  • the AUC of the validation set is 0.703 ⁇ 0.921.
  • Example 33 Constructing a colorectal cancer diagnostic model using tissue samples
  • Example 16 Referring to the method steps of Example 16 disclosed in the patent document CN114924073A "Colorectal Advanced Tumor Diagnostic Marker Combination and Its Application", the difference is:
  • the following 48 metabolites were quantitatively detected, including: 1-tetradecanoyl-2-hydroxylecithin, 2-hydroxyhippuric acid, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, acetaminophen, Propofol glucuronide, 3-hydroxyhippuric acid, phenylacetyl glutamine, 3-indolepropionic acid, hydroxydecanoic acid, ursodeoxycholic acid, DL-2-aminocaprylic acid, 3 ⁇ -ursodeoxycholic acid , 3-Hydroxybutyric acid, deoxycholic acid, chenodeoxycholic acid, 12-hydroxyeicosatetraenoic acid, 1,7-dimethylxanthine, 20-carboxyarachidonic acid, 2-azahexane Cyclone, glycolithocholic acid, 7-methylxanthine, cholic acid, S-(5'-adenosyl)-L-homocysteine,
  • Example 17 Referring to the method steps of Example 17 disclosed in the patent document CN114924073A "Colorectal Advanced Tumor Diagnostic Marker Combination and Its Application", the difference is:
  • Example 35 Construction of a diagnostic model for plasma-targeted metabolome diagnosis to distinguish patients with advanced colorectal tumors from patients with non-progressive colorectal tumors
  • a total of 120 healthy people, 60 patients with non-progressive adenomas, 70 patients with advanced adenomas, and 115 patients with colorectal cancer were collected from 3 independent clinical medical centers. of peripheral venous blood plasma.
  • the advanced colorectal tumor group included patients with advanced colorectal adenoma and colorectal cancer; the non-colorectal cancer group included healthy people and patients with non-advanced adenoma.
  • Metabolite detection includes Example 2 and Example 19. The analysis method is the same as Example 19, and the above 67 metabolites are quantitatively detected.
  • metabolic markers 1-tetradecanoyl-2-hydroxylecithin, 2-hydroxydecanoic acid, (2E, 4E, 8E)-tetradecane-2,4,8-trienoic acid, (R)- 3-Hydroxybutyric acid, 2,4-dihydroxybenzoic acid, 2-[2-[3-(2-carboxyethyl)-1H-indol-2-yl]ethyl]-1H-indole-3 -Carboxylic acid, 2-azahexanone, 3 ⁇ -deoxycholic acid, 3 ⁇ -ursodeoxycholic acid, 3 ⁇ -hyodeoxycholic acid, 3-sulfococool, 4,6-diamino-2-hydroxy-5 -Pyrimidinesulfonic acid, 5-methylfurfural, 7-methylxanthine, DL-2-aminooctanoic acid, N-cetyldiethanolamine, glycinolithocholic acid, pseudouridine, hippur
  • This embodiment provides a detection kit prepared based on the above metabolic markers.
  • the detection kit includes standards of metabolic markers: 3 ⁇ -deoxycholic acid, lysophosphatidylethanolamine (P-18:0), lithocholic acid , DL-2-aminocaprylic acid, 3 ⁇ -hyodeoxycholic acid, lysophosphatidylcholine (14:0), inositol, glutamic acid, pseudouridine, propionyl-L-carnitine hydrochloride, 4-aminobutyrate Acid, hydroxydecanoic acid, 20-carboxyarachidonic acid, L-pyroglutamic acid, cis-4-hydroxy-L-proline, symmetric N,N-dimethylarginine, S-adenosine Homocysteine, alpha-linolenic acid, hippuric acid, glycyl-L-leucine, 12-hydroxyeicosatetraenoic acid, L-valine, succin
  • Plasma sample metabolite extraction reagent 100% pure methanol and 50% acetonitrile aqueous solution are used for sample preparation; 50% acetonitrile aqueous solution can be used as a solvent for dissolving standards.

Abstract

一种用于结直肠进展期肿瘤诊断、监测或者风险评估的标志物组合及其应用。标志物选自溶血磷脂酰乙醇胺(P-18:0)、DL-2-氨基辛酸、3β-猪去氧胆酸、丙酰左旋肉碱盐酸盐、羟基癸酸、20-羧基花生四烯酸、牛磺石胆酸-3-硫酸盐、甘氨石胆酸、γ-鼠胆酸、DL-Β-苯乳酸、十六碳二酸、1-十四酰-2-羟基卵磷脂、2-氮己环酮、3-羟基丁酸、马尿酸、假尿苷等代谢标志物的组合。提供的代谢标志物能精确诊断结直肠进展期肿瘤(结直肠癌和/或进展期腺瘤)，灵敏度高。

Description

结直肠进展期肿瘤诊断标志物组合及其应用 技术领域
本发明涉及检测技术领域，尤其涉及一种结直肠进展期肿瘤诊断标志物组合及其应用。
背景技术
结直肠癌(Colorectal Cancer,CRC)又称大肠癌，是消化系统常见的恶性肿瘤。结直肠癌的发展是一个缓慢的发展过程，在早期通常无症状并且难以检测，绝大多数患者在出现便血、腹痛等症状后才到医院就诊，失去了最佳的手术治疗时机，就算进行了手术治疗，患者的5年生存率也大大降低。
结直肠癌的形成一般会经历正常粘膜增生、进展期腺瘤(恶性)、腺癌(恶性)的发展过程，一般需要5-10年的时间，这就为结直肠癌的预防提供了极有利的机会。进展期腺瘤(AA)是一种癌前病变，是指大小≥1cm或者具有≥25％的任何大小的绒毛状组分或高度发育异常，随时间推移，很容易发展为结直肠癌。若能够在结直肠癌发生的早期阶段，寻找到具有一定预警作用的肿瘤生物标志物，对结直肠癌和/或进展期腺瘤(恶性肿瘤)进行诊断，对于提高患者的治疗效果，改善患者预后有着重要的意义。
目前，结直肠癌诊断方法主要依靠影像学检查、生化检验和病理学检查，其中结直肠镜检查是结直肠癌诊断的金标准，是癌前病变和发病最有效的筛查方法。但是，上述检查技术针对进展期腺瘤的灵敏度更低，不到40％，远远低于结直肠癌的检测灵敏度。
发明内容
基于此，有必要提供一种结直肠进展期肿瘤诊断标志物组合及其应用，该代谢标志物组合可在制备用于诊断或监测进展期腺瘤及结直肠癌(简称结直肠进展期肿瘤)的试剂(盒)中进行应用。
目前，虽然有研究者利用公共数据库的代谢物信息采用非靶向代谢组检测技术筛选得到的一些代谢物，但是该非靶向代谢组学筛选的代谢物实际针对具体疾病诊断或监测的特异性不强，并且大多数研究都是样本量较少，单一样本来源，实际临床意义十分有限。血浆分析是临床上常用的一种疾病诊断方法，因其简便、快速、经济且相对无创的优点而被广泛采用。目前尚未有人使用血浆代谢物水平对结直肠癌和/或进展期腺瘤进行诊断。应用血浆靶向代谢组学寻找代谢标志物以诊断结直肠癌和/或进展期腺瘤，对于结直肠癌临床早期快速确诊具有重要意义。
而本申请通过构建结结直肠进展期肿瘤血浆特异性代谢组数据库进行血浆靶向定量代谢组学研究，获得大量与疾病相关的特异性代谢物，并且通过多个医学中心来源样本对代谢标志物进行验证，从而寻找灵敏度高、特异性好、稳定可靠的结直肠结直肠进展期肿瘤诊断血浆代谢标志物。
本发明采用如下技术方案：
本发明提供了一组进展期腺瘤和/或结直肠癌诊断、监测或者风险评估的代谢标志物，该代谢标志物为以下代谢标志物中的任意一种或多种组合：3β-脱氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、石胆酸、DL-2-氨基辛酸、3β-猪去氧胆酸、溶血磷脂酰胆碱(14:0)(也称为“1-十四酰-2-羟基卵磷脂”)、肌醇、谷氨酸、假尿苷、丙酰左旋肉碱盐酸盐、4-氨基丁酸、羟基癸酸(也称为“2-羟基癸酸”)、20-羧基花生四烯酸、L-焦谷氨酸、顺式-4-羟基-L-脯氨酸、对称N,N-二甲基精氨酸、S-腺苷同型半胱氨酸(也称为“S-(5'-腺苷)-L-高半胱氨酸”)、α-亚麻酸、马尿酸、甘氨酰-L-亮氨酸、12-羟基二十碳四烯酸、L-缬氨酸、琥 珀酸、不对称二甲基精氨酸、牛磺石胆酸-3-硫酸盐、甘氨石胆酸、γ-鼠胆酸、DL-Β-苯乳酸、鹅脱氧胆酸(也称为“鹅去氧胆酸”)、2-羟基马尿酸、3-(3-羟基苯基)丙酸、对乙酰氨基酚、异丙酚葡糖苷酸、3-羟基马尿酸、苯基乙酰谷氨酰胺、3-吲哚丙酸、熊去氧胆酸、3β-熊脱氧胆酸、3-羟基丁酸、去氧胆酸、1,7-二甲基黄嘌呤、2-氮己环酮、7-甲基黄嘌呤、胆酸、十六碳二酸、可可碱、胡椒碱、1-甲基黄嘌呤、反式-3-羟基可替宁、3-磺基可可醇、5-甲基糠醛、4,6-二氨基-2-羟基-5-嘧啶磺酸、2,4-二羟基苯甲酸、(2E，4E，8E)-十四烷-2,4,8-三烯酸、3-酮-4-氨基苯甲酸酯，环(异亮氨酸脯氨酰)、N-十六烷基二乙醇胺、(五十)-苏伯尔肉碱、4-羟基马尿酸、2-[2-[3-(2-羧乙基)-1H-吲哚-2-基]乙基]-1H-吲哚-3-羧酸、2-甲基三癸二酸、六酰基谷氨酰胺、N-乙酰-L-丝氨酸-L-亮氨酸、N-乙酰基甘氨酰丙氨酸、溶血磷脂酰胆碱(20:2)、对甲酚葡糖苷酸、7-去氧胆酸。
进一步地，代谢标志物组合至少包含任意两个、任意三个、任意四个、任意任意五个、任意六个、任意七个、任意八个、任意九个、任意十个及以上的代谢物。
在其中一些实施例中，所述代谢标志物至少选自3β-脱氧胆酸、石胆酸、溶血磷脂酰胆碱(14:0)、DL-2-氨基辛酸、3β-猪去氧胆酸、肌醇、谷氨酸、假尿苷、丙酰左旋肉碱盐酸盐、顺式-4-羟基-L-脯氨酸、对称N,N-二甲基精氨酸、S-腺苷同型半胱氨酸、α-亚麻酸、马尿酸中的至少一种，用于区分诊断结直肠癌患者和健康人。
在其中一些实施例中，所述代谢标志物至少选自溶血磷脂酰乙醇胺(P-18:0)、肌醇、4-氨基丁酸、L-焦谷氨酸、S-腺苷同型半胱氨酸、不对称二甲基精氨酸、牛磺石胆酸-3-硫酸盐中的至少一种，用于区分诊断进展期腺瘤和非结直肠进展期肿瘤。
在其中一些实施例中，所述代谢标志物至少选自石胆酸、3β-猪去氧胆酸、肌醇、假尿苷、羟基癸酸、20-羧基花生四烯酸、L-焦谷氨酸、马尿酸、12-羟基二十碳四烯酸、鹅脱氧胆酸中的至少一种，用于区分诊断结直肠进展期肿瘤和非结直肠进展期肿瘤。
在其中一些实施例中，所述代谢标志物至少选自3β-脱氧胆酸、石胆酸、3β-猪去氧胆酸、肌醇、羟基癸酸、20-羧基花生四烯酸、L-焦谷氨酸、12-羟基二十碳四烯酸、L-缬氨酸、不对称二甲基精氨酸、DL-Β-苯乳酸、鹅脱氧胆酸中的至少一种，用于区分诊断结直肠进展期肿瘤和健康人。
在其中一些实施例中，所述代谢标志物至少选自溶血磷脂酰胆碱(14:0)、马尿酸、2-羟基癸酸、熊去氧胆酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、3-羟基丁酸、12-羟基二十碳四烯酸、20-羧基花生四烯酸、2-氮己环酮、甘氨石胆酸、7-甲基黄嘌呤、胆酸、S-腺苷同型半胱氨酸、十六碳二酸、反式-3-羟基可替宁、胡椒碱中的至少一种。
在其中一些实施例中，用于对结直肠癌患者和健康人进行风险评估的代谢标志物组合至少选自：
A组分：选自十六碳二酸、3β-熊脱氧胆酸、1-十四酰-2-羟基卵磷脂、2-氮己环酮中的至少一种或者多种；以及
B组分：选自3-羟基丁酸、2-羟基癸酸、马尿酸、DL-2-氨基辛酸中的至少一种。优选上述8种代谢标志物的组合，整体的检测效果更精准。
在其中一些实施例中，所述代谢标志物至少选自1-十四酰-2-羟基卵磷脂、马尿酸、2-羟基癸酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、2-氮己环酮、胆酸、S-(5'-腺苷)-L-高半胱氨酸、十六碳二酸、胡椒碱和反式-3-羟基可替宁中的至少一种(优选五种及以上的组合)，用于对结直肠癌患者和非结直肠进展期肿瘤进行风险评估。
在其中一些实施例中，所述代谢标志物选自1-十四酰-2-羟基卵磷脂、马尿酸、DL-2-氨基辛酸、去氧胆酸、12-羟基二十碳四烯酸、7-甲基黄嘌呤、S-(5'-腺苷)-L-高半胱氨酸和胡椒碱中的多种组合(优选四种及以上的组合)，用于对结直肠进展期腺瘤患者和非结直肠进展期肿瘤进行风险评估。
在其中一些实施例中，所述代谢标志物至少选自1-十四酰-2-羟基卵磷脂、马尿酸、 DL-2-氨基辛酸、去氧胆酸、鹅去氧胆酸、12-羟基二十碳四烯酸、7-甲基黄嘌呤、胆酸、S-(5'-腺苷)-L-高半胱氨酸、胡椒碱和1-甲基黄嘌呤中的多种组合(优选五种及以上的组合)，用于对结直肠进展期腺瘤患者和健康人进行风险评估。
在其中一些实施例中，所述代谢标志物至少选自1-十四酰-2-羟基卵磷脂、马尿酸、2-羟基癸酸、熊去氧胆酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、3-羟基丁酸、12-羟基二十碳四烯酸、20-羧基花生四烯酸、2-氮己环酮、甘氨石胆酸、7-甲基黄嘌呤、S-(5'-腺苷)-L-高半胱氨酸、十六碳二酸、胡椒碱、1-甲基黄嘌呤和反式-3-羟基可替宁中的多种组合(优选五种及以上的组合)，用于对结直肠进展期肿瘤患者和非结直肠进展期肿瘤进行风险评估。
在其中一些实施例中，所述代谢标志物至少选自1-十四酰-2-羟基卵磷脂、马尿酸、熊去氧胆酸、DL-2-氨基辛酸、3-羟基丁酸、12-羟基二十碳四烯酸、7-甲基黄嘌呤、胆酸、S-(5'-腺苷)-L-高半胱氨酸、十六碳二酸和反式-3-羟基可替宁中的多种组合(优选五种及以上的组合)，用于对结直肠进展期肿瘤患者和健康人进行风险评估。
本发明目的提供了一种含有上述适合于结直肠癌和/或进展期腺瘤诊断的诊断标志物的试剂产品，包括所述代谢标志物的标准品，提取富集所述代谢标志物的溶剂，可用于结直肠癌和/或进展期腺瘤诊断。本发明提供的试剂盒可以用于诊断结直肠癌患者，还可以用于诊断进展期腺瘤，提高诊断便利性，对结直肠癌进行早诊早治。
本发明的有益效果：
本发明提供的血浆代谢物能对结直肠癌患者和进展期腺瘤患者进行有效诊断、监测和风险评估，灵敏度高。
本发明构建了结直肠癌血浆特异性代谢组数据库，诊断代谢标志物更具代表性。本发明仅通过取血检测就能实现诊断，无需额外采集组织样本，能够很好地替代或辅助现有肠镜检测。本发明简便、快捷、相对无内创，有利于结直肠癌和/或进展期腺瘤的早期筛查，具有很好的临床使用和推广价值。
具体实施方式
下面结合具体实施例对本发明作进一步的详细说明，以使本领域的技术人员更加清楚地理解本发明。以下各实施例，仅用于说明本发明，但不止用来限制本发明的范围。基于本发明中的具体实施例，本领域普通技术人员在没有做出创造性劳动的情况下，所获得的其他所有实施例，都属于本发明的保护范围。在本发明实施例中，若无特殊说明，所有原料组分均为本领域技术人员熟知的市售产品；在本发明实施例中，若未具体指明，所用的技术手段均为本领域技术人员所熟知的常规手段。
关键仪器信息分别见下表：
实施例1构建结直肠进展期肿瘤血浆特异性代谢物离子对数据库
本实施例提供一种构建结直肠进展期肿瘤血浆特异性代谢物离子对数据库的方法，包括如下步骤：
S1，采集样品
本研究在取得患者同意后，收集健康对照、非进展期腺瘤患者、进展期腺瘤患者、 结直肠癌患者各20例样本的外周静脉血血浆。其中，健康对照来源于体检后无肠道疾病的人群；疾病组均经过结直肠镜检查和术后确诊。所有样本均无其它任何恶性肿瘤病史、无其他全身性重大疾病、无长期用药的慢性病史。各组样本间的年龄和性别均相匹配，非结直肠癌组包括健康对照和非进展期腺瘤患者。采血时间均为清晨空腹状态。
所有血浆样本离心后置于-80℃冰箱内保存，研究时取出血浆样本解冻后进行后续分析。
S2，样品预处理
从-80℃冰箱中取出样品于冰上解冻至样本中没有冰块(后续操作都要求在冰上进行)；样本解冻后，涡旋10s混匀，取样本50μL加入到对应编号的离心管中；分别加入300μL纯甲醇内标提取液；涡旋5min，静置24h，12000r/min，4℃条件下离心10min；吸取上清液270μL浓缩24h；加入100μl复溶液(由体积比1:1的乙腈和水混合制成)，每个样本各取50μL混合成mix检测液。
S3，建库流程
液相色谱串联质谱(LC-MS/MS)实现从利用色谱进行物质分离到利用质谱进行物质鉴定的整个流程。迈维代谢基于广泛靶向代谢组方法，利用上述mix检测液建立结直肠癌血浆特异性代谢物离子对数据库，代谢物离子对主要有以下四种来源：MIM-EPI采集，TOF采集，迈维标准品数据库和结直肠癌相关文献代谢物。
其中，通过MIM-EPI检测模式共采集到1065个离子对，TOF检测模式共采集到1232个离子对，迈维标准品数据库共采集到572个离子对，以及结直肠癌文献相关代谢物71个，汇总和去重上述所有来源离子对信息，最终获得恶性肿瘤(包括进展期腺瘤和结直肠癌)血浆特异性代谢物离子对2832个。
实施例2筛选结直肠进展期肿瘤代谢标志物
本实施例提供一种结直肠进展期肿瘤代谢标志物的筛选方法，包括如下步骤：
S1，采集样品
本研究在取得患者同意后，从3个独立的临床医学中心共收集了795例健康对照、393例非进展期腺瘤患者、193例进展期腺瘤患者、494例结直肠癌患者的外周静脉血血浆。其中健康对照来源于体检后无肠道疾病的人群；疾病组均经过结直肠镜检查和术后确诊。所有样本均无其它任何恶性肿瘤病史、无其他全身性重大疾病、无长期用药的慢性病史。各组样本间的年龄和性别均相匹配，非结直肠癌组包括健康对照和非进展期腺瘤患者。采血时间均为清晨空腹状态。所有血浆样本离心后置于-80℃冰箱内保存，研究时取出血浆样本解冻后进行后续分析。
S2，血清广泛靶向代谢组学分析
(1)样品预处理
从-80℃冰箱中取出步骤S1采集的样品，于冰上解冻至样本中没有冰块(后续操作都要求在冰上进行)；样本解冻后，涡旋10s混匀，取样本50μL加入到对应编号的离心管中；加入300μL纯甲醇内标提取液(含100ppm浓度的L-2-苯丙氨酸，[2H3]-L-肉碱-d3盐酸盐，4-氟-L-2-苯基甘氨酸，L-苯基丙氨酸，[2H5]-马尿酸，[2H5]-犬尿酸，[2H5]-苯氧基乙酸内标)；涡旋5min，静置24h，再于12000r/min、4℃条件下离心10min；吸取上清液270μL浓缩24h；再加入100μL由乙腈和水按照体积比1:1组成的复溶液中，用于LC-MS/MS分析。每个样本各取20μL混合成质控样本(QC)，每间隔15个样本采集一次。
(2)样品代谢物检测分析
实验试剂

确定液相色谱条件如下：
色谱柱：Waters ACQUITY UPLC HSS T3 C18 1.8μm，2.1mm*100mm；柱温为40℃；进样量为2μL。
流动相：A相为含0.1％乙酸水溶液，B相为含0.1％乙酸的乙腈溶液。洗脱梯度程序为：0min，A相与B相的体积比为95:5；11.0min，A相与B相的体积比为10:90；12.0min，A相与B相的体积比为10:90；12.1min，A相与B相的体积比为95:5；14.0min，A相与B相的体积比为95:5V/V。流速0.4mL/min。
确定质谱条件如下：
电喷雾离子源(electrospray ionization，ESI)温度500℃，质谱电压5500V(positive)或者-4500V(negative)，离子源气体I(GS I)55psi，气体II(GS II)60psi，气帘气(curtain gas,CUR)25psi，碰撞诱导电离(collision-activated dissociation，CAD)参数设置为高。
在三重四极杆(Qtrap)中，每个离子对是根据优化的去簇电压(declustering potential，DP)和碰撞能(collision energy，CE)进行MRM模式扫描检测。
按照确定的液相色谱条件和质谱条件分别对样本进行分析检测。
(3)图谱峰面积预处理和积分
基于进展期腺瘤、结直肠癌血浆特异性代谢物数据库，对样本的代谢物进行质谱定性定量分析。通过液相色谱能够分离不同分子量的代谢物。通过三重四极杆的多反应监测模式(MRM)筛选出每个物质的特征离子，在检测器中获得特征离子的信号强度(CPS)。用MultiQuant 3.0.3软件打开样本下机质谱文件，进行色谱峰的积分和校正工作，每个色谱峰的峰面积(Area)代表对应物质的相对含量，设置S/N>5，保留时间偏移不超过0.2min的峰保留，最后导出所有色谱峰面积积分数据保存。
(4)实验质量控制
通过对不同质控QC样本质谱检测分析的总离子流图进行重叠展示分析，可以判断代谢物提取和检测的重复性，即技术重复。仪器的高稳定性为数据的重复性和可靠性提供了重要的保障。CV值即变异系数(Coefficient ofVariation)，是原始数据标准差与原始数据平均数的比，可反映数据离散程度。使用经验累积分布函数(Empirical Cumulative Distribution Function,ECDF)可以分析小于参考值的物质CV出现的频率，QC样本的CV值较低的物质占比越高，代表实验数据越稳定：QC样本CV值小于0.5的物质占比高于85％，表明实验数据较稳定；QC样本CV值小于0.3的物质占比高于75％，表明实验数据非常稳定。同时监测检测过程中所有内标CV值变化情况，内标CV值的变化小于20％，表明检测过程中仪器稳定性好。
(5)数据处理和分析
利用峰面积积分数据在两组间进行差异代谢物分析，并设定Pvalue<0.05为差异显著性标准，筛选差异代谢物作为诊断结直肠癌的候选代谢标志物。同时使用机器学习随机森林(Random Forest，RF)算法对两组间代谢物积分数据进行分析，将上述结直肠癌 患者样本及非结直肠癌患者血浆样本数据的2/3作为训练集，1/3作为测试集。对训练集进行决策树建模，然后组合多个决策树的预测，通过投票得出最终预测结果，该代谢物模型可有效诊断结直肠癌患者。通过测试集样本对上述模型进行验证，所筛选的代谢物成为候选代谢标志物。组间差异分析筛选的代谢物和机器学习筛选代谢物的并集作为诊断结直肠癌的候选代谢标志物集合。两组间比较，包括但不限于结直肠癌组和非结直肠进展期肿瘤组(健康对照和非进展期腺瘤)、结直肠癌组和健康人、进展期腺瘤组和非结直肠进展期肿瘤组(健康对照和非进展期腺瘤)、进展期腺瘤组和健康人、结直肠进展期肿瘤组(进展期腺瘤和结直肠癌组)和非结直肠进展期肿瘤组、结直肠进展期肿瘤组(进展期腺瘤和结直肠癌组)和健康人。
(6)血浆代谢物解谱
上述差异分析和随机森林模型筛选的代谢标志物集合，根据其保留时间，一级和二级推测标志物的分子质量和分子式，并且与代谢物谱图数据库中的谱图信息进行比对，从而对代谢物进行定性鉴定。
进一步采购上述所鉴定代谢物非同位素标准品，核对血浆样本中代谢物和对应非同位素标准品在高效液相色谱串联质谱检测中的保留时间，一级和二级质谱信息的一致性，以确定代谢物定性的准确性。
根据上述鉴定方法，我们成功鉴定出29种血浆代谢标志物作为适于结直肠进展期肿瘤的诊断标志物，见下表：
29种结直肠进展期肿瘤血浆代谢标志物

实施例3：构建血浆靶向代谢组结直肠进展期肿瘤诊断模型
S1，采集样品
本研究在取得患者同意后，从3个独立的临床医学中心共收集了311例健康对照，100例非进展期腺瘤患者，100例进展期腺瘤患者和355例结直肠癌患者的外周静脉血血浆。其中健康对照来源于体检后无肠道疾病的人群；疾病组均经过结直肠镜检查确诊。所有样本均无其它任何恶性肿瘤病史，无其他全身性重大疾病，无长期用药的慢性病史。各组样本间的年龄和性别均相匹配，非结直肠癌组包括健康对照和非进展期腺瘤患者，恶性肿瘤组包括进展期腺瘤患者和结直肠癌患者。采血时间均为清晨空腹状态。所有血浆样本离心后置于-80℃冰箱内保存，研究时取出血浆样本解冻后进行后续分析。
S2，样品代谢检测分析
本步骤所采用的实验试剂见下表：
试验试剂

(1)样品预处理
从-80℃冰箱中取出样品于冰上解冻至样本中没有冰块(后续操作都要求在冰上进行)；样本解冻后，涡旋10s混匀，取50μL项目样本加入150μL提取液(提取液含有浓度为100ppm的同位素内标混合液)，涡旋3min，12000rpm，4℃离心10min，-20冰箱低温静置过夜；12000rpm，4℃离心5min，取上清170μL，按顺序转移入96孔板内，过完蛋白沉淀板封口用于LC-MS/MS分析。每个样本各取20μl混合成质控样本(QC)，每间隔15个样本采集一次。
(2)确定检测条件进行检测
鉴于代谢标志物性质的差异，靶向定量检测使用T3柱和Amide柱两种方法进行代谢物分离，以保证代谢物定量的准确性。
确定T3柱液相色谱条件：
色谱柱：Waters ACQUITY UPLC HSS T3 C18 1.8μm，2.1mm*100mm；柱温40℃；进样量2μL。
流动相：A相为含0.04％的乙酸溶液，B相为含0.04％乙酸的乙腈溶液；洗脱梯度程序：0min，A相与B相的体积比为95:5；11.0min，A相与B相的体积比为10:90；12.0min，A相与B相的体积比为10:90；12.1min，A相与B相的体积比为95:5；14.0min，A相与B相的体积比为95:5V/V。流速0.4mL/min。
Amide柱液相色谱条件：
色谱柱：Waters ACQUITY UPLC BEH Amide 1.7μm，2.1mm*100mm；柱温40℃；进样量2μL。
流动相：A相为超纯水(10mM乙酸铵+0.3％氨水+1mg亚甲基二磷酸)，B相为90％乙腈水(包含1mg亚甲基二磷酸)；洗脱梯度程序：0min，A相与B相的体积比为10:90；9.0min，A相与B相的体积比为40:60；10.0min，A相与B相的体积比为60:40；11.0min，A相与B相的体积比为60:40；11.1min，A相与B相的体积比为10:90；15.0min，A相与B相的体积比为10:90。流速0.4mL/min。
质谱条件：
T3柱和Amide柱质谱采集条件相同，主要包括：电喷雾离子源(electrospray ionization，ESI)温度500℃，质谱电压5500V(positive)，-4500V(negative)，离子源气体I(GS I)55psi，气体II(GS II)60psi，气帘气(curtain gas,CUR)25psi，碰撞诱导电离(collision-activated dissociation，CAD)参数设置为高。在三重四极杆(Qtrap)中，每个离子对是根据优化的去簇电压(declustering potential，DP)和碰撞能(collision energy， CE)进行MRM模式扫描检测。
(3)图谱峰面积预处理和积分
采用MultiQuant 3.0.3软件处理质谱数据，参考标准品的保留时间与峰型信息，对待测物在不同样本中检测到的质谱峰进行积分校正，以确保定性定量的准确。
对所有样本进行定性定量分析，每个色谱峰的峰面积(Area)代表对应物质的相对含量，代入线性方程和计算公式，最终得到所有样本中待测物的定性定量分析结果。
(4)代谢物浓度计算
配制0.01ng/mL、0.05ng/mL、0.1ng/mL、0.5ng/mL、1ng/mL、5ng/mL、10ng/mL、50ng/mL、100ng/mL、200ng/mL、500ng/mL不同浓度的标准品溶液，获取各个浓度标准品的对应定量信号的质谱峰强度数据；以对应代谢物的外标与内标浓度比(Concentration Ratio)为横坐标，外标与内标峰面积比(Area Ratio)为纵坐标，绘制不同物质的标准曲线。将检测到的所有样本的积分峰面积比值代入标准曲线线性方程进行计算，进一步代入计算公式计算后，MultiQuant 3.0.3中稀释因子设置为3，最终样本中积分峰面积比值代入标准曲线得到的浓度值(ng/mL)即实际样本中该物质的含量数据。
(5)实验质量控制
通过对不同质控QC样本质谱检测分析的总离子流图进行重叠展示分析，可以判断代谢物提取和检测的重复性，即技术重复。仪器的高稳定性为数据的重复性和可靠性提供了重要的保障。CV值即变异系数(Coefficient of Variation)，是原始数据标准差与原始数据平均数的比，可反映数据离散程度。使用经验累积分布函数(Empirical Cumulative Distribution Function,ECDF)可以分析小于参考值的物质CV出现的频率，QC样本的CV值较低的物质占比越高，代表实验数据越稳定：QC样本所有的物质CV值小于0.3，表明实验数据较稳定；QC样本CV值小于0.2的物质占比高于90，表明实验数据非常稳定。同时监测检测过程中同位素内标CV值变化情况，内标CV值的变化小于20％，表明检测过程中仪器稳定性好。
(6)数据处理和分析
结直肠癌组和非结直肠癌组两组间代谢物浓度进行差异显著性分析，并设定Pvalue<0.05为差异显著性标准。筛选的差异代谢使用二元逻辑回归算法构建分类模型，得到结直肠癌的诊断模型。单个代谢标志物变化倍数结果见下表：
结直肠进展期肿瘤组VS非结直肠进展期肿瘤组代谢物变化倍数

该诊断模型包含以下29种代谢物：3β-脱氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、石胆酸、DL-2-氨基辛酸、3β-猪去氧胆酸、溶血磷脂酰胆碱(14:0)、肌醇、谷氨酸、假尿苷、丙酰左旋肉碱盐酸盐、4-氨基丁酸、羟基癸酸、20-羧基花生四烯酸、L-焦谷氨酸、顺式-4-羟基-L-脯氨酸、对称N,N-二甲基精氨酸、S-腺苷同型半胱氨酸、α-亚麻酸、马尿酸、甘氨酰-L-亮氨酸、12-羟基二十碳四烯酸、L-缬氨酸、琥珀酸、不对称二甲基精氨酸、牛磺石胆酸-3-硫酸盐、甘氨石胆酸、γ-鼠胆酸、DL-Β-苯乳酸、鹅脱氧胆酸。
这29个差异代谢物单个用于诊断区分结直肠进展期肿瘤和非结直肠进展期肿瘤的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义；这29个差异代谢物联合用于诊断时，AUC进一步提高，29个联合起来诊断结直肠进展期肿瘤的AUC达0.991。单个代谢标志物用于结直肠进展期肿瘤诊断的结果见下表：
结直肠进展期肿瘤组VS非结直肠进展期肿瘤组单个代谢物的AUC值

实施例4：使用2个血浆代谢标志物进行结直肠进展期肿瘤诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意2个血浆代谢标志物。
经构建模型统计分析：任意2个差异代谢物单个用于诊断区分结直肠进展期肿瘤和非结直肠进展期肿瘤的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
3β-脱氧胆酸和溶血磷脂酰乙醇胺(P-18:0)联合起来诊断结直肠癌的AUC为0.862。
L-焦谷氨酸和顺式-4-羟基-L-脯氨酸联合起来诊断结直肠癌的AUC为0.751。
DL-Β-苯乳酸和鹅脱氧胆酸联合起来诊断结直肠癌的AUC为0.716。
实施例5：使用5个血浆代谢标志物进行结直肠进展期肿瘤诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意5个血浆代谢标志物。
经构建模型统计分析：任意5个差异代谢物单个用于诊断区分结直肠进展期肿瘤和非结直肠进展期肿瘤的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
3β-脱氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、石胆酸、DL-2-氨基辛酸和3β-猪去氧胆酸联合用于诊断结直肠进展期肿瘤的AUC为0.892。
20-羧基花生四烯酸、L-焦谷氨酸、顺式-4-羟基-L-脯氨酸、对称N,N-二甲基精氨酸和S-腺苷同型半胱氨酸联合用于诊断结直肠进展期肿瘤的AUC为0.776。
牛磺石胆酸-3-硫酸盐、甘氨石胆酸、γ-鼠胆酸、DL-Β-苯乳酸和鹅脱氧胆酸联合用于 诊断结直肠进展期肿瘤的AUC为0.738。
实施例6：使用9个血浆代谢标志物进行结直肠进展期肿瘤诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意9个血浆代谢标志物。
经构建模型统计分析：任意9个差异代谢物单个用于诊断区分结直肠进展期肿瘤和非结直肠进展期肿瘤的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
3β-脱氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、石胆酸、DL-2-氨基辛酸、3β-猪去氧胆酸、溶血磷脂酰胆碱(14:0)、肌醇、谷氨酸和假尿苷联合用于诊断结直肠进展期肿瘤的AUC为0.925。
4-氨基丁酸、羟基癸酸、20-羧基花生四烯酸、L-焦谷氨酸、顺式-4-羟基-L-脯氨酸、对称N,N-二甲基精氨酸、S-腺苷同型半胱氨酸、α-亚麻酸和马尿酸联合用于诊断结直肠进展期肿瘤的AUC为0.791，
12-羟基二十碳四烯酸、L-缬氨酸、琥珀酸、不对称二甲基精氨酸、牛磺石胆酸-3-硫酸盐、甘氨石胆酸、γ-鼠胆酸、DL-Β-苯乳酸和鹅脱氧胆酸联合用于诊断结直肠进展期肿瘤的AUC为0.751。
实施例7：使用12个血浆代谢标志物进行结直肠进展期肿瘤诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意12个血浆代谢标志物。
经构建模型统计分析：任意12个差异代谢物单个用于诊断区分结直肠进展期肿瘤和非结直肠进展期肿瘤的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
3β-脱氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、石胆酸、DL-2-氨基辛酸、3β-猪去氧胆酸、溶血磷脂酰胆碱(14:0)、肌醇、谷氨酸、假尿苷、丙酰左旋肉碱盐酸盐、4-氨基丁酸和羟基癸酸联合用于诊断结直肠进展期肿瘤的AUC为0.941。
马尿酸、甘氨酰-L-亮氨酸、12-羟基二十碳四烯酸、L-缬氨酸、琥珀酸、不对称二甲基精氨酸、牛磺石胆酸-3-硫酸盐、甘氨石胆酸、γ-鼠胆酸、DL-Β-苯乳酸、鹅脱氧胆酸联合用于诊断结直肠进展期肿瘤的AUC为0.776。
实施例8：使用15个血浆代谢标志物进行结直肠进展期肿瘤诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意15个血浆代谢标志物。
经构建模型统计分析：任意15个差异代谢物单个用于诊断区分结直肠进展期肿瘤和非结直肠进展期肿瘤的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
3β-脱氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、石胆酸、DL-2-氨基辛酸、3β-猪去氧胆酸、溶血磷脂酰胆碱(14:0)、肌醇、谷氨酸、假尿苷、丙酰左旋肉碱盐酸盐、4-氨基丁酸、羟基癸酸、20-羧基花生四烯酸、L-焦谷氨酸和顺式-4-羟基-L-脯氨酸联合用于诊断结直肠进展期肿瘤的AUC为0.952。
顺式-4-羟基-L-脯氨酸、对称N,N-二甲基精氨酸、S-腺苷同型半胱氨酸、α-亚麻酸、马尿酸、甘氨酰-L-亮氨酸、12-羟基二十碳四烯酸、L-缬氨酸、琥珀酸、不对称二甲基精氨酸、牛磺石胆酸-3-硫酸盐、甘氨石胆酸、γ-鼠胆酸、DL-Β-苯乳酸和鹅脱氧胆酸联合用于诊断结直肠进展期肿瘤的AUC为0.788。
实施例9：使用19个血浆代谢标志物进行结直肠进展期肿瘤诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意19个血浆代谢标志物。
经构建模型统计分析：任意19个差异代谢物单个用于诊断区分结直肠进展期肿瘤和非结直肠进展期肿瘤的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
3β-脱氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、石胆酸、DL-2-氨基辛酸、3β-猪去氧胆酸、溶血磷脂酰胆碱(14:0)、肌醇、谷氨酸、假尿苷、丙酰左旋肉碱盐酸盐、4-氨基丁酸、羟基癸酸、20-羧基花生四烯酸、L-焦谷氨酸、顺式-4-羟基-L-脯氨酸、对称N,N-二甲基精氨酸、S-腺苷同型半胱氨酸、α-亚麻酸和马尿酸联合起来诊断结直肠进展期肿瘤的AUC达0.966。
实施例10：使用24个血浆代谢标志物进行结直肠进展期肿瘤诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意24个血浆代谢标志物。
经构建模型统计分析：任意24个差异代谢物单个用于诊断区分结直肠进展期肿瘤和非结直肠进展期肿瘤的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
3β-脱氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、石胆酸、DL-2-氨基辛酸、3β-猪去氧胆酸、溶血磷脂酰胆碱(14:0)、肌醇、谷氨酸、假尿苷、丙酰左旋肉碱盐酸盐、4-氨基丁酸、羟基癸酸、20-羧基花生四烯酸、L-焦谷氨酸、顺式-4-羟基-L-脯氨酸、对称N,N-二甲基精氨酸、S-腺苷同型半胱氨酸、α-亚麻酸、马尿酸、甘氨酰-L-亮氨酸、12-羟基二十碳四烯酸、L-缬氨酸、琥珀酸和不对称二甲基精氨酸联合起来诊断结直肠进展期肿瘤的AUC达0.975。
实施例11：血浆靶向代谢组针对性诊断区分结直肠癌和健康人诊断模型构建
本实施例的样本来源于实施例3，结直肠癌患者355例，健康人311例。代谢物检测和分析方法与实施例3相同，对上述29个代谢物进行定量检测。进一步优选代谢标志物组合为：3β-脱氧胆酸、石胆酸、溶血磷脂酰胆碱(14:0)、DL-2-氨基辛酸、3β-猪去氧胆酸、肌醇、谷氨酸、假尿苷、丙酰左旋肉碱盐酸盐、顺式-4-羟基-L-脯氨酸、对称N,N-二甲基精氨酸、S-腺苷同型半胱氨酸、α-亚麻酸、马尿酸。这些代谢物在结直肠癌患者体内发生显著变化，具体变化结果见下表：
结直肠癌患者VS健康人代谢物变化倍数

这14个差异代谢物单个用于诊断区分结直肠癌患者和健康人的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断结直肠癌患者的AUC为0.782～0.982。
实施例12：血浆靶向代谢组针对性诊断区分进展期腺瘤患者和非结直肠进展期肿瘤患者诊断模型构建
本实施例的样本来源于实施例3，进展期腺瘤患者100例。非结直肠进展期肿瘤患者200例，包括100例健康人和100例非进展期腺瘤患者。代谢物检测和分析方法与实施例3相同，对上述29个代谢物进行定量检测。
进一步优选代谢标志物溶血磷脂酰乙醇胺(P-18:0)、肌醇、4-氨基丁酸、L-焦谷氨酸、S-腺苷同型半胱氨酸、不对称二甲基精氨酸、牛磺石胆酸-3-硫酸盐。这些代谢物在进展期腺瘤患者体内发生显著变化，具体变化结果见下表：
进展期腺瘤患者VS非结直肠进展期肿瘤患者代谢物变化倍数
这7个差异代谢物单个用于诊断区分进展期腺瘤患者和非结直肠进展期肿瘤的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断进展期腺瘤患者的AUC为0.711～0.863。
实施例13：血浆靶向代谢组诊断区分进展期腺瘤患者和健康人诊断模型构建
进展期腺瘤患者100例，健康人100例。代谢物检测和分析方法与实施例3相同，对上述29个代谢物进行定量检测。
进一步优选代谢标志物溶血磷脂酰乙醇胺(P-18:0)、马尿酸、甘氨酰-L-亮氨酸、12-羟基二十碳四烯酸、琥珀酸、不对称二甲基精氨酸、甘氨石胆酸、γ-鼠胆酸。这些代谢物在进展期腺瘤患者体内发生显著变化，具体变化结果见下表：
进展期腺瘤患者VS健康人代谢物变化倍数
这8个差异代谢物单个用于诊断区分进展期腺瘤患者和健康人的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断进展期腺瘤患者的AUC为0.755～0.884。
实施例14：血浆靶向代谢组诊断区分结直肠进展期肿瘤患者和非结直肠进展期肿瘤患者诊断模型构建
本实施例的样本来源于实施例3，结直肠进展期肿瘤组患者455例，包括100例进展期腺瘤患者和355例结直肠癌患者；非结直肠进展期肿瘤患者411例，包括331例健康人和100例非进展期腺瘤。代谢物检测和分析方法与实施例3相同，对上述29个代谢物进行定量检测。
进一步优选代谢标志物：石胆酸、3β-猪去氧胆酸、肌醇、假尿苷、羟基癸酸、20-羧基花生四烯酸、L-焦谷氨酸、马尿酸、12-羟基二十碳四烯酸、鹅脱氧胆酸。这些代谢物在结直肠进展期肿瘤患者体内发生显著变化，具体变化结果见下表：
结直肠进展期肿瘤VS非结直肠进展期肿瘤患者代谢物变化倍数
这10个差异代谢物单个用于诊断区分结直肠进展期肿瘤患者和非结直肠进展期肿瘤患者的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断结直肠疾病患者的AUC为0.722～0.857。
实施例15：血浆靶向代谢组诊断区分结直肠进展期肿瘤患者和健康人诊断模型构建
本实施例的样本来源于实施例3，结直肠进展期肿瘤患者455例，包括100例进展期腺瘤患者和355例结直肠癌患者；健康人100例。代谢物检测和分析方法与实施例3相同，对上述29个代谢物进行定量检测。
进一步优选代谢标志物3β-脱氧胆酸、石胆酸、3β-猪去氧胆酸、肌醇、羟基癸酸、20-羧基花生四烯酸、L-焦谷氨酸、12-羟基二十碳四烯酸、L-缬氨酸、不对称二甲基精氨酸、DL-Β-苯乳酸、鹅脱氧胆酸。这些代谢物在结直肠癌患者体内发生显著变化，具体变化结果见下表：
结直肠进展期肿瘤患者VS健康人代谢物变化倍数

这12个差异代谢物单个用于诊断区分结直肠进展期肿瘤患者和健康人的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断结直肠疾病患者的AUC为0.752～0.885。
实施例16：使用组织样本构建结直肠癌诊断模型
1研究对象
本研究在取得患者同意后，在相同条件下收集了30例结直肠癌患者肿瘤病灶区组织和30例癌旁正常组织当做健康对照。采集的组织样品先通过纱布蘸取表面的血液，随后迅速转移至液氮中短期保存，最后转移至-80℃冰箱中长期保存。
2样品预处理
(1)从-80℃冰箱中取出样品于冰上解冻至能切动的状态(后续操作都要求在冰上进行)，准备好称量组织样本需要用的刀片、镊子、钢珠、滤纸、酒精、水等；
(2)取出样本，用滤纸吸掉样本表面的血液，用手术刀切下一块样本，用镊子夹到去皮后的离心管中，称量50±2mg，记录每个样本的称量重量；
(3)向称量好的样本中加入一粒钢珠，在30HZ的条件下匀浆4次，每次30s，根据匀浆情况，可适当增加匀浆时间；
(4)向匀浆好的离心管中加入1mL 70％甲醇内标提取液；
(5)振荡5min，冰上静置15min；
(6)在4℃条件下，12000r/min离心10min；
(7)离心后吸取上清液400uL到对应的离心管中；
(8)静置于-20℃冰箱，过夜；
(9)在4℃条件下，12000r/min再离心3min；
(10)离心后取上清200μL按顺序转移入96孔板内，过完蛋白沉淀板封口用于LC-MS/MS分析。
本实施例与实施例3的代谢物检测和分析方法相同，对上述29个代谢物进行定量检测。组织中单个代谢标志物用于结直肠癌诊断的结果见下表：
组织中单个代谢标志物用于结直肠癌诊断的AUC值

这29个差异代谢物单个用于诊断区分结直肠癌患者的能力较强；且各种代谢物组合用于诊断时，AUC进一步提高，其诊断结直肠癌患者的AUC值为0.781～0.999。
实施例17：使用粪便样本构建结直肠癌诊断模型
1研究对象
本研究在取得患者同意后，在相同条件下收集100例结直肠癌患者和100例健康对照粪便样本，-80℃冰箱中长期保存。
2样品预处理
(1)从-80℃冰箱中取出样品于冰上解冻至样本中没有冰块(后续操作都要求在冰上进行)；
(2)样本解冻后，称量样本50mg(±1mg)到2mL对应的离心管中，记录每个样本的称量重量；
(3)向离心管中加入500uL 70％甲醇内标提取液，涡旋3min混匀；(若样本仍是颗粒状，加入钢珠继续涡旋3min，冰水浴中超声10min，取出样本继续涡旋1min)；
(4)静置于-20℃冰箱30min；
(5)在4℃条件下，12000r/min离心10min；
(6)取250uL上清液于新的1.5mL EP管中；
(7)把上清液在4℃条件下，12000r/min离心5min；
(8)离心后取150μL上清液于对应进样瓶内衬管中，用于LC-MS/MS分析。
本实施例与实施例3的代谢物检测和分析方法相同，对上述29个代谢物进行定量检测。粪便中单个代谢标志物用于结直肠癌诊断的结果见下表：
粪便中单个代谢标志物用于结直肠癌诊断的AUC值
这29个差异代谢物单个用于诊断区分结直肠癌患者的能力较强；且各种代谢物组合用于诊断时，AUC进一步提高，其诊断结直肠癌的AUC值为0.741～0.991。
在实际应用中，可以按照本发明建模方法选取更多的样本进行建模，增加模型的准确度。
实施例18筛选结直肠进展期腺瘤和结直肠癌差异代谢标志物
参照专利文献CN114924073A公开《结直肠进展期肿瘤诊断标志物组合及其应用》中实施例2的方法步骤，本实施例提供一种结直肠进展期腺瘤和结直肠癌差异代谢标志物的筛选方法，区别在于步骤S1中采集样品的不同：
本试验研究在取得患者同意后，从3个独立的临床医学中心共收集了789例健康人、390例非进展期腺瘤患者、193例进展期腺瘤患者、452例结直肠癌患者的外周静脉血血浆。结直肠进展期肿瘤组包括结直肠进展期腺瘤患者和结直肠癌患者；非结直肠癌组包括健康人和非进展期腺瘤患者。
将收集的发现集所有样本通过LC-MS/MS进行代谢物全谱分析测试，通过Wilcoxon秩和检验所提供的任意两组间P值(P＜0.05)的筛选标准，从发现集样本中共得到48种初步差异性离子对。
根据上述鉴定方法，鉴定出48种血浆差异代谢标志物，见下表：
48种差异血浆代谢标志物

实施例19构建血浆靶向代谢组结直肠癌诊断模型
参照专利文献CN114924073A公开《结直肠进展期肿瘤诊断标志物组合及其应用》中实施例3的方法步骤，本实施例的试验步骤区别在于：
S1，采集样品
本研究在取得患者同意后，从3个独立的临床医学中心共收集了1981例血浆样本，分别作为训练集和验证集。其中训练集包括341例健康人，108例非进展期腺瘤患者，110例进展期腺瘤患者和383例结直肠癌患者的外周静脉血血浆。验证集包括417例健康人，100例非进展期腺瘤患者，209例进展期腺瘤患者和313例结直肠癌患者的外周静脉血血浆。其中健康人来源于体检后无肠道疾病的人群；疾病组均经过结直肠镜检查确诊。所有样本均无其它任何恶性肿瘤病史，无其他全身性重大疾病，无长期用药的慢性病史。各组样本间的年龄和性别均相匹配，非结直肠癌组包括健康人和非进展期腺瘤患者，结直肠进展期肿瘤组包括进展期腺瘤患者和结直肠癌患者。采血时间均为清晨空腹状态。所有血浆样本离心后置于-80℃冰箱内保存，研究时取出血浆样本解冻后进行后续分析。
S2，样品代谢检测分析
本步骤所采用的实验试剂见下表：
试验试剂


本试验发现集单个代谢标志物变化倍数结果见下表：
结直肠癌vs健康人代谢物变化倍数

利用逻辑回归模型对上述筛查得到的48个初步差异代谢物进行分析，发现30个代谢物：3-羟基丁酸、十六碳二酸、2-羟基癸酸、3β-熊脱氧胆酸、1-十四酰-2-羟基卵磷脂、2-氮己环酮、马尿酸、DL-2-氨基辛酸、熊去氧胆酸、去氧胆酸、甘氨石胆酸、20-羧基花生四烯酸、胡椒碱、1-甲基黄嘌呤、7-甲基黄嘌呤、假尿苷、反式-3-羟基可替宁、12-羟基二十碳四烯酸、鹅去氧胆酸、胆酸、1,7-二甲基黄嘌呤、S-(5'-腺苷)-L-高半胱氨酸、可可碱、异丙酚葡糖苷酸、对乙酰氨基酚、2-羟基马尿酸、3-羟基马尿酸、3-(3-羟基苯基)丙酸、3-吲哚丙酸、苯基乙酰谷氨酰胺作为结直肠进展期腺瘤及结直肠癌标记物的作用尤为重要。
训练集中这30个差异代谢物单个用于诊断区分结直肠癌和健康人的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义；这30个差异代谢物联合用于诊断时，AUC进一步提高，30个联合起来诊断区分结直肠癌和健康人的AUC达0.996。验证集中这30个差异代谢物单个用于诊断区分结直肠癌和健康人的ROC曲线下面积(AUC)均大于0.65，30个代谢物联合起来诊断区分结直肠癌和健康人的AUC达0.978。训练集中单个代谢标志物用于结直肠癌诊断的结果见下表：
诊断区分结直肠癌和健康人单个代谢物的AUC值

实施例20：使用2个血浆代谢标志物进行结直肠癌诊断模型的构建
本实施例与实施例19的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意2个血浆代谢标志物。
经构建模型统计分析：任意2个差异代谢物单个用于诊断区分结直肠癌和健康人的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
1-十四酰-2-羟基卵磷脂和2-羟基马尿酸联合起来诊断结直肠癌训练集的AUC为0.911，验证集的AUC为0.900。
去氧胆酸和鹅去氧胆酸联合起来诊断结直肠癌训练集的AUC为0.791，验证集的AUC为0.782。
1-甲基黄嘌呤和反式-3-羟基可替宁联合起来诊断结直肠癌训练集的AUC为0.726，验证集的AUC为0.668。
实施例21：使用5个血浆代谢标志物进行结直肠癌诊断模型的构建
本实施例与实施例19的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意5个血浆代谢标志物。
经构建模型统计分析：任意5个差异代谢物单个用于诊断区分结直肠癌和健康人的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
1-十四酰-2-羟基卵磷脂、2-羟基马尿酸、马尿酸、3-(3-羟基苯基)丙酸和对乙酰氨基酚联合用于诊断结直肠癌训练集的AUC为0.938，验证集的AUC为0.917。
3β-熊脱氧胆酸、3-羟基丁酸、去氧胆酸、鹅去氧胆酸和12-羟基二十碳四烯酸联合用于诊断结直肠癌训练集的AUC为0.808，验证集的AUC为0.810。
可可碱、假尿苷、胡椒碱、1-甲基黄嘌呤和反式-3-羟基可替宁联合用于诊断结直肠癌训练集的AUC为0.748，验证集的AUC为0.705。
实施例22：使用8个血浆代谢标志物进行结直肠癌诊断模型的构建
本实施例与实施例19的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意8个血浆代谢标志物。
经构建模型统计分析：任意8个差异代谢物单个用于诊断区分结直肠癌和健康人的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
1-十四酰-2-羟基卵磷脂、2-羟基马尿酸、马尿酸、3-(3-羟基苯基)丙酸、对乙酰氨基酚、异丙酚葡糖苷酸、3-羟基马尿酸和苯基乙酰谷氨酰胺联合用于诊断结直肠癌训练集的AUC为0.945，验证集的AUC为0.933。
DL-2-氨基辛酸、3β-熊脱氧胆酸、3-羟基丁酸、去氧胆酸、鹅去氧胆酸、12-羟基二十碳四烯酸、1,7-二甲基黄嘌呤、20-羧基花生四烯酸联合用于诊断结直肠癌训练集的AUC为0.821，验证集的AUC为0.818。
胆酸、S-(5'-腺苷)-L-高半胱氨酸、十六碳二酸、可可碱、假尿苷、胡椒碱、1-甲基黄嘌呤、反式-3-羟基可替宁联合用于诊断结直肠癌训练集的AUC为0.761，验证集的AUC为0.726。
实施例23：使用12个血浆代谢标志物进行结直肠癌诊断模型的构建
本实施例与实施例19的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意12个血浆代谢标志物。
经构建模型统计分析：任意12个差异代谢物单个用于诊断区分结直肠癌和健康人的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
1-十四酰-2-羟基卵磷脂、2-羟基马尿酸、马尿酸、3-(3-羟基苯基)丙酸、对乙酰氨基酚、异丙酚葡糖苷酸、3-羟基马尿酸、苯基乙酰谷氨酰胺、3-吲哚丙酸、2-羟基癸酸、熊去氧胆酸和DL-2-氨基辛酸联合用于诊断结直肠癌训练集的AUC为0.951，验证集的AUC为0.938。
20-羧基花生四烯酸、2-氮己环酮、甘氨石胆酸、7-甲基黄嘌呤、胆酸、S-(5'-腺苷)-L-高半胱氨酸、十六碳二酸、可可碱、假尿苷、胡椒碱、1-甲基黄嘌呤和反式-3-羟基可替宁联合用于诊断结直肠癌训练集的AUC为0.785，验证集的AUC为0.754。
实施例24：使用15个血浆代谢标志物进行结直肠癌诊断模型的构建
本实施例与实施例19的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意15个血浆代谢标志物。
经构建模型统计分析：任意15个差异代谢物单个用于诊断区分结直肠癌和健康人的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
1-十四酰-2-羟基卵磷脂、2-羟基马尿酸、马尿酸、3-(3-羟基苯基)丙酸、对乙酰氨基酚、异丙酚葡糖苷酸、3-羟基马尿酸、苯基乙酰谷氨酰胺、3-吲哚丙酸、2-羟基癸酸、熊去氧胆酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、3-羟基丁酸和去氧胆酸联合用于诊断结直肠癌训练集的AUC为0.972，验证集的AUC为0.961。
鹅去氧胆酸、12-羟基二十碳四烯酸、1,7-二甲基黄嘌呤、20-羧基花生四烯酸、2-氮己环酮、甘氨石胆酸、7-甲基黄嘌呤、胆酸、S-(5'-腺苷)-L-高半胱氨酸、十六碳二酸、可可碱、假尿苷、胡椒碱、1-甲基黄嘌呤和反式-3-羟基可替宁联合用于诊断结直肠癌训练集的AUC为0.798，验证集的AUC为0.766。
实施例25：使用20个血浆代谢标志物进行结直肠癌诊断模型的构建
本实施例与实施例19的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意20个血浆代谢标志物。
经构建模型统计分析：任意20个差异代谢物单个用于诊断区分结直肠癌和健康人的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
1-十四酰-2-羟基卵磷脂、2-羟基马尿酸、马尿酸、3-(3-羟基苯基)丙酸、对乙酰氨基酚、异丙酚葡糖苷酸、3-羟基马尿酸、苯基乙酰谷氨酰胺、3-吲哚丙酸、2-羟基癸酸、熊去氧胆酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、3-羟基丁酸、去氧胆酸、鹅去氧胆酸、12- 羟基二十碳四烯酸、1,7-二甲基黄嘌呤、20-羧基花生四烯酸和2-氮己环酮联合起来诊断结直肠癌训练集的AUC达0.983，验证集的AUC为0.968。
熊去氧胆酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、3-羟基丁酸、去氧胆酸、鹅去氧胆酸、12-羟基二十碳四烯酸、1,7-二甲基黄嘌呤、20-羧基花生四烯酸、2-氮己环酮、甘氨石胆酸、7-甲基黄嘌呤、胆酸、S-(5'-腺苷)-L-高半胱氨酸、十六碳二酸、可可碱、假尿苷、胡椒碱、1-甲基黄嘌呤和反式-3-羟基可替宁联合用于诊断结直肠癌训练集的AUC为0.811，验证集的AUC为0.786。
实施例26：使用25个血浆代谢标志物进行结直肠癌诊断模型的构建
本实施例与实施例19的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用上述任意25个血浆代谢标志物。
经构建模型统计分析：任意25个差异代谢物单个用于诊断区分结直肠癌和健康人的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，部分统计结果示例如下：
1-十四酰-2-羟基卵磷脂、2-羟基马尿酸、马尿酸、3-(3-羟基苯基)丙酸、对乙酰氨基酚、异丙酚葡糖苷酸、3-羟基马尿酸、苯基乙酰谷氨酰胺、3-吲哚丙酸、2-羟基癸酸、熊去氧胆酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、3-羟基丁酸、去氧胆酸、鹅去氧胆酸、12-羟基二十碳四烯酸、1,7-二甲基黄嘌呤、20-羧基花生四烯酸、2-氮己环酮、甘氨石胆酸、7-甲基黄嘌呤、胆酸、S-(5'-腺苷)-L-高半胱氨酸和十六碳二酸联合起来诊断结直肠癌训练集的AUC达0.991，验证集的AUC为0.977。
异丙酚葡糖苷酸、3-羟基马尿酸、苯基乙酰谷氨酰胺、3-吲哚丙酸、2-羟基癸酸、熊去氧胆酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、3-羟基丁酸、去氧胆酸、鹅去氧胆酸、12-羟基二十碳四烯酸、1,7-二甲基黄嘌呤、20-羧基花生四烯酸、2-氮己环酮、甘氨石胆酸、7-甲基黄嘌呤、胆酸、S-(5'-腺苷)-L-高半胱氨酸、十六碳二酸、可可碱、假尿苷、胡椒碱、1-甲基黄嘌呤和反式-3-羟基可替宁联合起来诊断结直肠癌训练集的AUC达0.878，验证集的AUC为0.835。
实施例27：血浆靶向代谢组针对性诊断区分结直肠癌和健康人诊断模型构建
本实施例的样本来源于实施例19，训练集结直肠癌患者383例，健康人341例；验证集结直肠癌患者313例，健康人417例。代谢物检测和分析方法与实施例3相同，对上述30个代谢物进行定量检测。
进一步优选代谢标志物组合为：1-十四酰-2-羟基卵磷脂、马尿酸、2-羟基癸酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、3-羟基丁酸、2-氮己环酮和十六碳二酸。这些代谢物在结直肠癌患者体内发生显著变化，训练集具体变化结果见下表：
结直肠癌患者vs健康人代谢物变化倍数
这8个差异代谢物单个用于诊断区分结直肠癌患者和健康人的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断区分结直肠癌患者和健康人训练集的AUC为0.720～0.988，验证集的AUC为0.702～0.967。
实施例28：血浆靶向代谢组针对性诊断区分结直肠癌和非进展期肿瘤患者诊断模型构建
本实施例的样本来源于实施例19，训练集结直肠癌患者383例；非结直肠进展期肿瘤患者449例，包括341例健康人和108例非进展期腺瘤患者。验证集结直肠癌患者313例；非结直肠进展期肿瘤患者517例，包括417例健康人和100例非进展期腺瘤患者。代谢物检测和分析方法与实施例3相同，对上述30个代谢物进行定量检测。
进一步优选代谢标志物组合为：1-十四酰-2-羟基卵磷脂、马尿酸、2-羟基癸酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、2-氮己环酮胆酸、S-(5'-腺苷)-L-高半胱氨酸、十六碳二酸、胡椒碱和反式-3-羟基可替宁。这些代谢物在结直肠癌患者体内发生显著变化，训练集中具体变化结果见下表：
结直肠癌患者vs非进展期肿瘤组代谢物变化倍数
这11个差异代谢物单个用于诊断区分结直肠癌患者和非进展期肿瘤组的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断区分结直肠癌患者和非进展期肿瘤组训练集的AUC为0.718～0.982，验证集的AUC为0.701～0.958。
实施例29：血浆靶向代谢组针对性诊断区分进展期腺瘤患者和非结直肠进展期肿瘤患者诊断模型构建
本实施例的样本来源于实施例19，训练集进展期腺瘤患者110例，非结直肠进展期肿瘤患者449例，包括341例健康人和108例非进展期腺瘤患者；验证集进展期腺瘤患者209例，非结直肠进展期肿瘤患者517例，包括417例健康人和100例非进展期腺瘤患者。代谢物检测和分析方法与实施例3相同，对上述30个代谢物进行定量检测。
进一步优选代谢标志物：1-十四酰-2-羟基卵磷脂、马尿酸、DL-2-氨基辛酸、去氧胆酸、12-羟基二十碳四烯酸、7-甲基黄嘌呤、S-(5'-腺苷)-L-高半胱氨酸和胡椒碱。这些代谢物在进展期腺瘤患者体内发生显著变化，训练集中具体变化结果见下表：
进展期腺瘤患者vs非结直肠进展期肿瘤患者代谢物变化倍数

这8个差异代谢物单个用于诊断区分进展期腺瘤患者和非结直肠进展期肿瘤的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断区分进展期腺瘤患者和非结直肠进展期肿瘤训练集的AUC为0.701～0.852，验证集的AUC为0.654～0.832。
实施例30：血浆靶向代谢组诊断区分进展期腺瘤患者和健康人诊断模型构建
本实施例的样本来源于实施例19，训练集进展期腺瘤患者110例，健康人341例；验证集进展期腺瘤患者209例，健康人417例。代谢物检测和分析方法与实施例3相同，对上述30个代谢物进行定量检测。
进一步优选代谢标志物：1-十四酰-2-羟基卵磷脂、马尿酸、DL-2-氨基辛酸、去氧胆酸、鹅去氧胆酸、12-羟基二十碳四烯酸、7-甲基黄嘌呤、胆酸、S-(5'-腺苷)-L-高半胱氨酸、胡椒碱和1-甲基黄嘌呤。这些代谢物在进展期腺瘤患者体内发生显著变化，训练集中具体变化结果见下表：
进展期腺瘤患者vs健康人代谢物变化倍数
这11个差异代谢物单个用于诊断区分进展期腺瘤患者和健康人的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断区分进展期腺瘤患者和健康人训练集的AUC为0.710～0.933，验证集的AUC为0.698～0.915。
实施例31：血浆靶向代谢组诊断区分结直肠进展期肿瘤患者和非结直肠进展期肿瘤患者诊断模型构建
本实施例的样本来源于实施例19，训练集结直肠进展期肿瘤组患者493例，包括110例进展期腺瘤患者和383例结直肠癌患者；非结直肠进展期肿瘤患者449例，包括341例健康人和108例非进展期腺瘤患者。验证集结直肠进展期肿瘤组患者522例，包括209例进展期腺瘤患者和313例结直肠癌患者；非结直肠进展期肿瘤患者517例，包括417例健康人和100例非进展期腺瘤患者。代谢物检测和分析方法与实施例19相同，对上述30个代谢物进行定量检测。
进一步优选代谢标志物：1-十四酰-2-羟基卵磷脂、马尿酸、2-羟基癸酸、熊去氧胆酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、3-羟基丁酸、12-羟基二十碳四烯酸、20-羧基花生 四烯酸、2-氮己环酮、甘氨石胆酸、7-甲基黄嘌呤、S-(5'-腺苷)-L-高半胱氨酸、十六碳二酸、胡椒碱、1-甲基黄嘌呤和反式-3-羟基可替宁。这些代谢物在结直肠进展期肿瘤患者体内发生显著变化，训练集中具体变化结果见下表：
结直肠进展期肿瘤vs非结直肠进展期肿瘤患者代谢物变化倍数
这17个差异代谢物单个用于诊断区分结直肠进展期肿瘤患者和非结直肠进展期肿瘤患者的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断区分结直肠进展期肿瘤患者和非结直肠进展期肿瘤患者训练集的AUC为0.703～0.927，验证集的AUC为0.685～0.913。
实施例32：血浆靶向代谢组诊断区分结直肠进展期肿瘤患者和健康人诊断模型构建
本实施例的样本来源于实施例19，训练集结直肠进展期肿瘤组患者493例，包括110例进展期腺瘤患者和383例结直肠癌患者；健康人341例。验证集结直肠进展期肿瘤组患者522例，包括209例进展期腺瘤患者和313例结直肠癌患者；健康人417例。代谢物检测和分析方法与实施例20相同，对上述30个代谢物进行定量检测。
进一步优选代谢标志物：1-十四酰-2-羟基卵磷脂、马尿酸、熊去氧胆酸、DL-2-氨基辛酸、3-羟基丁酸、12-羟基二十碳四烯酸、7-甲基黄嘌呤、胆酸、S-(5'-腺苷)-L-高半胱氨酸、十六碳二酸和反式-3-羟基可替宁。这些代谢物在结直肠癌患者体内发生显著变化，训练集中具体变化结果见下表：
结直肠进展期肿瘤患者vs健康人代谢物变化倍数

这11个差异代谢物单个用于诊断区分结直肠进展期肿瘤患者和健康人的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断区分结直肠进展期肿瘤患者和健康人训练集的AUC为0.722～0.938，验证集的AUC为0.703～0.921。
实施例33：使用组织样本构建结直肠癌诊断模型
参照专利文献CN114924073A公开《结直肠进展期肿瘤诊断标志物组合及其应用》中实施例16的方法步骤，区别在于：
研究对象：本研究在取得患者同意后，在相同条件下收集了25例结直肠癌患者肿瘤病灶区组织和25例癌旁正常组织当做健康人。
对以下48个代谢物进行定量检测，包括：1-十四酰-2-羟基卵磷脂、2-羟基马尿酸、马尿酸、3-(3-羟基苯基)丙酸、对乙酰氨基酚、异丙酚葡糖苷酸、3-羟基马尿酸、苯基乙酰谷氨酰胺、3-吲哚丙酸、羟基癸酸、熊去氧胆酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、3-羟基丁酸、去氧胆酸、鹅去氧胆酸、12-羟基二十碳四烯酸、1,7-二甲基黄嘌呤、20-羧基花生四烯酸、2-氮己环酮、甘氨石胆酸、7-甲基黄嘌呤、胆酸、S-(5'-腺苷)-L-高半胱氨酸、十六碳二酸、可可碱、假尿苷、胡椒碱、1-甲基黄嘌呤、反式-3-羟基可替宁、3-磺基可可醇、5-甲基糠醛、4,6-二氨基-2-羟基-5-嘧啶磺酸、2,4-二羟基苯甲酸、(2E，4E，8E)-十四烷-2,4,8-三烯酸、3-酮-4-氨基苯甲酸酯、环(异亮氨酸脯氨酰)、N-十六烷基二乙醇胺、(五十)-苏伯尔肉碱、4-羟基马尿酸、2-[2-[3-(2-羧乙基)-1H-吲哚-2-基]乙基]-1H-吲哚-3-羧酸、2-甲基三癸二酸、六酰基谷氨酰胺、N-乙酰-L-丝氨酸-L-亮氨酸、N-乙酰基甘氨酰丙氨酸、溶血磷脂酰胆碱(20:2)、对甲酚葡糖苷酸、7-去氧胆酸。组织中单个代谢标志物用于结直肠癌诊断的结果见下表：
组织中单个代谢标志物用于结直肠癌诊断的AUC值

这48个差异代谢物单个用于诊断区分结直肠癌患者的能力较强；且各种代谢物组合用于诊断时，AUC进一步提高，其诊断区分结直肠癌患者的AUC值为0.785～0.999。
实施例34：使用粪便样本构建结直肠癌诊断模型
参照专利文献CN114924073A公开《结直肠进展期肿瘤诊断标志物组合及其应用》中实施例17的方法步骤，区别在于：
研究对象：本研究在取得患者同意后，在相同条件下收集110例结直肠癌患者和110例健康人粪便样本，-80℃冰箱中长期保存。
对上述48个代谢物进行定量检测。粪便中单个代谢标志物用于结直肠癌诊断的结果见下表：
粪便中单个代谢标志物用于结直肠癌诊断的AUC值

这48个差异代谢物单个用于诊断区分结直肠癌患者的能力较强；且各种代谢物组合用于诊断时，AUC进一步提高，其诊断区分结直肠癌的AUC值为0.751～0.991。在实际应用中，可以按照本发明建模方法选取更多的样本进行建模，增加模型的准确度。
实施例35：血浆靶向代谢组诊断区分结直肠进展期肿瘤患者和非结直肠进展期肿瘤患者诊断模型构建
本实施例的样本在取得患者同意后，从3个独立的临床医学中心共收集了120例健康人、60例非进展期腺瘤患者、70例进展期腺瘤患者、115例结直肠癌患者的外周静脉血血浆。结直肠进展期肿瘤组包括结直肠进展期腺瘤患者和结直肠癌患者；非结直肠癌组包括健康人和非进展期腺瘤患者。代谢物检测包括实施例2和实施例19，分析方法与实施例19相同，对上述67个代谢物进行定量检测。
进一步优选代谢标志物：1-十四酰-2-羟基卵磷脂、2-羟基癸酸、(2E，4E，8E)-十四烷-2,4,8-三烯酸、(R)-3-羟基丁酸、2,4-二羟基苯甲酸、2-[2-[3-(2-羧乙基)-1H-吲哚-2-基]乙基]-1H-吲哚-3-羧酸、2-氮己环酮、3β-脱氧胆酸、3β-熊脱氧胆酸、3β-猪去氧胆酸、3-磺基可可醇、4,6-二氨基-2-羟基-5-嘧啶磺酸、5-甲基糠醛、7-甲基黄嘌呤、DL-2-氨基辛酸、N-十六烷基二乙醇胺、甘氨石胆酸、假尿苷、马尿酸、去氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、十六碳二酸、熊去氧胆酸。这些代谢物在结直肠进展期肿瘤患者体内发生显著变化，具体变化结果见下表：
结直肠进展期肿瘤vs非结直肠进展期肿瘤患者代谢物变化倍数

这23个差异代谢物单个用于诊断区分结直肠进展期肿瘤患者和非结直肠进展期肿瘤患者的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断区分结直肠进展期肿瘤患者和非结直肠进展期肿瘤患者的AUC为0.758～0.977。
实施例36检测试剂盒
本实施例提供一种基于上述代谢标志物制备的检测试剂盒，该检测试剂盒包括代谢标志物的标准品：3β-脱氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、石胆酸、DL-2-氨基辛酸、3β-猪去氧胆酸、溶血磷脂酰胆碱(14:0)、肌醇、谷氨酸、假尿苷、丙酰左旋肉碱盐酸盐、4-氨基丁酸、羟基癸酸、20-羧基花生四烯酸、L-焦谷氨酸、顺式-4-羟基-L-脯氨酸、对称N,N-二甲基精氨酸、S-腺苷同型半胱氨酸、α-亚麻酸、马尿酸、甘氨酰-L-亮氨酸、12-羟基二十碳四烯酸、L-缬氨酸、琥珀酸、不对称二甲基精氨酸、牛磺石胆酸-3-硫酸盐、甘氨石胆酸、γ-鼠胆酸、DL-Β-苯乳酸、鹅脱氧胆酸、2-羟基马尿酸、3-(3-羟基苯基)丙酸、对乙酰氨基酚、异丙酚葡糖苷酸、3-羟基马尿酸、苯基乙酰谷氨酰胺、3-吲哚丙酸、熊去氧胆酸、3β-熊脱氧胆酸、3-羟基丁酸、去氧胆酸、1,7-二甲基黄嘌呤、2-氮己环酮、7-甲基黄嘌呤、胆酸、十六碳二酸、可可碱、胡椒碱、1-甲基黄嘌呤、反式-3-羟基可替宁、3-磺基可可醇、5-甲基糠醛、4,6-二氨基-2-羟基-5-嘧啶磺酸、2,4-二羟基苯甲酸、(2E，4E，8E)-十四烷-2,4,8-三烯酸、3-酮-4-氨基苯甲酸酯，环(异亮氨酸脯氨酰)、N-十六烷基二乙醇胺、(五十)-苏伯尔肉碱、4-羟基马尿酸、2-[2-[3-(2-羧乙基)-1H-吲哚-2-基]乙基]-1H-吲哚-3-羧酸、2-甲基三癸二酸、六酰基谷氨酰胺、N-乙酰-L-丝氨酸-L-亮氨酸、N-乙酰基甘氨酰丙氨酸、溶血磷脂酰胆碱(20:2)、对甲酚葡糖苷酸、7-去氧胆酸。各标准品分别封装或标准品混合溶液封装。
血浆样本代谢物提取剂：100％纯甲醇和50％乙腈水溶液用于样品制备；50％乙腈水溶液可以用作溶解标准品的溶剂。
内标物：[2H3]-L-肉碱-d3盐酸盐，4-氟-L-2-苯基甘氨酸，L-苯基丙氨酸，[2H5]-马尿酸，[2H5]-犬尿酸，[2H5]-苯氧基乙酸。
当然，设计检测试剂盒时，并不需要完全包含上述全部代谢标志物的标准品，可以仅使用其中几个，还可以使用其中几个或全部与其他标志物进行组合。这些标准品可以单独封装，也可以制成混合物封装。采用本实施例提供的检测试剂盒，能够拥有对结直肠癌进展期腺瘤和结直肠癌进行风险评估、诊断和监测。
在此有必要指出的是，以上实施例仅限于对本发明的技术方案做进一步的阐述和说明，并不是对本发明的技术方案的进一步的限制，本发明的方法仅为较佳的实施方案，并非用于限定本发明的保护范围。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种用于结直肠进展期肿瘤诊断、监测或者风险评估的标志物，其特征在于，所述标志物至少选自3β-脱氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、石胆酸、DL-2-氨基辛酸、3β-猪去氧胆酸、溶血磷脂酰胆碱(14:0)、肌醇、谷氨酸、假尿苷、丙酰左旋肉碱盐酸盐、4-氨基丁酸、羟基癸酸、20-羧基花生四烯酸、L-焦谷氨酸、顺式-4-羟基-L-脯氨酸、对称N,N-二甲基精氨酸、S-腺苷同型半胱氨酸、α-亚麻酸、马尿酸、甘氨酰-L-亮氨酸、12-羟基二十碳四烯酸、L-缬氨酸、琥珀酸、不对称二甲基精氨酸、牛磺石胆酸-3-硫酸盐、甘氨石胆酸、γ-鼠胆酸、DL-Β-苯乳酸、鹅脱氧胆酸、2-羟基马尿酸、3-(3-羟基苯基)丙酸、对乙酰氨基酚、异丙酚葡糖苷酸、3-羟基马尿酸、苯基乙酰谷氨酰胺、3-吲哚丙酸、熊去氧胆酸、3β-熊脱氧胆酸、3-羟基丁酸、去氧胆酸、1,7-二甲基黄嘌呤、2-氮己环酮、7-甲基黄嘌呤、胆酸、十六碳二酸、可可碱、胡椒碱、1-甲基黄嘌呤、反式-3-羟基可替宁、3-磺基可可醇、5-甲基糠醛、4,6-二氨基-2-羟基-5-嘧啶磺酸、2,4-二羟基苯甲酸、(2E，4E，8E)-十四烷-2,4,8-三烯酸、3-酮-4-氨基苯甲酸酯，环(异亮氨酸脯氨酰)、N-十六烷基二乙醇胺、(五十)-苏伯尔肉碱、4-羟基马尿酸、2-[2-[3-(2-羧乙基)-1H-吲哚-2-基]乙基]-1H-吲哚-3-羧酸、2-甲基三癸二酸、六酰基谷氨酰胺、N-乙酰-L-丝氨酸-L-亮氨酸、N-乙酰基甘氨酰丙氨酸、溶血磷脂酰胆碱(20:2)、对甲酚葡糖苷酸、7-去氧胆酸中的至少一种。
  2. 根据权利要求1所述的标志物，其特征在于，所述标志物选自3β-脱氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、石胆酸、DL-2-氨基辛酸、3β-猪去氧胆酸、溶血磷脂酰胆碱(14:0)、肌醇、谷氨酸、假尿苷、丙酰左旋肉碱盐酸盐、4-氨基丁酸、羟基癸酸、20-羧基花生四烯酸、L-焦谷氨酸、顺式-4-羟基-L-脯氨酸、对称N,N-二甲基精氨酸、S-腺苷同型半胱氨酸、α-亚麻酸、马尿酸、甘氨酰-L-亮氨酸、12-羟基二十碳四烯酸、L-缬氨酸、琥珀酸、不对称二甲基精氨酸、牛磺石胆酸-3-硫酸盐、甘氨石胆酸、γ-鼠胆酸、DL-Β-苯乳酸、鹅脱氧胆酸、3-羟基丁酸、十六碳二酸3β-熊脱氧胆酸、2-氮己环酮、马尿酸、DL-2-氨基辛酸、熊去氧胆酸、去氧胆酸、甘氨石胆酸、20-羧基花生四烯酸、胡椒碱、1-甲基黄嘌呤、7-甲基黄嘌呤、反式-3-羟基可替宁、12-羟基二十碳四烯酸、鹅去氧胆酸、胆酸、1,7-二甲基黄嘌呤、可可碱、异丙酚葡糖苷酸、对乙酰氨基酚、2-羟基马尿酸、3-羟基马尿酸、3-(3-羟基苯基)丙酸、3-吲哚丙酸、苯基乙酰谷氨酰胺中的至少一种。
  3. 根据权利要求1所述的标志物，其特征在于，所述标志物选自溶血磷脂酰胆碱(14:0)、羟基癸酸、S-腺苷同型半胱氨酸、马尿酸、DL-2-氨基辛酸、12-羟基二十碳四烯酸、20-羧基花生四烯酸、甘氨石胆酸、假尿苷中的至少一种。
  4. 根据权利要求3所述的标志物，其特征在于，所述标志物还选自3β-猪去氧胆酸、牛磺石胆酸-3-硫酸盐、羟基癸酸、3-羟基丁酸、去氧胆酸、熊去氧胆酸、溶血磷脂酰乙醇胺(P-18:0)、丙酰左旋肉碱盐酸盐、γ-鼠胆酸、DL-Β-苯乳酸、7-甲基黄嘌呤、胡椒碱、中的至少一种。
  5. 根据权利要求1所述的标志物，其特征在于，所述标志物选自溶血磷脂酰胆碱(14:0)、马尿酸、2-羟基癸酸、熊去氧胆酸、DL-2-氨基辛酸、3β-熊脱氧胆酸、3-羟基丁酸、12-羟基二十碳四烯酸、20-羧基花生四烯酸、2-氮己环酮、甘氨石胆酸、7-甲基黄嘌呤、胆酸、S-腺苷同型半胱氨酸、十六碳二酸、反式-3-羟基可替宁、胡椒碱中的至少一种。
  6. 根据权利要求1所述的标志物，其特征在于，所述标志物组合至少选自3β-猪去氧胆酸、牛磺石胆酸-3-硫酸盐、DL-2-氨基辛酸、羟基癸酸、20-羧基花生四烯酸、溶血磷脂酰胆碱(14:0)、溶血磷脂酰乙醇胺(P-18:0)、丙酰左旋肉碱盐酸盐、甘氨石胆酸、γ-鼠胆酸、DL-Β-苯乳酸中的至少一种。
  7. 根据权利要求1所述的标志物，其特征在于，所述标志物至少选自石胆酸、3β-猪去氧胆酸、假尿苷、羟基癸酸、20-羧基花生四烯酸、12-羟基二十碳四烯酸、鹅脱氧胆酸中的至少一种。
  8. 根据权利要求1所述的标志物，其特征在于，所述标志物至少选自3β-脱氧胆酸、石胆酸、3β-猪去氧胆酸、羟基癸酸、20-羧基花生四烯酸、12-羟基二十碳四烯酸、L-缬氨酸、DL-Β-苯乳酸、鹅脱氧胆酸中的至少一种。
  9. 权利要求1至8任一项所述的标志物在构建代谢物数据库以及制备结直肠进展期肿瘤诊断试剂产品中的应用。
  10. 一种试剂产品，其特征在于，包括权利要求1至8任一项所述的结直肠进展期肿瘤诊断标志物作为标准品。
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