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WO2024159559A1 - Marqueur protéique et kit pour le dépistage précoce du cancer colorectal et leur utilisation - Google Patents

Marqueur protéique et kit pour le dépistage précoce du cancer colorectal et leur utilisation Download PDF

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WO2024159559A1
WO2024159559A1 PCT/CN2023/077068 CN2023077068W WO2024159559A1 WO 2024159559 A1 WO2024159559 A1 WO 2024159559A1 CN 2023077068 W CN2023077068 W CN 2023077068W WO 2024159559 A1 WO2024159559 A1 WO 2024159559A1
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colorectal cancer
protein
protein marker
marker combination
lrg1
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Chinese (zh)
Inventor
廖鲁剑
王婷婷
高飞
潘良选
杜逍遥
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Hangzhou Du'an Medical Laboratory Co Ltd
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Hangzhou Du'an Medical Laboratory Co Ltd
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    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
    • G01N27/626—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode using heat to ionise a gas
    • 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
    • G01N33/531—Production of immunochemical test materials
    • 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
    • G01N33/57407—Specifically defined cancers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/06—Gastro-intestinal diseases
    • G01N2800/065—Bowel diseases, e.g. Crohn, ulcerative colitis, IBS

Definitions

  • Colorectal cancer is one of the top five causes of cancer death worldwide. In the United States, the incidence of colorectal cancer ranks third and the mortality rate ranks second. Similarly, colorectal cancer has also become a high-incidence malignant tumor that seriously affects the health of Chinese people. Its incidence and mortality rate rank among the top three among all malignant tumors.
  • the main reason for the low survival rate of colorectal cancer patients is the lack of effective early diagnosis of early colorectal cancer.
  • a large number of clinical practices have shown that the five-year survival rate of patients who undergo surgery in the early stage of tumor development (stage I or stage IIa) can reach 90%, while the five-year survival rate of patients who undergo surgery in the late stage (stage III and stage IV) is less than 10%. It often takes 10-15 years for colorectal cancer to evolve from precancerous to diffuse and metastatic malignant tumors. Therefore, early diagnosis of cancer cells before they spread and metastasize is of great significance to improving the survival rate of patients.
  • the main means of colorectal cancer screening in clinical practice include colorectal endoscopy, imaging examination, fecal occult blood test, DNA test, CEA and other protein marker tests, etc.
  • These conventional technologies are difficult to use for early screening of large-scale risk groups because they are invasive or cause radiation damage, and more importantly, they have low sensitivity. In addition, the tolerance and acceptance of colonoscopy in the general population are also low.
  • the only non-invasive detection method used in clinical practice is chemical and immunological testing of fecal occult blood, but the sensitivity of this type of test for colorectal cancer is only 61-79% under the premise of 86-95% specificity. Although it is widely used in clinical practice, the detection rate of early colorectal cancer is difficult to meet clinical needs.
  • Cologuard can achieve a sensitivity of 95.55%, but its specificity will be reduced to 87.1%. Improving sensitivity and specificity at the same time will better improve the accuracy of detection and minimize the probability of missed diagnosis and misdiagnosis.
  • protein markers detected by CEA but their sensitivity and specificity are even more limited.
  • proteomics based on high-resolution mass spectrometry has greatly improved the accuracy of detection while also increasing the speed of detection, and has gradually become suitable for analyzing the proteome expression levels of large-scale clinical samples.
  • the industry generally believes that high-sensitivity and high-specificity early cancer screening methods need to shift from single protein markers to combined markers. At present, there is no early screening diagnostic kit for colorectal cancer based on protein markers in clinical practice.
  • the first aspect of the present invention provides a protein marker combination for prediction, diagnosis or prognosis of colorectal cancer, wherein the protein marker combination includes at least one selected from LRG1, SERPINA1, ITIH3, CP, ORM1, C9, IGFBP2, and CNDP1.
  • ITIH3 heavy chain H3 of inter-alpha trypsin inhibitor, the complex can stabilize the extracellular matrix through its ability to bind hyaluronic acid.
  • the polymorphism of this gene may be associated with an increased risk of schizophrenia and major depression.
  • LRG1 Belongs to the leucine-rich repeat protein family and plays an important role in protein-protein interactions, signal transduction, cell-cell adhesion and development.
  • C9 This protein is the final component of the complement system and is involved in the formation of the membrane attack complex (MAC).
  • MAC membrane attack complex
  • IGFBP2 insulin-like growth factors I and II (IGF-I and IGF-II). After being secreted into the blood, it can better bind to IGF-I and IGF-II, and can also interact with different ligands in cells. High expression of IGFBP2 can promote the growth of various tumors and predict the prognosis of patients.
  • CNDP1 This protein is a member of the M20 metalloproteinase family and a homodimeric dipeptidase specifically expressed in the brain.
  • the coding region of the gene contains a trinucleotide (CTG) repeat sequence.
  • SERPINA1 This protein is a serine protease inhibitor belonging to the serine superfamily, and its targets include elastase, plasmin, thrombin, trypsin, chymosin, and plasminogen activator.
  • the protein is produced in lymphocytes and monocytes in the liver, bone marrow, lymphoid tissue, and Paneth cells in the intestine. Defects in this gene have been reported to be associated with chronic obstructive pulmonary disease, emphysema, and chronic liver disease.
  • CP This protein is a metalloprotein that binds most of the copper in plasma and is involved in the peroxidation of iron (II) transferrin to iron (III) transferrin. Mutations in this gene cause acute plasminemia, leading to iron accumulation and tissue damage, and are associated with diabetes and neurological abnormalities.
  • ORM1 This protein is an acute phase plasma protein. Its expression level increases during acute inflammatory response. The specific function of this protein is unknown, but it may be related to immunosuppression.
  • the protein marker combination includes LRG1 and at least one of SERPINA1, ITIH3, CP, ORM1, C9, IGFBP2 and CNDP1.
  • the protein marker combination includes C9 and at least one of LRG1, SERPINA1, ITIH3, CP, ORM1, IGFBP2 and CNDP1.
  • the protein marker combination includes ITIH3, LRG1, C9, IGFBP2 and CNDP1.
  • the protein marker combination includes CP, LRG1, C9, IGFBP2 and CNDP1.
  • the protein marker combination includes ITIH3, CP, LRG1, C9 and CNDP1.
  • the protein marker combination includes SERPINA1, LRG1, C9, IGFBP2 and CNDP1.
  • the protein marker combination includes SERPINA1, CP, LRG1, C9 and CNDP1.
  • the protein marker combination includes LRG1, ORM1, C9, IGFBP2 and CNDP1.
  • the protein marker combination includes LRG1, SERPINA1, CP, ORM1, C9 and CNDP1.
  • the protein marker combination includes LRG1, SERPINA1, ITIH3, CP, C9 and CNDP1.
  • the protein marker combination includes LRG1, SERPINA1, ITIH3, C9, IGFBP2 and CNDP1.
  • the protein marker combination includes SERPINA1, ITIH3, LRG1, C9, IGFBP2 and CNDP1.
  • the protein marker combination includes SERPINA1, ITIH3, LRG1, ORM1, C9 and CNDP1.
  • the present invention by detecting the expression level of each protein in the protein marker group combination, it is possible to predict whether a subject has a risk of colorectal cancer, that is, it can be used for early screening of colorectal cancer; it is also possible to diagnose whether a subject has colorectal cancer, and the diagnosis can be an auxiliary diagnosis, which is made by a clinician in combination with other clinical indicators; it is also possible to evaluate the prognosis of subjects with colorectal cancer after treatment.
  • the second aspect of the present invention provides a polypeptide combination for prediction, diagnosis or prognosis of colorectal cancer, wherein the polypeptide combination comprises at least one polypeptide from each protein in any protein marker combination described in the first aspect of the present invention.
  • polypeptide from C9 comprises the amino acid sequence shown in SEQ ID No. 1 or SEQ ID No. 2.
  • polypeptide from SERPINA1 comprises the amino acid sequence shown in SEQ ID No. 3.
  • polypeptide from ITIH3 comprises the amino acid sequence shown in SEQ ID No. 4.
  • polypeptide from CP comprises the amino acid sequence shown in SEQ ID No. 5.
  • polypeptide from LRG1 comprises the amino acid sequence shown in SEQ ID No. 6 or SEQ ID No. 7.
  • polypeptide from IGFBP2 comprises the amino acid sequence shown in SEQ ID No.8.
  • polypeptide from KNG1 comprises the amino acid sequence shown in SEQ ID No. 9.
  • polypeptide from ORM1 comprises the amino acid sequence shown in SEQ ID No. 10.
  • polypeptide from PRDX2 comprises the amino acid sequence shown in SEQ ID No. 11.
  • polypeptide from CNDP1 comprises the amino acid sequence shown in SEQ ID No. 12.
  • the third aspect of the present invention provides the use of an expression level detection reagent of any one of the protein marker combinations described in the first aspect of the present invention in the preparation of a kit for prediction, diagnosis or prognosis of colorectal cancer.
  • the detection reagent detects the expression level of each protein in the protein marker combination based on mass spectrometry.
  • the expression level of each protein in the protein marker combination is detected by detecting the level of one or more polypeptides of each protein in the protein marker combination.
  • polypeptide from C9 comprises the amino acid sequence shown in SEQ ID No. 1 or SEQ ID No. 2.
  • polypeptide from SERPINA1 comprises the amino acid sequence shown in SEQ ID No. 3.
  • polypeptide from ITIH3 comprises the amino acid sequence shown in SEQ ID No. 4.
  • polypeptide from CP comprises the amino acid sequence shown in SEQ ID No. 5.
  • polypeptide from LRG1 comprises the amino acid sequence shown in SEQ ID No. 6 or SEQ ID No. 7.
  • polypeptide from IGFBP2 comprises the amino acid sequence shown in SEQ ID No.8.
  • polypeptide from KNG1 comprises the amino acid sequence shown in SEQ ID No. 9.
  • polypeptide from ORM1 comprises the amino acid sequence shown in SEQ ID No. 10.
  • polypeptide from PRDX2 comprises the amino acid sequence shown in SEQ ID No. 11.
  • polypeptide from CNDP1 comprises the amino acid sequence shown in SEQ ID No. 12.
  • the fourth aspect of the present invention provides a kit for prediction, diagnosis or prognosis of colorectal cancer, comprising an expression level detection reagent for any one of the protein marker combinations described in the first aspect of the present invention.
  • a fifth aspect of the present invention provides a method for predicting, diagnosing or prognosing colorectal cancer, comprising the following steps:
  • the machine learning model is trained using any one of the following algorithms:
  • Random forest algorithm support vector machine algorithm, linear regression algorithm, logistic regression algorithm, Bayesian classifier and neural network algorithm.
  • the machine learning model is trained using a logistic regression algorithm.
  • a preset threshold is obtained based on the machine learning model using a population sample, and for each subject sample, if the model measurement result is higher than the preset threshold, it is determined that the subject suffers from colorectal cancer or has a risk of colorectal cancer or has a poor prognosis for colorectal cancer. If it is not higher than the preset threshold, it is determined that the subject does not suffer from colorectal cancer or has no risk of colorectal cancer or has a good prognosis for colorectal cancer.
  • step S1 the subject's blood sample is anticoagulated with EDTA to obtain plasma, the plasma protein is denatured, reduced, and alkylated, and then trypsin is added for enzymatic cleavage to obtain polypeptide fragments, which are then desalted and evaporated to dryness for liquid phase separation and mass spectrometry detection, and the level of the protein marker combination is determined based on the level of the polypeptide.
  • the mass spectrometry detection is performed using a triple quadrupole mass spectrometry method.
  • a sixth aspect of the present invention provides a system for prediction, diagnosis or prognosis of colorectal cancer, comprising the following modules:
  • a data input module used to input the expression level data of each protein in any one of the protein marker combinations described in the first aspect of the present invention of the subject;
  • a data storage module used to store the expression level data of each protein in the protein marker combination in the group sample and the information of whether each sample is derived from a colorectal cancer patient;
  • the colorectal cancer analysis module is connected to the data input module and the data storage module respectively, and uses the expression level data of each protein in the protein marker combination in the storage population samples stored in the data storage module and the information on whether each sample is derived from a colorectal cancer patient to build a machine learning model, and judge whether the subject has colorectal cancer or has a risk of colorectal cancer or whether the prognosis of colorectal cancer is good based on the machine learning model.
  • the machine learning model is trained using any one of the following algorithms:
  • Random forest algorithm support vector machine algorithm, linear regression algorithm, logistic regression algorithm, Bayesian classifier and neural network algorithm.
  • the colorectal cancer analysis module further inputs the expression level data and the judgment result of each protein in the subject's protein marker combination into the data storage module.
  • the machine learning model is trained using a logistic regression algorithm.
  • the present invention has the following beneficial effects:
  • the protein marker combination of the present invention provides a plasma-based non-invasive screening method for early colorectal cancer.
  • the method and system of the present invention are used to predict, diagnose or prognose colorectal cancer, which is non-invasive to patients, easy to obtain samples, requires a small amount of plasma sample, has high sensitivity and specificity, and most importantly, fills the gap of no effective protein markers for early colorectal cancer.
  • the protein marker combination of the present invention has a high accuracy in predicting early colorectal cancer. After a positive result is determined, the patient is prompted to undergo further diagnosis, and in the long run, the mortality rate of colorectal cancer can be effectively reduced in the population.
  • Using machine learning to detect marker proteins in plasma can achieve the purpose of dynamically monitoring the patient's disease status.
  • Figure 1 shows the receiver operating characteristic curve of the single protein marker LRG1.
  • the areas under the curve (AUC) of the training set, test set, and independent validation set are 0.904, 0.85, and 0.8, respectively, where train represents the training set, test represents the test set, and valid represents the independent validation set; True positive rate (sensitivity) represents the true positive rate (sensitivity), and False positive rate (1-specificity) represents the false positive rate (1-specificity).
  • Figure 2 shows the receiver operating characteristic curve of the single protein marker SERPINA1.
  • the areas under the curve (AUC) of the training set, test set, and independent validation set are 0.837, 0.779, and 0.771, respectively, where train represents the training set, test represents the test set, and valid represents the independent validation set; True positive rate (sensitivity) represents the true positive rate (sensitivity), and False positive rate (1-specificity) represents the false positive rate (1-specificity).
  • Figure 3 shows the receiver operating characteristic curve of the single protein marker ITIH3.
  • the areas under the curve (AUC) of the training set, test set, and independent validation set were 0.835, 0.921, and 0.79, respectively, where train represents the training set, test represents the test set, and valid represents the independent validation set; True positive rate (sensitivity) represents the true positive rate (sensitivity), and False positive rate (1-specificity) represents the false positive rate (1-specificity).
  • Figure 4 shows the receiver operating characteristic curve of a single protein marker CP.
  • the areas under the curve (AUC) of the training set, test set, and independent validation set are 0.823, 0.842, and 0.624, respectively, where train represents the training set, test represents the test set, and valid represents the independent validation set; True positive rate (sensitivity) represents the true positive rate (sensitivity), and False positive rate (1-specificity) represents the false positive rate (1-specificity).
  • Figure 5 shows the receiver operating characteristic curve of the single protein marker ORM1.
  • the areas under the curve (AUC) of the training set, test set, and independent validation set are 0.818, 0.783, and 0.697, respectively, where train represents the training set, test represents the test set, and valid represents the independent validation set; True positive rate (sensitivity) represents the true positive rate (sensitivity), and False positive rate (1-specificity) represents the false positive rate (1-specificity).
  • Figure 6 shows the receiver operating characteristic curve of the single protein marker C9.
  • the areas under the curve (AUC) of the training set, test set, and independent validation set are 0.875, 0.91, and 0.81, respectively, where train represents the training set, test represents the test set, and valid represents the independent validation set; True positive rate (sensitivity) represents the true positive rate (sensitivity), and False positive rate (1-specificity) represents the false positive rate (1-specificity).
  • Figure 7 shows the receiver operating characteristic curve of the single protein marker IGFBP2.
  • the areas under the curve (AUC) of the training set, test set, and independent validation set are 0.728, 0.738, and 0.737, respectively, where train represents the training set, test represents the test set, and valid represents the independent validation set; True positive rate (sensitivity) represents the true positive rate (sensitivity), and False positive rate (1-specificity) represents the false positive rate (1-specificity).
  • Figure 8 shows the receiver operating characteristic curves of the combination of five protein markers.
  • the areas under the curve (AUC) of the training set, test set, and independent validation set are 0.956, 0.954, and 0.893, respectively, where train represents the training set, test represents the test set, and valid represents the independent validation set; True positive rate (sensitivity) represents the true positive rate (sensitivity), and False positive rate (1-specificity) represents the false positive rate (1-specificity).
  • FIG9 shows the confusion matrix of the combination of five protein markers, including 121 colorectal cancer patients and 186 healthy subjects. 1 represents positive and 0 represents negative. Train represents the training set, test represents the test set, valid represents the independent validation set, Truth represents the truth, and Prediction represents the prediction.
  • Numerical ranges in this application are approximate values, so unless otherwise specified, they may include values outside the range. Numerical ranges include all values from the lower limit to the upper limit increased by 1 unit, provided that there is an interval of at least 2 units between any lower value and any higher value. For a range containing a value less than 1 or containing a fraction greater than 1 (e.g., 1.1, 1.5, etc.), 1 unit is appropriately regarded as 0.0001, 0.001, 0.01 or 0.1. For a range containing a single digit less than 10 (e.g., 1 to 5), 1 unit is generally regarded as 0.1. These are only specific examples of what is intended to be expressed, and all possible combinations of the values between the lowest and highest values listed are considered to be clearly recorded in this application.
  • compositions using the terms “comprising”, “including”, or “having” in this application may include any additional additives, adjuvants or compounds.
  • the term “essentially consisting of" excludes any other components, steps or processes from the scope of any description of the term below.
  • Consisting of does not include any components, steps or processes that are not specifically described or listed. Unless explicitly stated, the term “or” refers to the listed individual members or any combination thereof.
  • the experimental methods in the following examples are all conventional methods unless otherwise specified.
  • the instruments and equipment used in the following examples are all conventional laboratory instruments and equipment unless otherwise specified; the experimental materials used in the following examples are all purchased from conventional biochemical reagent stores unless otherwise specified.
  • the inventors collected fresh blood samples from 101 colorectal cancer patients and 89 healthy controls matched in gender and age for protein marker discovery.
  • the plasma samples were diluted 50 times for BCA concentration determination: BSA standard was diluted to 2, 1, 0.5, 0.25, 0.125, 0.0625 mg/mL as a working curve to calibrate the plasma concentration. Diluted samples and standards were added to the 96-well plate, and the pre-configured BCA working solution was added. The reaction was carried out at 37°C for 30 min, and the plasma protein concentration was determined at an absorbance of 562 nm.
  • the selection of targets is first based on finding differentially expressed proteins.
  • the inventors collected mass spectrometry using the independent acquisition mode (DIA) on 190 plasma samples with symmetrical gender and age (89 healthy people and 101 colorectal cancer patients), and further used the DIA-NN software to analyze the expression data of proteins and peptides. The intensity of the total protein was used for normalization analysis, and a total of 714 proteins and 7988 peptides were quantified.
  • the inventors used the T test to find differentially expressed proteins and peptides.
  • the inventors used the Wilcoxon non-parametric test to find differentially expressed proteins and peptides. In the end, the inventors obtained a total of 96 differentially expressed proteins and 832 differentially expressed peptides. The differentially expressed peptides were obtained by integration.
  • the random forest method was used to select potential peptides that can distinguish colorectal cancer from healthy people.
  • the random forest calculated the average Gini coefficient of these targets and sorted them according to their importance.
  • the top 10 proteins were finally obtained, namely LRG1, SERPINA1, ITIH3, CP, ORM1, C9, IGFBP2, CNDP1, KNG1 and PRDX2.
  • the corresponding peptide sequences are shown in Table 1:
  • C 13 and N 15 heavy isotope labeled peptides were added to the digested plasma sample, mixed, and then desalted and evaporated using a 96-well SOLA solid phase extraction device.
  • MRM multiple reaction monitoring
  • the peptide concentrations corresponding to the respective protein markers were quantified and used to establish the model. 80% (152 cases) of the 190 samples were randomly selected as the training set, and the remaining 20% (38 cases) were used as the test set. The 10 potential protein markers were further used to establish a logistic regression model. The inventors found that a total of 7 single protein markers, LRG1, SERPINA1, ITIH3, CP, ORM1, C9 and IGFBP2, had very good predictive ability in both the training set and the test set, and their ROC curves are shown in Figures 1 to 7, respectively.
  • the inventors selected 121 colorectal cancer patients and 186 matched healthy people as the validation set to verify the model. In order to more accurately quantify the peptides and reduce the errors caused by cumbersome experimental processing, the inventors no longer removed the high peak protein, which can also greatly reduce the pre-processing cost of the experiment. After protein extraction and concentration determination, liquid phase separation and mass spectrometry detection were performed.
  • the inventors further used the optimal combination of the aforementioned proteins - the concentrations of 5 protein markers (ITIH3, LRG1, SERPINA1, IGFBP2 and CDNP1) to establish a logistic regression model to distinguish colorectal cancer patients from healthy people.
  • the logistic regression model used 77 colorectal cancer patients and 79 healthy people to learn the distinguishing effect of 5 protein markers.
  • the threshold in the logistic regression model was set to 0.34, and 44 colorectal cancer patients and 107 healthy people were used to independently verify the model.
  • the threshold was set based on the model results of all 307 plasma samples. For each sample, the model measurement result was judged to be positive if it was higher than this threshold. If the model measurement result of the sample is lower than this threshold, it is judged to be negative.
  • the ROC curve is shown in Figure 8. It can be seen that the areas under the curve (AUC) of the training set, test set, and independent validation set are 0.956, 0.954, and 0.893, respectively.
  • the final sensitivity is 92%, the specificity is 81%, the negative predictive value is 94%, and the positive predictive value is 76%, as shown in Figure 9.
  • Model Training set AUC Test set AUC Independent validation set AUC CP + LRG1 + C9 + IGFBP2 + CNDP1 0.955 0.945 0.870 ITIH3 + CP + LRG1 +C9+ CNDP1 0.953 0.945 0.872 SERPINA1 + LRG1 + C9 + IGFBP2 + CNDP1 0.952 0.939 0.884 SERPINA1+ CP+ LRG1+ C9 + CNDP1 0.952 0.942 0.870 LRG1+ ORM1+C9 + IGFBP2 + CNDP1 0.947 0.935 0.891 LRG1 +SERPINA1 +CP + ORM1 +C9 + CNDP1 0.950 0.939 0.861 LRG1 +SERPINA1+ ITIH3+ CP+ C9 + CNDP1 0.951 0.941 0.866 LRG1+SERPINA1+ITIH3 + C9 + IGFBP2+ CNDP1 0.949 0.936 0.892

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Abstract

L'invention concerne une combinaison de marqueurs protéiques pour la prédiction, le diagnostic ou le pronostic du cancer colorectal, et une utilisation et un système fondés sur la combinaison de marqueurs protéiques. La combinaison de marqueurs protéiques comprend au moins un élément choisi parmi LRG1, SERPINA1, ITIH3, CP, ORM1, C9, IGFBP2 et CNDP1. La combinaison de marqueurs protéiques fournit un procédé de criblage non invasif à base de plasma sanguin pour la prédiction du cancer colorectal précoce et même des lésions précancéreuses avancées. Utiliser le procédé et le système pour effectuer la prédiction, le diagnostic ou le pronostic du cancer colorectal a l'avantage d'être non invasif pour un patient, les matériaux sont accessibles, moins d'échantillons de plasma sanguin sont utilisés, et l'on obtient une sensibilité et une spécificité élevées.
PCT/CN2023/077068 2023-02-01 2023-02-20 Marqueur protéique et kit pour le dépistage précoce du cancer colorectal et leur utilisation Ceased WO2024159559A1 (fr)

Applications Claiming Priority (2)

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CN202310049892.3A CN116735889B (zh) 2023-02-01 2023-02-01 一种用于结直肠癌早期筛查的蛋白质标志物、试剂盒及应用
CN202310049892.3 2023-02-01

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