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CN119534824A - A blood metabolite marker combination for early diagnosis of colorectal cancer and its application - Google Patents

A blood metabolite marker combination for early diagnosis of colorectal cancer and its application Download PDF

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CN119534824A
CN119534824A CN202411692771.1A CN202411692771A CN119534824A CN 119534824 A CN119534824 A CN 119534824A CN 202411692771 A CN202411692771 A CN 202411692771A CN 119534824 A CN119534824 A CN 119534824A
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colorectal cancer
early diagnosis
model
blood metabolite
marker combination
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陈朋
周忠坤
马云浩
牛宇清
卢娟
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Lanzhou University
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Abstract

The invention belongs to the field of colorectal cancer detection, and relates to a blood metabolite marker combination for early diagnosis of colorectal cancer and application thereof. The screening of the invention results in a blood metabolite biomarker combination for early diagnosis of colorectal cancer comprising Ep silon-Caprolactam, triethyl Phosphate, 3-Carboxy-4-Methyl-5- (1-Hydroxypropyl) -2-Furanpropi onic Acid, arachidonic Acid, chrycorin; the method is used for diagnosing colorectal cancer through models such as random forests and linear regression, has high sensitivity and strong specificity, can avoid pains, discomfort and postoperative complications caused by enteroscopy invasive examination, obviously reduces the false positive rate of early diagnosis of colorectal cancer, and is widely used for early diagnosis of colorectal cancer.

Description

Blood metabolite marker combination for early diagnosis of colorectal cancer and application
Technical Field
The invention belongs to the field of colorectal cancer detection, and particularly relates to a biomarker group for early diagnosis of colorectal cancer and application thereof.
Background
Colorectal cancer is a common malignancy worldwide, accounting for about one tenth of cancer cases, with morbidity and mortality of 6.1% and 9.2%, respectively. Colorectal cancer incidence rates in the 380 ten thousand new cancer cases in china in 2018 are fourth in men, third in women and second in the world. If colorectal lesions can be detected early and resected by surgery, survival rates are over 90% and later are reduced to 13%, so early screening is the most effective means of reducing colorectal mortality. With the development of sequencing technology and information technology, artificial intelligence has become an important tool for assisting clinicians, such as imaging diagnosis. The data information can be deeply mined by utilizing an artificial intelligence algorithm and bioinformatics, a diagnosis model is built, and the diagnosis accuracy can be improved. The data show that the artificial intelligent model based on the algorithms such as neural network and deep learning can reach more than 80% accuracy, so that the patient benefits. Thus, combining artificial intelligence learning with early diagnosis will further reduce colorectal cancer mortality and medical burden.
Currently, invasive colonoscopy is still the gold standard for CRC diagnosis, the most commonly used method of diagnosis in the clinic, and the results of case-control and cohort studies indicate that colonoscopy has the potential to prevent CRC and cancer death. China recruited 1,381,561 qualified participants from 16 provinces in china from 2012 to 2015, aged 40-69 years, established a risk scoring system, then recommended for colonoscopy, and finally only 25,593 participants were colonoscopy with a participation rate of 14.0%. Although colonoscopy is considered the gold standard for CRC screening, it is an invasive procedure, requires a high level of expertise, is costly, can cause postoperative complications, and patient experience is poor, which limits its choice as an early screening. The non-invasive detection method, the American cancer society recommends that the high sensitivity FOBT and FIT be checked once every three years, but the sensitivity is lower (12% -56%), and the sensitivity is greatly influenced by diet and medication, so that the sensitivity is still further improved. Multi-target fecal DNA detection Cologuard, FDA approval in 2014, recommended screening every 3 years, but its sensitivity to early adenoma is low (42.4%), detection cost (599 merits) is high, performance verification is currently only obtained in a few countries such as the united states, canada, etc., and no clinical experiments have been performed in china. The large-scale use of these methods is limited due to the low accuracy of non-invasive tests and the damage caused by invasive tests. Thus, a need exists for a non-invasive, accurate method of CRC detection.
The occurrence of cancer is accompanied by global changes in metabolic status, affecting the tumor tissue as well as the surrounding microenvironment and macroscopical environment. Metabolic changes in tumor cell status can be more directly observed than genomic and proteomic changes and are therefore a promising source of biomarkers for detecting tumorigenesis. Various studies have shown that metabolites produced by intestinal bacteria can enter the circulation and exert regulatory functions in distant organs, key metabolic pathways being disrupted in the pathogenesis of CRC. In recent years, serum metabolites closely related to CRC have been intensively studied to develop diagnostic biomarkers. Intestinal microbiome reprogramming in colorectal cancer patients is associated with changes in serum metabolome, and changes in intestinal microbiome related serum metabolites (GMSM) can effectively distinguish CRC and adenoma patients from normal individuals. GMSM-based models can distinguish CRC and adenoma patients from healthy normal subjects more effectively than the clinical marker carcinoembryonic antigen. The GMSM model is superior to the clinical biomarkers CEA and FOBT in colorectal abnormality detection. Colorectal abnormalities were detected using CEA at a clinical cut-off of 5U/mL, AUC of 0.72, sensitivity of 35.8%, specificity of 86.4%. In contrast, AUC of GMSM model reached 0.92 (sensitivity=83.5%, specificity=84.9%) well above CEA. The detection sensitivity of FOBT/FIT is 65.2%, which is equivalent to that of the prior report. These results indicate that the GMSM model is superior to the FOBT/FI T test in detecting CRC.
The invention screens specific blood metabolites as biomarker compositions for early diagnosis of colorectal cancer, wherein the biomarker compositions comprise Epsilon-Caprolactam (caprolactam), triethyl Phosphate (triethyl phosphate), 3-Carboxy-4-Methyl-5- (1-Hydroxypropyl) -2-Furanpropionic Acid (3-carboxyl-4-Methyl-5- (1-hydroxypropyl) -2-furopropionic acid), arachidonic Acid (arachidonic acid) and Chrycorin (chloromycetin), and the biomarker compositions are used for the diagnosis of colorectal cancer by using the models of random forest, linear regression and the like.
Disclosure of Invention
Aiming at the technical problems, the invention aims to provide a blood metabolite marker combination for early diagnosis of colorectal cancer and application thereof. The method specifically comprises the following steps:
In a first aspect, the invention provides a blood metabolite marker combination for early diagnosis of colorectal cancer, comprising Epsilon-Caprolactam (caprolactam), triethyl Phosphate (triethyl phosphate), 3-Carboxy-4-Methyl-5- (1-Hydroxypropyl) -2-Furanpropionic Acid (3-carboxy-4-Methyl-5- (1-hydroxypropyl) -2-furanpropanoic acid), arachidonic Acid (arachidonic acid), chrycorin (chlorothalonil).
In a second aspect, the present invention provides the use of a reagent for detecting a blood metabolite marker combination according to the first aspect above, for the preparation of a reagent, kit or chip for early diagnosis or prognosis of colorectal cancer.
In a third aspect, the present invention provides the use of a reagent for detecting a blood metabolite marker combination according to the first aspect above, for the preparation of a colorectal cancer patient screening evaluation model.
In a fourth aspect, the present invention provides the use of a blood metabolite marker combination according to the first aspect above for the preparation of a reagent, kit or chip for early diagnosis or prognosis of colorectal cancer.
In a fifth aspect, the present invention provides the use of a blood metabolite marker combination according to the first aspect above for the preparation of a colorectal cancer patient screening evaluation model.
In a sixth aspect, the present invention provides a kit for early diagnosis or prognosis of colorectal cancer, the kit comprising reagents or apparatus for detecting a blood metabolite marker combination according to the first aspect above.
In a seventh aspect, the present invention provides a model for early diagnosis or prognosis of colorectal cancer, the model comprising:
(1) Sample processing, namely extracting the blood metabolite marker combination in the first aspect of the blood sample to be detected and carrying out LC-MS/MS full-scan detection to obtain the abundance value of the marker;
(2) Marking colorectal cancer samples determined by colonoscope and pathology detection as a value 1, marking healthy crowd samples as a value 0, inputting abundance values of biomarker combinations of different obtained samples into a random forest, constructing a model, and predicting diagnosis results;
(3) The result judges that the colorectal cancer diagnosis result is positive when the predictive value is 1, and negative when the predictive value is 0.
In an eighth aspect, the present invention provides a method for determining a blood metabolite marker combination according to the first aspect, comprising:
(1) Group difference statistics (Wilcoxon test) were performed on markers of colorectal cancer group and healthy group, using species with P <0.05 as potential biomarkers;
(2) The optimal combination of microbial biomarkers was determined using the R software package AUCRF (random forest method);
(3) Determining the optimal combination by randomly removing the markers using AUC as an evaluation index (pROC packs);
(4) And simultaneously constructing a logistic regression model by using the markers, and comparing the logistic regression model with the marker to obtain the blood metabolite marker combination.
In a ninth aspect, the present invention provides a model system for early diagnosis or prognosis of colorectal cancer, comprising:
(1) The pre-input module is at least used for inputting data to be evaluated;
(2) The evaluation module is at least used for evaluating the data to be evaluated and is executed by the model in the seventh aspect;
(3) And the display module is at least used for displaying the evaluation result.
In a tenth aspect, the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps using the system of the eighth aspect when executing the program, comprising:
(1) Collecting and/or inputting evaluation data for early diagnosis or prediction of colorectal cancer;
(2) Substituting the evaluation data into the model for calculation to obtain a judgment conclusion of whether the sample is colorectal cancer;
(3) And outputting a judging conclusion of whether the sample is colorectal cancer.
The kit has the beneficial effects that ① is subjected to a large number of screening to obtain metabolites Epsi lon-Caprolactam (caprolactam), triethyl Phosphate (triethyl phosphate), 3-Carboxy-4-Methyl-5- (1-Hydroxypropyl) -2-Furanpropionic Acid (3-carboxyl-4-Methyl-5- (1-hydroxypropyl) -2-furopropionic acid), arachi donic Acid (arachidonic acid) and Chrycorin (chloromycetin) which are derived from blood samples for the first time, and ② is used for constructing an early diagnosis or prediction model of colorectal cancer by using the markers, provides a new direction for clinical diagnosis of colorectal cancer, avoids pain, discomfort and postoperative complications caused by the conventional early diagnosis of colorectal cancer, remarkably reduces false positive rate of early diagnosis of colorectal cancer, has the characteristics of good specificity and high sensitivity, has good clinical application value for auxiliary diagnosis of colorectal cancer, and can be widely used for early diagnosis of colorectal cancer.
Drawings
Fig. 1 shows a graph of AUC versus markers constructed from a random forest model using pROC markers from 52 markers, the curve showing that the model consisting of 11 markers is optimal.
FIG. 2 shows the AUC curve of a diagnostic model constructed using 5 fecal metabolites, RF represents a random forest model, glm represents a logistic regression model.
Detailed Description
The following examples facilitate a better understanding of the present invention, but are not intended to limit the same. The experimental methods in the following examples are conventional methods unless otherwise specified. The test materials used in the examples described below are commercially available unless otherwise specified.
In one embodiment, sensitivity, specificity, accuracy, and other combinations are used to describe the well and reliability of the detection methods of the present invention. Several terms used with descriptions of sensitivity, specificity, accuracy include True Positive (TP), true Negative (TN), false Positive (FP), false Negative (FN), wherein a test result is considered true if a patient is demonstrated to have a disease and a given screening test also indicates the presence of the disease, a test result is considered true if a patient is demonstrated to have no disease and a given screening test also indicates the absence of the disease, a test result is false positive if a screening test result indicates that a patient who does not actually have a disease has the disease, and a test result is false negative if a screening test result indicates that a patient who does not actually have a disease does not have the disease.
Sensitivity = TP/(tp+fn) = number of true positive assessments/number of all positive assessments;
specificity = TN/(tn+fp) = number of true negative evaluations/number of all negative evaluations;
accuracy= (tn+tp)/(tn+tp+fn+fp) =number of correct evaluations/number of all evaluations.
In the following examples, 63 patients suffering from colorectal cancer and adenoma and 40 healthy people are selected, the above cases are screened by using the marker combination provided by the invention, and the sensitivity, the specificity and the accuracy of the marker combination provided by the invention for colorectal cancer detection are judged according to the detection result.
EXAMPLE 1 screening of blood metabolites for early diagnosis of colorectal cancer
1. Blood sample collection
A5 ml vacuum blood collection tube was prepared, blood from the patient was collected, immediately centrifuged at 3000rpm/min for 15 minutes, and the supernatant was aspirated and packaged into sterile centrifuge tubes and immediately stored at-80 ℃.
2. Blood biomarker detection
2.1 Metabolite extraction
(1) Taking out a sample stored at-80 ℃, thawing in an ice-water mixture, and transferring 80 mu L of the sample to a 1.5mL EP tube;
(2) 320. Mu.L of protein precipitant methanol-acetonitrile (V: V=2:1, containing mixed internal standard, 4. Mu.g/mL) was added and vortexed for 1min;
(3) Ultrasonic extraction in ice water bath for 10min at-40 ℃ and standing overnight;
(4) Centrifuge for 10min (12000 rpm,4 ℃) aspirate 150 μl of supernatant with syringe, filter with 0.22 μm organic phase pinhole filter, transfer to LC sample vials, store at-80 ℃ until LC-MS analysis.
(5) The quality control sample (QC) is prepared by mixing all the extracting solutions of all the samples in equal volume. All extraction reagents were pre-chilled at-20 ℃.
2.2 Liquid chromatography-Mass Spectrometry analysis
(1) The analytical instrument for the experiment is a liquid-mass combination system consisting of Waters ACQUITY UPLC I-Class plus/Thermo QE ultra-high performance liquid tandem high resolution mass spectrometer.
(2) The chromatographic column was ACQUITY UPLC HSS T (100 mm. Times.2.1 mm,1.8 μm), column temperature 45 ℃, mobile phase A-water (0.1% formic acid), B-acetonitrile, flow rate 0.35mL/min, and sample volume 3. Mu.L.
(3) Mass spectrometry conditions ：Spray Voltage(V)(3800 -3000),Capillary Temperature(℃)(320),Aux gas heater temperature(℃)(350),Sheath Gas Flow Rate(Arb)(35),Aux gas flow rate(Arb)(8),S-lens RF level(50),Mass range(m/z)(70-1050),Full ms resolution(70000),MS/MS resolution(17500),NCE/stepped NCE(10,20,40).
2.3 Data processing
(1) Data preprocessing before pattern recognition, the original data is subjected to baseline filtration, peak recognition, integration, retention time correction, peak alignment and normalization by metabonomics processing software Progenesis QI v 3.0.0, with main parameters of ：pre cursor tolerance：5ppm(HMDB+Lipidmaps)/10ppm(LuMet-Animal+METLIN);product to lerance：10ppm(HMDB+Lipidmaps)/20ppm(LuMet-Animal+METLIN).
(2) Identification of compounds based on multiple dimensions of RT (retention time), exact mass number, secondary fragmentation, and isotope distribution, identification analysis was performed using The Human Metabolome Database (HMDB), LIPIDMAPS (v 2.3), and METLIN databases and LuMet-animate 3.0 local databases.
(3) The extracted data is subjected to deletion value processing, 0 value replacement, score scoring screening, data merging and the like. Missing value processing and 0 value substitution by deleting ion peaks with missing values (0 value) >50% in the group and substituting the remaining 0 value with half of the minimum value of all ion intensities of all samples. Score scoring screening, namely screening the compounds obtained qualitatively according to the Score of the qualitative results of the compounds (Score), wherein the screening standard is 36 points (full Score of 80 points), and the qualitative results are inaccurate and deleted under 36 points.
(4) The multivariate statistical analysis firstly adopts an unsupervised Principal Component Analysis (PCA) to observe the overall distribution among samples and the stability of the whole analysis process, and then uses a supervised partial least squares analysis (PLS-DA) and an orthogonal partial least squares analysis (OPLS-DA) to distinguish the overall differences of the metabolic profiles among groups and find the differential metabolites among groups.
2.4 Marker combination screening
(1) The markers of colorectal cancer group and healthy group were subjected to inter-group difference statistics (Wilcoxon test) using species with P <0.05 as potential biomarkers, and by Wilcoxon inter-group difference test, markers with P value less than 0.05 after Bonferroni correction were selected for total of 52 metabolites.
(2) Colorectal cancer (colonoscope and pathology detection determination) samples were labeled as value 1, healthy population samples were labeled as value 0, and modeling analysis and prediction were performed on different sample abundance matrices. The optimal combination of microbial biomarkers was determined using the R software package AUCRF (random forest method). The results are shown in FIG. 1, which shows that the diagnosis is best when the combination contains 11 markers.
(3) By randomly removing the markers, the AUC was used as an evaluation index (pROC packs) to determine the optimal combination. The final determination of 5 metabolic markers will retain the diagnostic properties of the model, epsilon-Caprolactam (caprolactam), triethyl Phosphate (triethyl phosphate), 3-Carboxy-4-Methyl-5- (1-Hydroxypropyl) -2-Furanpropionic Acid (3-carboxy-4-Methyl-5- (1-hydroxypropyl) -2-furanopropionic acid), arachidonic Acid (arachidonic acid), chrycorin (chlorothalonil).
(4) And simultaneously constructing a logistic regression model by using the markers, and comparing the logistic regression model with the marker.
By the above analysis, Epsilon-Caprolactam、Triethyl Phosphate、3-Carboxy-4-Methyl-5-(1-Hydroxypropyl)-2-Furanpropionic Acid、Arachidonic Acid、Chrycorin metabolites were selected as marker combinations for early diagnosis of colorectal cancer for healthy and colorectal cancer population discrimination.
Example 2 establishment and validation of early diagnosis or prediction model of colorectal cancer
1. Model construction
The metabolite Epsilon-Caprolactam、Triethyl Phosphate、3-Carboxy-4-Methyl-5-(1-Hydroxypropyl)-2-Furanpropionic Acid、Arachidonic Acid、Chrycorin obtained by the final screening in example 1 was selected as a marker combination for early diagnosis of colorectal cancer, and an early diagnosis or prediction model of colorectal cancer was constructed by the following specific method:
(1) Extracting metabolites Epsilon-Caprolactam、Triethyl Phosphate、3-Carboxy-4-Met hyl-5-(1-Hydroxypropyl)-2-Furanpropionic Acid、Arachidonic Acid、Chrycorin, in the blood sample to obtain the abundance value of the marker;
(2) Marking colorectal cancer (colonoscope and pathological detection determination) samples as a value 1, marking healthy crowd samples as a value 0, inputting obtained abundance values (n x n table) of biomarker combinations of different samples into a random forest, constructing a model, and predicting diagnosis results;
(3) When the abundance matrix of the marker is used, the predicted value of the random forest algorithm is 1, the diagnosis result of the sample is judged to be positive, namely colorectal cancer, and when the predicted value is 0, the diagnosis result of the sample is judged to be negative, namely health.
2. Accuracy verification
The accuracy is determined by comparing the above-described predictive diagnosis with the true diagnosis (colonoscope and pathology detection are determined to be colorectal cancer).
3. Analysis of results
Healthy and colorectal cancer population discrimination was performed using Epsilon-Caprolactam、Triethyl Phosphate、3-Carboxy-4-Methyl-5-(1-Hydroxypropyl)-2-Furanpropionic Acid、Arachidonic Acid、Chrycorin markers, and the results are shown in fig. 2, with a specificity of 90.0% and an accuracy of 92.9% when the sensitivity was 81.0%.
The results show that the biomarker combination provided by the invention is used for colorectal cancer diagnosis, and has higher accuracy, specificity and sensitivity.
EXAMPLE 3 colorectal cancer early diagnosis or prediction System
The present embodiment provides a system for early diagnosis or prognosis of colorectal cancer, comprising:
(1) The pre-input module is at least used for inputting the biomarker combination abundance data in the fecal sample and transmitting the biomarker combination abundance data to the evaluation module;
(2) The evaluation module is at least used for analyzing the abundance data of the biomarker combinations in the fecal sample according to the model described in the embodiment 2 to obtain a result, wherein the abundance data of the biomarker combinations can be collected through the pre-input module and can be obtained from other sources.
(3) The display module is at least used for displaying the evaluation result, and the colorectal cancer diagnosis result is negative when the predicted value is 0, and positive when the predicted value is 1.
Example 4 computer apparatus
The present embodiment provides an electronic device, which may be expressed in the form of a computing device (e.g., may be a server device), including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor may implement the colorectal cancer early diagnosis or prediction model in embodiment 2 of the present invention when executing the computer program.
The processor, when executing the program, causes the system to perform the steps comprising:
(1) Collecting and/or inputting biomarker combination abundance data for early diagnosis or prediction of colorectal cancer;
(2) Substituting the evaluation data into the model for calculation to obtain a judgment conclusion that the sample is positive or negative to colorectal cancer;
(3) And outputting a judgment conclusion that the sample is positive or negative to colorectal cancer.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Embodiment 5 computer-readable storage Medium
The present embodiment provides a computer-readable storage medium having the computer device stored thereon. The program is executed by the processor to implement the steps of the method for evaluating the geodetic properties of rhubarb in the first embodiment of the invention.
More specifically, a readable storage medium may include, but is not limited to, a portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to execute a model for carrying out the early diagnosis or prognosis of colorectal cancer in embodiment 2 of the invention, when said program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
The foregoing description is directed to the preferred embodiments of the present invention, but the embodiments are not intended to limit the scope of the invention, and all equivalent changes or modifications made under the technical spirit of the present invention should be construed to fall within the scope of the present invention.

Claims (10)

1. A blood metabolite marker combination for early diagnosis of colorectal cancer, characterized in that the blood metabolite marker combination comprises Epsilon-Caprolactam、Triethyl Phosphate、3-Carboxy-4-Methyl-5-(1-Hydroxyp ropyl)-2-Furanpropionic Acid、Arachidonic Acid、Chrycorin.
2. Use of a reagent for detecting a blood metabolite marker combination according to claim 1 for the preparation of a reagent, kit or chip for early diagnosis or prognosis of colorectal cancer.
3. Use of a reagent for detecting a blood metabolite marker combination according to claim 1 for the preparation of a colorectal cancer patient screening evaluation model.
4. Use of a blood metabolite marker combination according to claim 1 for the preparation of a reagent, kit or chip for early diagnosis or prognosis of colorectal cancer.
5. Use of a blood metabolite marker combination according to claim 1 for the preparation of a colorectal cancer patient screening evaluation model.
6. A kit for early diagnosis or prognosis of colorectal cancer, the kit comprising reagents or instruments for detecting the blood metabolite marker combination of claim 1.
7. A model for early diagnosis or prognosis of colorectal cancer, characterized in that the model comprises:
(1) Sample processing, namely extracting the blood metabolite marker combination in the first aspect of the blood sample to be detected and carrying out LC-MS/MS full-scan detection to obtain the abundance value of the marker;
(2) Marking colorectal cancer samples determined by colonoscope and pathology detection as a value 1, marking healthy crowd samples as a value 0, inputting abundance values of biomarker combinations of different obtained samples into a random forest, constructing a model, and predicting diagnosis results;
(3) The result judges that the colorectal cancer diagnosis result is positive when the predictive value is 1, and negative when the predictive value is 0.
8. The method for determining a blood metabolite marker combination for early diagnosis of colorectal cancer according to claim 1:
(1) Group difference statistics (Wilcoxon test) were performed on markers of colorectal cancer group and healthy group, using species with P <0.05 as potential biomarkers;
(2) The optimal combination of microbial biomarkers was determined using the R software package AUCRF (random forest method);
(3) Determining the optimal combination by randomly removing the markers using AUC as an evaluation index (pROC packs);
(4) And simultaneously constructing a logistic regression model by using the markers, and comparing the logistic regression model with the marker to obtain the blood metabolite marker combination.
9. A model system for early diagnosis or prognosis of colorectal cancer, comprising:
(1) The pre-input module is at least used for inputting data to be evaluated;
(2) An evaluation module at least for evaluating the data to be evaluated, performed by the model of claim 7 or 8;
(3) And the display module is at least used for displaying the evaluation result.
10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, implements the steps comprising using the system of claim 9:
(1) Collecting and/or inputting evaluation data for early diagnosis or prediction of colorectal cancer;
(2) Substituting the evaluation data into the model for calculation to obtain a judgment conclusion of whether the sample is colorectal cancer;
(3) And outputting a judging conclusion of whether the sample is colorectal cancer.
CN202411692771.1A 2024-11-25 2024-11-25 A blood metabolite marker combination for early diagnosis of colorectal cancer and its application Pending CN119534824A (en)

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