Urine metabolism marker composition for diagnosis of generic cancer species as well as screening method and application of urine metabolism marker composition

Abstract

The invention discloses a urine metabolism marker composition for diagnosis of generic cancer species as well as a screening method and application, and relates to the technical field of biomedicine. The urine metabolism marker composition for the diagnosis of the panthenic cancer species comprises cane sugar, tyramine, acetyl homoserine, 3-methylglutaric acid and 3-ureapropionic acid. The urine metabolism marker composition provided by the invention has very high cancer species distinguishing capability, can quickly, accurately and non-invasively diagnose panthenic cancer species (such as three common high-incidence cancer species of colorectal cancer, gastric cancer and lung cancer), is beneficial to improving the diagnosis rate of cancer patients, timely provides treatment opportunities, carries out early intervention and individualized precise treatment, and has a good clinical application prospect. The treatment effect is improved, so that the survival rate of patients is increased. In addition, urine samples are adopted for diagnosis, and the advantages of being easy to collect, low in treatment cost, capable of being frozen and stored for a long time and the like are achieved.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a urine metabolic marker composition for pan-cancer diagnosis and a screening method and application thereof. Background Art

[0002] Cancer is the most common type of malignant tumor, with millions of deaths caused by cancer each year, and has become a major global health problem. Due to the complexity of the cancer development mechanism, different types of cancer may have common pathological features and biological mechanisms at the molecular level. This makes the diagnosis of cancer more complicated, thus posing a huge challenge to the timely diagnosis and treatment of cancer.

[0003] Existing clinical cancer diagnostic methods can only examine specific organs or types, such as tissue biopsy and imaging examinations, and can only confirm a few common cancer types. They are invasive, have high false positives and radiation problems, have certain limitations, and often cannot meet the individual needs of different patients. This limitation has, to a certain extent, restricted the timely detection and effective treatment of cancer, resulting in poor subsequent treatment effects and prognosis. Therefore, the development of non-invasive cancer diagnostic methods for detecting multiple cancer types is of great practical significance.

[0004] Pan-cancer diagnosis breaks through the traditional cancer boundary restrictions and can comprehensively diagnose and classify multiple cancer types, providing more accurate and comprehensive cancer diagnosis. This development represents an important advancement in the field of cancer diagnosis, laying the foundation for more accurate and personalized cancer diagnosis and treatment in the future, and helping to provide more accurate and effective treatment strategies for individual cancer patients. Highly prevalent cancers include common colorectal cancer, gastric cancer, and lung cancer. These cancers have multiple pathological types and subtypes, and different types of cancer cells have different morphological and immunohistochemical characteristics, which increases the complexity of cancer diagnosis. There is currently no tumor marker that can effectively distinguish these three types of cancer.

[0005] Therefore, the existing pan-cancer diagnosis technology needs to be improved and developed. Summary of the invention

[0006] Based on the above-mentioned deficiencies of the prior art, the purpose of the present invention is to provide a urine metabolic marker composition and screening method and application for pan-cancer diagnosis, aiming to enable rapid, accurate and non-invasive diagnosis of colorectal cancer, gastric cancer and lung cancer.

[0007] The technical solution of the present invention is as follows:

[0008] In a first aspect of the present invention, a urine metabolite marker composition for pan-cancer cancer diagnosis is provided, wherein the urine metabolite marker composition for pan-cancer cancer diagnosis comprises sucrose, tyramine, acetyl homoserine, 3-methylglutaric acid and 3-acylurea propionic acid.

[0009] Optionally, the urine metabolic marker composition for pan-cancer diagnosis also includes at least one of neonicotinoids, N-acetylhistamine, 2,4,6-trihydroxyacetophenone, 3-hydroxybutyric acid, and imidazoleacetic acid.

[0010] Optionally, the urine metabolic marker composition for pan-cancer diagnosis also includes at least one of inositol, glutamate, thymine, S-(2-carboxyethyl)-L-cysteine, and cinnamoylglycine.

[0011] Optionally, the urine metabolic marker composition for pan-cancer diagnosis further includes at least one of 3-hydroxyhippuric acid, succinic acid, β-aminopropionitrile, pyruvic acid and (S)-5'-deoxy-5'-(methylthio)adenosine.

[0012] Optionally, the urine metabolic marker composition for pan-cancer diagnosis consists of sucrose, tyramine, acetylhomoserine, 3-methylglutaric acid, 3-acyluidepropionic acid, neonicotinoids, N-acetylhistamine, 2,4,6-trihydroxyacetophenone, 3-hydroxybutyric acid, imidazoleacetic acid, inositol, glutamate, thymine, S-(2-carboxyethyl)-L-cysteine, cinnamoylglycine, 3-hydroxyhippuric acid, succinic acid, β-aminopropionitrile, pyruvic acid and (S)-5'-deoxy-5'-(methylthio)adenosine.

[0013] The second aspect of the present invention provides a method for screening the urine metabolite marker composition for pan-cancer diagnosis as described above, which comprises the following steps:

[0014] Extracting urine from patients with gastric cancer, colorectal cancer and lung cancer respectively to obtain extracts;

[0015] Using a chromatography-mass spectrometer to detect the water phase in the extract to obtain detection data;

[0016] Processing the detection data, and then identifying metabolites to obtain metabolomics data;

[0017] The metabolomics data are analyzed to screen out a urine metabolite marker composition for pan-cancer diagnosis.

[0018] Optionally, the step of analyzing the metabolomics data to screen and obtain a urine metabolite marker composition for pan-cancer diagnosis specifically comprises:

[0019] The metabolomics data were subjected to Lasso regression analysis to screen out a urine metabolite marker composition for pan-cancer diagnosis.

[0020] The third aspect of the present invention provides a use of the urine metabolic marker composition for pan-cancer diagnosis as described above in the preparation of a product for diagnosing pan-cancer.

[0021] Optionally, the sample used by the product for diagnosing pan-cancer is urine.

[0022] Optionally, the pan-cancer types include gastric cancer, colorectal cancer or lung cancer.

[0023] Beneficial effects: The urine metabolite marker composition provided by the present invention has a high ability to distinguish cancer types, and can quickly, accurately and non-invasively diagnose pan-cancer types (such as the three common cancer types with high incidence, such as colorectal cancer, gastric cancer and lung cancer), thereby helping to improve the diagnosis rate of cancer patients, provide timely treatment opportunities, conduct early intervention and individualized precision treatment, improve treatment effects, and thus improve the survival rate of patients. In addition, the use of urine samples for diagnosis has the advantages of easy collection, low processing cost and long-term frozen storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a multivariate ROC curve diagram of 20 urine metabolite markers in the urine samples of the modeling group in Example 2.

[0025] Figure 2 : This is a multivariate ROC curve diagram of 20 urine metabolic markers in the urine samples of the validation group in Example 2.

[0026] Figure 3 This is a multivariate ROC curve diagram of 15 urine metabolite markers in the urine samples of the validation group in Example 3.

[0027] Figure 4 This is a multivariate ROC curve diagram of 10 urine metabolite markers in the urine samples of the validation group in Example 4.

[0028] Figure 5 This is a multivariate ROC curve diagram of 5 urine metabolite markers in the urine samples of the validation group in Example 5. DETAILED DESCRIPTION

[0029] The present invention provides a urine metabolic marker composition and screening method and application for pan-cancer diagnosis. In order to make the purpose, technical scheme and effect of the present invention clearer and more specific, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0030] Unless otherwise defined, all technical terms and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0031] Urine metabolite markers are non-invasive and easy to collect, and can reflect the biological changes of cancer. Metabolomics technology can analyze a variety of metabolites in urine, covering multiple biological metabolic pathways and molecular level information, and can fully understand the metabolic changes in the process of cancer. Therefore, the use of metabolomics technology to analyze urine metabolite markers can provide more comprehensive information for the detection of cancer types, help determine biomarkers that can distinguish different types of cancer, and provide a broader foundation for personalized treatment and precision medicine. Therefore, the embodiment of the present invention provides a urine metabolite marker composition for pan-cancer cancer diagnosis based on the metabolomics method, wherein the urine metabolite marker composition for pan-cancer cancer diagnosis includes sucrose, tyramine, acetyl homoserine, 3-methylglutaric acid and 3-acylurea propionic acid.

[0032] In this embodiment, the urine metabolite marker composition containing 5 urine metabolite markers has significant differences between different cancer types, has a high ability to distinguish cancer types, and can quickly, accurately, and non-invasively diagnose (or detect) pan-cancer types (such as the three common cancers with high incidence, such as colorectal cancer, gastric cancer, and lung cancer), thereby helping to improve the diagnosis rate of cancer patients, provide treatment opportunities in a timely manner, help provide early intervention and individualized precision treatment, improve treatment effects, and thus improve patient survival rates. In addition, the use of urine samples for diagnosis has the advantages of easy collection, low processing costs, and long-term frozen storage.

[0033] In some embodiments, the urine metabolite marker composition for pan-cancer cancer diagnosis also includes at least one of neonicotinoids, N-acetylhistamine, 2,4,6-trihydroxyacetophenone, 3-hydroxybutyric acid, and imidazoleacetic acid. That is to say, in this embodiment, the urine metabolite marker composition for pan-cancer cancer diagnosis includes sucrose, tyramine, acetyl homoserine, 3-methylglutaric acid and 3-acylurea propionic acid, as well as at least one of neonicotinoids, N-acetylhistamine, 2,4,6-trihydroxyacetophenone, 3-hydroxybutyric acid, and imidazoleacetic acid. The above-mentioned urine metabolite marker composition has a high ability to distinguish cancer types, and can quickly, accurately and non-invasively diagnose pan-cancer cancers (such as three common high-incidence cancers such as colorectal cancer, gastric cancer and lung cancer).

[0034] In some embodiments, the urine metabolite marker composition for pan-cancer cancer diagnosis also includes at least one of inositol, glutamate, thymine, S-(2-carboxyethyl)-L-cysteine, and cinnamoyl glycine. That is to say, in this embodiment, the urine metabolite marker composition for pan-cancer cancer diagnosis includes sucrose, tyramine, acetyl homoserine, 3-methylglutaric acid and 3-acylurea propionic acid, as well as at least one of neonicotinoids, N-acetyl histamine, 2,4,6-trihydroxyacetophenone, 3-hydroxybutyric acid, and imidazole acetic acid, and inositol, glutamate, thymine, S-(2-carboxyethyl)-L-cysteine, and cinnamoyl glycine. The above-mentioned urine metabolite marker composition has a high ability to distinguish cancer types, and can quickly, accurately, and non-invasively diagnose pan-cancer cancers (such as three common cancers with high incidence, such as colorectal cancer, gastric cancer, and lung cancer).

[0035] In some embodiments, the urine metabolite marker composition for pan-cancer cancer diagnosis also includes at least one of 3-hydroxyhippuric acid, succinic acid, β-aminopropionitrile, pyruvic acid and (S)-5'-deoxy-5'-(methylthio)adenosine. That is, in this embodiment, the urine metabolite marker composition for pan-cancer cancer diagnosis includes sucrose, tyramine, acetylhomoserine, 3-methylglutaric acid and 3-acylurea propionic acid, and at least one of neonicotinoids, N-acetylhistamine, 2,4,6-trihydroxyacetophenone, 3-hydroxybutyric acid, and imidazoleacetic acid, and at least one of inositol, glutamic acid, thymine, S-(2-carboxyethyl)-L-cysteine, and cinnamoylglycine, and at least one of 3-hydroxyhippuric acid, succinic acid, β-aminopropionitrile, pyruvic acid and (S)-5'-deoxy-5'-(methylthio)adenosine. The above-mentioned urine metabolic marker composition has a high ability to distinguish cancer types, and can quickly, accurately and non-invasively diagnose pan-cancer types (such as the three common high-incidence cancer types such as colorectal cancer, gastric cancer and lung cancer).

[0036] In some embodiments, the urine metabolite marker composition for pan-cancer cancer diagnosis is composed of sucrose, tyramine, acetylhomoserine, 3-methylglutaric acid, 3-acylurea propionic acid, neonicotinoids, N-acetylhistamine, 2,4,6-trihydroxyacetophenone, 3-hydroxybutyric acid, imidazoleacetic acid, inositol, glutamic acid, thymine, S-(2-carboxyethyl)-L-cysteine, cinnamoylglycine, 3-hydroxyhippuric acid, succinic acid, β-aminopropionitrile, pyruvic acid and (S)-5'-deoxy-5'-(methylthio)adenosine. The above-mentioned urine metabolite marker composition has a high ability to distinguish cancer types, and can quickly, accurately and non-invasively diagnose pan-cancer cancer types (such as three common cancer types with high incidence, such as colorectal cancer, gastric cancer and lung cancer).

[0037] The present invention also provides a method for screening the urine metabolic marker composition for pan-cancer diagnosis as described above, which comprises the following steps:

[0038] S1. Extracting urine from patients with gastric cancer, patients with colorectal cancer, and patients with lung cancer respectively to obtain extracts;

[0039] S2. Detecting the aqueous phase in the extract using a chromatography-mass spectrometer to obtain detection data;

[0040] S3, processing the detection data, and then identifying metabolites to obtain metabolomics data;

[0041] S4. Analyze the metabolomics data to screen and obtain a urine metabolite marker composition for pan-cancer diagnosis.

[0042] The embodiment of the present invention is based on the metabolomics method, which realizes high-throughput detection of small molecule metabolites in the body by preparing urine samples, and combines data analysis to screen metabolic markers with significant differences, so as to distinguish different types of cancer in cancer patients, and provide a new option for pan-cancer screening or diagnosis.

[0043] In step S4, in some embodiments, the step of analyzing the metabolomics data to screen and obtain a urine metabolite marker composition for pan-cancer diagnosis specifically includes:

[0044] The metabolomics data were subjected to Lasso regression analysis to screen out a urine metabolite marker composition for pan-cancer diagnosis.

[0045] The present invention also provides a use of the urine metabolite marker composition for pan-cancer diagnosis as described above in the present invention in the preparation of a product for pan-cancer diagnosis. The product includes but is not limited to a kit.

[0046] In some embodiments, the sample used by the product for diagnosing pan-cancer is urine.

[0047] In some embodiments, the pan-cancer includes gastric cancer, colorectal cancer or lung cancer. In the present invention, the above-mentioned urine metabolite marker composition can be used to diagnose three major cancers: gastric cancer, colorectal cancer and lung cancer.

[0048] The present invention will be further described below by means of specific examples.

[0049] First, in Example 1, urine samples of patients with gastric cancer, lung cancer and colorectal cancer were obtained and divided into a modeling group and a validation group to detect and identify small molecule metabolites in urine samples; then, the modeling group samples were used to screen out metabolites with significant differences between patients with gastric cancer, colorectal cancer and lung cancer, and potential metabolic markers for diagnosing pan-cancer cancers were determined. Next, in Examples 2 to 5, multivariate ROC analysis was used to evaluate the diagnostic effects of the screened metabolic markers in distinguishing patients with gastric cancer, colorectal cancer and lung cancer, and a diagnostic model for pan-cancer cancers was constructed. The validation group data was used as an unknown sample to verify the effectiveness and accuracy of the constructed diagnostic model in distinguishing patients with gastric cancer, colorectal cancer and lung cancer.

[0050] Example 1 Detection, identification and screening of small molecule metabolites in urine samples

[0051] 1. Subjects’ conditions and sample collection

[0052] (1) The inclusion and exclusion criteria for patients with gastric cancer, lung cancer, and colorectal cancer are as follows:

[0053] Inclusion criteria:

[0054] 1) Male or female aged ≥18 years;

[0055] 2) Patients diagnosed with primary gastric cancer, lung cancer or colorectal cancer by biopsy, postoperative pathological confirmation or comprehensive evaluation by clinicians.

[0056] Exclusion criteria:

[0057] 1) Pregnancy or lactation;

[0058] 2) Emergency or emergency treatment required;

[0059] 3) history of malignant tumor or any anti-tumor treatment before sampling;

[0060] 4) Multiple primary malignant tumors are present simultaneously.

[0061] (2) Sample collection

[0062] A total of 968 cancer urine samples (from 968 subjects who met the above criteria) were collected from three medical centers. All urine samples were collected in the morning when fasting and stored in a refrigerator at -80°C after centrifugation. All subjects obtained written informed consent before participating in the study. The 968 cancer urine samples were randomly divided into a modeling group and a validation group. The urine samples of the modeling group and the validation group came from different patients to ensure the independence and representativeness of the samples.

[0063] Specifically, the number of urine samples in the modeling group: 154 gastric cancer (GC) urine samples, 157 colorectal cancer (CRC) urine samples, and 452 lung cancer (LC) urine samples; the number of urine samples in the validation group: 39 gastric cancer (GC) urine samples, 39 colorectal cancer (CRC) urine samples, and 127 lung cancer (LC) urine samples (see Table 1 for details).

[0064] Table 1. Sample information

[0065] GC CRC LC total Urine samples of modeling group (pcs) 154 157 452 763 Validation group urine samples (pieces) 39 39 127 205 Total (pcs) 193 196 579 968

[0066] 2. Reagents

[0067] Methanol, acetonitrile, water, and acetic acid of mass spectrometry grade purity, and formic acid, ammonium acetate, and methyl tert-butyl ether of HPLC grade purity were purchased from Sigma-Aldrich, USA.

[0068] 3. Sample preparation

[0069] After the urine sample was taken out of the -80°C refrigerator and thawed, 40 μL of the urine sample was taken into an extraction tube, and 400 μL of pre-cooled methyl tert-butyl ether and methanol mixed extract (the volume ratio of methyl tert-butyl ether and methanol was 3:1) was added, and the extract was obtained after vortexing and ultrasonic mixing; 360 μL of methanol-water mixed solution (the volume ratio of methanol and water was 3:1) was added to the extract, and vortexed and centrifuged to separate the layers; 300 μL of the lower aqueous phase solution was taken, and 900 μL of pre-cooled methanol was added thereto. After protein precipitation, 1000 μL of the supernatant was taken and dried by rotation, and 200 μL of water was added to re-dissolve it. After re-dissolution (aqueous phase polar substance), it was used for LC-MS (liquid chromatography-mass spectrometry) detection.

[0070] 4. Metabolite detection

[0071] (1) Testing instruments

[0072] The organic phase was analyzed using Waters ACQUTTY UPLC BEH C8 1.7μm 2.1*100mm column, water phase using Waters ACQUTTY UPLC HSS T3 1.8μm 2.1*100mm column was used for small molecule separation; ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific) were used for liquid chromatography and mass spectrometry.

[0073] (2) Mobile phase parameters

[0074] Organic phase: Mobile phase A is an aqueous solution containing 0.1% acetic acid and 10mM ammonium acetate (the mass content of acetic acid is 0.1%, the mass content of ammonium acetate is 1%), and mobile phase B is a mixed solution of acetonitrile and isopropanol containing 0.1% acetic acid and 10mM ammonium acetate (the volume ratio of acetonitrile to isopropanol is 7:3, and the mass content of acetic acid is 0.1%). The separation gradient is as follows: 55% mobile phase B is maintained for 0-1 minute, 1-4 minutes is 55%-75% mobile phase B, 4-12 minutes is 75%-89% mobile phase B, 12-15 minutes is 89%-100% mobile phase B, 15-19.5 minutes is maintained at 100% mobile phase B, 19.5-19.51 minutes is 100%-55% mobile phase B, and maintained at 55% mobile phase B from 19.51 to 24 minutes.

[0075] Aqueous phase: Mobile phase A is an aqueous solution containing 0.1% formic acid (the mass content of formic acid is 0.1%), and mobile phase B is an acetonitrile solution containing 0.1% formic acid (the mass content of formic acid is 0.1%). The separation gradient is as follows: 1% mobile phase B is maintained for 0-1 minute, 1%-40% mobile phase B is maintained for 1-11 minutes, 40%-70% mobile phase B is maintained for 11-13 minutes, 70%-99% mobile phase B is maintained for 13-15 minutes, 99% mobile phase B is maintained for 15-18 minutes, 99%-1% mobile phase B is maintained for 18-19 minutes, and 1% mobile phase B is maintained for 19-22 minutes.

[0076] (3) Mass spectrometry parameters

[0077] Mass spectrometry data were collected in Full MS and Full MS / dd-MS2 modes (each with positive and negative modes). The parameters used by QExactive were as follows:

[0078] In Full MS mode, the resolution is 70,000, the scan range is 100-1500 m / z (m / z is the ratio of ion mass to charge number), and the AGC (automatic gain control) is 3E6 (i.e. 3×10 6 ), Maximum IT (maximum injection time) is 200ms;

[0079] In Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer is 17,500, the quadrupole window is 1.5 m / z, and the AGC is 1E5 (i.e., 1×10 5 ), the maximum ion injection time is 50ms, and the HCD (high energy collision dissociation) relative collision energy is 30eV.

[0080] 5. Data processing

[0081] First, effective peak signals were extracted from all mass spectrometry data, and then baseline correction was applied to remove noise and retain the original signal peak; the original data was converted into central discrete data; then, the retention time in the chromatogram corresponding to each mass spectrometry peak in the sample was corrected, and the peaks with differences between samples within the error range were defined as the same peak, thereby obtaining a matrix data set. The mass spectrometry data were normalized by the MSTUS (total useful MS signals, MSTUS) method to eliminate the dilution effect in urine samples. In order to make the data distribution more reflective of the real differences between samples, the Normalization Autoencoder (NormAE) was used for homogenization.

[0082] 6. Identification of metabolites

[0083] Using public databases such as the Human Metabolite Database (HMDB; www.hmdb.ca), the Metabolomics Database (Metlin Database; https: / / metlin.scripps.edu), the Mass Spectrum Database (http: / / www.massbank.jp / ), and the Lipid Database (https: / / www.lipidmaps.org / ), as well as the primary chromatogram, secondary chromatogram, and mass spectrometry spectra of the standard separated on the same chromatographic column; the retention time was within 0.1min difference, and the mass-to-charge ratio was less than 10ppm, and the database and standard were matched for identification. A total of 360 small molecule metabolites were successfully identified.

[0084] 7. Screening of urine metabolic markers for distinguishing gastric cancer, colorectal cancer and lung cancer

[0085] Important metabolites that can effectively distinguish patients with gastric cancer, colorectal cancer and lung cancer were screened out from the 360 ​​metabolites obtained above.

[0086] First, based on the modeling group data of urine samples, Lasso (Least Absolute Shrinkage and Selection Operator) was used to screen differential metabolites for three groups of samples: gastric cancer, colorectal cancer, and lung cancer. By shrinking the coefficients of other metabolites with small contributions to the differences between the three groups to zero, only the important metabolite combinations with high contributions to the differences between the three groups of samples of gastric cancer, colorectal cancer, and lung cancer were retained. After model optimization, 20 urine metabolite marker combinations with significant differences in distinguishing patients with gastric cancer, colorectal cancer, and lung cancer were finally determined (Table 2).

[0087] Table 2. Differential metabolites used to distinguish gastric cancer group, colorectal cancer group and lung cancer group

[0088]

[0089]

[0090] Example 2 Construction and validation of a pan-cancer diagnosis model based on 20 urine metabolic markers

[0091] A multivariate ROC (receiver operating characteristic curve) analysis was performed for the combination of the 20 urine metabolic markers with significant differences in Example 1, namely 3-acyluide propionic acid, 3-methylglutaric acid, acetylhomoserine, imidazoleacetic acid, 3-hydroxybutyric acid, cinnamoylglycine, S-(2-carboxyethyl)-L-cysteine, thymine, tyramine, sucrose, glutamate, 2,4,6-trihydroxyacetophenone, N-acetylhistamine, inositol, neonicotinoids, 3-hydroxyhippuric acid, succinic acid, β-aminopropionitrile, pyruvic acid and (S)-5'-deoxy-5'-(methylthio)adenosine. Randomly use 3 / 4 of the urine samples of the modeling group in Example 1 as a training set, and the remaining 1 / 4 as a test set. The training set is used to build and train the machine learning classification model, and the test set is used to evaluate the discrimination ability of the trained model. The model is built using the support vector machine (SVM) algorithm. During the construction process, 2000 random cycle iterations of SVM training were performed to improve the stability and generalization ability of the model. A diagnostic model for distinguishing gastric cancer, colorectal cancer, and lung cancer was constructed based on the above 20 urine metabolic markers with significant differences.

[0092] The results showed that the model based on the combination of 20 urine metabolite markers performed well on the constructed model test set, with an AUC of 0.851 ( Figure 1 ).

[0093] This embodiment uses the area under the ROC curve (AUC) of metabolites as a diagnostic evaluation indicator. The ROC curve is used to evaluate the relationship between the sensitivity (Sensitivity) and 1-specificity (1-Specificity) of the model. In the ROC curve, the ordinate is the sensitivity (also called the true positive rate), and the abscissa is 1-specificity (i.e., the false positive rate). The area under the ROC curve (AUC) is an indicator for evaluating the classification performance of the model, and its value range is between 0 and 1. An AUC value close to 1 indicates that the model has excellent classification performance and can effectively distinguish between positive and negative samples; an AUC value close to or less than 0.5 indicates that the model performance is poor, similar to random guessing.

[0094] In order to evaluate the accuracy of the constructed diagnostic model, the urine samples of the validation group obtained in Example 1 were used as unknown samples, and 20 urine metabolite markers were used to validate the pan-cancer diagnostic model constructed above. The validation results were: AUC = 0.837 ( Figure 2 ).

[0095] The above analysis and verification process helps to evaluate the accuracy and practicality of the model, and provides strong support and verification for future clinical applications.

[0096] The above results indicate that the combination of the 20 screened urine metabolic markers has practical clinical diagnostic value, can provide reliable diagnostic support for the differentiation of gastric cancer, colorectal cancer and lung cancer, and is expected to become an important reference for clinical cancer diagnosis.

[0097] Example 3 Construction and validation of a pan-cancer diagnosis model based on 15 urine metabolic markers

[0098] The only difference between this embodiment and embodiment 2 is that a pan-cancer diagnosis model is constructed using a combination of 15 urine metabolite markers, namely neonicotinoids, inositol, N-acetylhistamine, 2,4,6-trihydroxyacetophenone, glutamic acid, sucrose, tyramine, thymine, S-(2-carboxyethyl)-L-cysteine, cinnamoylglycine, 3-hydroxybutyric acid, imidazoleacetic acid, acetylhomoserine, 3-methylglutaric acid and 3-acylurea propionic acid.

[0099] The pan-cancer diagnosis model constructed with 15 urine metabolite markers had an AUC of 0.832. The combination of 15 urine metabolite markers performed well on the constructed model test set.

[0100] The urine samples of the validation group were used as unknown samples, and 15 urine metabolic markers were used to validate the constructed pan-cancer diagnosis model. The validation results were: AUC was 0.822 ( Figure 3 ).

[0101] The above results indicate that the combination of 15 urine metabolic markers has practical clinical diagnostic value, can provide reliable diagnostic support for the differentiation of gastric cancer, colorectal cancer and lung cancer, and is expected to become an important reference for clinical cancer diagnosis.

[0102] Example 4 Construction and validation of a pan-cancer diagnosis model based on 10 urine metabolic markers

[0103] The only difference between this embodiment and embodiment 2 is that a pan-cancer diagnosis model is constructed using a combination of 10 urine metabolite markers, namely neonicotinoids, N-acetylhistamine, 2,4,6-trihydroxyacetophenone, sucrose, tyramine, 3-hydroxybutyric acid, imidazoleacetic acid, acetylhomoserine, 3-methylglutaric acid and 3-acylurea propionic acid.

[0104] The results showed that the diagnostic model constructed with 10 urine metabolite markers had an AUC of 0.839, and the combination of 10 urine metabolite markers performed well on the constructed model test set.

[0105] The urine samples of the validation group were used as unknown samples, and 10 urine metabolic markers were used to validate the constructed pan-cancer diagnosis model. The validation results were: AUC = 0.820 ( Figure 4 ).

[0106] The above results indicate that the combination of 10 urine metabolic markers has practical clinical diagnostic value, can provide reliable diagnostic support for the differentiation of gastric cancer, colorectal cancer and lung cancer, and is expected to become an important reference for clinical cancer diagnosis.

[0107] Example 5 Construction and validation of a pan-cancer diagnosis model based on five urine metabolic markers

[0108] The only difference between this embodiment and embodiment 2 is that a pan-cancer diagnosis model is constructed using a combination of five urine metabolic markers, namely sucrose, tyramine, acetyl homoserine, 3-methylglutaric acid and 3-acylurea propionic acid.

[0109] The results showed that the diagnostic model constructed with 5 urine metabolite markers had an AUC of 0.806, and the combination of 5 urine metabolite markers performed well on the constructed model test set.

[0110] The urine samples of the validation group were used as unknown samples, and five urine metabolic markers were used to validate the constructed pan-cancer diagnosis model. The validation results were: AUC = 0.801 ( Figure 5 ).

[0111] The above results indicate that the combination of the five urine metabolic markers has practical clinical diagnostic value, can provide reliable diagnostic support for the differentiation of gastric cancer, colorectal cancer and lung cancer, and is expected to become an important reference for clinical cancer diagnosis.

[0112] Therefore, from the results of Example 2 to Example 5, it can be seen that the urine metabolite marker compositions containing 20, 15, 10 and 5 urine metabolite markers can effectively distinguish patients with gastric cancer, colorectal cancer and lung cancer, showing high accuracy and differentiation ability, and have potential clinical application value. Therefore, the diagnostic model provided by the present invention is expected to become a reliable choice in actual clinical applications in the future, providing important support and guidance for the diagnosis of pan-cancer and the health management of cancer patients.

[0113] In summary, the present invention provides a urine metabolite marker composition for pan-cancer diagnosis, a screening method and an application. The urine metabolite marker composition has significant differences in different cancer types, has a high ability to distinguish cancer types, and can achieve pan-cancer detection. The use of urine metabolite marker compositions for pan-cancer detection is non-invasive and more suitable for large-scale screening and detection of the general population, which helps to improve the diagnosis rate of cancer patients, provide treatment opportunities in a timely manner, conduct early intervention and individualized precision treatment, improve treatment effects, and thus improve patient survival rates. In addition, the use of urine samples for diagnosis has the advantages of easy collection, low processing costs, and long-term frozen storage.

[0114] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A composition of urine metabolic markers for pan-cancer diagnosis, characterized in that: The urine metabolic marker composition for pan-cancer diagnosis comprises sucrose, tyramine, acetyl homoserine, 3-methylglutaric acid and 3-acylurea propionic acid.

2. The urine metabolic marker composition for pan-cancer diagnosis according to claim 1, characterized in that: The urine metabolic marker composition for pan-cancer diagnosis also includes at least one of neonicotinoids, N-acetylhistamine, 2,4,6-trihydroxyacetophenone, 3-hydroxybutyric acid, and imidazoleacetic acid.

3. The urine metabolic marker composition for pan-cancer diagnosis according to claim 2, characterized in that: The urine metabolic marker composition for pan-cancer diagnosis also includes at least one of inositol, glutamic acid, thymine, S-(2-carboxyethyl)-L-cysteine, and cinnamoylglycine.

4. The urine metabolic marker composition for pan-cancer diagnosis according to claim 3, characterized in that: The urine metabolic marker composition for pan-cancer diagnosis also includes at least one of 3-hydroxyhippuric acid, succinic acid, β-aminopropionitrile, pyruvic acid and (S)-5'-deoxy-5'-(methylthio)adenosine.

5. The urine metabolic marker composition for pan-cancer diagnosis according to claim 1, characterized in that: The urine metabolic marker composition for pan-cancer diagnosis consists of sucrose, tyramine, acetylhomoserine, 3-methylglutaric acid, 3-acylurea propionic acid, neonicotinoids, N-acetylhistamine, 2,4,6-trihydroxyacetophenone, 3-hydroxybutyric acid, imidazoleacetic acid, inositol, glutamic acid, thymine, S-(2-carboxyethyl)-L-cysteine, cinnamoylglycine, 3-hydroxyhippuric acid, succinic acid, β-aminopropionitrile, pyruvic acid and (S)-5'-deoxy-5'-(methylthio)adenosine.

6. A method for screening a urine metabolic marker composition for pan-cancer diagnosis according to any one of claims 1 to 5, characterized in that: The steps include: Extracting urine from patients with gastric cancer, colorectal cancer and lung cancer respectively to obtain extracts; Using a chromatography-mass spectrometer to detect the water phase in the extract to obtain detection data; Processing the detection data, and then identifying metabolites to obtain metabolomics data; The metabolomics data are analyzed to screen out a urine metabolite marker composition for pan-cancer diagnosis.

7. The screening method according to claim 6, characterized in that The step of analyzing the metabolomics data to screen and obtain a urine metabolite marker composition for pan-cancer diagnosis specifically includes: The metabolomics data were subjected to Lasso regression analysis to screen out a urine metabolite marker composition for pan-cancer diagnosis.

8. Use of the urine metabolic marker composition for pan-cancer diagnosis according to any one of claims 1 to 5 in the preparation of a product for diagnosing pan-cancer.

9. The use according to claim 8, characterized in that: The sample used by the product for diagnosing pan-cancer types is urine.

10. The use according to claim 9, characterized in that: The pan-cancer types include gastric cancer, colorectal cancer or lung cancer.

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