A urine metabolic marker composition and screening method and application for pan-cancer screening

Abstract

The application discloses a urine metabolic marker composition and a screening method and application for general cancer screening, and relates to the field of biomedical technology. The urine metabolic marker composition for general cancer screening comprises pyruvic acid, succinic acid, 3-hydroxyhippuric acid, myo-inositol, N-acetylhistamine, glutamic acid, sucrose, tyramine, thymine, cinnamoyl glycine, 3-hydroxybutyric acid, imidazole acetic acid, acetylhomoserine, 3-methyl glutaric acid and 3-ureidopropionic acid. The urine metabolic marker composition has the functions of general cancer screening and tumor tracing, has high sensitivity and specificity, can quickly, accurately and non-invasively distinguish healthy people from cancer patients, and can trace tumors. When the urine metabolic marker composition is used for general cancer screening, urine samples are used, and the urine metabolic marker composition has the advantages of easy collection, low processing cost and long-term refrigerated storage, and is non-invasive, convenient and suitable for large-scale screening.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a urine metabolic biomarker composition, screening method and application for pan-cancer screening. Background Technology

[0002] Cancer is the most common type of malignant tumor, causing millions of deaths each year and becoming a major global health problem. Due to the complexity of cancer development mechanisms, different types of cancer may share common pathological features and biological mechanisms at the molecular level. This makes cancer diagnosis more complex, posing a significant challenge to timely diagnosis and treatment.

[0003] Currently used clinical methods for tumor screening generally suffer from problems such as complex operation, poor patient compliance, and high cost. They can only be used to examine specific organs or types of tumors and cannot meet the individualized needs of different patients. For example, traditional tissue biopsies are invasive and difficult to use for routine screening, while imaging examinations such as PET / CT are expensive and pose a risk of radiation exposure.

[0004] Therefore, developing a non-invasive, simple, low-cost cancer screening method applicable to a large population has become an urgent technical problem to be solved in the field of tumor diagnosis. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a urine metabolic biomarker composition, screening method and application for pan-cancer screening, which aims to achieve accurate and non-invasive pan-cancer screening.

[0006] The technical solution of the present invention is as follows: In a first aspect, the present invention provides a urinary metabolic biomarker composition for pan-cancer screening, wherein the urinary metabolic biomarker composition for pan-cancer screening comprises pyruvate, succinic acid, 3-hydroxyhippuric acid, inositol, N-acetylhistamine, glutamic acid, sucrose, tyramine, thymine, cinnamoylglycine, 3-hydroxybutyric acid, imidazole acetic acid, acetylhomoserine, 3-methylglutaric acid, and 3-ureidopropionic acid.

[0007] Optionally, the urine metabolic marker composition is used to distinguish between healthy individuals and individuals with cancer, including stomach cancer, colorectal cancer, and lung cancer.

[0008] Optionally, the urine metabolic marker composition is used for tumor tracing in cancer patients.

[0009] A second aspect of the present invention provides a method for screening a urine metabolic biomarker composition for pan-cancer screening as described above, comprising the following steps: Urine samples were provided from a healthy population group and a pan-cancer patient group, and the urine was extracted to obtain an extract; the pan-cancer patient group consisted of a gastric cancer patient group, a colorectal cancer patient group, and a lung cancer patient group. The aqueous phase in the extract was detected using chromatography-mass spectrometry to obtain detection data; The detection data is processed and then the metabolites are identified to obtain metabolomics data. The metabolomics data were analyzed to screen for a urinary metabolic biomarker composition for pan-cancer screening.

[0010] Optionally, the step of analyzing the metabolomics data to screen for a urinary metabolic biomarker composition for pan-cancer screening specifically includes: Differential metabolites were screened between the healthy population and the pan-cancer patient group using orthogonal partial least squares discriminant analysis with VIP>1 and FDR<0.05 as screening criteria. The selected differential metabolites were subjected to absolute value convergence and selection operator analysis to obtain a urinary metabolic biomarker composition for pan-cancer screening.

[0011] Optionally, the step of performing absolute value convergence and selection operator analysis on the screened differential metabolites to obtain a urinary metabolic biomarker composition for pan-cancer screening specifically includes: Between the healthy population and the pan-cancer patient group, the differentially identified metabolites were subjected to absolute value convergence and selection operator analysis to obtain the first result; In the three groups of patients with lung cancer, colorectal cancer, and gastric cancer, the differentially identified metabolites were subjected to absolute value convergence and selection operator analysis to obtain the second result. The intersection of the first and second results yields a urinary metabolic biomarker composition for pan-cancer screening.

[0012] A third aspect of the present invention provides the use of the urinary metabolic marker composition for pan-cancer screening as described above in the preparation of products for pan-cancer screening.

[0013] A fourth aspect of the present invention provides the use of the urinary metabolic marker composition for pan-cancer screening as described above in the preparation of products for pan-cancer screening and tumor tracing.

[0014] Optionally, the product may include reagents or kits.

[0015] Optionally, the product may be used as a urine sample when used for pan-cancer screening.

[0016] Beneficial effects: The urine metabolic biomarker composition provided by this invention has a high pan-cancer screening capability, with high sensitivity and specificity. It can quickly, accurately, and non-invasively distinguish between healthy people and cancer patients (such as patients with common 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.

[0017] The urine metabolic biomarker composition provided by this invention, when used for pan-cancer screening, uses urine samples and has the advantages of easy collection, low processing cost and long-term cryopreservation. At the same time, the detection is non-invasive and convenient, making it suitable for large-scale screening.

[0018] In addition, the urine metabolic marker composition provided by the present invention not only has pan-cancer screening function, but also tumor tracing function, which can realize tumor tracing for cancer patients (gastric cancer, lung cancer and colorectal cancer patients). Attached Figure Description

[0019] Figure 1 Multivariate ROC curve analysis of 15 key biomarkers that distinguish between health and pan-cancer in the modeling group of Example 2.

[0020] Figure 2 This is a multivariate ROC curve analysis of 15 important biomarkers that distinguish between healthy individuals and pan-cancer in the validation group of Example 2.

[0021] Figure 3 This is a multivariate ROC curve analysis of 15 important biomarkers used for tumor origin tracing in the modeling group of Example 2.

[0022] Figure 4 This is a multivariate ROC curve analysis of 15 important biomarkers used for tumor tracing in the validation group of Example 2. Detailed Implementation

[0023] This invention provides a urinary metabolic biomarker composition, screening method, and application for pan-cancer screening. To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention is further described in detail below. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0025] Liquid chromatography-mass spectrometry (LC-MS), as a core tool in metabolomics research, features high throughput, high sensitivity, and high resolution, enabling precise qualitative and quantitative analysis of small-molecule metabolites in biological samples. Urine, as an ideal clinical sample, offers significant advantages such as non-invasive acquisition, high reproducibility, and the ability to be collected independently. In recent years, increasing research has shown that when cancer develops in the body, abnormal metabolism of tumor cells leads to significant changes in the composition of metabolites in urine, and these differences can serve as important evidence for cancer diagnosis. However, current technologies still have the following shortcomings: 1) Lack of pan-cancer screening capability: Most reported urine biomarkers target single cancer types, such as prostate cancer, bladder cancer, and colorectal cancer, lacking combinations of pan-cancer biomarkers capable of simultaneously screening multiple cancers. This means that screening for multiple cancers often requires multiple tests simultaneously, increasing testing costs and reducing screening efficiency. 2) Lack of tumor origin tracing capability: Even existing studies attempting pan-cancer screening only differentiate between healthy individuals and cancer patients, failing to further clarify the origin of the cancer. However, precision treatment of cancer highly depends on a clear diagnosis of the primary site. If only a diagnosis of "cancer" can be made without pinpointing the location, the patient still needs to undergo numerous imaging examinations or tissue biopsies to determine the type of cancer, which undoubtedly increases the physical and mental burden on the patient and the consumption of medical resources.

[0026] Based on this, the present invention provides a set of metabolic biomarker compositions based on metabolomics data from urine samples for gastric cancer, lung cancer, and colorectal cancer, which are among the most common cancers. This not only distinguishes between healthy individuals and cancer but also allows for accurate tracing of the origins of these three types of cancer, potentially providing a non-invasive, convenient, and reliable new tool for large-scale pan-cancer screening and clinical auxiliary diagnosis. Specifically, an embodiment of the present invention provides a urine metabolic biomarker composition for pan-cancer screening, wherein the urine metabolic biomarker composition for pan-cancer screening includes pyruvate, succinic acid, 3-hydroxyhippuric acid, inositol, N-acetylhistamine, glutamic acid, sucrose, tyramine, thymine, cinnamoylglycine, 3-hydroxybutyric acid, imidazole acetic acid, acetylhomoserine, 3-methylglutaric acid, and 3-ureidopropionic acid.

[0027] In some embodiments, the urine metabolic marker composition is used to distinguish between healthy individuals and individuals with cancer, including stomach cancer, colorectal cancer, and lung cancer.

[0028] In some embodiments, the urine metabolic biomarker composition is used for tumor tracing in cancer patients.

[0029] The urine metabolic biomarker composition provided by this invention has a high pan-cancer screening capability, with high sensitivity and specificity. It can quickly, accurately, and non-invasively distinguish between healthy people and cancer patients (such as patients with common 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.

[0030] The urine metabolic biomarker composition provided by this invention, when used for pan-cancer screening, uses urine samples and has the advantages of easy collection, low processing cost and long-term cryopreservation. At the same time, the detection is non-invasive and convenient, making it suitable for large-scale screening.

[0031] In addition to its pan-cancer screening function, the urinary metabolic biomarker composition provided by this invention also has a tumor tracing function, enabling tumor tracing in cancer patients (gastric cancer, lung cancer, and colorectal cancer patients). In other words, the urinary metabolic biomarker composition provided by this invention can simultaneously achieve pan-cancer screening and tumor tracing, accurately distinguishing between healthy individuals and those at risk for cancer (gastric cancer, lung cancer, and colorectal cancer) through a single urine test, and precisely tracing the origins of these three common cancers.

[0032] This invention also provides a method for screening a urine metabolic biomarker composition for pan-cancer screening as described above, comprising the following steps: S1. Provide urine samples from a healthy population group and a pan-cancer patient group, and extract the urine to obtain an extract; the pan-cancer patient group consists of a gastric cancer patient group, a colorectal cancer patient group, and a lung cancer patient group. S2. The aqueous phase in the extract was detected using a chromatography-mass spectrometry system to obtain detection data; S3. The detection data is processed, and then the metabolites are identified to obtain metabolomics data. S4. Analyze the metabolomics data to screen and obtain a urine metabolic biomarker composition for pan-cancer screening.

[0033] This invention utilizes a metabolomics-based method to achieve high-throughput detection of small molecule metabolites in the body by preparing urine samples. Combined with data analysis, it screens for significantly different metabolic biomarkers, enabling pan-cancer screening and tumor tracing.

[0034] In step S4, in some embodiments, the step of analyzing the metabolomics data to screen for a urinary metabolic biomarker composition for pan-cancer screening specifically includes: S41. In the healthy population group and the pan-cancer patient group, orthogonal partial least squares discriminant analysis was used to screen out differential metabolites using VIP>1 and FDR<0.05 as screening criteria. S42. Perform Least Absolute Shrinkage and Selection Operator (Lasso) analysis on the screened differential metabolites to obtain a urinary metabolic biomarker composition for pan-cancer screening.

[0035] In this embodiment, firstly, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to distinguish between the healthy population and the pan-cancer patient group in the modeling group. Variable importance projection (VIP) was used to measure the influence and explanatory power of each metabolite component content on sample classification, assisting in the screening of metabolic biomarkers. Metabolites with a VIP > 1 and a free error rate (FDR) < 0.05 between the two groups were identified as differentially expressed metabolites, thus identifying differentially expressed metabolites between the healthy population and the pan-cancer patient group. Then, Lasso analysis was performed on the identified differentially expressed metabolites to obtain a urinary metabolic biomarker composition for pan-cancer screening.

[0036] In step S42, in some embodiments, the step of performing Lasso analysis on the screened differential metabolites to obtain a urinary metabolic biomarker composition for pan-cancer screening specifically includes: S421. Between the healthy population and the pan-cancer patient group, Lasso analysis was performed on the screened differential metabolites to obtain the first result; S422. Among the three groups of patients—lung cancer, colorectal cancer, and gastric cancer—the selected differential metabolites were analyzed using Lasso to obtain the second result. S423. Take the intersection of the first and second results to obtain a urine metabolic biomarker composition for pan-cancer screening.

[0037] This invention also provides the application of the urine metabolic marker composition for pan-cancer screening described above in the preparation of products for pan-cancer screening.

[0038] This invention also provides an application of the urine metabolic biomarker composition for pan-cancer screening described above in the preparation of products for pan-cancer screening and tumor tracing.

[0039] In some embodiments, the product includes reagents or kits.

[0040] In some embodiments, the kit includes standards and / or quality control products.

[0041] In some implementations, the product uses urine as a sample when used for pan-cancer screening.

[0042] The present invention will be further described below through specific embodiments.

[0043] Example 1: Detection, identification, and screening of small molecule metabolites and metabolic biomarkers in urine samples 1. Subject conditions and sample collection (1) The inclusion and exclusion criteria for patients with gastric cancer, lung cancer and colorectal cancer are as follows: Inclusion criteria: Participants must meet all of the following inclusion criteria to be eligible to participate in this study: 1) Males or females aged 18-65; 2) Read and fully understand the informed consent form, sign it, and be able to provide a urine sample for metabolomics testing; 3) Patients diagnosed by biopsy, postoperative pathology, or clinical assessment by a clinician as having primary gastric cancer, lung cancer, or colorectal cancer.

[0044] Exclusion criteria: Subjects meeting any of the following criteria will be excluded and are ineligible to participate in this study: 1) During pregnancy or lactation; 2) Emergency room visit or resuscitation required; 3) History of blood transfusion within 7 days prior to sampling; 4) People who have received organ transplants or have previously received non-autologous (allogeneic) bone marrow or stem cell transplants; 5) History of malignant tumors within the past 5 years or any anti-tumor treatment prior to sampling; 6) Simultaneous co-occurrence of multiple primary malignant tumors.

[0045] (2) The inclusion and exclusion criteria for the healthy control group are as follows: Inclusion criteria: Participants must meet all of the following inclusion criteria to be eligible to participate in this study: (1) Females or males aged 18 to 65; (2) Read and fully understand the informed consent form, sign it, and be able to provide samples for metabolomics testing; (3) Those who report that they are in good health, have no history of heart, liver, kidney, digestive tract, metabolic or nervous system diseases, and have undergone a health check that is normal and has no clinical significance.

[0046] Exclusion criteria: Participants meeting any of the following criteria will be excluded and are not eligible to participate in this study: (1) BMI greater than 28, waist circumference > 85cm; (2) Having any of the following clinically significant diseases within the past 3 months (including but not limited to cardiovascular, pulmonary, renal, gastrointestinal, liver metabolic, blood, endocrine, tumor, immune, neurological or psychiatric diseases); (3) History of long-term medication use within the past 6 months; (4) History of mental illness, drug abuse, or drug dependence; (5) Those who have undergone surgery within the past 3 months, or those who plan to undergo surgery in the near future; (6) Those who have donated blood or experienced massive bleeding (>450mL) within the past 3 months, or who have received blood transfusions or used blood products; (7) Women with a positive pregnancy test, breastfeeding women, or women who plan to have children within the past six months; (8) Other reasons deemed unsuitable for enrollment by the researchers (physical weakness).

[0047] (2) Sample collection Urine samples were collected from 1050 participants across two medical centers (all meeting the above criteria). All samples were collected in the morning on an empty stomach, centrifuged, and stored at -80°C. Written informed consent was obtained from all participants before they joined the study. The 1050 urine samples were randomly assigned to a modeling group and a validation group. Urine samples from the modeling and validation groups were from different patients to ensure independence and representativeness of the samples.

[0048] Specifically, the urine samples in the modeling group included: 166 healthy individuals (Healthy Control, HC) samples, 414 lung cancer (LC) samples, 105 colorectal cancer (CRC) samples, and 102 gastric cancer (GC) samples.

[0049] The number of urine samples in the validation group included: 55 healthy individuals (Healthy Control, HC) samples, 139 lung cancer (LC) samples, 35 colorectal cancer (CRC) samples, and 34 gastric cancer (GC) samples (see Table 1 for specific sample grouping information).

[0050] Table 1. Sample Grouping Information

[0051] 2. Reagents Methanol, acetonitrile, water, acetic acid, methyl tert-butyl ether of mass spectrometry grade, and formic acid of chromatographic (HPLC) grade were all purchased from Sigma-Aldrich, USA.

[0052] 3. Sample preparation After thawing the urine sample from a -80°C freezer, 40 μL of the urine sample was placed in an extraction tube, and 400 μL of a pre-chilled mixture of methyl tert-butyl ether (MTBE) and methanol (3:1 volume ratio) was added. The mixture was vortexed and sonicated to obtain the extract. 360 μL of a methanol-water mixture (3:1 volume ratio) was added to the extract, and the mixture was vortexed and centrifuged to separate the layers. 300 μL of the lower aqueous phase was taken, and 900 μL of pre-chilled methanol was added to it. After protein precipitation, 1000 μL of the supernatant was taken and evaporated to dryness. 200 μL of water was added to redissolve the protein. The redissolved protein (aqueous phase polarity) was then analyzed by LC-MS (liquid chromatography-mass spectrometry).

[0053] 4. Detection of small molecule metabolites (1) Testing instruments Small molecule separation was performed using a Waters ACQUTTY UPLC® HSS T3 1.8µm 2.1*100mm column; the liquid chromatography and mass spectrometry systems used were an ACQUITY UPLC I-Class liquid chromatography system (Waters) and a Q-Exactive mass spectrometry system (Thermo Fisher Scientific), respectively.

[0054] (2) Mobile phase parameters Mobile phase A is an aqueous solution containing 0.1% formic acid (the mass content of formic acid is 0.1%); mobile phase B is an acetonitrile solution containing 0.1% formic acid (the mass content of formic acid is 0.1%); the separation elution gradient is as follows: 0-13 minutes is 1%-70% mobile phase B, 13-18 minutes is 99% mobile phase B.

[0055] (3) Mass spectrometry parameters Mass spectrometry data were acquired using Full MS and Full MS / dd-MS2 methods (each including both positive and negative modes). The parameters used by QExactive are as follows: 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), and the AGC (Automatic Gain Control) is 3E+6 (i.e., 3×10⁻⁶). 6 The Maximum Injection Time (IT) is 200 milliseconds. In Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer is 17,500 m / z, the quadrupole window is 1.5 m / z, and the AGC is 1E+5 (i.e., 1×10⁻⁵). 5 The maximum ion implantation time is 50 milliseconds, and the relative collision energy of HCD (high-energy collision dissociation) is 30 eV.

[0056] 5. Metabolomics Data Processing (1) Extract peaks from the RAW format file of the mass spectrometer and convert it into FeatureXML format file to reduce the dimensionality of the original mass spectrometry data and improve the signal-to-noise ratio; (2) Use the peak alignment algorithm of OpenMS software to correct and align the retention time of the extracted peak format data between samples, thereby converting the mass spectrometry data into a data matrix; (3) Match and filter the isotope peaks in the data matrix obtained in step (2), and replace abnormal data (0, negative values, background noise, etc.) with missing values; (4) Remove the feature peaks with a detection rate <80% from all the feature peaks obtained in step (3), fill the median value of the feature peak with a detection rate >80%, and add 5% random noise (following a standard normal distribution); (5) Use MSTUS (total useful MS) to convert the mass spectrometry data into a data matrix. (6) To reduce the difference in metabolite concentrations between samples and make the data distribution more symmetrical, the Normalization Autoencoder (NormAE) was used to normalize the mass spectrometry data in order to eliminate the dilution effect in the urine sample; (7) In order to reduce the difference in metabolite concentrations between samples and make the data distribution more symmetrical, the Normalization Autoencoder (NormAE) was used to normalize the data in order to remove systematic errors such as batch effects.

[0057] 6. Identification of metabolites After analyzing the raw data using software, the spectral information of the primary precursor ion (MS1) and secondary fragment ion (MS2) of the compound is obtained. This information, such as the mass-to-charge ratio (m / z) of the primary mass spectrometer and the fragment ion data, is matched with the spectral information of primary and secondary metabolites in public databases to qualitatively identify the metabolites. Commonly used metabolite databases include the Human Metabolite Database (HMDB, www.hmdb.ca), the Metabolomics Database (Metlin, metlin.scripps.edu), and the Mass Spectrometry Database (www.massbank.jp). Metabolites identified based on these databases are then finally validated using retention times, MS1, and MS2 mass spectrometry data obtained from separation of standards under the same chromatographic column and mass spectrometry conditions. The criteria for metabolite identification are a retention time difference within 0.1 min and a theoretical and measured molecular weight difference of less than 10 ppm.

[0058] 7. Screening of metabolic biomarkers for pan-cancer screening and tumor origin tracing First, the OPLS-DA method was used to distinguish between the healthy population group and the pan-cancer group (lung cancer group + colorectal cancer group + gastric cancer group) in the modeling group. The VIP value was used to measure the influence and explanatory power of the content of each metabolite component on the sample classification and to assist in the screening of metabolic biomarkers. Metabolites with VIP > 1 and FDR < 0.05 between the two groups were selected as differential metabolites to identify differential metabolites between the healthy population group and the pan-cancer group.

[0059] Secondly, within the range of differentially expressed metabolites identified above, Lasso analyses were performed between the healthy control group in the modeling group and the pan-cancer group (lung cancer group + colorectal cancer group + gastric cancer group), and between the three groups of lung cancer, colorectal cancer, and gastric cancer. Then, the intersection of the two results was taken, and 15 metabolic biomarkers were finally selected as important metabolic biomarkers for pan-cancer screening and tumor tracing (Table 2).

[0060] Table 2. 15 metabolic biomarkers for pan-cancer screening and tumor tracing

[0061] Example 2: Construction and Validation of a Diagnostic Model for Pan-Cancer Screening and Tumor Origin Tracing Based on 15 Urinary Metabolic Markers The ROC curve (Receiving Operating Characteristic curve) is a method for studying the relationship between model sensitivity and specificity. It plots sensitivity on the ordinate. Specificity (1) The x-axis represents the specificity of the model, and the evaluation is based on the area under the curve (AUC). If the AUC is greater than 0.5, the closer the AUC is to 1, the better the model performance and the better the diagnostic effect. If it is less than 0.5, it indicates that the model's accuracy is poor.

[0062] (1) Construction of a diagnostic model for pan-cancer screening To verify the diagnostic efficacy of the 15 metabolic biomarkers selected in Example 1 for pan-cancer screening, multivariate ROC curve analysis was performed on these 15 metabolic biomarkers in the modeling group. Specifically, three-quarters of the healthy control group and three-quarters of the pan-cancer group samples in the modeling group of Example 1 were randomly selected as the training set, and one-quarter of the healthy control group and one-quarter of the pan-cancer group samples were selected as the test set. A support vector machine (SVM) was used for 1000 random iterations, and a diagnostic model that distinguishes between healthy individuals and pan-cancer individuals was constructed by statistically analyzing the average accuracy of the final model. The results are as follows: Figure 1As shown, AUC=0.867, indicating that the constructed diagnostic model has high diagnostic efficacy in distinguishing between healthy individuals and pan-cancer.

[0063] (2) Validation of diagnostic models for pan-cancer screening To further validate the effectiveness of the diagnostic model for distinguishing between healthy individuals and pan-cancer based on the modeling group data, the diagnostic model was validated using the validation group data from Example 1. Specifically, multivariate ROC curve analysis was performed to evaluate the independent validation performance of the diagnostic model on unknown datasets outside the modeling group dataset. The results are as follows: Figure 2 As shown, AUC=0.884, sensitivity=0.731, and specificity=0.891. These results indicate that the constructed diagnostic model for distinguishing between healthy individuals and pan-cancer also demonstrated good diagnostic performance in the validation group.

[0064] (3) Construction of diagnostic models for tumor origin tracing To further validate the diagnostic efficacy of the 15 metabolic biomarkers selected in Example 1 for tumor attribution, multivariate ROC curve analysis was performed on these 15 biomarkers in the modeling group. Specifically, three-quarters of the sample data from the lung cancer group, colorectal cancer group, and gastric cancer group in Example 1 were randomly selected as the training set, and one-quarter of the sample data from the lung cancer group, colorectal cancer group, and gastric cancer group were selected as the test set. A support vector machine (SVM) was used for 1000 random iterations, and the diagnostic model for cancer attribution was constructed by statistically analyzing the average accuracy of the final model. The results are as follows: Figure 3 As shown, AUC=0.908.

[0065] (4) Validation of diagnostic models for tumor origin tracing To further validate the effectiveness of the diagnostic model for tumor origin tracing constructed based on the modeling group data, the diagnostic model was validated using the validation group data from Example 1. Specifically, multivariate ROC curve analysis was performed to evaluate the independent validation performance of the diagnostic model on unknown datasets outside the modeling group dataset. The results are as follows: Figure 4 As shown, AUC=0.909, sensitivity=0.728, and specificity=0.889. These results indicate that the constructed diagnostic model for tumor origin tracing also exhibits good diagnostic performance in the validation group.

[0066] In summary, this invention provides a urinary metabolic biomarker composition, screening method, and application for pan-cancer screening. The urinary metabolic biomarker composition provided by this invention has dual functions of pan-cancer screening and tumor tracing, with high sensitivity and specificity. It can quickly, accurately, and non-invasively distinguish between healthy individuals and cancer patients (colorectal cancer, gastric cancer, and lung cancer patients), and trace the origin of these three types of tumors. This helps 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 patient survival rates.

[0067] The urine metabolic biomarker composition provided by this invention, when used for pan-cancer screening, uses urine samples and has the advantages of easy collection, low processing cost and long-term cryopreservation. At the same time, the detection is non-invasive and convenient, making it suitable for large-scale screening.

[0068] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A urinary metabolic biomarker composition for pan-cancer screening, characterized in that, The urine metabolic biomarker composition for pan-cancer screening includes pyruvate, succinic acid, 3-hydroxyhippuric acid, inositol, N-acetylhistamine, glutamic acid, sucrose, tyramine, thymine, cinnamoylglycine, 3-hydroxybutyric acid, imidazole acetic acid, acetylhomoserine, 3-methylglutaric acid, and 3-ureopropionic acid.

2. The urinary metabolic biomarker composition for pan-cancer screening according to claim 1, characterized in that, The urine metabolic marker composition is used to distinguish between healthy individuals and individuals with cancer, including stomach cancer, colorectal cancer, and lung cancer.

3. The urinary metabolic biomarker composition for pan-cancer screening according to claim 2, characterized in that, The urine metabolic biomarker composition is used for tumor tracing in cancer patients.

4. A method for screening a urinary metabolic biomarker composition for pan-cancer screening as described in any one of claims 1-3, characterized in that, Includes the following steps: Urine samples were provided from a healthy population group and a pan-cancer patient group, and the urine was extracted to obtain an extract; the pan-cancer patient group consisted of a gastric cancer patient group, a colorectal cancer patient group, and a lung cancer patient group. The aqueous phase in the extract was detected using chromatography-mass spectrometry to obtain detection data; The detection data is processed and then the metabolites are identified to obtain metabolomics data. The metabolomics data were analyzed to screen for a urinary metabolic biomarker composition for pan-cancer screening.

5. The screening method according to claim 4, characterized in that, The steps of analyzing the metabolomics data and screening for urinary metabolic biomarker compositions for pan-cancer screening specifically include: Differential metabolites were screened between the healthy population and the pan-cancer patient group using orthogonal partial least squares discriminant analysis with VIP>1 and FDR<0.05 as screening criteria. The differentially identified metabolites were subjected to absolute value convergence and selection operator analysis to obtain a urinary metabolic biomarker composition for pan-cancer screening.

6. The screening method according to claim 5, characterized in that, The steps for obtaining a urinary metabolic biomarker composition for pan-cancer screening by performing absolute value convergence and selection operator analysis on the screened differential metabolites specifically include: Between the healthy population and the pan-cancer patient group, the differentially identified metabolites were subjected to absolute value convergence and selection operator analysis to obtain the first result; In the three groups of patients with lung cancer, colorectal cancer, and gastric cancer, the differentially identified metabolites were subjected to absolute value convergence and selection operator analysis to obtain the second result. The intersection of the first and second results yields a urinary metabolic biomarker composition for pan-cancer screening.

7. The use of a urinary metabolic marker composition for pan-cancer screening according to any one of claims 1-3 in the preparation of a product for pan-cancer screening.

8. The use of a urinary metabolic marker composition for pan-cancer screening according to any one of claims 1-3 in the preparation of products for pan-cancer screening and tumor tracing.

9. The application according to claim 7, characterized in that, The products include reagents or kits.

10. The application according to claim 7, characterized in that, The product uses urine as a sample when used for pan-cancer screening.

Images