Plasma metabolism marker composition as well as screening method and application thereof

Abstract

The invention discloses a plasma metabolism marker composition as well as a screening method and application thereof, and relates to the technical field of biomedicine. The plasma metabolism marker composition comprises glycyl-L-glutamic acid, phosphatidylcholine (38: 7), pyrrole-2-formic acid and phosphatidyl ethanolamine (36: 2p). The metabolic marker composition has obvious difference in different cancer species, and can be used for distinguishing different cancer species of cancer patients, so that the detection of the general cancer species is realized. The metabolic marker composition has non-invasive property and minimally invasive property when being used for detecting the generic cancer species, is more suitable for screening and detecting large-scale common people, has higher sensitivity and specificity, is more suitable for early screening of cancers, is beneficial to early screening, early diagnosis and early treatment of the cancers, and has a wide application prospect. The survival rate and the cure rate of cancer patients are favorably improved.

Description

Technical Field

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

[0002] Cancer refers to a multi-stage process that originates from the transformation of normal cells into tumor cells, usually developing from precancerous lesions to malignant tumors. Common types of cancer include lung cancer, colon and rectal cancer, stomach cancer, breast cancer, liver cancer, and prostate cancer, which have become major factors threatening human life and health. At present, common clinical cancer screening and diagnosis methods mainly include pathological examination, imaging examination, and molecular screening.

[0003] Pathological examination, as the gold standard for cancer diagnosis, has been widely used in clinical work and scientific research. First, pathological examination can clarify and verify the preoperative diagnosis and improve the clinical diagnosis level. Secondly, after the diagnosis is clear, it can determine further treatment plans and estimate prognosis, thereby improving the clinical treatment level and obtaining a large amount of extremely valuable scientific research data. However, since pathological examination is usually invasive, patient compliance is low and it is not suitable for early screening of large populations.

[0004] Imaging examinations can provide images of the body's interior, provide details such as the location and size of lesions, help doctors diagnose diseases, determine the severity of illnesses, and provide monitoring for patients after disease diagnosis. However, since they are radioactive and can only be detected when lesions reach a certain size, they are also not suitable for early screening of cancer.

[0005] The non-invasive tumor markers currently used in molecular screening are CEA, CA19-9, etc., which have low sensitivity or specificity in cancer diagnosis, and the test results are easily affected by various factors such as diet, drugs and lifestyle habits. Therefore, there is an urgent need to find new tumor markers to promote early intervention and treatment of cancer and prolong the survival of patients.

[0006] Metabolomics, as a new type of tumor marker that has become popular in recent years, is mainly based on the metabolomics method. It reflects a series of biological events that occur in a certain pathophysiological process by revealing the overall metabolic changes under the influence of internal and external factors. It is also one of the most widely used directions of metabolomics to find disease metabolic markers. Although it requires highly professional technical and equipment support, data analysis is complex, and the degree of standardization is relatively low, this method can improve the accuracy of diagnosis and has the potential to become a tool for early diagnosis and prognosis assessment of cancer.

[0007] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0008] Based on the above-mentioned deficiencies of the prior art, the purpose of the present invention is to provide a plasma metabolic marker composition and a screening method and application thereof, aiming to solve the problem that the existing methods for diagnosing cancer are either invasive or have low accuracy.

[0009] The technical solution of the present invention is as follows:

[0010] In a first aspect of the present invention, a plasma metabolic marker composition is provided, wherein the plasma metabolic marker composition comprises glycyl-L-glutamate, phosphatidylcholine 38:7, pyrrole-2-carboxylic acid and phosphatidylethanolamine 36:2p.

[0011] Optionally, the plasma metabolic marker composition further comprises at least one of hypoxanthine, phosphatidylethanolamine 34:2p, phosphatidylethanolamine 36:1p and pyroglutamate.

[0012] Optionally, the plasma metabolic marker composition further comprises at least one of allantoic acid, phosphatidylethanolamine 36:3p, arabinose, oxalic acid and phosphatidylcholine 34:2e.

[0013] Optionally, the plasma metabolic marker composition further includes at least one of L-malic acid, niacin, oxamic acid, phosphatidylethanolamine 38:5p and uridine.

[0014] Optionally, the plasma metabolic marker composition consists of glycyl-L-glutamate, phosphatidylcholine 38:7, pyrrole-2-carboxylic acid, phosphatidylethanolamine 36:2p, hypoxanthine, phosphatidylethanolamine 34:2p, phosphatidylethanolamine 36:1p, pyroglutamic acid, allantoic acid, phosphatidylethanolamine 36:3p, arabinose, oxalic acid and phosphatidylcholine 34:2e.

[0015] The second aspect of the present invention provides a method for screening the plasma metabolic marker composition as described above, comprising the following steps:

[0016] The plasma of patients with gastric cancer, colorectal cancer and lung cancer is extracted respectively to obtain extracts;

[0017] Using a chromatography-mass spectrometer to detect the organic phase and the aqueous phase in the extract to obtain detection data;

[0018] Processing the detection data, and then identifying metabolites to obtain metabolomics data;

[0019] The metabolomics data are analyzed to screen and obtain the plasma metabolite marker composition.

[0020] Optionally, the step of analyzing the metabolomics data to screen and obtain the plasma metabolite marker composition specifically comprises:

[0021] The metabolomics data are subjected to linear correlation analysis, and the Pearson correlation coefficient is obtained by calculation. The absolute value of the Pearson correlation coefficient being greater than a preset value is used as a screening condition to screen out the plasma metabolite marker composition.

[0022] Optionally, the absolute value of the Pearson correlation coefficient being greater than or equal to 0.3 is used as a screening condition.

[0023] The third aspect of the present invention provides use of the plasma metabolic marker composition as described above in the preparation of a product for detecting pan-cancer species.

[0024] Optionally, the pan-cancer types include gastric cancer, colorectal cancer and lung cancer.

[0025] Beneficial effects: The metabolite marker composition of the present invention has significant differences in different cancer types (such as gastric cancer, colorectal cancer and lung cancer), and can distinguish different cancer types in cancer patients, and realize pan-cancer detection. The use of the metabolite marker composition for pan-cancer detection is non-invasive and minimally invasive, and is more suitable for large-scale screening and detection of the general population. The metabolite marker composition has high sensitivity and specificity, and is more suitable for early screening of cancer, which is helpful for early screening, early diagnosis and early treatment of cancer, and helps to improve the survival rate and cure rate of cancer patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a linear correlation curve diagram of hypoxanthine in three cancer modeling group samples in Example 1.

[0027] Figure 2 This is a linear correlation curve diagram of glycyl-L-glutamate in three cancer modeling group samples in Example 1.

[0028] Figure 3 This is a linear correlation curve diagram of allantoic acid in three cancer modeling group samples in Example 1.

[0029] Figure 4 This is a linear correlation curve diagram of phosphatidylethanolamine 36:3p in three cancer modeling group samples in Example 1.

[0030] Figure 5 This is a linear correlation curve diagram of arabinose in three cancer modeling group samples in Example 1.

[0031] Figure 6 This is a linear correlation curve diagram of phosphatidylethanolamine 34:2p in three cancer modeling group samples in Example 1.

[0032] Figure 7 This is a linear correlation curve diagram of L-malic acid in three cancer modeling group samples in Example 1.

[0033] Figure 8 This is a linear correlation curve diagram of oxalic acid in three cancer modeling group samples in Example 1.

[0034] Fig. 9 This is a linear correlation curve diagram of phosphatidylethanolamine 36:1p in three cancer modeling group samples in Example 1.

[0035] Fig.10 This is a linear correlation curve diagram of niacin in three cancer modeling group samples in Example 1.

[0036] Fig.11 This is a linear correlation curve diagram of phosphatidylcholine 38:7 in three cancer modeling group samples in Example 1.

[0037] Fig.12 This is a linear correlation curve diagram of pyroglutamate in three cancer modeling group samples in Example 1.

[0038] Fig.13 This is a linear correlation curve diagram of oxalic acid in three cancer modeling group samples in Example 1.

[0039] Fig.14 This is a linear correlation curve diagram of phosphatidylethanolamine 38:5p in three cancer modeling group samples in Example 1.

[0040] Fig.15 This is a linear correlation curve diagram of uridine in three cancer modeling group samples in Example 1.

[0041] Fig.16 This is a linear correlation curve diagram of phosphatidylcholine 34:2e in three cancer modeling group samples in Example 1.

[0042] Fig.17 This is a linear correlation curve diagram of pyrrole-2-carboxylic acid in three cancer modeling group samples in Example 1.

[0043] Fig.18 This is a linear correlation curve diagram of phosphatidylethanolamine 36:2p in three cancer modeling group samples in Example 1.

[0044] Fig.19 This is a multivariate and multi-classification ROC curve analysis diagram of 18 metabolite markers in the plasma samples of the modeling group in Example 2.

[0045] Fig. 20This is a multivariate and multi-classification ROC curve analysis diagram of 18 metabolite markers in the plasma samples of the validation group in Example 2. DETAILED DESCRIPTION

[0046] The present invention provides a plasma metabolic marker composition and a screening method and application thereof. 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 intended to limit the present invention.

[0047] 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.

[0048] Some of the metabolic markers appearing in the following embodiments are explained below.

[0049] Phosphatidylcholine belongs to the glycerol phospholipids. One of the three side chains is phosphatidylcholine, and the other two side chains are fatty acids.

[0050] The 38:7 in phosphatidylcholine 38:7 refers to the additional two fatty acid side chains, which contain 38 carbon atoms and 7 double bonds.

[0051] The 34:2 in phosphatidylcholine 34:2e refers to the other two fatty acid side chains, which contain 34 carbon atoms and 2 double bonds; e represents one of the two fatty acid side chains. The covalent ester bond originally formed by the esterification of one hydroxyl group and one fatty acid molecule is replaced by an ether bond, thus having one less oxygen atom.

[0052] Phosphatidylethanolamine belongs to the glycerol phospholipids. One of the three side chains is phosphatidylethanolamine, and the other two side chains are fatty acids.

[0053] The 34:2 in phosphatidylethanolamine 34:2p refers to the other two fatty acid side chains, which contain 34 carbon atoms and 2 double bonds; p represents one of the two fatty acid side chains. The covalent ester bond originally formed by the esterification of one hydroxyl group and one fatty acid molecule is replaced by an ether bond, so there is one less oxygen atom and one more double bond.

[0054] The 36:1 in phosphatidylethanolamine 36:1p refers to the other two fatty acid side chains, which contain 36 carbon atoms and 1 double bond; p represents one of the two fatty acid side chains. The covalent ester bond originally formed by the esterification of one hydroxyl group and one fatty acid molecule is replaced by an ether bond, so there is one less oxygen atom and one more double bond.

[0055] The 36:2 in phosphatidylethanolamine 36:2p refers to the other two fatty acid side chains, which contain 36 carbon atoms and 2 double bonds; p represents one of the two fatty acid side chains. The covalent ester bond originally formed by the esterification of one hydroxyl group and one fatty acid molecule is replaced by an ether bond, so there is one less oxygen atom and one more double bond.

[0056] The 36:3 in phosphatidylethanolamine 36:3p refers to the other two fatty acid side chains, which contain 36 carbon atoms and 3 double bonds; p represents one of the two fatty acid side chains. The covalent ester bond originally formed by the esterification of one hydroxyl group and one fatty acid molecule is replaced by an ether bond, so there is one less oxygen atom and one more double bond.

[0057] The 38:5 in phosphatidylethanolamine 38:5p refers to the other two fatty acid side chains, which contain 38 carbon atoms and 5 double bonds; p represents one of the two side chains. The covalent ester bond originally formed by the esterification of one hydroxyl group and one fatty acid molecule is replaced by an ether bond, so there is one less oxygen atom and one more double bond.

[0058] An embodiment of the present invention provides a plasma metabolic marker composition, wherein the plasma metabolic marker composition includes glycyl-L-glutamate, phosphatidylcholine 38:7, pyrrole-2-carboxylic acid and phosphatidylethanolamine 36:2p.

[0059] In this embodiment, the plasma metabolite marker composition containing 4 plasma metabolite markers has significant differences in different cancers, and can distinguish different cancers (such as gastric cancer, colorectal cancer and lung cancer) in cancer patients, and realize the detection of pan-cancers (including gastric cancer, colorectal cancer and lung cancer). The detection of pan-cancers using the metabolite marker composition is non-invasive and minimally invasive, and is more suitable for screening and detection of large-scale general populations. The metabolite marker composition has high sensitivity and specificity, and is more suitable for early screening of cancer, which is helpful for early screening, early diagnosis and early treatment of cancer, and helps to improve the survival rate and cure rate of cancer patients.

[0060] In some embodiments, the plasma metabolic marker composition also includes at least one of hypoxanthine, phosphatidylethanolamine 34:2p, phosphatidylethanolamine 36:1p and pyroglutamic acid. That is, in this embodiment, the plasma metabolic marker composition includes glycyl-L-glutamate, phosphatidylcholine 38:7, pyrrole-2-carboxylic acid, phosphatidylethanolamine 36:2p, and at least one of hypoxanthine, phosphatidylethanolamine 34:2p, phosphatidylethanolamine 36:1p and pyroglutamic acid. In this embodiment, the above-mentioned plasma metabolic marker composition has significant differences in different cancer types, and can distinguish different cancer types in cancer patients, and realize pan-cancer detection.

[0061] In some embodiments, the plasma metabolic marker composition also includes at least one of allantoic acid, phosphatidylethanolamine 36:3p, arabinose, oxalic acid and phosphatidylcholine 34:2e. That is, the plasma metabolic marker composition includes glycyl-L-glutamate, phosphatidylcholine 38:7, pyrrole-2-carboxylic acid, phosphatidylethanolamine 36:2p, and at least one of hypoxanthine, phosphatidylethanolamine 34:2p, phosphatidylethanolamine 36:1p and pyroglutamic acid, and at least one of allantoic acid, phosphatidylethanolamine 36:3p, arabinose, oxalic acid and phosphatidylcholine 34:2e. The above-mentioned plasma metabolic marker composition has significant differences in different cancers, and can distinguish different cancers in cancer patients and realize pan-cancer detection.

[0062] In some embodiments, the plasma metabolic marker composition also includes at least one of L-malic acid, nicotinic acid, oxamic acid, phosphatidylethanolamine 38:5p and uridine. That is, in this embodiment, the plasma metabolic marker composition includes glycyl-L-glutamic acid, phosphatidylcholine 38:7, pyrrole-2-carboxylic acid, phosphatidylethanolamine 36:2p, and at least one of hypoxanthine, phosphatidylethanolamine 34:2p, phosphatidylethanolamine 36:1p, pyroglutamic acid, and allantoic acid, phosphatidylethanol 36:3p, arabinose, oxalic acid, phosphatidylcholine 34:2e, and at least one of L-malic acid, nicotinic acid, oxamic acid, phosphatidylethanolamine 38:5p, and uridine. The above-mentioned plasma metabolic marker composition has significant differences in different cancers, and can distinguish different cancers in cancer patients and realize the detection of pan-cancers.

[0063] In some embodiments, the plasma metabolic marker composition is composed of glycyl-L-glutamate, phosphatidylcholine 38:7, pyrrole-2-carboxylic acid, phosphatidylethanolamine 36:2p, hypoxanthine, phosphatidylethanolamine 34:2p, phosphatidylethanolamine 36:1p, pyroglutamic acid, allantoic acid, phosphatidylethanolamine 36:3p, arabinose, oxalic acid and phosphatidylcholine 34:2e. The above plasma metabolic marker composition is significantly different in different cancer types, and can distinguish different cancer types in cancer patients and realize pan-cancer detection.

[0064] The second aspect of the present invention provides a method for screening the plasma metabolic marker composition as described above, comprising the following steps:

[0065] S1. respectively extracting plasma from patients with gastric cancer, patients with colorectal cancer, and patients with lung cancer to obtain extracts;

[0066] S2, using a chromatography-mass spectrometer to detect the organic phase and the aqueous phase in the extract to obtain detection data;

[0067] S3, processing the detection data, and then identifying metabolites to obtain metabolomics data;

[0068] S4. Analyze the metabolomics data and screen to obtain a plasma metabolite marker composition.

[0069] The embodiment of the present invention is based on the metabolomics method, which prepares plasma samples to achieve high-throughput detection of small molecule metabolites in the body, combines multivariate statistical analysis to screen metabolic markers with significant differences, and achieves differentiation of different cancer types in cancer patients, providing a new option for pan-cancer screening.

[0070] In step S4, in some embodiments, the step of analyzing the metabolomics data to screen and obtain the plasma metabolite marker composition specifically includes:

[0071] The metabolomics data are subjected to linear correlation analysis, and the Pearson correlation coefficient is obtained by calculation. The absolute value of the Pearson correlation coefficient being greater than a preset value is used as a screening condition to screen out the plasma metabolite marker composition.

[0072] In some embodiments, the absolute value of the Pearson correlation coefficient is greater than or equal to 0.3 as a screening condition. The Pearson correlation coefficient is widely used to measure the degree of correlation between two variables (the two variables in the present invention refer to the intensity of metabolites and different types of cancer). The Pearson correlation coefficient (i.e., R value) is between -1 and 1. If the Pearson correlation coefficient is close to -1, it means that the two variables have a high negative correlation; if the Pearson correlation coefficient is close to 1, it means that the two variables have a high positive correlation; if the Pearson correlation coefficient is close to 0, it means that the two variables are independent of each other and have no correlation; therefore, the present invention uses the absolute value of the Pearson correlation coefficient greater than or equal to 0.3, that is, |R|≥0.3 as a screening condition to screen out metabolic markers with a high correlation with distinguishing different types of cancer.

[0073] The present invention also provides a use of the plasma metabolic marker composition described above in the present invention in the preparation of a product for detecting pan-cancer species. The product includes but is not limited to a kit.

[0074] In some embodiments, the pan-cancer includes gastric cancer, colorectal cancer and lung cancer. In the present invention, the above plasma metabolic marker composition can be used to distinguish the three major cancers of gastric cancer, colorectal cancer and lung cancer.

[0075] The present invention will be further described below by means of specific examples.

[0076] Example 1 Detection, identification and screening of small molecule metabolites in plasma samples and metabolic markers

[0077] 1. Subjects’ conditions and sample collection

[0078] The inclusion and exclusion criteria for patients with gastric cancer, lung cancer, and colorectal cancer were as follows:

[0079] Inclusion criteria: (1) All subjects obtained written informed consent before participating in the study; (2) Male or female aged ≥18 years; (3) Patients diagnosed with primary gastric cancer, lung cancer, or colorectal cancer by biopsy / postoperative pathology or by comprehensive evaluation by clinicians; patients who met all three of the above criteria were included.

[0080] Exclusion criteria: (1) pregnancy or lactation; (2) emergency or rescue; (3) history of malignant tumor or any anti-tumor treatment before sampling; (4) concurrent multiple primary malignant tumors; patients were excluded if any of the four items was met.

[0081] A total of 222 patients with gastric cancer (GC), 232 patients with colorectal cancer (CRC), and 579 patients with lung cancer (LC) were collected (Table 1). Plasma samples were collected in the early morning on an empty stomach, and all collected plasma samples were stored in a -80°C refrigerator. Plasma samples from patients with gastric cancer were recorded as plasma samples in the GC group, plasma samples from patients with colorectal cancer were recorded as plasma samples in the CRC group, and plasma samples from patients with lung cancer were recorded as plasma samples in the LC group.

[0082] The 222 plasma samples in the GC group were randomly divided into a modeling group and a validation group. The 232 plasma samples in the CRC group were randomly divided into a modeling group and a validation group. The 579 plasma samples in the LC group were also randomly divided into a modeling group and a validation group. The plasma samples in the modeling group were different from those in the validation group. Specifically,

[0083] Number of plasma samples in the modeling groups: 167 plasma samples in the GC group, 174 plasma samples in the CRC group, and 434 plasma samples in the LC group;

[0084] The number of plasma samples in the validation group: 55 plasma samples in the GC group, 58 plasma samples in the CRC group, and 145 plasma samples in the LC group (see Table 1 for details).

[0085] Table 1. Subjects

[0086] GC Group CRC Group LC group Number of samples in the modeling group (pieces) 167 174 434 Number of samples in the validation group (pieces) 55 58 145 Total (pcs) 222 232 579

[0087] 2. Reagents

[0088] Methanol, acetonitrile, water, acetic acid, isopropanol and methyl tert-butyl ether of mass spectrometry grade purity and formic acid and ammonium acetate of HPLC grade purity were purchased from Sigma-Aldrich, USA.

[0089] 3. Sample preparation

[0090] Take 100 μL of plasma, place it in 1000 μL of pre-cooled mixed solution (composed of methyl tert-butyl ether and methanol, with a volume ratio of methyl tert-butyl ether to methanol of 3:1), vortex mix, and obtain a sample extract;

[0091] Add 500 μL of a mixed solution of methanol and water (the volume ratio of methanol to water is 3:1) to the sample extract, sonicate, let stand, vortex and centrifuge to separate the layers;

[0092] After the sample was layered, 500 μL of the upper organic phase was taken into a centrifuge tube. After the organic phase was dried, 200 μL of a mixed solution of acetonitrile and isopropanol (the volume ratio of acetonitrile to isopropanol was 3:1) was added, and incubated at room temperature for 15 minutes. After incubation, the centrifuge tube was vortexed and ultrasonic-assisted for 5 minutes, and then the centrifuge tube was centrifuged at room temperature (12000 rpm) for 5 minutes. 180 μL of the supernatant was taken from the centrifuge tube and put into a 2 mL glass injection vial, which was the organic phase material, and was detected by a machine (LC-MS, liquid chromatography-mass spectrometry).

[0093] After the samples were layered, 400 μL of the lower aqueous phase was taken into a centrifuge tube, and 1100 μL of ice methanol was added thereto. After protein precipitation, the centrifuge tube was centrifuged, and 1000 μL of the supernatant was transferred to a new centrifuge tube and dried overnight; 200 μL of water was added to the dried centrifuge tube and incubated at room temperature for 15 minutes; after incubation, the centrifuge tube was vortexed and ultrasonically treated for 5 minutes, and then the centrifuge tube was centrifuged at room temperature (12000 rpm) for 5 minutes; 180 μL of the supernatant was taken from the centrifuge tube into a 2 mL glass injection vial, which was the aqueous phase material, and was detected by the machine (LC-MS).

[0094] 4. Small molecule metabolite detection:

[0095] The organic phase was purified by Waters ACQUTTY BEH C8 1.7 μm 2.1 mm × 100 mm column, the aqueous phase uses Waters ACQUTTY HSS T3 1.8μm2.1mm×100mm column was used for small molecule separation; liquid chromatography and mass spectrometry were performed using ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific);

[0096] The mobile phase parameters are as follows:

[0097] Organic phase: Mobile phase A is an aqueous solution containing 0.1% acetic acid and 1% ammonium acetate (the mass content of acetic acid is 0.1%, and the mass content of ammonium acetate is 1%); Mobile phase B is a mixed solution of acetonitrile and isopropanol containing 0.1% acetic acid and 1% ammonium acetate (the volume ratio of acetonitrile to isopropanol is 7:3, the mass content of acetic acid is 0.1%, and the mass content of ammonium acetate is 1%), and the separation elution gradient is as follows: 0-12 minutes is 55%-89% mobile phase B, 12-19.5 minutes is 100% mobile phase B;

[0098] Aqueous phase: 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;

[0099] The mass spectrometry parameters are as follows:

[0100] Mass spectrometry data were collected in Full MS and Full MS / dd-MS2 modes (each including positive and negative modes). The parameters used by the QExactive mass spectrometry system are as follows:

[0101] 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 3E+6 (i.e. 3×10 6 ), Maximum IT (maximum injection time) is 200 milliseconds;

[0102] 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 1E+5 (i.e., 1×10 5 ), the maximum ion injection time is 50 milliseconds, and the HCD (high energy collision dissociation) relative collision energy is 30 eV.

[0103] 5. Metabolomics data processing

[0104] First, the effective peak signal is extracted from all the mass spectrometry data, and then the baseline correction is applied to remove the noise and retain the original signal peak; the original data is converted into central discrete data; then, the retention time in the chromatogram corresponding to each mass spectrometry peak in the sample is corrected, and the peaks with differences between samples within the error range are defined as the same peak, thereby obtaining a matrix data set. Due to the unavoidable systematic fluctuations in the experimental process (such as instrument fluctuations, fluctuations caused by human operation, etc.), in order to make the data distribution more reflect the real differences between samples, the Normalization Autoencoder (NormAE) is used for homogenization.

[0105] 6. Identification of metabolites

[0106] Public databases such as the Human Metabolite Database (HMDB; www.hmdb.ca), the Metabolomics Database (Metlin Database; https: / / metlin.scripps.edu), and the Mass Spectrum Database (http: / / www.massbank.jp / ), as well as the primary, secondary chromatograms, and mass spectra of the standards separated on the same chromatographic column were used; the retention times were within 0.1 min and the mass-to-charge ratio was less than 10 ppm, and the identification was carried out by matching with the databases and standards.

[0107] 7. Metabolomics data analysis

[0108] The metabolomics data of the modeling group samples in the three groups of GC, CRC, and LC were subjected to linear correlation analysis (linear correlation), and the Pearson correlation coefficient (i.e., R value) was calculated. The metabolite markers with high correlation with different cancer types were screened out by |R|≥0.3, and finally 18 differential metabolites were obtained (as shown in Table 2). Among them, the linear correlation curves of hypoxanthine in the three cancer modeling group samples are shown in Figure 1 As shown (which reflects the intensity change trend of xanthine among three cancers, Figures 2 to 18 The linear correlation curves of glycyl-L-glutamate in the three cancer modeling group samples are shown in Figure 2 As shown in Figure 2, the linear correlation curves of allantoic acid in the three cancer modeling group samples are as follows: Figure 3 As shown, the linear correlation curves of phosphatidylethanolamine 36:3p in the three cancer modeling group samples are as follows Figure 4 As shown, the linear correlation curves of arabinose in the three cancer modeling group samples are as follows Figure 5 As shown, the linear correlation curves of phosphatidylethanolamine 34:2p in the three cancer modeling group samples are as follows Figure 6 As shown in Figure 2, the linear correlation curves of L-malic acid in the three cancer modeling group samples are as follows: Figure 7 As shown in Figure 2, the linear correlation curves of oxalic acid in the three cancer modeling group samples are as follows: Figure 8 As shown, the linear correlation curves of phosphatidylethanolamine 36:1p in the three cancer modeling group samples are as follows Fig. 9 As shown, the linear correlation curves of niacin in the three cancer modeling group samples are as follows Fig.10 As shown, the linear correlation curves of phosphatidylcholine 38:7 in the three cancer modeling group samples are as follows Fig.11 As shown, the linear correlation curves of pyroglutamate in the three cancer modeling group samples are as follows Fig.12 As shown, the linear correlation curves of oxalic acid in the three cancer modeling group samples are as follows Fig.13 As shown, the linear correlation curves of phosphatidylethanolamine 38:5p in the three cancer modeling group samples are as follows Fig.14 As shown, the linear correlation curves of uridine in the three cancer modeling group samples are as follows Fig.15 As shown, the linear correlation curves of phosphatidylcholine 34:2e in the three cancer modeling group samples are as follows Fig.16 As shown, the linear correlation curves of pyrrole-2-carboxylic acid in the modeling group samples of three cancers are as follows Fig.17 As shown, the linear correlation curves of phosphatidylethanolamine 36:2p in the three cancer modeling group samples are as follows Fig.18 shown.

[0109] Table 2. 18 metabolic markers used to distinguish different types of cancer

[0110]

[0111]

[0112] Example 2: Using 18 metabolic markers to construct a diagnostic model for distinguishing different types of cancer

[0113] In order to evaluate the effect of the metabolic markers screened in Example 1 on distinguishing different types of cancer, this example again performed a multivariate multi-classification ROC (receiver operating characteristic curve) analysis on these 18 metabolic markers. Randomly use 3 / 4 of the modeling group sample data (obtained from Example 1) as a training set (train), and 1 / 4 as a test set (test), where the training set is used to build and train the machine learning classification model, and the test set is used to evaluate the discriminative ability of the trained model. The support vector machine (SVM) algorithm is randomly cycled 1000 times, and the evaluation model is constructed by statistically calculating the average value of the final model accuracy. The results show that AUC = 0.859 (as shown in Figure 2). Fig.19 This indicates that the diagnostic model is efficient.

[0114] The ROC curve is a method to study the relationship between model sensitivity and specificity, with sensitivity as the vertical axis and 1-specificity as the horizontal axis. The evaluation basis is to compare the area under the curve (AUC). When AUC is greater than 0.5, the closer the AUC is to 1, the better the model performance is, indicating a better diagnostic effect. If it is less than 0.5, it means that the model accuracy is poor.

[0115] In order to further verify the effectiveness of the constructed diagnostic model, the plasma samples of the validation group in Example 1 were used as unknown samples, and the validation group data were put into the above-constructed diagnostic model for verification. The results showed that AUC = 0.844 (as shown in Figure 2). Fig. 20 This indicates that the diagnostic model has clinical diagnostic significance.

[0116] Example 3: Using 13 metabolic markers to construct a diagnostic model for distinguishing different types of cancer

[0117] The only difference from the method in Example 2 is that 13 metabolic markers are used when constructing a diagnostic model using the SVM method, namely, hypoxanthine, glycyl-L-glutamate, allantoic acid, phosphatidylethanolamine 36:3p, arabinose, phosphatidylethanolamine 34:2p, oxalic acid, phosphatidylethanolamine 36:1p, phosphatidylcholine 38:7, pyroglutamic acid, phosphatidylcholine 34:2e, pyrrole-2-carboxylic acid and phosphatidylethanolamine 36:2p are combined to construct a diagnostic model.

[0118] The results showed that AUC = 0.869, which has clinical diagnostic significance.

[0119] To further verify the effectiveness of the constructed diagnostic model, the plasma samples of the validation group in Example 1 were used as unknown samples and put into the above-constructed diagnostic model for verification. The results showed that AUC = 0.845, indicating that the diagnostic model has clinical diagnostic significance.

[0120] Example 4: Using 8 metabolic markers to construct a diagnostic model for distinguishing different types of cancer

[0121] The only difference from the method in Example 2 is that when constructing the diagnostic model using the SVM method, 8 metabolic markers, namely hypoxanthine, glycyl-L-glutamate, phosphatidylethanolamine 34:2p, phosphatidylethanolamine 36:1p, phosphatidylcholine 38:7, pyroglutamic acid, pyrrole-2-carboxylic acid and phosphatidylethanolamine 36:2p, are combined to construct the diagnostic model.

[0122] The results showed that AUC = 0.851, which has clinical diagnostic significance.

[0123] To further verify the effectiveness of the constructed diagnostic model, the plasma samples of the validation group in Example 1 were used as unknown samples and put into the above-constructed diagnostic model for verification. The results showed that AUC = 0.844, indicating that the diagnostic model has clinical diagnostic significance.

[0124] Example 5: Using four metabolic markers to construct a diagnostic model for distinguishing different types of cancer

[0125] The difference from the method of Example 2 is that when constructing the diagnostic model using the SVM method, four metabolic markers, namely glycyl-L-glutamate, phosphatidylcholine 38:7, pyrrole-2-carboxylic acid and phosphatidylethanolamine 36:2p, are combined to construct the diagnostic model. The result shows that AUC = 0.843, which has clinical diagnostic significance.

[0126] To further verify the effectiveness of the constructed diagnostic model, the plasma samples of the validation group in Example 1 were used as unknown samples and put into the above-constructed diagnostic model for verification. The results showed that AUC = 0.828, indicating that the diagnostic model has clinical diagnostic significance.

[0127] In summary, the metabolic marker composition of the present invention has significant differences in different cancer types, and can distinguish different cancer types in cancer patients, and realize pan-cancer detection. The use of the metabolic marker composition for pan-cancer detection is non-invasive and minimally invasive, and is more suitable for large-scale screening and detection of the general population. The metabolic marker composition has high sensitivity and specificity, and is more suitable for early screening of cancer, which is helpful for early screening, early diagnosis and early treatment of cancer, and can significantly improve the survival rate and cure rate of cancer patients.

[0128] 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 plasma metabolic marker composition, characterized in that: The plasma metabolic marker composition includes glycyl-L-glutamate, phosphatidylcholine 38:7, pyrrole-2-carboxylic acid and phosphatidylethanolamine 36:2p.

2. The plasma metabolic marker composition according to claim 1, characterized in that: The plasma metabolic marker composition further includes at least one of hypoxanthine, phosphatidylethanolamine 34:2p, phosphatidylethanolamine 36:1p and pyroglutamic acid.

3. The plasma metabolic marker composition according to claim 2, characterized in that: The plasma metabolic marker composition also includes at least one of allantoic acid, phosphatidylethanolamine 36:3p, arabinose, oxalic acid and phosphatidylcholine 34:2e.

4. The plasma metabolic marker composition according to claim 3, characterized in that: The plasma metabolic marker composition also includes at least one of L-malic acid, niacin, oxamic acid, phosphatidylethanolamine 38:5p and uridine.

5. The plasma metabolic marker composition according to claim 3, characterized in that: The plasma metabolic marker composition consists of glycyl-L-glutamate, phosphatidylcholine 38:7, pyrrole-2-carboxylic acid, phosphatidylethanolamine 36:2p, hypoxanthine, phosphatidylethanolamine 34:2p, phosphatidylethanolamine 36:1p, pyroglutamic acid, allantoic acid, phosphatidylethanolamine 36:3p, arabinose, oxalic acid and phosphatidylcholine 34:2e.

6. A method for screening the plasma metabolic marker composition according to any one of claims 1 to 5, characterized in that: The steps include: The plasma of patients with gastric cancer, colorectal cancer and lung cancer is extracted respectively to obtain extracts; Using a chromatography-mass spectrometer to detect the organic phase and the aqueous 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 and obtain the plasma metabolite marker composition.

7. The screening method according to claim 6, characterized in that The step of analyzing the metabolomics data to screen and obtain the plasma metabolite marker composition specifically comprises: The metabolomics data are subjected to linear correlation analysis, and the Pearson correlation coefficient is obtained by calculation. The absolute value of the Pearson correlation coefficient being greater than a preset value is used as a screening condition to screen out the plasma metabolite marker composition.

8. The screening method according to claim 7, characterized in that The absolute value of the Pearson correlation coefficient is greater than or equal to 0.3 as a screening condition.

9. Use of the plasma metabolic marker composition according to any one of claims 1 to 5 in the preparation of a product for detecting pan-cancer species.

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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