Serum metabolic markers for diagnosis of benign and malignant pulmonary nodules and their application

Abstract

The present invention provides a metabolic marker for diagnosing benign and malignant pulmonary nodules and its application, wherein the metabolic marker is selected from one or more combinations of 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamic acid, asparagine, formononetin, alanine and kynurenic acid. The 12 metabolic markers provided by the present invention can accurately diagnose and distinguish benign and malignant pulmonary nodules, with high sensitivity and strong specificity, and can well replace the existing tissue biopsy and imaging diagnosis mode, reduce trauma and radiation risks, and have clinical use and promotion value.

Description

Technical Field

[0001] The present invention relates to the technical field of detection and diagnosis, and in particular to a serum metabolic marker for diagnosing benign and malignant pulmonary nodules, and a screening method and application thereof. Background Art

[0002] Lung cancer is the disease with the highest incidence and mortality in China. The mortality rate of lung cancer has increased by about 5 times in the past 30 years. A big reason for this is that 75% of cancer patients are diagnosed in the middle and late stages. The detection rate of early lung cancer is less than 25%, but the 5-year survival rate of early lung cancer is more than 90%. However, because its early characteristics are not obvious, the best way to detect lung cancer is to regularly screen for lung nodules.

[0003] Pulmonary nodules are a term used in imaging. When a round or oval gray or white shadow of less than 3 cm appears in an obstructed area on a lung image, it is called a nodule. Nodules can be granulomas or exudates formed by irritation of the lungs and tissue hyperplasia, or enlarged lymph nodes, vascular malformations, etc. Nodules can be formed for many reasons.

[0004] When a nodule is found, it is necessary to distinguish between benign and malignant. Malignant nodules are mainly lung cancer. Lung cancer appears as small nodules in the early stage of imaging, and gradually grows over time. Therefore, when a lung nodule is found, especially when the diameter is >1cm, it is necessary to distinguish whether it is benign or malignant. If it is malignant, surgical treatment should be performed as soon as possible to achieve the purpose of radical cure. If it is a benign nodule, the cause must also be clarified, such as spherical pneumonia, tuberculosis foci, fungi, or hemangiomas and vascular malformations in the lungs, etc., and treatment should be carried out according to the cause. Most nodules that are asymptomatic and have a diameter of <7mm are benign nodules. Regular observation is adopted to see if there is a trend of continued enlargement over time on the image. When the nodule grows to more than 1cm, it is necessary to confirm the diagnosis through blood tests, bronchoscopy, lung puncture, etc., and take appropriate treatment.

[0005] Due to the increasingly deteriorating environment, the probability of people suffering from lung nodules has greatly increased, resulting in more and more people suffering from lung nodules. Early removal of lung nodules is the key to effectively preventing lung nodules from turning into lung cancer. At present, ultrasound imaging is mainly used to screen lung nodules, but the test results are usually accompanied by a large number of false-positive lung nodules. A large number of false-positive lung nodules will seriously interfere with the doctor's diagnosis, thereby increasing the chance of misdiagnosis and increasing the medical burden. Data from the National Lung Screening Trial (NLST) in the United States showed that early lung cancer screening for high-risk populations using low-dose computed tomography (LDCT) can reduce lung cancer mortality by 20% and total mortality by 7% (Bethesda, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011; 365: 395-409.). However, LDCT has problems such as radiation exposure and high false-positive rate, which affects the practicality of LDCT-based screening worldwide. Therefore, how to effectively reduce the false positive of lung nodules in the test results is a problem to be solved.

[0006] Metabolomics is an emerging discipline after genomics and proteomics, and is an important part of systems biology. Metabolomics has developed and rapidly penetrated into many fields. Its purpose is to study the overall metabolic differences in biological systems by monitoring the levels of small molecule metabolites in biological fluids or tissues, and to find the relative relationship between metabolites and pathophysiological changes. The occurrence of tumors is inevitably accompanied by metabolic changes, especially in the early stages, when the changes in small molecule metabolites are very weak and not easy to detect (Pei-Hsuan, C., Ling, C., Kenneth, H. et al. Metabolic diversity in human non-small cell lung cancer cells. Molecular Cell. 2019, 76, 1-14. Brandon, F., Ashley, S., Ralph, J. D. Metabolic reprogramming and cancer progression. Science. 2020, April 10; 368.). At present, the commonly used metabolome analysis methods include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These technologies are radiation-free and have high throughput in the application process. NMR detection has low sensitivity, and GC-MS detection sample pretreatment is complex, while LC-MS has the characteristics of high sensitivity and simple sample pretreatment. It is very suitable for large-scale population screening and clinical diagnosis, and can detect changes in tumor-related metabolites and effectively distinguish between benign and malignant lung nodules.In the past decade, scientists have applied metabolomics to conduct a large number of studies in the fields of lung cancer screening, diagnosis, and prognosis. They have discovered some metabolites and metabolic pathways that change during the occurrence and development of lung cancer, and obtained some reliable biomarkers for lung cancer diagnosis (Mathe, EA, Patterson, AD, Haznadar, M. et al. Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Res. 2014, 74: 3259-3270. William, RW, Samir, H., Brian, D. et al. Diacetylspermine is a novel prediagnostic serum biomarker for non-small-cell lung cancer and has additive performance with pro-surfactant protein BJ Clin Oncol. 2015, Nov 20; 33(33): 3880-6. Agnieszka, K., Szymon, P., Mariusz, K. et al. Serum lipidome screening in patients with stage I non-small cell lung cancer. Clin Exp Med. 2019; 19(4): 505-513.). However, these studies basically use metabolite information from public databases, the metabolites screened are not disease-specific, and most studies are based on a single sample source, so their actual clinical significance is very limited. Summary of the invention

[0007] Based on this, it is necessary to provide a metabolic marker for the diagnosis of benign and malignant lung nodules and its screening method and application, which can well replace the existing tissue biopsy and imaging diagnosis mode, reduce trauma and radiation risks, and have clinical use and promotion value.

[0008] At present, no one has used serum metabolite levels to screen and diagnose benign and malignant lung nodules. The present invention uses serum metabolomics to find metabolite markers to diagnose and distinguish benign and malignant lung nodules, which is of great significance for the rapid diagnosis of lung cancer in the early clinical stage. The present invention constructs a serum-specific metabolome database of benign and malignant lung nodules to conduct serum metabolomics research, obtains a large number of disease-related specific metabolites, and verifies the metabolite markers through samples from multiple medical centers to find serum metabolite markers with high sensitivity, good specificity, stability and reliability for the diagnosis of benign and malignant lung nodules.

[0009] The present invention adopts the following technical solution:

[0010] The present invention provides a metabolic marker for diagnosing or monitoring benign and malignant pulmonary nodules, wherein the metabolic marker is at least one selected from 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamic acid, asparagine, formononetin, alanine, and kynurenic acid.

[0011] The present invention provides a metabolic marker for diagnosing or monitoring benign and malignant pulmonary nodules, wherein the metabolic marker is at least selected from at least one of 4-hydroxy-L-proline, hyodeoxycholic acid, and xanthine. Further, the metabolic marker is also selected from at least one of 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, and glutamic acid. Further, the metabolic marker is also selected from at least one of guanine, glutamic acid, asparagine, formononetin, alanine, and kynurenic acid.

[0012] The present invention also provides the use of the above-mentioned metabolic markers for diagnosing or monitoring benign and malignant pulmonary nodules in preparing a metabolite database, reagent product or kit for diagnosing or monitoring benign and malignant pulmonary nodules.

[0013] The present invention provides a reagent product or a detection kit, comprising a standard substance of a metabolic marker for diagnosing or monitoring benign or malignant pulmonary nodules. The detection kit may also comprise a dissolving reagent, an extracting reagent and an internal standard substance. The internal standard substance is L-phenylalanine.

[0014] The present invention also provides a screening method for metabolite markers for diagnosing or monitoring benign and malignant pulmonary nodules, comprising the following steps: collecting samples from a benign pulmonary nodule group and a malignant pulmonary nodule group respectively; randomly selecting 20% ​​of the samples from the benign pulmonary nodule group and the malignant pulmonary nodule group, using a metabolomics method combining enhanced ion scanning mass spectrometry and time-of-flight mass spectrometry with a multiple reaction monitoring acquisition mode, and integrating a local standard database to construct a serum metabolite database for benign and malignant pulmonary nodules; analyzing the collected serum samples by constructing a serum metabolite database for benign and malignant pulmonary nodules and LC-MS detection to obtain original mass spectrometry data of each serum sample; using MultiQu ant software was used to preprocess and correct the original mass spectrometry data according to the mass-to-charge ratio and retention time; the peak area was calculated according to the mass spectrometry peak intensity to obtain the relative content information of metabolites; the relative content information of metabolites was subjected to multivariate statistical orthogonal-partial least squares discriminant analysis, and candidate differential metabolites were obtained according to the screening criteria of variable weight value greater than 1 and P value less than 0.05 of univariate statistical analysis; binary logistic regression modeling was performed on the candidate differential metabolites to screen excellent metabolites and their combinations for diagnosis of patients with benign and malignant pulmonary nodules, and receiver operating characteristic curve analysis was performed on the screened excellent metabolites and their combinations to determine the metabolic markers for diagnosis or monitoring of benign and malignant pulmonary nodules.

[0015] In some embodiments, the conditions for LC-MS detection are:

[0016] Chromatographic column: Waters ACQUITY UPLC HSS T3 C18 1.8μm, 2.1mm*100mm;

[0017] Mobile phase: Phase A is an aqueous solution containing 0.1% acetic acid, and phase B is an acetonitrile solution containing 0.1% acetic acid, with a flow rate of 0.4 mL / min;

[0018] The elution gradient program was:

[0019] 0min, the volume ratio of phase A to phase B was 95:5;

[0020] 11.0min, the volume ratio of phase A to phase B was 10:90;

[0021] 12.0min, the volume ratio of phase A to phase B was 10:90;

[0022] 12.1min, the volume ratio of phase A to phase B was 95:5;

[0023] At 14.0 min, the volume ratio of phase A to phase B was 95:5.

[0024] Compared with the prior art, the present invention uses large-scale clinical samples to conduct serum metabolomics research to obtain 12 markers for the diagnosis of benign and malignant lung nodules. The area under the ROC curve AUC value of a single metabolite marker is greater than 0.6, between 0.611-0.808; the performance of a combination of multiple metabolite markers is significantly better than that of a single metabolite marker, and the area under the ROC curve AUC value is 0.685-0.861. When the 12 serum metabolite markers of the present invention are used for detection and diagnosis, diagnosis can be achieved only by blood sampling, without the need for additional tissue sample collection, and can well replace the existing tissue biopsy and imaging diagnosis mode, reduce trauma and radiation risks, and have clinical use and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is the S-plot of the metabolite OPLS-DA provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0026] The present invention is further described in detail below in conjunction with specific embodiments so that those skilled in the art can understand the present invention more clearly.

[0027] The following embodiments are only used to illustrate the present invention, but are not limited to the scope of the present invention. Based on the specific embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work belong to the protection scope of the present invention.

[0028] In the examples of the present invention, unless otherwise specified, all raw material components are commercially available products well known to those skilled in the art; in the examples of the present invention, unless otherwise specified, the technical means used are conventional means well known to those skilled in the art. The key instrument information is shown in Table 1 below:

[0029] Table 1 Experimental instrument information

[0030] name model brand HPLC-TOF-MS TripleTOF 6600 SCIEX LC-MS / MS QTRAP 6500+ SCIEX Centrifuge 5424R Eppendorf Centrifugal Concentrator CentriVap LABCONCO Vortex mixer VORTEX-5 Kyllin-Be11

[0031] Terminology

[0032] The "local standard database" mentioned in the present invention refers to the collection of mass spectrometry information of a large number of related metabolite molecular standards after mass spectrometry detection, thereby forming a localized standard database.

[0033] Example 1

[0034] This embodiment provides a method for screening serum metabolic markers, comprising the following steps:

[0035] S1, sample collection

[0036] After obtaining the patients' consent, this study collected peripheral venous serum samples from 140 patients with benign pulmonary nodules (benign pulmonary nodule group) and 143 patients with malignant pulmonary nodules (malignant pulmonary nodule group) from Shanghai Chest Hospital. All patients with malignant pulmonary nodules had no history of other malignant tumors, other major systemic diseases, or chronic diseases with long-term medication. Blood was collected in the early morning on an empty stomach. All serum samples were centrifuged and stored in a -80°C refrigerator. During the study, serum samples were taken out and thawed for subsequent analysis.

[0037] S2, serum extensive targeted metabolomics analysis

[0038] (1) Sample pretreatment

[0039] Take out the sample collected in step S1 from the -80℃ refrigerator, thaw on ice until there is no ice in the sample (subsequent operations are required to be performed on ice); after the sample is thawed, vortex for 10s to mix, take 50μL of the sample and add it to the corresponding numbered centrifuge tube; add 300μL of pure methanol internal standard extract (containing 100ppm concentration of L-phenylalanine internal standard); vortex for 5min, let stand for 24h, and then centrifuge at 12000r / min and 4℃ for 10min; take 270μL of the supernatant and concentrate for 24h; then add 100μL of a reconstituted solution composed of acetonitrile and water in a volume ratio of 1:1 for LC-MS / MS analysis. Take 20μL of each sample and mix it into a quality control sample (QC), and collect it once every 15 samples.

[0040] (2) Sample metabolite detection

[0041] Table 2 Experimental reagents

[0042] Compound CAS Number brand Methanol 67-56-1 Merck Acetonitrile 75-05-8 Merck Acetic acid 64-19-7 Aladdin L-Phenylalanine 63-91-2 isoreag

[0043] The liquid chromatography conditions were determined as follows: chromatographic column: Waters ACQUITY UPLC HSS T3 C18 1.8 μm, 2.1 mm*100 mm; column temperature was 40°C; injection volume was 2 μL.

[0044] Mobile phase: Phase A is an aqueous solution containing 0.1% acetic acid, and phase B is an acetonitrile solution containing 0.1% acetic acid. The elution gradient program is: 0 min, the volume ratio of phase A to phase B is 95:5; 11.0 min, the volume ratio of phase A to phase B is 10:90; 12.0 min, the volume ratio of phase A to phase B is 10:90; 12.1 min, the volume ratio of phase A to phase B is 95:5; 14.0 min, the volume ratio of phase A to phase B is 95:5. Flow rate 0.4 mL / min.

[0045] The mass spectrometry conditions were determined as follows: electrospray ionization (ESI) temperature of 500°C, mass spectrometry voltage of 5500 V (positive) or -4500 V (negative), ion source gas I (GS I) 55 psi, gas II (GSII) 60 psi, curtain gas (CUR) 25 psi, and collision-activated dissociation (CAD) parameters were set to high.

[0046] In a triple quadrupole (Qtrap), each ion pair is scanned and detected in MRM mode according to the optimized declustering potential (DP) and collision energy (CE).

[0047] The samples were analyzed and tested according to the determined liquid chromatography conditions and mass spectrometry conditions: 20% of the samples in the benign lung nodule group and the malignant lung nodule group were randomly selected, and the metabolomics method of enhanced ion scanning mass spectrometry (MIM-EPI) and time-of-flight mass spectrometry (TOF) combined with multiple reaction monitoring acquisition mode was used, and the local standard database was integrated to construct the serum metabolite database of benign and malignant lung nodules. The collected serum samples were analyzed using the liquid chromatography-mass spectrometry metabolomics method and the constructed serum metabolite database of benign and malignant lung nodules to obtain the original mass spectrometry data of each serum sample.

[0048] (3) Spectral peak area preprocessing and integration

[0049] Based on the serum metabolite database of benign and malignant lung nodules, the metabolites of the samples were qualitatively and quantitatively analyzed by mass spectrometry. Metabolites of different molecular weights can be separated by liquid chromatography. The characteristic ions of each substance were screened out by the multiple reaction monitoring mode (MRM) of the triple quadrupole, and the signal intensity (CPS) of the characteristic ions was obtained in the detector. The mass spectrometry file of the sample was opened with MultiQuant software, and the original mass spectrometry data was preprocessed and corrected according to the mass-to-charge ratio and retention time. The chromatographic peaks were integrated and corrected. The peak area (Area) of each chromatographic peak represents the relative content of the corresponding substance. The peaks with S / N>5 and retention time offset not exceeding 0.2min were retained; the peak area was calculated according to the mass spectrometry peak intensity to obtain the relative content information of the metabolites, and finally all the chromatographic peak area integral data were exported and saved for the next statistical analysis.

[0050] (4) Experimental quality control

[0051] By overlapping and displaying the total ion current graph (TIC graph) of mass spectrometry analysis of different quality control QC samples, the repeatability of metabolite extraction and detection, i.e., technical repetition, can be determined. The high stability of the instrument provides an important guarantee for the repeatability and reliability of the data. The CV value, or coefficient of variation, is the ratio of the standard deviation of the original data to the mean of the original data, which can reflect the degree of data dispersion. The empirical cumulative distribution function (ECDF) can be used to analyze the frequency of CV of substances less than the reference value. The higher the proportion of substances with low CV values ​​in QC samples, the more stable the experimental data: the proportion of substances with CV values ​​less than 0.5 in QC samples is higher than 85%, indicating that the experimental data is relatively stable; the proportion of substances with CV values ​​less than 0.3 in QC samples is higher than 75%, indicating that the experimental data is very stable. At the same time, the changes in the CV value of the internal standard of L-phenylalanine during the detection process are monitored. The change in the CV value of the internal standard is less than 20%, indicating that the instrument has good stability during the detection process.

[0052] (5) Data processing, analysis and marker screening

[0053] The peak area integration data were imported into SIMCA software (Version 14.1, Sweden) for multivariate statistical analysis. The orthogonal partial least squares discriminant (OPLS-DA) model was established to find the metabolites with greater contribution (VIP>1.0) between patients with benign lung nodules and patients with malignant lung nodules. Figure 1 As shown in the figure, the black points are metabolites with VIP>1.0, and the gray points are metabolites with VIP<1.0. Then, the T-test was performed, and p<0.05 was set as the significant difference standard. Finally, the differential metabolites with VIP>1.0 and p<0.05 were screened out, which may be potential metabolic biomarkers for diagnosing benign and malignant lung nodules.

[0054] The potential metabolite markers of benign and malignant lung nodules screened by the above analysis were inferred based on their retention time, primary and secondary mass spectra, and compared with the spectrum information in the metabolite spectrum database to qualitatively identify the metabolites. Finally, the structure of the metabolite marker was verified by purchasing standard products and comparing the molecular weight, chromatographic retention time and corresponding multi-stage MS fragmentation spectrum of the standard products.

[0055] Using binary logistic regression forward stepwise method, 12 differential metabolites were screened out to diagnose and distinguish benign and malignant pulmonary nodules:

[0056] 4-Hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamic acid, asparagine, formononetin, alanine, kynurenic acid.

[0057] The specific information of metabolites is shown in Tables 3 and 4 below:

[0058] Table 3 12 serum metabolic markers

[0059] Table 4 Metabolites in patients with malignant pulmonary nodules VS patients with benign pulmonary nodules

[0060]

[0061]

[0062] The receiver operating characteristic (ROC) curve was used to analyze the diagnostic performance of metabolites for benign and malignant pulmonary nodules. The results showed that the 12 differential metabolites, 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamic acid, asparagine, formononetin, alanine, and kynurenic acid, had strong ability to diagnose and distinguish benign and malignant pulmonary nodules, and the area under the ROC curve (AUC) was greater than 0.6, which was of clinical diagnostic significance.

[0063] When these 12 differential metabolites were used in combination for diagnosis, the AUC was further improved. The AUC value of the 12 combined metabolites for diagnosing benign and malignant lung nodules reached 0.855. At the optimal cutoff value, the sensitivity and specificity were 83.3% and 82.1%, respectively. The specific data statistics are shown in Tables 5 and 6 below:

[0064] Table 5 AUC values ​​of individual metabolites for the diagnosis of benign and malignant pulmonary nodules

[0065] serial number Chinese name AUC Sensitivity Specificity 1 4-Hydroxy-L-proline 0.785 77.1％ 76.5％ 2 Hyodeoxycholic acid 0.768 76.1％ 74.8％ 3 Xanthine 0.745 73.8％ 73.5％ 4 2-Hydroxy-6-aminopurine 0.722 70.6％ 71.1％ 5 5,6-Dihydrothymidine 0.711 70.1％ 69.2％ 6 Isobutyrylcarnitine 0.705 69.2％ 68.5％ 7 Guanine 0.699 68.8％ 67.7％ 8 Glutamate 0.678 66.2％ 66.4％ 9 Asparagine 0.656 64.3％ 63.2％ 10 Formononetin 0.637 62.1％ 61.8％ 11 Alanine 0.625 61.8％ 60.9％ 12 kynurenic acid 0.611 60.5％ 60.1％

[0066] Table 6 AUC values ​​of any metabolite combination for diagnosis of benign and malignant pulmonary nodules

[0067] Number of joints AUC Sensitivity Specificity Any two ≥0.685 ≥67.1％ ≥66.8％ Any three ≥0.698 ≥68.8％ ≥67.2％ Any four ≥0.712 ≥69.6％ ≥68.5％ Any five ≥0.733 ≥70.8％ ≥70.2％ Any six ≥0.758 ≥71.5％ ≥70.6％ Any seven ≥0.768 ≥72.6％ ≥71.7％ Any eight ≥0.780 ≥76.6％ ≥75.7％ Any nine ≥0.795 ≥77.6％ ≥75.2％ Any ten ≥0.823 ≥80.1％ ≥78.6％ Any eleven ≥0.842 ≥81.9％ ≥80.7％

[0068] The diagnostic model values ​​of some preferred metabolic marker combinations are further exemplified:

[0069] The metabolite marker combination was further optimized as follows: 4-hydroxy-L-proline, hyodeoxycholic acid, and xanthine, to construct a model for diagnosing benign and malignant pulmonary nodules. The AUC value of these three metabolites combined for diagnosing benign and malignant pulmonary nodules reached 0.803, and at the optimal cutoff value, the sensitivity and specificity were 79.5% and 78.3%, respectively.

[0070] A further preferred combination of metabolic markers is: 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, and isobutyrylcarnitine, to construct a model for diagnosing and distinguishing benign and malignant lung nodules.

[0071] The AUC value of these six metabolites combined for diagnosing benign and malignant pulmonary nodules reached 0.822. At the optimal cutoff value, the sensitivity and specificity were 80.0% and 78.8%, respectively.

[0072] A further preferred combination of metabolic markers is: 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamate, and asparagine, to construct a model for diagnosing and distinguishing benign and malignant lung nodules.

[0073] The combined AUC value of these nine metabolites for diagnosing benign and malignant pulmonary nodules reached 0.836. At the optimal cutoff value, the sensitivity and specificity were 81.4% and 80.2%, respectively.

[0074] Example 2

[0075] The research subjects of this embodiment include 70 serum samples of patients with benign pulmonary nodules and 71 serum samples of malignant pulmonary nodules from 2 independent medical centers. All patients with benign and malignant pulmonary nodules have no history of any other malignant tumors, no other major systemic diseases, and no history of chronic diseases with long-term medication. The blood was collected in the early morning on an empty stomach. All serum samples were centrifuged and stored in a -80°C refrigerator. The serum samples were taken out and thawed for subsequent analysis during the study.

[0076] The detection conditions and data analysis methods of this embodiment are the same as those of Example 1. The differential metabolites detected and analyzed are the following 12 kinds: 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamic acid, asparagine, formononetin, alanine, and kynurenic acid, which are used for the diagnosis of benign and malignant pulmonary nodules. The above 12 metabolite markers change significantly in patients with malignant pulmonary nodules. The specific information is shown in Table 7:

[0077] Table 7 Metabolites in patients with malignant pulmonary nodules VS patients with benign pulmonary nodules

[0078] Chinese name Difference multiple VIP P value 4-Hydroxy-L-proline 0.78 1.67 0.019 Xanthine 0.85 1.82 0.026 Hyodeoxycholic acid 0.86 1.53 0.048 2-Hydroxy-6-aminopurine 0.93 1.44 0.012 5,6-Dihydrothymidine 0.93 1.56 0.007 Isobutyrylcarnitine 1.10 1.32 0.028 Guanine 0.87 1.60 0.014 Glutamate 0.85 1.28 0.035 Formononetin 1.03 1.15 0.031 Asparagine 1.03 1.33 0.045 Alanine 1.04 1.06 0.048 kynurenic acid 0.96 1.04 0.043

[0079] These 12 differential metabolites have strong ability to diagnose and distinguish patients with benign and malignant pulmonary nodules, and the area under the ROC curve (AUC) is greater than 0.6, which has clinical diagnostic significance.

[0080] When these 12 differential metabolites were used in combination for diagnosis, the AUC was further improved. The AUC value of the 12 metabolites combined for diagnosis of benign and malignant lung nodules reached 0.881. At the optimal cutoff value, the sensitivity and specificity were 86.1% and 86.9%, respectively. The AUC of a single metabolite and any combination of 2-11 metabolites for diagnosis are shown in Tables 8 and 9:

[0081] Table 8 AUC values ​​of individual metabolites for the diagnosis of benign and malignant pulmonary nodules

[0082] serial number Chinese name AUC Sensitivity Specificity 1 4-Hydroxy-L-proline 0.808 79.6％ 80.2％ 2 Xanthine 0.777 76.3％ 77.0％ 3 Hyodeoxycholic acid 0.764 74.2％ 75.1％ 4 2-Hydroxy-6-aminopurine 0.731 71.8％ 72.2％ 5 5,6-Dihydrothymidine 0.719 70.1％ 70.9％ 6 Isobutyrylcarnitine 0.710 69.5％ 70.2％ 7 Guanine 0.705 68.4％ 69.7％ 8 Glutamate 0.688 66.3％ 67.8％ 9 Formononetin 0.662 64.6％ 65.5％ 10 Asparagine 0.643 63.1％ 63.8％ 11 Alanine 0.628 61.7％ 62.0％ 12 kynurenic acid 0.615 60.9％ 61.4％

[0083] Table 9 AUC values ​​of any differential metabolites combined for diagnosis of benign and malignant pulmonary nodules

[0084]

[0085]

[0086] The diagnostic model values ​​of some preferred metabolic marker combinations are further exemplified:

[0087] The metabolic marker combination of 4-hydroxy-L-proline, xanthine, and hyodeoxycholic acid was further optimized to construct a diagnostic model for distinguishing benign and malignant lung nodules.

[0088] The AUC value of these three metabolites combined for diagnosing benign and malignant pulmonary nodules reached 0.815. At the optimal cutoff value, the sensitivity and specificity were 80.2% and 78.8%, respectively.

[0089] A further preferred metabolic marker combination: 4-hydroxy-L-proline, xanthine, hyodeoxycholic acid, 2-hydroxy-6-aminopurine, 5,6-dihydrothymidine, and isobutyrylcarnitine was used to construct a model for diagnosing and distinguishing benign and malignant lung nodules.

[0090] The AUC value of these six metabolites combined for diagnosing benign and malignant pulmonary nodules reached 0.833. At the optimal cutoff value, the sensitivity and specificity were 81.5% and 80.6%, respectively.

[0091] A further preferred combination of metabolic markers is: 4-hydroxy-L-proline, xanthine, hyodeoxycholic acid, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamate, and formononetin, to construct a model for diagnosing and distinguishing benign and malignant lung nodules.

[0092] The combined AUC value of these nine metabolites for diagnosing benign and malignant pulmonary nodules reached 0.847. At the optimal cutoff value, the sensitivity and specificity were 82.1% and 81.8%, respectively.

[0093] Example 3

[0094] This embodiment provides a detection kit, comprising:

[0095] (1) Metabolic marker standards: hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamic acid, asparagine, formononetin, alanine, and kynurenic acid, packaged individually or mixed.

[0096] (2) Solvent:

[0097] Pure methanol and 50% acetonitrile in water were used for sample extraction.

[0098] 50% acetonitrile aqueous solution can be used as a solvent for dissolving the standard.

[0099] (3) Internal standard: L-phenylalanine.

[0100] It is necessary to point out that the above embodiments are limited to further elaboration and explanation of the technical solution of the present invention, and are not further limitations of the technical solution of the present invention. The method of the present invention is only a preferred implementation scheme, and is not used to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Use of a metabolite marker for diagnosing or monitoring benign or malignant pulmonary nodules in the preparation of a metabolite database, reagent product or kit for diagnosing or monitoring benign or malignant pulmonary nodules, characterized in that: The metabolic markers are 4-hydroxy-L-proline, hyodeoxycholic acid, and xanthine.

2. The use according to claim 1, characterized in that: The metabolic markers are 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, and isobutyrylcarnitine.

3. The use according to claim 2, characterized in that: The metabolic markers are 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamic acid, and formononetin.

4. The use according to claim 1, characterized in that: The metabolic markers are 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamic acid, asparagine, formononetin, alanine, and kynurenic acid.

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