WO2023082821A1 - Serum metabolism marker for diagnosing benign and malignant pulmonary nodules and use thereof - Google Patents

Abstract

Provided in the present invention are a metabolism marker for diagnosing benign and malignant pulmonary nodules and the use thereof. The metabolism marker is one or a combination of more than one selected from 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyryl carnitine, guanine, glutamic acid, asparagine, formononetin, alanine and kynuric acid. The 12 metabolism markers provided in the present invention can accurately diagnose and distinguish benign and malignant pulmonary nodules, are high in sensitivity and high in specificity, can fully replace existing tissue biopsy and imaging diagnosis modes, reduce trauma and radiation risks, and have value in terms of clinical use and popularization.

Description

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

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

Background technique

Lung cancer is the disease with the highest morbidity and mortality in China. In the past three decades, the mortality rate of lung cancer has increased by about 5 times. The main reason 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 over 90%, but because its early features are not obvious, the best way to find lung cancer is to regularly screen for lung nodules.

Pulmonary nodule is an imaging term. When there is a round or oval <3cm gray or white shadow in a place that should not be occluded on the lung image, we call it a nodule. Nodules can be granulomas or exudates formed by lung stimulation and tissue hyperplasia, or enlarged lymph nodes, abnormal blood vessels, etc. There are many reasons for the formation of nodules.

It is necessary to distinguish between benign and malignant nodules. Malignancy is mainly lung cancer. In the early stage of lung cancer, it is a small nodule on the image, which gradually grows over time. Therefore, when a pulmonary nodule is found, especially when the diameter is >1 cm, 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, fungi, or hemangiomas in the lungs, vascular malformations, etc., and treatment will be performed according to the cause. Asymptomatic nodules with a diameter of <7mm are mostly benign nodules, and should be observed regularly to see if there is a trend of continued enlargement over time on the images. When the nodules grow larger than 1 cm, blood tests, bronchoscopy, lung biopsy, etc. are required to confirm the diagnosis and take appropriate treatment.

Due to the 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 transforming into lung cancer . At present, ultrasound images are mainly used to screen for pulmonary nodules, but the detection results are usually accompanied by a large number of false positive pulmonary nodules. A large number of false positive pulmonary 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) show that early lung cancer screening in high-risk groups using low-dose computed tomography (LDCT) reduces lung cancer mortality by 20% and overall 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 affect the practicality of LDCT-based screening on a global scale. Therefore, how to effectively reduce the false positive of pulmonary nodules in the detection results is a problem to be solved.

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, and 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 bound to be accompanied by metabolic changes, especially in the early stage, the changes of small molecule metabolites are very weak and not easy to be found (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 metabolite analysis methods include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). specialty. The sensitivity of NMR detection is low, and the pretreatment of GC-MS detection samples is complicated, while LC-MS has the characteristics of high sensitivity and simple sample pretreatment, which is very suitable for large-scale population screening and clinical diagnosis, and can find metabolites related to tumors Changes to effectively distinguish between benign and malignant pulmonary nodules. In the past ten years, scientists have conducted a lot of research in the fields of lung cancer screening, diagnosis, and prognosis using metabolomics. They have discovered some metabolites and metabolic pathways that change during the occurrence and development of lung cancer, and obtained some reliable data on lung cancer. Diagnostic biomarkers (Mathe, E.A., Patterson, A.D., Haznadar, M. et al. Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Res. 2014, 74:3259-3270. William, R.W., Sam ir, 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 B.J 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 the metabolite information in public databases, and the metabolite disease specificity obtained by screening is not strong, and most of the studies are from a single sample source, and the actual clinical significance is very limited.

disclosure of invention

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

Currently, no one has used serum metabolite levels to screen and diagnose benign and malignant pulmonary nodules. The present invention uses serum metabolomics to search for metabolic markers to diagnose and distinguish benign and malignant pulmonary nodules, which is of great significance for early and rapid clinical diagnosis of lung cancer. The present invention builds a serum-specific metabolome database for benign and malignant pulmonary nodules to conduct serum metabolomics research, obtains a large number of specific metabolites related to diseases, and verifies metabolic markers through samples from multiple medical centers to find sensitivity High, specific, stable and reliable serum metabolic markers for the diagnosis of benign and malignant pulmonary nodules.

The present invention adopts following technical scheme:

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

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

The present invention also provides the application of the above 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.

The invention provides a reagent product or a detection kit, including standard products of metabolic markers for diagnosing or monitoring benign and malignant pulmonary nodules. The detection kit may also include dissolution reagents, extraction reagents and internal standards. The internal standard is L-phenylalanine.

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

In some of these embodiments, the conditions for LC-MS detection are:

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

Mobile phase: Phase A is an aqueous solution containing 0.1% acetic acid, phase B is an acetonitrile solution containing 0.1% acetic acid, and the flow rate is 0.4mL/min;

The elution gradient program is:

0min, the volume ratio of phase A to phase B is 95:5;

11.0min, the volume ratio of phase A and phase B is 10:90;

12.0min, the volume ratio of phase A and phase B is 10:90;

12.1min, the volume ratio of phase A to phase B is 95:5;

14.0min, the volume ratio of phase A to phase B is 95:5.

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 pulmonary nodules. The AUC value of the area under the ROC curve of a single metabolic marker is greater than 0.6. Between 0.611-0.808; the performance of the combination of multiple metabolic markers is significantly better than that of a single metabolic marker, and the area under the ROC curve AUC value is 0.685-0.861. When the 12 serum metabolic markers of the present invention are used for detection and diagnosis, the diagnosis can be realized only by taking blood for detection, without additional collection of tissue samples, which can well replace the existing tissue biopsy and imaging diagnosis modes, and reduce trauma and radiation risk, and has the value of clinical use and promotion.

Brief description of the drawings

Fig. 1 is an S-plot diagram of the metabolite OPLS-DA provided in Example 1 of the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION

The present invention will be further described in detail below in conjunction with specific embodiments, so that those skilled in the art can understand the present invention more clearly.

The following examples are only used to illustrate the present invention, but not to limit the scope of the present invention. Based on the specific embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

In the embodiments 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 embodiments of the present invention, if not specifically specified, the technical means used are all well-known conventional means. The key instrument information is shown in Table 1 below:

Table 1 Experimental instrument information

name	model	brand
HPLC-TOF-MS	Triple TOF 6600	SCIEX
LC-MS/MS	QTRAP 6500+	SCIEX
centrifuge	5424R	Eppendorf
Centrifugal concentrator	CentriVap	LABCONCO
Vortex mixer	VORTEX-5	Kyllin-Be11

Glossary

The "local standard substance database" mentioned in the present invention refers to the mass spectrometric detection of a large number of relevant metabolite molecular standards in the present invention, and the collection of mass spectrometry information of these metabolites, thereby forming a localized standard substance database.

Example 1

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

S1, collecting samples

In this study, after obtaining the consent of the patients, peripheral venous blood was collected from 140 patients with benign pulmonary nodules (benign pulmonary nodules group) and 143 patients with malignant pulmonary nodules (malignant pulmonary nodules group) in Shanghai Chest Hospital. Serum samples. All patients with pulmonary malignant nodules had no history of other malignant tumors, other major systemic diseases, and no chronic medical history of long-term medication. The time of blood collection was in the 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.

S2, Extensive targeted metabolomics analysis of serum

(1) Sample pretreatment

Take out the sample collected in step S1 from the -80°C refrigerator, and thaw it on ice until there are no ice cubes in the sample (subsequent operations are required to be carried out on ice); Into a numbered centrifuge tube; add 300 μL pure methanol internal standard extraction solution (containing 100 ppm L-phenylalanine internal standard); vortex for 5 min, let stand for 24 h, and then centrifuge at 12000 r/min, 4 °C for 10 min ; Take 270 μL of the supernatant and concentrate for 24 hours; then add 100 μL of acetonitrile and water in a complex solution with a volume ratio of 1:1 for LC-MS/MS analysis. 20 μL of each sample was mixed to form a quality control sample (QC), which was collected every 15 samples.

(2) Sample metabolite detection

Table 2 Experimental Reagents

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

Determine the liquid chromatography conditions as follows: chromatographic column: Waters ACQUITY UPLC HSS T3 C18 1.8 μm, 2.1mm*100mm; column temperature is 40°C; injection volume is 2 μL.

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: 0min, the volume ratio of phase A to B is 95:5; 11.0min, the volume ratio of phase A to B is 10:90; 12.0min, the volume ratio of phase A to B is 10 :90; 12.1min, the volume ratio of phase A to phase B is 95:5; 14.0min, the volume ratio of phase A to phase B is 95:5. Flow rate 0.4mL/min.

Determine the mass spectrometry conditions as follows: electrospray ionization (ESI) temperature 500°C, mass spectrometry voltage 5500V (positive) or -4500V (negative), ion source gas I (GS I) 55psi, gas II (GS II) 60psi, The curtain gas (CUR) was 25psi, and the collision-activated dissociation (CAD) parameter was set to high.

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

The samples were analyzed and detected according to the determined liquid chromatography conditions and mass spectrometry conditions: randomly selected 20% of the samples from the benign pulmonary nodule group and the malignant pulmonary nodule group, and used enhanced ion scanning mass spectrometry (MIM-EPI) and time-of-flight Mass spectrometry (TOF) combined with the metabolomics method of the multiple reaction monitoring acquisition mode, and the integration of the local standard database for the construction of a serum metabolite database for benign and malignant pulmonary nodules. The collected serum samples were analyzed by liquid chromatography-mass spectrometry combined with metabolomics method and the constructed serum metabolite database of benign and malignant pulmonary nodules, and the original mass spectrometry data of each serum sample were obtained.

(3) Spectrum peak area preprocessing and integration

Based on the serum metabolite database of benign and malignant pulmonary 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 are screened out by the multiple reaction monitoring mode (MRM) of the triple quadrupole, and the signal intensity (CPS) of the characteristic ions is obtained in the detector. Use MultiQuant software to open the mass spectrum file of the sample off-machine, preprocess and correct the original mass spectrum data according to the mass-to-charge ratio and retention time, and perform the integration and calibration of the chromatographic peaks. The peak area (Area) of each chromatographic peak represents the corresponding substance. For relative content, set S/N>5, and retain peaks whose retention time shift does not exceed 0.2min; calculate the peak area according to the mass spectrum peak intensity to obtain the relative content information of metabolites, and finally export all chromatographic peak area integral data and save them for the next step Statistical Analysis.

(4) Experimental quality control

The repeatability of metabolite extraction and detection can be judged by overlaying and displaying the total ion chromatograms (TIC charts) of different quality control QC samples for mass spectrometry detection and analysis, that is, technical repetition. The high stability of the instrument provides an important guarantee for the repeatability and reliability of the data. The CV value is the coefficient of variation (Coefficient of Variation), which is the ratio of the standard deviation of the original data to the mean of the original data, which can reflect the degree of dispersion of the data. The Empirical Cumulative Distribution Function (ECDF) can be used to analyze the frequency of the CV of substances less than the reference value. The higher the proportion of substances with lower CV values in the QC sample, the more stable the experimental data: the CV value of the QC sample is less than The proportion of substances with 0.5 is higher than 85%, indicating that the experimental data is relatively stable; the proportion of substances with QC sample CV value less than 0.3 is higher than 75%, indicating that the experimental data is very stable. At the same time, the change of the CV value of the internal standard L-phenylalanine during the detection process was monitored, and the change of the CV value of the internal standard was less than 20%, indicating that the stability of the instrument during the detection process was good.

(5) Data processing, analysis and marker screening

The peak area integration data were imported into SIMCA software (Version 14.1, Sweden) for multivariate statistical analysis. By establishing an orthogonal-partial least squares discriminant (OPLS-DA) model, the metabolites with greater contribution (VIP>1.0) between patients with benign pulmonary nodules and patients with malignant pulmonary nodules were found. As shown in Figure 1, the black points are the metabolites with VIP>1.0, and the gray marked points are the metabolites with VIP<1.0. Then through T-test test, set p<0.05 as the standard of significant difference. Finally, differential metabolites with VIP>1.0 and p<0.05 were screened out, which may be potential metabolic biomarkers for the diagnosis of benign and malignant pulmonary nodules.

The potential metabolic markers of benign and malignant pulmonary nodules screened by the above analysis, the molecular mass and molecular formula of the markers were estimated according to their retention time, primary and secondary mass spectra, and compared with the spectral information in the metabolite spectral database , so as to qualitatively identify the metabolites. Finally, the structures of metabolic markers were verified by purchasing standard products and comparing their molecular weights, chromatographic retention times, and corresponding multilevel MS fragmentation profiles.

Using the binary logistic regression forward stepwise method to screen 12 differential metabolites can diagnose and distinguish benign and malignant pulmonary nodules:

4-Hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamic acid, day Paragine, formononetin, alanine, kynuric acid.

The specific information of metabolites is shown in Table 3 and Table 4 below:

Table 3 12 serum metabolic markers

Table 4 Metabolites in patients with malignant pulmonary nodules VS patients with benign pulmonary nodules

Chinese name	multiple of difference	VIP	P value
4-Hydroxy-L-proline	0.82	1.88	0.010
hyodeoxycholic acid	0.85	1.81	0.037
Xanthine	0.87	1.75	0.033
2-Hydroxy-6-aminopurine	0.90	1.33	0.041
5,6-Dihydrothymine	0.94	1.46	0.038
Isobutyrylcarnitine	1.10	1.40	0.025
Guanine	0.88	1.55	0.013
glutamic acid	0.86	1.21	0.026
Asparagine	1.07	1.22	0.044
Formononetin	1.02	1.05	0.047
Alanine	1.05	1.13	0.039
Kynuric acid	0.95	1.02	0.042

The receiver operating characteristic curve (ROC) was used to analyze the diagnostic performance of metabolites for benign and malignant pulmonary nodules. The results showed that 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamine Acid, asparagine, formononetin, alanine, and kynuric acid, which are 12 differential metabolites, have a strong ability to distinguish benign and malignant pulmonary nodules individually, and the areas under the ROC curve (AUC) are all greater than 0.6. It has clinical diagnostic significance.

When these 12 differential metabolites are combined for diagnosis, the AUC is further improved, and the AUC value of the 12 joint diagnosis of benign and malignant pulmonary nodules reaches 0.855. Under the optimal cutoff value, the sensitivity and specificity are 83.3% and 82.1 respectively. %, see Table 5 and Table 6 below for specific statistics:

Table 5 AUC values of individual metabolites for the diagnosis of benign and malignant pulmonary nodules

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-Dihydrothymine	0.711	70.1%	69.2%
6	Isobutyrylcarnitine	0.705	69.2%	68.5%
7	Guanine	0.699	68.8%	67.7%
8	glutamic acid	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	Kynuric acid	0.611	60.5%	60.1%

Table 6 The AUC value of any metabolite combined for the diagnosis of benign and malignant pulmonary nodules

joint number	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%

Further examples of diagnostic model values for some preferred metabolic marker combinations:

A further preferred combination of metabolic markers is: 4-hydroxy-L-proline, hyodeoxycholic acid, and xanthine, to construct a diagnostic model for distinguishing benign and malignant pulmonary nodules. The AUC value of these three metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.803. Under the optimal cutoff value, the sensitivity and specificity were 79.5% and 78.3%, respectively.

A further preferred combination of metabolic markers is: 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, Construct a model for diagnosing benign and malignant pulmonary nodules.

The AUC value of these 6 metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.822. Under the optimal cutoff value, the sensitivity and specificity were 80.0% and 78.8%, respectively.

A further preferred combination of metabolic markers is: 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, Guanine, glutamic acid, and asparagine were used to construct a model for diagnosing benign and malignant pulmonary nodules.

The AUC value of these 9 metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.836. Under the optimal cutoff value, the sensitivity and specificity were 81.4% and 80.2%, respectively.

Example 2

The research objects of this example included 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 had no other history of malignant tumors, other major systemic diseases, and no chronic medical history of long-term medication. The time of blood collection was in the 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.

The detection conditions and data analysis methods of this example are the same as those of Example 1, and 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, kynuric acid, for benign and malignant lungs Nodule diagnosis. The above 12 metabolic markers have significant changes in patients with pulmonary malignant nodules, and the specific information is shown in Table 7:

Table 7 Metabolites in patients with malignant pulmonary nodules VS in patients with benign pulmonary nodules

Chinese name	multiple of difference	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-Dihydrothymine	0.93	1.56	0.007
Isobutyrylcarnitine	1.10	1.32	0.028
Guanine	0.87	1.60	0.014
glutamic acid	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
Kynuric acid	0.96	1.04	0.043

These 12 differential metabolites have a 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.

When the 12 differential metabolites are combined for diagnosis, the AUC is further improved, and the AUC value of the 12 combined diagnosis of benign and malignant pulmonary nodules reaches 0.881. Under the optimal cutoff value, the sensitivity and specificity are 86.1% and 86.9 respectively %. See Table 8 and Table 9 for the AUC of single and any combination of 2-11 metabolites for diagnosis:

Table 8 AUC values of individual metabolites for the diagnosis of benign and malignant pulmonary nodules

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-Dihydrothymine	0.719	70.1%	70.9%
6	Isobutyrylcarnitine	0.710	69.5%	70.2%
7	Guanine	0.705	68.4%	69.7%
8	glutamic acid	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	Kynuric acid	0.615	60.9%	61.4%

Table 9 The AUC value of any differential metabolite combined for the diagnosis of benign and malignant pulmonary nodules

joint number	AUC	sensitivity	specificity
two	≥0.693	≥68.2%	≥68.5%
three	≥0.708	≥69.5%	≥70.2%
four	≥0.722	≥70.4%	≥71.1%
five	≥0.734	≥71.2%	≥72.1%
six	≥0.764	≥72.9%	≥74.2%

seven	≥0.783	≥74.8%	≥76.4%
eight	≥0.808	≥78.8%	≥79.4%
nine	≥0.821	≥80.3%	≥81.4%
ten	≥0.844	≥82.5%	≥83.6%
eleven	≥0.868	≥84.1%	≥85.3%

Further examples of diagnostic model values for some preferred metabolic marker combinations:

Further optimize the combination of metabolic markers: 4-hydroxy-L-proline, xanthine, hyodeoxycholic acid, and construct a model for diagnosing benign and malignant pulmonary nodules.

The AUC value of these three metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.815. Under the optimal cutoff value, the sensitivity and specificity were 80.2% and 78.8%, respectively.

Further preferred combination of metabolic markers: 4-hydroxy-L-proline, xanthine, hyodeoxycholic acid, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine to construct a diagnosis Distinguish between benign and malignant pulmonary nodule models.

The AUC value of these 6 metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.833. Under the optimal cutoff value, the sensitivity and specificity were 81.5% and 80.6%, respectively.

A further preferred combination of metabolic markers is: 4-hydroxy-L-proline, xanthine, hyodeoxycholic acid, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, Guanine, glutamic acid and formononetin were used to construct a model for diagnosing benign and malignant pulmonary nodules.

The AUC value of these 9 metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.847. Under the optimal cutoff value, the sensitivity and specificity were 82.1% and 81.8%, respectively.

Example 3

This embodiment provides a detection kit, comprising:

(1) Standards for metabolic markers: hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, Guanine, glutamic acid, asparagine, formononetin, alanine, kynuuric acid, individually packaged or mixed packaged.

(2) Solvent:

Pure methanol and 50% acetonitrile in water for sample extraction.

50% acetonitrile in water can be used as a solvent to dissolve the standards.

(3) Internal standard: L-phenylalanine.

It must be pointed out here that the above examples are only limited to further elaboration and description of the technical solution of the present invention, and are not further limitations on the technical solution of the present invention. The method of the present invention is only a preferred implementation, not a Used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (10)

1. A metabolic marker for diagnosing or monitoring benign and malignant pulmonary nodules, characterized in that the metabolic marker is at least selected from 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2- At least one of hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamic acid, asparagine, formononetin, alanine, kynuric acid .
2. The metabolic marker for diagnosing or monitoring benign and malignant pulmonary nodules according to claim 1, wherein the metabolic marker is at least selected from the group consisting of 4-hydroxyl-L-proline, hyodeoxycholic acid, At least one of xanthines.
3. The metabolic marker for diagnosing or monitoring benign and malignant pulmonary nodules according to claim 2, wherein the metabolic marker is also selected from 2-hydroxyl-6-aminopurine, 5,6-dihydro At least one of thymine, isobutyrylcarnitine, guanine, and glutamic acid.
4. The metabolic marker for diagnosing or monitoring benign and malignant pulmonary nodules according to claim 2 or 3, wherein the metabolic marker is also selected from asparagine, formononetin, alanine , at least one of kynuric acid.
5. Application of the metabolic markers for diagnosing or monitoring benign and malignant pulmonary nodules described in any one of claims 1 to 4 in the preparation of metabolite databases, reagent products or kits for diagnosing or monitoring benign and malignant pulmonary nodules .
6. A reagent product or detection kit, characterized in that it includes the standard product of metabolic markers for diagnosing or monitoring benign and malignant pulmonary nodules according to any one of claims 1 to 4.
7. The reagent product or detection kit according to claim 6, further comprising an extraction reagent and an internal standard.
8. The reagent product or detection kit according to claim 7, wherein the internal standard is L-phenylalanine.
9. A screening method for metabolic markers for diagnosing or monitoring pulmonary benign and malignant nodules according to any one of claims 1 to 4, characterized in that it comprises the following steps:

   The samples of benign pulmonary nodules and malignant pulmonary nodules were collected respectively;

   Randomly select 20% of the samples from the benign pulmonary nodule group and the malignant pulmonary nodule group, use the metabolomics method of enhanced ion scanning mass spectrometry and time-of-flight mass spectrometry combined with multiple reaction monitoring acquisition mode, and integrate the local standard database for lung Construction of serum metabolite database for benign and malignant nodules;

   The collected serum samples were analyzed by constructing a serum metabolite database of benign and malignant pulmonary nodules and detected by LC-MS, and the original mass spectrometry data of each serum sample were obtained;

   Use MultiQuant software to preprocess and correct the original mass spectrum data according to the mass-to-charge ratio and retention time; calculate the peak area according to the mass spectrum peak intensity to obtain the relative content information of metabolites;

   Perform multivariate statistical orthogonal-partial least squares discriminant analysis on the relative content information of metabolites, and obtain candidate differential metabolites according to the screening criteria that the variable weight value is greater than 1 and the P value of univariate statistical analysis is less than 0.05;

   Perform binary logistic regression modeling on candidate differential metabolites, screen excellent metabolites and their combinations to diagnose patients with benign and malignant pulmonary nodules, and perform receiver operating characteristic curve analysis on the screened excellent metabolites and their combinations to determine Metabolic markers for diagnosis or monitoring of benign and malignant pulmonary nodules.
10. The screening method for metabolic markers for diagnosing or monitoring benign and malignant pulmonary nodules according to claim 9, wherein the conditions for LC-MS detection are:

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

    Mobile phase: Phase A is an aqueous solution containing 0.1% acetic acid, phase B is an acetonitrile solution containing 0.1% acetic acid, and the flow rate is 0.4mL/min;

    The elution gradient program is:

    0min, the volume ratio of phase A to phase B is 95:5;

    11.0min, the volume ratio of phase A and phase B is 10:90;

    12.0min, the volume ratio of phase A and phase B is 10:90;

    12.1min, the volume ratio of phase A to phase B is 95:5;

    14.0min, the volume ratio of phase A to phase B is 95:5.

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