WO2023082820A1 - Marker for lung adenocarcinoma diagnosis and application thereof - Google Patents

Abstract

The present invention provides a metabolic marker for diagnosing or monitoring lung adenocarcinoma, a screening method therefor and an application thereof. The metabolic marker system is selected from one or more combinations of D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L-proline, hexadecanedioic acid, guanine, glutamic acid, creatine, alanine and kynuric acid. The metabolic marker provided by the present invention can accurately diagnose patients with lung adenocarcinoma, can distinguish patients with early lung adenocarcinoma from healthy persons, is high in sensitivity and strong in specificity, can well replace the existing tissue biopsy and imaging diagnosis modes, and reduces trauma and radiation risks.

Description

Markers for the diagnosis of lung adenocarcinoma and its application technical field

The invention relates to the technical field of detection and diagnosis, in particular to a marker for lung adenocarcinoma diagnosis and its screening method and application.

Background technique

According to histopathology, lung cancer is mainly divided into two types: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The proportion of non-small cell lung cancer is as high as 85% to 90%. NSCLC further includes lung adenocarcinoma, lung squamous cell carcinoma, and large cell carcinoma. Compared with small cell carcinoma, the growth and division of cancer cells are slower, and the spread and metastasis are relatively late. Among them, the most common subtype of lung cancer is lung adenocarcinoma. Clinically, non-small cell lung cancer is often diagnosed at an advanced stage. More than half of NSCLC patients die within 1 year after diagnosis, and the 5-year survival rate is less than 20%. However, the 5-year survival rate of patients with early lung cancer can be as high as 90%. Therefore, early diagnosis of lung cancer is an important method for lung cancer patients to obtain a good prognosis and reduce mortality.

The means of clinical diagnosis of lung cancer mainly rely on ultrasound imaging and lung puncture. Among them, the sensitivity of ultrasound diagnosis is low, and lung puncture is harmful to the lungs of patients, which is risky and difficult to promote. As a result, many patients are not diagnosed until the decompensated stage of lung cancer. Studies have found that gene molecules can be used as markers for the diagnosis of lung cancer, but the sensitivity and specificity of single gene diagnosis need to be improved urgently.

In addition, data from the National Lung Screening Trial (NLST) in the United States showed that early lung cancer screening in high-risk groups using low-dose computed tomography (LDCT) can reduce 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.

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, but in the early stages, 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.) . A large number of studies have shown that the occurrence and development of tumors are closely related to energy metabolism, such as the Warburg effect (Warburg effect), tricarboxylic acid cycle (TCA), glycolysis pathway, etc., which provide energy requirements for cancer cell proliferation (Vander Heiden , MG., Cantley, LC., Thompson, CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 2009; 324(5930): 1029-33.). Therefore, blood- or urine-based biomarkers or multiple marker combinations can complement the deficiencies of LDCT screening and may be able to make a significant contribution in the implementation of lung cancer screening.

In the past ten years, some scientists have also tried to apply metabolomics in the fields of lung cancer screening, diagnosis, and prognosis. Studies have discovered some metabolites and metabolic pathways that change during the occurrence and development of lung cancer, and obtained some reliable data. Lung cancer diagnostic biomarkers, such as 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. , Samir, H., Brian, D.et Al.DiaceTylSpermine is a Novel Prediagnostic Serum Biomarker for Non-Small-Cell LUNG CANGER and Has AdditIVANCE PRO-SURFActan T propein 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 metabolite information in public databases. Most of the studies have small sample sizes and a single source of samples. The metabolites obtained from the screening are not specific for early lung cancer screening and have little value in actual clinical applications. big.

disclosure of invention

Based on this, it is necessary to provide a marker for the diagnosis of lung adenocarcinoma and its screening method and application. The metabolic marker obtained through screening has great clinical application value, and is especially suitable for the prediction and diagnosis of early lung cancer.

The present invention adopts following technical scheme:

The present invention provides a marker for diagnosing or monitoring lung adenocarcinoma, wherein the metabolic marker is at least selected from D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L - at least one of proline, hexadecanedioic acid, guanine, glutamic acid, creatine, alanine, kynuric acid.

The present invention provides a marker for diagnosing or monitoring lung adenocarcinoma, the combination of metabolic markers is at least selected from D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy- At least one of L-proline, hexadecanedioic acid, and guanine. Further, the marker combination is also selected from at least one of glutamic acid, creatine, alanine, and kynuric acid.

In the ROC curve evaluation method, the AUC value of the area under the ROC curve of a single metabolic marker in the present invention is 0.7-0.9. The performance of multiple metabolite groups is significantly better than that of a single metabolite, and the area under the ROC curve AUC value is 0.86-0.99, which can effectively diagnose patients with early lung adenocarcinoma.

Application of the above-mentioned metabolic markers for diagnosing or monitoring lung adenocarcinoma in the preparation of a metabolite database, reagent product or kit for diagnosing or monitoring lung adenocarcinoma.

The present invention also provides a reagent product or kit, including the above-mentioned standard product of metabolic markers for early diagnosis or monitoring of lung adenocarcinoma.

Further, the reagent product or kit also includes solvents and/or internal standards for extracting and enriching the metabolic markers.

The present invention also provides a method for screening metabolic markers for diagnosing or monitoring lung adenocarcinoma, comprising the following steps:

The samples of the lung adenocarcinoma group and the serum samples of the healthy group were collected separately, and the TNM staging of the patients in the lung adenocarcinoma group included stage I, stage II, stage III, and stage IV;

Randomly select 20% of the samples from the lung adenocarcinoma group and the healthy group, adopt 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 adenocarcinoma serum metabolite database Construction; using the construction of lung adenocarcinoma serum metabolite database and LC-MS detection to analyze the collected serum samples to obtain the original mass spectrometry data of each serum sample; use MultiQuant software to predict the original mass spectrometry data according to the mass-to-charge ratio and retention time Processing and correction; 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 value greater than 1 and univariate statistical analysis The screening criteria with a P value less than 0.05 were used to obtain candidate differential metabolites; the candidate differential metabolites were subjected to binary logistic regression modeling, and excellent metabolites and their combinations were screened for diagnosis of lung adenocarcinoma patients. Receiver operating characteristic curve analysis was performed in combination to identify metabolic markers for diagnosis or monitoring of lung adenocarcinoma.

Compared with the prior art, the 10 metabolic markers screened by the present invention can effectively diagnose lung adenocarcinoma patients. The invention can realize the diagnosis of lung adenocarcinoma only by taking blood for detection, without additional collection of tissue samples, can well replace the existing tissue biopsy and imaging diagnosis modes, and reduce trauma and radiation risks.

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
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" in the present invention refers to the mass spectrometric detection of a large number of related 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 method for screening metabolic markers for lung adenocarcinoma diagnosis, comprising the following steps:

S1, collecting samples

In this study, after obtaining the consent of the patients, peripheral vein samples from 242 patients with lung adenocarcinoma (lung adenocarcinoma group, including 172 patients with early lung adenocarcinoma) and 150 healthy people (healthy group) were collected from Shanghai Chest Hospital. blood serum. Among them, the diagnostic standard for patients with lung adenocarcinoma is confirmed by postoperative pathology. According to TNM staging, patients with stage I and II lung adenocarcinoma were defined as patients with early-stage lung adenocarcinoma. All patients with lung adenocarcinoma and samples from the non-lung adenocarcinoma group had no history of other malignant tumors, no 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 and analysis

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 1.8μm, 2.1mm*100mm; column temperature is 40°C; injection volume is 2μL.

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 A phase and B phase is 95:5; 14.0min, the volume ratio of A phase and B phase is 95:5V/V. Flow rate 0.4mL/min.

Determine the mass spectrometry conditions: electrospray ionization (ESI) temperature 500°C, mass spectrometer voltage 5500V (positive) or -4500V (negative), ion source gas I (GS I) 55psi, gas II (GS II) 60psi, gas curtain Gas (curtain gas, CUR) 25psi, collision-induced ionization (collision-activated dissociation, CAD) parameters were 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: 20% of the samples in the lung adenocarcinoma group and the healthy group were randomly selected, and enhanced ion scanning mass spectrometry (MIM-EPI) and time-of-flight mass spectrometry (TOF) were used. Combined with the metabolomics method of multiple reaction monitoring acquisition mode, the local standard database was integrated to construct the lung adenocarcinoma serum metabolite database. The collected serum samples were analyzed to obtain the original mass spectrometry data of each serum sample.

(3) Spectrum peak area preprocessing and integration

Based on the lung adenocarcinoma serum metabolite database, 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 instrument was stable during the detection process.

(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, we searched for metabolites with greater contribution (VIP>1.0) between healthy people and lung adenocarcinoma patients. As shown in Figure 1, the points marked in black are metabolites with VIP>1.0, and the points marked in light gray are 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.

The potential lung adenocarcinoma metabolic markers screened by the above analysis, according to their retention time, primary and secondary mass spectrometry, speculate the molecular mass and molecular formula of the marker, and compare it with the spectral information in the metabolite spectrum database, so as to Metabolites were qualitatively identified. 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.

Ten differential metabolites screened by binary logistic regression forward stepwise method can diagnose and distinguish lung adenocarcinoma patients from healthy people: D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy -L-proline, hexadecanedioic acid, guanine, glutamic acid, creatine, alanine, kynuric acid. See Table 3 to Table 5 for specific information on metabolites:

Table 3 10 serum metabolic markers

Table 4 Metabolites of lung adenocarcinoma patients VS healthy people

Chinese name	multiple of difference	VIP	P value
D-galactose	1.31	2.06	2.71E-05
high citrulline	1.25	1.85	5.06E-04
N6-Acetyl-L-lysine	0.87	1.88	3.03E-02
4-Hydroxy-L-proline	1.15	1.35	6.77E-03
hexadecanedioic acid	0.84	1.41	3.88E-02
Guanine	0.92	1.22	2.11E-02
glutamic acid	0.86	1.05	4.50E-02
Creatine	1.18	1.16	3.04E-02
Alanine	1.02	1.08	4.63E-02
Kynuric acid	0.94	1.04	3.48E-02

Table 5 Metabolites of early lung adenocarcinoma patients VS healthy people

Chinese name	multiple of difference	VIP	P value
D-galactose	1.43	2.11	2.50E-05
high citrulline	1.21	1.78	3.16E-03
N6-Acetyl-L-lysine	0.89	1.66	6.20E-02
4-Hydroxy-L-proline	1.17	1.35	1.64E-03
hexadecanedioic acid	0.84	1.41	1.56E-02
Guanine	0.91	1.22	3.82E-02
glutamic acid	0.87	1.05	4.54E-02
Creatine	1.13	1.16	3.12E-02
Alanine	1.06	1.08	4.65E-02
Kynuric acid	0.98	1.04	9.77E-02

The diagnostic performance of metabolites in patients with lung adenocarcinoma was analyzed by receiver operating characteristic curve (ROC). The results showed that D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L-proline, hexadecanedioic acid, guanine, glutamic acid, creatine, propane The 10 differential metabolites such as aminoacid and kynuric acid have a strong ability to diagnose and distinguish lung adenocarcinoma, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance.

When these 10 differential metabolites are combined for diagnosis, the AUC is further improved, and the AUC value of the 10 joint diagnosis of lung adenocarcinoma reaches 0.988. Under the optimal cutoff value, the sensitivity and specificity are 97.7% and 94.0% respectively; diagnosis The AUC of early lung adenocarcinoma reached 0.995, and the sensitivity and specificity were 98.2% and 95.7% respectively under the optimal cutoff value. Please refer to Table 6 to Table 9 below for specific statistics:

Table 6 AUC values of individual metabolites for the diagnosis of lung adenocarcinoma

serial number	Chinese name	AUC	sensitivity	specificity
1	D-galactose	0.858	85.5%	85.1%
2	high citrulline	0.844	85.2%	84.3%
3	N6-Acetyl-L-lysine	0.832	83.1%	83.0%
4	4-Hydroxy-L-proline	0.798	80.4%	81.1%
5	hexadecanedioic acid	0.773	79.3%	77.8%
6	Guanine	0.761	78.5%	77.6%
7	glutamic acid	0.734	72.5%	75.5%
8	Creatine	0.720	71.8%	74.3%
9	Alanine	0.708	71.7%	72.5%
10	Kynuric acid	0.703	70.3%	71.8%

Table 7 The AUC value of any differential metabolite combined for the diagnosis of lung adenocarcinoma

joint number	AUC	sensitivity	specificity
any two	≥0.752	≥74.2%	≥73.3%
any three	≥0.775	≥76.4%	≥75.0%
any four	≥0.794	≥78.1%	≥76.5%
any five	≥0.835	≥81.9%	≥80.2%

any six	≥0.877	≥86.2%	≥85.1%
any seven	≥0.903	≥88.9%	≥87.2%
any eight	≥0.934	≥92.0%	≥90.1%
any nine	≥0.951	≥93.6%	≥92.3%

Example metabolic marker combinations are as follows:

A further preferred combination of metabolic markers is: D-galactose, homocitrulline, and N6-acetyl-L-lysine to construct a model for diagnosing lung adenocarcinoma. The AUC value of these three metabolites combined for the diagnosis of lung adenocarcinoma reached 0.925. Under the optimal cutoff value, the sensitivity and specificity were 90.8% and 90.3%, respectively.

A further preferred combination of metabolic markers is: D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L-proline, hexadecanedioic acid, and guanine to construct a diagnostic lung gland cancer model. The AUC value of these 6 metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.956. Under the optimal cutoff value, the sensitivity and specificity were 94.1% and 93.3%, respectively.

Table 8 The AUC value of a single metabolite for the diagnosis of early lung adenocarcinoma

serial number	Chinese name	AUC	sensitivity	specificity
1	D-galactose	0.865	87.5%	84.1%
2	high citrulline	0.857	86.2%	83.3%
3	N6-Acetyl-L-lysine	0.838	84.8%	83.0%
4	4-Hydroxy-L-proline	0.816	82.6%	81.1%
5	hexadecanedioic acid	0.795	80.4%	77.8%
6	Guanine	0.766	78.5%	75.6%
7	glutamic acid	0.754	76.5%	74.3%
8	Creatine	0.741	75.2%	73.7%
9	Alanine	0.722	72.5%	71.8%
10	Kynuric acid	0.709	71.7%	70.5%

Table 9 The AUC value of any differential metabolites combined for the diagnosis of early lung adenocarcinoma

joint number	AUC	sensitivity	specificity
any two	≥0.766	≥75.6%	≥74.8%
any three	≥0.790	≥77.8%	≥76.5%
any four	≥0.818	≥80.3%	≥79.5%

any five	≥0.841	≥83.1%	≥81.8%
any six	≥0.885	≥86.2%	≥85.1%
any seven	≥0.916	≥90.2%	≥89.3%
any eight	≥0.947	≥93.0%	≥92.1%
any nine	≥0.971	≥95.6%	≥94.3%

Example metabolic marker combinations are as follows:

A further preferred combination of metabolic markers is: D-galactose, homocitrulline, and N6-acetyl-L-lysine to construct a model for diagnosing early lung adenocarcinoma. The AUC value of these three metabolites combined to diagnose early lung adenocarcinoma reached 0.925. Under the optimal cutoff value, the sensitivity and specificity were 90.8% and 90.3%, respectively.

A further preferred combination of metabolic markers is: D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L-proline, hexadecanedioic acid, guanine, constructing an early diagnosis Lung adenocarcinoma model. The combined AUC value of these 6 metabolites in the diagnosis of early lung adenocarcinoma reached 0.956. Under the optimal cutoff value, the sensitivity and specificity were 94.1% and 93.3%, respectively.

Example 2: Validation of diagnostic markers for lung adenocarcinoma

The subjects of this study included 266 serum samples of patients with lung adenocarcinoma from 2 independent medical centers, including 118 patients with early lung adenocarcinoma; and 149 serum samples of healthy people, which were from the same source as the feature screening samples (150 cases) . Among them, the diagnostic standard of lung adenocarcinoma is lung adenocarcinoma diagnosed by postoperative pathology; healthy people are healthy people without lung diseases after physical examination. All lung adenocarcinoma patients and healthy samples had no history of any other malignant tumors, no other major systemic diseases, and no chronic medical history of long-term medication. According to TNM staging, patients with stage I and II lung adenocarcinoma were defined as patients with early-stage lung adenocarcinoma.

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 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 10 kinds: D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy - L-proline, hexadecanedioic acid, guanine, glutamic acid, creatine, alanine, kynuric acid for the diagnosis of lung adenocarcinoma. These 10 differential metabolites are individually used to diagnose and distinguish between patients with lung adenocarcinoma and healthy people. The ability of early lung adenocarcinoma patients and healthy people is relatively strong, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance; When the differential metabolites are combined for diagnosis, the AUC is further improved, and the AUC value of 10 joint diagnosis of lung adenocarcinoma reaches 0.955. Under the optimal cutoff value, the sensitivity and specificity are 91.5% and 93.6%, respectively; the diagnosis of early lung adenocarcinoma The AUC of cancer was 0.983, and the sensitivity and specificity were 93.2% and 96.0% at the best cutoff value. See Table 11 to Table 14 for the AUC of a single and any combination of 2 to 9 metabolites used for diagnosis:

Table 11 AUC values of individual metabolites for the diagnosis of lung adenocarcinoma

serial number	Chinese name	AUC	sensitivity	specificity
1	D-galactose	0.842	83.2%	85.1%
2	high citrulline	0.832	82.5%	84.8%
3	N6-Acetyl-L-lysine	0.825	82.2%	83.9%
4	4-Hydroxy-L-proline	0.801	79.5%	82.2%
5	hexadecanedioic acid	0.762	75.3%	79.5%
6	Guanine	0.755	74.4%	77.6%
7	glutamic acid	0.733	72.5%	75.8%
8	Alanine	0.716	71.1%	74.0%
9	Kynuric acid	0.711	70.7%	73.2%
10	Creatine	0.705	70.1%	72.5%

Table 12 The AUC value of any differential metabolite combined for the diagnosis of lung adenocarcinoma

joint number	AUC	sensitivity	specificity
any two	≥0.756	≥71.8%	≥73.6%
any three	≥0.780	≥74.0%	≥76.1%
any four	≥0.797	≥76.1%	≥77.5%
any five	≥0.825	≥78.9%	≥80.4%
any six	≥0.857	≥81.2%	≥83.1%
any seven	≥0.888	≥84.9%	≥86.2%
any eight	≥0.913	≥88.0%	≥90.3%
any nine	≥0.931	≥90.6%	≥91.5%

Further optimized metabolic markers D-galactose, homocitrulline, and N6-acetyl-L-lysine were used to construct a model for diagnosing lung adenocarcinoma. The AUC value of these three metabolites combined for the diagnosis of lung adenocarcinoma reached 0.913. Under the optimal cutoff value, the sensitivity and specificity were 88.7% and 89.6%, respectively.

Further preferred metabolic markers D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L-proline, hexadecanedioic acid, and guanine were used to construct a model for diagnosing lung adenocarcinoma. The AUC value of these 6 metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.931. Under the optimal cutoff value, the sensitivity and specificity were 90.2% and 91.4%, respectively.

Table 13 The AUC value of a single metabolite for the diagnosis of early lung adenocarcinoma

serial number	Chinese name	AUC	sensitivity	specificity
1	D-galactose	0.858	83.2%	85.1%
2	high citrulline	0.842	82.5%	84.8%
3	4-Hydroxy-L-proline	0.829	82.2%	83.9%
4	N6-Acetyl-L-lysine	0.815	79.5%	82.2%
5	Guanine	0.788	75.3%	79.5%
6	hexadecanedioic acid	0.761	74.4%	77.6%
7	glutamic acid	0.745	72.5%	75.8%
8	Alanine	0.728	71.1%	74.0%
9	Kynuric acid	0.715	70.7%	73.2%
10	Creatine	0.708	70.1%	72.5%

Table 14 The AUC value of any differential metabolite combined for the diagnosis of early lung adenocarcinoma

joint number	AUC	sensitivity	specificity
any two	≥0.759	≥72.6%	≥74.5%
any three	≥0.781	≥74.8%	≥76.1%
any four	≥0.803	≥76.4%	≥78.7%
any five	≥0.832	≥79.1%	≥80.8%
any six	≥0.875	≥82.2%	≥84.4%
any seven	≥0.906	≥85.2%	≥87.3%
any eight	≥0.937	≥88.5%	≥91.1%
any nine	≥0.951	≥91.7%	≥93.5%

Metabolic markers D-galactose, homocitrulline, and N6-acetyl-L-lysine were further optimized to construct a model for diagnosing early lung adenocarcinoma. The AUC value of these three metabolites combined to diagnose early lung adenocarcinoma reached 0.916. Under the optimal cutoff value, the sensitivity and specificity were 89.3% and 90.1%, respectively.

Further optimization of metabolic markers D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L-proline, hexadecanedioic acid, and guanine to construct a model for diagnosing early lung adenocarcinoma . The AUC value of these 6 metabolites combined to diagnose early lung adenocarcinoma reached 0.951, and the sensitivity and specificity were 91.8% and 93.2% respectively under the optimal cutoff value.

Embodiment 3 detection kit

This embodiment provides a detection kit, comprising:

(1) Standards for metabolic markers: D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L-proline, hexadecanedioic acid, guanine, gluten amino acid, creatine, alanine, kynuric acid, each standard product is packaged separately or a mixed solution of standard products is packaged.

(2) Extraction solvent:

100% pure methanol and 50% acetonitrile in water for sample preparation;

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 lung adenocarcinoma, characterized in that the metabolic marker is at least selected from D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy- At least one of L-proline, hexadecanedioic acid, guanine, glutamic acid, creatine, alanine, and kynuric acid.
2. The metabolic marker for diagnosing or monitoring lung adenocarcinoma according to claim 1, wherein the metabolic marker is selected from D-galactose, homocitrulline, N6-acetyl-L-lysine at least one of.
3. The metabolic marker for diagnosing or monitoring lung adenocarcinoma according to claim 2, wherein the metabolic marker is also selected from 4-hydroxy-L-proline, hexadecandioic acid, guanine at least one of the
4. The metabolic marker for diagnosing or monitoring lung adenocarcinoma according to claim 2 or 3, wherein the metabolic marker is also selected from at least one of glutamic acid, creatine, alanine, and kynuric acid. A sort of.
5. Application of the metabolic markers for diagnosing or monitoring lung adenocarcinoma according to any one of claims 1 to 4 in preparing metabolite databases, reagent products or kits for diagnosing or monitoring lung cancer.
6. A reagent product or kit, characterized in that it comprises the standard product of metabolic markers for diagnosing or monitoring lung adenocarcinoma according to any one of claims 1 to 4.
7. The reagent product or kit according to claim 6, further comprising an internal standard and/or an extraction reagent.
8. The reagent product or kit according to claim 7, wherein the internal standard is L-phenylalanine.
9. A method for screening metabolic markers for diagnosing or monitoring lung adenocarcinoma according to any one of claims 1 to 4, characterized in that it comprises the steps of:

   The samples of the lung adenocarcinoma group and the serum samples of the healthy group were collected separately, and the TNM staging of the patients in the lung adenocarcinoma group included stage I, stage II, stage III, and stage IV;

   Randomly select 20% of the samples from the lung adenocarcinoma group and the healthy group, adopt 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 adenocarcinoma serum metabolite database Construct;

   The collected serum samples were analyzed by constructing a lung adenocarcinoma serum metabolite database and LC-MS detection, and the original mass spectrometry data of each serum sample were obtained;

   Use MultiQuant software to preprocess and correct the original mass spectrometry 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; conduct multivariate statistical orthogonal-partial least squares discriminant analysis on the relative content information of metabolites, and according to the variable weight value greater than 1 and the P value of univariate statistical analysis less than 0.05 screening criteria to obtain candidate differential metabolites;

   Carry out binary logistic regression modeling of candidate differential metabolites, screen excellent metabolites and their combinations for diagnosis of lung adenocarcinoma patients, perform receiver operating characteristic curve analysis on screened excellent metabolites and their combinations, and determine them for diagnosis Or monitor metabolic markers in lung adenocarcinoma.
10. The method for screening metabolic markers for diagnosis or monitoring lung cancer 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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