CN108931587B - Method for quantitatively screening diagnostic biomarkers of NSCLC - Google Patents

Abstract

The invention relates to the technical field of diagnosis and treatment of NSCLC, in particular to a method for quantitatively screening diagnostic biomarkers of NSCLC, which comprises the following steps: s1, comparing the metabolic fingerprint changes of the NSCLC group and the normal control group; s2, confirming that Phosphatidylcholine (PCs) is a NSCLC related differential metabolic marker; s3, establishing a quantitative detection method of NSCLC serum phosphatidylcholine, and confirming that 12 phosphatidylcholines are diagnostic biomarkers of NSCLC; s4, confirming that the 12 phosphatidylcholine combinations are used as serum diagnosis biomarkers of NSCLC. The method for quantitatively screening the diagnostic biomarker of the NSCLC is beneficial to the diagnosis and treatment of the NSCLC, thereby improving the survival rate of patients with the lung cancer and having obvious social and economic benefits.

Description

Method for quantitatively screening diagnostic biomarkers of NSCLC

Technical Field

The invention relates to the technical field of diagnosis and treatment of NSCLC, in particular to a method for quantitatively screening diagnostic biomarkers of NSCLC.

Background

NSCLC, non-small cell lung cancer, is a malignant tumor that seriously harms human health and life. Current studies have found that 5-year survival rates in NSCLC patients are only about 15%. Early screening becomes a key for preventing and treating NSCLC, and the exploration and establishment of a simple, rapid, high-sensitivity and strong-specificity early diagnosis technology and the like are urgent needs in clinical medicine. The diagnosis biomarker of the NSCLC is mined and confirmed by applying metabonomics technology, an adaptive target analysis method is established, and the diagnosis biomarker of the NSCLC is quantitatively detected, so that the diagnosis and treatment of the NSCLC are facilitated, and the survival rate of a patient with the lung cancer is improved.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: in order to solve the problems in the background art, a method for quantitatively screening a diagnostic biomarker of NSCLC is provided, clinical application transformation research is further developed, the method is expected to become an ideal novel serum tumor metabolic marker of NSCLC, technical support is provided for developing a rapid, sensitive and basic NSCLC diagnostic kit, and the clinical application and development prospect is wide.

The technical scheme adopted by the invention for solving the technical problems is as follows: a method of quantitatively screening a diagnostic biomarker for NSCLC comprising the steps of: s1, performing non-target metabonomics analysis on metabolites in blood serums of patients in a non-small cell lung cancer NSCLC group and a normal control group by jointly adopting ultra-high performance liquid chromatography/quadrupole-time-of-flight mass spectrometry combined with UPLC-Q-TOF/MS and gas chromatography-time-of-flight mass spectrometry combined with GC-TOF/MS, and comparing the metabolic fingerprint changes of the NSCLC group and the normal control group by adopting a multivariate statistical method; s2, comparing the metabolite changes in the blood serum of NSCLC and normal control population, carrying out metabolic pathway attribution analysis on the target differential metabolic markers, combining clinical NSCLC diagnostic indexes, and confirming that Phosphatidylcholine (PCs) are NSCLC-related differential metabolic markers according to the diagnostic efficacy of the clinical NSCLC diagnostic indexes; s3, establishing a quantitative detection method of NSCLC serum phosphatidylcholine by adopting UHPLC-Q-TOF/MS technology, and confirming that 12 phosphatidylcholines are diagnostic biomarkers of NSCLC, wherein the content of saturated and monounsaturated phosphatidylcholine PC15:0/18:1, PC18:0/16:0 and PC18:0/20:1 is remarkably increased by P <0.05 in NSCLC, the content of polyunsaturated phosphatidylcholine PC17:2/2:0, PC18:4/3:0, PC15:0/18:2, PC16:0/18:2, PC17:0/18:2, PC18:2/18:2, PC16:0/20:3, PC15:0/22:6 and PC 36 24:4/17:2 is remarkably reduced by P <0.05 in NSCLC; s4, analyzing the diagnostic efficacy of the combined action of the single index and the multiple indexes, and confirming the 12 phosphatidylcholine combinations as serum diagnostic biomarkers of NSCLC.

Further, in the above technical solution, the non-target metabonomics analysis of the metabolites in the serum in step S1 includes non-target UPLC-Q-TOF/MS detection and non-target GC-TOF/MS detection.

Further, in the above technical solution, the metabolites of the serum sample in step S1 further need to be pretreated, and the pretreatment includes UPLC-Q-TOF/MS serum sample pretreatment and GC-TOF/MS serum sample pretreatment.

Further, in the above technical solution, the method for preprocessing the UPLC-Q-TOF/MS serum sample comprises: firstly, thawing and shaking a serum sample stored at a low temperature at room temperature, adding 700 mu L of methanol into 200 mu L of the sample, then adding 40 mu L L-2-chlorophenylalanine, and carrying out vortex oscillation for 30 s; then, carrying out ultrasonic treatment for 10min, and standing for 1h at-20 ℃; centrifuging at 13000r/min for 15min at 4 ℃; and finally, transferring 400 mu L of supernatant into a sample injection bottle for UPLC-Q-TOF/MS detection and analysis.

Further limited, in the above technical solution, the GC-TOF/MS serum sample pretreatment is: firstly, unfreezing a serum sample stored at a low temperature at room temperature, putting 200 mu L of the sample into a 1.5mL EP tube, adding 700 mu L of methanol, adding 40 mu L L-2-chlorophenylalanine, and carrying out vortex oscillation for 10 s; then, centrifuging for 15min at 4 ℃ and 13000 r/min; taking out 300 mu L of supernatant, putting the supernatant into a 2mL sampling bottle, drying the extract in a vacuum concentrator, adding 40 mu L of methoxylamine reagent into the dried metabolite, slightly mixing the mixture evenly, and putting the mixture into an oven to incubate for 30min at the temperature of 80 ℃; to each sample was added 60 μ L BSTFA rapidly and the mixture was incubated at 70 ℃ for 2 h; and finally, cooling to room temperature, adding 10 mu L of FAMEs into the mixed sample, uniformly mixing, and carrying out GC-TOF/MS detection analysis.

The invention has the beneficial effects that: the method for quantitatively screening the diagnostic biomarker of the NSCLC provided by the invention is expected to become an ideal novel serum tumor metabolic marker of the NSCLC by further developing the clinical application transformation research, provides technical support for developing a rapid, sensitive and basic-level NSCLC diagnostic kit in the future, and has wide clinical application and development prospects.

Drawings

In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.

FIG. 1 is the metabolism finger print of ultra high performance liquid chromatography/quadrupole-time of flight mass spectrometry (UHPLC-Q-TOF/MS) in the non-small cell lung cancer group (A: positive ion mode, B: negative ion mode) and normal control group (C: positive ion mode, D: negative ion mode) according to the present invention;

FIG. 2 is a gas chromatography/time-of-flight mass spectrometry (GC-TOF/MS) metabolic fingerprint of a non-small cell lung cancer group (A) and a normal control group (B) according to the present invention;

FIG. 3 is a main component analysis (PCA) score chart of a non-small cell lung cancer group (NSCLC) and a normal Control group (Control) based on ultra-high performance liquid chromatography/quadrupole-time of flight mass spectrometry (UHPLC-Q-TOF/MS) and gas chromatography-time of flight mass spectrometry (GC-TOF/MS) in the present invention (A: UHPLC-Q-TOF/MS positive ion mode, B: UHPLC-Q-TOF/MS negative ion mode, C: GC-TOF/MS);

FIG. 4 is an orthogonal partial least squares-discriminant analysis (OPLS-DA) score plot of a non-small cell lung cancer group (NSCLC) and a normal Control group (Control) based on ultra-high performance liquid chromatography/quadrupole-time of flight mass spectrometry (UHPLC-Q-TOF/MS) and gas chromatography-time of flight mass spectrometry (GC-TOF/MS) in the present invention (A: UHPLC-Q-TOF/MS positive ion mode, B: UHPLC-Q-TOF/MS negative ion mode, C: GC-TOF/MS);

FIG. 5 shows the results of OPLS-DA model replacement test of non-small cell lung cancer (NSCLC) and normal Control (Control) based on ultra-high performance liquid chromatography/quadrupole-time of flight mass spectrometry (UHPLC-Q-TOF/MS) and gas chromatography-time of flight mass spectrometry (GC-TOF/MS) in the present invention (A: UHPLC-Q-TOF/MS positive ion mode, B: UHPLC-Q-TOF/MS negative ion mode, C: GC-TOF/MS);

FIG. 6 is a volcano plot of differential metabolites of the non-small cell lung cancer group (NSCLC) and the normal Control group (Control) based on ultra high performance liquid chromatography/quadrupole-time of flight mass spectrometry (UHPLC-Q-TOF/MS) in accordance with the present invention (A: positive ion mode B: negative ion mode);

FIG. 7 is a volcano plot of differential metabolites of the non-small cell lung cancer (NSCLC) group and the normal Control (Control) group based on gas chromatography-time of flight/mass spectrometry (GC-TOF/MS) in accordance with the present invention;

FIG. 8 is a diagram of an analysis of NSCLC pathway based on UHPLC-Q-TOF/MS and GC-TOF/MS detection in the present invention (A: glycerophospholipid metabolism B: synthesis and degradation of ketone bodies C: galactose metabolism D: caffeine metabolism E: amino sugar and nucleotide sugar metabolism);

FIG. 9 is a diagram of a NSCLC differential metabolite network in accordance with the present invention;

FIG. 10 is a scattergram of the serum levels of Phosphatidylcholine (PC) in the non-small cell lung cancer group and the normal control group according to the present invention (black horizontal line is median);

FIG. 11 is a ROC graph of the combination of Phosphatidylcholine (PC) in the non-small cell lung cancer group and the normal control group according to the present invention.

Detailed Description

Non-target NSCLC metabonomics research (1) instrument and reagent based on UHPLC-Q-TOF/MS and GC-TOF/MS technologies

Instrument

Ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry is adopted, and comprises an Agilent 1290 ultra-high performance liquid chromatograph, an Agilent 6550 quadrupole-time-of-flight mass spectrometer (Agilent, USA) and an AB Sciex 6600 triple time-of-flight mass spectrometer (AB Sciex, USA); gas chromatography-time-of-flight mass spectrometry including Agilent 7890B gas chromatograph (Agilent, usa) and LECO Pegasus HT time-of-flight mass spectrometer (LECO, usa); waters ACQUITY UHPLC HSS T3C 18 chromatography column (1.7 μm,2.1 mm. times.100 mm, Water, USA), Agilent DB-5MS capillary column (0.25 μm, 30 m. times.250 μm, Agilent, USA); heal Force Neoflux 23R low temperature high speed centrifuge (Lixin, China), -80 ℃ ultra low temperature refrigerator (Haier, China).

② reagent

Chromatographic grade methanol and acetonitrile were obtained from merck, L-2-chlorophenylalanine from conifer biosciences, shanghai, bis (trimethylsilyl) trifluoroacetamide (BSTFA) containing 1% TMCS from gis, and ultrapure water was prepared from Milli-Q ultrapure water system, usa.

(2) Study object

90 pathological or cytologically confirmed NSCLC patients are selected from a tumor biological sample library of a central hospital in Huzhou city, and all the patients are initially-diagnosed untreated patients, and clinical pathological data of the patients are collated. Age 33-73 years, mean 58.1 ± 9.0 years; 40 male cases and 50 female cases; adenocarcinoma 50 cases, squamous carcinoma 30 cases; 81 cases in phase I and 9 cases in phase II. 90 normal control groups are all healthy normal persons in Huzhou city, the age is 39-78 years, and the average age is 53.0 +/-11.8 years; 42 male and 48 female. The study was approved by the ethical committee of the central hospital in lazhou city, and all subjects gave informed consent to participate in the study.

(3) Sample collection

Blood samples of confirmed NSCLC patients and healthy volunteers were collected by the hospital according to the norms (approved by the ethics committee of the hospital, and the patients and volunteers signed informed consent) and a rational procedure: collecting 5mL of elbow venous blood in the early morning (fasting for more than 8 hr), centrifuging at 4 deg.C for 5min at 2500r/min, collecting upper layer serum, and storing in refrigerator at-80 deg.C.

(4) Establishment of UHPLC-Q-TOF/MS and GC-TOF/MS detection methods

UHPLC-Q-TOF/MS serum sample pretreatment

Thawing and shaking the blood serum sample stored at low temperature at room temperature, adding 200 μ L of the sample into 700 μ L of methanol, adding 40 μ L L-2-chlorophenylalanine, vortexing and shaking for 30s, performing ultrasound for 10min (ice water bath), and standing at 20 deg.C for 1 h; centrifuging at 13000r/min for 15min at 4 ℃, taking 400 mu L of supernatant, transferring the supernatant into a sample injection bottle (silicane), and carrying out UPLC-Q-TOF/MS detection analysis. 10 μ L of each sample was mixed into a Quality Control (QC) sample and tested in the same batch as the sample.

② pretreatment of GC-TOF/MS serum sample

Unfreezing a serum sample stored at a low temperature at room temperature, putting 200 mu L of the sample into a 1.5mL EP tube, adding 700 mu L of methanol, adding 40 mu L L-2-chlorophenylalanine, and performing vortex oscillation for 10 s; centrifuging at 13000r/min for 15min at 4 ℃; taking out 300 mu L of supernatant, putting the supernatant into a 2mL sampling bottle (silicane-based), drying the extract in a vacuum concentrator, adding 40 mu L of methoxylamine reagent (methoxylamine hydrochloride, dissolved in 20mg/mL of pyridine) into the dried metabolite, gently mixing the mixture, and putting the mixture into an oven to incubate for 30min at 80 ℃; to each sample was added 60 μ L BSTFA (containing 1% TCMS, v/v) rapidly and the mixture was incubated at 70 ℃ for 2 h; after cooling to room temperature, 10. mu.L of FAMEs (saturated fatty acid methyl ester standard mixture, in chloroform C8-C16: 1 mg/mL; C18-C24: 0.5mg/mL) was added to the mixed sample, mixed and analyzed by GC-TOF/MS. 10 μ L of each sample was mixed into a Quality Control (QC) sample and tested in the same batch as the sample.

③ UHPLC-Q-TOF/MS analysis conditions

The serum metabolic profiles of NSCLC patients and healthy normal examinees were analyzed by ultra-high performance liquid-quadrupole-time-of-flight mass spectrometry (Agilent 1290Infinity LC, Agilent 6550Q-TOF/MS and AB Sciex Triple TOF 6600).

Chromatographic conditions are as follows:

a Waters ACQUITY UPLC HSS T3C 18 column (100 mm. times.2.1 mm,1.7 μm) with a flow rate of 0.5ml/min and a column temperature of 25 ℃. The sample volume was 1. mu.L in positive ion mode, 3. mu.L in negative ion mode, and the autosampler temperature was 4 ℃.

Mobile phase: a: 0.1% formic acid-water, B: 0.1% formic acid-acetonitrile (positive ion mode);

a: 0.5mmol/L ammonium acetate-water, B: acetonitrile (negative ion mode);

the gradient elution procedure is shown in table 1.

TABLE 1 chromatographic gradient elution procedure

Agilent 6550Q-TOF/MS Mass Spectrometry conditions:

a. positive ion mode: ESI ion source, gas temperature 250 deg.C, dry gas flow rate 16L/min, atomizer pressure 20psig, sheath gas temperature 400 deg.C, sheath gas flow 12L/min, capillary voltage 3000V, nozzle voltage 0V.

b. Negative ion mode: ESI ion source, gas temperature 250 deg.C, dry gas flow rate 16L/min, atomizer pressure 40psig, sheath gas temperature 400 deg.C, sheath gas flow 12L/min, capillary voltage 3000V, nozzle voltage 0V.

Voltage of the fragmenter: 175V, data acquisition range: 50-1200m/z, acquisition rate: 4Hz, cycle time: 250 ms.

AB Sciex Triple TOF 6600 mass spectrometry conditions:

atomization gas pressure (GS1)40psi, assist gas pressure (GS2)80psi, gas curtain gas pressure (CUR) 25psi, ion source Temperature (TEM)650 ℃, spray voltage (ISVF)5000V (positive ion mode)/-4000 (negative ion mode), declustering voltage 60V, and collision voltage (CE)35 + -15 eV. The data acquisition is segmented according to the mass ranges of 50-300m/z, 290-600m/z, 590-900m/z and 890-1200m/z, so that the acquisition rate of a secondary spectrogram is expanded; four replicates per segment were collected per method. The acquired data are analyzed by using self-built MetDDA and LipDA databases in the laboratory.

GC-TOF/MS analysis conditions

Serum metabolic profiles of NSCLC patients and healthy normal examinees were analyzed using gas chromatography-time of flight mass spectrometry (Agilent 7890B GC, LECO Chroma TOF PEGASUS HT) technique. The capillary column was DB-5MS (30 m.times.250. mu.m.times.0.25 μm) from J & W Scientific. The instrument parameters were set as: high-purity helium is used as carrier gas, the split-flow sample injection is not carried out, the sample injection amount is 1 mu L, the purge flow rate of a sample inlet is 3mL/min, and the column flow rate is 1 mL/min; the temperature rising procedure is as follows: the initial column temperature is 50 ℃, the initial column temperature is maintained for 1min, the speed of 20 ℃/min is increased to 310 ℃, and the initial column temperature is maintained for 6 min; sample inlet temperature 280 ℃, transmission line temperature 270 ℃, ion source temperature 220 ℃, ionization voltage: -70 eV; performing mass spectrum detection in a full-scanning mode, wherein the mass spectrum detection range is 50-550m/z, and the scanning speed is 20 spectra/s; the solvent delay 366 s.

(5) Data processing

UHPLC-Q-TOF/MS data processing

Visualization was performed by recording the total ion current chromatogram (TIC) for each serum sample using the Agilent MassHunter workstation (version number: B05.01) and the AB SCIEX analysis TF 1.7.1 data acquisition software. Using XCMS (XCMS4dda, XCMS4lipid) program, minfrac was set to 0.5 and cutoff was set to 0. l. The secondary data is first screened for those identified ion peaks. The screening principle is forward (forward) and reverse (reverse) as long as one of them is identified to retain the ion peak. And secondly, matching the ion peaks of the primary data and the secondary data, namely searching the ion peak corresponding to the ion peak in the primary data in the secondary data. The matching is mainly carried out by using a characteristic ion mass-to-charge ratio (mz) and Retention Time (RT) according to mz tolerance +/-30 ppm and RT tolerance +/-60 s.

② GC-TOF/MS data processing

Raw data analysis was performed using the chromo TOF 4.3X software from LECO corporation and the LECO-Fiehn Rtx5 database. The method is mainly used for original peak identification, noise filtration, baseline correction, peak alignment, spectrogram deconvolution analysis, peak qualitative determination and quantification. Qualitative analysis is carried out by using a retention time index method, and the retention time index qualitative deviation is 5000. The LECO-Fiehn Rtx5 database was used to identify metabolites.

(6) Screening of differential metabolic markers associated with NSCLC

UHPLC-Q-TOF/MS data preprocessing

Simulating missing values in the original data, and filling the missing values by a numerical simulation method which is one-half of the minimum value; data normalization process, normalization with Total Ion Current (TIC) for each sample.

② preprocessing GC-TOF/MS data

Filtering the single data to remove noise, and filtering the deviation value based on the quartile range; filtering the single peak, and only retaining peak area data with a null value of not more than 50% in a single group or with a null value of not more than 50% in all groups; simulating missing values in the original data, and filling the missing values by a numerical simulation method which is one-half of the minimum value; and (5) carrying out data standardization by using an internal standard.

Identification and analysis of multivariate variable mode

a. Principal Component Analysis (PCA)

PCA may reveal the internal structure of the data, thereby better interpreting the data variables. Data were subjected to LOG (LOG) transform plus centralization (Ctr) formatting using SIMCA V14.1 software (MKS Data Analytics Solutions, Umea, Sweden) and then subjected to automated modeling analysis.

b. Orthogonal partial least squares-discriminant analysis (OPLS-DA)

LOG conversion and UV formatting are carried out on the data by using SIMCA V14.1 software, OPLS-DA modeling analysis is carried out on the first main component, and the quality of the model is checked by 7-fold cross validation (7-fold cross validation); then judging the effectiveness of the model by using R2Y (model interpretability of the classification variable Y) and Q2 (model predictability) obtained after cross validation; and finally, randomly changing the arrangement sequence of the classification variables Y for multiple times through a displacement test (simulation test) to obtain different random Q2 values, and further testing the validity of the model.

OPLS-DA displacement assay

The permutation test has an important role in avoiding overfitting of the test model and evaluating the statistical significance of the model by randomly changing the arrangement sequence of the classification variables Y and establishing a corresponding OPLS-DA model for multiple times (the time n is 200) to obtain the R2 and Q2 values of the random model.

Screening of differential metabolites of NSCLC

The first principal component Variable Projection Importance (VIP) value (threshold >1) of the OPLS-DA model was used in combination with the P value (threshold <0.05) of Student's t-test (Student's t-test) to find NSCLC differential metabolites.

(7) KEGG analysis and metabolic pathway analysis of NSCLC differential metabolites

All metabolic pathways involved in NSCLC differential metabolic markers are collated through Kyoto Encyclopedia of Genes and Genes (KEGG) Pathway databases of Kyoto Encyclopedia of Genomes, the metabolic pathways are comprehensively analyzed (including enrichment analysis and topological analysis), and further screening is carried out on the metabolic pathways to find the key pathways with the highest correlation with the metabolites.

(8) Analysis of the "Gene-enzyme-response-metabolite" network of NSCLC differential metabolites

The Fold values (Fold change) and P values of all NSCLC differential metabolites were imported into the MetScap plug-in of CytosScap (http:// metScap. ncibi. org /), yielding an overall network of "genes-enzymes-biochemical reactions-metabolites" and all sub-networks in which the differential metabolites participate.

(9) Confirmation of serum diagnostic biomarkers for NSCLC

And synthesizing the screening of NSCLC differential metabolites and the analysis result of metabolic pathways to obtain the candidate NSCLC serum diagnosis biomarker. Selecting clinical diagnostic indexes of NSCLC carcinoembryonic antigen (CEA) and cytokeratin 19 serum fragment 21-1(CYFRA21-1), obtaining detection values of CEA and CYFRA21-1 of NSCLC patients and normal control populations, and carrying out ROC curve analysis on candidate serum differential metabolic markers of the research through SPSS19.0 software.

2. UPLC-Q-TOF/MS technology-based target NSCLC metabonomics research

(1) Instruments and reagents

Instrument

Ultra-high performance liquid-quadrupole-time-of-flight mass spectrometry, including Agilent 1290 ultra-high performance liquid chromatograph and AB Sciex 6600 triple time-of-flight mass spectrometer (AB Sciex, usa); phenomenex Kinetex C18100A column (1.7 μm,2.1 × 100mm, Phenomenex, usa), health Force neodrug 23R low temperature high speed centrifuge (li xin, china), -80 ℃ ultra low temperature refrigerator (hail, china).

② reagent

Chromatographic grade methanol, acetonitrile, methyl tert-butyl ether (MTBE) and methylene chloride were purchased from Merck, lipid Mass Spectroscopy Standard (cat # 330707, containing 160. mu.g/mL phosphatidylcholine (15:0/18:1) (d7)) was purchased from Avanti polar lipids, USA, and ultrapure water was prepared by Milli-Q ultrapure water system, USA.

(2) Study object

30 pathological or cytologically confirmed NSCLC patients are selected from a tumor biological sample bank in a central hospital of Huzhou city, and all the patients are initially-diagnosed untreated patients, and clinical pathological data of the patients are collated. Age 47-74 years, mean 62.1 ± 6.7 years; 21 male and 9 female; adenocarcinoma 15 cases, squamous carcinoma 15 cases; 15 cases in I phase and 15 cases in II phase. 30 normal control groups are all healthy normal persons in Huzhou city, and the age of the normal control group is 34-73 years, and the average age is 51.7 +/-7.1 years; 19 of the women and 11 of the women. The study was approved by the ethical committee of the central hospital in lazhou city, and all subjects gave informed consent to participate in the study.

(3) Sample collection

Blood samples of confirmed NSCLC patients and healthy volunteers were collected by the hospital according to the norms (approved by the ethics committee of the hospital, and the patients and volunteers signed informed consent) and a rational procedure: collecting 5mL of elbow venous blood in the early morning (fasting for more than 8 hr), centrifuging at 4 deg.C for 5min at 2500r/min, collecting upper layer serum, and storing in refrigerator at-80 deg.C.

(4) Establishment of UHPLC-Q-TOF/MS quantitative detection method

UHPLC-Q-TOF/MS serum sample pretreatment

Thawing and shaking a serum sample stored at a low temperature at room temperature, taking 40 mu L of the sample, adding 160 mu L of water, adding 480 mu L of an extracting solution (V MTBE: V methanol: 5:1) and 10 mu L of a lipid mass spectrum standard (containing 160 mu g/mL phosphatidylcholine (15:0/18:1) (d7)), taking 6 mu L of each sample, mixing to obtain a Quality Control (QC) sample, and preparing 4 QC samples according to the same operation as the experiment samples; vortex for 60 seconds, and ultrasonically process for 10 min; centrifuging the sample at 4 ℃ and 3000rpm for 15min, and taking 200 mu L of supernatant; adding 200 μ L MTBE again, vortexing for 60s, and performing ultrasonic treatment for 10 min; centrifuging at 4 deg.C and 3000rpm for 15min, and collecting supernatant 200 μ L; adding 200 mu L of MTBE again, vortexing for 60s, and performing ultrasonic treatment for 10 min; centrifuging at 4 deg.C and 3000rpm for 15min, and collecting supernatant 200 μ L; and combining the three supernatants, spin-drying, re-dissolving 80 mu L of 1:1 dichloromethane/methanol, and detecting the QC sample and the sample on a machine in the same batch.

② UHPLC-Q-TOF/MS quantitative analysis condition

The concentration level of Phosphatidylcholine (PC) in serum of NSCLC patients and healthy normal examinees was quantitatively determined by ultra-high performance liquid-quadrupole-time-of-flight mass spectrometry (Agilent 1290Infinity LC and AB Sciex Triple TOF 6600).

Chromatographic conditions are as follows:

phenomenex Kinetex C18100A column (2.1X 100mm, Phenomenex, 1.7 μm), flow rate 0.3mL/min, column temperature 25 ℃. The sample size was 1. mu.L, and the autosampler temperature was 4 ℃.

Mobile phase: a: 10mmol/L ammonium formate, 40% water and 60% acetonitrile

B: 10mmol/L ammonium formate + 10% acetonitrile + 90% n-propanol

The gradient elution procedure is shown in table 2.

TABLE 2 chromatographic gradient elution procedure

Mass spectrum conditions:

atomization gas pressure (GS1)60psi, assist gas pressure (GS2)60psi, gas curtain gas pressure (CUR) 30psi, ion source Temperature (TEM)600 ℃, spray voltage (ISVF) -4500V (negative ion mode), and collision voltage (CE)45 + -25 eV.

(5) Data processing

Data acquisition and processing are carried out by adopting AB SCIEX analysis TF 1.7.1 data acquisition software. The MS raw data file is converted to mzXML format using mscontroller and processed by the R software package XCMS (version 1.41.0). The results of the pre-processing yielded a data matrix consisting of Retention Time (RT), mass-to-charge ratio (m/z) and peak intensity. The cutoff value for the match score was set to 0.8 and minfrac was set to 0.5. All m/z errors are less than 30ppm and all RT errors are less than 60 s. Less than 50% of the detected metabolic features in all QC samples were discarded. Lipid identification was performed by matching the obtained MS/MS data with MS/MS data in an in-house developed database. The absolute concentration of each PC (ng/ml) was calculated from the area of the PC peak identified in the sample and the area of the peak corresponding to the PC (15:0/18:1) internal standard of the sample.

(6) Validating serum diagnostic biomarkers for NSCLC

Calculating the absolute concentration of each PC in each sample serum, comparing the difference of the PCs of the NSCLC group and the normal control group through t test of the two samples, and considering the PC as a serum difference metabolic marker when the P value is less than 0.05 and the fold change is more than 1.0.

(7) Confirming serum diagnostic biomarkers for NSCLC

And (3) performing ROC curve drawing of a single index on the serum difference PC metabolic marker obtained by screening through SPSS19.0 software, calculating the sensitivity and specificity of the combined action of the indexes, analyzing the diagnostic efficiency of the combined action of multiple indexes, and confirming the NSCLC serum diagnosis biomarker.

(III) main research results and technical innovation points

1. Results of the main study

(1) Non-target NSCLC metabonomics research based on UHPLC-Q-TOF/MS and GC-TOF/MS technologies

Establishing metabolic fingerprint of NSCLC and normal control population

Serum metabolite components are complex, a positive ion mode and a negative ion mode are adopted during UHPLC-Q-TOF/MS detection, 1865 ion peaks in the positive ion mode and 359 ion peaks in the negative ion mode are obtained after screening of metabolites obtained by detection. The metabolic substances obtained by GC-TOF/MS detection are screened to obtain 223 peaks in total. The metabolic fingerprints of the NSCLC group and the normal control group obtained by UHPLC-Q-TOF/MS and GC-TOF/MS detection are shown in figure 1 and figure 2. From the figure, it is known that there is a certain difference between the metabolites and their ionic strength in the serum of NSCLC patients and normal control.

② multivariate data analysis

a. Principal Component Analysis (PCA)

Principal component analysis is a statistical method that transforms a set of observed, possibly correlated variables into linearly uncorrelated variables (i.e., principal components) by orthogonal transformation. PCA may reveal the internal structure of the data, thereby better interpreting the data variables. The data obtained by detection in positive ion mode of UHPLC-Q-TOF/MS of NSCLC group and normal control group are processed by Logarithm (LOG) conversion and Centralization (CTR) formatting by using SIMCA V14.1 software, and then are automatically modeled and analyzed to obtain 2 main components (PC), wherein R2X is 0.627; data obtained by detection of the NSCLC group and the normal control group in a UHPLC-Q-TOF/MS negative ion mode are subjected to LOG (LOG) conversion and Centralization (CTR) formatting treatment and then subjected to automatic modeling analysis. A total of 2 Principal Components (PC) were obtained, with R2X being 0.529. Data obtained from GC-TOF/MS tests in NSCLC and normal control groups were subjected to LOG (LOG) transformation plus Centralization (CTR) formatting and then to automated modeling analysis. A total of 10 Principal Components (PC) were obtained, with R2X ═ 0.416. A scatter plot of PCA scores for the NSCLC group and the normal control group is shown in FIG. 3, wherein the abscissa PC1 and the ordinate PC2 in the PCA score plot represent the scores of the first and second ranked principal components, respectively, and the scatter color and shape represent the experimental grouping of samples. It can be seen that the discrimination between groups is very significant on the top ranking principal component, with the samples all within the 95% confidence interval. The whole model is ideal.

b. Orthogonal partial least squares-discriminant analysis (OPLS-DA)

The UHPLC-QTOFMS-based metabonomics data has the characteristics of high dimension (more detected metabolites) and small sample (less detected sample amount), and the variables comprise differential variables related to classification variables and a large number of non-differential variables possibly related to each other. This results in that if we use the PCA model or the PLS model for analysis, the difference variables are scattered over more principal components due to the influence of the related variables, and better visualization and subsequent analysis cannot be performed. Therefore, we used the statistical method of OPLS-DA to analyze the results. Through OPLS-DA analysis, orthogonal variables irrelevant to classification variables in the metabolites can be filtered out, and non-orthogonal variables and orthogonal variables are analyzed respectively, so that more reliable correlation degree information of interclass differences of the metabolites and experimental groups is obtained.

Performing LOG conversion and UV formatting treatment on data obtained by UHPLC-Q-TOF/MS and GC-TOF/MS detection of NSCLC groups and normal control groups by using SIMCA V14.1 software, firstly performing OPLS-DA modeling analysis on a first main component, and verifying the quality of a model by 7-fold cross validation (7-fold cross validation); then judging the effectiveness of the model by using R2Y (model interpretability of the classification variable Y) and Q2 (model predictability) obtained after cross validation; and finally, randomly changing the arrangement sequence of the classification variables Y for multiple times through a displacement test (simulation test) to obtain different random Q2 values, and further testing the validity of the model. The data result obtained by detection in a UHPLC-Q-TOF/MS positive ion mode obtains 1 main component and 1 orthogonal component, wherein R2X is 0.637, R2Y is 0.873, and Q2 is 0.866; the data obtained by detection in the UHPLC-Q-TOF/MS negative ion mode result in 1 main component and 1 orthogonal component, wherein R2X is 0.268, R2Y is 0.804, and Q2 is 0.760; the data obtained by GC-TOF/MS examination resulted in 1 main component and 1 orthogonal component, R2X ═ 0.134, R2Y ═ 0.905, and Q2 ═ 0.860. The score chart is shown in fig. 4. In the figure, the abscissa t 1P represents the predicted principal component score of the first principal component, the ordinate t 1O represents the orthogonal principal component score, and the scatter point shape and color represent different experimental groups. It can be seen that the two groups of samples are very significantly differentiated, and the samples are substantially within the 95% confidence interval.

OPLS-DA displacement assay

The substitution test results of the OPLS-DA model are shown in fig. 5, in which the abscissa indicates the substitution retention degree of the substitution test (the ratio is consistent with the original model Y variable sequence, the points where the substitution retention degree is equal to 1 are the R2 and Q2 values of the original model), the ordinate indicates the value of R2 or Q2, the green dots indicate the R2 values obtained by the substitution test, the blue squares indicate the Q2 values obtained by the substitution test, and the two dotted lines indicate the regression lines of R2 and Q2, respectively. It can be seen that the original model R2 is close to 1, indicating that the established model conforms to the true condition of sample data; q2 is close to 1, which shows that if a new sample is added into the model, an approximate distribution is obtained, and the original model can well explain the difference between two groups of samples in general. Meanwhile, along with the gradual reduction of the replacement retention degree, the proportion of the replaced Y variable is increased, and R2 and Q2 of the random model are both gradually reduced, which shows that the original model has no overfitting phenomenon and the model has good robustness.

The substitution test results of the OPLS-DA model are shown in fig. 8, in which the abscissa indicates the substitution retention degree of the substitution test (the ratio is consistent with the original model Y variable sequence, the points where the substitution retention degree is equal to 1 are the R2 and Q2 values of the original model), the ordinate indicates the value of R2 or Q2, the green dots indicate the R2 values obtained by the substitution test, the blue squares indicate the Q2 values obtained by the substitution test, and the two dotted lines indicate the regression lines of R2 and Q2, respectively. It can be seen that the original model R2 is close to 1, indicating that the established model conforms to the true condition of sample data; q2 is close to 1, which shows that if a new sample is added into the model, an approximate distribution is obtained, and the original model can well explain the difference between two groups of samples in general. Meanwhile, along with the gradual reduction of the replacement retention degree, the proportion of the replaced Y variable is increased, and R2 and Q2 of the random model are both gradually reduced, which shows that the original model has no overfitting phenomenon and the model has good robustness.

Screening of differential metabolites of NSCLC

a. Screening of NSCLC differential metabolites based on UHPLC-Q-TOF/MS detection

The first principal component Variable Projection Importance (VIP) value (threshold >1) of the OPLS-DA model was used in combination with the P value (threshold <0.05) of the t-test (Student's t-test) to find NSCLC differential metabolites. 690 different metabolites of the NSCLC group and the normal control group are obtained in a positive ion mode, wherein 20 different metabolites matched with the secondary mass spectrum, 350 different metabolites matched with the primary mass spectrum and 320 different metabolites without matching are obtained in the primary mass spectrum. 119 different metabolites of the NSCLC group and the normal control group are obtained together in a negative ion mode, wherein 17 different metabolites matched with the secondary mass spectrum, 73 different metabolites matched with the primary mass spectrum and 29 different metabolites without matching are obtained in the primary mass spectrum. The volcano plot of the differential metabolites is shown in fig. 6, wherein each point in the volcano plot represents a metabolite, the abscissa represents the fold change (logarithm base 2) of the group compared with each substance, the ordinate represents the P-value (logarithm base 10) of the t-test, the size of the scatter point represents the VIP value of the OPLS-DA model, and the VIP value is larger as the scatter point is larger. The scatter color represents the final screening results, with significantly up-regulated metabolites represented in red, significantly down-regulated metabolites represented in blue, and non-significantly different metabolites in gray. The list of differential metabolites is shown in table 3.

b. Screening of NSCLC differential metabolites based on GC-TOF/MS detection

The first principal component Variable Projection Importance (VIP) value (threshold >1) of the OPLS-DA model was used in combination with the P value (threshold <0.05) of Student's t-test (Student's t-test) to find NSCLC differential metabolites. 30 different metabolites were obtained for the NSCLC group and the normal control group together, and the volcano pattern of the different metabolites is shown in fig. 7.

TABLE 3 differential metabolite List based on ultra high performance liquid chromatography/quadrupole-time of flight mass spectrometry (UHPLC-Q-TOF/MS) for the non-small cell lung cancer group and the normal control group

*: a "+" positive ion mode, a "-" negative ion mode

TABLE 4 differential metabolite List based on gas chromatography-time of flight/Mass Spectrometry (GC-TOF/MS) for the non-small cell Lung cancer group and the Normal control group

KEGG analysis and metabolic pathway analysis of NSCLC differential metabolite combination based on UHPLC-Q-TOF/MS and GC-TOF/MS detection

The mapping path of NSCLC differential metabolites detected based on UHPLC-Q-TOF/MS and GC-TOF/MS is collated by a KEGG Pathway database and is shown in a table 5, the metabolic paths in the table are the KEGG paths mapped by the differential metabolites, the numbers in brackets represent the number of the differential metabolites contained in the path, and the metabolites are information of the differential metabolites mapped to the path.

TABLE 5 USCLC differential metabolite KEGG pathway annotation information table based on UHPLC-Q-TOF/MS and GC-TOF/MS detection

The metabolic pathways are comprehensively analyzed (including enrichment analysis and topological analysis), the metabolic pathways are further screened, the key pathways with the highest correlation with metabolites are found, and the following detailed results of the metabolic pathways are obtained and shown in table 6. Pathway is the name of a metabolic Pathway, and Total is the number of all metabolites in the Pathway; hits are the number of differential metabolites hitting the pathway, Raw p: the P value obtained by enrichment analysis, log (P) is a negative common logarithm of the P value, FDR is the P value corrected by multiple hypothesis testing by a False Discovery Rate (FDR) method, and Impact is an influence factor obtained by topology analysis. The results of the metabolic pathway analysis are shown in the bubble chart, see FIG. 8. Each bubble in the bubble diagram represents a metabolic pathway, the abscissa of the bubble and the size of the bubble represent the size of an influence factor of the pathway in topological analysis, and the larger the size, the larger the influence factor; the ordinate where the bubble is located and the bubble color represent the P-value (in a negative common logarithm, i.e., -log 10P-value) of the enrichment analysis, and the deeper the color, the smaller the P-value, the more significant the enrichment degree. Comprehensive analysis shows that the influence factor of the glycerol phospholipid metabolism in the topological analysis is the largest, and the enrichment degree of the caffeine metabolism in the enrichment analysis is the most obvious.

TABLE 6 summary of the assignment of metabolic pathways of serum differential metabolites of NSCLC based on UHPLC-Q-TOF/MS and GC-TOF/MS measurements

'Gene-enzyme-reaction-metabolite' network analysis of NSCLC differential metabolite

The Fold values (Fold change) and P values of all NSCLC differential metabolites were imported into the MetScap plug-in of Cytoscape (http:// metScap. ncibi. org /) to generate an overall network of "genes-enzymes-biochemical reactions-metabolites" and all sub-networks in which the differential metabolites participate, and the NSCLC differential metabolite network is shown in FIG. 9.

Confirmation of serum diagnosis biomarker for NSCLC

The obtained candidate NSCLC serum diagnosis biomarkers by integrating the screening of NSCLC differential metabolites and the analysis result of metabolic pathways are respectively as follows: ethanolamine (ethanomine), Galactonic acid (Galactonic acid), Glucose-1-phosphate (Glucose-1-phosphate), Xanthine (Xanthine), Theophylline (Theophylline) and 12 phosphatidylcholines (phsophatidylcholine, PC). The concentration of ethanolamine and galactonic acid in the serum of the NSCLC group is obviously increased compared with the normal control group, the concentration of glucose-1-phosphate, theophylline and xanthine is obviously reduced compared with the normal control group, and the difference has statistical significance (P < 0.05). Of 12 kinds of phosphatidylcholine, the concentration of PC (18:3/0:0), PC (O-16:1/0:0), PC (O-16:0/0:0), PC (19:0/0:0), PC (18:4/22:6), PC (18:0/22:6), PC (17:2/24:4), PC (16:1/0:0) and PC (15:1/22:6) in the serum of the NSCLC group is obviously higher than that of a normal control group; the concentrations of PC (10:0/22:2), PC (20:0/0:0) and PC (O-18:3/0:0) were significantly reduced compared to the normal control group.

The results of the area under the curve, sensitivity and specificity after ROC curve analysis of the serum differential metabolic markers, carcinoembryonic antigen (CEA) and cytokeratin 19 serum fragment 21-1(CYFRA21-1) NSCLC patients and normal control population are shown in Table 7. From the results, the area under the curve of the combination of Phosphatidylcholine (PCs) was 0.989 (P <0.01), which showed higher diagnostic potency (AUC >0.9, P <0.01), sensitivity was 97.8%, and specificity was 98.9%. Combining with metabolic pathway analysis, Phosphatidylcholine (PCs) is selected as a metabolic marker for guiding the clinical diagnosis of the NSCLC, a quantitative detection method needs to be further established, the absolute concentration of the NSCLC is detected by adopting target metabonomics, and serum diagnosis biomarkers of the NSCLC are verified and confirmed.

TABLE 7 area under the curve (AUC), sensitivity and specificity of the individual indices of NSCLC serum differential metabolic marker, CEA and CYFRA21-1

(2) UHPLC-Q-TOF/MS technology-based target NSCLC metabonomics research

Verification of serum diagnosis biomarker of NSCLC

The absolute concentration of each PC in serum of each sample in NSCLC group and HC group was quantitatively analyzed by UHPLC-Q-TOF/MS, and 85 PC concentration changes were detected in total. Fold-changes in mean values of p-value and PC concentration were calculated for the Student's t test between NSCLC and HC groups, and 12 PCs were selected as biomarkers for early diagnosis of NSCLC according to p <0.01 and fold > 1. As shown in fig. 10, the content of saturated monounsaturated phosphatidylcholine PC (15:0/18:1), PC (18:0/16:0) and PC (18:0/20:1) was significantly increased in NSCLC (P < 0.05). And the content of polyunsaturated phosphatidylcholine PC (17:2/2:0), PC (18:4/3:0), PC (15:0/18:2), PC (16:0/18:2), PC (17:0/18:2), PC (18:2/18:2), PC (16:0/20:3), PC (15:0/22:6) and PC (24:4/17:2) is significantly reduced in NSCLC relative to HC (P < 0.05).

② confirmation of serum diagnostic biomarkers for NSCLC

The sensitivity and specificity are calculated by plotting ROC curves of single index and multiple indexes on the serum difference PC metabolic markers obtained by screening through SPSS19.0 software, the sensitivity, specificity and AUC of each metabolite and PC combination are shown in Table 8, and the ROC curves are shown in figure 11. A single PC was found to have no good diagnostic performance in distinguishing NSCLC from HC. However, analysis of the diagnostic efficacy of the multi-index combination revealed that the 12 PC combinations had the best diagnostic performance, confirming that the 12 PC combinations were serum diagnostic biomarkers for NSCLC.

TABLE 8 Area Under Curve (AUC), sensitivity and specificity of serum Phosphatidylcholine (PC) Single index and multiple indices

Combination a: down-regulating the PC combination; combination b: c, up-regulating a PC combination; and c: 12 kinds of PC combination

2. Technical innovation point

(1) The project jointly adopts the ultra-high performance liquid chromatography/quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF/MS) and the gas chromatography-time of flight-mass spectrometry (GC-TOF/MS) methods, has high accuracy and short time consumption, and can perform high-throughput qualitative and quantitative analysis, thereby greatly shortening the screening time of serum markers, enhancing the reliability of results and providing a better method for the research of tumor markers.

(2) The diagnosis biomarker of the NSCLC is mined and confirmed by adopting a large-sample non-target metabonomics analysis method, and the diagnosis biomarker of the NSCLC is quantitatively verified by adopting a target metabonomics analysis method.

(IV) Overall evaluation conclusion

1. The research simultaneously applies the detection and analysis means of ultra-high performance liquid chromatography/quadrupole-time of flight mass spectrometry (UPLC-Q-TOF/MS) and gas chromatography-time of flight mass spectrometry (GC-TOF/MS), and the obtained metabolic marker has wider coverage and certain advantages. And carrying out non-target metabonomics analysis on the metabolites in the blood serum of the NSCLC group and the normal control group patients, and comparing the metabolic fingerprint changes of the NSCLC group and the normal control group by adopting a multivariate statistical method. The result shows that the metabolic substances and the ionic strength thereof in the blood serum of the NSCLC patient and the normal control have certain difference, which is beneficial to guiding the clinical diagnosis of the lung cancer.

(2) Through comparing the metabolite changes in the blood serum of NSCLC and normal control population, the metabolic pathway attribution analysis is carried out on the target differential metabolic markers, clinical NSCLC diagnosis indexes are combined, and Phosphatidylcholine (PCs) is confirmed to be NSCLC related differential metabolic markers according to the diagnosis efficiency, and NSCLC is closely related to the abnormal glycerophospholipid metabolic pathway.

(3) Establishing a quantitative detection method of NSCLC serum phosphatidylcholine by adopting UHPLC-Q-TOF/MS technology, confirming 12 phosphatidylcholines as NSCLC diagnosis biomarkers, wherein the content of saturated and monounsaturated phosphatidylcholine PC (15:0/18:1), PC (18:0/16:0) and PC (18:0/20:1) is significantly increased in NSCLC (P <0.05), the content of polyunsaturated phosphatidylcholine PC (17:2/2:0), PC (18:4/3:0), PC (15:0/18:2), PC (16:0/18:2), PC (17:0/18:2), PC (18:2/18:2), PC (16:0/20:3), PC (15:0/22:6) and PC (24:4/17:2) is significantly decreased in NSCLC (P < 0.05). Analyzing the diagnostic efficacy of the combined action of the single index and the multiple indexes, and confirming the 12 phosphatidylcholine combinations as serum diagnostic biomarkers of the NSCLC.

The beneficial effect of this application is as follows: 1. carrying out non-target metabonomics analysis on metabolites in serums of large-sample non-small cell lung cancer (NSCLC) patients and normal control populations by adopting a metabonomics method of ultra-high performance liquid chromatography/high resolution quadrupole-time-of-flight tandem mass spectrometry (UPLC-Q-TOF/MS) and gas chromatography/mass spectrometry (GC/MS), establishing a NSCLC serum metabolism fingerprint spectrum database, and mining and confirming diagnostic biomarkers of the NSCLC by adopting a multivariate statistical analysis method and combining clinical pathological characteristics and diagnostic efficiency; 2. taking differential metabolites discovered by non-target metabonomics as research objects, developing the NSCLC target metabonomics analysis based on UPLC/MS/MS and GC/MS, establishing a target analysis method suitable for the serum diagnosis biomarker of NSCLC, and quantitatively verifying the diagnosis biomarker of NSCLC; 3. by bioinformatics database analysis, the discovered diagnostic biomarkers of NSCLC are subjected to functional analysis and verification by adopting adenovirus overexpression and siRNA interference technology, and a foundation is laid for developing a tumor molecular diagnosis strategy of metabolic biomarkers.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention are equivalent to or changed within the technical scope of the present invention.

Claims (5)

1. A method of quantitatively screening a diagnostic biomarker for NSCLC comprising the steps of: s1, performing non-target metabonomics analysis on metabolites in blood serums of patients in a non-small cell lung cancer NSCLC group and a normal control group by jointly adopting ultra-high performance liquid chromatography/quadrupole-time-of-flight mass spectrometry combined with UPLC-Q-TOF/MS and gas chromatography-time-of-flight mass spectrometry combined with GC-TOF/MS, and comparing the metabolic fingerprint changes of the NSCLC group and the normal control group by adopting a multivariate statistical method; s2, comparing the metabolite changes in the blood serum of NSCLC and normal control population, carrying out metabolic pathway attribution analysis on the target differential metabolic markers, combining clinical NSCLC diagnostic indexes, and confirming that Phosphatidylcholine (PCs) are NSCLC-related differential metabolic markers according to the diagnostic efficacy of the clinical NSCLC diagnostic indexes; s3, establishing a quantitative detection method of NSCLC serum phosphatidylcholine by adopting UHPLC-Q-TOF/MS technology, and confirming that 12 phosphatidylcholines are diagnostic biomarkers of NSCLC, wherein the content of saturated and monounsaturated phosphatidylcholine PC15:0/18:1, PC18:0/16:0 and PC18:0/20:1 is remarkably increased by P <0.05 in NSCLC, the content of polyunsaturated phosphatidylcholine PC17:2/2:0, PC18:4/3:0, PC15:0/18:2, PC16:0/18:2, PC17:0/18:2, PC18:2/18:2, PC16:0/20:3, PC15:0/22:6 and PC24:4/17:2 is remarkably reduced by P <0.05 in NSCLC; s4, analyzing the diagnostic efficacy of the combined action of the single index and the multiple indexes, and confirming the 12 phosphatidylcholine combinations as serum diagnostic biomarkers of NSCLC.

2. The method of claim 1, wherein the method comprises the step of: the non-target metabonomic analysis of the metabolites in serum in step S1 includes non-target UPLC-Q-TOF/MS detection and non-target GC-TOF/MS detection.

3. The method of claim 1, wherein the method comprises the step of: the metabolites of the serum sample in step S1 further need to be pretreated, and the pretreatment includes UPLC-Q-TOF/MS serum sample pretreatment and GC-TOF/MS serum sample pretreatment.

4. A method of quantitatively screening a diagnostic biomarker for NSCLC according to claim 3, wherein: the method for preprocessing the UPLC-Q-TOF/MS serum sample comprises the following steps: firstly, thawing and shaking a serum sample stored at a low temperature at room temperature, adding 700 mu L of methanol into 200 mu L of the sample, then adding 40 mu L L-2-chlorophenylalanine, and carrying out vortex oscillation for 30 s; then, carrying out ultrasonic treatment for 10min, and standing for 1h at-20 ℃; centrifuging at 13000r/min for 15min at 4 ℃; and finally, transferring 400 mu L of supernatant into a sample injection bottle for UPLC-Q-TOF/MS detection and analysis.

5. A method of quantitatively screening a diagnostic biomarker for NSCLC according to claim 3, wherein: the GC-TOF/MS serum sample pretreatment comprises the following steps: firstly, unfreezing a serum sample stored at a low temperature at room temperature, putting 200 mu L of the sample into a 1.5mL EP tube, adding 700 mu L of methanol, adding 40 mu L L-2-chlorophenylalanine, and carrying out vortex oscillation for 10 s; then, centrifuging for 15min at 4 ℃ and 13000 r/min; taking out 300 mu L of supernatant, putting the supernatant into a 2mL sampling bottle, drying the extract in a vacuum concentrator, adding 40 mu L of methoxylamine reagent into the dried metabolite, slightly mixing the mixture evenly, and putting the mixture into an oven to incubate for 30min at the temperature of 80 ℃; to each sample was added 60 μ L BSTFA rapidly and the mixture was incubated at 70 ℃ for 2 h; and finally, cooling to room temperature, adding 10 mu L of FAMEs into the mixed sample, uniformly mixing, and carrying out GC-TOF/MS detection analysis.

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