CN107917971B - Application of myristic acid and glycerol composition in evaluating curative effect of chronic myelogenous leukemia TKI - Google Patents

Abstract

The invention provides application of a myristic acid and glycerol composition in evaluating the curative effect of chronic myelogenous leukemia TKI, and belongs to the technical field of metabonomics and clinical examination. In the experiment, a GC-MS combined technology is adopted to research plasma micromolecule metabolites of CML patients, 26 samples of a primary diagnosis CML group, 26 samples of a TKI treatment success group, 26 samples of a TKI treatment failure group and 26 samples of a healthy control group are randomly selected as substance discovery models, and 194 samples are verified according to substances screened from the small samples. Through sample collection, pretreatment, sample separation and detection and data processing, the screened combination level of myristic acid and glycerol in the blood plasma of a CML patient can be used as a potential marker for predicting the curative effect of TKI, and a new blood plasma metabolic marker is provided for clinically judging the curative effect of TKI.

Description

Application of myristic acid and glycerol composition in evaluating curative effect of chronic myelogenous leukemia TKI

Technical Field

The invention belongs to the technical field of metabonomics and clinical examination, and particularly relates to a method for screening the combination level of myristic acid and glycerol in the plasma of a CML patient by using a gas chromatography-mass spectrometry (GC-MS) metabonomics technology to be used as a potential marker for predicting the curative effect of TKI.

Background

Chronic Myelogenous Leukemia (CML) is a group of malignant clonal proliferative diseases of a blood system, the molecular genetics of the CML is characterized in that long-arm translocations of chromosomes 9 and 22 generate BCR-ABL fusion genes, and the expression of the BCR-ABL fusion genes is mainly realized by changing the protein tyrosine phosphorylation level of malignant tumor cells, so that a series of normal signal transduction pathways are destroyed, and finally, the apoptosis is inhibited. Tyrosine Kinase Inhibitor (TKI) is a targeted antitumor drug designed according to the biological activities of tumor cells, selectively acts on BCR-ABL fusion genes, blocks downstream signal transduction pathways, and inhibits malignant proliferation of leukemia cells, and is a first-line treatment scheme of CML at present. However, TKI also has drug resistance problem, which affects clinical efficacy. The TKI-treated CML patient is subjected to early curative effect evaluation, and timely selective administration of tumor patients with poor drug sensitivity is facilitated, so that curative effect is improved, adverse reaction is reduced, and individual precise treatment is realized. However, there is currently no effective method for early prediction of the efficacy of CML patients following TKI application. In recent years, screening biomarkers related to drug efficacy by using various omics technologies has become a hotspot of tumor pharmacokinetic research.

Disclosure of Invention

The invention aims to provide application of a myristic acid and glycerol composition in evaluating the curative effect of chronic myelogenous leukemia TKI, plasma samples of CML patients and patients with different curative effects obtained through treatment of medicine TKI are used as research objects, a metabonomics method is utilized, a gas chromatography-mass spectrometry (GC-MS) combined technology is adopted, multiple statistical analysis methods are combined, the myristic acid and glycerol combined level of the plasma of the CML patients is screened out to be used as a potential marker for predicting the curative effect of the TKI, and a new plasma metabolic marker is provided for clinical judgment of the curative effect of the TKI.

In order to achieve the purpose, the invention adopts the following technical scheme:

the application of the myristic acid and glycerol composition in evaluating the curative effect of the chronic myelogenous leukemia TKI.

In the application of the myristic acid and glycerol composition in evaluating the curative effect of the chronic myelogenous leukemia TKI, the myristic acid and the glycerol are derived from blood plasma.

In the application of the myristic acid and glycerol composition in evaluating the curative effect of the chronic myelogenous leukemia TKI, the conventional detection method is a gas chromatography-mass spectrometry combined method.

In the application of the myristic acid and glycerol composition in evaluating the curative effect of the chronic myelogenous leukemia TKI, the gas chromatography analysis conditions are as follows: injecting 1 mu L of sample, the split ratio is 10:1, the initial temperature is 80 ℃, keeping for 5min, raising to 170 ℃ at a constant speed of 10 ℃/min, then raising to 250 ℃ at 5 ℃/min, finally raising to 300 ℃ at 10 ℃/min, keeping for 5min, the whole procedure is 46min in total, the carrier gas is helium, the constant current is constant, and the flow rate is 1.0 mL/min;

the mass spectrometry conditions were: the sample inlet temperature is 300 ℃, and the ionization voltage is 70 eV; the ion source temperature is 230 ℃; the detector voltage is 1.2kV, and the ionization mode is as follows: and (3) bombarding an ion source by electrons, wherein the ionization voltage is 70eV, the mass scanning range is 30-600m/z, and the solvent cutting time is 5 min.

Further, the detection kit comprises a standard substance composed of myristic acid and glycerol, and the standard substance is a chemical monomer of myristic acid and glycerol or a mixture of myristic acid and glycerol.

Furthermore, the detection kit also comprises a buffer solution and a color developing agent.

Further, the detection kit also comprises a solvent for dissolving the standard substance and a solvent for extracting the myristic acid and the glycerol.

Has the advantages that: the invention provides application of a myristic acid and glycerol composition in evaluating the curative effect of chronic myelocytic leukemia TKI. Researches show that profile differences exist in metabolism of patients with different curative effects after TKI treatment, myristic acid and glycerol are main contributing substances, and large sample verification shows that the combination of the myristic acid and the glycerol can well predict and evaluate the curative effect of the CML after the TKI treatment and can be used as a diagnostic biomarker for potential CML after the TKI treatment.

Drawings

FIG. 1 is a graph of the distribution trend of response RSD% values of metabolic features in QC samples by plasma GC-MS analysis, with bars representing the percentage of the total number of metabolic features and lines representing the cumulative percentage of the total number of metabolic features.

Figure 2 GC-MS acquired plasma typical total ion flow graph. (A) Healthy control group, (B) CML group.

FIG. 3 is a diagram of pattern recognition analysis under the OSC-PLSDA model. (A) Model score graph, (B) 200 displacement response test.

FIG. 4 is a graph showing the change in the content of two metabolites in different groups. (A) Glycerol, (B) myristic acid. Both substances decreased significantly in the initial CML patients, with the successful group undergoing TKI treatment showing a tendency to return to normal levels, and the failed group showing a trend of change opposite to that of the successful group.

Figure 5 clinical efficacy evaluation plots of single and combination therapy markers. (A) ROC curve for material discovery group data, (B) ROC curve for external validation group data.

Detailed Description

The present invention is further described below with reference to specific examples, which are only exemplary and do not limit the scope of the present invention in any way. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention, and that such changes and modifications may be made without departing from the spirit and scope of the invention.

Materials and methods

1.1 study object

The study subjects in the substance discovery group selected 26 patients in the CML chronic stage (CML-CP stage) at the first diagnosis of the first hospital affiliated to Suzhou university, and 52 patients in the CML-CP stage who received TKI for 12 months or more (26 patients in the treatment success group and 26 patients in the treatment failure group according to the treatment effect), and alternatively, 26 healthy volunteers in the same period of the physical examination center were selected as the healthy control group. Age, gender, BMI index of each group were matched. The subjects in the substance verification group were 194 TKI-treated CML-CP phase patients randomly selected, 112 in the successful treatment group and 82 in the failed treatment group.

1.2 instruments and reagents

1.2.1 Experimental reagents

(1) Acetonitrile: TEDIA corporation, USA.

(2) 2, 4-Dichlorobenzoic acid (internal standard): Sigma-Aldrich, USA.

(3) Chlorotrimethylsilane (TMCS): Sigma-Aldrich, USA.

(4) Methoxy amine: Sigma-Aldrich, USA.

(5) Pyridine: Sigma-Aldrich, USA.

(6) N-Methyltrimethylsilyltrifluoroacetamide (MSTFA): Sigma-Aldrich, USA.

(7) N-heptane: purchased from TEDIA corporation, usa.

(8) Amino acid standards, fatty acid standards: purchased from Sigma-Aldrich, usa.

(9) Heparin sodium anticoagulation tube: shanghai Xinrui Biotechnology Limited, Specification: 6 mL.

1.2.2 Main instrumentation

Agilent7890A/5975C gas chromatography-mass spectrometer (GC-MS, Agilent corporation, USA), Agilent DB-5MS capillary column (30 mmx 0.25 μm), laboratory ultrapure water system (Direct-Q5 UV, Kyowa Baineng instruments, Ltd.), general purpose frozen high speed centrifuge (Eppendorf, Germany), thermostatic water bath (HSC-24A), freeze dryer, etc.

1.3 Experimental design and methods

In the experiment, a GC-MS combined technology is adopted to research plasma micromolecule metabolites of CML patients, 26 samples of a primary diagnosis CML group, 26 samples of a TKI treatment success group, 26 samples of a TKI treatment failure group and 26 samples of a health control group are randomly selected as substance discovery models, and 194 samples are verified according to substances screened from the small samples. The experimental method comprises the steps of sample collection and pretreatment, sample separation and detection and data processing.

1.3.1 Collection of plasma specimens

Venous blood samples were taken from all patients, 4mL each, and whole blood was anticoagulated with heparin. Immediately centrifuging at 1500rpm/min for 15min in laboratory, extracting blood plasma, subpackaging, and freezing at-80 deg.C in refrigerator for use.

1.3.2 GC-MS plasma sample preparation method

Taking out plasma from a refrigerator at the temperature of-80 ℃, unfreezing the plasma at the normal temperature, uniformly mixing the plasma by vortex, effectively precipitating and centrifuging proteins in the plasma by using acetonitrile, and then performing derivatization (derivanted) treatment on the metabolites by adopting an oximation-silanization two-step derivatization method. The steps are briefly described as follows: mu.L of plasma was taken, added with 300. mu.L of acetonitrile and 100. mu.L of an internal standard solution (2, 4-dichlorobenzoic acid acetonitrile solution, 0.2 mg/mL), vortexed, mixed well, subjected to ultrasonic ice-bath for 15min, and centrifuged at 13000 r/min (4 ℃) for 15 min. And (3) taking 320 mu L of supernatant, carrying out vacuum freeze drying treatment in a new EP tube, adding 50 mu L of methoxylamine pyridine solution (15 mg/mL) into a freeze-dried sample, uniformly shaking, adding 50 mu L N-methyl trimethylsilyl trifluoroacetamide MSTFA (containing 1% trimethylchlorosilane TMCS as a catalyst), uniformly mixing, centrifuging at 13000 r/min (4 ℃) and taking supernatant for GC-MS analysis.

1.3.3 GC-MS

Chromatographic conditions are as follows: injecting 1 mu L of sample, and the split ratio is 10: 1. The initial temperature is 80 deg.C (maintained for 5 min), the temperature is increased to 170 deg.C at a constant speed of 10 deg.C/min, then increased to 250 deg.C at 5 deg.C/min, and finally increased to 300 deg.C at 10 deg.C/min (maintained for 5 min), the total time of the whole procedure is 46 min. The carrier gas is helium; constant flow, flow rate 1.0 mL/min.

Mass spectrum conditions: the sample inlet temperature is 300 ℃, and the ionization voltage is 70 eV; the ion source temperature is 230 ℃; the detector voltage is 1.2kV, and the ionization mode is as follows: and (3) electron bombardment ion source (EI), wherein the ionization voltage is 70eV, the mass scanning range is 30-600m/z, and the solvent cutting time is 5 min.

And (3) introducing the original data acquired by the GC-MS into an automatic mass spectrum deconvolution qualitative system (AMDIS) for deconvolution and ion peak screening. A quantitative integral table is established for ion peaks with signal-to-noise ratio S/N >3, batch sample integration is carried out by using a GC-MS workstation, and normalization is carried out by using an internal standard substance (2, 4 dichlorobenzoic acid). Identification of plasma metabolites is mainly by library search (NIST), online Database (HMDB), and combined retention index and standard validation

1.4 data processing

The output of GC-MS raw data is integrated by using MSD Chem station (Agilent in USA) and data correction is carried out by using an internal standard 2, 4-dichlorobenzoic acid integration result, the data is subjected to dimensionality reduction and noise reduction treatment by SIMCA-P v 13.0.0 software (Umetrics in Sweden), and differential metabolites are screened by combining univariate statistical analysis (Mann-Whitney's U-test) corrected by Benjamini-Hochberg method (FDR < 0.10). We also performed univariate statistical analysis and ROC analysis during the characteristic metabolite validation phase. Statistical processing is done by SPSS software.

Second, result in

2.1 quality control in GC-MS data acquisition

To monitor the stability of the assay system during the entire sample run, Quality Control (QC) samples were inserted uniformly into the detection sequence, with 1 QC sample inserted every 10 plasma samples. In the metabolic characteristics of the QC sample collected by the GC-MS technology in the final peak table, the proportion of more than 89% of the total peak area of the sample meets the condition that the Relative Standard Deviation (RSD) is less than 15%, and the method is shown in figure 1, which shows that the stability and the repeatability of the methods are good, and the requirements of plasma sample metabonomics research can be met.

2.2 plasma metabolism Spectroscopy

FIG. 2 shows a typical total ion flux chromatogram of plasma from GC-MS analysis, where FIG. 2A is a healthy control group and FIG. 2B is a CML group, showing different degrees of differences in plasma metabolite levels over certain retention times, suggesting that there is a difference in plasma metabolic phenotype between the two groups.

After sample ion peak extraction, peak identification, peak matching and substance identification, 305 and 406 metabolite ion information are respectively identified in normal human and CML group plasma. The data collected by GC-MS are imported into AMDIS software and NIST11.0 database for chromatographic peak identification and matching, and finally 44 metabolites including amino acids, organic acids, carbohydrates, fatty acids and the like are identified through database standard map, standard and retention index verification (Table 1). Note: the first ion of the characteristic fragment ions is a quantitative ion, and the second and third are qualitative ions.

TABLE 1 identification of plasma endogenous metabolites

Component name	Component name	Retention time (min)	Degree of matching
				Oxalic acid	Oxalic acid	5.913	803
Propionic acid	Propanoicacid	7.298	905
				Lactic acid	Lactic acid	7.894	887
Octanol (I)	Octanol	8.398	856
				Norvaline	L-Norvaline	8.405	899
Hexyl amyl ester	Hexyl neopentyl ester	8.716	801
				Butyric acid	Butanoicacid	9.11	869
Leucine	Leucine	9.33	911
				3-hydroxybutyric acid	3-Hydroxybutyricacid	10.132	911
Valeric acid	Pentanoic acid	10.742	879
				Isoleucine	Isoleucine	10.779	932
Valine	Valine	11.101	877
				Urea	Urea	11.122	957
Serine	Serine	11.135	893
				Glycerol	Glycerol	11.546	920
Threonine	Threonine	11.670	926
				Heptanol (Heptan)	Heptanol	12.014	911
Glycine	Glycine	12.2	811
				Decanol	11-Methyldodecanol	12.493	924
N-octyl alcohol	1-Octanol	12.523	835
				L-threonine	L-threonine	12.852	927
Proline	L-Proline	14.671	854
				Triazine	Tirazine	14.742	822
Phenylalanine	L-phenylalanine	14.8410	868
				Indole propionic acid	Indolepropionate	15.2	947
2, 4-Dichlorobenzoic acid (internal standard)	2,4-Dichlorobenzoate	15.3	947
				Myristic acid	Tetradecanoic acid	16.077	840
Phthalic acid salt	Phalate	16.200	937
				Sorbitol	D-sorbitol	19.124	878
D-glucose	D-Glucose	19.687	925
				D-talose	D-Talose	20.171	947
D-galactose	D-Galactose	20.259	878
				Tyrosine	Tyrosine	20.424	908
Palmitic acid	Hexadecanoic acid	20.870	889
				Heptadecanoic acid	Heptadecanoic acid	20.964	832
Inositol	Myo-Inositol	21.193	900
				Uric acid	Uric acid	21.369	883
Tryptophan	Tryptophan	22.691	929
				Octadecadienoic acid	Octadecadienoic acid	23.070	963
Oleic acid	Oleic acid	23.671	844
				Stearic acid	Octadecanoic acid	23.693	878
Ethylenediaminetetraacetic acid	Ethylenediaminetetraacetic acid	23.934	876
				Phthalic acid	Phthalic acid	31.713	865
Tocopherol	α-Tocopherol	37.052	951
				Cholesterol	Cholesterol	37.262	913

2.3 Pattern Recognition (PR) analysis

We performed pattern recognition analysis on healthy control groups and CML patients using supervised partial least squares discriminant with orthogonal signal filtering (OSC-PLSDA). As shown in FIG. 3A, two groups of samples are well separated in the space formed by the two groups of principal components t1 and t2, the circle shows a 95% confidence interval, and 1 sample in the CML group is a discrete value. FIG. 3A shows a model score plot, the predictive model interpretability (R)²X=30.3％，R²Y = 86.1%), predictability (Q)²=0.748), further verifying the reliability of the classification. The verification result of 200 times of permutation response permutation shows that the classification model has no overfitting phenomenon (R)²=0.308<0.4，Q²=-0.277<0, fig. 3B).

2.4 discovery of potential markers associated with CML

Differentially expressed metabolites were screened based on VIP values under the OSC-PLSDA model and P values from independent sample t-test. The results show that compared with a healthy control group, the CML patients have reduced plasma propionic acid, myristic acid, glycerol, sorbitol and abundance, and increased inositol, galactose, glucose, lactic acid, glycine, isoleucine, valine and indole propionic acid (VIP >1 and P <0.05, Table 2) and can be used as potential CML diagnosis biomarkers.

Table 2 CML patient plasma differential metabolite identification table (n =26)

Identification of substances	Name (R)	VIP value	P value	Trend of the
					Propionic acid	Propanoic acid	1.21	0.040	Descend
Lactic acid	Lactic acid	2.91	0.004	Rise up
					Isoleucine	Isoleucine	1.06	0.020	Rise up
Valine	L-Valine	1.01	0.032	Rise up
					Glycerol	Glycerol	1.26	0.005	Descend
Glycine	Glycine	1.18	0.006	Rise up
					Indole propionic acid	Indolepropionate	1.02	0.032	Rise up
Myristic acid	Tetradecanoic acid	1.31	0.004	Descend
					Sorbitol	D-sorbitol	1.18	0.024	Descend
Galactose	D-galactose	2.81	0.010	Rise up
					Glucose	D-glucose	1.39	0.004	Rise up
Inositol	Myo-Inositol	1.68	<0.001	Rise up

2.5 screening of therapeutic markers for TKI

Initial diagnosis and changes in plasma metabolites from TKI-treated CML patients were examined and differentially expressed metabolites were screened based on VIP values from the PLS-DA model and P values from the FDR-corrected Whitney U test. Further, the trend of change after the plasma characteristic change metabolite treatment of the primarily diagnosed CML patients was observed in two groups that obtained different effects by TKI treatment, and for two groups of substances with the same trend of change, two metabolite (identified as myristic acid and glycerol) variables were screened as the difference variables related to the TKI effect, and as can be seen from fig. 4, it was found that myristic acid and glycerol had opposite trends of change before and after TKI treatment success group and failure group, and two substances tended to approach to normal level in the treatment success group and the difference had statistical significance (p < 0.05), and showed opposite trends of change in the treatment failure group, but the change had no statistical significance (p > 0.05). The corresponding levels of the two markers in the normal group (95% confidence interval) were glycerol (0.093261-0.116505) and myristic acid (0.072245-0.081827). The above marker levels, glycerol (0.050631-0.091904) and myristic acid (0.061114-0.071716), respectively, indicate the occurrence of tumors. When the levels of the markers are respectively glycerol (0.066534-0.096718) and myristic acid (0.070224-0.079025), the TKI treatment is indicated to be successful. The levels of the markers are respectively glycerol (0.048284-0.071578) and myristic acid (0.056619-0.068569), which indicates that the TKI treatment fails.

2.6 validation of TKI efficacy markers

The ROC curve is firstly used for testing and analyzing the clinical efficacy of two metabolites, and the area under the combined ROC curve of the two metabolites is greatly improved. This combined marker was also observed to be significantly different between the different efficacy groups (p < 0.001 for myristic acid and p = 0.001 for glycerol) in an additional set of 194 independent samples randomly selected. ROC screening was also performed in the validation group, and the results are shown in fig. 5, where the combined markers were clinically efficacious and superior to the single metabolites.

Claims (3)

1. The application of myristic acid and glycerol as combined markers in preparing a detection kit for evaluating the curative effect of the chronic myelogenous leukemia TKI.

2. Use according to claim 1, wherein the myristic acid and glycerol are derived from plasma.

3. A method for detecting myristic acid and glycerol as in claim 1, wherein the detection method is a gas chromatography-mass spectrometry combination;

the gas chromatographic analysis conditions were: injecting 1 mu L of sample, the split ratio is 10:1, the initial temperature is 80 ℃, keeping for 5min, raising to 170 ℃ at a constant speed of 10 ℃/min, then raising to 250 ℃ at 5 ℃/min, finally raising to 300 ℃ at 10 ℃/min, keeping for 5min, the whole procedure is 46min in total, the carrier gas is helium, the constant current is constant, and the flow rate is 1.0 mL/min;

the mass spectrometry conditions were: the sample inlet temperature is 300 ℃, and the ionization voltage is 70 eV; the ion source temperature is 230 ℃; the detector voltage is 1.2kV, and the ionization mode is as follows: and (3) bombarding an ion source by electrons, wherein the ionization voltage is 70eV, the mass scanning range is 30-600m/z, and the solvent cutting time is 5 min.

Images