CN113138274B - Composition, application and lung cancer patient diagnosis kit - Google Patents
Composition, application and lung cancer patient diagnosis kit Download PDFInfo
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Abstract
The invention relates to a new application of micromolecular metabolites in a tissue sample in preparing a non-small cell lung cancer diagnosis kit, wherein the micromolecular metabolites comprise lactic acid, arachidonic acid and phosphatidylcholine PC (33. The invention also relates to a kit for detecting a lung cancer patient in a subject, which is used for judging whether the subject has lung cancer or not by detecting the relative concentration of each combined marker in a tissue sample from the subject, calculating the variable P value of the combined marker based on a binary logistic regression equation and then judging whether the subject has the lung cancer or not based on the determined cutoff point (cutoff). In addition, the combined marker also has better diagnostic capability on patients with early lung cancer. The kit can realize high-sensitivity and high-efficiency detection. The micromolecular metabolites have the characteristics of low detection cost and good repeatability. The invention can be applied to the auxiliary clinical diagnosis of the non-small cell lung cancer, has the characteristics of high diagnosis sensitivity, good diagnosis effect and capability of distinguishing the lung cancer at an early stage, and has better application prospect.
Description
Technical Field
The invention relates to the fields of analytical chemistry, biochemistry and clinical medicine, in particular to a composition, application and a lung cancer patient diagnosis kit.
Background
Lung cancer is the highest mortality cancer worldwide, accounting for one fourth of all cancer deaths worldwide, and the main reason for this phenomenon is the difficulty in diagnosing lung cancer, especially early lung cancer. Because early symptoms of lung cancer are not obvious, an effective early lung cancer screening method is lacked at present. Recent Cancer big data indicate that the five-year survival rate of early lung Cancer is 56%, however, only 16% of lung Cancer patients are diagnosed in the early stage, and 57% of lung Cancer patients are found at a late stage, with a five-year survival rate of only 5% (document 1. Therefore, the development of a new method for lung cancer diagnosis, especially for early lung cancer diagnosis, has very important practical significance for reducing lung cancer death rate.
Metabolomics has been widely used in cancer diagnosis as a new and advantageous tool in recent years. The technology based on chromatography-mass spectrometry is a main research means of metabonomics, the detection of small molecule metabolites as markers has been successfully applied in cancer diagnosis, and the researched samples comprise serum, plasma, urine and other body fluids, as well as placenta, tissues and the like. The marker has a single metabolite and also has several small molecule metabolites which form a combined metabolic marker. Specific examples are the detection of phenylalanine tryptophan and glycocholic acid as combined markers for liver cancer in blood samples (reference 2. It is worth mentioning that tissues can more intuitively reflect the original metabolic changes caused by disease relative to body fluids, which is important in finding disease markers (document 4. Tissue-based metabolomics is receiving increasing attention.
The invention utilizes the high performance liquid chromatography-mass spectrometry technology to obtain the metabolic profiles of non-small cell lung cancer tissues and normal lung tissues. Through multiple optimization, the combination of lactic acid, arachidonic acid and phosphatidylcholine PC (33) is determined to be used as a diagnostic marker of the non-small cell lung cancer. Lactic acid is produced by the process of glycolysis and elevated levels of lactic acid are important metabolic markers for cancer cells. Studies have shown that cancer cells exhibit an abnormally active glycolytic process, consuming large amounts of glucose to produce lactate accumulation. Cancer cells, even under oxygen-rich conditions, utilize glycolysis, a less efficient means of energy production to power their massive proliferation, a phenomenon known as The warburg effect (document 5. Arachidonic acid is an important anti-inflammatory factor, and its downstream metabolites, prostaglandin, leukotriene, etc., have been considered as novel preventive and therapeutic targets in Cancer (document 6. Phosphatidylcholine PC (33. Although the functions of the three metabolites have been discovered and focused on, the research shows that the concentrations of lactic acid, arachidonic acid and Phosphatidylcholine (PC) (33).
Disclosure of Invention
The invention aims to solve the practical problem that the diagnosis of lung cancer, particularly early lung cancer is difficult, provide an application of a combined small molecule biomarker in the diagnosis of non-small cell lung cancer patients, and provide an analysis and detection method for the small molecule metabolite.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
(1) Carrying out metabonomic fingerprint analysis on lung cancer tissues and normal lung tissues by using a high performance liquid chromatography-mass spectrometry combined metabonomic technology, and identifying 340 small molecule metabolites in total, wherein 48 metabolites have significant difference in the lung cancer tissues and the normal lung tissues;
(2) The combined marker variables for lung cancer diagnosis were determined by random combination among 48 differential metabolites between lung cancer tissue and normal lung tissue by an analytical method of binary logistic regression using the data statistics software SPSS. The sensitivity and specificity of the combination markers was then evaluated using the ROC (receiver operating characterization) curve. And finally, taking three factors of high sensitivity, high specificity and less marker number into comprehensive consideration, and determining the lactic acid, the arachidonic acid and the phosphatidylcholine PC (33.
(3) Use of a combination marker in the diagnosis of lung cancer patients: lung cancer tissues all had elevated concentrations of lactic acid, arachidonic acid, and phosphatidylcholine PC (33. These 3 metabolites were regressed to the combined marker variable P by a binary logistic regression method using the data statistics software SPSS, preferably the binary logistic regression equation is as follows:
the equation:
P＝1/(1+e -(0.024*a+0.039*b+0.451*c-9.365) )
where a is the relative concentration of lactic acid in the tissue sample, b is the relative concentration of arachidonic acid in the tissue sample, and c is the relative concentration of phosphatidylcholine PC (33.
The resulting variable P is elevated in lung cancer tissues and the value of this variable can be used to aid in the diagnosis of lung cancer. Here, the cut-off value of the combined marker variable is set to 0.758, based on the sample involved in the test, according to the optimal principle of diagnostic sensitivity and specificity, above which a lung cancer is likely.
(4) Use of a combination marker in the diagnosis of early stage lung cancer patients: early lung cancer tissues also showed elevated concentrations of all lactic acid, arachidonic acid and phosphatidylcholine PC (33. Therefore, the combined markers show good diagnostic capability in early lung cancer patients, the concentrations of the three metabolites of the early lung cancer patients are substituted into the regression equation, the three metabolites are judged by using the intercept value of 0.758, and the diagnostic accuracy can reach 94.1%.
(5) The model established has good discrimination ability for lung cancer including early lung cancer diagnosis (see fig. 3 and 4). In the diagnosis of lung cancer, AUC =0.963, sensitivity =0.826, and specificity =0.978 of the combined marker. In early lung cancer diagnosis, AUC =0.945, sensitivity =0.800, and specificity =0.950 for the combined markers. In conclusion, excellent ROC curve results show that the markers in the group have good diagnosis potential in the diagnosis of lung cancer including early lung cancer.
(6) The invention has the following effects: the metabolites small molecules lactic acid, arachidonic acid and phosphatidylcholine PC (33) in tissue samples can be used in combination for the diagnosis of lung cancer, including early stage lung cancer. The combined marker provided by the invention has the characteristics of high specificity and high sensitivity for diagnosing lung cancer including early lung cancer. In conclusion, the invention has very important practical significance for diagnosing lung cancer, timely discovering early lung cancer and reducing lung cancer death rate.
The kit can realize high-sensitivity and high-efficiency detection of several micromolecular metabolites, and has the characteristics of low detection cost and good repeatability. The invention can be applied to the auxiliary clinical diagnosis of the non-small cell lung cancer, has the characteristics of high diagnosis sensitivity, good diagnosis effect and capability of distinguishing the lung cancer at an early stage, and has better application prospect.
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The above features and advantages of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which:
figure 1. Content variation (mean ± standard error) of lactic acid, arachidonic acid and phosphatidylcholine PC (33) in example 1 in normal lung tissue and lung cancer tissue samples. * Represents p <0.05, represents p <0.01, represents p <0.001.
Fig. 2. Content variation (mean ± standard error) of lactic acid, arachidonic acid, and phosphatidylcholine PC (33) in normal lung tissue and early lung cancer tissue in example 2. * Represents p <0.05, represents p <0.01, represents p <0.001.
FIG. 3 is a graph showing the results of diagnosis of discrimination between normal lung tissue and lung cancer tissue for each of the combined markers in example 1. (A) Representing the ROC curve of the combination markers in the diagnosis of lung cancer. (B) And a scatter diagram showing the probability P value obtained according to the discriminant formula.
FIG. 4 is a graph showing the results of diagnosis of discrimination between normal lung tissue and early lung cancer tissue for each of the combined markers in example 2. (A) Representing the ROC curve of the combined markers in the diagnosis of early lung cancer. (B) And a scatter diagram showing the probability P value obtained according to the discriminant formula.
Detailed Description
Example 1
1. Tissue sample collection
All volunteers enrolled in the study signed informed consent prior to sampling.
Tissues from tumor foci were collected under identical conditions from 46 patients with non-small cell lung cancer, 17 of which were early stage (stage I) lung cancer, 17 of which were intermediate stage (stage II) lung cancer, and 12 of which were locally advanced (stage III) lung cancer. All patients should take enema or cathartic one day before lung tumor resection, and should be prohibited to eat for 8 hr or more before operation, and smoker should stop smoking for at least 2-3 weeks before operation. Under the same conditions, another 46 cases of normal lung tissues were taken as healthy controls. The collected tissue samples are dipped with blood on the surface through gauze, then are quickly transferred to liquid nitrogen for short-term storage, and finally are transferred to a refrigerator with the temperature of 80 ℃ below zero for long-term storage.
2. Analytical method
2.1 separate pretreatment of tissue samples
The tissue samples were thawed separately on ice at room temperature, 10mg of the tissue samples were weighed into 2ml centrifuge tubes, and the ground magnetic beads were added, followed by 600. Mu.L of methanol/water solution (8/2 vol/vol) containing the extract of the stable isotope labeled standard (internal standard), i.e., containing: D5-phenylalanine (5-10. Mu.g/ml), D4-chenodeoxycholic acid (1-5. Mu.g/ml) and D3-palmitoyl carnitine (0.1-0.5. Mu.g/ml), to precipitate the proteins. At a vibration frequency of 28HZ, grinding for 2 minutes, sufficiently breaks up the tissue to extract the metabolites therein. After homogenization, the tissue was centrifuged at 13000g at 4 ℃ for 10 minutes. Two 200. Mu.L portions of the supernatant were lyophilized in duplicate for each tissue sample and stored in a-80 ℃ freezer. Before sample injection, the lyophilized sample is redissolved by 80 μ L acetonitrile/water (1/1 vol/vol), vortexed for 1 minute, centrifuged at 13000g for 10 minutes at 4 ℃, and the supernatant is collected for instrumental analysis, and the sample injection volume is 5 μ L.
2.2 ultra high performance liquid chromatography-Mass Spectrometry
One of two parallel samples of each tissue sample is detected in a positive ion mode, and the other is detected in a negative ion mode;
(1) Liquid phase conditions:
and (3) separating the metabolites in the tissue supernatant by using a Waster ultra-performance liquid chromatography system. The column in positive ion mode was a Waters BEH C8 column (specification: 50 mm. Times.2.1mm, 1.7 μm) (Waters, milford, mass.), the column temperature was 60 ℃ and the flow rate was 0.4ml/min. The mobile phase was water with a final volume concentration of 0.1% formic acid (phase a) and acetonitrile with a final volume concentration of 0.1% formic acid (phase B). Elution (v/v) started with a gradient of 5%B, maintained for 0.5min, then increased linearly to 40% B in 1.5min, increased linearly to 100% B in 6min and maintained for 2min, then decreased linearly to 10.1min back to initial gradient 5%B, equilibrated for 1.9min. The column in the negative ion mode was ACQUITY UPLC HSST 3 (specification: 50 mm. Times.2.1mm, 1.8 μm) (Waters, milford, mass.), the column temperature was 60 ℃ and the flow rate was 0.4ml/min. Mobile phase A is water with a final concentration of 6.5mM NH 4 HCO 3 Phase B is 6.5mM NH 4 HCO 3 95% methanol-water solution (v/v). Elution start gradient 2%B (v/v), maintained for 0.5min, linearly increased to 40% b in 1.5min, then linearly increased to 100% b in 6min and maintained for 5min, then linearly decreased back to initial gradient 2%B at 13.1min, equilibrium for 1.9min.
(2) Conditions of Mass Spectrometry
The mass spectrometry system adopts tripleTOF TM 5600+ mass spectrometer (AB SCIEX). The air curtain gas (CUR), atomizing gas (GS 1) and assist gas (GS 2) pressures were 35, 55 and 55psi, respectively. The declustering voltage is 100V, and the collision energy is 10V. The scanning range is 80-1000Da. Electrospray ionization is adopted, and the spraying voltage is 5500V and the temperature is 550 ℃ in a positive ion mode. The spray voltage in the negative ion mode was 4500V, temperature 450 ℃. The second level adopts an information-dependent acquisition mode, the first 15 ions with higher response are selected to automatically perform second-level fragment scanning and second-level collisionCan be respectively 15V,30V,45V and 30 +/-15V.
2.3 tissue test results and methods of aided diagnosis
The peak area in the chromatogram for each metabolite was divided by the peak area of the corresponding internal standard and corrected to the relative concentration value per gram of tissue for each metabolite. The relative concentration values of lactic acid, arachidonic acid and phosphatidylcholine PC (33).
Table 1 relative concentration values of lactic acid, arachidonic acid, and phosphatidylcholine PC (33
Quantitative analysis the relative concentrations of lactic acid, arachidonic acid and phosphatidylcholine PC (33) in lung cancer tissue and normal lung tissue are shown in figure 1; lung cancer tissues all had elevated concentrations of lactic acid, arachidonic acid, and phosphatidylcholine PC (33.
These 3 metabolites were regressed to the combined marker variable P by a binary logistic regression method using the data statistics software SPSS, preferably the binary logistic regression equation is as follows:
the equation:
P＝1/(1+e -(0.024*a+0.039*b+0.451*c-9.365) )
wherein a is the relative concentration of lactic acid in the tissue sample, b is the relative concentration of arachidonic acid in the tissue sample, c is the relative concentration of phosphatidylcholine PC (33; e is the base of the natural logarithmic function, sometimes referred to as the Euler number.
The content of each marker (table 1) was substituted into the above regression equation to calculate the probability P. When the combined marker is used for diagnosing a lung cancer patient through a discriminant variable P, the optimal cut-off value is 0.758, namely when the probability P value of the combined marker is more than 0.758, the lung cancer is considered. The diagnostic results were as follows: the area under the ROC curve is 0.963, the sensitivity value is 0.826, and the specificity value is 0.978; the accuracy of the model on normal lung tissue and lung cancer tissue is 84.9% and 97.4% respectively. The results of the diagnosis of the combined markers distinguishing normal lung tissue from lung cancer tissue are shown in FIGS. 3A and B. The results show that the combined markers have good lung cancer diagnosis capability.
Example 2
In order to verify that the combined marker has a good diagnosis effect on early lung cancer patients, 20 early non-small cell lung cancer patient tissues and 20 normal lung tissue samples are added for verification. The analytical methods, instruments and reagents used in this experiment were all in full agreement with example 1.
1. Tissue samples were collected and placed in volunteers for informed consent prior to collection.
Under the same conditions, 20 early stage lung cancer tissue samples and 20 normal lung tissue samples were collected, stored and examined as described in example 1.
2. Tissue testing and aided diagnosis results
The concentrations of lactic acid, arachidonic acid and phosphatidylcholine PC (33.
The content of each marker (table 2) was substituted into the regression equation described in example 1, and the probability P was calculated. When the combined marker is used for diagnosing patients with early lung cancer by the discrimination variable P, the optimal cut-off value of 0.758 in example 1 is still adopted, namely, when the probability P value of the combined marker is more than 0.758, the combined marker is considered as early lung cancer. The diagnostic results were as follows: the area under the ROC curve is 0.963, the sensitivity value is 0.826, and the specificity value is 0.978; the accuracy of the model on normal lung tissue and lung cancer tissue is 84.9% and 97.4% respectively. The diagnosis results of the combined markers distinguishing normal lung tissue and lung cancer tissue are shown in fig. 3A and B. Therefore, the group of markers also has good diagnosis effect on early lung cancer.
Table 2 relative concentration values of lactic acid, arachidonic acid, and phosphatidylcholine PC (33
The kit has the characteristics of low detection cost and good stability in the diagnosis of lung cancer patients. Meanwhile, the invention can also be applied to clinical diagnosis of early lung cancer patients, and has high diagnosis accuracy. In conclusion, the invention has higher development value.
It should be understood that while the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein, and any combination of the various embodiments may be made therein without departing from the spirit and scope of the present invention as defined by the following claims.
Claims (7)
1. Use of a combined small molecule marker composition for diagnosing non-small cell lung cancer or non-small cell early lung cancer for preparing a lung cancer diagnostic kit, wherein the composition is a tissue metabolite composition which can be used for diagnosing non-small cell lung cancer or non-small cell early lung cancer patients and consists of the following tissue metabolites: lactic acid, arachidonic acid and phosphatidylcholine PC (33: 2); wherein the kit comprises standards for each component of the composition, a tissue sample extraction solution, an internal standard required to detect the concentration of the tissue metabolite, and an eluent to elute a chromatographic column.
2. The application of the composition of claim 1, wherein the mass ratio of the components is as follows: lactic acid: arachidonic acid: phosphatidylcholine PC (33: 2) =3-13:1-5:0.05-0.1.
3. The use of claim 1, wherein the internal standards for detecting the various tissue metabolites of claim 1 are D5-phenylalanine, D4-chenodeoxycholic acid, and D3-palmitoyl carnitine.
4. The use according to claim 1, the kit comprising a standard, an extraction solution reagent and an internal standard to detect the concentration of each tissue metabolite of the corresponding standard in the tissue sample, and an eluent to elute the chromatographic column:
and (3) standard substance: including lactic acid, arachidonic acid, and phosphatidylcholine PC (33: 2).
5. Use according to claim 4, (1) an extraction solution containing an internal standard comprising: the methanol/water solution of the D5-phenylalanine, the D4-chenodeoxycholic acid and the D3-palmitoyl carnitine has the volume ratio of 9/1-8/2;
(2) Eluting the eluent of the chromatographic column; the method comprises the following steps:
positive ion mode:
mobile phase A: formic acid/water with volume concentration of 0.1-0.5%;
mobile phase B: formic acid/acetonitrile at a volume concentration of 0.1% to 0.5%;
negative ion mode:
mobile phase A: an aqueous solution containing 6-7 mM ammonium bicarbonate;
mobile phase B: methanol/water solution containing 6-7 mM ammonium bicarbonate in a volume ratio of 95/5.
6. The use according to claim 3 or 4, wherein the kit comprises standard quality concentration ranges of: 3-13. Mu.g/ml lactic acid, 1-5. Mu.g/ml arachidonic acid and 0.05-0.1. Mu.g/ml phosphatidylcholine PC (33: 2).
7. The use according to claim 4, wherein,
the internal standard concentrations are: 5-10 mug/ml of stable isotope labeled phenylalanine, 1-5 mug/ml of stable isotope labeled chenodeoxycholic acid and 0.1-0.5 mug/ml of stable isotope labeled palmitoyl carnitine.
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| Agnieszka Klupczynska等.Serum lipidome screening in patients with stage I non‑small cell lung cancer.《Clinical and Experimental Medicine》.2019,第19卷 * |
| Jinbo Liu等.Serum Free Fatty Acid Biomarkers of Lung Cancer.《Chest》.2014,第146卷(第3期), * |
| Long T. Hoang等.Metabolomic, transcriptomic and genetic integrative analysis reveals important roles of adenosine diphosphate in haemostasis and platelet activation in non-small-cell lung cancer.《Molecular Oncology》.2019,第13卷 * |
| 卢兴兵 等.６种血清肿瘤标志物在肺癌辅助诊断中的应用评价.《检验医学与临床》.2018,第15卷(第18期), * |
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