CN115219705A

CN115219705A - Application of biomarker in Cushing syndrome diagnosis

Info

Publication number: CN115219705A
Application number: CN202210854261.4A
Authority: CN
Inventors: 胡晓敏; 李菡钰; 郑威扬; 周瑞林; 邓侃; 范阅; 王泽源; 孙玥燊; 赵心悦; 吴清扬; 卢琳; 姚勇; 苏婉; 刘继方
Original assignee: Peking Union Medical College Hospital Chinese Academy of Medical Sciences
Current assignee: Peking Union Medical College Hospital Chinese Academy of Medical Sciences
Priority date: 2022-07-14
Filing date: 2022-07-14
Publication date: 2022-10-21
Anticipated expiration: 2042-07-14
Also published as: CN115219705B

Abstract

The invention discloses application of a biomarker in Cushing syndrome diagnosis, and the application is characterized in that through carrying out non-targeted omics detection on a sample, differential metabolites in healthy people and patients with Cushing syndrome are identified, the level of the metabolite LysoPE 22.

Description

Application of biomarker in Cushing syndrome diagnosis

Technical Field

The invention belongs to the field of biological medicines, and relates to application of a biomarker in Cushing syndrome diagnosis.

Background

Cushing Syndrome (CS) is a group of clinical symptoms caused by excessive secretion of chronic glucocorticoids, and Cushing Disease and adrenocortical adenoma are the two most common types. The cushing's disease accounts for about 70% of cushing's syndrome and is caused by excessive secretion of ACTH by the pituitary. The clinical manifestations of cushing's syndrome are mainly obesity, hypertension, secondary diabetes, central obesity, muscular atrophy, hirsutism, menstrual disorder, sexual dysfunction, purple streaks, lunar face, osteoporosis, acne and pigmentation, edema, headache, unhealed wound, etc. The mean diagnosis time after the first symptom of cushing's syndrome is 6.0 years, and the typical symptom of cushing's syndrome is usually slow in progress, strong in non-specificity, and probably ignored or attributed to other common diseases due to its clinical manifestations. In addition, investigations have shown that untreated cushing's syndrome population has a mortality rate of 50% within 5 years, mainly due to cardiovascular events (congestive heart failure or myocardial infarction) or infections. The incidence of cushing's syndrome is high and can affect the muscular system, leading to myopathy and congestive heart failure; affecting bone integrity, leading to early osteoporosis; affecting reproductive function, resulting in menstrual disorder and infertility; causing mood disorders (Prague, julia Kate, stephanie May, and Benjamin Cameron Whitelaw. "Cushing's syndrome." Bmj (2013)). Whereas delayed diagnosis of cushing's syndrome may result in irreversible organ damage.

At present, besides the traditional diagnostic method, the gene technology is tried to be used for the qualitative diagnosis of CS, but the research of large samples is still lacked, so that the search of reliable and stable biomarkers is important for the qualitative diagnosis of Cushing's syndrome. The serum marker is convenient to detect, simple to operate and low in cost, and is approved to be applied to diagnosis, treatment and the like of some diseases. Metabolomics is an emerging omics technology and is an important part of the composition of "system biology". The metabonomics mainly aims at researching small molecular compounds with the mass less than 1000 in body fluid, cells and tissues of a biological organism, and a main analysis platform is analyzed by a modern instrument with high resolution, high sensitivity and high flux, such as a chromatography-mass spectrometry combined technology, a nuclear magnetic resonance technology and the like. It reveals the change of metabolic pathway in vivo by qualitatively or quantitatively researching the change of the type, quantity, content and the like of disturbed metabolic products (endogenous metabolites) in the organism. Metabonomics is positioned at the terminal of transcription, gene and protein expression, can directly and accurately reflect the current pathophysiological state of an organism, is widely applied to the fields of disease diagnosis, drug research and development, nutrition, toxicology, sports medicine and the like, and particularly provides reliable theoretical basis and means for clinical disease diagnosis. The research on the metabolites with significant differences in blood and the search for serum markers are of great significance for realizing the early diagnosis of Cushing's syndrome.

Disclosure of Invention

In order to evaluate the correlation between the metabolites and the Cushing syndrome, the invention discovers the biomarker suitable for the Cushing syndrome diagnosis by collecting samples of healthy controls and the Cushing syndrome, comprehensively analyzing the metabonomics of the samples, screening the metabolites with the contents showing significant difference in the two groups and further analyzing the diagnosis effectiveness of the different metabolites.

Specifically, the invention provides the following technical scheme:

the invention provides application of a reagent for detecting a biomarker in a sample in preparing a product for diagnosing cushing's syndrome, wherein the biomarker is LysoPE 22.

The term "biomarker" refers to a substance within a biological system that serves as an indicator of the biological state of the system. In the art, the term "biomarker" is sometimes also applied to the means of detecting said endogenous substance (e.g. antibodies, nucleic acid probes etc., imaging systems). A biomarker may be any kind of molecule present in a living organism, such as a nucleic acid (DNA, mRNA, miRNA, rRNA, etc.), a protein (cell surface receptor, cytoplasmic protein, etc.), a metabolite or hormone (blood glucose, insulin, estrogen, etc.), a molecule with another molecule (e.g. a sugar moiety or phosphoryl residue on a protein, a methyl residue on genomic DNA) or a substance that has been internalized by the organism or some modified characteristic of a metabolite of this substance.

As used herein, the terms "biomarker", "molecular marker" are interchangeable and refer to a molecule that is differentially present in a sample taken from a healthy subject not suffering from cushing's syndrome compared to a comparable sample taken from a control subject, e.g., a subject suffering from cushing's syndrome. Thus, the biomarkers of the invention provide a possibility for the development of cushing's syndrome.

The term "subject" as used herein refers to a living animal or human that is to be sensitive to a condition, particularly cushing's syndrome. Preferably, the subject is a mammal, including human and non-human mammals such as dogs, cats, pigs, cows, sheep, goats, horses, rats, and mice. More preferably, the subject is a human. The term "subject" does not exclude normal persons who do not have cushing's syndrome disease.

The term "diagnosing" as used herein refers to identifying a disease in a subject suffering from the symptoms of cushing's syndrome.

Further, the reagent includes a reagent for detection by chromatography, spectroscopy, mass spectrometry, chemical analysis, or a combination thereof.

As known to those skilled in the art, mass spectrometers generally consist of three components: an ion source, a mass analyzer, and a detector. The ionizer converts a portion of the sample into ions. As described below, there are a wide variety of ionization techniques depending on the phase of the sample (solid, liquid, gas) and the efficiency of the various ionization mechanisms of unknown species. Mass spectrometers also typically include an extraction system that removes ions from a sample before passing the ions through a mass analyzer and onto a detector. The difference in the mass-to-charge ratios (m/z) of the fragments allows the mass analyser to classify ions by their mass-to-charge ratios. Finally, the detector measures the value of the amount of the indicator, thereby providing data for calculating the abundance of each ion present.

In a typical mass spectrometry procedure, the first step involves ionization of the sample. In one embodiment, ionizing comprises Electron Ionization (EI), which comprises bombarding the sample with electrons. In another embodiment, the ionization comprises Chemical Ionization (CI) according to which ions are generated by the collision of the analyte with ions of a reactant gas present in the ion source (examples of suitable reactant gases include methane, ammonia, and isobutane). In another embodiment, the ionization comprises Atmospheric Pressure Chemical Ionization (APCI). In another embodiment, the ionization comprises Atmospheric Pressure Photon Ionization (APPI).

When the ionization is electron ionization, this typically results in the mass ion having the same mass (M) as the parent molecule, but being charged (M + or M-). When the ionization is chemical ionization, this generally results in a mass ion having the mass of the parent molecule and the chemical species used to ionize the molecule, well-known examples include [ M + H ] +, [ M-H ] -, [ M + NH4] +, and [ M + Na ] +. Such molecular ions are also referred to as "pseudo molecular ions" in the present specification.

In another embodiment, the ionization comprises electrospray ionization (ESI), wherein a liquid containing the analyte of interest is dispersed by electrospray into a fine aerosol. In another embodiment, the ionization comprises matrix-assisted laser desorption/ionization (MALDI), which typically comprises a three-step method, as follows: (1) Mixing the sample in a suitable matrix material and applying it to a surface, typically a metal plate; (2) The sample is typically irradiated with a pulsed laser, triggering ablation and desorption of the sample and matrix material; (3) Analyte molecules are ionized by protonation or deprotonation in a hot plume of ablated gas, accelerating the ions into a mass spectrometer for their analysis. These ionization techniques are well known to those skilled in the art. Ionization, particularly electron ionization, may cause the fragmentation of some sample molecules into charged fragments.

After ionization, the ions generated in the first step are separated according to the mass-to-charge ratio (m/z) in the mass analyzer. This is typically done by one or more of the following mass to charge separation techniques: the electric field and/or magnetic field deflections used conventionally in quadrupole mass spectrometers are determined by the quadrupole electric field used in quadrupole mass spectrometers, by the ion trap quadrupole electric field used in ion trap mass spectrometers, by the longitudinal ion travel time used in time-of-flight mass spectrometers, and by the electric and magnetic field deflections used in electrical and magnetic sector mass spectrometers. The last technique involves accelerating the ions and subjecting them to an electric or magnetic field, such that the electric or magnetic field deflects the ions. Ions having the same mass-to-charge ratio will experience the same amount of deflection.

After separation, the ions are detected. Typically, the detector records the induced charge or current generated when the ions pass through or impact the surface. In a scanning instrument, the signal generated in the detector during scanning and the position of the instrument during scanning will produce a mass spectrum, a record of ions as a function of m/z.

Further, the reagent is selected from a reagent for combined detection of chromatography-mass spectrometry.

As an alternative embodiment, the chromatographic technique is gas chromatography, and the combined method is referred to as gas chromatography-mass spectrometry (GC/MS, GCMS or GC-MS). As known to those skilled in the art, in this technique, a gas chromatograph is used to separate the different compounds. The separated compound stream is sent to a mass spectrometer for ionization, mass analysis, and detection as described above.

In another preferred embodiment, the chromatographic method is liquid chromatography and the combined method is known as liquid chromatography-mass spectrometry (LC/MS, LCMS or LC-MS). As known to those skilled in the art, this technique uses liquid mobile phase chromatography to separate compounds. Typically, the liquid phase is a mixture of water and an organic solvent. The separated compound stream is then fed to a mass spectrometer for ionization, mass analysis and detection as described above.

In a preferred embodiment, the mass spectrometry is tandem mass spectrometry. The tandem mass spectrometry is selected from the group consisting of ion trap mass spectrometry, quadrupole time-of-flight mass spectrometry, triple quadrupole mass spectrometry, quadrupole ion trap mass spectrometry, ion mobility-quadrupole ion trap-time-of-flight mass spectrometry, quadrupole-orbitrap mass spectrometry, ion mobility spectrometer-quadrupole ion trap mass spectrometry, quadrupole-orbitrap mass spectrometry, triple quadrupole-orbitrap mass spectrometry, quadrupole ion trap-orbitrap mass spectrometry, time-of-flight or ion trap-fourier transform mass spectrometry.

"sample" and "sample" are used interchangeably herein and, as used herein, refer to a composition obtained or derived from a subject (e.g., an individual of interest) that comprises cells and/or other molecular entities to be characterized and/or identified based on, for example, physical, biochemical, chemical, and/or physiological characteristics. For example, the phrase "disease sample" or variants thereof refers to any sample obtained from a subject of interest that is expected or known to contain the cells and/or molecular entities to be characterized. Samples include, but are not limited to, tissue samples, primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreous humor, lymph, synovial fluid, follicular fluid (follicullar fluid), semen, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, urine, and tissue culture fluid (tissue culture medium), tissue extracts such as homogenized tissue, cell extracts, and combinations thereof.

Further, the sample is selected from blood, serum, plasma.

After separation and analysis by appropriate mass spectrometry, the metabolites identified in the sample of the subject can be used to detect cushing's syndrome in the subject. Typically, this step comprises comparing the level of the biomarker in the sample from the subject to a reference value, wherein the level of the biomarker in the sample compared to the reference value is indicative of cushing's syndrome in the subject.

As an alternative embodiment, a decrease in the level of the biomarker in the sample compared to the reference value is indicative of cushing's syndrome in the subject. In one embodiment, an increase in the level of a metabolite in the sample compared to the reference value is indicative of cushing's syndrome in the subject. The difference compared to the reference value may be an increase as defined and exemplified below or a decrease as defined and exemplified below.

Typically, an increase or decrease in the level of a biomarker in a sample compared to a reference value is measured as a% mean difference. In the present specification, the term "% mean difference" refers to the% difference in total ion count per mass ion in a subject with cushing's syndrome compared to the total ion count in a reference subject (i.e., control).

Where the measurement values include an increase in total ion count of the appropriate mass ion in a subject with cushing's syndrome, the% mean difference is measured as (mean disease/mean control) × 100% compared to the reference value. Where the measurement values include a decrease in total ion count of the appropriate mass ions in a subject with a disease,% mean difference, as compared to the reference value, is measured as (mean control/mean disease) × 100%. Thus, the% mean difference always exceeds 100%, except for the case where the total ion count of the appropriate mass ions in the subject with cushing's syndrome is exactly the same as the reference value.

In embodiments wherein an increase in the level of the biomarker in the sample compared to the reference value is indicative of cushing's syndrome in the subject, the% average difference in the level of the one or more biomarkers in the sample compared to the reference value is not particularly limited. In an embodiment, the% average difference is at least 100%, such as at least 101%, such as at least 102%, such as at least 103%, such as at least 104%, such as at least 105%, such as at least 106%, such as at least 107%, such as at least 108%, such as at least 109%, such as at least 110%, such as at least 112%, such as at least 114%, such as at least 116%, such as at least 118%, such as at least 120%, such as at least 130%, such as at least 140%, such as at least 150%, such as at least 160%, such as at least 170%, such as at least 180%, such as at least 190%, such as at least 200%, such as at least 250%, such as at least 300%, such as at least 350%, such as at least 400%, such as at least 450%, such as at least 500%, such as at least 550%, such as at least 600%, such as at least 650%, such as at least 700%, such as at least 750%, such as at least 800%, for example at least 850%, such as at least 900%, such as at least 950%, such as at least 1000%, such as at least 1100%, such as at least 1200%, such as at least 1300%, such as at least 1400%, such as at least 1500%, such as at least 1600%, such as at least 1700%, such as at least 1800%, such as at least 1900%, such as at least 2000%, such as at least 2500%, such as at least 3000%, such as at least 3500%, such as at least 4000%, such as at least 4500%, such as at least 5000%, such as at least 5500%, such as at least 6000%, such as at least 6500%, such as at least 7000%, such as at least 7500%, such as at least 8000%, such as at least 8500%, such as at least 9000%, such as at least 9500%, such as at least 10,000%, such as at least 11,000%, such as at least 12,000%, such as at least 13,000%, such as at least 14,000%, such as at least 15,000%, such as at least 16,000%, for example at least 17,000%, such as at least 18,000%, such as at least 19,000%, such as at least 20,000%, such as at least 25,000%, such as at least 30,000%, such as at least 35,000%, such as at least 40,000%, such as at least 45,000%, such as at least 50,000%, such as at least 55,000%, such as at least 60,000%, such as at least 65,000%, such as at least 70,000%, such as at least 75,000%, such as at least 80,000%, such as at least 85,000%, such as at least 90,000%, such as at least 95,000%, such as at least 100,000%.

In embodiments wherein an increase in the level of the biomarker in the sample compared to the reference value is indicative of cushing's syndrome in the subject, the% mean difference is typically 101% to 15,000%, e.g. 105% to 12,000%, e.g. 110% to 10,000%, e.g. 110% to 9000%, e.g. 120% to 8000%, e.g. 130% to 7000%, e.g. 140% to 6000%, e.g. 150% to 5000%, e.g. 160% to 4000%, e.g. 170% to 3000%, e.g. 180% to 2500%, e.g. 190% to 2250%, e.g. 200% to 2000%, e.g. 250% to 1900%, e.g. 300% to 1800%, e.g. 350% to 1700%, e.g. 400% to 1600%, e.g. 450% to 1550%, e.g. 500% to 1500%.

In embodiments wherein a decreased level of the biomarker in the sample as compared to the reference value is indicative of cushing's syndrome in the subject, the% average difference in the level of the biomarker in the sample as compared to the reference value is not particularly limited. In one embodiment, the% mean difference is at least 100%, such as at least 101%, such as at least 102%, such as at least 103%, such as at least 104%, such as at least 105%, such as at least 106%, such as at least 107%, such as at least 108%, such as at least 109%, such as at least 110%, such as at least 112%, such as at least 114%, such as at least 116%, such as at least 118%, such as at least 120%, such as at least 130%, such as at least 140%, such as at least 150%, such as at least 160%, such as at least 170%, such as at least 180%, such as at least 190%, such as at least 200%, such as at least 250%, such as at least 300%, such as at least 350%, such as at least 400%, such as at least 450%, such as at least 500%, such as at least 550%, such as at least 600%, such as at least 650%, such as at least 700%, such as at least 750%, such as at least 800%, for example at least 850%, such as at least 900%, such as at least 950%, such as at least 1000%, such as at least 1100%, such as at least 1200%, such as at least 1300%, such as at least 1400%, such as at least 1500%, such as at least 1600%, such as at least 1700%, such as at least 1800%, such as at least 1900%, such as at least 2000%, such as at least 2500%, such as at least 3000%, such as at least 3500%, such as at least 4000%, such as at least 4500%, such as at least 5000%, such as at least 5500%, such as at least 6000%, such as at least 6500%, such as at least 7000%, such as at least 7500%, such as at least 8000%, such as at least 8500%, such as at least 9000%, such as at least 9500%, such as at least 10,000%, such as at least 11,000%, such as at least 12,000%, such as at least 13,000%, such as at least 14,000%, such as at least 15,000%, such as at least 16,000%, for example at least 17,000%, such as at least 18,000%, such as at least 19,000%, such as at least 20,000%, such as at least 25,000%, such as at least 30,000%, such as at least 35,000%, such as at least 40,000%, such as at least 45,000%, such as at least 50,000%, such as at least 55,000%, such as at least 60,000%, such as at least 65,000%, such as at least 70,000%, such as at least 75,000%, such as at least 80,000%, such as at least 85,000%, such as at least 90,000%, such as at least 95,000%, such as at least 100,000%.

In embodiments wherein a decrease in the level of the biomarker in the sample compared to the reference value is indicative of cushing's syndrome in the subject, the% mean difference is typically 101% to 15,000%, e.g. 105% to 12,000%, e.g. 110% to 10,000%, e.g. 110% to 9000%, e.g. 120% to 8000%, e.g. 130% to 7000%, e.g. 140% to 6000%, e.g. 150% to 5000%, e.g. 160% to 4000%, e.g. 170% to 3000%, e.g. 180% to 2500%, e.g. 190% to 2250%, e.g. 200% to 2000%, e.g. 250% to 1900%, e.g. 300% to 1800%, e.g. 350% to 1700%, e.g. 400% to 1600%, e.g. 450% to 1550%, e.g. 500% to 1500%.

Preferably the biomarkers are present at levels that are statistically significant (i.e. p-value less than 0.05 and/or q-value less than 0.10, as determined using Welch's T-Test (Welch's T-Test) or Wilcoxon rank-sum Test).

The second aspect of the present invention provides a kit for diagnosing cushing's syndrome, comprising a reagent for detecting LysoPE 22; and instructions for using the kit to assess whether a subject is suffering from or susceptible to cushing's syndrome.

Further, the reagent detects the content and/or concentration of LysoPE 22.

Further, the kit also comprises reagents for processing the sample.

The most reliable results are possible when processing samples in a laboratory environment. For example, a sample may be taken from a subject in a doctor's office and then sent to a hospital or commercial medical laboratory for further testing. However, in many cases, it may be desirable to provide immediate results at the clinician's office or to allow the subject to perform the test at home. In some cases, the need for testing that is portable, prepackaged, disposable, ready to use by the subject without assistance or guidance, etc., is more important than a high degree of accuracy. In many cases, especially in the presence of a physician's follow-up, it may be sufficient to perform a preliminary test, even a test with reduced sensitivity and/or specificity. Thus, assays provided in kit form can involve the detection and measurement of relatively small amounts of metabolites to reduce the complexity and cost of the assay.

Any form of sample assay capable of detecting a metabolite in a sample as described herein may be used. Typically, the assay will quantify the metabolites in the sample to an extent, such as whether their concentration or amount is above or below a predetermined threshold. Such kits may take the form of test strips, dipsticks, cartridges, chip-based or bead-based arrays, multi-well plates, or a series of containers, and the like. One or more reagents are provided to detect the presence and/or concentration and/or amount of a selected sample metabolite. The sample from the subject may be dispensed directly into the assay or indirectly from a stored or previously obtained sample. The presence or absence of a metabolite above or below a predetermined threshold may be indicated, for example, by chromogenic, fluorogenic, electrochemiluminescent or other output (e.g., in an Enzyme Immunoassay (EIA), such as an enzyme-linked immunoassay (ELISA)).

In one embodiment, the kit may comprise a solid substrate such as a chip, slide, array, or the like, having reagents capable of detecting and/or quantifying one or more metabolites of a sample immobilized at predetermined locations on the substrate. As an illustrative example, the chip may be provided with reagents immobilized at discrete predetermined locations for detecting and quantifying the presence and/or concentration and/or amount of a biomarker in a sample. As described above, elevated levels of the biomarker are found in a sample from a subject with cushing's syndrome. The chip may be configured such that a detectable output (e.g. a colour change) is provided only when the concentration of one or more of these metabolites exceeds a threshold value selected or differentiated between a concentration and/or amount of a biomarker indicative of a control subject and a concentration and/or amount of a biomarker indicative of a patient suffering from or susceptible to cushing's syndrome. Thus, the presence of a detectable output (such as a color change) immediately indicates that a significantly elevated level of the biomarker is contained in the sample, indicating that the subject is suffering from or susceptible to cushing's syndrome.

In a third aspect, the invention provides the use of a biomarker for constructing a computational model for predicting cushing's syndrome, or a system or apparatus in which the computational model is embedded, wherein the biomarker is LysoPE 22.

The computational model employs an algorithm developed and obtained by applying statistical methods. For example, suitable statistical methods are Discriminant Analysis (DA) (i.e., linear, quadratic, regular DA), kernel methods (i.e., SVM), nonparametric methods (i.e., k-nearest neighbor classifiers), PLS (partial least squares), tree-based methods (i.e., logistic regression, CART, random forest methods, boosting/bagging methods), generalized linear models (i.e., logistic regression), principal component-based methods (i.e., SIMCA), generalized additive models, fuzzy logic-based methods, neural network-based and genetic algorithm-based methods. The skilled person will not have any problem in selecting a suitable statistical method to evaluate the marker combinations of the invention and thereby obtain a suitable mathematical algorithm. In one embodiment, the statistical method used to obtain the mathematical algorithm used in assessing cushing's syndrome is selected from DA (i.e., linear, quadratic, rule-discriminant analysis), kernel method (i.e., SVM), non-parametric method (i.e., k-nearest neighbor classifier), PLS (partial least squares), tree-based method (i.e., logistic regression, CART, random forest method, boosting method), or generalized linear model (i.e., logarithmic regression).

The area under the receiver operating curve (= AUC) is an indicator of the performance or accuracy of the diagnostic procedure. The accuracy of a diagnostic method is best described by its Receiver Operating Characteristics (ROC). ROC plots are line graphs of all sensitivity/specificity pairs derived from continuously varying decision thresholds across the entire data range observed.

The clinical performance of a laboratory test depends on its diagnostic accuracy, or the ability to correctly classify a subject into a clinically relevant subgroup. Diagnostic accuracy measures the ability to correctly discriminate between two different conditions of the subject under investigation. Such conditions are, for example, health and disease or disease progression versus no disease progression. In each case, the ROC line graph depicts the overlap between the two distributions by plotting sensitivity versus 1-specificity over the entire range of decision thresholds. On the y-axis is the sensitivity, or true positive score [ defined as (number of true positive test results)/(number of true positives + number of false negative test results) ]. This is also referred to as a positive for the presence of a disease or condition. It is calculated from the affected subgroups only. On the x-axis is the false positive score, or 1-specificity [ defined as (number of false positive results)/(number of true negatives + number of false positive results) ]. It is an indicator of specificity and is calculated entirely from unaffected subgroups. Because the true positive and false positive scores are calculated completely separately using test results from two different subgroups, the ROC line graph is independent of the prevalence of disease in the sample. Each point on the ROC line graph represents a sensitivity/1-specificity pair corresponding to a particular decision threshold. One test with perfect discrimination (no overlap of the two result distributions) has a ROC line graph that passes through the upper left corner where the true positive score is 1.0, or 100% (perfect sensitivity), and the false positive score is 0 (perfect specificity). A theoretical line graph for an undifferentiated test (results from both groups are equally distributed) is a 45 ° diagonal from the bottom left to the top right. Most line graphs fall between these two extremes. (if the ROC line plot falls well below the 45 ° diagonal, then this is easily corrected by reversing the criteria of "positive" from "greater" to "less" or vice versa.) qualitatively, the closer the line plot is to the upper left corner, the higher the overall accuracy of the test.

One convenient goal to quantify the diagnostic accuracy of a laboratory test is to express its performance by a single numerical value. The most common global metric is the area under the ROC curve (AUC). Conventionally, this area is always ≧ 0.5 (if not, the decision rule can be reversed to do so). The range of values was between 1.0 (test values that perfectly separated the two groups) and 0.5 (no significant distribution difference between the test values of the two groups). The area depends not only on a particular part of the line graph, such as the point closest to the diagonal or the sensitivity at 90% specificity, but also on the entire line graph. This is a quantitative, descriptive representation of how ROC plots are close to perfect (area = 1.0).

Overall assay sensitivity will depend on the specificity required to carry out the methods disclosed herein. In certain preferred settings, a specificity of 75% may be sufficient, and statistical methods and resulting algorithms may be based on this specificity requirement. In a preferred embodiment, the method for assessing an individual at risk for cushing's syndrome is 80%, 85%, or further preferably 90% or 95% based on specificity.

A fourth aspect of the present invention provides a calculation model for predicting cushing's syndrome, which outputs a discrimination value with the level of LysoPE 22.

The discrimination value is calculated based on the concentration and/or magnitude of the biomarker in the sample and the discrimination with the concentration and/or magnitude of the biomarker as an explanatory variable stored in a computational model.

The invention has the advantages and beneficial effects that:

the invention discovers the LysoPE 22 which is a biomarker related to the cushing syndrome for the first time, and can judge whether a subject suffers from or is susceptible to the cushing syndrome by detecting the level of the biomarker compared with a healthy control so as to realize the early diagnosis of the cushing syndrome, thereby carrying out intervention treatment at the early stage of the cushing syndrome and improving the life and the life quality of the patient.

Drawings

FIG. 1 is a bar graph of the differential analysis of LysoPE 22 in Cushing's syndrome patients and healthy populations; wherein 1A is a differential analysis bar graph of LysoPE 22; 1B is a bar graph demonstrating differential analysis of LysoPE 22 in patients with cushing's syndrome and healthy populations.

FIG. 2 is a bar graph of the differential analysis of LysoPE 20; wherein 2A is a bar graph of the differential analysis of LysoPE 20; 2B is a bar graph demonstrating differential analysis of LysoPE 20 in patients with cushing's syndrome and healthy populations.

Fig. 3 is a graph of the LysoPE 22; wherein 3A is a discovery queue recipient operational signature graph; 3B is a verification queue recipient operational signature graph.

Fig. 4 is a graph of LysoPE 20; wherein 4A is a discovery queue recipient operational signature graph; 4B is a verification queue recipient operational signature graph.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. The following examples are intended to illustrate the invention only and are not intended to limit the scope of the invention. The experimental methods in the examples, in which specific conditions are not specified, are generally carried out under conventional conditions.

Example screening and potency determination of metabolites associated with cushing's syndrome

1. Crowd recruited in queue

A total of 30 healthy volunteers and 34 patients diagnosed with cushing's syndrome in the beijing counseling hospital were recruited as a discovery cohort for this study. 25 healthy volunteers and 23 patients diagnosed with cushing's syndrome at the Beijing coordination hospital were recruited as a validation cohort. Diagnosis is verified by clinical symptoms, family history, images, and blood cortisol measurements.

Exclusion criteria included:

1) The antibiotic treatment is carried out for more than or equal to 3 days within 3 months before the group is added;

2) Now suffering from organic digestive system diseases, or receiving digestive tract surgery within 1 year;

3) Complicated with hyperthyroidism, diabetic ketoacidosis, adrenal cortex insufficiency, pregnancy, severe anemia, severe renal insufficiency (eGFR is less than or equal to 15mL/min/1.73 square meters), poisoning or drug side reaction;

4) Systemic autoimmune diseases (such as systemic lupus erythematosus) or malignant tumors are diagnosed.

The standard of the healthy control group is as follows: doctors make inquiries, physical examination and blood tests to identify healthy people.

2. Collecting and preserving samples

Peripheral venous blood was drawn the next morning after admission of participants and fresh serum samples were collected and immediately frozen at-80 ℃. All clinical information was collected according to standard procedures (note: the study was approved by the Beijing coordination and Hospital ethics Committee and performed according to the principles of the declaration of Helsinki. All subjects provided written informed consent to participate in the study).

Peripheral venous blood of a subject in an empty stomach state in the morning is extracted, about 5mL samples of 1 tube are respectively collected by using an EDTA-K2 anticoagulation blood collection tube (plasma tube) and a separation gel procoagulant tube (serum tube), the samples are obtained and then centrifuged within 1 hour (the plasma tube is 500g multiplied by 10 minutes, and the serum tube is 3500rpm multiplied by 5 minutes), and supernatant is taken and distributed into 1.5mL eppendorf tubes and is placed in a refrigerator at the temperature of 80 ℃ below zero for storage.

3. Serum non-targeted metabonomics detection and differential metabolite screening

3.1 serum non-targeted metabolomics detection

Serum metabolomics was analyzed using ultra high performance liquid chromatography-mass spectrometry (UHPLC-MS), during which a Vanquish ultra high performance liquid chromatography system (Thermo Fisher, germany) and an Orbitrap Q exact HF mass spectrometer (Thermo Fisher, germany) were used, in Novogene co.

Firstly, extracting metabolites of a sample, then detecting a molecular characteristic peak by an LC-MS/MS machine, and performing data quality control by using a QC sample prepared by mixing experiment samples in equal volumes in the detection process. Then, performing data preprocessing on the original off-line result by using Compound discover 3.1 (CD 3.1) software, firstly simply screening through parameters such as retention time, mass-to-charge ratio and the like, and performing peak alignment on different samples according to retention time deviation and mass deviation (Part per million, ppm) so as to ensure more accurate identification; subsequently, peak extraction is carried out according to information such as set ppm, signal-to-noise ratio (S/N), adduct ions and the like, and meanwhile, the peak area is quantified.

3.2 serum metabolite identification

And comparing the high-resolution secondary spectrogram database mzCloud, mzVault and MassList for primary database retrieval, and identifying the metabolites. The specific principle is as follows: determining the molecular weight of a metabolite according to the mass-to-charge ratio (m/z) of parent ions in the primary mass spectrum, predicting the molecular formula according to information such as ppm and adduct ions, and matching with a database; and the database containing the secondary spectrogram is matched with information such as fragment ions, collision energy and the like of each metabolite in the database according to the actual secondary spectrogram, so that secondary identification of the metabolite is realized. Only features with Coefficient of Variation (CV) values less than 30% in Quality Control (QC) samples were filtered for downstream analysis.

3.3 screening of serum differential metabolites

Metabolites were annotated using the KEGG database (https:// www.genome.jp/KEGG/pathway. HtmL), the HMDB database (https:// HMDB. Ca/metablites), and the LIPDMaps database (http:// www.lipidmaps.org /). After obtaining serum metabolite annotation and quantitative tables, OPLS-DA was subjected to multivariate statistical analysis using SIMCA software (v 14.1, umetrics, sweden), VIP values were calculated for each feature, significance was calculated using Wilcoxon rank-sum test, and finally metabolites with VIP > 1 and P < 0.05 were selected as differential metabolites. Results as shown in fig. 1 and 2, in the discovery cohort, the levels of LysoPE 22.

To verify the accuracy of the LysoPE 22, lysoPE 20.

4. Evaluating diagnostic efficacy of differential metabolites

Using the R package p ROC to calculate the Receiver Operating Curve (ROC) of the difference metabolite, and calculating the area under the curve (AUC) and the optimal Cut-off value, thereby obtaining a plurality of diagnosis models. ROC curves for the discovery and validation cohorts from which the differential metabolites LysoPE 22, lysoPE 20; AUC values of LysoPE 20 in the discovery and validation queues were 0.822, 0.798, respectively.

LysoPE 22, lysoPE 20. However, the patients with cushing's syndrome can have metabolic disorders due to the general disorder of endocrine, and the change of phosphatidylethanolamine lipid can reflect the metabolic disorders to a certain extent, so that the LysoPE 22, lysoPE 20.

The above description of the embodiments is only intended to illustrate the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall into the protection scope of the claims of the present invention.

Claims

1. Use of a reagent for detecting a biomarker in a sample for the preparation of a product for the diagnosis of cushing's syndrome, wherein the biomarker is LysoPE 22.

2. The use of claim 1, wherein the reagent comprises a reagent for detection by chromatography, spectroscopy, mass spectrometry, chemical analysis, or a combination thereof.

3. The use of claim 2, wherein the reagent is selected from the group consisting of reagents for combined chromatography-mass spectrometry detection.

4. Use according to claim 2, wherein said mass spectrometry is tandem mass spectrometry.

5. The use of claim 4, wherein said tandem mass spectrometry is selected from the group consisting of ion trap mass spectrometry, quadrupole time-of-flight mass spectrometry, triple quadrupole mass spectrometry, quadrupole ion trap mass spectrometry, ion mobility-quadrupole ion trap-time-of-flight mass spectrometry, quadrupole-orbitrap mass spectrometry, ion mobility spectrometer-quadrupole ion trap mass spectrometry, quadrupole-orbitrap mass spectrometry, triple quadrupole-orbitrap mass spectrometry, quadrupole ion trap-orbitrap mass spectrometry, time-of-flight or ion trap-Fourier transform mass spectrometry.

6. The use according to any one of claims 1 to 5, wherein the sample is selected from blood, serum, plasma.

7. A kit for diagnosing cushing's syndrome, comprising reagents for detecting LysoPE 22; and instructions for using the kit to assess whether the subject is suffering from or susceptible to cushing's syndrome.

8. The kit of claim 7, further comprising reagents for processing the sample.

9. Use of a biomarker for constructing a computational model for predicting cushing's syndrome or a system or device in which said computational model is embedded, wherein said biomarker is LysoPE 22.

10. A computational model for predicting cushing's syndrome or a system or apparatus incorporating the computational model, wherein a discrimination value is output with the level of LysoPE 22.