A method for detecting a characteristic marker of an immune microenvironment of a brain glioma

Abstract

The present application relates to the technical field of environmental feature detection, and in particular to a brain glioma immune microenvironment characteristic marker detection method. According to the ion intensity of each internal standard molecule at different time, the internal standard reference inhibition rate of each internal standard molecule at the internal standard peak time is obtained; according to the ion intensity distribution of the target metabolite at different time, multiple discrete slice time is obtained; according to the total ion flow intensity distribution at different time, the internal standard reference inhibition rate at the internal standard peak time and the initial slice transient inhibition rate at the discrete slice time, the final slice transient inhibition rate at each discrete slice time is obtained; according to the ion intensity of the target metabolite at different discrete slice time, the final slice transient inhibition rate and the target exosome concentration, the immune microenvironment characteristic value is obtained. The present application can accurately obtain the immune microenvironment characteristic value, and improve the effectiveness of microenvironment characteristic marker detection.

Description

Technical Field

[0001] This invention relates to the field of environmental feature detection technology, specifically to a method for detecting characteristic biomarkers of the immune microenvironment in glioma. Background Technology

[0002] Impurities in cerebrospinal fluid exhibit a non-uniform, pulsed distribution during the chromatographic elution stage, causing non-linear fluctuations in impurity concentrations at different elution times. In conventional engineering practice, to eliminate dynamic matrix interference, it is usually necessary to add a stable isotope internal standard that can be completely co-eluted with all target metabolites to the sample. However, in actual pathological testing, especially in non-targeted mass screening scenarios, obtaining isotope standards that can be completely co-eluted with all unknown metabolites is extremely costly and often impractical.

[0003] When co-eluting internal standards are lacking, existing technologies rely on a few internal standards with known elution times for single-point static calibration, or reduce the overall impurity concentration by directly diluting the sample. However, the single compensation coefficient obtained by single-point static calibration cannot track the impurity interference caused by pulse-like mutations during the broad peak elution, resulting in nonlinear bias in the quantitative data. Directly diluting the sample can easily cause the originally trace concentration of tumor markers to drop below the instrument's detection limit, resulting in poor assessment of the immune microenvironment. Summary of the Invention

[0004] To address the technical problem that existing techniques, such as single-point static calibration and direct sample dilution, cannot eliminate dynamic matrix interference when co-eluting internal standards are lacking, resulting in poor assessment of the immune microenvironment, this invention aims to provide a method for detecting characteristic biomarkers of the immune microenvironment in gliomas. The specific technical solution adopted is as follows: This invention proposes a method for detecting characteristic biomarkers of the immune microenvironment in gliomas, the method comprising: Cerebrospinal fluid samples containing step-eluting isotope internal standard solutions were obtained from glioma patients. The concentration of target exosomes, total ion current intensity, target metabolite ion intensity, and intensity of multiple internal standard molecular ions were extracted from the samples at each time point. Based on the ion intensity of each internal standard molecule at different times, the internal standard baseline inhibition rate of each internal standard molecule at the internal standard elution time is obtained; based on the ion intensity distribution of the target metabolites at different times, multiple discrete slice times are obtained; based on the total ion current intensity distribution at different times, the environmental similarity weight between each discrete slice time and different internal standard elution times and other discrete slice times is obtained. Based on the environmental similarity weights between each discrete slice time and different internal index peak times and other discrete slice times, the internal index baseline suppression rate at the corresponding internal index peak time, and the initial slice instantaneous suppression rate at the discrete slice time, the final slice instantaneous suppression rate at each discrete slice time is obtained. The immune microenvironment characteristics were obtained based on the target metabolite ion intensity, the instantaneous inhibition rate of the final slice, and the target exosome concentration at different discrete slice times.

[0005] Furthermore, the method for obtaining the internal standard suppression rate includes: For any internal standard molecule, obtain the maximum numerical value of the internal standard molecule ion intensity at all times, and take the corresponding time as the internal standard elution time; The cumulative value of the internal standard ion intensity of the internal standard molecule at all times in the neighborhood of the internal standard peak time is obtained as the total intensity of the interfered ions; the ratio of the total intensity of the interfered ions of the internal standard molecule at the internal standard peak time to the ideal calibrated ion intensity is obtained and negatively correlated and mapped as the initial suppression rate of the internal standard. The minimum value between the initial internal standard inhibition rate and the preset upper limit of inhibition threshold is obtained as the internal standard baseline inhibition rate.

[0006] Furthermore, the method for obtaining the discrete slice time includes: The signal-to-noise ratio at each time point is obtained based on the target metabolite ion intensity distribution at different times. If the signal-to-noise ratio at a given moment is greater than or equal to a preset threshold, the corresponding moment will be taken as the peak-out moment. The range between adjacent peak times is divided according to a preset time step, and the divided times are used as discrete slice times.

[0007] Furthermore, the method for obtaining the signal-to-noise ratio includes: The average intensity of target metabolite ions at all times is obtained as the average intensity of target metabolite ions; The standard deviation of the target metabolite ion intensity at all times is obtained as the intensity fluctuation coefficient; The intensity difference between the target metabolite ion intensity and the average intensity of the target metabolite ion is obtained at each time step. The ratio of the intensity difference to the intensity fluctuation coefficient is calculated as the signal-to-noise ratio at each time step.

[0008] Furthermore, the method for obtaining the environmental similarity weight includes: The total ion current intensity at different times is normalized and used as the standardized intensity. For the time when the inner index peaks or the time when the discrete slices are taken as the time to be analyzed, the absolute difference of the standardized intensity between each discrete slice time and the time to be analyzed is obtained, and negative correlation mapping is performed as the environmental similarity weight between each discrete slice time and the time to be analyzed.

[0009] Furthermore, the method for obtaining the instantaneous suppression rate of the final slice includes: Based on the environmental similarity weights between each discrete slice time and different internal index peak times and other discrete slice times, the internal index baseline suppression rate at the corresponding internal index peak time, and the initial slice instantaneous suppression rate at the discrete slice time, the slice instantaneous suppression rate at the next iteration is obtained. Based on the absolute difference in the instantaneous suppression rate of each discrete slice between the next iteration and the previous iteration, and the latest iteration number, determine whether to stop the iteration; If it is determined that the iteration has not stopped, the instantaneous suppression rate of the slice at each discrete slice time in the next iteration is used as the initial instantaneous suppression rate of the slice, and the instantaneous suppression rate of the slice at each discrete slice time in the next iteration is obtained. The judgment is repeated. If the iteration is stopped, the instantaneous suppression rate of the slice at each discrete slice time in the next iteration is taken as the final instantaneous suppression rate of the slice at each discrete slice time.

[0010] Furthermore, the method for obtaining the instantaneous suppression rate of each discrete slice in the next iteration includes: The cumulative sum of the environmental similarity weights and the internal standard suppression rate between each discrete slice time and all internal standard peak times is obtained as the weighted internal standard suppression rate; The environmental similarity weight between each discrete slice time and other discrete slice times and the initial slice instantaneous suppression rate of other discrete slice times are multiplied and accumulated to form the initial weighted slice instantaneous suppression rate between each discrete slice time and other discrete slice times. Obtain the sum of the weighted internal standard suppression rate and the initial weighted slice instantaneous suppression rate; calculate the sum of the suppression values ​​divided by the cumulative sum of the environmental similarity weights between each discrete slice moment and the internal standard peak moment and other discrete slice moments, and use this as the slice instantaneous suppression rate for each discrete slice moment in the next iteration.

[0011] Furthermore, the method for determining whether the iteration has stopped includes: If the absolute difference in the instantaneous suppression rate of each discrete slice is less than or equal to a preset difference threshold between the next and previous iterations for a consecutive preset number of iterations, or if the latest iteration order is equal to a preset order threshold, the iteration is stopped. If, at any given time, there is no consecutive preset number of iterations where the absolute difference in instantaneous suppression rate between the next and previous iterations is less than or equal to a preset difference threshold, and the latest iteration order is less than a preset order threshold, then the iteration is stopped before iteration begins.

[0012] Furthermore, the method for obtaining the immune microenvironment feature values ​​includes: The theoretical response value of the target metabolite is obtained based on the ion intensity of the target metabolite at different discrete slice times and the instantaneous inhibition rate of the final slice. The maximum value between the target exosome concentration and the preset lower limit threshold is selected as the target exosome baseline concentration; The ratio of the theoretical response value of the target metabolite to the baseline concentration of the target exosome was obtained as a characteristic value of the immune microenvironment.

[0013] Furthermore, the method for obtaining the theoretical response value of the target metabolite includes: The sum of the products of the target metabolite ion intensities at all discrete slice times and the instantaneous inhibition rate of the final slice is obtained as the damaged signal intensity; the sum of the target metabolite ion intensities at all discrete slice times is obtained as the total target metabolite ion intensity. If the total intensity of the target metabolite ions is less than or equal to the preset lower limit threshold, the total intensity of the target metabolite ions will be used as the theoretical response value of the target metabolite. If the total intensity of the target metabolite ions is greater than the preset lower limit threshold, the theoretical response value of the target metabolite is obtained based on the intensity ratio of the damaged signal intensity to the total intensity of the target metabolite ions, as well as the total intensity of the target metabolite ions. The intensity ratio and the total intensity of the target metabolite ions are both positively correlated with the theoretical response value of the target metabolite.

[0014] The present invention has the following beneficial effects: This invention obtains the internal standard inhibition rate of each internal standard molecule at the internal standard elution time based on the internal standard ionic intensity of each molecule at different times, quantifying the degree of signal suppression due to impurity interference. Based on the target metabolite ion intensity distribution at different times, multiple discrete slice times are obtained, which helps to capture and quantify high-frequency interference changes, dividing continuous broad peaks into fine-grained discrete micro-nodes. Based on the total ion current intensity distribution at different times, the environmental similarity weight between each discrete slice time and different internal standard elution times and other discrete slice times is obtained, reflecting the macroscopic characteristics of impurities at any two slice times throughout the chromatographic elution process. The invention assesses the environmental connectivity strength of the target metabolites. Based on the environmental similarity weights between each discrete slice moment and different internal standard peak moments, as well as other discrete slice moments, the internal standard baseline inhibition rate at the corresponding internal standard peak moment, and the initial instantaneous inhibition rate of the discrete slice moment, the final instantaneous inhibition rate of each discrete slice moment is obtained. This continuously and dynamically reflects the proportion of signal loss due to pulse matrix impurities within a very short segment at each discrete slice moment, preventing detection by the instrument. Based on the target metabolite ion intensity, the final instantaneous inhibition rate of the final slice, and the target exosome concentration at different discrete slice moments, the characteristic values ​​of the immune microenvironment are obtained. This invention improves the effectiveness of detecting characteristic biomarkers of the immune microenvironment by accurately obtaining these characteristic values. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a method for detecting characteristic biomarkers of the immune microenvironment in gliomas, as provided in one embodiment of the present invention. Detailed Implementation

[0017] The following describes in detail, with reference to the accompanying drawings, a specific scheme for the detection method of characteristic biomarkers of the immune microenvironment in glioma provided by the present invention.

[0018] Please see Figure 1 The diagram illustrates a flowchart of a method for detecting characteristic biomarkers of the immune microenvironment in gliomas according to an embodiment of the present invention. The specific method includes: Step S1: Obtain cerebrospinal fluid samples containing step-eluting isotope internal standard solutions from glioma patients, and extract the target exosome concentration, total ion current intensity, target metabolite ion intensity, and intensity of multiple internal standard molecular ions from the samples at each time point.

[0019] In embodiments of the present invention, in actual physicochemical detection scenarios, due to the difficulty in obtaining stable isotope standards that can be completely co-eluted with all target metabolites, conventional single-point static calibration cannot accurately characterize the dynamic matrix interference that fluctuates continuously over a wide peak time period; therefore, cerebrospinal fluid samples from glioma patients and externally configured step-eluting isotope internal standard solutions are received as initial inputs, and physical centrifugation layering and liquid chromatography-mass spectrometry full scan detection are performed. The step-eluting isotope internal standard solution contains at least three internal standard factors with known elution orders, corresponding to the early, middle and late internal standards eluted in chronological order.

[0020] First, because cerebrospinal fluid samples from glioma patients contain both free metabolites and exosome vesicles, conventional whole-sample testing methods for unseparated samples would prevent the system from distinguishing the specific pathological source of metabolic substrate consumption. Therefore, the operator transferred the cerebrospinal fluid sample containing the step-eluting isotope internal standard solution to a molecular centrifuge tube with a preset pore size. The operator then initiated a centrifugation separation process to separate the sample into two parts from top to bottom: the retentate on the upper retentate mesh and the ultrafiltrate centrifuged through the filter membrane to the bottom of the tube. The method for obtaining the target exosome concentration includes: adding a lipophilic fluorescent dye to the retentate to specifically label the lipid bilayer structure of the exosome vesicles; after staining, the retentate is placed in a fluorescence spectrometer, which emits excitation light of a specific wavelength and reads the fluorescence intensity signal generated by the retentate; the fluorescence intensity signal is measured in advance using a standard exosome vesicle sample with a known target exosome concentration to construct a concentration-intensity curve; the target exosome concentration of the sample to be treated is output using the concentration-intensity curve, representing the total number of vesicles carried per unit volume of cerebrospinal fluid.

[0021] The methods for obtaining the total ion current intensity, target metabolite ion intensity, and multiple internal standard molecular ion intensities include: using liquid chromatography-mass spectrometry to simultaneously acquire total ion current data and target ion data, which can record the intensity changes of specific target signals and the fluctuations of global background interference in the same time coordinate. Therefore, the ultrafiltrate is input into the automatic injection module of the liquid chromatography-mass spectrometry system, and the total ion current intensity, target metabolite ion intensity, and multiple internal standard molecular ion intensities are output at each time step.

[0022] Step S2: Based on the ion intensity of each internal standard molecule at different times, obtain the internal standard baseline inhibition rate of each internal standard molecule at the internal standard elution time; based on the target metabolite ion intensity distribution at different times, obtain multiple discrete slice times; based on the total ion current intensity distribution at different times, obtain the environmental similarity weight between each discrete slice time and different internal standard elution times and other discrete slice times.

[0023] Because high-concentration background impurities compete with internal standard molecules for surface charge when entering the mass spectrometer electrospray ion source, the actual reading signal of the instrument deviates from the theoretical response value in the impurity-free state. By analyzing the ion intensity of internal standard molecules at different times, the degree of signal suppression due to impurity interference is quantified. Based on the ion intensity of each internal standard molecule at different times, the internal standard reference suppression rate of each internal standard molecule at the internal standard peak time is obtained.

[0024] Preferably, in one embodiment of the present invention, the method for obtaining the internal standard inhibition rate includes: For any internal standard molecule, obtain the maximum numerical value of the internal standard molecule ion intensity at all times, and take the corresponding time as the internal standard elution time; The cumulative value of the internal standard ion intensity of the internal standard molecule at all times in the neighborhood of the internal standard peak time is obtained as the total intensity of the interfered ions; the ratio of the total intensity of the interfered ions of the internal standard molecule at the internal standard peak time to the ideal calibrated ion intensity is obtained and negatively correlated and mapped as the initial suppression rate of the internal standard. It should be noted that, in the embodiments of the present invention, the method for obtaining the neighborhood range includes: taking the internal standard elution time as a reference, obtaining the signal-to-noise ratio at each time based on the ion intensity distribution of the internal standard molecules at different times; if there is a time when the signal-to-noise ratio is greater than or equal to a preset threshold, the corresponding time is taken as the baseline time; obtaining the range formed by the internal standard elution time on the left and right adjacent baseline times as the neighborhood range; the ideal calibrated ion intensity is the total ion intensity of the internal standard molecules pre-determined in a pure water solvent free of impurities, obtained according to relevant professional knowledge; wherein, based on the industry standard recognized in the field of chromatography-mass spectrometry, the preset threshold is set to 3, and its statistical basis is a 99.7% confidence level.

[0025] It should be noted that, in one embodiment of the present invention, the difference between the positive integer 1 and the ratio is used as the initial suppression rate of the internal standard. The larger the ratio, the greater the total intensity of the interfered ions relative to the ideal calibrated ion intensity, the less likely it is to be interfered with by extreme matrix, and the smaller the initial suppression rate of the internal standard.

[0026] The minimum value between the initial internal standard inhibition rate and the preset upper limit of inhibition threshold is obtained as the internal standard baseline inhibition rate.

[0027] It should be noted that, in one embodiment of the present invention, in order to avoid excessive amplification of values, the preset suppression upper limit threshold is set to 0.99. When the internal standard signal is suppressed by the matrix to less than 1% of the original signal, it is considered to have exceeded the reliable detection range, and forced compensation will lead to numerical distortion or division by zero error. In other embodiments of the present invention, the size of the preset suppression upper limit threshold can be set according to specific circumstances, and will not be limited or described in detail here.

[0028] In full-scan quantitative analysis by chromatography-mass spectrometry, the elution of target metabolites with the mobile phase is not instantaneous. In order to capture and quantify high-frequency interference changes, the system divides the continuous broad peaks into fine-grained discrete micro nodes; based on the ion intensity distribution of target metabolites at different times, multiple discrete slice times are obtained.

[0029] Preferably, in one embodiment of the present invention, the method for obtaining the discrete slice time includes: The first step is to obtain the signal-to-noise ratio at each time step based on the target metabolite ion intensity distribution at different times. It should be noted that, in one embodiment of the present invention, the method for obtaining the signal-to-noise ratio includes: The mean intensity of target metabolite ions at all times is obtained as the average intensity of target metabolite ions; the standard deviation of target metabolite ion intensity at all times is obtained as the intensity fluctuation coefficient. The intensity difference between the target metabolite ion intensity and the average intensity of the target metabolite ion is obtained at each time step. The ratio of the intensity difference to the intensity fluctuation coefficient is calculated as the signal-to-noise ratio at each time step.

[0030] The second step is to take the time when the signal-to-noise ratio is greater than or equal to the preset threshold. If there is a time when the signal-to-noise ratio is greater than or equal to the preset threshold, the corresponding time is taken as the peak time. It should be noted that, in the embodiments of the present invention, based on the recognized industry standard in the field of chromatography-mass spectrometry, the preset threshold is set to 3, and its statistical basis is a 99.7% confidence level.

[0031] The third step is to divide the range between adjacent peak times according to a preset time step, and use the divided times as discrete slice times.

[0032] It should be noted that, in one embodiment of the present invention, the preset time step can be configured as an integer multiple of the instrument scanning cycle, such as 0.1 seconds; in other embodiments of the present invention, the size of the preset time step can be set according to specific circumstances, and will not be limited or elaborated here.

[0033] The total ion current intensity is used as proxy data to assess the characteristics of the local microscopic ionization competition environment. In order to reflect the environmental similarity, the distribution of total ion current intensity at different times is analyzed, and the environmental similarity weight between times is quantified. Based on the distribution of total ion current intensity at different times, the environmental similarity weight between each discrete slice time and different internal index peak times and other discrete slice times is obtained.

[0034] Preferably, in one embodiment of the present invention, the method for obtaining the environmental similarity weight includes: The total ion current intensity at different times is normalized and used as the standardized intensity. It should be noted that, in the embodiments of the present invention, the maximum value of the total ion current intensity at all times is selected, and the ratio of the total ion current intensity to the maximum value at each time is calculated, that is, normalized, to obtain the standardized intensity.

[0035] For the time when the inner index peaks or the time when the discrete slices are taken as the time to be analyzed, the absolute difference of the standardized intensity between each discrete slice time and the time to be analyzed is obtained, and negative correlation mapping is performed as the environmental similarity weight between each discrete slice time and the time to be analyzed.

[0036] It should be noted that the absolute difference represents the absolute value of the difference. In one embodiment of the present invention, the reciprocal of the absolute difference is taken. To avoid the absolute difference of the standardization intensity between different times being zero, which would render the formula meaningless, a very small positive number with consistent dimensions is added to the denominator. The value of this number is specifically set according to the range of values ​​of the denominator, so that the smaller the absolute difference, the closer the standardization intensity between times, and the greater the environmental similarity weight. In other embodiments of the present invention, an exponential function with the natural constant as the base can also be used. Negative correlation mapping is performed; the specific methods are well known to those skilled in the art and will not be elaborated here.

[0037] Step S3: Based on the environmental similarity weights between each discrete slice time and different internal index peak times and other discrete slice times, the internal index baseline suppression rate at the corresponding internal index peak time, and the initial slice instantaneous suppression rate at the discrete slice time, obtain the final slice instantaneous suppression rate for each discrete slice time.

[0038] The environmental similarity weight reflects the environmental connectivity strength between any two slices based on the macroscopic characteristics of impurities throughout the chromatographic elution process. The greater the environmental similarity weight, the greater the environmental connectivity strength, and the more likely a state transition will occur. The internal standard inhibition rate and the initial slice instantaneous inhibition rate reflect the inhibited characteristics. Based on the inhibition rate influence transmitted proportionally from the degree of impurity similarity, the final slice instantaneous inhibition rate at each discrete slice moment is quantified.

[0039] Preferably, in one embodiment of the present invention, the method for obtaining the instantaneous suppression rate of the final slice includes: The first step is to obtain the instantaneous suppression rate of each discrete slice moment in the next iteration based on the environmental similarity weight between each discrete slice moment and different internal index peak moments and other discrete slice moments, the internal index baseline suppression rate at the corresponding internal index peak moment, and the initial instantaneous suppression rate of the discrete slice moment. It should be noted that, in one embodiment of the present invention, the method for obtaining the instantaneous suppression rate of the slice in the next iteration at each discrete slice time includes: The cumulative sum of the environmental similarity weights and the internal standard baseline suppression rates between each discrete slice time and all internal standard peak times is obtained as the weighted internal standard suppression rate. The environmental similarity weight between each discrete slice time and other discrete slice times and the initial slice instantaneous suppression rate of other discrete slice times are multiplied and accumulated to form the initial weighted slice instantaneous suppression rate between each discrete slice time and other discrete slice times. Obtain the sum of the weighted internal standard suppression rate and the initial weighted slice instantaneous suppression rate; calculate the sum of the suppression values ​​divided by the cumulative sum of the environmental similarity weights between each discrete slice moment and the internal standard peak moment and other discrete slice moments, and use this as the slice instantaneous suppression rate for each discrete slice moment in the next iteration.

[0040] It should be noted that, in the embodiments of the present invention, the initial instantaneous suppression rate of the slice is 0.

[0041] The formula is expressed as: ;in, Indicates the first Each discrete slice time in the next iteration The instantaneous inhibition rate of the slice; Indicates the first The discrete slice time and the first Each internal standard molecule corresponds to an environmental similarity weight between the times when the internal standard peaks appear; Indicates the first The internal standard inhibition rate corresponding to the internal standard peak time of each internal standard molecule; Indicates the number of internal standard molecules; Indicates the first The discrete slice time and the first Environmental similarity weights between discrete slice time points; Indicates the first Each discrete slice is at the time of the previous iteration. The initial slice instantaneous inhibition rate; This indicates the number of other discrete slice times.

[0042] The second step is to determine whether to stop the iteration based on the absolute difference of the instantaneous suppression rate of each discrete slice between the next iteration and the previous iteration, and the latest iteration number. It should be noted that, in one embodiment of the present invention, the method for determining whether the iteration has stopped includes: If the absolute difference in the instantaneous suppression rate of each discrete slice is less than or equal to a preset difference threshold between the next and previous iterations for a consecutive preset number of iterations, or if the latest iteration order is equal to a preset order threshold, the iteration is stopped. If, at any given time, there is no consecutive preset number of iterations where the absolute difference in instantaneous suppression rate between the next and previous iterations is less than or equal to a preset difference threshold, and the latest iteration order is less than a preset order threshold, then the iteration is stopped before iteration begins.

[0043] It should be noted that, in one embodiment of the present invention, in order to avoid accidental phenomena, the preset quantity is set to 3; in order to avoid potentially masking minor oscillations and slow convergence that may not be achieved, the preset difference threshold is set to 0.001; in order to cover most convergence scenarios, the preset order threshold is set to 1000; in other embodiments of the present invention, the preset quantity, preset difference threshold, and preset order threshold can be set according to specific circumstances, and are not limited or elaborated here.

[0044] The third step is to determine if the iteration has stopped before stopping. Then, take the instantaneous suppression rate of the slice at each discrete slice time in the next iteration as the initial instantaneous suppression rate of the slice, obtain the instantaneous suppression rate of the slice at each discrete slice time in the next iteration, and repeat the determination. If the iteration is stopped, the instantaneous suppression rate of the slice at each discrete slice time in the next iteration is taken as the final instantaneous suppression rate of the slice at each discrete slice time.

[0045] Based on this, the instantaneous inhibition rate of the final slice continuously and dynamically reflects the proportion of signal loss of the target metabolite that is not detected by the instrument due to interference from pulse matrix impurities in the extremely short segment at each discrete slice moment. The higher the inhibition rate, the greater the possibility of interference from matrix impurities.

[0046] Step S4: Obtain the characteristic values ​​of the immune microenvironment based on the target metabolite ion intensity, the instantaneous inhibition rate of the final slice, and the target exosome concentration at different discrete slice times.

[0047] The target metabolite ion intensity reflects the instrument read signal output by the detector. The final slice instantaneous inhibition rate characterizes the continuous proportion of the target being disturbed within a very short slice interval at the discrete slice time. The greater the final slice instantaneous inhibition rate, the greater the target metabolite ion intensity, and the greater the loss of theoretical signal suppressed by ionization competition. The target exosome concentration represents the total number of vesicles carried per unit volume of cerebrospinal fluid, quantifying the metabolic consumption activity of local micro-regions.

[0048] Preferably, in one embodiment of the present invention, the method for obtaining immune microenvironment feature values ​​includes: The first step is to obtain the theoretical response value of the target metabolite based on the ion intensity of the target metabolite at different discrete slice times and the instantaneous inhibition rate of the final slice. Preferably, in one embodiment of the present invention, the method for obtaining the theoretical response value of the target metabolite includes: The sum of the products of the target metabolite ion intensities at all discrete slice times and the instantaneous inhibition rate of the final slice is obtained as the damaged signal intensity; the sum of the target metabolite ion intensities at all discrete slice times is obtained as the total target metabolite ion intensity. If the total intensity of the target metabolite ions is less than or equal to the preset lower limit threshold, the total intensity of the target metabolite ions will be used as the theoretical response value of the target metabolite. It should be noted that, in the embodiments of the present invention, in order to prevent the total intensity of target metabolite ions from approaching the zero value of the machine when the content of target metabolites in the biological sample is extremely low or even undetectable, thus causing the subsequent mean division to collapse, the preset intensity lower limit threshold is set to 10 times the standard deviation of the target metabolite ion intensity during the pure water blank sample operation stage; in other embodiments of the present invention, the size of the preset intensity lower limit threshold can be set according to the specific situation, and is not limited or described here.

[0049] If the total intensity of the target metabolite ions is greater than the preset lower limit threshold, the theoretical response value of the target metabolite is obtained based on the intensity ratio of the damaged signal intensity to the total intensity of the target metabolite ions, as well as the total intensity of the target metabolite ions. The intensity ratio and the total intensity of the target metabolite ions are both positively correlated with the theoretical response value of the target metabolite.

[0050] It should be noted that the larger the intensity ratio, the greater the intensity of the damaged signal relative to the total intensity of the target metabolite ions, the greater the possibility of suppression, the smaller the effective retention of the target metabolite, and the larger the theoretical response value of the target metabolite. The total intensity of the target metabolite ions reflects the accumulated value of the original response surface actually recorded by the detector hardware before any mathematical baseline adjustment or compensation. The greater the total intensity of the target metabolite ions, the larger the theoretical response value of the target metabolite.

[0051] In one embodiment of the present invention, the difference between the positive integer 1 and the intensity ratio is obtained, and the ratio of the total intensity of the target metabolite ions to the difference result is calculated as the theoretical response value of the target metabolite.

[0052] The second step is to select the maximum value between the target exosome concentration and the preset lower limit threshold as the target exosome baseline concentration; It should be noted that, in the embodiments of the present invention, in order to prevent the computer from crashing due to the target exosome concentration approaching zero because the tumor does not secrete a large number of vesicles or the vesicle concentration is below the fluorescence detection limit, the preset lower limit threshold is set to a very small positive number with consistent dimensions. Its value can be specifically set according to the range of target exosome concentration, such as... This will not be elaborated upon here.

[0053] The third step is to obtain the ratio of the theoretical response value of the target metabolite to the baseline concentration of the target exosome, which is used as a characteristic value of the immune microenvironment.

[0054] Based on this, the immune microenvironment characteristic value reflects the relative abundance of local free metabolites corresponding to a unit vesicle. When the immune microenvironment characteristic value increases, the theoretical response value of the target metabolite increases relatively while the baseline concentration of the target exosome decreases relatively, indicating that under the same scale of tumor exosome release, the content of specific free metabolic substrates remaining in the surrounding tissue of the sample is relatively high. When the immune microenvironment characteristic value decreases, the theoretical response value of the target metabolite decreases relatively, indicating that compared with the scale of exosomes secreted by the tumor, the surrounding free metabolic substrates have been rapidly consumed, which helps to further assess the characteristic state of the tumor microenvironment.

[0055] In summary, this invention obtains the internal standard baseline inhibition rate of each internal standard molecule at the internal standard elution point based on the internal standard ionic intensity of each molecule at different times; obtains multiple discrete slice times based on the target metabolite ion intensity distribution at different times; obtains the final instantaneous inhibition rate of each discrete slice time based on the total ion current intensity distribution at different times, the internal standard baseline inhibition rate at the internal standard elution point, and the initial instantaneous inhibition rate of the discrete slice times; and obtains the immune microenvironment characteristic values ​​based on the target metabolite ion intensity, the final instantaneous inhibition rate of the slices, and the target exosome concentration at different discrete slice times. This invention improves the effectiveness of detecting characteristic biomarkers of the immune microenvironment by accurately obtaining immune microenvironment characteristic values.

[0056] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for detecting characteristic biomarkers of the immune microenvironment in gliomas, characterized in that, The method includes: Cerebrospinal fluid samples containing step-eluting isotope internal standard solutions were obtained from glioma patients. The concentration of target exosomes, total ion current intensity, target metabolite ion intensity, and intensity of multiple internal standard molecular ions were extracted from the samples at each time point. Based on the ion intensity of each internal standard molecule at different times, the internal standard baseline inhibition rate of each internal standard molecule at the internal standard elution time is obtained; based on the ion intensity distribution of the target metabolites at different times, multiple discrete slice times are obtained; based on the total ion current intensity distribution at different times, the environmental similarity weight between each discrete slice time and different internal standard elution times and other discrete slice times is obtained. Based on the environmental similarity weights between each discrete slice time and different internal index peak times and other discrete slice times, the internal index baseline suppression rate at the corresponding internal index peak time, and the initial slice instantaneous suppression rate at the discrete slice time, the final slice instantaneous suppression rate at each discrete slice time is obtained. The immune microenvironment characteristics were obtained based on the target metabolite ion intensity, the instantaneous inhibition rate of the final slice, and the target exosome concentration at different discrete slice times.

2. The method for detecting characteristic biomarkers of the immune microenvironment in gliomas according to claim 1, characterized in that, The method for obtaining the internal standard baseline suppression rate includes: For any internal standard molecule, obtain the maximum numerical value of the internal standard molecule ion intensity at all times, and take the corresponding time as the internal standard elution time; The cumulative value of the internal standard ion intensity of the internal standard molecule at all times in the neighborhood of the internal standard peak time is obtained as the total intensity of the interfered ions; the ratio of the total intensity of the interfered ions of the internal standard molecule at the internal standard peak time to the ideal calibrated ion intensity is obtained and negatively correlated and mapped as the initial suppression rate of the internal standard. The minimum value between the initial internal standard inhibition rate and the preset upper limit of inhibition threshold is obtained as the internal standard baseline inhibition rate.

3. The method for detecting characteristic biomarkers of the immune microenvironment in gliomas according to claim 1, characterized in that, The method for obtaining the discrete slice time includes: The signal-to-noise ratio at each time point is obtained based on the target metabolite ion intensity distribution at different times. If the signal-to-noise ratio at a given moment is greater than or equal to a preset threshold, the corresponding moment will be taken as the peak-out moment. The range between adjacent peak times is divided according to a preset time step, and the divided times are used as discrete slice times.

4. The method for detecting characteristic biomarkers of the immune microenvironment in gliomas according to claim 3, characterized in that, The method for obtaining the signal-to-noise ratio includes: The average intensity of target metabolite ions at all times is obtained as the average intensity of target metabolite ions; The standard deviation of the target metabolite ion intensity at all times is obtained as the intensity fluctuation coefficient; The intensity difference between the target metabolite ion intensity and the average intensity of the target metabolite ion is obtained at each time step. The ratio of the intensity difference to the intensity fluctuation coefficient is calculated as the signal-to-noise ratio at each time step.

5. The method for detecting characteristic biomarkers of the immune microenvironment in gliomas according to claim 1, characterized in that, The method for obtaining the environmental similarity weight includes: The total ion current intensity at different times is normalized and used as the standardized intensity. For the time when the inner index peaks or the time when the discrete slices are taken as the time to be analyzed, the absolute difference of the standardized intensity between each discrete slice time and the time to be analyzed is obtained, and negative correlation mapping is performed as the environmental similarity weight between each discrete slice time and the time to be analyzed.

6. The method for detecting characteristic biomarkers of the immune microenvironment in gliomas according to claim 1, characterized in that, The method for obtaining the instantaneous suppression rate of the final slice includes: Based on the environmental similarity weights between each discrete slice time and different internal index peak times and other discrete slice times, the internal index baseline suppression rate at the corresponding internal index peak time, and the initial slice instantaneous suppression rate at the discrete slice time, the slice instantaneous suppression rate at the next iteration is obtained. Based on the absolute difference in the instantaneous suppression rate of each discrete slice between the next iteration and the previous iteration, and the latest iteration number, determine whether to stop the iteration; If it is determined that the iteration has not stopped, the instantaneous suppression rate of the slice at each discrete slice time in the next iteration is used as the initial instantaneous suppression rate of the slice, and the instantaneous suppression rate of the slice at each discrete slice time in the next iteration is obtained. The judgment is repeated. If the iteration is stopped, the instantaneous suppression rate of the slice at each discrete slice time in the next iteration is taken as the final instantaneous suppression rate of the slice at each discrete slice time.

7. The method for detecting characteristic biomarkers of the immune microenvironment in gliomas according to claim 6, characterized in that, The method for obtaining the instantaneous suppression rate of each discrete slice in the next iteration includes: The cumulative sum of the environmental similarity weights and the internal standard suppression rate between each discrete slice time and all internal standard peak times is obtained as the weighted internal standard suppression rate; The environmental similarity weight between each discrete slice time and other discrete slice times and the initial slice instantaneous suppression rate of other discrete slice times are multiplied and accumulated to form the initial weighted slice instantaneous suppression rate between each discrete slice time and other discrete slice times. Obtain the sum of the weighted internal standard suppression rate and the initial weighted slice instantaneous suppression rate; calculate the sum of the suppression values ​​divided by the cumulative sum of the environmental similarity weights between each discrete slice moment and the internal standard peak moment and other discrete slice moments, and use this as the slice instantaneous suppression rate for each discrete slice moment in the next iteration.

8. The method for detecting characteristic biomarkers of the immune microenvironment in gliomas according to claim 6, characterized in that, The method for determining whether the iteration has stopped includes: If the absolute difference in the instantaneous suppression rate of each discrete slice is less than or equal to a preset difference threshold between the next and previous iterations for a consecutive preset number of iterations, or if the latest iteration order is equal to a preset order threshold, the iteration is stopped. If, at any given time, there is no consecutive preset number of iterations where the absolute difference in instantaneous suppression rate between the next and previous iterations is less than or equal to a preset difference threshold, and the latest iteration order is less than a preset order threshold, then the iteration is stopped before iteration begins.

9. The method for detecting characteristic biomarkers of the immune microenvironment in gliomas according to claim 1, characterized in that, The method for obtaining the immune microenvironment feature values ​​includes: The theoretical response value of the target metabolite is obtained based on the ion intensity of the target metabolite at different discrete slice times and the instantaneous inhibition rate of the final slice. The maximum value between the target exosome concentration and the preset lower limit threshold is selected as the target exosome baseline concentration; The ratio of the theoretical response value of the target metabolite to the baseline concentration of the target exosome was obtained as a characteristic value of the immune microenvironment.

10. The method for detecting characteristic biomarkers of the immune microenvironment in gliomas according to claim 9, characterized in that, The method for obtaining the theoretical response value of the target metabolite includes: The sum of the products of the target metabolite ion intensities at all discrete slice times and the instantaneous inhibition rate of the final slice is obtained as the damaged signal intensity; the sum of the target metabolite ion intensities at all discrete slice times is obtained as the total target metabolite ion intensity. If the total intensity of the target metabolite ions is less than or equal to the preset lower limit threshold, the total intensity of the target metabolite ions will be used as the theoretical response value of the target metabolite. If the total intensity of the target metabolite ions is greater than the preset lower limit threshold, the theoretical response value of the target metabolite is obtained based on the intensity ratio of the damaged signal intensity to the total intensity of the target metabolite ions, as well as the total intensity of the target metabolite ions. The intensity ratio and the total intensity of the target metabolite ions are both positively correlated with the theoretical response value of the target metabolite.

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