Marker substance spectrum detection method for evaluating immune microenvironment of HCC patient

Abstract

The invention relates to the technical field of mass spectrum detection, in particular to a marker substance spectrum detection method for HCC patient immune microenvironment evaluation. According to the method, mass spectrum data characteristic analysis of each marker is obtained for different tissue space regions, and characteristic indexes are obtained through mass spectrum peak shapes of the markers in tissue region samples; analyzing the incidence relation of characteristic indexes of different markers, and obtaining spatial heterogeneity in combination with an incidence difference expression condition; characterizing the contribution degree of the feature expression of the marker in different tissue region samples by combining the spatial heterogeneity with the significant deviation of the feature index; and obtaining a mass spectrum evaluation characteristic value of the marker through contribution degree adjustment, and carrying out environment evaluation. According to the invention, through mass spectrometric detection of a multi-tissue space sample and combined analysis, the composition state of the immune microenvironment is compared from different space regions, so that the interference of spatial heterogeneity on a marker detection result is reduced, and the real characteristics of the HCC immune microenvironment can be evaluated more comprehensively and accurately.

Description

Technical Field

[0001] This invention relates to the field of mass spectrometry detection technology, and more specifically to a biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients. Background Technology

[0002] Hepatocellular carcinoma (HCC) is often accompanied by hepatitis B or C infection, cirrhosis, and long-term chronic inflammation. Immunotherapy has become one of the important strategies for HCC treatment. The immune microenvironment, a complex environment surrounding tumor cells composed of immune cells, cytokines, immune checkpoint molecules, stromal cells, etc., determines the response of HCC patients to immunotherapy.

[0003] Currently, mass spectrometry is a highly sensitive and high-throughput analytical technique that can perform qualitative and quantitative analysis of molecules in complex biological samples. It is generally used to detect immune-related biomarkers in blood, tissue and other samples from HCC patients to assess the immune microenvironment.

[0004] In the process of using mass spectrometry to detect biomarkers in blood or tissue samples from HCC patients, the immune status of the tumor interior and surrounding tissues differs significantly. Due to factors such as different blood supply conditions, immune cell recruitment pathways, inconsistent local immunosuppression mechanisms, and complex tumor-stromal interactions in the tumor center, invasive margin, and adjacent normal tissue areas, the immune microenvironment of hepatocellular carcinoma exhibits a high degree of spatial variability. As a result, when performing mass spectrometry detection, the collected blood or single-region tissue samples often only reflect local characteristics, and the detection signal is easily averaged in the overall analysis, failing to fully reflect the true situation of biomarkers in the overall immune microenvironment. Summary of the Invention

[0005] To address the technical problems in the prior art, the present invention aims to provide a biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients. The specific technical solution adopted is as follows: This invention provides a biomarker mass spectrometry detection method for assessing the immune microenvironment in HCC patients, the method comprising: For each biomarker, mass spectrometry peaks were extracted from samples from each tissue region. For each biomarker, the mass spectrometry peak shape in each tissue region sample was analyzed to determine the degree of Gaussianity, thereby obtaining the characteristic indicators of each tissue region sample. The trend correlation of the biomarker with each other biomarker among samples of different tissue regions was analyzed to screen out the biomarkers with strong correlation with each biomarker; between each biomarker and each strongly correlated biomarker, the degree of correlation difference in each tissue region sample was determined according to the degree of deviation of the correlation of the biomarkers in each tissue region sample relative to the correlation of all tissue region samples. The spatial heterogeneity of each biomarker in each tissue region sample is obtained by measuring the difference in association performance and trend correlation between each biomarker and all strongly associated biomarkers in each tissue region sample. Based on the significant deviation of the characteristic indicators of each biomarker in each tissue region sample, and combined with spatial heterogeneity, the contribution of each biomarker in each tissue region sample is obtained; based on the contribution and characteristic indicators of the biomarkers in different tissue region samples, the mass spectrometry evaluation characteristic values ​​of the biomarkers are obtained. The immune microenvironment was assessed based on mass spectrometry characteristic values.

[0006] Furthermore, the method for obtaining the feature indicators includes: For any biomarker in any tissue region sample, the highest relative intensity value of the mass spectrum peak is taken as the peak value. For each data point outside the peak, calculate the difference between the relative intensity of the data point and the peak, as the intensity variability; calculate the difference between the mass-to-charge ratio of the data point and the mass-to-charge ratio corresponding to the peak, as the neighbor spacing; and use the ratio of the intensity variability of the data point to the neighbor spacing as the Gaussian trend of the data point. The characteristic index of the mass spectrum peak is obtained by normalizing the mean Gaussian trend of all data points.

[0007] Furthermore, the method for obtaining the trend correlation includes: The characteristic indicators of each marker are arranged in a preset region order to obtain the characteristic indicator sequence of each marker; For any given marker, calculate the correlation between that marker and every other marker in the feature index sequence, which is used as the trend correlation between that marker and every other marker.

[0008] Furthermore, the method for obtaining the strongly associated markers for each marker is as follows: When the trend correlation is greater than the preset correlation threshold, other corresponding markers will be used as strong correlation markers for that marker.

[0009] Furthermore, the method for obtaining the correlation difference performance includes: For any given marker, each strongly correlated marker of that marker is used as the analysis marker in turn; After calculating the differences in characteristic indicators between the biomarker and the analytical biomarker in each tissue region sample, the mean of the differences in characteristic indicators in all tissue region samples is used as the regional association distribution coefficient between the biomarker and the analytical biomarker. The standard deviation of the differences in characteristic indicators among all tissue region samples is used as the distribution disorder of the biomarker and the analytical biomarker. For any tissue region sample, calculate the difference between the characteristic index difference and the correlation distribution coefficient between the marker and the analytical marker in the tissue region sample, and use it as a significant deviation indicator between the marker and the analytical marker in the tissue region sample. By combining the significant deviation index and distribution disorder of the marker with the analytical marker, the association difference between the marker and the analytical marker in the tissue region samples is obtained.

[0010] Furthermore, the method for obtaining the spatial heterogeneity includes: For any biomarker in any tissue region sample, the product of the trend correlation degree and the correlation difference performance between the biomarker and each strongly correlated biomarker in that tissue region sample is used as the confidence level of the difference between the biomarker and each strongly correlated biomarker in that tissue region sample. The mean of the confidence values ​​of the differences between the marker and all strongly associated markers in the tissue region samples is taken as the spatial heterogeneity of the marker in the tissue region samples.

[0011] Furthermore, the method for obtaining the contribution level includes: For any biomarker, the mean of the biomarker’s characteristic indices across all tissue region samples is taken as the characteristic mean of the biomarker. The differences between the characteristic indices and the characteristic mean of the biomarker in each tissue region sample were normalized to obtain the significant characteristic difference index of the biomarker in each tissue region sample. The contribution of the biomarker to each tissue region sample is obtained by multiplying the significant difference index of the biomarker in each tissue region sample with the spatial heterogeneity.

[0012] Furthermore, the method for obtaining the mass spectrometry evaluation feature values ​​includes: For any given biomarker, the contribution of the biomarker to each tissue region sample is used as the weight, and the characteristic indicators of the biomarker in each tissue region sample are weighted and averaged to obtain the mass spectrometry evaluation characteristic value of the biomarker.

[0013] Furthermore, the assessment of the immune microenvironment based on mass spectrometry characteristic values ​​includes: The mass spectrometry evaluation characteristics of the biomarkers are input into the immune microenvironment evaluation model, and the immune microenvironment characteristic score is output.

[0014] Furthermore, the horizontal axis of the mass spectrum peak represents the mass-to-charge ratio, and the vertical axis represents the relative intensity.

[0015] The present invention has the following beneficial effects: This invention analyzes the mass spectrometry data of various biomarkers from different tissue spatial regions. By comparing the mass spectrometry peak characteristics of samples from different regions, it reveals the spatial differences in the immune microenvironment, avoiding the bias caused by relying solely on samples from a single region. By comprehensively considering multi-level biomarker information such as genes, proteins, and metabolites, a correlation network between biomarkers is established. This correlation clarifies the spatial distribution patterns of different molecules, helping to distinguish whether immune heterogeneity is caused by local tissue characteristics or systemic factors. The integrated spatial heterogeneity data shows significant characteristic bias; by adjusting for the influence of different spaces, more accurate and stable biomarker characteristic detection results are obtained, providing a reliable basis for assessing the immune microenvironment in HCC patients. This invention, through mass spectrometry detection of multiple tissue spatial samples and combined analysis to compare the composition of the immune microenvironment from different spatial regions, reduces the interference of spatial heterogeneity on biomarker detection results, enabling a more comprehensive and accurate assessment of the true characteristics of the HCC immune microenvironment. Attached Figure Description

[0016] 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.

[0017] Figure 1 This is a flowchart of a biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients, provided in one embodiment of the present invention. Figure 2 This is a schematic diagram of a mass spectrum in a single tissue region sample provided in an embodiment of the present invention. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] The following describes in detail, with reference to the accompanying drawings, a specific scheme for a biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients provided by the present invention.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients, provided by an embodiment of the present invention. The method includes the following steps: S1: For each biomarker, extract the mass spectrometry peaks from each tissue region sample.

[0022] In this embodiment of the invention, mass spectrometry data were collected from blood and different tissue samples obtained preoperatively from HCC patients. Residual cells were removed by low-speed centrifugation. Frozen tissue was ground into powder and added to a lysis buffer. The tissue cells were then disrupted by sonication, and impurities were removed by centrifugation. The supernatant was used as the detection material. Liquid chromatography (LC) was used to separate complex samples, reducing intermolecular ion inhibition. High-resolution mass spectrometry (LC-MS / MS) was employed for detection, and biomarkers were identified and quantified through characteristic ion peaks.

[0023] Further, full-scan and fragment ion (MS / MS) data were acquired using mass spectrometry software. Peak identification, alignment, and normalization were performed to reduce sample variability and batch effects. For each tissue region sample, a separate mass spectrum was obtained. Then, the corresponding mass spectrometry peaks were extracted based on known databases (such as UniProt, HMDB, and KEGG) and a list of candidate immunomarkers. It should be noted that the acquisition of mass spectrometry data is a technique well-known to those skilled in the art and will not be elaborated upon here. Please refer to [link to relevant documentation]. Figure 2 The diagram shows a schematic of a mass spectrum in a single tissue region sample provided by an embodiment of the present invention, with the horizontal axis representing the mass-to-charge ratio and the vertical axis representing the relative intensity.

[0024] S2: For each biomarker, analyze the Gaussianity of the mass spectrometry peak shape in each tissue region sample to obtain the characteristic indicators of each tissue region sample; analyze the trend correlation between the biomarker and each other biomarker in different tissue region samples to screen out the biomarkers with strong correlation to each biomarker.

[0025] Blood and tissue samples contain metabolites, proteins, lipids, and other molecules associated with hepatocellular carcinoma (HCC). Before assessing the immune microenvironment of HCC patients, it is necessary to perform mass spectrometry data characterization analysis on biomarkers in preoperative blood and tissue samples. Mass spectrometry peaks can accurately characterize the compositional state of biomarker molecules. By extracting the characteristics of the mass spectrometry peaks of biomarker molecules, a molecular fingerprint of the biomarker can be established, enabling cross-regional comparability.

[0026] Ideally, the mass spectrometry peaks of a biomarker should exhibit a sharp Gaussian distribution. Therefore, by analyzing the Gaussian distribution of the signal distribution of each mass spectrometry peak, the characteristic indicators of each sample's mass spectrometry peak are measured. In this embodiment of the invention, for any biomarker in any tissue region sample, the highest relative intensity value of that mass spectrometry peak is taken as the peak value, characterizing the sharp point.

[0027] Furthermore, for each data point outside the peak, the difference between the relative intensity of that data point and the peak is calculated as the intensity variability, reflecting the degree of distributional distance between each data point and the peak on the vertical axis. The difference between the mass-to-charge ratio of that data point and the mass-to-charge ratio corresponding to the peak is calculated as the proximity interval, reflecting the magnitude of the distance between each data point and the peak on the horizontal axis. The ratio of the intensity variability of that data point to the proximity interval is used as the Gaussian trend of that data point. When the spacing on the horizontal axis and the spacing on the vertical axis change simultaneously, and the larger the span of the vertical axis spacing, the more likely the mass spectrum peak is to exhibit a sharp Gaussian distribution, thus the greater the Gaussian trend.

[0028] By analyzing all data points, the mean Gaussian trend of all data points was normalized to obtain the characteristic index of the mass spectrometry peak. It should be noted that normalization is a technique well-known to those skilled in the art, and the choice of normalization method can be standard normalization or linear normalization, etc., without restriction on the normalization method.

[0029] In another embodiment of the present invention, the kurtosis of the mass spectrometry peak can be calculated and normalized as a characteristic index. The kurtosis reflects the sharpness of Gaussianness, which will not be elaborated here.

[0030] S3: Analyze the trend correlation of the biomarker with each other biomarker among samples from different tissue regions, and screen out the biomarkers with strong correlations for each biomarker; between each biomarker and each strongly correlated biomarker, determine the degree of correlation difference in each tissue region sample based on the degree of deviation of the correlation of the biomarkers in each tissue region sample relative to the correlation between all tissue region samples; obtain the spatial heterogeneity of each biomarker in each tissue region sample by the degree of correlation difference and trend correlation between each biomarker and all strongly correlated biomarkers in each tissue region sample.

[0031] Significant spatial heterogeneity exists within and across different regions of HCC tumor tissue. Differences in blood supply, immune cell infiltration, and tumor-stromal interactions across different regions lead to the high expression of certain biomarkers at the tumor center and low expression at the tumor margin or adjacent normal tissue. Comparing and correlating the mass spectrometry characteristics of different regions can visually reveal the spatial distribution differences of these biomarkers, avoiding information gaps caused by single-point sampling. Furthermore, introducing multi-omics joint analysis can comprehensively characterize molecular changes from multiple dimensions, including proteins and metabolites, enhancing the ability to elucidate the sources and mechanisms of different spatial differences and improving the overall accuracy of the immune microenvironment assessment.

[0032] Different biomarkers in the immune microenvironment of HCC patients often have functional or regulatory relationships. In the process of analyzing the spatial heterogeneity of biomarkers, if the biomarker mass spectrometry results differ in samples from different regions, they may also be affected by other factors, such as high-abundance proteins masking low-abundance immune biomarkers. In order to determine whether the regional characteristics of biomarkers are caused by spatial heterogeneity, it is necessary to construct the association between different omics biomarkers.

[0033] Based on the changes in the characteristic indicators of mass spectrometry peaks of different markers in samples from different regions, the characteristic correlation trend between two markers can be preliminarily determined. In this embodiment of the invention, the characteristic indicators of each marker are arranged in a preset regional order to obtain a characteristic indicator sequence of each marker. The preset regional order can be adjusted by the implementer according to different implementation scenarios, and is not limited here.

[0034] For any given marker, the correlation between that marker and every other marker in the feature index sequence is calculated, which serves as the trend correlation between the marker and every other marker. A higher trend correlation indicates a stronger correlation between the feature changes of the two markers across regions. In this embodiment of the invention, the correlation is calculated using the Pearson correlation coefficient. Calculating the correlation between sequences is a technique well-known to those skilled in the art; Manhattan distance or the DTW algorithm can also be used, but these methods are not limited or elaborated upon here.

[0035] To determine whether the difference in mass spectrometry characteristics of a biomarker in a tissue region sample compared to other tissue regions is due to spatial heterogeneity, the reliability of the difference analysis is highlighted by analyzing the deviation of the biomarker's characteristics from those of other biomarkers in tissue region samples.

[0036] For preliminary trend correlation analysis, a correlation effect will only occur between markers with a high trend correlation. Therefore, markers with strong correlation are selected by trend correlation degree. In this embodiment of the invention, when the trend correlation degree is greater than a preset correlation threshold, other corresponding markers are used as strongly correlated markers of that marker. The preset correlation threshold can be set to 0.7, and the specific value can be adjusted by the implementer and is not limited here.

[0037] By combining the association performance between biomarkers and strongly associated biomarkers, and considering the potential differences in the performance of samples from different tissue regions, the reliability of the spatial heterogeneity analysis of the characteristic differences in tissue sample regions can be assessed. Preferably, in this embodiment of the invention, the method for obtaining the degree of association difference performance includes: First, for any given biomarker, each strongly correlated biomarker is used as an analytical biomarker in turn, and each strongly correlated biomarker is analyzed sequentially. After calculating the differences in characteristic indicators between the biomarker and the analytical biomarker in each tissue region sample, the mean of the differences in characteristic indicators across all tissue region samples is used as the regional correlation distribution coefficient between the biomarker and the analytical biomarker, quantifying the degree of distributional correlation of the characteristic manifestations in a single tissue region sample when the overall trend correlation is high.

[0038] The standard deviation of the differences in characteristic indicators among all tissue region samples is used as the distribution disorder of the marker and the analytical marker. The higher the distribution disorder, the lower the correlation stability between the distributions of the overall characteristic performance, and the higher the possible differences.

[0039] Furthermore, for any tissue region sample, the difference between the characteristic index difference and the correlation distribution coefficient between the marker and the analytical marker in the tissue region sample is calculated as a significant deviation indicator between the marker and the analytical marker in the tissue region sample. For a single tissue region sample, the higher the deviation of the characteristic index distribution from the overall distribution, the more significant the difference and the more prominent the difference in the tissue region sample.

[0040] Finally, by combining the significant deviation index and the distribution disorder of the marker and the analytical marker, the association difference performance between the marker and the analytical marker in the tissue region sample is obtained. In this embodiment of the invention, the product of the significant deviation index and the distribution disorder of the marker and the analytical marker is calculated to obtain the association difference performance between the marker and the analytical marker in the tissue region sample. The larger the significant deviation index and the distribution disorder, the higher the confidence level that there is spatial heterogeneity between the marker and the analytical marker in the tissue region sample.

[0041] Further analysis of the association anomalies between the biomarker and all strongly associated biomarkers, combined with trend correlation adjustment, yields the spatial heterogeneity of the biomarker in each tissue region sample, reflecting the significance of regional characteristic differences as spatial heterogeneity.

[0042] In this embodiment of the invention, for any biomarker in any tissue region sample, the product of the trend correlation degree and the correlation difference performance degree between the biomarker and each strongly correlated biomarker in the tissue region sample is used as the difference confidence degree between the biomarker and each strongly correlated biomarker in the tissue region sample. The greater the trend correlation degree between the strongly correlated biomarker and the biomarker, the more reliable the result of the difference influence analysis of the strongly correlated biomarker is, and therefore the higher the difference confidence degree.

[0043] Finally, the mean of the confidence scores of the differences between this marker and all strongly associated markers in the tissue region samples is used as the spatial heterogeneity of this marker in the tissue region samples. By combining all association analyses, this reflects the significance of the spatial bias of this marker in the tissue region samples.

[0044] S4: Based on the significant deviation of the characteristic indicators of each biomarker in each tissue region sample, and combined with spatial heterogeneity, obtain the contribution of each biomarker in each tissue region sample; based on the contribution and characteristic indicators of the biomarker in different tissue region samples, obtain the mass spectrometry evaluation characteristic value of the biomarker.

[0045] The immune microenvironment of hepatocellular carcinoma (HCC) exhibits spatial heterogeneity. Different regions, such as the tumor center, periphery, and surrounding normal tissue, differ in blood supply, cellular composition, and immune activity, leading to variations in the characteristics of the same biomarker in samples from different regions. Mass spectrometry patterns of biomarkers differ across tumor regions in HCC patients. To accurately assess the immune microenvironment, it is necessary to differentiate the contributions of biomarkers in samples from different regions. The greater the spatial heterogeneity of a biomarker within each regional sample, and the greater the characteristic differences, the more significant the contribution of that biomarker's heterogeneous performance in that regional sample to the assessment.

[0046] Therefore, the deviation of characteristic indicators reflects the differences, and the contribution is analyzed in conjunction with spatial heterogeneity. Preferably, in this embodiment of the invention, the method for obtaining the contribution includes: For any biomarker, the mean of the biomarker's characteristic indicators across all tissue region samples is taken as the characteristic mean of the biomarker. The difference between the characteristic indicators of the biomarker in each tissue region sample and the characteristic mean is normalized to obtain the characteristic difference significance index of the biomarker in each tissue region sample. The degree of deviation between the characteristic difference of a single tissue region sample and the overall mean reflects the characteristic difference. The larger the characteristic difference significance index, the more significant the characteristic difference appears in the tissue region sample.

[0047] The contribution of the biomarker to each tissue region sample is obtained by multiplying the significant difference index of the biomarker in each tissue region sample with the spatial heterogeneity. At the same time, the greater the spatial heterogeneity, the higher the probability that it is caused by spatial heterogeneity, and the higher the attention it should receive in the evaluation.

[0048] To accurately assess the immune microenvironment and prevent biomarker signals from being averaged and thus masking key regional differences, the greater the contribution of a biomarker in a tissue region sample, the more meaningful its mass spectrometry peak characteristics are. By combining the characteristic performance of multiple tissue region samples, mass spectrometry evaluation characteristic values ​​of biomarkers that are beneficial for assessing the immune microenvironment are obtained.

[0049] Preferably, in this embodiment of the invention, the method for obtaining mass spectrometry evaluation feature values ​​includes: For any given biomarker, the contribution of the biomarker to each tissue region sample is used as the weight, and the characteristic indicators of the biomarker in each tissue region sample are weighted and averaged to obtain the mass spectrometry evaluation characteristic value of the biomarker. For tissue region samples with higher contribution, more attention is paid to the characteristic performance.

[0050] S5: Evaluate the immune microenvironment based on mass spectrometry characteristic values.

[0051] After analyzing the evaluation characteristic values ​​of each biomarker in mass spectrometry detection, an immune microenvironment assessment model is further constructed using a multi-dimensional integration method to obtain the final score. In this embodiment of the invention, the mass spectrometry evaluation characteristic values ​​of the biomarkers are input into the immune microenvironment assessment model, and the immune microenvironment characteristic score is output. In other embodiments of the invention, biomarkers can be functionally classified and categorized into groups such as immune activating factors, immunosuppressive factors, immune cell infiltration-related molecules, and metabolic regulatory molecules to clarify their role in the immune microenvironment. An immune microenvironment assessment model is obtained through statistical modeling or machine learning methods, and the final score is output. Model training and construction are techniques well-known to those skilled in the art and will not be elaborated upon here.

[0052] In summary, this invention analyzes the mass spectrometry data of various biomarkers from different tissue spatial regions. By comparing the mass spectrometry peak characteristics of samples from different regions, it reveals the spatial differences in the immune microenvironment within tissues, avoiding the bias caused by relying solely on samples from a single region. By comprehensively considering multi-level biomarker information such as genes, proteins, and metabolites, a correlation network between biomarkers is established. This correlation clarifies the spatial distribution patterns of different molecules, helping to distinguish whether immune heterogeneity is caused by local tissue characteristics or systemic factors. The integrated spatial heterogeneity-based characteristic bias is significant; by adjusting for the influence of different spatial factors, more accurate and stable biomarker characteristic detection results are obtained, providing a reliable basis for assessing the immune microenvironment in HCC patients. This invention, through mass spectrometry detection of multiple tissue spatial samples combined with joint analysis to compare the composition of the immune microenvironment from different spatial regions, reduces the interference of spatial heterogeneity on biomarker detection results, enabling a more comprehensive and accurate assessment of the true characteristics of the HCC immune microenvironment.

[0053] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0054] 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 markers for the evaluation of the immune microenvironment in HCC patients by mass spectrometry, characterized by, The method comprises: For each marker, the mass spectrum peak in each tissue region sample is extracted respectively; For each marker, the Gaussian degree of the mass spectrum peak shape in each tissue region sample is analyzed to obtain a feature index of each tissue region sample; The trend correlation degree of the feature index of the marker and each other marker between different tissue region samples is analyzed to screen a strong correlation marker of each marker; between each marker and each strong correlation marker, the deviation degree of the correlation in each tissue region sample relative to the correlation between all tissue region samples in the feature index is determined to obtain a correlation difference performance of each marker in each tissue region sample; The spatial heterogeneity of each marker in each tissue region sample is obtained by the correlation difference performance and the trend correlation degree of each marker and all strong correlation markers in each tissue region sample; The contribution degree of each marker in each tissue region sample is obtained according to the significant degree of the deviation of the feature index of each marker in each tissue region sample, combined with the spatial heterogeneity; and the mass spectrum evaluation characteristic value of the marker is obtained based on the contribution degree and the feature index of the marker in different tissue region samples; The immune microenvironment is evaluated according to the mass spectrum evaluation characteristic value.

2. The biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients according to claim 1, characterized in that, The feature index acquisition method comprises: For the mass spectrum peak of any marker in any tissue region sample, the highest relative intensity value in the mass spectrum peak is taken as a peak value; For each data point outside the peak value, the difference between the relative intensity of the data point and the peak value is calculated as an intensity change degree; the difference between the mass-to-charge ratio of the data point and the mass-to-charge ratio corresponding to the peak value is calculated as a neighboring interval; and the ratio of the intensity change degree and the neighboring interval of the data point is taken as the Gaussian trend degree of the data point; The mean value of the Gaussian trend degrees of all data points is normalized to obtain the feature index of the mass spectrum peak.

3. The biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients according to claim 1, characterized in that, The trend correlation degree acquisition method comprises: The feature index of each marker is arranged in a preset region sequence to obtain a feature index sequence of each marker; For any marker, the correlation between the marker and each other marker in the feature index sequence is calculated as the trend correlation degree between the marker and each other marker.

4. The biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients according to claim 3, characterized in that, The strong correlation marker acquisition method of each marker comprises: When the trend correlation degree is greater than a preset correlation threshold, the corresponding other marker is taken as the strong correlation marker of the marker.

5. The biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients according to claim 1, characterized in that, The correlation difference performance acquisition method comprises: For any marker, each strong correlation marker of the marker is sequentially taken as an analysis marker; After the difference between the feature index of the marker and the analysis marker in each tissue region sample is calculated, the mean value of the feature index difference in all tissue region samples is taken as a regional correlation distribution coefficient of the marker and the analysis marker; The standard deviation of the feature index difference in all tissue region samples is taken as a distribution confusion degree of the marker and the analysis marker; For any one tissue region sample, the difference between the feature index difference and the correlation distribution coefficient of the marker and the analysis marker in the tissue region sample is calculated as a deviation significant index of the marker and the analysis marker in the tissue region sample. In combination with the deviation significant index and the distribution confusion degree of the analysis marker, the correlation difference performance of the marker and the analysis marker in the tissue region sample is obtained.

6. The biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients according to claim 1, characterized in that, The method for obtaining the spatial heterogeneity comprises: For any marker in any tissue region sample, the product of the trend correlation degree and the correlation difference performance of the marker and each strongly correlated marker in the tissue region sample is taken as the difference reliability of the marker and each strongly correlated marker in the tissue region sample; The mean value of the difference reliability of the marker and all strongly correlated markers in the tissue region sample is taken as the spatial heterogeneity of the marker in the tissue region sample.

7. The biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients according to claim 1, characterized in that, The method for obtaining the contribution degree comprises: For any marker, the mean value of the feature index of the marker in all tissue region samples is taken as the feature mean degree of the marker; The difference between the feature index and the feature mean value of the marker in each tissue region sample is normalized to obtain the feature difference significant index of the marker in each tissue region sample; The product of the feature difference significant index and the spatial heterogeneity of the marker in each tissue region sample is calculated to obtain the contribution degree of the marker in each tissue region sample.

8. The biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients according to claim 1, characterized in that, The method for obtaining the mass spectrum evaluation feature value comprises: For any marker, the contribution degree of the marker in each tissue region sample is taken as the weight, and the feature index of the marker in each tissue region sample is weighted and averaged to obtain the mass spectrum evaluation feature value of the marker.

9. The biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients according to claim 1, characterized in that, The immune microenvironment evaluation according to the mass spectrum evaluation feature value comprises: The mass spectrum evaluation feature value of the marker is input into an immune microenvironment evaluation model to output an immune microenvironment feature score.

10. The biomarker mass spectrometry detection method for assessing the immune microenvironment of HCC patients according to claim 1, characterized in that, The horizontal axis of the mass spectrum peak is the mass-to-charge ratio, and the vertical axis is the relative intensity.

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