Myopia risk detection method based on tear protein analysis

Abstract

The invention relates to a myopia risk detection method based on tear protein analysis. The method comprises the following steps: detecting a concentration value of a target protein in to-be-detected tears; giving a discrete score to each target protein according to the concentration interval of the concentration value; accumulating the discrete score of each target protein to obtain a myopia risk total score corresponding to the tears to be detected; wherein the target protein is determined by combining a tandem mass tag technology with a liquid chromatography-tandem mass spectrometry technology and has differential expression aiming at different myopia degrees, and the target protein comprises one or more of a tumor necrosis factor related apoptosis inducing ligand, a chemotactic factor ligand 21 and an epidermal growth factor; the myopia risk level represented by the concentration interval is in positive correlation with the discrete score; the myopia risk total score is positively correlated with the myopia risk. According to the scheme provided by the invention, the myopia risk of the individual corresponding to the tears to be detected can be detected by utilizing the biological protein with differentiated expression aiming at different myopia degrees.

Description

Technical Field

[0001] This application relates to the field of myopia risk detection technology, and in particular to a myopia risk detection method based on tear protein analysis. Background Technology

[0002] Myopia, a prevalent eye disease worldwide, is rapidly increasing among children and adolescents, posing a significant public health challenge to eye health. The pathogenesis of myopia in children and adolescents is not fully understood. Current risk assessment techniques for myopia rely heavily on measurements of refractive error and axial length. While clinical methods using structural indicators such as refractive error and axial length for myopia management are convenient, they are essentially passive records of existing lesions, failing to reflect molecular dynamics before morphological changes and making it difficult to predict individual progression risk. In other words, structural indicators such as refractive error and axial length are neither early biomarkers for myopia development nor provide dynamic monitoring of disease progression in clinical practice.

[0003] Therefore, developing a biomarker detection system that can identify risks early, monitor processes dynamically, and reveal mechanisms has become an urgent need for precision ophthalmology.

[0004] Tears, as a non-invasive ocular surface secretion, are rich in proteins related to eye health. Changes in their composition can directly reflect the state of the local microenvironment. The protein profile of tears may be closely related to the development of myopia, making them a highly promising source of biomarkers. However, current research on tear proteomics and myopia is insufficient, and there is an urgent need to explore new biomarkers.

[0005] Traditional protein analysis techniques, such as enzyme-linked immunosorbent assays (ELISA), while widely used in biomarker detection, exhibit significant limitations in sensitivity and sample size requirements. Currently, ELISA is widely used for specific protein quantification due to its standardized operation. Based on the principle of antigen-antibody specific binding, it achieves detection through a double-antibody sandwich structure and enzymatic colorimetric reaction. Although this method has been used to detect certain common inflammatory factors in tears, such as lactoferrin, its application in screening for low-abundance myopia-related proteins has significant limitations. These limitations are mainly reflected in the following aspects: First, the sensitivity is typically in the picogram to nanogram per milliliter range, making it difficult to effectively capture extremely low concentrations of regulatory proteins in tears; second, a single test requires tens of microliters of sample, while the volume of tears collected from children is often less than ten microliters, making sample size a rigid bottleneck; third, the complex composition of tears easily interferes with the stability of solid-phase coated antibodies, leading to non-specific binding or signal attenuation; and fourth, antibody reagents are easily inactivated and have a short shelf life, making it difficult to meet the stability requirements for large-scale applications.

[0006] In short, ELISA methods typically have low detection sensitivity and require large sample volumes, limiting their effectiveness in detecting low-concentration biomarkers in practical applications. Furthermore, most ELISA methods rely on antigen-antibody binding reactions, usually performed on a solid-phase surface. While relatively simple and cost-effective, the accuracy of this method is limited by the stability of reagents and the platform. The stability of reagent kits, especially their short shelf life, has become a bottleneck restricting the widespread adoption and clinical application of ELISA technology. For kits requiring long-term storage, insufficient reagent stability and interference resistance often lead to performance degradation, affecting the accuracy and reliability of test results.

[0007] To address these issues, this invention employs a novel technique combining tandem mass tagging (TMT) with liquid chromatography-tandem mass spectrometry (LC-MS / MS) to perform comprehensive proteomics analysis on tear fluid samples from children and adolescents, providing a new technical approach for the early diagnosis and monitoring of myopia. This method can obtain more accurate and efficient detection results with lower sample volumes, demonstrating significant clinical application value. Compared to traditional ELISA methods, TMT technology offers higher sensitivity and a wider range of applications, enabling efficient and high-throughput quantitative analysis by labeling protein peptides in different samples. Simultaneously, LC-MS / MS technology provides higher-resolution mass spectrometry data, enabling the identification and quantification of more complex protein profiles, suitable for detecting low-concentration biomarkers. This technology not only overcomes the limitations of ELISA in terms of sensitivity and sample requirements but also allows for the analysis of protein changes in large numbers of samples, significantly improving detection throughput and accuracy. It is particularly suitable for tear fluid proteomics research, revealing potential biomarkers related to myopia. Summary of the Invention

[0008] To overcome the problems existing in related technologies, this application provides a myopia risk detection method based on tear protein analysis. This method can utilize target proteins that are differentially expressed for different degrees of myopia to realize a myopia risk identification and assessment scheme based on proteomics analysis.

[0009] The first aspect of this application provides a method for myopia risk detection based on tear protein analysis, comprising: detecting the concentration value of a target protein in the tear to be tested; assigning a discrete score to each target protein according to the concentration range in which the concentration value falls; accumulating the discrete scores of each target protein to obtain a total myopia risk score corresponding to the tear to be tested; wherein the target protein is determined by tandem mass tagging technology combined with liquid chromatography-tandem mass spectrometry and it exhibits differential expression for different degrees of myopia; the target protein includes one or more of tumor necrosis factor-associated apoptosis-inducing ligand, chemokine ligand 21, and epidermal growth factor; the myopia risk level characterized by the concentration range is positively correlated with the discrete score; and the total myopia risk score is positively correlated with myopia risk.

[0010] In some embodiments, the target protein further includes one or more of ribosomal proteins, lysosome-associated proteins, and lipid metabolism-associated proteins.

[0011] In some embodiments, before assigning a discrete score to each target protein based on the concentration range in which the concentration value falls, the method further includes: constructing a tear sample set and a myopia risk grading model, wherein the tear sample set includes tear samples from individuals with high myopia, moderate myopia, mild myopia, and normal refractive status, wherein the number of each tear sample is greater than or equal to 50; using the tear sample set and the myopia risk grading model to determine multiple concentration ranges corresponding to each target protein, with different concentration ranges corresponding to different myopia risk levels; the expression of the myopia risk grading model is as follows: ;in, The total myopia risk score represents the total score of tear samples from individuals with high myopia, moderate myopia, mild myopia, and normal refractive status, with the total myopia risk score decreasing in that order. This represents the weighting coefficient of the target protein numbered i in the tear sample. This represents the concentration data of the target protein numbered i in the tear sample. This represents the intercept term.

[0012] In some embodiments, determining multiple concentration ranges corresponding to each target protein using a tear film sample set and a myopia risk grading model includes: collecting concentration data of the target protein in the tear film sample set; training the myopia risk grading model using the concentration data to obtain weight coefficients and intercept terms in the myopia risk grading model; generating a receiver operating characteristic (ROC) curve based on the intercept term; and determining the optimal cutoff point on the ROC curve according to the Youden index to obtain the boundary values ​​of multiple concentration ranges corresponding to each target protein.

[0013] In some embodiments, the collection of target protein concentration data in the tear sample group includes: quantitatively detecting the tear sample group to obtain target protein concentration values; generating a target protein concentration vector based on the target protein concentration values; and standardizing the target protein concentration vector using the reference mean and standard deviation of the tear reference group to obtain standardized target protein concentration data, wherein the tear reference group is a tear sample group of people with normal refractive status.

[0014] In some embodiments, determining the optimal cutoff point on the receiver operating characteristic curve based on the Youden index to obtain the boundary values ​​of multiple concentration intervals corresponding to each target protein includes: determining the optimal cutoff point on the receiver operating characteristic curve based on the Youden index to generate a first threshold and a second threshold corresponding to each target protein; wherein the first threshold is a boundary value that distinguishes between low myopia risk concentration intervals and moderate myopia risk concentration intervals, and the second threshold is a boundary value that distinguishes between moderate myopia risk concentration intervals and high myopia risk concentration intervals.

[0015] In some embodiments, for tumor necrosis factor-related apoptosis-inducing ligands, the concentration range corresponding to low myopia risk is [0–0.50) μg / L, the concentration range corresponding to moderate myopia risk is [0.50–1.50) μg / L, and the concentration range corresponding to high myopia risk is [1.50– The concentration range for chemokine ligand 21 with low myopia risk is [0–0.20) μg / L; the concentration range for moderate myopia risk is [0.20–0.60) μg / L; and the concentration range for high myopia risk is [0.60–0.20) μg / L. The concentration range for epidermal growth factor (EGFR) with low myopia risk is [0–0.30) μg / L, with moderate myopia risk range [0.30–1.00) μg / L, and with high myopia risk range [1.00–...]. ) μg / L.

[0016] In some embodiments, before detecting the concentration of the target protein in the tear fluid, the method further includes: using tandem mass tagging technology combined with liquid chromatography-tandem mass spectrometry to screen out the target protein from multiple biological proteins contained in the tear fluid, wherein the target protein is differentially expressed for different degrees of myopia.

[0017] In some embodiments, the screening of target proteins from multiple biological proteins contained in tear fluid includes: constructing a tear fluid sample group, the tear fluid sample group including tear fluid samples from people with high myopia, people with moderate myopia, people with mild myopia, and people with normal refractive status; pretreating the tear fluid samples in the tear fluid sample group to obtain a sample peptide solution; mixing a tandem mass spectrometry tag 16-fold labeling reagent with the sample peptide solution and reacting it in a preset environment for a preset time to obtain a labeled solution; adding a stop solution to the labeled solution to terminate the reaction to obtain a solution to be separated; performing a tandem mass spectrometry tag fractionation operation on the solution to be separated to obtain a separated sample; performing mass spectrometry analysis on the separated sample using liquid chromatography-tandem mass spectrometry; and determining the target protein based on the mass spectrometry comparison results of tear fluid samples with different degrees of myopia.

[0018] In some embodiments, the pretreatment includes: performing high-pressure assisted reduction and alkylation on the tear sample to obtain a first sample; enzymatically digesting the first sample using a dual-enzyme synergistic digestion strategy to obtain a second sample; and sequentially performing desalting, centrifugation concentration, and reconstitution operations on the second sample to obtain a sample peptide solution containing multiple small molecule peptides.

[0019] The technical solution provided in this application may include the following beneficial effects: This application utilizes tandem mass tagging technology combined with liquid chromatography-tandem mass spectrometry (LC-MS / MS) to screen several target proteins from over 3000 biological proteins contained in tears, including tumor necrosis factor-related apoptosis-inducing ligands, chemokine ligand 21, and epidermal growth factor. These target proteins exhibit differential expression for different degrees of myopia, making them particularly suitable for tear proteomics research as potential biomarkers for assessing myopia risk. By using one or more of the screened target proteins, the concentration range of their values ​​is detected, and a discrete score is assigned to each protein to represent the myopia risk level exhibited by that target protein in the tested tear. This allows for the determination of the total myopia risk score for the tested tear based on the performance of a single target protein or a combination of multiple target proteins, thus realizing a myopia risk identification and assessment scheme based on proteomics analysis.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0021] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0022] Figure 1 This is a schematic flowchart illustrating a myopia risk detection method based on tear fluid protein analysis, as shown in an embodiment of this application. Figure 2 This is a schematic flowchart illustrating the concentration range division method shown in the embodiments of this application; Figure 3 This is a flowchart illustrating a concentration range division method according to some embodiments of this application; Figure 4 This is a schematic flowchart illustrating the target protein screening method in the embodiments of this application. Detailed Implementation

[0023] Preferred embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0024] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0025] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0026] The technical solutions of the embodiments of this application are described in detail below with reference to the accompanying drawings.

[0027] Figure 1 This is a schematic flowchart illustrating a myopia risk detection method based on tear protein analysis, as shown in an embodiment of this application.

[0028] See Figure 1 In step S101, the concentration of the target protein in the tear fluid to be tested is detected; In step S102, each target protein is assigned a discrete score based on the concentration range in which its concentration value falls. In step S103, the discrete scores of each target protein are accumulated to obtain the total myopia risk score corresponding to the tear fluid to be tested.

[0029] In this embodiment, the target proteins are selected from over 3000 biological proteins contained in tear fluid using tandem mass tagging technology combined with liquid chromatography-tandem mass spectrometry. These proteins are then identified through bioinformatics techniques and are differentially expressed for different degrees of myopia. Specifically, the target proteins screened in this embodiment include one or more of tumor necrosis factor-associated apoptosis-inducing ligands, chemokine ligand 21, and epidermal growth factor.

[0030] Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), also known as Tumor Necrosis Factor Superfamily Member 10 (TNFSF10), mediates tumor cell apoptosis and immune regulation by encoding the TRAIL protein. Currently, in clinical applications, TRAIL is primarily used as a core gene target for tumor-targeted therapy. During the target protein screening process, this application discovered that the concentration of this biological protein in tear fluid samples from normal refractive states, low-to-moderate myopia, and high myopia showed a progressively increasing expression pattern with increasing myopia degree. In other words, this biological protein exhibits differential expression for different degrees of myopia, making it a highly promising myopia-related biomarker.

[0031] Chemokine ligand 21 (CCL21) is a CC-class homeostatic chemokine whose core function is to mediate immune cell homing and lymphoid tissue localization by binding to the receptor CCR7, thereby regulating immune initiation and tumor metastasis. Currently, in clinical applications, CCL21 is mainly used in tumor immunology, autoimmune diseases, and vaccine development. During the target protein screening process, this application discovered a significantly upregulated expression pattern of CCL21 in tear fluid samples from individuals with high myopia. In other words, this protein exhibits differential expression for different degrees of myopia, making it a highly promising myopia-related biomarker.

[0032] Epidermal growth factor (EGF) is a multifunctional polypeptide growth factor and a core member of the epidermal growth factor family. It primarily regulates cell proliferation, differentiation, migration, and survival. Currently, in clinical applications, EGF is mainly used for wound repair, targeted tumor therapy, and cosmetic dermatology. During the target protein screening process, this application discovered a selective upregulation pattern of EGF expression in tear fluid samples from individuals with high myopia. In other words, this protein exhibits differential expression for different degrees of myopia, making it a highly promising myopia-related biomarker.

[0033] Furthermore, in some embodiments, the target protein may also include one or more of ribosomal proteins, lysosomal-associated proteins, and lipid metabolism-associated proteins.

[0034] As an example, ribosomal proteins can be ribosomal protein S3 (RPS3) or ribosomal protein S18 (RPS18). The core function of RPS3 is to participate in ribosome maturation and translation initiation, while also possessing non-ribosomal functions such as DNA repair, playing a crucial role in tumor, inflammation, and DNA damage responses. The core function of RPS18 is to stabilize ribosome structure, ensuring precise and efficient translation initiation and elongation, while also participating in non-ribosomal functions (such as cytoskeleton regulation and signal transduction), and is closely related to diseases such as tumors and hereditary anemia. Currently, in clinical applications, RPS3 is mainly used for tumor diagnosis and treatment, DNA damage repair, and inflammatory immunity, while RPS18 is mainly used for the treatment of tumors, hereditary anemia, and neurodegenerative diseases. During the screening of target proteins, this application discovered that the concentration of ribosomal protein exhibits a downregulated expression pattern in tear fluid samples from normal refractive states, low-to-moderate myopia, and high myopia. In other words, this biological protein shows differential expression for different degrees of myopia and is a highly promising myopia-related biomarker.

[0035] Lysosome-associated proteins (LAPs) and lipid metabolism-associated proteins (LTMs) are two core intracellular proteins involved in substance degradation, lipid synthesis, lipid transport, and lipid breakdown. Currently, in clinical applications, LAPs and LTMs primarily provide combined intervention strategies for lipid storage diseases, metabolic syndrome, and tumors. This application discovers that LAPs and LTMs can reflect changes in the lysosome-autophagy and fatty acid metabolism pathways. The lysosome-autophagy pathway, by regulating intracellular substance degradation and energy supply, affects retinal function, scleral fibroblast activity, and extracellular matrix remodeling, thereby promoting axial elongation. Therefore, these proteins, to some extent, reflect the differences in myopia severity and possess the potential to serve as myopia-related biomarkers.

[0036] In practical applications, one or more of TNFSF10, CCL21, and EGF can be selected as indicators for myopia risk assessment. In addition, one or more of ribosomal proteins, lysosomal-associated proteins, and lipid metabolism-associated proteins can be selected as supplementary indicators to be used together for the detection of the tear fluid to determine the patient's myopia risk.

[0037] For ease of description, the following example uses the selection of three target proteins, TNFSF10, CCL21, and EGF, to illustrate the myopia risk detection method based on tear protein analysis shown in the embodiments of this application.

[0038] In step S101, the concentrations of TNFSF10, CCL21, and EGF in the test tear fluid are detected to obtain the concentration values ​​of TNFSF10 (C_TNF), CCL21 (C_CCL21), and EGF (C_EGF).

[0039] Given that each target protein is expressed differently for different degrees of myopia, for example, the concentration of TNFSF10 shows a progressively increasing expression pattern with the degree of myopia in tear samples from normal refractive state, low-to-moderate myopia, and high myopia, several concentration intervals can be divided to correspond to different degrees of myopia for easy grading. Thus, based on the concentration interval of each target protein, the following information can be obtained: the myopia risk level of the test tear when the target protein is used as a single assessment indicator.

[0040] When multiple target proteins are selected, a discrete score can be assigned to the detection results of each target protein. Then, the total myopia risk score for the tested tear fluid is obtained by combining the discrete scores of all selected target proteins. The concentration range, representing the myopia risk level, is positively correlated with the discrete score, and the total myopia risk score is positively correlated with the myopia risk itself. In other words, the higher the myopia risk level, the larger the assigned discrete score, and the higher the accumulated total myopia risk score.

[0041] As an example, the discrete score corresponding to the low myopia risk concentration range can be set to 0, the discrete score corresponding to the moderate myopia risk concentration range can be set to 1, and the discrete score corresponding to the high myopia risk concentration range can be set to 2. It should be noted that the discrete score corresponding to each concentration range can also be set to other specific values.

[0042] In some embodiments, for tumor necrosis factor-related apoptosis-inducing ligands, the concentration range corresponding to low myopia risk is [0–0.50) μg / L, the concentration range corresponding to moderate myopia risk is [0.50–1.50) μg / L, and the concentration range corresponding to high myopia risk is [1.50– ) μg / L.

[0043] For chemokine ligand 21, the concentration range corresponding to low myopia risk is [0–0.20) μg / L, the concentration range corresponding to moderate myopia risk is [0.20–0.60) μg / L, and the concentration range corresponding to high myopia risk is [0.60– ) μg / L.

[0044] For epidermal growth factor, the concentration range corresponding to low myopia risk is [0–0.30) μg / L, the concentration range corresponding to moderate myopia risk is [0.30–1.00) μg / L, and the concentration range corresponding to high myopia risk is [1.00– ) μg / L.

[0045] Assuming the discrete score range is {0, 1, 2}, then during step S102, if the C_TNF in the test tear fluid ∈ [0–0.50) μg / L, then the C_TNF biomarker reflects information about low myopia risk, and the discrete score of TNFSF10 is... TNFSF10 The value is 0; if C_TNF in the test tear fluid is ∈ [0.50-1.50) μg / L, then the biomarker C_TNF reflects the information of moderate myopia risk, and the discrete score of TNFSF10, ScoreTNFSF10, is 1; if C_TNF in the test tear fluid is ∈ [1.50– If the concentration of C_CCL21 in the tear fluid is [0–0.20) μg / L, then the biomarker C_CCL21 reflects information about a high risk of myopia, and the discrete score of CCL21 is 2. If the concentration of C_CCL21 in the tear fluid is [0–0.20) μg / L, then the biomarker C_CCL21 reflects information about a low risk of myopia, and the discrete score of CCL21 is 2. CCL21 The value is 0; if C_CCL21 in the test tear fluid ∈ [0.20–0.60) μg / L, then the biomarker C_CCL21 reflects information about the risk of moderate myopia, and the discrete score of CCL21 is 0. CCL21 The value is 1; if C_CCL21∈[0.60– ] in the tear fluid to be tested If the concentration of CCL21 is μg / L, then the biomarker C_CCL21 reflects information about the risk of high myopia. The discrete score of CCL21 is... CCL21 The value is 2. If C_EGF in the test tear fluid ∈ [0–0.30) μg / L, then the biomarker C_EGF reflects information about low myopia risk, and the discrete score of EGF is 2. EGF The value is 0; if C_EGF in the test tear fluid ∈ [0.30–1.00) μg / L, then the biomarker C_EGF reflects information about the risk of moderate myopia, and the discrete score of EGF is 0. EGF The value is 1; if C_EGF ∈ [1.00– ] in the tear fluid to be tested If the concentration of C-EGF is μg / L, then the biomarker C-EGF reflects information about the risk of high myopia. The discrete score of EGF is... EGF The value is 2.

[0046] Furthermore, in practical applications, standard protein samples were used to detect standard and blank control samples at concentrations of 0.25 μg / L, 0.5 μg / L, 1.0 μg / L, 2.0 μg / L, 4.0 μg / L, and 8.0 μg / L, respectively, with 12 measurements taken for each sample. The mean and standard deviation were calculated. The absorbance of the blank control mean plus three times the blank standard deviation was taken as the detection limit, resulting in a detection limit of 0.10 μg / L. In other words, 0.10 μg / L is the lowest range for protein concentration detection. Therefore, the concentration ranges for TNFSF10, CCL21, and EGF corresponding to low myopia risk can be adjusted to [0.10–0.50) μg / L, [0.10–0.20) μg / L, and [0.10–0.30) μg / L, respectively. If a target protein is not detected (below the detection limit of 0.10 μg / L), the discrete score is recorded as 0 by default.

[0047] When performing step S103, the formula TotalScore=Score can be used. TNFSF10 +Score CCL21 +Score EGF Calculate the total myopia risk score corresponding to the tear fluid being tested.

[0048] Furthermore, in some embodiments, the range of the total myopia risk score can be determined based on the configuration of the discrete scores and the number of target proteins selected. For example, when assuming the discrete score range is {0, 1, 2} and the selected target proteins are TNFSF10, CCL21, and EGF, the range of the total myopia risk score is {0, 1, 2, 3, 4, 5, 6}. Based on this, {0, 1, 2} can be divided into the range of the total myopia risk score corresponding to the low-risk level, {3, 4} into the range of the total myopia risk score corresponding to the medium-risk level, and {5, 6} into the range of the total myopia risk score corresponding to the high-risk level. Here, the low-risk level indicates that the patient has normal refractive status or a mild myopia risk, the medium-risk level indicates that the patient has a moderate risk of developing myopia or progressing to high myopia, and the high-risk level indicates that the patient has high myopia or a high risk of progressing to high myopia.

[0049] It should be noted that in practical applications, the range of values ​​for the total myopia risk score can be further subdivided, for example, the lowest risk level, the second lowest risk level, the medium risk level, the second highest risk level, and the high risk level.

[0050] In other embodiments, based on the degree of differential expression of different target proteins for different degrees of myopia, different evaluation weights can be assigned to different target proteins to increase the contribution of certain target proteins to the total myopia risk score. Therefore, when performing step S103, the formula TotalScore=K can be used. TNFSF10 ×Score TNFSF10 +K CCL21 ×Score CCL21 +K EGF ×Score EGF Calculate the total myopia risk score corresponding to the tear film being tested, K. TNFSF10 The evaluation weights configured for TNFSF10, K CCL21 Evaluation weights configured for CCL21, K EGF The assessment weights configured for EGF. If assessment weights are used, the total myopia risk score can be divided into several consecutive value intervals. Consistent with the example described above, the number of consecutive value intervals can also be determined according to actual needs.

[0051] The above example illustrates the myopia risk detection method provided in this application, using the selection of three target proteins: TNFSF10, CCL21, and EGF. When other target proteins are selected, the calculation terms in the formula for calculating the total myopia risk score will be adjusted accordingly. For example, when selecting TNFSF10, CCL21, EGF, and ribosomal protein as the three target proteins, TotalScore = Score. TNFSF10 +Score CCL21 +Score EGF +Score RP Or TotalScore=K TNFSF10 ×Score TNFSF10 +K CCL21 ×Score CCL21 +K EGF ×Score EGF +K RP ×Score RP And so on.

[0052] In the above embodiments, the concentration range corresponding to different myopia risks for each target protein is a predetermined concentration range. During execution... Figure 1 Prior to the method shown, tear samples can be collected from people with high myopia, people with moderate myopia, people with mild myopia, and people with normal refractive status to construct a tear sample group. Based on the performance of different target proteins in tear samples of different myopia levels in the tear sample group, multiple concentration ranges corresponding to different myopia risk levels for each target protein can be determined.

[0053] Figure 2 This is a schematic flowchart illustrating the concentration range division method shown in the embodiments of this application. See also... Figure 2 In step S201, tear sample groups and myopia risk grading models are constructed. In step S202, multiple concentration ranges corresponding to each target protein are determined using tear fluid sample groups and a myopia risk grading model.

[0054] In this embodiment, the tear sample set includes tear samples from individuals with high myopia, moderate myopia, mild myopia, and normal refractive status. Furthermore, to ensure the reliability of the results, the number of each type of tear sample in the tear sample set is greater than or equal to 50; that is, the tear sample set should include at least 50 tear samples from individuals with high myopia, 50 from individuals with moderate myopia, 50 from individuals with mild myopia, and 50 from individuals with normal refractive status.

[0055] In this embodiment, different concentration ranges correspond to different myopia risk levels.

[0056] In this embodiment, the expression for the myopia risk grading model is as follows: ; in, This represents the total myopia risk score for the individual corresponding to the tear sample. The total myopia risk score decreases from the tear sample of highly myopic individuals to the tear sample of moderately myopic individuals and the tear sample of mildly myopic individuals. This represents the weighting coefficient of the target protein numbered i in the tear sample. This represents the concentration data of the target protein numbered i in the tear sample. This represents the intercept term.

[0057] Furthermore, the myopia risk grading model is constructed using support vector machines or XGBoost algorithms. The input variables can be standardized core biomarker values, and the output is a score for three myopia risk levels: low risk, medium risk, and high risk. Additionally, the training and validation sets can be divided in a 7:3 ratio, and cross-validation uses the 50-fold method. Performance evaluation metrics include AUC, sensitivity, specificity, and / or accuracy.

[0058] In this embodiment, the total myopia risk score for each tear sample can be determined based on the predetermined discrete score configuration and the number of selected target proteins. For specific implementation details, please refer to the description in the preceding embodiments. For example, assuming the discrete score range is {0, 1, 2} and three target proteins are selected, {0, 1, 2} can be divided into the range of total myopia risk scores corresponding to low risk levels, {3, 4} into the range of total myopia risk scores corresponding to medium risk levels, and {5, 6} into the range of total myopia risk scores corresponding to high risk levels. Then, based on the myopia status of each tear sample, the total myopia risk score for each individual is set.

[0059] Based on the predefined total myopia risk score for each individual corresponding to a tear sample and the concentration of the target protein in that tear sample, the weighting coefficients and intercept term in the myopia risk grading model can be calculated. The weighting coefficients reflect, to some extent, the contribution of each target protein to the total myopia risk score for the individual corresponding to the tear sample, while the intercept term affects the fitting effect of the myopia risk grading model and the degree of deviation in myopia risk grading, thus affecting the accuracy of myopia risk level classification.

[0060] After obtaining the intercept term, the optimal cutoff point, also known as the optimal threshold, can be determined based on the validation set using the Receiver Operating Characteristic Curve (ROC) and the Youden index. This threshold is the boundary line between the concentration ranges corresponding to each risk level.

[0061] In some embodiments, step S202 may be performed as follows: Figure 3 The method shown is executed. Figure 3 This is a flowchart illustrating a concentration range division method as shown in some embodiments of this application.

[0062] In step S301, the concentration data of the target protein in the tear sample group are collected; In step S302, a myopia risk grading model is trained using concentration data to obtain the weight coefficients and intercept term in the myopia risk grading model. In step S303, a receiver operating characteristic curve is generated based on the intercept term; In step S304, the optimal cutoff point on the subject operating characteristic curve is determined based on the Youden index to obtain the boundary values ​​of multiple concentration ranges corresponding to each target protein.

[0063] In some embodiments, step S301 is performed as follows: quantitative detection of the tear sample group is performed to obtain the target protein concentration value; then, a target protein concentration vector is generated based on the target protein concentration value; then, the target protein concentration vector is standardized using the reference mean and standard deviation of the tear reference group to obtain standardized target protein concentration data, wherein the tear reference group is the tear sample group of people with normal refractive status.

[0064] Furthermore, a microsphere-immunofluorescence multiplex detection platform can be used for quantitative detection, which converts the fluorescence signal into actual concentration values ​​through a standard curve. For each tear sample, the target protein concentration value can be constructed as a target protein concentration vector C=(C_TNF, C_CCL21, C_EGF, ...), which is then standardized using the reference mean and standard deviation of tear samples from people with normal refractive status, thus obtaining the standardized expression of the target protein concentration vector Z=(Z_TNF, Z_CCL21, Z_EGF, ...).

[0065] When performing step S302, the myopia risk grading model can be trained using the standardized expression described above. Specifically, multivariate logistic regression, random forest, gradient boosting tree, or other supervised learning algorithms can be used to train the myopia risk grading model on a training queue with known refractive states (normal, low myopia, moderate myopia, high myopia), thereby obtaining the weight coefficients and intercept terms of each target protein.

[0066] Furthermore, in this embodiment, the intercept term b is a multidimensional vector, wherein one component contains a mathematical expression of the optimal cutoff point corresponding to a target protein. Therefore, after the intercept term b is obtained in step S302, the optimal cutoff point in the direction of each component can be solved by using the Youden exponent and ROC curve, thereby generating the first threshold and the second threshold corresponding to each target protein.

[0067] In step S304, the optimal cutoff point on the receiver operating characteristic (ROC) curve is determined based on the Youden index to generate a first threshold and a second threshold for each target protein. That is, the optimal cutoff point is found by maximizing the Youden index, representing the point on the ROC curve furthest from the diagonal of random guessing. In the myopia risk grading model, the optimal cutoff point provides two thresholds for each target protein: a first threshold C_low and a second threshold C_high. The first threshold is the boundary between the low and moderate myopia risk concentration ranges, and the second threshold is the boundary between the moderate and high myopia risk concentration ranges.

[0068] This application has been approved. Figure 2 and Figure 3The described method determined that the first threshold corresponding to TNFSF10 was 0.50 μg / L, the second threshold corresponding to TNFSF10 was 1.50 μg / L, the first threshold corresponding to CCL21 was 0.20 μg / L, the second threshold corresponding to CCL21 was 0.60 μg / L, the first threshold corresponding to EGF was 0.30 μg / L, and the second threshold corresponding to EGF was 1.00 μg / L.

[0069] Combination Figures 1-3 The illustrated embodiment shows how to determine the concentration range of each target protein based on tear samples, and how to use the determined concentration range to calculate the total myopia risk score of the individual corresponding to the tear sample, thereby determining the individual's myopia risk.

[0070] Furthermore, in addition to using the determined concentration range to calculate the total myopia risk score for the individual corresponding to the tear fluid being tested, thereby determining the individual's myopia risk, it is also necessary to screen for target proteins from multiple biological proteins contained in the tear fluid. Even further, some embodiments of this application utilize tandem mass tagging technology combined with liquid chromatography-tandem mass spectrometry to screen from over 3000 biological proteins in the tear fluid and ultimately determine the target protein using bioinformatics technology. The screened target protein exhibits differential expression for different degrees of myopia.

[0071] Figure 4 This is a schematic flowchart illustrating the target protein screening method in an embodiment of this application. See also... Figure 4 In step S401, a tear sample group is constructed; In step S402, the tear samples in the tear sample group are pretreated to obtain a sample peptide solution; In step S403, the tandem mass spectrometry tag 16-fold labeling reagent is mixed with the sample peptide solution and placed in a preset environment for a preset reaction time to obtain the labeling solution; In step S404, a stop solution is added to the labeled solution to terminate the reaction, resulting in the solution to be separated. In step S405, a tandem mass spectrometry tag fractionation operation is performed on the solution to be separated to obtain a separated sample; In step S406, the separated sample is analyzed by mass spectrometry using liquid chromatography-tandem mass spectrometry. In step S407, the target protein is determined based on the mass spectrometry comparison results of tear samples with different degrees of myopia.

[0072] In this embodiment, the tear sample set includes tear samples from individuals with high myopia, moderate myopia, mild myopia, and normal refractive status. Furthermore, to ensure the reliability of the results, the number of each type of tear sample in the tear sample set is greater than or equal to 50; that is, the tear sample set should include at least 50 tear samples from individuals with high myopia, 50 from individuals with moderate myopia, 50 from individuals with mild myopia, and 50 from individuals with normal refractive status.

[0073] In some embodiments, the pretreatment includes: performing high-pressure assisted reduction and alkylation on the tear sample to obtain a first sample; enzymatically digesting the first sample using a dual-enzyme synergistic digestion strategy to obtain a second sample; and sequentially performing desalting, centrifugation concentration, and reconstitution operations on the second sample to obtain a sample peptide solution containing multiple small molecule peptides.

[0074] Specifically, during the high-pressure assisted reduction and alkylation of tear samples, each sample was initially treated with 30 μL of lysis buffer containing 6 mol / L urea and 2 mol / L thiourea. Subsequently, 5 μL of 0.2 mol / L tris(2-carboxyethyl)phosphine (TCEP) was added as a reducing agent and 2.5 μL of 0.8 mol / L iodoacetamide (IAA) was added as an alkylating agent. The process was performed in the Barocycler high-pressure sample processing system at a pressure of 45 kpsi, a high-pressure holding time of 30 seconds, and a normal pressure recovery cycle of 10 seconds, for a total of 90 cycles. The reaction temperature was kept constant at 30 degrees Celsius to ensure complete depolymerization of the protein structure and to prevent the reformation of disulfide bonds.

[0075] After reduction and alkylation, 75 μL of 0.1 mol / L triethylammonium bicarbonate (TEAB) buffer was added to the first sample to adjust the ionic strength. Then, 5 μg of trypsin and 1.25 μg of lysine C-terminal specific protease (rLys-C) were added sequentially, both pre-dissolved in 1 mM hydrochloric acid or 50 mM acetate buffer, respectively. After adjusting the pH to 8.0, the sample was placed back into the Barocycler system and subjected to enzymatic digestion at 30°C under 20 kpsi pressure, 50 seconds of high pressure, 10 seconds of normal pressure, and 120 cycles to achieve efficient and specific peptide generation. Large proteins in the tear sample were degraded into smaller peptides for easy mass spectrometry detection. The digestion was then terminated by adding 15 μL of 10% trifluoroacetic acid (TFA) solution, ensuring a final TFA concentration of 1%. The Barocycler high-pressure treatment technology significantly shortened the preparation time and improved the integrity of protein extraction.

[0076] After enzymatic hydrolysis was terminated, the resulting second sample was desalted and purified using a SOLAμ solid-phase extraction column to remove residual salts, buffer components, and other interfering substances. The desalted second sample was then centrifuged and concentrated to dryness at 45°C under a vacuum of less than 10 mbar. After reconstitution, a peptide solution containing multiple small peptide molecules was obtained. Furthermore, the peptide concentration in the peptide solution was determined using the A280 wavelength. The A280 wavelength is a core characteristic wavelength in ultraviolet-visible spectroscopy used for quantitative protein detection, based on the ultraviolet absorption characteristics of specific amino acids in protein molecules.

[0077] After obtaining the sample peptide solution through the above pretreatment, the extracted protein peptides are labeled with TMT by performing steps S403 and S404. TMT can label protein peptides in different samples and perform efficient and high-throughput quantitative analysis, with higher sensitivity and a wider range of applications.

[0078] Specifically, in step S403, 5 micrograms of peptide from all sample peptide solutions are used for TMT labeling according to a uniform quality standard. The labeling operation is performed in a 0.6 mL centrifuge tube. After reconstitution of the dried peptide, 40 micrograms of tandem mass spectrometry tagging reagent (TMTpro 16-plex) at a concentration of 20 micrograms per microliter are added, ensuring a peptide to labeling reagent mass ratio of 1:8. The labeling reaction is carried out at 25°C and 1200 rpm for 90 minutes to obtain the labeled solution. TMTpro 16-plex labeling reagent supports parallel analysis of up to 16 samples, significantly improving the efficiency of cohort studies.

[0079] Specifically, in step S404, 0.5 μL of 5% hydroxylamine solution is added to the labeling solution to terminate the reaction, and the reaction is continued at 25 degrees Celsius and 600 rpm for 15 minutes to remove unreacted active groups, resulting in the solution to be separated.

[0080] After labeling, step S405 is executed to complete the TMT fractionation operation. Specifically, a DIONX μLtiMate 3000 high-performance liquid chromatography system with an XBridge Peptide BEH C18 reversed-phase column is used for high-pH reversed-phase fractionation to obtain separated samples. The TMT fractionation operation uses acetonitrile and a 10 mmol / L ammonium hydroxide aqueous solution as the mobile phase, with the pH of the ammonium hydroxide aqueous solution equal to 10.0, and gradient elution is performed at a flow rate of 0.5 mL / min. During the fractionation process, 60 fractions are initially obtained, which are combined into 30 main fractions based on the retention time distribution. Each fraction is then centrifuged, concentrated, and dried again, and reconstituted with a solution containing 98% water, 2% acetonitrile, and 0.1% formic acid for mass spectrometry analysis.

[0081] In this embodiment, mass spectrometry analysis was performed using a Vanquish Neo ultra-high performance liquid chromatography system coupled with an OrbitrapExploris 480 high-resolution mass spectrometer, employing a data-dependent acquisition mode. Chromatographic separation used a 60-minute gradient program. Mobile phase A was an aqueous system containing 2% acetonitrile, 98% water, and 0.1% formic acid; mobile phase B was an organic system containing 80% acetonitrile, 20% water, and 0.1% formic acid. The gradient range was set to 8% to 35% of mobile phase B. The first-stage full scan mass range was limited to 375 to 1800 m / s, with a resolution of 60,000. The second-stage fragment ion scan resolution was set to 30,000, and TurboTMT was used for TMT label detection. Furthermore, the obtained mass spectrometry analysis results were used for database searching using Fragpipe software, with MSFragger as the search tool, allowing for a maximum of two missed cleavage sites. Data quality control used an FDR of 0.01, and the Unique+Razor mode was used to determine peptide-protein uniqueness. Furthermore, all data were normalized using the median-centered method to ensure data reliability and comparability, facilitating subsequent screening for biological proteins with differential expression depending on the degree of myopia, i.e., target proteins. Compared to ELISA, which is limited by low throughput and a limited number of targets, the target protein screening method shown in this embodiment can more effectively screen for key proteins related to different degrees of myopia.

[0082] Furthermore, in order to achieve myopia risk assessment for children and adolescents, tear samples were collected from children and adolescents aged 8 to 18 according to their degree of myopia when constructing the tear sample group. The collected tear samples were temporarily stored in a -80°C frozen environment until experimental processing to ensure the reliability and validity of the samples.

[0083] Based on the preceding text Figure 4 In addition to the described embodiments, this application also provides a kit for tear protein proteometry analysis, which includes a tear collection device and... Figure 4 The examples shown include various reagents and solutions, such as lysis buffer, reducing agent, alkylating agent, buffer, dual-enzyme mixture, trifluoroacetic acid solution, TMTpro 16-plex labeling reagent, and hydroxylamine solution. The dual-enzyme mixture contains trypsin and a lysine C-terminal specific protease. TMTpro isotope labeling reagent is also included. In the experimental phase, the above-mentioned kit was tested. Regarding the standard curve range for TMT labeling, tests were conducted using protein samples of different concentrations. The standard curve range was 1.0 μg / L to 200 μg / L, with corresponding absorbance ranges of 0.3 to 1.5, exhibiting a curved relationship. The blank absorbance was less than 0.2. Regarding analytical sensitivity, standard protein samples at concentrations of 0.25 μg / L, 0.5 μg / L, 1.0 μg / L, 2.0 μg / L, 4.0 μg / L, and 8.0 μg / L, as well as a blank control, were measured 12 times each, and the mean and standard deviation were calculated. The limit of detection was set at the absorbance of the blank control mean plus three times the blank standard deviation, which was 0.10 μg / L. Regarding the accuracy indicator, in the experimental stage, standards with concentrations of 100 μg / L and 200 μg / L were added to a certain amount of sample for detection. The recovery rate of thyroglobulin was between 93% and 106%, with an average recovery rate of 101.6%.

[0084] The solution of this application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the descriptions of each embodiment have different emphases; for parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. Those skilled in the art should also understand that the actions and modules involved in the specification are not necessarily essential to this application. Furthermore, it is understood that the steps in the method of this application's embodiments can be adjusted, combined, and deleted according to actual needs, and the modules in the device of this application's embodiments can be combined, divided, and deleted according to actual needs.

[0085] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for myopia risk detection based on tear fluid protein analysis, characterized in that, include: The concentration of the target protein in the tear fluid to be tested was detected; Each target protein is assigned a discrete score based on the concentration range in which the concentration value falls. The discrete scores of each target protein are summed to obtain the total myopia risk score corresponding to the tear fluid being tested; The target protein is identified by tandem mass tagging technology combined with liquid chromatography-tandem mass spectrometry and is differentially expressed for different degrees of myopia. The target protein includes one or more of tumor necrosis factor-associated apoptosis-inducing ligand, chemokine ligand 21, and epidermal growth factor. The myopia risk level characterized by the concentration range is positively correlated with the discrete score. The total myopia risk score is positively correlated with myopia risk.

2. The myopia risk detection method according to claim 1, characterized in that, The target protein also includes one or more of ribosomal proteins, lysosome-associated proteins, and lipid metabolism-associated proteins.

3. The myopia risk detection method according to claim 1, characterized in that, Before assigning a discrete score to each target protein based on the concentration range in which the concentration value falls, the process also includes: A tear sample set and a myopia risk classification model were constructed. The tear sample set included tear samples from people with high myopia, people with moderate myopia, people with mild myopia, and people with normal refractive status, with each tear sample having a quantity greater than or equal to 50. The tear fluid sample set and the myopia risk grading model are used to determine multiple concentration ranges for each target protein, with different concentration ranges corresponding to different myopia risk levels. The expression for the myopia risk grading model is as follows: ； in, The total myopia risk score represents the total score of tear samples from individuals with high myopia, moderate myopia, mild myopia, and normal refractive status, with the total myopia risk score decreasing in that order. This represents the weighting coefficient of the target protein numbered i in the tear sample. This represents the concentration data of the target protein numbered i in the tear sample. This represents the intercept term.

4. The myopia risk detection method according to claim 3, characterized in that, The method of determining multiple concentration ranges for each target protein using tear film sample sets and a myopia risk grading model includes: Concentration data of the target protein were collected from tear fluid samples. The myopia risk grading model is trained using the concentration data to obtain the weight coefficients and intercept term in the myopia risk grading model; The subject operating characteristic curve is generated based on the intercept term; The optimal cutoff point on the subject operating characteristic curve is determined based on the Youden index to obtain the boundary values ​​of multiple concentration ranges corresponding to each target protein.

5. The myopia risk detection method according to claim 4, characterized in that, The concentration data of the target protein collected in the tear sample group includes: Quantitative analysis was performed on tear fluid samples to obtain the concentration values ​​of the target protein. Generate a target protein concentration vector based on the target protein concentration value; The target protein concentration vector was standardized using the reference mean and standard deviation of the tear reference group to obtain standardized target protein concentration data. The tear reference group was a tear sample group of people with normal refractive status.

6. The myopia risk detection method according to claim 4, characterized in that, The step of determining the optimal cutoff point on the receiver operating characteristic curve based on the Youden index to obtain the boundary values ​​of multiple concentration ranges corresponding to each target protein includes: The optimal cutoff point on the receiver operating characteristic curve is determined based on the Youden index to generate the first and second thresholds for each target protein. The first threshold is the dividing value that distinguishes between the low myopia risk concentration range and the moderate myopia risk concentration range, and the second threshold is the dividing value that distinguishes between the moderate myopia risk concentration range and the high myopia risk concentration range.

7. The myopia risk detection method according to any one of claims 1-6, characterized in that, For tumor necrosis factor-associated apoptosis-inducing ligands, the concentration range corresponding to low myopia risk is [0–0.50) μg / L, the concentration range corresponding to moderate myopia risk is [0.50–1.50) μg / L, and the concentration range corresponding to high myopia risk is [1.50– μg / L; For chemokine ligand 21, the concentration range corresponding to low myopia risk is [0–0.20) μg / L, the concentration range corresponding to moderate myopia risk is [0.20–0.60) μg / L, and the concentration range corresponding to high myopia risk is [0.60– μg / L; For epidermal growth factor, the concentration range corresponding to low myopia risk is [0–0.30) μg / L, the concentration range corresponding to moderate myopia risk is [0.30–1.00) μg / L, and the concentration range corresponding to high myopia risk is [1.00– ) μg / L.

8. The myopia risk detection method according to claim 1, characterized in that, Before detecting the concentration of the target protein in the tear sample, the following steps are also included: Using tandem mass tagging technology combined with liquid chromatography-tandem mass spectrometry, target proteins were screened from multiple biological proteins contained in tears. These target proteins showed differential expression for different degrees of myopia.

9. The myopia risk detection method according to claim 8, characterized in that, The process of screening for target proteins from multiple biological proteins contained in tears includes: A tear sample set was constructed, which included tear samples from people with high myopia, people with moderate myopia, people with mild myopia, and people with normal refractive status. Tear samples from the tear sample group were pretreated to obtain sample peptide solutions; The tandem mass spectrometry tag 16-fold labeling reagent is mixed with the sample peptide solution and placed in a preset environment for a preset reaction time to obtain the labeling solution; A stop solution is added to the labeled solution to terminate the reaction, resulting in the solution to be separated. The solution to be separated was subjected to tandem mass spectrometry tag fractionation to obtain a separated sample; The separated samples were analyzed by mass spectrometry using liquid chromatography-tandem mass spectrometry. The target protein was determined based on the mass spectrometry comparison results of tear samples with different degrees of myopia.

10. The myopia risk detection method according to claim 9, characterized in that, The preprocessing includes: The tear fluid sample was subjected to high-pressure assisted reduction and alkylation treatment to obtain the first sample; The first sample was enzymatically digested using a dual-enzyme synergistic digestion strategy to obtain the second sample; The second sample was subjected to desalting, centrifugation and concentration, and reconstitution in sequence to obtain a sample peptide solution containing multiple small molecule peptides.

Images