Protein marker for predicting hypoxia reaction and kit thereof

Abstract

The invention provides a protein marker combination for predicting hypoxia reaction risk and a kit and application thereof. The marker combination comprises six differential expression proteins of RALY, PADI4, RPL12, PRKCB, GRIK5 and BPNT1, a hypoxia intolerance prediction model is constructed through logistic regression and an OPLS-DA analysis method, and effective distinguishing between a hypoxia reaction group and a non-reaction group is achieved. The core markers RALY and PADI4 are respectively in significant up-regulation and down-regulation expression under a hypoxia condition, and the AUC value of joint detection reaches 0.784. The ELISA kit developed on the basis of the marker combination can be used for quantitatively detecting the protein in a human sample, is suitable for evaluating the hypoxia tolerance of patients related to metabolic diseases and immune system disorder, and provides reference for clinical risk stratification and intervention decision.

Description

Technical Field

[0001] The present invention relates to the fields of biotechnology and molecular diagnostics, and specifically to a protein marker combination for predicting hypoxia response, a detection kit thereof, and its application in assessing the risk of hypoxia intolerance in patients with metabolic diseases and immune system disorders. The marker combination is particularly suitable for quantitative detection based on an ELISA method, enabling the construction of a hypoxia intolerance prediction model, enabling personalized risk assessment and auxiliary diagnosis. Background Art

[0002] Hypoxia is a common feature of the pathological processes of many diseases, particularly cardiovascular disease, tumors, metabolic disorders, and inflammatory diseases. The induced changes in cellular function and abnormal signal transduction pose a serious threat to the body's health. The human body's response to a hypoxic environment varies from person to person, and some people may experience hypoxia intolerance, which manifests as abnormal metabolic stress response, over-activation of inflammation, or imbalanced immune regulation. In severe cases, this can lead to tissue damage and even organ failure. Therefore, developing a reliable and quantifiable biomarker tool for early identification of people at risk of hypoxia intolerance is of great significance for disease early warning, precision treatment, and personalized management.

[0003] Existing studies have attempted to identify biological markers associated with hypoxic response at the transcriptomic, metabolomic, or proteomic levels, but a systematic, actionable panel of protein markers and their detection methods for hypoxic risk are still lacking. In particular, in clinical applications, existing detection methods have limited ability to monitor the dynamic response to hypoxic stress and lack comprehensive assessment schemes that address the intersection of immunity, inflammation, and apoptosis. Therefore, there is an urgent need to develop a reproducible, sensitive, and highly specific multi-marker combined detection tool to predict an individual's physiological tolerance to hypoxic conditions. Summary of the Invention

[0004] Individual physiological responses to hypoxic environments vary significantly, and hypoxia intolerance is closely associated with the development and progression of various diseases. Existing technologies lack a stable and sensitive biomarker panel and detection solution that can accurately predict the risk profile of individuals experiencing hypoxic stress.

[0005] Therefore, the present invention aims to provide a novel combination of hypoxia response protein markers, a kit thereof, and its use for assessing an individual's physiological tolerance to hypoxia, particularly for monitoring hypoxic stress and risk stratification in patients with metabolic diseases and immune system abnormalities. By combining molecular marker detection with computational modeling, this approach enables accurate assessment of hypoxia intolerance risk, addressing the current lack of stable, sensitive, and clinically applicable hypoxia response prediction tools.

[0006] To achieve the above objectives, the inventors of the present invention have, through unremitting efforts, discovered protein markers and combinations thereof for predicting the risk of hypoxia reaction, wherein the protein markers are selected from the group consisting of the following protein markers:

[0007] RALY (RNA-binding protein RALY)

[0008] PADI4 (protein arginine deiminase 4)

[0009] RPL12 (ribosomal protein L12)

[0010] PRKCB (protein kinase C-β)

[0011] GRIK5 (glutamate receptor K5)

[0012] BPNT1 (3′-phosphoadenylic acid tyrosine kinase)

[0013] The above-mentioned markers were screened from the proteomic data of hypoxia stress response population and normal population through statistical methods (univariate analysis and multivariate modeling), and can effectively distinguish individuals at risk of hypoxia intolerance.

[0014] To this end, the present invention first provides a protein marker for predicting the risk of hypoxia reaction, wherein the marker is selected from any one, two, three, four, five or six of the following six proteins:

[0015] RALY (RNA-binding protein RALY)

[0016] PADI4 (protein arginine deiminase 4)

[0017] RPL12 (ribosomal protein L12)

[0018] PRKCB (protein kinase C-β)

[0019] GRIK5 (glutamate receptor K5)

[0020] BPNT1 (3′-phosphoadenylic acid tyrosine kinase)

[0021] Preferably, the protein markers are a combination of RALY and PADI4, which show significant expression changes during hypoxia stress response and serve as core predictive markers. RALY and PADI4 were significantly downregulated in the hypoxia-intolerant group, while RPL12, PRKCB, GRIK5, and BPNT1 were significantly upregulated. These differences in expression can serve as a basis for distinguishing hypoxia-tolerant individuals.

[0022] The present invention also provides the use of reagents for detecting the above protein markers in the preparation of a kit or detection device for predicting the risk of hypoxia. The reagents include but are not limited to:

[0023] Antibody or antigen pairs for immunoassays;

[0024] PCR primers for gene expression detection;

[0025] Reagents for mass spectrometry or chromatography analysis of protein levels.

[0026] This application covers a variety of detection methods at the protein level and nucleic acid level, which can be flexibly selected according to the detection platform.

[0027] Based on the aforementioned markers and reagents, the present invention further provides a kit for predicting hypoxia risk, characterized by comprising a detection component for quantitatively detecting target proteins such as RALY and PADI4, preferably an ELISA kit. The kit can quantitatively measure the target protein in a subject's blood, plasma, serum, or other biological samples to assess their hypoxia tolerance risk.

[0028] The kit can also be used in non-therapeutic and non-diagnostic scenarios such as scientific research, physical examinations, chronic disease screening, and risk warning. It is particularly suitable for dynamic monitoring of individuals with metabolic diseases, inflammatory diseases, or immune disorders.

[0029] The present invention also provides for the use of the aforementioned protein markers for predicting an individual's risk of hypoxia response, for purposes other than disease diagnosis or treatment. This application can provide bioinformatics support for sports medicine, environmental adaptation research, and personalized health management.

[0030] Furthermore, to facilitate standardized interpretation of results and risk warnings, the present invention also provides a computer system that performs the following steps:

[0031] a) Receive and read the quantitative detection results of the target protein (such as RALY, PADI4, etc.) in the subject's blood sample, compare it with the reference population level, and calculate the expression ratio;

[0032] b) determining whether each marker falls within the hypoxia risk range based on a preset threshold;

[0033] c) Output comprehensive judgment results and, when necessary, prompt the user of the risk of hypoxia intolerance.

[0034] The system can serve as a supporting analytical tool for the test kit, realizing automated data analysis and intelligent risk classification, making it easier for doctors or researchers to make scientific decisions.

[0035] The present invention also provides an application method of the above-mentioned kit, which can be used, for example, to detect the risk of hypoxia intolerance in individuals with metabolic disorders, inflammation-related diseases or immune system disorders, to assist in early identification and risk management.

[0036] Compared with the prior art, the present invention has the following significant advantages:

[0037] The protein marker combination is highly targeted, covering key pathways such as immune regulation, inflammatory response, and cell apoptosis, and can comprehensively reflect the systemic biological response under hypoxic stress;

[0038] The detection method is based on ELISA or mass spectrometry platform, etc., which has high sensitivity, simple operation and strong clinical applicability, providing a scientific basis for precision medicine.

[0039] The present invention further provides a hypoxia reaction risk assessment model based on protein markers, which uses statistical modeling and machine learning methods to predict the risk of hypoxia intolerance in individual subjects.

[0040] (1) Model construction method

[0041] This model is based on a previous large-sample human hypoxia stimulation experiment. It collects protein expression data from biological samples of different individuals before and after hypoxia exposure, and annotates them based on clinical response characteristics. The samples are divided into "hypoxia response group" and "non-response group".

[0042] The modeling steps are as follows:

[0043] Feature screening: Protein markers with significant differences were screened through univariate statistical analysis (such as t-test) and multivariate analysis (such as PLS-DA, OPLS-DA).

[0044] Model Algorithm: Logistic Regression is preferred for building a binary classification prediction model. Other classification methods such as Random Forest and Support Vector Machine can also be used.

[0045] Variable importance score (VIP): The OPLS-DA method is used to evaluate the contribution of each variable in the model.

[0046] Finally, six core differential proteins (RALY, PADI4, RPL12, PRKCB, GRIK5 and BPNT1) were selected as model input features.

[0047] (2) Model parameter description

[0048] Model formula (taking logistic regression as an example):

[0049] Risk Score=β0+β1·[RALY]+β2·[PADI4]+…+β6·[BPNT1]

[0050] Where [X] represents the expression level of the target protein, and β0 to β6 are the regression coefficients obtained after model training. This score is used to measure an individual's risk of hypoxia intolerance.

[0051] Classification threshold setting: The optimal cut-off value is determined through the ROC curve to divide individuals into low-risk and high-risk groups.

[0052] (3) Performance verification

[0053] Model validation was performed on an independent validation set.

[0054] (4) Model application instructions

[0055] The model can be embedded in the analysis system of the test kit or deployed as an online or local software tool to receive test data and automatically generate risk reports. Typical applications include but are not limited to:

[0056] Risk prediction of potential plateau workers by medical examination institutions;

[0057] Screening for hypoxia tolerance in individuals with metabolic diseases or immune disorders;

[0058] Biomarker support for environmental adaptability in precision medicine research.

[0059] (5) Advantages and innovations

[0060] Combination biomarkers are superior to single indicators and more robust;

[0061] Logistic regression + OPLS-DA joint modeling enhances prediction stability and interpretability;

[0062] Complementary to existing methods: This model does not rely on imaging or physiological function tests, but is based on the molecular level and is suitable for early screening and dynamic monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without inventive efforts. Among them:

[0064] Figure 1 .Supervised OPLSDA can significantly distinguish the two groups.

[0065] Figure 2 .Volcano plot analysis of hypoxia proteomics.

[0066] Figure 3 .Thermal map analysis of proteins.

[0067] Figure 4 .GO enrichment analysis results.

[0068] Figure 5 .KEGG enrichment analysis results.

[0069] Figure 6 .Reactome enrichment analysis results.

[0070] Figure 7 .ROC analysis curve graph. DETAILED DESCRIPTION

[0071] Example 1 Collection of case-control samples

[0072] (1) Based on informed consent, a hypoxia intolerance hypobaric oxygen chamber simulation experiment and susceptibility scale screening were conducted.

[0073] The inventors of the present invention used a hypobaric oxygen chamber to simulate plateau conditions to screen for people who are sensitive to hypoxia and recruited volunteers (aged 18-60).

[0074] (2) Low-pressure cabin implementation plan:

[0075] Ascend to 5000m and stay for 30 minutes without oxygen. The subject first ascends to 4000m at a speed of 30-40m / s and stays for 5 minutes. If everything is good, continue to ascend to 5000m at the same speed and stay for 30 minutes, then descend to the ground at a speed of 10-20m / s. During the stay at 5000m, take a test of writing and calculation ability. If conditions permit, record physiological indicators such as respiration, pulse, blood pressure, electrocardiogram, and electroencephalogram. Pay attention to observe and record the ascender's expression, speed and accuracy of response, etc. Hypoxia tolerance should be analyzed and evaluated based on the ascender's main complaint, expression, respiration, pulse, blood pressure, and the results of various objective examinations.

[0076] The evaluation criteria for hypoxia tolerance are as follows.

[0077] Respiratory Circulation Assessment:

[0078] Normal: Pulse variation <20 beats / min, >5 beats / min, respiratory variation <10 beats / min

[0079] Abnormal: Pulse variation >20 beats / min, <5 beats / min, respiratory variation >10 beats / min

[0080] Symptom and sign assessment:

[0081] No obvious symptoms or signs: There is no obvious subjective discomfort and no obvious changes in physical signs.

[0082] Mild symptoms and signs: at least one reaction such as mild headache, dizziness, and drowsiness.

[0083] Moderate symptoms and signs: sometimes nausea, severe but not unbearable headache, fullness of the head, chest tightness, etc., at least one reaction.

[0084] Severe symptoms and signs: vomiting, severe nausea, obvious paleness, blurred vision, or at least one reaction such as unbearable headache, head fullness, chest tightness, etc.

[0085] The inventors of the present invention regarded volunteers with mild, moderate and severe symptoms as the hypoxia-sensitive population, and those without obvious symptoms as the hypoxia-insensitive population.

[0086] Hypoxia endurance assessment:

[0087] Good hypoxia tolerance: No discomfort, expression, behavior, response and working ability are normal. Respiration and pulse are slightly increased (pulse increase value does not exceed 20 beats / minute, respiration increase value does not exceed 10 beats / minute)

[0088] Hypoxia tolerance is relatively good: General malaise, mild headache, and sometimes nausea. Moderate skin pallor, cyanosis of lips and nails. Blood pressure may slightly decrease or increase significantly, and pulse rate may slightly decrease or increase significantly. This condition usually lasts for a few minutes, after which the patient's feeling and expression improve. Work capacity may be slightly reduced.

[0089] Poor tolerance to hypoxia: severe headache and dizziness, obvious general weakness, "golden spots" in front of the eyes, and even symptoms before collapse.

[0090] (3) Termination of the research plan

[0091] When the volunteer's heart rate is greater than or equal to 140 beats / minute or he or she experiences intolerable severe symptoms and signs such as severe headache and pale complexion, the volunteer should immediately receive oxygen.

[0092] (4) Blood sample collection and research

[0093] 2 ml of venous blood was drawn from volunteers before they entered the hypobaric oxygen chamber. The genetic susceptibility and proteomic differences between the hypoxia-sensitive and insensitive groups were compared to study the predictive indicators for hypoxia sensitivity.

[0094] A total of 164 samples were collected, including 38 hypoxia-intolerant individuals and 126 hypoxia-tolerant individuals serving as the control group.

[0095] Example 2 Protein extraction and magnetic bead enrichment

[0096] Using the Low-Abundance Protein Enrichment Magnetic Bead Kit (MagicOmics-DMB8X), mix 100 μL of sample with an equal volume of Dilution Buffer and add to DMB beads pre-incubated with Dilution Buffer. Incubate at 37°C, 1000 rpm, shaking for 30 minutes. Discard the supernatant by magnetic aspiration and wash three times with Dilution Buffer to obtain magnetic beads containing low-abundance proteins. Add 30 μL of Lysis Buffer and incubate at 95°C for 10 minutes. Return to room temperature, add 100 μL of Digestion Buffer and 2 μL of trypsin, and incubate at 37°C, 1000 rpm, shaking for at least 4 hours. After incubation, add 5 μL of Quench Buffer to terminate enzymatic digestion and lyophilize.

[0097] Using high-resolution mass spectrometry detection:

[0098] Prepare mobile phases A (100% water, 0.1% formic acid) and B (80% acetonitrile, 0.1% formic acid). Dissolve the lyophilized powder in 10 μL of A, centrifuge at 14000 g for 20 min at 4°C, take 1 μg of the supernatant and inject it into the sample for LC-MS / MS analysis. The LC elution conditions are shown in Table 1. Use Orbitrap Exploris TM 480 mass spectrometer, compensation voltage CV switched between -45 and -65 every 1s, Nanospray Flex TM The (NSI) ion source was set to 1.85 kV, the ion transfer tube temperature was set to 320°C, the mass spectrometer adopted a non-data-dependent acquisition mode, the full scan range of the mass spectrometer was m / z 350-1500, the primary mass spectrometer resolution was set to 120,000 (200 m / z), the AGC was 300%, and the maximum C-trap injection time was 50 ms; the secondary mass spectrometer detection adopted the "TopSpeed" mode, the secondary mass spectrometer resolution was set to 15,000 (200 m / z), the AGC was 75%, the maximum injection time was 22 ms, and the peptide fragmentation collision energy was set to 33% to generate mass spectrometry detection raw data (.raw).

[0099] The separation gradient is as follows:

[0100] Time (minutes) Mobile phase B ratio (%) 0 8 2 12 17 30 20 40 21 95 30 95

[0101] Dissolve the peptide powder in 0.1% FA formic acid aqueous solution and add iRT standard peptides. Take 1 μg sample and inject it for LC-MS detection. The LC elution conditions are shown in Table 1. Orbitrap Exploris TM 480 Mass Spectrometer, NanosprayFlex TMThe MS was acquired in data-dependent acquisition mode with a full scan range of m / z 3500-1500, a primary mass spectrometry resolution of 120,000 (200 m / z), an AGC of 300%, and a C-trap maximum injection time of 50 ms. The secondary mass spectrometry detection adopted the "Top Speed" mode with a secondary mass spectrometry resolution of 30,000 (200 m / z), an AGC of 200%, a maximum injection time of Auto, and a peptide fragmentation collision energy of 33% to generate raw mass spectrometry data (.raw).

[0102] The separation flow rate was 600 nl / min, and the separation gradient was as follows:

[0103]

[0104]

[0105] Protein bioinformatics analysis:

[0106] The selection of a database is based on the desired species, completeness of the database annotation, and sequence reliability. When selecting a database, follow these guidelines: for sequenced organisms, select the database for that species directly; for unsequenced organisms, select the broad proteome database most relevant to the sample being tested.

[0107] The database used this time was: Homo sapiens SP (number of proteins: 20,361, database: uniprot, download time: 2022.03.17).

[0108] The search parameters for the Spectronaunt software are set as follows.

[0109] Parameters:Value

[0110] Enzyme:Trypsin

[0111] Static Modification:Carbamidomethyl(C)

[0112] Dynamic Modification: M Oxidation (15.995Da); Acetyl (Protein N-terminal)

[0113] Max Missed Cleavages: 2

[0114] The obtained data were quality controlled, and the quality controlled data were statistically analyzed and plotted using R language.

[0115] Example 3 PCA analysis of proteomics of hypoxia cohort

[0116] The inventors of this invention established a hypoxia investigation cohort of nearly 164 cases and obtained basic data, clinical data and proteomic data. Through large-scale proteomics, it is possible to significantly distinguish between hypoxia-responsive and hypoxia-nonresponsive samples. The results are shown in Figure 1 .

[0117] Example 4 Discovery of related proteins

[0118] Six differentially expressed proteins were found to be significantly different. Figure 2 As can be seen from the figure, there are 4 up-regulated and 2 down-regulated proteins, a total of 6 differentially expressed proteins, see Table 1.

[0119] Table 1: Differentially expressed proteins

[0120]

[0121]

[0122] Heatmap analysis

[0123] Heatmap is a display of quality control and difference data of experimental data, which visualizes the differences between data through color gradients. Areas of different colors represent different clustering grouping information, and the actual differences between groups can be displayed through sample clustering and color gradients. The corresponding colors of the color band are mapped to the heatmap matrix data. Generally, colors close to positive values indicate high expression and positive correlation, while colors close to negative values indicate low expression and negative correlation. Taking the average expression level of metabolites in the same sample as the benchmark, expressions above the average value are positive and marked in red, and expressions below the average value are negative and marked in blue. See the results. Figure 3 .

[0124] Functional enrichment analysis

[0125] Functional annotation and analysis is aimed at the list of experimentally identified genes or other molecules, and extracting various structural and functional annotation information of these molecules in current annotation databases (such as gene-related Gene Ontology, KEGG, etc.).

[0126] Based on these annotation information, functional enrichment analysis can be further performed. Enrichment analysis is to find a class of genes or proteins (such as a functional pathway) that are overexpressed in a group of genes or proteins (such as differentially expressed genes). This method is easy to discover valuable functional categories or pathways from omics identification or differential protein list data. Functional enrichment analysis uses the over-representation analysis method, performs statistical significance tests based on hypergeometric distribution, and calculates the p-value of the significant enrichment degree of its reaction identification or differential protein on a KEGG pathway based on a certain functional category annotation (such as KEGG), as well as the FDR correction value based on multiple hypothesis testing. The value of enrichment is obtained by calculating -log (p value). The smaller the p-value or FDR value, the higher the enrichment value, which means that the pathway may have important biological significance in this set of data. Results are shown in Figure 4 、 Figure 5 and Figure 6 .

[0127] Example 5: Building a model

[0128] Logistic regression was used to build the model. All protein expression levels were normalized and trained with the label (response variable) of "hypoxia response group". The model performance was evaluated by cross-validation method. The performance evaluation indicators included: ROC curve (Receiver Operating Characteristic Curve), AUC value (Area Under Curve), etc. The results showed that the AUC value of the model established by 6 differential proteins reached 0.784. Figure 7 The results for individual proteins are shown in the table below.

[0129] Name Auc Q9UKM9 0.65 Q9UM07 0.64 P30050 0.59 P05771 0.62 Q16478 0.64 O95861 0.60

[0130] The results of the two protein combinations are shown in the following table:

[0131] Serial number serial number combination AUC 1 R2-1 RALY&PADI4 0.7206 2 R2-4 RALY&GRIK5 0.6980 3 R2-6 PADI 4 & GRIK 5 0.6959 4 R2-3 RALY&RPL12 0.6924 5 R2-5 PADI 4 & RPL 12 0.6759 6 R2-2 RPL12&GRIK5 0.6598

[0132] Example 6: Kit

[0133] This embodiment provides an ELISA kit for predicting the risk of hypoxia reaction. The kit is designed to detect the content level of RALY and / or PADI4 or one of the two markers in the plasma of a subject, thereby achieving accurate diagnosis of hypoxia risk.

[0134] Kit Composition:

[0135] RALY's ELISA analysis:

[0136] Includes reagents and materials such as antibodies, standards, substrates, wash buffers, etc. for detecting RALY.

[0137] Used to detect the level of RALY in the subject's plasma and perform quantitative analysis based on the standard curve.

[0138] ELISA analysis of PADI4:

[0139] Includes reagents and materials such as antibodies, standards, substrates, wash buffer, etc. for detecting PADI4.

[0140] Used to detect the level of PADI4 in the plasma of subjects and perform quantitative analysis based on the standard curve.

[0141] The detection process can be as follows:

[0142] Take the subject's plasma sample and perform specimen processing and pretreatment steps according to the kit instructions.

[0143] The pretreated plasma samples were added to the respective ELISA plate wells and reacted specifically with antibodies against RALY and PADI4.

[0144] Wash the plate with wash buffer to remove unbound material.

[0145] A substrate is added and the reaction is allowed to proceed under appropriate conditions to produce a measurable color.

[0146] The absorbance of the reaction product was measured using a microplate reader, and the contents of RALY and PADI4 in the sample were calculated based on the standard curve.

[0147] Based on the content levels of each marker and the preset diagnostic criteria, it is determined whether the subject is at risk of hypoxia.

[0148] Example 7: Further confirmation of biomarkers

[0149] Sample selection and detection methods

[0150] To further verify the stability and predictive performance of the six hypoxia response-related protein markers screened in the present invention, another 8 patients with clinical symptoms of hypoxia intolerance (hypoxia response group) and 10 control subjects without obvious hypoxia stress response (control group) were selected, and their peripheral blood was collected and plasma samples were separated.

[0151] The ELISA method was used to determine the levels of the following six proteins in plasma: RALY, PADI4, RPL12, PRKCB, GRIK5, and BPNT1.

[0152] The test results were compared with the expression levels of the six proteins in the control group samples.

[0153] Variance Analysis and Statistical Standards

[0154] A fold change of ≥1.2 or ≤0.83, with a P value of <0.05, was considered significant. The results showed that any of the six proteins could be detected as significantly abnormal in the eight selected hypoxic response subjects (all positive individuals), meaning that these eight individuals fell significantly into the risk range for at least one marker, suggesting that the six proteins exhibited good individual discrimination capabilities in this small sample.

[0155] According to the expression ratio model constructed above, the expression values of the six proteins in the subject's plasma were compared with the mean values of the control group, and the following risk ranges were set:

[0156] RALY: An index value less than 0.67 is considered to be at risk of hypoxia;

[0157] PADI 4: An index value less than 0.71 is considered to be at risk of hypoxia;

[0158] RPL12: An index value greater than 1.32 is considered to be at risk of hypoxia;

[0159] PRKCB: An index value greater than 1.45 is considered to be at risk of hypoxia;

[0160] GRIK5: An index value greater than 1.51 is considered to be at risk of hypoxia;

[0161] BPNT1: An index value greater than 1.38 is considered to be at risk of hypoxia.

[0162] The results showed that among the eight individuals with hypoxia reactions, each had at least two proteins falling into the aforementioned risk range, indicating that this set of protein markers has good combined predictive performance. In particular, the performance evaluation (ROC analysis) of the combined markers of RALY and PADI4 was superior to that of a single marker. The ROC curve was used to evaluate the overall predictive performance of the model, and the results showed that the sensitivity of the model constructed by combining these two proteins was over 82% and the specificity was over 77%. This combined model is superior to a single marker in predicting the risk of hypoxia intolerance and is expected to be used for clinical screening and early intervention.

[0163] Example 8: Computer system for predicting the risk of hypoxic reaction

[0164] This embodiment provides a method for evaluating whether a subject is at risk of hypoxia intolerance using a computer system. The system can read the subject's test data, automatically calculate the index values of relevant protein markers, determine whether the subject is in the hypoxia risk range, and output corresponding prompt information, thereby providing support for clinical intervention and individualized health management.

[0165] 1. Methods and Steps

[0166] Collect test data:

[0167] Plasma samples were collected from the subjects using medical testing equipment to measure the levels of six hypoxia response risk-related protein markers, including RALY, PADI4, RPL12, PRKCB, GRIK5, and BPNT1.

[0168] Calculate the indicator value:

[0169] The ratio between the level of each of the above proteins in the subject's plasma and the mean value in the plasma of people without hypoxia reaction was calculated as the "index value". The calculation formula is as follows:

[0170] Index value =

[0171] (Target marker level in subject's plasma) ÷

[0172] (The average level of this marker in the plasma of the control population)

[0173] For example, the RALY index value is calculated as: Index value = RALY (subject) / RALY (control population mean)

[0174] Determine the risk range:

[0175] Based on the preset risk range of each marker obtained from clinical validation, determine whether the value of each marker is in the hypoxic reaction risk range, as follows:

[0176]

[0177]

[0178] Output evaluation results:

[0179] After the system determines the values of the six indicators, it gives prediction tips based on the following logic:

[0180] If the index values of any three or more markers fall within the risk range, a prompt "The subject is at risk of hypoxia reaction" will be output;

[0181] Otherwise, the assessment conclusion of "the risk of hypoxia is not significantly increased" is output.

[0182] 2. Specific description of system functions

[0183] Data reading module: reads the concentration data of target protein in plasma from laboratory testing equipment.

[0184] Ratio calculation module: automatically calls the control mean in historical data and calculates the expression ratio (i.e. index value) of each marker.

[0185] Risk judgment module: compares the indicator value with the respective risk range to determine whether it is in the abnormal range.

[0186] Assessment prompt module: Outputs an assessment report based on the judgment results, including the specific marker name, indicator value, whether it falls into the risk range, overall risk judgment conclusion, etc. It can also be used to generate PDF reports or push them to the hospital's electronic medical record system.

[0187] 3. Scope of application

[0188] The system is suitable for the following scenarios:

[0189] Individual tolerance screening before high altitude environments and aerospace missions;

[0190] Early warning for patients with chronic respiratory diseases;

[0191] Risk assessment before major surgery or anesthesia;

[0192] Physiological monitoring and recovery guidance for athletes undergoing high-intensity training.

[0193] The above descriptions are merely embodiments of the present disclosure and are not intended to limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A protein marker for predicting the risk of hypoxia reaction, characterized in that: The protein markers are selected from any one, two, three, four, five or six of the following group: RALY, PADI4, RPL12, PRKCB, GRIK5 and BPNT1.

2. The protein marker according to claim 1, wherein The protein marker is selected from a combination of RALY and PADI4.

3. The protein marker according to claim 1 or 2, wherein RALY and PADI4 are predictive markers that show significant down-regulation in hypoxia intolerance; and RPL12, PRKCB, GRIK5, and BPNT1 show significant up-regulation.

4. Use of a reagent for detecting a marker according to any one of claims 1 to 3 in preparing a kit or a detection device for predicting the risk of hypoxia reaction.

5. The use according to claim 4, wherein the reagent is an antibody against the marker, a primer for PCR, a reagent for mass spectrometry analysis, or a reagent for chromatography analysis.

6. A kit for predicting the risk of hypoxia, characterized in that: Comprising a reagent for detecting the marker according to claim 1 or 2, optionally, the detection is a quantitative detection of the biomarker level in the subject's blood. 7 . The kit according to claim 6 , wherein the reagent is an antibody against the marker, a primer for PCR, a reagent for mass spectrometry analysis, or a reagent for chromatography analysis.

8. The kit for predicting the risk of hypoxia according to claim 5, wherein The kit is an ELISA kit.

9. Use of the marker according to any one of claims 1 to 3 in predicting the risk of hypoxic reaction for purposes other than disease diagnosis or treatment.

10. A computer system comprising the steps of: a) calculating the ratio between the level of a marker in the plasma or blood of an assessment subject who may have a risk of hypoxia and the level of a marker in the plasma or blood of a human being who does not have a risk of hypoxia, wherein the marker is as described in any one of claims 1 to 3; b) Determine whether the calculated marker value is within the preset risk range; c) Outputting the judgment result of step b) and optionally giving a risk prompt. If there is a situation where the index value of one or more markers is within the hypoxia reaction risk range, giving a corresponding hypoxia reaction risk assessment result prompt.

Images