Use of ggcx in the preparation of a reagent for diagnosing ischemic stroke

Abstract

This invention discloses the application of GGCX in the preparation of reagents for the diagnosis or auxiliary diagnosis of ischemic stroke. First, a six-layer progressive genetic evidence system was constructed to confirm that upregulation of gamma-glutamyl carboxylase (GGCX) is a protective factor against ischemic stroke, and that the protective effect is subtype-specific. Second, a diagnostic model was constructed based on peripheral blood GGCX expression data. Finally, the significant downregulation of GGCX under ischemic conditions was verified through three levels: clinical sample qPCR detection, tMCAO / R animal model, and primary hippocampal neuronal OGD / R cell model, with an AUC value of 0.889 in the clinical validation cohort. This invention provides a reliable approach for the early auxiliary diagnosis of ischemic stroke based on the peripheral blood biomarker GGCX. It has clear clinical value and significance for improving the early diagnosis rate of ischemic stroke and shortening the time from onset to treatment.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to the application of GGCX in the preparation of reagents for diagnosing ischemic stroke. Background Technology

[0002] Ischemic stroke is one of the leading causes of death and disability worldwide, and the leading cause of death among adults in China. It is characterized by "five highs": high incidence, high recurrence rate, high disability rate, high mortality rate, and high economic burden, seriously endangering human health and social development. The key to clinical emergency treatment of stroke lies in initiating reperfusion therapy (such as intravenous thrombolysis or mechanical thrombectomy) as early as possible, as the improvement in the patient's neurological prognosis is more significant. Therefore, early detection, early diagnosis, and early treatment are emphasized. However, the diagnosis and assessment of ischemic stroke in clinical practice still heavily rely on imaging examinations (such as CT and MRI). However, the accessibility of imaging equipment in primary healthcare institutions is limited, and some patients have atypical imaging manifestations in the early stages of the disease, easily leading to missed diagnoses or delayed diagnosis. Therefore, developing auxiliary diagnostic methods based on peripheral blood biomarkers has clear clinical value and significance for improving the early diagnosis rate of ischemic stroke and shortening the time from onset to treatment.

[0003] Gamma-glutamyl carboxylase (GGCX) is an intact endoplasmic reticulum membrane protein, also known as vitamin K-dependent glutamyl carboxylase. It catalyzes the post-translational carboxylation of various vitamin K-dependent proteins (VKDPs) into active clotting factors (II, VII, IX, and X), thereby promoting the coagulation cascade and maintaining normal coagulation pathways in the body. Previous studies have shown that key enzymes encoded by the GGCX gene can lead to alterations in vitamin K metabolic pathways through single nucleotide polymorphisms (SNPs) that affect their function. Summary of the Invention

[0004] Current clinical research on GGCX mainly focuses on the association between its gene polymorphism and stroke risk, and there is a lack of research reports on its use as an auxiliary diagnostic biomarker for ischemic stroke and in the preparation of in vitro diagnostic reagents. Therefore, the development of GGCX-based diagnostic reagents has significant clinical translational value for achieving early, minimally invasive, and convenient auxiliary diagnosis of ischemic stroke. To address the shortcomings of existing technologies, this invention provides the application of γ-glutamyl carboxylase (GGCX) in the preparation of reagents for diagnosing ischemic stroke.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution: The first aspect of the present invention provides the use of a reagent for detecting the expression level of a biomarker in a sample in the preparation of products for diagnosing or assisting in the diagnosis of ischemic stroke, wherein the biomarker is GGCX.

[0006] In this invention, the biomarker GGCX has a genetic causal association with ischemic stroke, and the diagnostic or auxiliary diagnostic product is used for early screening or auxiliary diagnosis of ischemic stroke, especially suitable for subjects in the early stage of ischemic stroke and those at potential risk of ischemic stroke.

[0007] Furthermore, the sample is a peripheral blood sample.

[0008] In some embodiments, the reagent used to detect the expression level of biomarkers in the sample is based on at least one of the following technologies: nucleic acid sequencing technology and nucleic acid hybridization technology.

[0009] In some embodiments, the reagents used to detect the expression level of biomarkers in the sample include probes that specifically recognize GGCX, or primers that specifically amplify GGCX.

[0010] In some embodiments, the sequences of the probe / primer are as shown in SEQ ID NO:1-2.

[0011] In some embodiments, the product includes a kit, probe set, primer set, chip, and nucleic acid membrane strip.

[0012] In some embodiments, the kit includes a qPCR kit, an RT-PCR kit, a competitive RT-PCR kit, an RT-qPCR kit, or a DNA microarray kit.

[0013] In this invention, the product may comprise a solid substrate such as a chip, a glass slide, an array, etc., having reagents capable of detecting and / or quantifying one or more blood biomarkers or other sample-derived biomarkers immobilized at predetermined locations on the substrate. As an illustrative example, reagents immobilized at discrete predetermined locations may be provided to the chip for detecting and quantifying the concentration of any amount or any combination of biomarkers in a blood sample.

[0014] In some embodiments, the chip includes a microarray chip, a microfluidic chip, a nanopore chip, or a liquid phase chip.

[0015] In some embodiments, the nucleic acid membrane strip includes a chemically chromogenic membrane strip, a fluorescently labeled membrane strip, or a quantum dot labeled membrane strip.

[0016] In some embodiments, the kit includes one or more of genomic DNA extraction reagents, library construction reagents, and sequencing reagents.

[0017] In some embodiments, the genomic DNA extraction reagent includes a blood genomic DNA extraction column, proteinase K, and lysis buffer.

[0018] In some embodiments, the library construction reagents include adapter sequences, PCR amplification premix, and purified magnetic beads.

[0019] In some embodiments, the sequencing reagent includes sequencing primers and fluorescently labeled nucleotides.

[0020] In some embodiments, the product includes a reference cutoff value determined by an ROC curve, used to compare the expression level of GGCX in the test sample with the reference cutoff value to assess the risk of ischemic stroke.

[0021] In some embodiments, the reference cutoff value is 1.110.

[0022] The second aspect of the present invention provides a method for constructing a risk assessment model for ischemic stroke, the method comprising the following steps: obtaining biomarker expression level data of ischemic stroke patients and healthy controls, and constructing a risk assessment model based on the biomarker expression level data, wherein the biomarker is GGCX as described in claim 1.

[0023] In some embodiments, the risk assessment model calculates the probability of a subject developing the disease by comparing GGCX expression data with a reference cutoff value.

[0024] In some embodiments, the risk assessment model is constructed based on the correlation between GGCX expression levels and disease risk, and a high risk is determined when the GGCX expression level is below a reference cutoff value.

[0025] In some embodiments, the threshold of the risk assessment model is determined by ROC curve analysis.

[0026] In a specific embodiment of the present invention, the reference cutoff value is 1.110, which is based on the relative expression level of GGCX obtained by qPCR detection (2). -ΔΔCt The value (determined by maximizing the Youden index) is used to compare the expression level of the gene GGCX with the reference cutoff value to assess the risk of ischemic stroke.

[0027] In some embodiments, the risk assessment model has a threshold or reference cutoff value. If the analysis result is lower than the threshold or reference cutoff value, it is determined that the subject from the sample source has a high risk of ischemic stroke or ischemic stroke; otherwise, it is determined that the subject from the sample source does not have a low risk of ischemic stroke or ischemic stroke.

[0028] In this invention, the term "threshold" or "reference cutoff value" refers to a value that is statistically relevant to a particular outcome when compared with the analysis results. In some embodiments, the threshold or reference cutoff value is determined based on statistical conclusions from analyses of biomarker expression levels in patients with ischemic stroke or high-risk populations and healthy controls. Some such studies are shown in the Examples section of this document, but studies from the literature and the experience of users of the methods described herein can also be used to generate or adjust thresholds or reference cutoff values.

[0029] In some embodiments, the risk assessment model is constructed using an algorithm.

[0030] In some embodiments, the algorithm is logistic regression.

[0031] In some embodiments, the methods for constructing the risk assessment model are known to those skilled in the art, and the steps of associating biomarker expression levels with a certain probability or risk can be implemented and realized in different ways. Preferably, the biomarker expression levels are mathematically associated with the fundamental question of whether or not an ischemic stroke has been diagnosed. The determination of biomarker expression levels can be combined with other clinical characteristics using any suitable existing mathematical methods, and the risk assessment model can be constructed using machine learning algorithms.

[0032] A third aspect of the present invention provides a computer-aided diagnostic system for ischemic stroke, the computer-aided diagnostic system comprising the following units: The acquisition unit acquires data on the expression level of a biomarker in a sample, wherein the biomarker is GGCX as described in the first aspect.

[0033] The analysis unit inputs the biomarker expression level data into the risk assessment model. The risk assessment model analyzes the risk of ischemic stroke in the subjects from the sample source based on the biomarker expression level data in the sample or outputs a risk score. The risk assessment model is the risk assessment model obtained by the construction method described in the second aspect.

[0034] Output unit, output results.

[0035] The system may be a user's electronic device or a computer system remotely located relative to that electronic device.

[0036] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0037] As used herein, the terms "patient" or "subject" refer to any animal (e.g., mammal) that is at risk of ischemic stroke, including but not limited to humans, non-human primates, rodents, etc. Generally, the terms "subject" and "patient" are used interchangeably herein when referring to human subjects. Most preferably, the subject is a human.

[0038] A fourth aspect of the present invention provides an auxiliary diagnostic device for ischemic stroke, comprising a memory, a processor, and instructions stored in the memory, wherein the processor executes the instructions to perform the following steps: Data acquisition is used to acquire biomarker expression level data in the sample to be tested, wherein the biomarker is GGCX as described in the first aspect.

[0039] In some embodiments, the step further includes: preprocessing the data, normalizing or relatively quantifying the raw data (e.g., 2). -ΔΔCt The expression level of GGCX was calculated to obtain standardized GGCX expression data.

[0040] The data is analyzed to input the biomarker expression level data into a risk assessment model. The risk assessment model analyzes the risk of ischemic stroke in the subjects from the sample source based on the biomarker expression level data in the sample or outputs a risk score. The risk assessment model is the risk assessment model obtained by the construction method described in the second aspect.

[0041] Output data, used to produce results, generates and outputs auxiliary diagnostic suggestions based on probability values ​​or risk scores.

[0042] In some embodiments, the processor may also be referred to as a Central Processing Unit (CPU). The processor may be an integrated circuit chip with signal processing capabilities. The processor may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor. The device further includes a display unit for visually presenting risk assessment curves or reports.

[0043] A fifth aspect of the present invention provides a computer program product comprising a computer program for assisting in the diagnosis of ischemic stroke, wherein the computer program, when executed by a processor, implements the following method: A configuration acquisition module is used to receive and acquire data, configured to acquire biomarker expression level data in the sample, wherein the biomarker is GGCX as described in the first aspect. Specifically, after receiving the data, the module parses the GGCX expression level data from the detection terminal.

[0044] A logic calculation module is configured to analyze data and input the biomarker expression level data into a risk assessment model. The biomarker is GGCX as described in the first aspect. The risk assessment model analyzes the risk of ischemic stroke in the subjects from the sample source based on the biomarker expression level data in the sample, or outputs a risk score. The risk assessment model is the risk assessment model obtained by the construction method described in the second aspect. Specifically, a built-in machine learning model is invoked, and a binary classification algorithm is executed based on the low expression characteristics of GGCX in the subject sample and the endogenous causal association with the occurrence of the disease.

[0045] The configuration results generation module is used to output data and is configured to output results. Specifically, it matches corresponding clinical reference recommendations based on the judgment results.

[0046] It should be understood that the systems, devices, and program products described in this invention can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings, direct couplings, or communication connections may be indirect couplings or communication connections between devices or units through some interfaces, and may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0047] Advantages and beneficial effects of this invention: This invention constructs a six-layer progressive genetic evidence system consisting of "forward Mendelian randomization (MR) + SMR and HEIDI tests + Steiger directionality tests + reverse MR tests + subtype-specific analysis + colocalization," enabling in-depth screening of auxiliary diagnostic biomarkers. This system not only provides solid causal directional support for GGCX as a therapeutic target but also ensures the specificity and reliability of diagnostic biomarkers from a genomic perspective. It provides solid genetic directional support for GGCX as a therapeutic target (GGCX upregulation as a protective direction), and subtype-specific analysis confirms that the protective effect of GGCX specifically targets ischemic stroke, providing a clear direction for subsequent target validation and drug screening. Unlike conventionally screened "correlation" indicators, this invention, based on genetic causal inference (MR / SMR), identifies an essential pathophysiological link between GGCX and ischemic stroke, thus providing scientific logical support for the high accuracy of subsequent clinical auxiliary diagnostic protocols and reducing the risk of missed diagnoses and misdiagnoses.

[0048] During the target validation phase, this invention validated the downregulation of GGCX under ischemic conditions at multiple levels—clinical, animal, and cellular—confirming GGCX as a target that needs to be salvaged. The auxiliary diagnostic protocol, based on peripheral blood GGCX genomic data analysis, achieved an AUC of 0.851, a sensitivity of 75.0%, a specificity of 90.9%, and an accuracy of 81.1%, demonstrating good diagnostic efficacy and predictive accuracy. Furthermore, peripheral blood sample collection is simple, ensuring good clinical accessibility and possessing potential application value in reducing the risk of missed diagnoses in atypical patients in the early clinical stages. Attached Figure Description

[0049] Figure 1 This is a schematic diagram illustrating the screening of ischemic stroke targets, construction of a diagnostic model, and multi-level validation based on GGCX, as provided in an embodiment of the present invention.

[0050] Figure 2 This invention provides evidence for Mendelian randomization causal inference of GGCX and ischemic stroke in embodiments of the present invention. Figure A shows a forest plot of the PsychENCODE brain tissue discovery dataset in stroke MR analysis results; Figure B shows a forest plot of the eQTLGen blood discovery dataset in stroke MR analysis results; Figure C shows a forest plot of the GTEx brain tissue validation dataset in stroke MR analysis results; Figure D shows the GTEx blood validation dataset; and Figure E shows forest plots of the four datasets in ischemic stroke MR analysis results.

[0051] Figure 3The SMR / HEIDI test and Steiger directionality test results provided in this embodiment of the invention are shown in Figure A, which shows the SMR and HEIDI test results of GGCX in ischemic stroke; Figure B shows the Steiger directionality test results of GGCX in ischemic stroke.

[0052] Figure 4 The following figures illustrate the results of reverse MR and Steiger directionality tests provided in embodiments of the present invention. Figure A shows the results of reverse MR analysis of ischemic stroke and GGCX; Figure B shows the Steiger directionality test results of reverse MR analysis of ischemic stroke and GGCX.

[0053] Figure 5 The identification results of MR subtypes excluding hemorrhagic stroke provided in the embodiments of the present invention are shown. Figure A shows the MR analysis results of GGCX and cerebral hemorrhage; Figure B shows the MR analysis results of GGCX and subarachnoid hemorrhage.

[0054] Figure 6 The colocalization analysis and fine localization results provided in this embodiment of the invention are shown in Figure A. Figure B shows the colocalization analysis results for GGCX; Figure C shows the sentinel variant rs1972297 of brain tissue signals located within the GGCX gene region; Figure D shows the sentinel variant SNP rs6547623 of blood signals located within the GGCX gene region; Figure D shows the linkage disequilibrium diagram of the brain tissue-associated sentinel variant rs1972297; and Figure E shows the linkage disequilibrium diagram of the blood-associated sentinel variant rs6547623.

[0055] Figure 7 This invention provides a method for constructing a diagnostic prediction model based on GGCX. Figure A shows the area under the receiver characteristic curve (AUC) of the ischemic stroke diagnostic model constructed based on peripheral blood GGCX; Figure B shows the calibration curve of the ischemic stroke diagnostic model constructed based on peripheral blood GGCX.

[0056] Figure 8 The following figures illustrate the validation results of GGCX in clinical samples and multi-level models provided in this invention. Figure A shows the relative expression levels of GGCX mRNA in peripheral blood of patients with ischemic stroke and normal controls; Figure B shows representative images of TTC-stained coronal brain sections from each group of mice; Figure C shows a quantitative statistical graph of cerebral infarction volume in each group of mice; Figure D shows a quantitative statistical graph of neurological function scores in each group of mice; Figure E shows representative GGCX protein blots from each group of mice; Figure F shows a quantitative statistical graph of representative GGCX from each group of mice; Figure G shows the results of cell viability testing of primary hippocampal neurons; Figure H shows a representative GGCX protein blot from primary hippocampal neurons; and Figure I shows a quantitative statistical graph of representative GGCX from primary hippocampal neurons.

[0057] Figure 9 This is to validate the ischemic stroke diagnostic model based on GGCX provided in the embodiments of the present invention. Detailed Implementation

[0058] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0059] Example 1: Establishment of a genetic evidence system for the target GGCX 1. Forward MR causal inference and target orientation confirmation Table 1 presents the overall genome-wide association study (GWAS) data for stroke obtained from the GWAS Catalog (ebi-a-GCST90038613), which includes 6,925 clinically confirmed stroke cases and 477,673 control samples; it also presents the GWAS Catalog (ebi-a-GCST90018864) data for ischemic stroke, which includes 11,929 ischemic stroke cases and 472,192 control samples.

[0060] Table 2 presents the quantitative trait locus (eQTL) data for GGCX gene expression obtained from PsychENCODE (brain tissue), eQTLGen (blood), and GTEx v8 (brain tissue and blood). PsychENCODE included 1387 brain tissue samples covering multiple brain regions; eQTLGen included 31684 samples; and GTEx v8 came from 838 donors, encompassing 670 brain tissue samples and 175 blood samples. The study employed multiple Mendelian randomization (MR) methods, including inverse variance weighted (IVW), Wald, MR-Egger, and MR-PRESSO, to assess the causal relationship between γ-glutamyl carboxylase (GGCX) expression and stroke and its subtypes (ischemic stroke and hemorrhagic stroke).

[0061] Table 1. Sources of GWAS data and sample characteristics used in MR analysis

[0062] Table 2. Sources of eQTL data and sample characteristics used in MR analysis

[0063] GGCX and overall stroke: Results as follows Figure 2 As shown in AD, in the PsychENCODE brain tissue data, the odds ratio (OR) for SLC33A1 was 0.997 (P = 3.0 × 10⁻⁶). -4 The odds ratio (OR) for HTR6 was 0.997 (P = 0.022). In the GTEx brain tissue data, the OR for GGCX was 0.916 (95% CI: 0.856–0.981, P = 0.012), showing a strong protective effect (generally, OR < 1 corresponds to a reduced disease risk, and OR > 1 corresponds to an increased disease risk). In the eQTLGen blood data, the OR for ALDH16A1 was 0.998 (P = 0.009), the OR for GGCX was 0.999 (P = 0.048), the OR for SLC33A1 was 0.994 (P = 0.019), and the OR for HTR6 was 1.001 (P = 0.010). In the GTEx blood data, the odds ratio (OR) for GGCX was 0.902 (95% CI: 0.824–0.988, IVW P = 0.027), while that for SLC33A1 did not reach significance (OR = 1.034, 95% CI: 0.921–1.161, P = 0.572).

[0064] GGCX and ischemic stroke (the main subtype of stroke, accounting for 80% of all strokes): Results are as follows Figure 2 As shown in Figure E, the OR in brain tissue was 0.916, P = 0.012; the OR in blood was 0.902, P = 0.027. Notably, GGCX was the only gene that maintained a significant protective association in both brain and blood tissues, and the central effect direction was completely consistent in both the discovery and validation datasets in the two tissues (OR < 1, P < 0.05). In conclusion, GGCX is significantly causally associated with ischemic stroke.

[0065] Further sensitivity analysis showed that the Cochran's QP of the GGCX blood signal was 0.197, indicating no heterogeneity; MR-Egger P was 0.821; and MR-PRESSO P was 0.652, indicating no level of pleiotropic effects.

[0066] 2. SMR and HEIDI tests and Steiger directionality test Summary-based Mendelian randomization (SMR) combined with the HEIDI test was used to exclude artifacts of linkage disequilibrium (LD). The results are as follows: Figure 3As shown in Figure A, the P(SMR) values ​​for all four independent datasets (PsychENCODE, eQTLGen, GTEx brain, and GTEx blood) were <0.05, and the P(HEID) values ​​were >0.05, confirming that the association between GGCX expression and ischemic stroke is due to a non-linkage disequilibrium. Meanwhile, the results are as follows... Figure 3 As shown in B, the Steiger directionality test further confirms the causal direction as GGCX as the exposure and ischemic stroke as the outcome. In summary, the SMR and HEIDI tests rule out linkage disequilibrium artifacts, and the Steiger directionality test reinforces the causal direction.

[0067] 3. Reverse MR exclusion Using significant GWAS variants in ischemic stroke as an instrumental variable, we conducted a reverse test to examine whether ischemic stroke affects GGCX expression. The results are as follows: Figure 4 As shown in Figure A, reverse MR analysis ruled out the possibility that ischemic stroke affected GGCX expression. Results are as follows: Figure 4 As shown in B, the MRSteiger directionality test results show that the causal direction of all datasets is FALSE (incorrect), excluding reverse causality, and further confirming that the causal direction is GGCX as exposure and ischemic stroke as outcome.

[0068] 4. Subtype specificity analysis In one embodiment, as shown in Table 3, the subtype data of hemorrhagic stroke were obtained from the FinnGen database. This included two main subtypes: intracerebral hemorrhage (ICH, 1,224 cases / 163,533 controls) and subarachnoid hemorrhage (SAH, 1,019 cases / 163,508 controls).

[0069] Table 3. Sources and sample characteristics of GWAS data used in MR analysis of hemorrhagic stroke.

[0070] No significant association was observed between GGCX and either intracerebral hemorrhage or subarachnoid hemorrhage (P>0.05), suggesting that the protective effect of GGCX is specifically targeted at ischemic stroke. Results are as follows... Figure 5 As shown in AB, MR subtype analysis excluded the association in hemorrhagic stroke and confirmed that the protective effect of GGCX is subtype specific, that is, only for "ischemic stroke".

[0071] 5. Colocation Analysis The core purpose of colocalization analysis is to assess whether gene expression (eQTL signaling) and disease risk (GWAS signaling) within the same genomic region are driven by the same causal variant, which is crucial for the validity of MR analysis. To further elucidate the probability of genetic variants that may contribute to the potential causal relationship between GGCX and ischemic stroke, we performed Bayesian colocalization analysis, genetic sentinel variant analysis, and fine colocalization analysis.

[0072] PPH3 represents stroke risk and gene expression, but driven by different causal variants; PPH4 represents stroke risk and gene expression, driven by the same shared causal variant. This patent uses the PPH3+PPH4 ≥0.8 method to assess whether GGCX expression and ischemic stroke share the same causal variant. Results are as follows... Figure 6 As shown in Figure A, in brain tissue, PPH3 + PPH4 = 0.846. In blood, PPH3 + PPH4 = 0.808.

[0073] Genetic sentinel variants provide a clear central anchor for subsequent fine mapping analysis to identify substitution variants and potential functional variants in high linkage disequilibrium with GGCX. Results are as follows: Figure 6 As shown in Figure B, the sentinel variant rs1972297, representing brain tissue signaling, is located near chromosome 2 at 85.8 Mb (chr2:85808573), with an allele of T / C and a minor allele frequency (MAF) of 0.4314. In the regional localization map, rs1972297 serves as the strongest associated site for both eQTL and GWAS signals: its eQTL... log 10 A p-value exceeding 60 indicates that this variant has a highly significant effect on GGCX expression in brain tissue; simultaneously, its GWAS... log 10 The (P) value is approximately 4, corresponding to a P value of approximately 10. -4 This indicates that the variant is also statistically significantly associated with the risk of stroke.

[0074] Fine-mapping analysis further narrows down the range of candidate functional variants within the high-probability regions identified in co-mapping analysis, and assesses the potential biological functions of GGCX. Results are as follows: Figure 6 As shown in Figure C, the sentinel variant rs6547623 of the blood signal is also located within the GGCX gene region (chr2:85753553), with an allele of T / A and an MAF of 0.326. In the regional localization map, rs6547623 is the strongest associated site for GWAS signaling in this region (GWAS). log 10 (P) is approximately 4.1), while also exhibiting a moderate level of eQTL signal (eQTL log 10 (P) is approximately 5). Results are shown below. Figure 6 In the D region, the sentinel variant rs1972297 associated with brain tissue is in complete linkage disequilibrium with the synonymous exon variant rs1009 (164 bp away) in the coding region of the GGCX gene. The results are as follows... Figure 6 As shown in E, the blood-associated sentinel variant rs6547623 is located in the intron region of the GGCX gene.

[0075] The results of the Bayesian colocalization, genetic sentinel variant, and fine colocalization analyses above collectively confirm that GGCX expression shares causal variants with ischemic stroke.

[0076] In summary, the six-layer genetic evidence system confirms that GGCX upregulation is a protective effect, and that the protective effect is specific to stroke subtypes, providing a clear causal direction for subsequent target validation and drug screening.

[0077] Example 2: Construction of a diagnostic model based on peripheral blood GGCX 1. Research Subjects To further validate the universality of GGCX in assisting the diagnosis of ischemic stroke, this invention introduces the ischemic stroke cohort dataset GSE16561 from the international public database GEO (Gene Expression Omnibus). This dataset contains peripheral blood whole blood gene expression profiles of 37 ischemic stroke patients and 23 healthy controls, analyzed using the Illumina platform. In this invention, this dataset serves as a validation cohort for independently evaluating the validation efficacy of peripheral blood GGCX expression levels in local clinical ischemic stroke patients and healthy controls.

[0078] The GSE16561 dataset encompasses patients with acute ischemic stroke, with samples collected within the critical diagnostic window after onset. During the validation of this invention, all subjects underwent rigorous screening to ensure they met the clinical diagnostic criteria for ischemic stroke and complied with ethical and informed consent guidelines for biomedical research.

[0079] The baseline characteristics of the two groups are shown in Table 4. The validation cohort (GSE16561) included 23 healthy controls and 37 patients with ischemic stroke, for a total of 60 subjects.

[0080] Table 4 Baseline characteristics of the study subjects

[0081] 2. Evaluation of the effectiveness of diagnostic prediction models like Figure 7As shown in Figure A, ROC curve analysis revealed that using GGCX gene expression level as an auxiliary diagnostic biomarker for ischemic stroke, the AUC value was 0.851 (95% CI: 0.753–0.937), demonstrating good diagnostic efficacy in an independent validation cohort (23 healthy controls and 37 patients with ischemic stroke). As shown in Table 5, maximizing the Youden index determined the optimal cutoff value to be 0.671, at which the sensitivity was 75.0%, specificity was 90.9%, and accuracy was 81.1%. Meanwhile, as... Figure 7 As shown in Figure B, the calibration curve analysis shows that the predicted incidence rate of the nomogram fits the actual incidence rate well (both the Apparent curve and the Bias-corrected curve are close to the ideal reference line), indicating that the model has good predictive accuracy.

[0082] Table 5. Performance of the GGCX-assisted diagnostic model

[0083] Example 3: Multi-level validation of the target GGCX 1. Clinical validation To further validate the clinical applicability of GGCX in the auxiliary diagnosis of ischemic stroke, this patent collected peripheral blood samples from 12 subjects at the Aerospace Center Hospital as a single-center ischemic stroke cohort in the Asian population. Six of these subjects were diagnosed with acute ischemic stroke by MRI or CT, and the other six were healthy controls. All subjects signed informed consent forms, and the research protocol was approved by the Medical Ethics Committee of the Aerospace Center Hospital. The study used designed GGCX and GAPDH primers and qRT-PCR technology to detect the mRNA expression level of the GGCX gene in the peripheral blood of ischemic stroke patients and healthy controls, aiming to verify the causal inference results of Mendelian randomization.

[0084] The GGCX primers are as follows: The forward primer is 5′-GAGTCGGCGATGGAAGGAT-3′ (SEQ ID NO:1); The reverse primer is 5′-CCTCTGCTGGAAGCGGTCAT-3′ (SEQ ID NO:2).

[0085] The GAPDH primers are as follows: The forward primer is 5′-GGAAGCTTGTCATCAATGGAAATC-3′ (SEQ ID NO:3); The reverse primer is 5′-TGATGACCCTTTTGGCTCCC-3′ (SEQ ID NO:4).

[0086] The results are as follows Figure 8 As shown in Figure A, compared with the healthy control group, the mRNA expression level of the GGCX gene in the peripheral blood of patients with ischemic stroke was significantly downregulated (P<0.05).

[0087] 2. Animal-level verification Two animal experiments were conducted: (1) sham surgery group; (2) ischemic stroke model group (tMCAO / R). C57BL / 6 mice were used to construct a transient middle cerebral artery occlusion / reperfusion (tMCAO / R) animal model using the suture occlusion method. After occlusion for 1 hour, reperfusion was restored for 24 hours. The sham surgery group underwent the same procedure as the ischemic stroke model group, except that the suture occlusion was not inserted. TTC staining was used to assess the infarct area; the Longa score was used to assess neurological function (ranging from 0 to 4, with higher scores indicating more severe neurological deficits); Western blot was used to detect the expression levels of GGCX and ACTB proteins on the ischemic and normal sides of tMCAO / R mice (GGCX antibody was purchased from Proteintech, USA, catalog number 16209-1-AP; ACTB antibody was purchased from Proteintech, USA, catalog number 60008-1-Ig). The results are as follows: Figure 8 As shown in BD, compared with the sham-operated control group, tMCAO / R mice exhibited significant cerebral infarction (P<0.01) and neurological deficits (P<0.0001). The results are as follows... Figure 8 As shown in EF, the level of GGCX protein in the ischemic side of the cerebral cortex of tMCAO / R mice was significantly reduced (P<0.01), while there was no significant difference in GGCX protein expression between the ischemic and normal sides of the cerebral cortex in the sham-operated group mice (P>0.05).

[0088] 3. Cell-level verification Three groups were set up for cell experiments: (1) control group (Normoxia); (2) simple ischemia group (OGD); (3) ischemic stroke model group (OGD / R). Primary hippocampal neurons isolated from Wistar suckling mice were subjected to oxygen-glucose deprivation (OGD) for 1 hour under hypoxic (2% oxygen) conditions in sugar-free medium, followed by reoxygenation and re-glucose (R) for 24 hours to construct an oxygen-glucose deprivation / reperfusion (OGD / R) cell model. Cell viability was detected using a CCK-8 kit (purchased from Beijing Jinpulai Biotechnology Co., Ltd., catalog number P04D30L); the expression levels of GGCX protein and internal control ACTB protein were detected by Western blot. The results are as follows. Figure 8 As shown in FG, ischemia alone resulted in significant neuronal cell damage and a marked decrease in activity (P<0.0001). The results are as follows... Figure 8As shown in HI, GGCX protein expression was significantly decreased in the ischemic stroke group (P<0.001).

[0089] In summary, clinical, animal, and cellular studies have consistently confirmed that GGCX is significantly downregulated under ischemic conditions, confirming GGCX as a therapeutic target that needs to be salvaged.

[0090] 4. Validation of the diagnostic prediction model's effectiveness The effectiveness and applicability of the GGCX-based diagnostic model constructed in Example 2 were verified using the Logistic regression learning algorithm on the GGCX mRNA data of peripheral blood samples obtained by RT-qPCR analysis. The results are as follows: Figure 9 As shown, using GGCX gene expression level as an auxiliary diagnostic biomarker for ischemic stroke, the AUC value was 0.889 (95% CI: 0.667-1.000). GGCX has good efficacy and applicability in the diagnosis of ischemic stroke patients, indicating that the GGCX-based predictive model has high robustness in the diagnosis of ischemic stroke.

[0091] The above description of the embodiments is only for understanding the method and core ideas of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from the principles of the invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. Use of a reagent for detecting the expression level of a biomarker in a sample for the manufacture of a diagnostic or an aid-diagnostic product for ischemic stroke, characterized in that, The biomarker is GGCX.

2. The application according to claim 1, characterized in that, The sample was a peripheral blood sample.

3. The application according to claim 1, characterized in that, The reagent used to detect the expression level of biomarkers in the sample is based on at least one of the following technologies: nucleic acid sequencing technology, nucleic acid hybridization technology; Preferably, the reagents used to detect the expression level of biomarkers in the sample include probes that specifically recognize GGCX, or primers that specifically amplify GGCX; Preferably, the sequence of the probe / primer is shown in SEQ ID NO:1-2.

4. The application according to claim 1, characterized in that, The products include reagent kits, probe sets, primer sets, chips, and nucleic acid membrane strips; Preferably, the kit includes a qPCR kit, an RT-PCR kit, a competitive RT-PCR kit, an RT-qPCR kit, or a DNA microarray kit; Preferably, the chip includes a microarray chip, a microfluidic chip, a nanopore chip, or a liquid phase chip; Preferably, the nucleic acid membrane strip includes a chemically chromogenic membrane strip, a fluorescently labeled membrane strip, or a quantum dot labeled membrane strip.

5. The application according to claim 4, characterized in that, The kit includes one or more of the following: genomic DNA extraction reagents, library construction reagents, and sequencing reagents; Preferably, the genomic DNA extraction reagent includes a blood genomic DNA extraction column, proteinase K, and lysis buffer; Preferably, the library construction reagent includes adapter sequences, PCR amplification premix, and purified magnetic beads; Preferably, the sequencing reagent includes sequencing primers and fluorescently labeled nucleotides.

6. The application according to claim 1, characterized in that, The product includes a reference cutoff value determined by ROC curves, which is used to compare the expression level of GGCX in the test sample with the reference cutoff value to assess the risk of ischemic stroke.

7. A method for constructing a risk assessment model for ischemic stroke, characterized in that, The method includes the following steps: obtaining biomarker expression level data of ischemic stroke patients and healthy controls, and constructing a risk assessment model based on the biomarker expression level data, wherein the biomarker is GGCX as described in claim 1; Preferably, the risk assessment model calculates the probability of a subject developing the disease by comparing GGCX expression data with a reference cutoff value; Preferably, the risk assessment model is constructed based on the correlation between GGCX expression level and disease risk, and is judged as high risk when GGCX expression level is lower than the reference cutoff value; Preferably, the threshold of the risk assessment model is determined through ROC curve analysis; Preferably, the risk assessment model is constructed using an algorithm; Preferably, the algorithm is logistic regression.

8. A computer-aided diagnostic system for ischemic stroke, characterized in that, The computer-aided diagnostic system includes the following units: The acquisition unit acquires data on the expression level of a biomarker in a sample, wherein the biomarker is GGCX as described in claim 1. The analysis unit inputs the biomarker expression level data into the risk assessment model. The risk assessment model analyzes the risk of ischemic stroke in the subjects from the sample source based on the biomarker expression level data in the sample or outputs a risk score. The risk assessment model is the risk assessment model obtained by the construction method described in claim 7. Output unit, output results.

9. An auxiliary diagnostic device for ischemic stroke, comprising a memory, a processor, and instructions stored in the memory, characterized in that, The processor executes the instructions to perform the following steps: Data is acquired to obtain biomarker expression level data in the sample, wherein the biomarker is GGCX as described in claim 1; The data is analyzed to input the biomarker expression level data into a risk assessment model. The risk assessment model analyzes the risk of ischemic stroke in the subjects from the sample source based on the biomarker expression level data in the sample or outputs a risk score. The risk assessment model is the risk assessment model obtained by the construction method described in claim 7. Output data, used to output results.

10. A computer program product comprising a computer program for assisting in the diagnosis of ischemic stroke, characterized in that, When this computer program is executed by the processor, it implements the following method: Data acquisition is configured to acquire biomarker expression level data in the sample, wherein the biomarker is GGCX as described in claim 1; The data is analyzed and configured to input the biomarker expression level data into a risk assessment model. The risk assessment model analyzes the risk of ischemic stroke in the subjects from the sample source based on the biomarker expression level data in the sample or outputs a risk score. The risk assessment model is the risk assessment model obtained by the construction method described in claim 7. Output data, configured as the output result.

Images