Biomarker panel, kit and system for predicting poor prognosis in patients with ischemic stroke

Abstract

The present invention discloses a biomarker, a kit and a system for predicting poor prognosis of ischemic stroke patients, which relate to the technical field of molecular diagnosis. The biomarker group disclosed by the present invention includes a protein biomarker group and a single nucleotide polymorphism (SNP) biomarker group. Using the biomarker group, kit and system provided by the present invention can better predict the risk of death or severe disability occurring 3 months after the onset of ischemic stroke patients, so as to early identify high-risk patients with poor prognosis of ischemic stroke, and guide doctors to take corresponding treatment and nursing measures to prevent the occurrence of death or severe disability after the onset of ischemic stroke patients, which has important clinical and public health significance.

Description

Technical Field

[0001] The present invention relates to the field of molecular diagnostic technologies, and in particular, to a biomarker, a kit, and a system for predicting poor prognosis in patients with ischemic stroke. Background Art

[0002] Ischemic stroke is a complex disease involving multiple pathophysiological pathways. Common poor post-illness prognoses mainly include death, disability, and recurrence of stroke, etc. Currently known traditional risk factors for poor prognosis of ischemic stroke include gender, age, smoking, hypertension, etc. Research shows that compared with individual traditional factors, the combination of multiple traditional factors may play a better predictive efficacy in predicting poor prognosis in patients with ischemic stroke. Since these traditional risk factors can only explain part of the occurrence of poor prognosis, there is still room for improvement in the predictive ability of the prediction model constructed based on traditional risk factors for poor prognosis in patients with ischemic stroke. Therefore, searching for protein markers and genetic markers related to the prognosis of patients with ischemic stroke, or constructing an improved prediction model covering multiple pathophysiological pathways, can further improve the ability to predict poor prognosis and early identify high-risk patients, which helps in the early detection and early intervention of poor prognosis.

[0003] In view of this, the present invention is specifically proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a biomarker group, a kit, and a system for predicting poor prognosis in patients with ischemic stroke. Through the biomarker group, kit, and system provided by the present invention, the risk of poor outcomes 3 months after illness in patients with ischemic stroke can be better predicted, so as to early identify high-risk patients with poor prognosis of ischemic stroke, and guide doctors to take corresponding treatment and nursing measures to prevent the occurrence of poor prognosis in patients with ischemic stroke, which has important clinical and public health significance.

[0005] The present invention is implemented as follows:

[0006] On the one hand, the present invention provides a biomarker group for predicting poor prognosis in patients with ischemic stroke. This biomarker group is suitable for being combined with traditional factors to predict poor prognosis in patients with ischemic stroke, and the biomarker group includes a protein biomarker group and an SNP biomarker group;

[0007] The protein biomarker panel includes the following proteins: hepatocyte growth factor (HGF), galectin-3, high-sensitivity C-reactive protein (hsCRP), S100 calcium-binding protein A8 / A9 complex protein (S100A8 / A9), Netrin-1, osteopontin, chemokine ligand 21 (CCL21), and angiopoietin-like protein 4 (ANGPTL4);

[0008] The SNP biomarker panel includes the following SNPs: rs12248560, rs17346334, rs2149041, rs4244285, rs429358, rs465401, rs5745695, and rs704;

[0009] The traditional risk factors include: gender, age, smoking status, systolic blood pressure, NIHSS score, history of diabetes, and the concentration level of low-density lipoprotein cholesterol (LDL-C).

[0010] The protein biomarker panel plays a key role in the possible pathological mechanisms of the occurrence and development of ischemic stroke. On the other hand, clinical studies have also shown that these biomarkers have potential predictive value in predicting the risk of poor prognosis in patients with ischemic stroke.

[0011] The association between the SNP biomarker panel and the poor prognosis of patients with ischemic stroke is not very clear, but the genes to which these SNPs belong may be related to the functions or pathways associated with the pathology of ischemic stroke. Indirectly indicating that these SNPs may play a crucial role in the occurrence, development, and even prognosis of ischemic stroke.

[0012] The present invention discloses for the first time that the above protein biomarker panel and SNP biomarker panel can be used for predicting the poor prognosis of patients with ischemic stroke. Combining with traditional risk factors, it can better predict the risk of poor outcomes in patients with ischemic stroke 3 months after the onset of the disease, so as to early identify high-risk patients with poor prognosis of ischemic stroke, and guide doctors to take corresponding treatment and nursing measures to prevent the occurrence of poor prognosis in patients with ischemic stroke, which has important clinical and public health significance.

[0013] Optionally, in some embodiments, the poor prognosis refers to the risk of death or severe disability in patients with ischemic stroke 3 months after the onset of the disease.

[0014] On the other hand, the present invention provides a detection kit for predicting the poor prognosis of patients with ischemic stroke, which includes detection reagents for detecting the above-mentioned biomarker panel. The detection reagents include a first detection reagent for detecting the protein biomarker panel and a second detection reagent for detecting the genotypes of the SNP biomarker panel.

[0015] Based on the technologies known in the art, those skilled in the art can easily obtain reagents for detecting the concentrations of the above-mentioned protein markers histone and the genotypes of the SNP markers. For example, ELISA reagents and corresponding protein antibodies; and for another example, those applicable to SNPscan TM multiple SNP genotyping, detecting SNP genotypes by technologies such as direct sequencing method (PCR-sequencing method), Taqman-MGB probe SNP genotyping, PCR-RFLP enzyme digestion SNP genotyping, PCR-LDR ligation detection SNP genotyping, Multiplex SNaPshot SNP genotyping, Sequenom MassArray SNP genotyping, and KASP SNP genotyping, etc.

[0016] Optionally, in some embodiments, the poor prognosis refers to the risk of death or severe disability in ischemic stroke patients 3 months after the onset of the disease.

[0017] Optionally, in some embodiments, the test samples of the kit are plasma or serum or DNA.

[0018] On the other hand, the present invention provides the use of a detection reagent in the preparation of a detection kit for predicting the poor prognosis of ischemic stroke patients, the detection reagent comprising a first detection reagent for detecting the protein concentration in the protein marker group and a second detection reagent for detecting the SNP genotype in the SNP marker group;

[0019] The protein marker group comprises the following proteins: HGF, Galectin-3, hsCRP, S100A8 / A9, Netrin-1, Osteopontin, CCL21, and ANGPTL4;

[0020] The SNP marker group comprises the following SNPs: rs12248560, rs17346334, rs2149041, rs4244285, rs429358, rs465401, rs5745695, and rs704.

[0021] On the other hand, the present invention provides a system for predicting the poor prognosis of ischemic stroke patients, the system comprising an information acquisition module, a calculation module, and a prediction module;

[0022] The information acquisition module is used to perform the operation of acquiring the traditional risk factor information and the above-mentioned basic information of the ischemic stroke patient to be predicted;

[0023] The basic information includes the traditional risk factor information, protein marker information, and SNP marker information;

[0024] Among them, the traditional risk factor information includes the information of the following indicators: gender, age, smoking status, systolic blood pressure, NIHSS score, history of diabetes, and concentration level of low-density lipoprotein cholesterol (LDL-C).

[0025] The protein biomarker information includes the concentration level information of the following proteins: HGF, Galectin-3, hsCRP, S100A8 / A9, Netrin-1, Osteopontin, CCL21, and ANGPTL4.

[0026] The SNP biomarker information includes the genotype information of the following SNPs: rs12248560, rs17346334, rs2149041, rs4244285, rs429358, rs465401, rs5745695, and rs704;

[0027] The calculation module is used to perform operations of calculating the traditional risk factor score (TRS), biomarker score (BMS), and genetic risk score (GRS) based on the basic information;

[0028] The prediction module is used to perform predicting the risk of poor prognosis of ischemic stroke patients based on the traditional risk factor score, the biomarker score, and the genetic risk score.

[0029] Optionally, in some embodiments, the formula for calculating TRS is as follows:

[0030] TRS = -4.8340 + 0.0964×A1 + 0.6782×A2 - 0.2524×A3 + 0.00707×A4 + 0.2893×A5 + 0.2554×A6 + 0.1292×A7;

[0031] Among them, if the gender is female, then A1 = 0, if the gender is male, then A1 = 1,

[0032] if the age < 65 years old, then A2 = 0, if the age ≥ 65 years old, then A2 = 1,

[0033] if the smoking status is no, then A3 = 0, if the smoking status is yes, then A3 = 1,

[0034] if the history of diabetes is none, then A6 = 0, if the history of diabetes is present, then A6 = 1,

[0035] A4 is the systolic blood pressure value, A5 is the NIHSS score value, and A7 is the LDL-C concentration value.

[0036] In some embodiments, the formula for calculating the biomarker score (BMS) is as follows:

[0037] BMS = Beta HGF × B1 + Beta Galectin-3 × B2 + Beta hsCRP × B3 + Beta S100A8 / A9 × B4 + Beta Netrin-1 × B5 + Beta Osteopontin × B6 + Beta CCL21 × B7 + Beta ANGPTL4 × B8;

[0038] Wherein, Beta HGF = 0.2287. If the HGF concentration < 1687.283 pg / mL, then B1 = 0; if the HGF concentration ≥ 1687.283 pg / mL, then B1 = 1;

[0039] Beta Galectin-3 = 0.2447. If the Galectin-3 concentration < 9.268 ng / mL, then B2 = 0; if the Galectin-3 concentration ≥ 9.268 ng / mL, then B2 = 1;

[0040] Beta hsCRP = 0.3118. If the hsCRP concentration < 2.50 mg / L, then B3 = 0; if the hsCRP concentration ≥ 2.50 mg / L, then B3 = 1;

[0041] Beta S100A8 / A9 = 0.3071. If the S100A8 / A9 concentration < 353.1584 ng / mL, then B4 = 0; if the S100A8 / A9 concentration ≥ 353.1584 ng / mL, then B4 = 1;

[0042] Beta Netrin-1 = -0.3248. If the Netrin-1 concentration < 444.003 pg / mL, then B5 = 0; if the Netrin-1 concentration ≥ 444.003 pg / mL, then B5 = 1;

[0043] Beta Osteopontin = 0.4149. If the Osteopontin concentration < 100.9548 ng / mL, then B6 = 0; if the Osteopontin concentration ≥ 100.9548 ng / mL, then B6 = 1;

[0044] Beta CCL21 = 0.8458. If the CCL21 concentration < 308.07 pg / mL, then B7 = 0; if the CCL21 concentration ≥ 308.07 pg / mL, then B7 = 1;

[0045] Beta ANGPTL4= 0.3219. If the ANGPTL4 concentration < 102.84 ng / mL, then B8 = 0; if the ANGPTL4 concentration ≥ 102.84 ng / mL, then B8 = 1.

[0046] The grouping and assignment of the protein biomarkers are based on the optimal cut-off points of each biomarker determined by the ROC curve. According to the optimal cut-off points, the biomarkers are grouped respectively, and the biomarker levels less than the optimal cut-off points are assigned a value of 0, and the biomarker levels greater than or equal to the optimal cut-off points are assigned a value of 1. Among them, the cut-off value of HGF is 1687.283 pg / mL, the cut-off value of Galectin-3 is 9.268 ng / mL, the cut-off value of hsCRP is 2.50 mg / L, the cut-off value of S100A8 / A9 is 353.1584 ng / mL, the cut-off value of Netrin-1 is 444.003 pg / mL, the cut-off value of Osteopontin is 100.9548 ng / mL, the cut-off value of CCL21 is 308.07 pg / mL, and the cut-off value of ANGPTL4 is 102.84 ng / mL.

[0047] In some embodiments, the formula for calculating the genetic risk score (GRS) is as follows: GRS = 0.6507 × SNP1 + 0.3863 × SNP2 + 0.1550 × SNP3 + 0.1601 × SNP4 + 0.2596 × SNP5 + 0.1645 × SNP6 + 0.2050 × SNP7 + 0.2026 × SNP8;

[0048] SNP1 to SNP8 are assigned values as shown in the following table according to the genotypes of their corresponding SNP loci:

[0049]

[0050] This algorithm believes that each SNP has a different impact on the disease, and assigns corresponding weights to each SNP. By calculating the number of risk alleles of a specific SNP possessed by the individual to be tested (i.e., 0, 1, or 2), multiplying it by beta, and then adding up the risk contributions of each genetic marker, a weighted combined GRS is provided for each individual.

[0051] In some embodiments, the prediction module makes predictions according to the following conditions:

[0052] If the total score of TRS, BMS, and GRS is higher than 2.3415 points, it indicates that the ischemic stroke patient to be predicted is a high-risk patient who will die or have severe disability three months after the disease.

[0053] By calculating the total scores of TRS + BMS + GRS, the BMS-GRS-TRS score was constructed. The optimal cut-off value of the BMS-GRS-TRS score was 2.3415 points. The sensitivity for predicting the composite outcome event of death or severe disability three months after the onset of ischemic stroke patients was 74.61% (95% CI: 70.90% - 78.10%), and the specificity was 79.85% (95% CI: 77.90% - 81.70%). Therefore, when the BMS-GRS-TRS score of ischemic stroke patients is higher than 2.3415 points, they can be diagnosed as high-risk patients with death or severe disability three months after the onset. Brief Description of the Drawings

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0055] Figure 1 It is the decile distribution diagram of the predicted probability and the actual probability of the composite outcome of death or severe disability three months after the onset of ischemic stroke patients by TRS. The Hosmer Lemeshow test shows that the calibration effect of the TRS model is good.

[0056] Figure 2 It is the decile distribution diagram of the predicted probability and the actual probability of the composite outcome of death or severe disability three months after the onset of ischemic stroke patients by BMS-GRS-TRS. The Hosmer Lemeshow test shows that after adding BMS and GRS on the basis of TRS, the calibration effect of the constructed BMS-GRS-TRS score model is good. Detailed Embodiments

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. For those not specified in the embodiments, they are carried out according to conventional conditions or the conditions recommended by the manufacturer. For the reagents or instruments not specified by the manufacturer, they are all conventional products that can be obtained through commercial purchase.

[0058] The features and performance of the present invention will be further described in detail below in conjunction with the embodiments.

[0059] Example 1

[0060] This example study is based on a Chinese stroke cohort study conducted in 26 hospitals in China from August 2009 to May 2013. A total of 4,071 patients aged 22 and above who had their first ischemic stroke were recruited, and 3,500 study subjects provided blood specimens. The admission data of ischemic stroke patients included demographic characteristics (such as age and gender), clinical characteristics (time from onset to admission, NIHSS scale, systolic blood pressure, diastolic blood pressure, and blood glucose, etc.), medical history, and medication use (hypertensive history, hyperlipidemia history, heart disease history, diabetes history, and drug use, etc.), family history of stroke, and lifestyle habits (smoking and drinking), etc. Through literature screening and bioinformatics analysis, in the plasma or serum samples of 3,500 ischemic stroke patients, 27 protein biomarkers that may be related to the prognosis of ischemic stroke were detected (Table 1), and 136 SNPs that may be related to the prognosis of ischemic stroke were detected in DNA samples using SNPscan TM The multiplex SNP genotyping technique detected 136 SNPs that may be related to the prognosis of ischemic stroke (Table 2).

[0061] Table 1. List of 27 blood biomarkers detected

[0062]

[0063] Table 2. List of 136 SNP genotyping

[0064]

[0065]

[0066]

[0067] Trained and qualified clinicians conducted follow-up interviews with the study subjects face-to-face 3 months after the onset of the disease, measured their blood pressure, and filled out the modified Rankin scale (mRS) for self-care ability, NIHSS scale, and death diagnosis assessment form; the primary outcome of this study was the composite outcome of death or severe disability after ischemic stroke. The primary outcome was evaluated using the mRS scale. The mRS scale score ranged from 0 to 6 points. The higher the score, the more severe the disability. 0 points indicated no symptoms, 3 points indicated mild severe disability, 4 points indicated moderate severe disability, 5 points indicated severe severe disability, and 6 points indicated death. The composite outcome of death or severe disability was defined as an mRS scale score of 3-6.

[0068] Using the stepwise regression method, on the premise of forcibly including two variables of gender and age, the Logistic regression model was used to screen traditional risk factors related to death or severe disability three months after onset in ischemic stroke patients from smoking status, drinking status, history of hypertension, history of taking antihypertensive drugs, history of hyperlipidemia, history of diabetes, family history of stroke, admission systolic blood pressure, blood glucose level, blood lipid level, NIHSS score, time from onset to admission, and body mass index.

[0069] The detection methods of blood protein biomarker levels are shown in Table 3 in detail. According to the optimal cut-off values of each biomarker determined by the ROC curve, each protein biomarker level was divided into a low-level group and a high-level group according to the optimal cut-off values. The relationship between these protein biomarkers and the risk of death or severe disability at 3 months after onset in ischemic stroke patients was evaluated by the Logistic regression model, and the odds ratio (OR) and 95% confidence interval (CI) were calculated. According to the number of alleles of a specific SNP owned by the patient (i.e., 0, 1, or 2), the relationship between these SNPs and the risk of death or severe disability was calculated using the Logistic regression model.

[0070] Table 3. Detection of blood protein biomarkers

[0071]

[0072]

[0073] Based on the screened protein biomarkers and SNPs related to the risk of death or severe disability in ischemic stroke, the BMS and GRS were calculated respectively. The TRS was modified according to the BMS and GRS, the total score of TRS + BMS + GRS was calculated, and the BMS-GRS-TRS score was constructed.

[0074] The likelihood ratio test was used to evaluate whether the goodness of fit of the overall BMS-GRS-TRS model was improved compared with the TRS, and the Hosmer Lemeshow χ 2 statistic was used to evaluate the calibration of the model. By calculating the net reclassification improvement index (NRI) and the integrated discrimination index (IDI), it was evaluated whether the predictive ability of the BMS-GRS-TRS model for the poor prognosis of ischemic stroke patients was improved compared with the TRS. All P values were two-sided tests, the significance level P was 0.05, and the statistical analysis was completed using SAS 9.4, R 4.1.0, and MedCalc statistical software.

[0075] The results are as follows:

[0076] (1) Using logistic stepwise regression analysis, it was found that on the premise of including gender and age, smoking status, admission systolic blood pressure, NIHSS score, history of diabetes, and LDL-C level were associated with the risk of death or severe disability three months after onset in patients with ischemic stroke (P<0.05; see Table 4). Based on these selected traditional risk factors, a TRS was constructed, and the calculation formula is as follows:

[0077] TRS = -4.8340 + 0.0964 × gender (female = 0, male = 1) + 0.6782 × age (<65 years old = 0, ≥65 years old = 1) - 0.2524 × smoking status (no = 0, yes = 1) + 0.00707 × systolic blood pressure + 0.2893 × NIHSS + 0.2554 × history of diabetes (none = 0, yes = 1) + 0.1292 × LDL-C level.

[0078] Table 4. Construction of TRS for the composite outcome of death or severe disability 3 months after onset in patients with ischemic stroke

[0079]

[0080] * TRS = -4.8340 + 0.0964 × gender + 0.6782 × age - 0.2524 × smoking + 0.00707 × systolic blood pressure + 0.2893 × NIHSS + 0.2554 × history of diabetes + 0.1292 × LDL-C

[0081] (2) The optimal cut-off point of each protein biomarker was determined according to the ROC curve. The cut-off value of HGF was 1687.283 pg / mL, the cut-off value of Galectin-3 was 9.268 ng / mL, the cut-off value of hsCRP was 2.50 mg / L, the cut-off value of S100A8 / A9 was 353.1584 ng / mL, the cut-off value of Netrin-1 was 444.003 pg / mL, the cut-off value of Osteopontin was 100.9548 ng / mL, the cut-off value of CCL21 was 308.07 pg / mL, and the cut-off value of ANGPTL4 was 102.84 ng / mL. The biomarkers were grouped according to the optimal cut-off point, and the protein biomarker levels less than the optimal cut-off value were assigned 0, and the protein biomarker levels greater than or equal to the optimal cut-off value were assigned 1.

[0082] Using a multifactorial Logistic regression model, after adjusting for gender, age, smoking status, systolic blood pressure, admission NIHSS score, history of diabetes, and LDL-C level in the TRS system, it was found that HGF, Galectin-3, hsCRP, S100A8 / A9, Netrin-1, Osteopontin, CCL21, and ANGPTL4 were associated with poor prognosis in patients with ischemic stroke (P<0.05; see Table 5). On this basis, the BMS of each patient was calculated according to the levels of HGF, Galectin-3, hsCRP, S100A8 / A9, Netrin-1, Osteopontin, CCL21, and ANGPTL4 in each patient. The calculation formula is as follows:

[0083] BMS = Beta HGF ×Group assignment of HGF (0 or 1) + Beta Galectin-3 ×Group assignment of Galectin-3 (0 or 1) + Beta hsCRP ×Group assignment of hsCRP (0 or 1) + Beta S100A8 / A9 ×Group assignment of S100A8 / A9 (0 or 1) + Beta Netrin-1 ×Group assignment of Netrin-1 (0 or 1) + Beta Osteopontin ×Group assignment of Osteopontin (0 or 1) + Beta CCL21 ×Group assignment of CCL21 (0 or 1) + Beta ANGPTL4 ×Group assignment of ANGPTL4 (0 or 1). Table 5. Relationship between biomarkers and the composite outcome of death or severe disability 3 months after ischemic stroke

[0084]

[0085]

[0086] * BMS = 0.2287×Group of HGF + 0.2447×Group of Galectin-3 + 0.3118×Group of hsCRP + 0.3071×Group of S100A8 / A9 - 0.3248×Group of Netrin-1 + 0.4149×Group of Osteopontin + 0.8458×Group of CCL21 + 0.3219×Group of ANGPTL4

[0087] (3) Using the multivariable Logistic regression model, after adjusting for gender, age, smoking status, systolic blood pressure, admission NIHSS score, history of diabetes, and LDL-C level in the TRS system, it was found that rs12248560, rs17346334, rs2149041, rs4244285, rs429358, rs465401, rs5745695, and rs704 were closely associated with the composite outcome of death or severe disability at 3 months after stroke in patients with ischemic stroke (P<0.05). On this basis, the homozygotes of the risk alleles were scored as 2 points, the heterozygotes were scored as 1 point, and the homozygotes without the risk alleles were scored as 0 points. According to the beta values of the relationship between each SNP and the composite outcome of death or severe disability, the weighted GRS was calculated, and the calculation formula was as follows:

[0088] GRS = Beta rs12248560 × SNP rs12248560 (0 or 1) + Beta rs17346334 × SNP rs17346334 (0, 1, or 2) + Beta rs2149041 × SNP rs2149041 (0, 1, or 2) + Beta rs4244285 × SNP rs4244285 (0, 1, or 2) + Beta rs429358 × SNP rs429358 (0, 1, or 2) + Beta rs465401 × SNP rs465401 (0, 1, or 2) + Beta rs5745695 × SNP rs5745695 (0, 1, or 2) + Beta rs704 × SNP rs704 (0, 1, or 2) (Table 6).

[0089] Table 6. Relationship between SNPs and the composite outcome of death or severe disability at 3 months after ischemic stroke

[0090]

[0091] * GRS = 0.6507 × SNP rs12248560 + 0.3863 × SNP rs17346334 + 0.1550 × SNP rs2149041 + 0.1601 × SNP rs4244285 + 0.2596 × SNP rs429358 + 0.1645 × SNP rs465401 + 0.2050 × SNP rs5745695 + 0.2026 × SNP rs704

[0092] (4) On the basis of TRS, by combining BMS and GRS, a new adverse prognosis risk warning model (BMS-GRS-TRS system) more suitable for patients with ischemic stroke was constructed. The study found that compared with TRS, the new prediction model combining TRS with BMS and GRS could better predict the risk of adverse outcomes 3 months after the onset of ischemic stroke in patients (Table 7). Among them, the likelihood ratio test showed that compared with TRS, the model fitting degree of the BMS-GRS-TRS score was significantly improved (P < 0.001); the Hosmer Lemeshow test showed that after adding BMS and GRS on the basis of TRS, the calibration effect of the constructed BMS-GRS-TRS score model was good (see Figure 1 and Figure 2 ). In addition, compared with TRS, adding BMS and GRS on the basis of TRS significantly improved the predictive ability of TRS for the risk of death or severe disability after the onset of ischemic stroke in patients (NRI = 48.4%, P < 0.001; IDI = 4.00%, P < 0.001). The optimal cut-off value of the BMS-GRS-TRS score was 2.3415 points, and the sensitivity for predicting the composite outcome event of death or severe disability 3 months after the onset of ischemic stroke in patients was 74.61% (95% CI: 70.90% - 78.10%), and the specificity was 79.85% (95% CI: 77.90% - 81.70%). When the BMS-GRS-TRS score of patients with ischemic stroke is higher than 2.3415 points, they can be diagnosed as high-risk patients with death or severe disability 3 months after the onset.

[0093] Thus, it can be seen that BMS-GRS-TRS can identify high-risk patients with poor prognosis of ischemic stroke at an early stage, and guide doctors to take corresponding treatment and nursing measures to prevent the occurrence of poor prognosis in patients with ischemic stroke, which has important clinical and public health significance.

[0094] Table 7. Predictive ability of BMS-GRS-TRS for the risk of death or severe disability 3 months after ischemic stroke

[0095]

[0096]

[0097] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A biomarker panel for predicting poor prognosis in patients with ischemic stroke, which is suitable for combination with traditional risk factors to predict poor prognosis in patients with ischemic stroke, characterized in that, The biomarker panel includes a protein biomarker panel and an SNP biomarker panel; The protein biomarker panel includes the following proteins: HGF, Galectin-3, hsCRP, S100A8 / A9, Netrin-1, Osteopontin, CCL21, and ANGPTL4; The SNP biomarkers include the following SNPs: rs12248560, rs17346334, rs2149041, rs4244285, rs429358, rs465401, rs5745695, and rs704; The traditional risk factors include: gender, age, smoking status, systolic blood pressure, NIHSS score, history of diabetes, and LDL-C concentration.

2. The biomarker panel according to claim 1, wherein The poor prognosis refers to the risk of death or severe disability in ischemic stroke patients 3 months after the onset of the disease.

3. A detection kit for predicting poor prognosis in patients with ischemic stroke, characterized in that, It includes a detection reagent for detecting the biomarker panel as described in claim 1, and the detection reagent includes a first detection reagent for detecting the protein biomarker panel and a second detection reagent for detecting the genotypes of the SNP biomarker panel.

4. The kit according to claim 3, characterized in that, The detection sample of the kit is plasma or serum or DNA.

5. Use of a detection reagent in the preparation of a detection kit for predicting poor prognosis in patients with ischemic stroke, characterized in that, The detection reagent includes a first detection reagent for detecting the protein concentrations in the protein biomarker panel and a second detection reagent for detecting the genotypes of the SNP biomarker panel; The protein biomarker panel includes the following proteins: HGF, Galectin-3, hsCRP, S100A8 / A9, Netrin-1, Osteopontin, CCL21, and ANGPTL4; The SNP biomarker panel includes the following SNPs: rs12248560, rs17346334, rs2149041, rs4244285, rs429358, rs465401, rs5745695, and rs704.

6. A system for predicting poor prognosis in patients with ischemic stroke, characterized in that, The system includes an information acquisition module, a calculation module, and a prediction module; The information acquisition module is used to perform the operation of acquiring the basic information of the ischemic stroke patient to be predicted; The basic information includes traditional risk factor information, protein biomarker information, and SNP biomarker information; Among them, the traditional risk factor information includes the information of the following indicators: gender, age, smoking status, systolic blood pressure, NIHSS score, history of diabetes, and LDL-C concentration level; The protein biomarker information includes the concentration level information of the following proteins: HGF, Galectin-3, hsCRP, S100A8 / A9, Netrin-1, Osteopontin, CCL21, and ANGPTL4; The SNP biomarker information includes the genotype information of the following SNPs: rs12248560, rs17346334, rs2149041, rs4244285, rs429358, rs465401, rs5745695, and rs704; The calculation module is used to perform the operation of calculating the traditional risk factor score TRS, the biomarker score BMS, and the genetic risk score GRS based on the basic information; The prediction module is used to execute the prediction of the risk of poor prognosis of ischemic stroke patients based on the traditional risk factor score, the biomarker score, and the genetic risk score.

7. The system according to claim 6, characterized in that, The formula for calculating the TRS is as follows: TRS = -4.8340 + 0.0964 × A1 + 0.6782 × A2 - 0.2524 × A3 + 0.00707 × A4 + 0.2893 × A5 + 0.2554 × A6 + 0.1292 × A7; Wherein, if the gender is female, then A1 = 0, if the gender is male, then A1 = 1, if the age < 65 years old, then A2 = 0, if the age ≥ 65 years old, then A2 = 1, if the smoking status is no, then A3 = 0, if the smoking status is yes, then A3 = 1, if there is no history of diabetes, then A6 = 0, if there is a history of diabetes, then A6 = 1, A4 is the systolic blood pressure value, A5 is the NIHSS score value, and A7 is the LDL-C concentration value.

8. The system according to claim 7, characterized in that, The formula for calculating the BMS is as follows: BMS = Beta HGF × B1 + Beta Galectin-3 × B2 + Beta hsCRP × B3 + Beta S100A8 / A9 × B4 + Beta Netrin-1 × B5 + Beta Osteopontin × B6 + Beta CCL21 × B7 + Beta ANGPTL4 × B8; Among them, Beta HGF = 0.2287. If the HGF concentration < 1687.283 pg / mL, then B1 = 0; if the HGF concentration ≥ 1687.283 pg / mL, then B1 = 1. Beta Galectin-3 = 0.2447. If the Galectin-3 concentration < 9.268 ng / mL, then B2 = 0; if the Galectin-3 concentration ≥ 9.268 ng / mL, then B2 = 1. Beta hsCRP = 0.3118. If the hsCRP concentration < 2.50 mg / L, then B3 = 0; if the hsCRP concentration ≥ 2.50 mg / L, then B3 = 1. Beta S100A8 / A9 = 0.3071. If the concentration of S100A8 / A9 < 353.1584 ng / mL, then B4 = 0; if the concentration of S100A8 / A9 ≥ 353.1584 ng / mL, then B4 = 1. Beta Netrin-1 =-0.3248. If the concentration of Netrin-1 is < 444.003 pg / mL, then B5 = 0; if the concentration of Netrin-1 ≥ 444.003 pg / mL, then B5 = 1; Beta Osteopontin = 0.4149. If the Osteopontin concentration < 100.9548 ng / mL, then B6 = 0; if the Osteopontin concentration ≥ 100.9548 ng / mL, then B6 = 1; Beta CCL21 = 0.8458. If the CCL21 concentration < 308.07 pg / mL, then B7 = 0; if the CCL21 concentration ≥ 308.07 pg / mL, then B7 = 1. Beta ANGPTL4 = 0.3219. If the ANGPTL4 concentration < 102.84 ng / mL, then B8 = 0; if the ANGPTL4 concentration ≥ 102.84 ng / mL, then B8 = 1.

9. The system according to claim 8, wherein The formula for calculating the GRS is as follows: GRS = 0.6507 × SNP1 + 0.3863 × SNP2 + 0.1550 × SNP3 + 0.1601 × SNP4 + 0.2596 × SNP5 + 0.1645 × SNP6 + 0.2050 × SNP7 + 0.2026 × SNP8; SNP1 to SNP8 are assigned values as shown in the following table according to the genotypes of their corresponding SNP loci: 。 10. The system according to claim 9, characterized in that, The prediction module makes predictions according to the following conditions: If the total score of TRS, BMS, and GRS is higher than 2.3415 points, it indicates that the ischemic stroke patient to be predicted is a high-risk patient who will die or have severe disability three months after the disease.

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