CN108802379B

CN108802379B - Group of molecular markers for judging aortic dissection prognosis

Info

Publication number: CN108802379B
Application number: CN201810614415.6A
Authority: CN
Inventors: 杜杰; 李玉琳; 卢洁; 刘卓惠
Original assignee: BEIJING INSTITUTE OF HEART LUNG AND BLOOD VESSEL DISEASES
Current assignee: BEIJING INSTITUTE OF HEART LUNG AND BLOOD VESSEL DISEASES
Priority date: 2018-06-14
Filing date: 2018-06-14
Publication date: 2021-04-16
Anticipated expiration: 2038-06-14
Also published as: CN108802379A

Abstract

The invention relates to a group of molecular markers for judging aortic dissection prognosis, wherein the molecular markers are protein factors, and the protein factors are as follows: TRAIL: tumor necrosis factor-related apoptosis-inducing ligand (TNF-related apoptosis inducing ligand); OPG: osteoprotegerin (osteoprotegerin); d-dimer: a D-dimer; the judging method comprises the following steps: death risk score (LP) — 5.2903+0.9318 × Ln (OPG/TRAIL) +0.5227 × Ln (D-dimer) within one year; the probability of adverse reaction occurrence within One year (One-year risk of all-cause reliability) < 1-0.947^ exp { LP }.

Description

Group of molecular markers for judging aortic dissection prognosis

Technical Field

The invention belongs to the technical field of medical biology, and particularly relates to a group of molecular markers for judging aortic dissection prognosis.

Background

Aortic Dissection (AD) is a common cardiovascular critical disease, which is a potentially fatal disease caused by local or diffuse abnormal dilation of the aortic wall, compressing the surrounding organs. The current definitive diagnosis of aortic dissection mainly relies on imaging examination, but it lacks simplicity. Therefore, the diagnosis, prevention and prediction of aortic dissection using effective laboratory examination items has become a very active research area, and more importantly, the monitoring and prediction of disease evolution over time to assess clinical risk.

Aortic dissection is one of the most aggressive aortic diseases, however, there is currently no good biomarker clinically to predict its adverse outcome. The biomarker can shorten the diagnosis time, can accelerate the implementation of treatment, and has significant meaning for the prognosis prompt of the acute arterial dissection. Previous researches report that D-dimer, CRP and NT-proBNP can predict the nosocomial death of AAAD patients, but have certain limitations and unknown long-term prediction value. Therefore, it is clinically important to find a new reliable marker that can predict the adverse outcome early.

Disclosure of Invention

The invention firstly relates to a group of serological diagnosis markers for judging the prognosis of an aortic dissection patient, wherein the serological markers are protein factors which are:

TRAIL: tumor necrosis factor-related apoptosis-inducing ligand (TNF-related apoptosis inducing ligand);

OPG: osteoprotegerin (osteoprotegerin);

d-dimer: a D-dimer;

the method for judging the prognosis of the aortic dissection patient refers to predicting the incidence rate of clinical adverse reactions or grading death risks in a certain time period, wherein the adverse reactions include but are not limited to: death, diffuse or ischemic nerve injury, acute renal failure, acute heart failure, dissection, cerebral infarction or stroke, and postoperative thoracotomy.

The certain time period refers to: within a certain time, preferably within one year, after detection of said serological diagnostic marker;

the death risk grading is three grades, namely low risk (<5 points), medium risk (5-15 points) and high risk (>15 points), according to the size of a death risk score (LP) value; the calculation formula of the LP value is as follows:

LP＝-5.2903+0.9318*Ln(OPG/TRAIL)+0.5227*Ln(D-dimer)

wherein the unit of the D-dimer is ng/ml;

the calculation formula of the adverse reaction probability (One-year risk of all-house reliability) is as follows:

the probability of adverse reaction is 1-0.947^ exp { LP }.

The invention also relates to application of the serological diagnosis markers in preparing a detection kit for predicting aortic dissection prognosis, and the kit also comprises reagents for quantifying the content of OPG, TRAIL and D-dimer in the serum of a patient.

The method for predicting aortic dissection prognosis by using the kit comprises the following steps:

(1) collecting a serum sample of a patient to be detected;

(2) detecting the amount of the serological diagnostic marker in the sample using an ELISA method;

(3) calculating the death risk score within one year and the adverse reaction probability within one year by substituting the following formulas

Death risk score (LP) ═ 5.2903+0.9318 Ln (OPG/TRAIL) +0.5227 Ln (D-dimer) within one year

Wherein the unit of the D-dimer is ng/ml;

and according to the calculation result, the death risk in one year is divided into three levels, namely low risk (<5 points), medium risk (5-15 points) and high risk (>15 points).

The probability of adverse reaction occurrence within One year (One-year risk of all-cause reliability) < 1-0.947^ exp { LP }.

The diagnostic kit is a diagnostic kit for diagnosing by utilizing an ELISA principle.

The kit also comprises a sample diluent, an antibody aiming at the diagnostic marker and a color developing agent.

The invention also relates to a method for predicting aortic dissection prognosis using the serological diagnostic marker, the method comprising the steps of:

(1) collecting a serum sample of a patient to be detected;

Drawings

FIG. 1 is a scatter plot comparing serum OPG concentrations in the no event group and the event group (events including death, diffuse or ischemic injury, acute renal failure, acute heart failure, dissection, cerebral infarction or stroke, post-operative re-thoracotomy). OPG was significantly higher in the event group compared to the no event group.

Figure 2, OPG one SD per liter (standard deviation), risk of mortality and hospital adverse events increase 3.153 and 2.628 times respectively (P both <0.05), SD values were calculated: the OPG data was ln-transformed, the transformed values were divided into 4 layers according to the mean-standard deviation, mean + standard deviation, and HR values were calculated for each rising standard deviation layer, with the risk of mortality and occurrence of hospital adverse events increasing 3.153 and 2.628 times, respectively (P all <0.05)

FIG. 3 shows that OPG is divided into four layers from low to high according to quartile, and the death risk of the second, third and fourth layers is respectively increased by 0.676(P >0.05), 1.257(P >0.05) and 4.990(P <0.05) times relative to that of the first layer. The risk of occurrence of adverse events in the second, third and fourth floors relative to the first floor was increased by a factor of 2.023(P >0.05), 3.790(P <0.05) and 7.497(P <0.05), respectively.

FIG. 4, ROC curves: the OPG distinguishes between event-group and event-group-free area under the curve (AUC) of 0.718 (0.654-0.783).

Figure 5 compares the difference in serum TRAIL concentration between no event group and event group. The concentration of TRAIL was significantly reduced in the event group compared to the no event group.

Figure 6, the risk of mortality and the occurrence of hospital adverse events increased 0.408 and 0.457 fold for every SD (standard deviation) rise in TRAIL, respectively (P < 0.05).

Figure 7, TRAIL is divided into four layers from low to high according to quartile, and the risk of death and occurrence of hospital adverse events in the second, third and fourth layers are respectively increased by 0.278(P >0.05), 0.278(P >0.05) and 0.098(P <0.05) times relative to the first layer. The risk of occurrence of adverse events in the second, third and fourth layers is respectively increased by 0.298(P <0.05), 0.366(P <0.05) and 0.094(P <0.05) times relative to the first layer.

Figure 8, area under the curve (AUC) for TRAIL distinguishing event and no event groups was 0.715 (0.650-0.781).

FIG. 9 is a scatter plot of OPG/TRAIL and D-dimer comparing event versus no event sets (note: log10 transformation of the ordinate for OPG/TRAIL or D-dimer detection values, respectively).

FIG. 10, COX regression analysis of OPG/TRAIL and D-dimer to predict death and nosocomial events, respectively (mean-standard deviation, mean + standard deviation after ln conversion for OPG/TRAIL and Ddimer, respectively, into four layers, and calculating the risk of death or nosocomial event risk for each standard deviation increase for these two indices using COX risk regression model, 95% confidence interval in parentheses).

FIG. 11 is a K-M diagram for low, medium and high risk stratification according to an OTD model.

FIG. 12, K-M diagram for OTD model external validation.

Detailed Description

Example 1 differential validation of molecular markers for prognosis of aortic dissection

418 cases of aortic dissection patients are selected, follow-up of 19.8 months is carried out, the patients are divided into no-event groups and event groups according to follow-up results, ELISA experiments of serum protein markers are carried out, the expression levels of 9 protein factors are detected by using corresponding RayBiotech Human ELISA kits, and whether the histone factors can effectively predict the prognosis condition of the aortic dissection patients is verified.

The experimental steps are as follows:

1. reagent preparation

(1) Equilibrating the kit and sample to room temperature (18-25 ℃);

(2) samples serum samples of the test subjects were diluted according to the fold shown in table 1 below according to the preliminary experimental results.

TABLE 1 dilution factor for the detection of different diagnostic markers

Detecting an object	Dilution factor
		TRAIL	1.5
FN1	20000
		LCN2	200
PLG	50000
		OPG	5
ANG	30
		LOX-1	10
SAA	50
		PF4	4000

(3) Assay dilution (Item E) was diluted 5-fold with deionized water for use;

(4) preparing a standard substance:

centrifuging the Item C tubule, adding 400 mu L of Assay Diluent (Item E) into the standard tubule, and mixing uniformly to obtain a standard product stock solution with the concentration of 50 ng/ml;

preparing 8 small centrifuge tubes of 1.5ml, adding 475. mu.L of Assay dilution buffer to the first tube, then extracting 25. mu.L of standard stock solution of 50ng/ml, adding to the first tube, mixing well to 2500pg/ml, and labeling as STD 1;

adding 300 μ L of Assay dilution buffer to the remaining 7 tubes, respectively, and sequentially labeling STD2, STD3, STD4, STD5, STD6, and STD 7;

then diluting the standard substance with 50ng/ml STD1 gradient, extracting 200 μ L of 50ng/ml standard solution (namely STD1) and adding into STD2 tubule, mixing uniformly, extracting 200 μ L of the solution in the tube and adding into STD3 tubule, mixing uniformly, and sequentially until STD7 is prepared, wherein STD 8 is only 300 μ L of Assay Diluent, namely standard substance 0 pg/ml;

(5) dilution of washing liquor: diluting the concentrated washing solution by 20 times with deionized water for later use;

(6) centrifuging to detect antibody tubule (tem F), adding 100 μ L of Diluent 1 × Assay Diluent (Item E), dissolving thoroughly, gently blowing up and down with a pipette, and diluting with Diluent 1 × Assay Diluent 80 times for use;

7) centrifuging HRP-streptavidin (Item G), and diluting with 1x Assay Diluent by 200 times for use;

2. procedure for the preparation of the

(1) Equilibrating the kit and sample to room temperature (18-25 ℃); detecting a standard substance and a part of samples by using multiple wells, and detecting a part of samples by using a single well;

(2) after the antibody-coated ELISA plate is balanced to room temperature, 100 microliter of prepared standard substance and sample are added into the corresponding hole, the whole plate strip is sealed by a sealing plate membrane, and the mixture is incubated overnight at 4 ℃;

(3) adding the prepared 1x lotion to a plate washing machine, washing the lath for 4 times by using the plate washing machine, and adding 300 mu L lotion into each hole;

(4) after washing the plate, adding 100 mu L of prepared detection antibody (biotin labeled antibody) into each hole, and incubating for 1h at room temperature;

(5) cleaning, the step is the same as (3);

(6) adding 100 mu L of prepared HRP-streptavidin into each hole, and incubating for 45min at room temperature;

(7) cleaning, the step is the same as (3);

(8) adding 100 mu L of TMB color development liquid into each hole, and incubating for 30min at room temperature in a dark place;

(9) adding 50 mu L of stop solution into each hole, and immediately reading at 450nm in an enzyme labeling instrument;

(10) concentration values were calculated using sigmaplot 12.0 software.

3. And (5) counting results:

(1) the expression level differences of 9 protein factors in the tested serum are shown in the following table 2.

TABLE 2 ELISA for the results of exploring the differential expression of proteins in serum sample 9

LCN 2: lipocalin-2, Lipocalin 2

LOX-1: hemagglutinin-like oxidized low density lipoprotein receptor 1

TRAIL: TNF-related apoptosis inducing ligand, TNF-related apoptosis inducing ligand

FN 1: fibrinectin, fibronectin

PF 4: plain factor 4, CXCL4, platelet factor 4

SAA: serum Amyloid A, Serum Amyloid A

OPG: osteoprotegerin, tumor necrosis factor receptor superfamily, member 11b (TNFRSF11B)

PLG: plasminogen, Plasminogen

ANG: angiopoetin, an angiogenic factor.

OPG is elevated in case-group patients, TRAIL is reduced in case-group patients, and OPG and TRAIL are reported to act on one pathway in the past, so OPG and TRAIL were selected as follow-up validation factors among 4 factors whose expression is different between case-group and no-case group. And meanwhile, dividing OPG and TRAIL to obtain a ratio, and combining the ratio with Ddimer to obtain an OTD model for predicting the prognosis of the aortic dissection patient.

(2) A scatter plot of the OPG expression difference between event groups and no event groups is shown in FIG. 1. OPG is elevated in event group expression.

(3) The risk of mortality or hospital side adverse events is predicted for each standard deviation increase in OPG as shown in figure 2. After OPG is subjected to log10 conversion, the OPG is divided into four layers according to the mean number-standard deviation, the mean number and the standard deviation, the two indexes of each rising standard deviation are counted by a COX regression method to increase the death or nosocomial event risk by a factor respectively, 95% confidence intervals are included in brackets, and the OPG is higher by one SD (standard deviation) per liter, and the death and nosocomial adverse event occurrence risk is increased by a factor of 3.153 and 2.628 respectively (P is less than 0.05).

(4) OPG predicts risk of death or hospitalization events in quartile hierarchy see figure 3. OPG is divided into four layers according to quartile from low to high, and the death risk of the second layer, the third layer and the fourth layer is respectively increased by 0.676(P >0.05), 1.257(P >0.05) and 4.990(P <0.05) times relative to the first layer. The risk of occurrence of adverse events in the second, third and fourth layers was increased by 2.023(P >0.05), 3.790(P <0.05) and 7.497(P <0.05) times, respectively, relative to the first layer (four layers were divided according to the OPG quartile, and the factor of risk of death or increase in hospital events in the other three layers was compared to the first layer by COX regression method with the lowest value layer, i.e., the first layer as a reference).

(5) The ROC curve for the OPG to distinguish aortic dissection patients from those without events is shown in FIG. 4. The OPG distinguishes between event-group and event-group-free area under the curve (AUC) of 0.718 (0.654-0.783).

(6) A scatter plot of TRAIL differentially expressed in event and no event groups is shown in figure 5. TRAIL is reduced in event group expression.

(7) The risk of mortality or hospital side adverse events is predicted for each standard deviation of TRAIL rise as shown in figure 6. After TRAIL is subjected to log10 conversion, the TRAIL is divided into four layers according to the average number-standard deviation, the average number and the standard deviation, the two indexes of increasing one standard deviation by COX regression method are respectively used for increasing the death or nosocomial event risk times, 95% confidence intervals are included in brackets, and the death and nosocomial adverse event occurrence risk increases by 0.408 and 0.457 times (P is less than 0.05) respectively when the TRAIL is increased by one SD (standard deviation)

(8) The risk of death or hospitalization events for TRAIL was predicted by quartile stratification as shown in figure 7. TRAIL is divided into four layers according to quartile from low to high, and the death risk of the second, third and fourth layers is respectively increased by 0.278(P >0.05), 0.278(P >0.05) and 0.098(P <0.05) times relative to the first layer. The risk of occurrence of adverse events in the second, third and fourth layers was increased by 0.298(P <0.05), 0.366(P <0.05) and 0.094(P <0.05) times, respectively, relative to the first layer (four layers were divided according to the quartile of TRAIL, and the factor of risk of death or increase in hospital events in the other three layers was compared with the first layer by COX regression method, using the lowest-value layer, i.e., the first layer as a reference).

(9) The ROC curve for TRAIL to distinguish aortic dissection patients with and without events is shown in FIG. 8. TRAIL distinguishes between event-group and no event-group areas under the curve (AUC) of 0.715 (0.650-0.781).

(10) The expression difference between OPG/TRAIL and Ddimer in no event group and event group is shown in FIG. 9. OPG/TRAIL and Ddimer were elevated in the event group, with statistical differences (P < 0.05).

(11) COX regression analyses of OPG/TRAIL and D-dimer predicted death and nosocomial events, respectively, are shown in FIG. 10. The OPG/TRAIL and Ddimer were log10 transformed into four layers according to mean-standard deviation, mean + standard deviation, and COX regression was used to count the HR and 95% confidence interval for each standard deviation increase in mortality or hospitalization event risk. The result shows that both OPG/TRAIL and Ddimer have good prognosis prediction value, and the prediction value of OPG/TRAIL is higher than that of D-dimer.

(12) The K-M curve of the OTD model for predicting 1-year mortality in aortic dissection patients is shown in FIG. 11. Fitting an OTD model by combining two indexes of OPG/TRAIL and Ddimer, calculating 1-year death risk of all patients by a COX regression formula, dividing the death risk into three groups of low-risk (< 5), medium-risk (5-15) and high-risk (> 15), and making a Kaplan-Meier survival curve according to the group, wherein the result shows that the higher the score is, the higher the 1-year death rate of the group is. The 1-year death risk score LP is calculated by the formula:

LP(linear predictor)＝-5.2903+0.9318*Ln(OPG/TRAIL)+0.5227*Ln(D-dimer)

the probability of adverse reaction occurrence within One year (One-year risk of all-cause mortality) is 1-0.947^ exp { LP }

Example 2 Large sample validation of molecular markers for prognosis of aortic dissection

271 cases of the patients with the aortic dissection in the forecourt are selected to carry out ELISA experiments of serum protein markers, and the expression levels of the OPG and TRAIL two protein factors selected in the example 1 are detected by using a corresponding RayBiotech Human ELISA Kit, so as to verify whether the OTD model can effectively predict the prognosis condition of the patients with the aortic dissection.

The experimental procedure was as follows:

1. reagent preparation

(1) Equilibrating the kit and sample to room temperature (18-25 ℃);

(2) samples serum samples of the test subjects were diluted according to the fold shown in table 2 below according to the preliminary experimental results.

TABLE 2 dilution factor for the detection of different diagnostic markers

Detecting an object	Dilution factor
		TRAIL	1.5
OPG	5

(3) Assay dilution (Item E) was diluted 5-fold with deionized water for use;

(4) preparing a standard substance:

(7) centrifuging HRP-streptavidin (Item G), and diluting with 1x Assay Diluent by 200 times for use;

2. procedure for the preparation of the

(5) cleaning, the step is the same as (3);

(7) cleaning, the step is the same as (3);

(9) add 50. mu.L of stop buffer to each well and read immediately on a microplate reader at 450 nm.

(10) Concentration values were calculated using sigmaplot 12.0 software.

The experimental results are as follows:

the Kaplan-Meier curve of the OTD model for predicting 1-year mortality of aortic dissection patients is shown in the figure.

The predicted risk of death for 1 year for each patient was obtained by substituting OPG/TRAIL and D-dimer according to the 1 year mortality calculation formula of the discovery cohort (see Table 3), and divided into three groups of low, medium and high according to the score <5, 5-15 and score >15, and Kaplan-Meier curves were constructed according to this group. The results show that the higher the score, the higher the annual death rate of the grouped patients 1, and according to the follow-up results, the number of deaths of the patients in the high, medium and low risk groups within 1 year in the queue is respectively: 25. 10, 5 persons; in the verification queue, the number of 1-year deaths of high-risk patients, medium-risk patients and low-risk patients is respectively as follows: 14. and 9, 1 person, which shows that the OTD model can well predict the 1-year death rate of the aortic dissection patient.

TABLE 3 predicted value of death risk in 271 cases of out-hospital aortic dissection patients

Finally, it should be noted that the above examples only help those skilled in the art understand the essence of the present invention, and should not be construed as limiting the scope of the present invention.

Claims

1. The application of a group of serological diagnosis markers in the preparation of a detection kit for judging aortic dissection prognosis is characterized in that,

the serum marker is a protein factor, and the protein factor is:

TRAIL: a tumor necrosis factor-related apoptosis-inducing ligand,

and OPG: the bone protective agent is a compound of bone protective agent,

and D-dimer: a D-dimer;

the kit comprises reagents for quantifying the amount of the set of serum markers in the serum of a patient;

the judgment of the aortic dissection patient prognosis refers to predicting the incidence rate of clinical adverse reactions or grading death risks in a certain time period,

the adverse reaction includes but is not limited to: death, diffuse or ischemic nerve injury, acute renal failure, acute heart failure, dissection, cerebral infarction or stroke, and postoperative thoracotomy.

2. The use according to claim 1,

the death risk classification is classified into three levels of low-risk, medium-risk and high-risk according to the death risk score value; respectively corresponding to death risk score value <5, death risk score value 5-15 and death risk score value > 15;

the death risk score value is calculated according to the formula:

death risk score-5.2903 +0.9318 Ln (OPG/TRAIL) +0.5227 Ln (D-dimer);

wherein the unit of the D-dimer is ng/ml.

3. The use according to claim 2, wherein the incidence of adverse reactions is calculated by the formula:

adverse reaction incidence is 1-0.947^ exp { death risk score value }.

4. The use according to any one of claims 1-3, wherein said certain period of time is: within one year after detection of the serological diagnostic marker panel.

5. A test kit for determining the prognosis of aortic dissection, characterized in that the kit comprises reagents for quantifying the amount of the serum marker panel in the serum of a patient;

the serum marker is a protein factor, and the protein factor is:

TRAIL: a tumor necrosis factor-related apoptosis-inducing ligand,

and OPG: the bone protective agent is a compound of bone protective agent,

and D-dimer: a D-dimer;

6. The detection kit according to claim 5,

the death risk score value is calculated according to the formula:

death risk score-5.2903 +0.9318 Ln (OPG/TRAIL) +0.5227 Ln (D-dimer);

wherein the unit of the D-dimer is ng/ml.

7. The detection kit according to claim 6,

the calculation formula of the adverse reaction incidence rate is as follows:

the probability of adverse reaction occurrence is 1-0.947^ exp { death risk score value }.

8. The test kit according to any one of claims 5 to 7, wherein the test kit is a diagnostic kit for diagnosis using ELISA principle.

9. The test kit according to any one of claims 5 to 7, wherein the kit further comprises necessary sample diluents, antibodies against the serological diagnostic marker panel, and a color-developing agent.