CN104200060A - Model and method for predicting probability of post-operation recent relapse and metastasis of giant liver caner of a patient - Google Patents

Abstract

The invention relates to a mathematical model and a method for predicting the short-term recurrence and metastasis probability of patients with huge liver cancer after operation. The mathematical model is: P=1/(1+Y), P is the probability of recurrence and metastasis in patients with huge liver cancer after surgery, and when P is the probability of recurrence and metastasis within 6 months: Y=exp(4.092+2.073*X12 -2.719*X14-2.58*X17+3.039*X19); when P is the probability of recurrence and metastasis within 6 to 12 months, Y=exp(2.528+1.633*X8+0.971*X12-1.517*X17). The beneficial effects of the present invention are mainly reflected in: comprehensively analyzing multiple indicators related to the recent recurrence and metastasis of giant liver cancer from multiple factors and multiple proteins, and achieving the effect of predicting the short-term recurrence and metastasis after surgery through the prediction model, and individualizing the clinical practice and treatment plan Choices matter.

Description

Model and method for predicting the probability of short-term recurrence and metastasis in patients with huge liver cancer

(1) Technical field

The invention relates to a mathematical model and method for predicting the short-term recurrence and metastasis probability of patients with huge liver cancer after operation.

(2) Background technology

Hepatocellular carcinoma ranks fifth in the incidence of malignant tumors in the world, and its mortality rate ranks third in the cause of death of malignant tumors in the world (Jemal A, Siegel R, Ward E, et al. Cancer statistics, 2008. CancerJ Clin, 2008; 58: 71-96), my country is a big country with liver cancer, and there are about 450,000 new cases of liver cancer every year, and it has become the second killer of cancer patients (Zhang Weidong, Miao Guojun; Analysis of epidemiological characteristics of malignant tumor mortality in my country ; China Health Education, 2009, 25:246-248), especially giant liver cancer.

Huge HCC refers to a subtype of HCC with a diameter greater than 10 cm. Surgical resection is currently the first choice for patients with giant liver cancer, but postoperative metastasis and recurrence and poor prognosis are the main reasons for the poor treatment effect and high mortality rate of giant liver cancer. Statistics show that the probability of recurrence of giant liver cancer within 5 years after surgery reaches 70%. Studies have shown that the earliest postoperative recurrence and metastasis of giant liver cancer can be within 2 months after surgery; 1 to 2 years after surgery is the high incidence period of recurrence and metastasis. Shimul et al. reported 56 patients with recurrence after radical resection of huge liver cancer, 21 cases (38%) relapsed within 1 year, and 31 cases (55%) relapsed after 1 year; and found that the survival rate after recurrence was closely related to the recurrence time: within 1 year after operation The 3-year survival rate of relapse patients was significantly lower than that of relapse patients after 1 year (reference: Shimul A Shah, Paul D Greig, Steven Gallinger, et al. Factors associated with early recurrence after resection for hepatocellular carcinoma and outcomes. J Am Coll Surg, 2005, 10(5):275-283.). Hayashi et al. observed the relationship between postoperative recurrence time and prognosis of liver cancer. The 1-, 3-, and 5-year survival rates of patients with recurrence within 1 year were 75.7%, 36.6%, and 28.3%, respectively, far lower than the survival rate of recurrence after 2 years (1 , 3, and 5-year survival rates were 100%, 92.2%, and 68.6%, respectively) (references: Hayashi M, Shimizu T, Hirokawa F, et al. Clinicopathological risk factors for recurrence with one year after initial hepatitis for hepatocellular carcinoma. J Am Surg , 2011, 77(5):572-578.). It can be seen that the recurrence rate of liver cancer within 1 year after operation is relatively high, and it has a significant impact on the survival rate. However, the mechanism of recurrence and metastasis has not been clearly elucidated, so there is a lack of prognostic indicators and corresponding treatment methods for giant liver cancer.

At present, there are many analyzes related to the factors related to postoperative recurrence and metastasis of giant liver cancer, but most of them are based on single factor. Although this provides a relatively accurate judgment to a certain extent, it is still limited and one-sided. A multifactorial and complex pathological process, in which the molecular characteristics of proteins determine the biological behavior of liver cancer. So far, there is no relevant literature reporting the multifactorial and multiprotein mathematical factors of the recent recurrence and metastasis of massive liver cancer. For the prediction model, if multiple indicators related to the recurrence and metastasis of liver cancer can be comprehensively analyzed from multiple factors and proteins, a mathematical prediction model for the recurrence and metastasis of giant liver cancer can be established, which can provide theoretical and experimental basis for individualized clinical treatment and prediction of postoperative efficacy.

(3) Contents of the invention

The purpose of the present invention is to provide a mathematical model and method for predicting the short-term recurrence and metastasis probability of patients with huge liver cancer after surgery.

The technical scheme adopted in the present invention is:

A mathematical model for predicting the probability of postoperative recurrence and metastasis in patients with huge liver cancer P=1/(1+Y), P is the probability of recurrence and metastasis in the short term after surgery in patients with huge liver cancer, and P is the probability of recurrence and metastasis within 6 months : Y=exp(4.092+2.073*X12-2.719*X14-2.58*X17+3.039*X19); when P is the probability of recurrence and metastasis in 6-12 months, Y=e xp(2.528+1.633*X8+0.971* X12-1.517*X17); where X8 is intraoperative tumor thrombus, if there is intraoperative tumor thrombus, X8=1, if there is no intraoperative tumor thrombus, then X8=0; X12 is tumor grade, when the tumor grade is grade I, X12=1, II- X12=2 for grade III and X12=3 for grade IV; X14 is the immunohistochemical expression of IQGAP-2 protein, X14=2 for high expression and X14=1 for low expression; X17 is the immunohistochemical expression of S100A12 protein, X17=2 for high expression and X17=1 for low expression; X19 is the immunohistochemical expression of APOA2 protein, X19=2 for high expression and X19=1 for low expression.

The inventor collected the specimens of massive liver cancer undergoing radical resection, and adopted a case-control study method. All the cases had not received any radiotherapy and chemotherapy before surgical resection, and were confirmed as hepatocellular carcinoma by pathology. According to the recurrence group within 6 months (group A) and the recurrence group within 6-12 months (group B) as the experimental group, and the non-relapse group within 12 months as the control group, the clinicopathological factors were analyzed by univariate and multivariate factors (See the table below for the statistical variable assignment of each factor), and screen out the independent risk factors affecting the short-term recurrence and metastasis of massive liver cancer. Combined with the screening of differential proteins related to recurrence and metastasis through proteomics, the relative expression of proteins was detected by immunohistochemistry, and multi-factor logistic regression analysis was used to obtain a mathematical equation for predicting the recurrence and metastasis of massive liver cancer in the near future. The sensitivity, accuracy, and specificity of the equation were judged by retrospective analysis, and it was judged that the equation could predict the short-term recurrence and metastasis of massive liver cancer after surgery.

Statistical variable assignment for each factor

In the study, the inventors found that tumor grade, IQGAP-2, S100A12, and APOA2 are independent risk factors for the recurrence and metastasis of giant liver cancer within 6 months. The higher the tumor grade, the abnormal expression of IQGAP-2, S100A12, and APOA2 can increase the risk of recurrence and metastasis within 6 months after operation, and the corresponding postoperative recurrence and metastasis can be obtained through the established multi-factor and multi-protein mathematical prediction model probability of occurrence. In the analysis of the postoperative group of giant liver cancer within 6 to 12 months, it was found that intraoperative tumor thrombus, tumor grade, and S100A12 were independent risk factors for recurrence and metastasis of giant liver cancer within 6 to 12 months. Intraoperative tumor thrombi, higher tumor grades, and abnormal expression of S100A12 can all increase the risk of recurrence and metastasis within 6 to 12 months after surgery. Therefore, through the established multi-factor and multi-protein mathematical prediction model, the corresponding probability of postoperative recurrence and metastasis can be obtained.

The present invention also relates to a method for predicting the probability of postoperative recurrence and metastasis in patients with huge liver cancer, the method comprising:

(1) Take biopsy tissue or intraoperative and postoperative pathological materials from patients with huge liver cancer as pathological specimens, and record and analyze intraoperative tumor thrombi and tumor grading of pathological specimens;

(2) Using SP staining method, the immunohistochemical scores of IQGAP-2 protein, S100A12 protein and APOA12 protein of pathological specimens were respectively obtained (the sum of staining intensity and positive cell rate was taken as Judgment criteria (ShimizuM, SaitonY, Itoh H. Immunohistochemical staining of H-rasoncogene product in normal, benign and malignant human pancreatic tissues. Hum Pathol, 1990, 21(6):607-612.): divided into four grades according to the staining intensity, No staining of cells is scored as 0 points, weak staining (light yellow) is scored as 1 point, moderate staining (brownish yellow) is scored as 2 points, and strong staining (tan) is scored as 3 points; according to the positive cell rate, it is also divided into four grades, 0≤positive 0 points for cell rate ≤ 5%, 1 point for 5% < positive cell rate ≤ 25%, 2 points for 25% < positive cell rate ≤ 50%, 3 points for positive cell rate > 50%. Adding the two points, 0 is negative (-), 1-2 is weakly positive (+), 3-4 is moderately positive (++), and 5-6 is strongly positive (+++). Negative (—) and weak positive (+) were defined as low expression, moderate positive (++) and strong positive (+++) were high expression.)

(3) Substitute the above values into the mathematical model of recurrence and transfer probability within 6 months P=1/(1+Y), Y=exp(4.092+2.073*X12-2.719*X14-2.58*X17+3.039*X19 ); and the mathematical model of recurrence and transfer probability of 6 to 12 months P=1/(1+Y), Y=exp(2.528+1.633*X8+0.971*X12-1.517*X17), respectively calculated to obtain 6 Probability of recurrence and metastasis within one month and 6 to 12 months; where X8 is intraoperative tumor thrombus, if there is X8=1, if not, then X8=0; X12 is tumor grade, X12=1 for grade I, II-III X12=2 for grade IV, X12=3 for grade IV; X14 is the immunohistochemical expression of IQGAP-2 protein, X14=2 for high expression, X14=1 for low expression; X17 is the immunohistochemical expression of S100A12 protein, high X17=2 when expressed and X17=1 when low expressed; X19 is the immunohistochemical expression of APOA2 protein, X19=2 when high expressed and X19=1 when low expressed.

The steps of the immunohistochemical SP staining method involved in the present invention to detect protein are as follows (IQGAP-2 protein, S100A12 protein and APOA12 protein are carried out according to the following steps respectively):

i. Take pathological specimens to prepare paraffin sections of liver cancer tissues, and put them in an oven at 60°C overnight.

ii. Slices are dewaxed. Soak in sequence: Xylene I: 10min; Xylene II: 10min; Xylene III: 10min.

iii. Slice hydration. Soak in sequence: absolute ethanol: 3min; 90% (v/v) ethanol: 3min; 80% ethanol: 3min; 75% ethanol: 3min.

iv. Wash with PBS 3 times, 5min each time.

v. EDTA antigen high-pressure repair: soak the slices in 0.01M EDTA repair solution, soak in boiling water for 5 minutes, and cool to room temperature. Wash 3 times with PBS, 5min each time.

vi. Add 300 μL of 3% (w/w) hydrogen peroxide aqueous solution, 37° C. for 10 min. Wash 3 times with PBS, 5min each time.

vii. Add 300 μL of 3% (w/w) BSA blocking solution (prepared in PBS), and keep at 37° C. for 1 hour. Wash 3 times with PBS, 5min each time.

viii. Add the primary antibody of the corresponding protein, the concentration of each antibody is as follows: IQGAP-2 protein antibody concentration is 1:300, S100A12 protein antibody concentration is 1:50, APOA2 protein antibody concentration is 1:300; Take it out, rewarm at room temperature for 15 minutes, and then wash with PBS 4 times, 5 minutes each time.

ix. Add the secondary antibody dropwise, which is the corresponding secondary antibody labeled with horseradish peroxidase (purchased from Fuzhou Maixin Reagent Co., Ltd., ready-to-use, without dilution), at 37° C. for 45 minutes. Wash 4 times with PBS, 5min each time.

x. Wash 3 times with PBS, 5min each time. DAB (DAB Chromogenic Kit, purchased from Shanghai Sangong) developed color for 2-10 minutes, observed under a microscope; washed with double distilled water to stop color development, counterstained with hematoxylin for 10 seconds, rinsed and soaked in tap water.

xi. Dehydration. Soak in sequence: 75% ethanol: 2min; 80% ethanol: 2min; 90% ethanol: 2min; absolute ethanol: 2min.

xii. Blow dry with a hair dryer, add neutral gum, and cover with a cover glass.

xiii. Two pathologists used the double-blind method to read the slices, and the slices with differences were determined after discussion. Now observe the positive expression of each index in the whole tissue microarray under the low power lens (×40 times), and then in the high power field ( ×200 times) to count the number of positive cells, and take the average value of 2 or 3 lattices for each sample as the score of the sample.

The sum of staining intensity and positive cell rate is used as the judgment standard (Shimizu M, Saiton Y, Itoh H. Immunohistochemical staining of H-ras oncogene product abnormal, benign and malignant human pancreatic tissues. HumPathol, 1990, 21 (6): 607 -612.): Divided into four grades according to the staining intensity, 0 points for no staining, 1 point for weak staining (light yellow), 2 points for moderate staining (brownish yellow), 3 points for strong staining (tan); According to the positive cell rate, it is also divided into four grades, 0 ≤ positive cell rate ≤ 5% is scored as 0 points, 5% < positive cell rate ≤ 25% is scored as 1 point, 25% < positive cell rate ≤ 50% is scored as 2 points , the positive cell rate > 50% was recorded as 3 points. The above two points are added together, 0 is negative (-), 1-2 is weakly positive (+), 3-4 is moderately positive (++), 5-6 is strongly positive (+++) . Negative (—) and weak positive (+) were defined as low expression, moderate positive (++) and strong positive (+++) were high expression.

The beneficial effects of the present invention are mainly reflected in: comprehensively analyzing multiple indicators related to the recent recurrence and metastasis of giant liver cancer from multiple factors and multiple proteins, and achieving the effect of predicting the short-term recurrence and metastasis after surgery through the prediction model, and individualizing the clinical practice and treatment plan Choices matter.

(4) Specific implementation methods

The present invention is further described below in conjunction with specific embodiment, but protection scope of the present invention is not limited thereto:

Example 1:

The specimens of massive hepatocellular carcinoma undergoing radical resection were collected, and the method of case-control study was adopted. All cases had not received any radiotherapy and chemotherapy before surgical resection, and were confirmed as hepatocellular carcinoma by pathology. According to whether there is recurrence within 6 months or within 6-12 months, they are divided into experimental group and control group, and the clinicopathological factors are analyzed by univariate and multivariate analysis (see Tables 1-4), and the patients with massive liver cancer are screened out. Independent risk factors for recent recurrence and metastasis.

Combined with the screening of differential proteins related to recurrence and metastasis through proteomics, the relative expression of proteins was detected by immunohistochemistry (see Tables 5-6), and a prediction was obtained through multivariate logistic regression analysis (see Tables 7-8). Mathematical equation for the short-term recurrence and metastasis of massive liver cancer after operation. The sensitivity, accuracy and specificity of the equation were judged through back-substitution analysis (Table 9-10), and it was judged that the equation can predict the short-term recurrence and metastasis of massive liver cancer after surgery. .

The protein detection steps involved are as follows:

(a) Pathological specimens were obtained from biopsies or intraoperative and postoperative pathological materials of patients with giant liver cancer.

(b) The immunohistochemical method uses the SP staining method, and the specific steps are as follows:

(c) Paraffin sections of liver cancer tissues were prepared, and the oven was set at 60° C. overnight.

(d) Dewaxing of slices. Soak in sequence: Xylene I: 10min; Xylene II: 10min; Xylene III: 10min.

(e) Section hydration. Soak in sequence: absolute ethanol: 3min; 90% (v/v) ethanol: 3min; 80% ethanol: 3min; 75% ethanol: 3min.

(f) Washing with PBS 3 times, 5 min each time.

(h) EDTA antigen high-pressure heat repair: soak the slices in 0.01M EDTA repair solution, soak in boiling water for 5 minutes, and cool to room temperature. Wash 3 times with PBS, 5min each time.

(I) Add 300 μL of 3% (w/w) hydrogen peroxide aqueous solution, 37° C. for 10 min. Wash 3 times with PBS, 5min each time.

(J) Add 300 μL of 3% (w/w) BSA blocking solution (prepared in PBS), and keep at 37° C. for 1 hour. Wash 3 times with PBS, 5min each time.

(K) Add primary antibody: the corresponding protein antibody concentration (as shown in Table 11). Place in the refrigerator at 4°C for 16 hours, take it out, rewarm at room temperature for 15 minutes, and then wash with PBS 4 times, 5 minutes each time.

(L) Secondary antibody was added dropwise, said secondary antibody was the corresponding secondary antibody labeled with horseradish peroxidase (purchased from Fuzhou Maixin Reagent Company, ready-to-use, without dilution), 37°C for 45min. Wash 4 times with PBS, 5min each time.

(M) Wash with PBS 3 times, 5 min each time. DAB (DAB Chromogenic Kit, purchased from Shanghai Sangong) developed color for 2-10 minutes, observed under a microscope; washed with double distilled water to stop color development, counterstained with hematoxylin for 10 seconds, rinsed and soaked in tap water.

(N) Dehydration. Soak in sequence: 75% ethanol: 2min; 80% ethanol: 2min; 90% ethanol: 2min; absolute ethanol: 2min.

(O) Blow dry with a hair dryer, add neutral gum, and cover with a cover glass.

(P) Two pathologists used the double-blind method to read the slices, and the discrepancies were determined after discussion. Now observe the positive expression of each index in the whole tissue chip under the low power lens (×40 times), and then in the high power field Count the number of positive cells under (×200 times), and take the average value of 2 or 3 lattices for each sample as the score of the sample.

The sum of staining intensity and positive cell rate is used as the judgment standard (Shimizu M, Saiton Y, Itoh H. Immunohistochemical staining of H-ras oncogene product abnormal, benign and malignant human pancreatic tissues. HumPathol, 1990, 21 (6): 607 -612.): Divided into four grades according to the staining intensity, 0 points for no staining, 1 point for weak staining (light yellow), 2 points for moderate staining (brownish yellow), 3 points for strong staining (tan); According to the positive cell rate, it is also divided into four grades, 0 ≤ positive cell rate ≤ 5% is scored as 0 points, 5% < positive cell rate ≤ 25% is scored as 1 point, 25% < positive cell rate ≤ 50% is scored as 2 points , the positive cell rate > 50% was recorded as 3 points. The above two points are added together, 0 is negative (-), 1-2 is weakly positive (+), 3-4 is moderately positive (++), 5-6 is strongly positive (+++) . Negative (—) and weak positive (+) were defined as low expression, moderate positive (++) and strong positive (+++) were high expression.

(L) SPSS 17.0 was used for statistical analysis, and Pearson chi-square test was used for count data between test indicators and clinical data. Multivariate analysis was performed using logistic regression analysis.

The results show that:

1. Mathematical prediction model for the probability of recurrence and metastasis within 6 months after operation in patients with huge liver cancer P=1/(1+Y), Y=exp(4.092+2.073*x12-2.719*x14-2.58*x17+3.039*x19 ));

2. Mathematical prediction model of recurrence and metastasis probability within 6-12 months: P=1/(1+Y), Y=exp(2.528+1.633*x8+0.971*x12-1.517*x17). Among them, P is the probability of recurrence and metastasis in patients with huge liver cancer after surgery, X8 is tumor thrombus during operation, X12 is tumor grade, X14 is IQGAP-2 protein immunohistochemical expression, X17 is S100A12 protein immunohistochemical expression, X19 is APOA2 Protein immunohistochemical expression.

Table 1: Results of univariate analysis of clinicopathological data in the 6-month recurrence and metastasis group

*P<0.05 for comparison between two groups

Table 2: Results of univariate analysis of clinicopathological data in the recurrence and metastasis group within 6 to 12 months

Table 3: Results of multivariate logistic regression analysis of clinicopathological data in recurrence and metastasis group within 6 months (α=0.05 backward method)

*P<0.05 for comparison between two groups

Table 4: Results of multivariate logistic regression analysis of clinicopathological data in the recurrence and metastasis group within 6 to 12 months (α = 0.05 backward method)

*P<0.05 for comparison between two groups

Table 5: The expression of 6 proteins between the recurrence group and the non-relapse group at 6 months

Table 6: The expression of 6 proteins between the recurrence group and the non-relapse group at 6 to 12 months

Table 7: Results of multivariate and multiprotein logi regression analysis for the 6-month recurrence group

Table 8: Results of multivariate and multiprotein logi regression analysis for the 6-12 month recurrence group

Table 9: Back-substitution analysis was performed on the equation of the recurrence and metastasis prediction model within 6 months, and the sensitivity, specificity and accuracy of the model were obtained;

The evaluation table of the prediction model is a model containing constant items and 4 variables, with a probability value of 0.5 as the junction point, the comparison table between the predicted value and the actual data is shown in Attached Table 5. The sensitivity of early recurrence and metastasis was 92.3% (36/39), the specificity was 96.9% (62/64), and the accuracy was 95.1% (98/103), which can predict the recurrence and metastasis of giant liver cancer to a certain extent. effect.

Table 10: Back-substitution analysis was performed on the equation of the prediction model for recurrence and metastasis within 6 to 12 months, and the sensitivity, specificity, and accuracy of the model were obtained;

The evaluation table of the prediction model is a model including constant items and 3 variables, with a probability value of 0.5 as the junction point, the comparison table between the predicted value and the actual data is shown in Attached Table 5. The sensitivity of early recurrence and metastasis was 58.1% (18/31), the specificity was 85.9% (55/64), and the accuracy was 76.8% (73/95), which can predict the recent recurrence and metastasis of giant liver cancer to a certain extent. effect.

Table 11: Various antibody experimental methods

The basic principles of the present invention, main features and advantages of the present invention have been shown and described above, which only illustrate the principles of the present invention. The present invention also has various changes and improvements without departing from the spirit and scope of the present invention. These changes All modifications and improvements are within the scope of the claimed invention. The protection scope of the present invention is defined by the appended claims and their equivalents.

Claims (3)

1. one kind for predicting the mathematical model P=1/ (1+Y) of relapse and metastasis probability after huge liver cancer operation in patients, P is short-term relapse transition probability after huge liver cancer operation in patients, and P is while being the relapse and metastasis probability in 6 months: Y=exp (4.092+2.073*X12-2.719*X14-2.58*X17+3.039*X19); When P is the 6-12 relapse and metastasis probability of individual month, Y=e xp (2.528+1.633*X8+0.971*X12-1.517*X17); Wherein X8 is cancer embolus in art, if if having X8=1 without X8=0; X12 is tumor grade, X12=3 when X12=2, IV level when X12=1, II-III level during I level; X14 is IQGAP-2 protein immunization group expression, X14=1 when X14=2, low expression during high expressed; X17 is S100A12 protein immunization group expression, X17=1 when X17=2, low expression during high expressed; X19 is APOA2 protein immunization group expression, X19=1 when X19=2, low expression during high expressed.

2. a method of predicting short-term relapse transition probability after huge liver cancer operation in patients, described method comprises:

(1) get in huge liver cancer patient biopsy or art, postoperative pathologic sampling is as Pathologic specimen, and cancer embolus, tumor grade situation in the art of Pathologic specimen carried out to record analysis;

(2) adopt SP immunohistochemical decoration method, obtain respectively the SABC scoring of the SABC scoring of Pathologic specimen IQGAP-2 albumen, the SABC scoring of S100A12 albumen and APOA12 albumen;

(3) by the above-mentioned value relapse and metastasis probability mathematical model P=1/ (1+Y) in substitution 6 months respectively, Y=exp (4.092+2.073*X12-2.719*X14-2.58*X17+3.039*X19); With the relapse and metastasis probability mathematical model P=1/ (1+Y) of 6～12 months, Y=e xp (2.528+1.633*X8+0.971*X12-1.517*X17), calculate respectively and obtain in 6 months and the relapse and metastasis probability of 6～12 months; Wherein X8 is cancer embolus in art, if if having X8=1 without X8=0; X12 is tumor grade, X12=3 when X12=2, IV level when X12=1, II-III level during I level; X14 is IQGAP-2 protein immunization group expression, X14=1 when X14=2, low expression during high expressed; X17 is S100A12 protein immunization group expression, X17=1 when X17=2, low expression during high expressed; X19 is APOA2 protein immunization group expression, X19=1 when X19=2, low expression during high expressed.

3. method as claimed in claim 2, is characterized in that described SP immunohistochemical decoration method method is as follows:

(a) get Pathologic specimen and prepare liver cancer tissue paraffin section, 60 ℃ of baking boxs spend the night;

(b) section is de-cured; Dimethylbenzene I:10min; Dimethylbenzene II:10min; Dimethylbenzene III:10min;

(c) section aquation; Soak successively: absolute ethyl alcohol: 3min; 90% ethanol: 3min; 80% ethanol: 3min; 75% ethanol: 3min;

(d) PBS cleans 3 times, each 5min;

(e) EDTA antigen Pressure method: section is put into 0.01M EDTA and repaired immersion bubble, and boiling water bath 5min, is cooled to room temperature, and PBS cleans 3 times, each 5min;

(f) add 3% aqueous hydrogen peroxide solution of 300 μ L, 37 ℃ of 10min, PBS cleans 3 times, each 5min;

(g) add the 3%BSA confining liquid of 300 μ L, 37 ℃ of 1h; PBS cleans 3 times, each 5min;

(h) add primary antibodie, the primary antibodie antibody concentration of corresponding protein is as follows: IQGAP-2 protein antibodies concentration is 1:300, and the antibody concentration of S100A12 albumen is 1:50, and APOA2 protein antibodies concentration is 1:300; After placing 16h, take out by 4 ℃ of refrigerators, room temperature rewarming 15min, and then PBS washes 4 times, each 5min;

(i) drip two anti-, described two and resist corresponding two anti-for horseradish peroxidase-labeled, 37 ℃ of 45min; PBS washes 4 times, each 5min;

(j) PBS washes 3 times, each 5min; DAB 2～the 10min that develops the color, Microscopic observation; Distilled water is washed only colour developing, and haematoxylin is redyed 10s, with tap water, rinses and soaks;

(k) dehydration; Soak successively: 75% ethanol: 2min; 80% ethanol: 2min; 90% ethanol: 2min; Absolute ethyl alcohol: 2min;

(l) electricity consumption dries up, and adds neutral gum, and cover glass covers;

(m) using the score sum of staining power and positive cell rate as criterion: according to staining power, be divided into level Four, cell dye-free note 0 minute, weak dyeing note 1 minute, moderate stain note 2 minutes, strong dyeing note 3 minutes; According to positive cell rate, be also divided into level Four, 0≤positive cell rate≤5% is designated as 0 minute, 5% < positive cell rate≤25% is designated as 1 minute, and 25% < positive cell rate≤50% is designated as 2 minutes, and positive cell rate > 50% is designated as 3 minutes; Above-mentioned two integrations are added, and 0 minute negative, and 1～2 is divided into the weak positive, and 3～4 are divided into the medium positive, and 5～6 are divided into strong positive, and feminine gender and the weak positive are defined as to low expression, and the medium positive and strong positive are high expressed.