CN116908451A - Application of protein markers in preparation of reagent for identifying lung metastasis of primary lung adenocarcinoma and colorectal cancer - Google Patents
Application of protein markers in preparation of reagent for identifying lung metastasis of primary lung adenocarcinoma and colorectal cancer Download PDFInfo
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
- G01N33/57407—Specifically defined cancers
- G01N33/57423—Specifically defined cancers of lung
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
- G01N33/57407—Specifically defined cancers
- G01N33/57419—Specifically defined cancers of colon
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- G—PHYSICS
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- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
- G01N33/57407—Specifically defined cancers
- G01N33/57446—Specifically defined cancers of stomach or intestine
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
- G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
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Abstract
The application relates to application of a group of protein markers in a reagent for identifying primary lung adenocarcinoma and colorectal cancer lung metastasis, in particular to application of FABP1, MLPH or NARR in identifying primary lung adenocarcinoma and colorectal cancer lung metastasis with intestinal or mucus differentiation characteristics, and a kit for identifying primary lung adenocarcinoma and colorectal cancer lung metastasis with intestinal or mucus differentiation characteristics. In the application, the detection of FABP1, MLPH or NARR can accurately diagnose the patient suspected to suffer from primary lung adenocarcinoma or colorectal cancer lung metastasis with intestinal type or mucous differentiation characteristics, has great significance for improving the prognosis of the patient, and can be used as a potential treatment target point for subsequent research.
Description
Technical Field
The application belongs to the field of molecular biology, and particularly relates to application of a group of protein markers in identifying primary lung adenocarcinoma and colorectal cancer lung metastasis with intestinal or mucus differentiation characteristics.
Background
Primary lung adenocarcinoma characterized by intestinal or mucosal differentiation is pathologically confusing with colorectal adenocarcinoma, and it is necessary to exclude metastatic colorectal cancer to diagnose primary lung adenocarcinoma. Differential diagnosis of primary and metastasis remains challenging in current clinical practice. The primary lung adenocarcinoma can not be distinguished according to the conventional histomorphology, immunohistochemistry and gene mutation, and the primary lung adenocarcinoma can be diagnosed by comprehensively considering the clinical history and comprehensive and detailed imaging examination such as CT, PET-CT, digestive endoscopy and the like after the gastrointestinal tumor is eliminated. This process is complex, time consuming, expensive, and stressful, and may miss the optimal treatment window, delaying patient outcome.
The immunohistochemical markers currently used clinically to identify lung metastasis of primary lung adenocarcinoma and colorectal cancer with intestinal or mucus differentiation characteristics are CK7, CK20, CDX2, TTF-1. It is noted that the above immunohistochemical markers are not diagnostic and differential diagnostic specific. The binding PubMed records involved lung and intestine adenocarcinoma literature analyses, with a lung and intestine adenocarcinoma CK7 positive rate of 88.2% (149/169), a CDX2 positive rate of 78.1% (132/169), a CK20 positive rate of 48.2% (82/170), and a TTF-1 positive rate of 38.8% (66/170). TTF-1 is often expressed in primary lung adenocarcinomas characterized by intestinal or mucoid differentiation, and CK 7-negative primary lung adenocarcinomas characterized by intestinal or mucoid differentiation are not uncommon. In addition, CK7 positive expression can be found in some colorectal cancer cases, and colorectal cancer lung metastasis cases with TTF-1 positive expression are reported in researches. The limitations of the existing immunohistochemical markers increase the difficulty of differential diagnosis. The differential diagnostic efficacy of several emerging immunohistochemical markers, such as SATB2, beta-Catenin, etc., is still controversial and still requires further evaluation.
At present, the immunohistochemical markers CK7, CK20, CDX2 and TTF-1 for identifying the primary lung adenocarcinoma and the metastatic colorectal cancer with intestinal or mucus differentiation characteristics are limited, and only the differential diagnosis is carried out according to the immunohistochemical markers, so that partial cases can be misdiagnosed, and huge loss is caused to the life health of patients. Thus, there remains a need to develop new immunohistochemical markers to accurately identify primary lung adenocarcinoma and metastatic colorectal cancer characterized by intestinal or mucoid differentiation.
Disclosure of Invention
The technical aim of the present application is to develop a set of protein markers that can be used to identify primary lung adenocarcinoma with intestinal or mucoid differentiation characteristics from lung metastatic colorectal cancer.
It is another technical object of the present application to provide the use of the above protein markers for identifying primary lung adenocarcinoma and lung metastatic colorectal cancer characterized by intestinal or mucoid differentiation.
Yet another technical object of the present application is to develop a kit useful for identifying primary lung adenocarcinoma with intestinal or mucoid differentiation characteristics from lung metastatic colorectal cancer.
In one aspect, the application provides the use of FABP1, MLPH or NARR as a biomarker for identifying primary lung adenocarcinoma with intestinal or mucolytic differentiation characteristics and lung metastatic colorectal cancer.
In specific embodiments, when MLPH, NARR are highly expressed or immunohistochemical positive, it is determined that the lung adenocarcinoma is primary with intestinal or mucosal differentiation characteristics; when FABP1 is highly expressed or immunohistochemical positive, lung metastatic colorectal cancer is judged.
In particular embodiments, primary lung adenocarcinoma characterized by intestinal or mucoid differentiation and lung metastatic colorectal cancer are identified by detecting FABP1, MLPH or NARR of a test sample, comprising detecting expression of FABP1, MLPH or NARR using one or several technical means selected from the group consisting of: protein immunoblotting (Western blot), enzyme-linked immunosorbent assay (ELISA), immunohistochemistry (IHC), immunocytochemistry (ICC), immunohistochemistry (IHF), immunocytofluorescence (ICF), immunoprecipitation (IP), flow cytometry, protein labeling, and mass spectrometry.
In specific embodiments, the sample to be tested is a tumor cell or a tumor tissue.
In specific embodiments, the sample to be tested is freshly frozen tissue of tumor tissue or paraffin embedded tissue.
In another aspect, the application provides a kit for identifying primary lung adenocarcinoma with intestinal or mucolytic differentiation characteristics from lung metastatic colorectal cancer, the kit comprising reagents for detecting FABP1, MLPH or NARR.
In specific embodiments, the reagent for detecting FABP1, MLPH or NARR detects expression of FABP1, MLPH or NARR at the protein level.
In particular embodiments, the agent comprises an antibody.
In specific embodiments, the kit comprises an immunohistochemical detection reagent comprising at least an antibody directed against FABP1, MLPH or NARR.
In specific embodiments, the kit further comprises an enzyme-labeled anti-mouse/rabbit IgG polymer.
In specific embodiments, the kit further comprises xylene, absolute ethanol, 85% alcohol, 75% alcohol, distilled water, antigen retrieval buffer solution, PBS buffer solution, 3% hydrogen peroxide solution, DAB color development solution, hematoxylin, hydrochloric acid alcohol, and bluing solution.
Advantageous effects
Based on proteomics data, the application utilizes a machine learning algorithm to screen and obtain a group of protein markers with identification capability, and after independent queue verification is carried out on the immunohistochemical level, a group of immunohistochemical markers for identifying primary lung adenocarcinoma and metastatic colorectal cancer with intestinal or mucus differentiation characteristics are obtained: FABP1, MLPH, NARR, when MLPH, NARR are highly expressed or immunohistochemically positive, support diagnosis of primary lung adenocarcinoma with intestinal or mucosal differentiation characteristics; diagnosis of lung metastatic colorectal cancer is supported when primary panel high expression FABP1 is high expression or immunohistochemical is positive. The detection of the protein marker can accurately diagnose a patient as soon as possible, has great significance for accurate treatment of the patient, and can be used as a potential treatment target for subsequent research.
Drawings
Fig. 1: proteomic analysis quality control results. A 7871 protein was identified; b, sample detection results of different batches of on-machines; and C, repeating the sample detection result by the technology.
Fig. 2: protein markers with differential diagnosis ability are screened by applying 3 machine learning algorithms. A, performing cluster analysis on 35 protein markers obtained by screening by using an RF machine learning algorithm; b, screening 33 protein markers by using an SVM machine learning algorithm; screening to obtain 24 protein markers by using a LASSO machine learning algorithm; and D, synthesizing 3 machine learning algorithm results to make a Venn diagram.
Fig. 3: candidate protein markers in the cohort were found to be immunohistochemical.
Fig. 4: the results of immunohistochemical analysis of 5 protein markers in the queue were independently verified.
Detailed Description
The technical contents of the present application are described below by means of specific examples so that those skilled in the art can better understand the present application.
In the present application, CDH17 refers to cadherein 17, cadherin 17.
FABP1 refers to fatty acid binding protein 1, fatty acid binding protein 1.
MLPH means melanopylin, melanin.
NARR refers to the Nine amino acid residues, is RAB34 (Ras-related protein Rab-34, ras-related protein Rab 34) alternatively spliced and generated subtype, the antibodies of the application also directed against Rab34.
CK7 refers to cytokeratin 7, cytokeratin 7.
ATP1B3 refers to ATPase Na + /K + transporting subunit beta 3, atpase sodium potassium ion transport subunit beta 3. The method comprises the steps of carrying out a first treatment on the surface of the
OXNAD1 refers to oxidoreductase NAD binding domain containing 1, oxidoreductase NAD binding domain 1.
S100A14 refers to S100 calcium binding protein A, S100 calbindin A14.
GLB1 refers to galactosidase beta 1, galactosidase β1.
Reagents used in the immunohistochemical detection hereinafter are shown below
The above, except for hematoxylin provided by the Pinctada martensii biological Co., ltd, were all from the biological medical engineering Co., ltd.
Example 1: proteomic analysis of primary lung adenocarcinoma and colorectal carcinoma lung metastasis tumor tissue with intestinal or mucoid differentiation characteristics
1. Sample preparation
22 cases of primary lung adenocarcinoma with intestinal or mucus differentiation characteristics and 17 cases of lung metastatic colorectal cancer patients were collected and subjected to surgical resection of tumor tissue, and paraffin-embedded to prepare paraffin-embedded samples.
2. Proteomic analysis
1) 1-1.5mg of paraffin embedded samples were dewaxed with heptane, hydrated with graded strength ethanol solution, then acid hydrolyzed with 0.1% formic acid, and alkaline hydrolyzed with 0.1M Tris-HCl (pH 10.0). Subsequently, the protein lysate was cleaved with 6M urea/2M thiourea buffer, followed by addition of the reduced and alkylated protein lysate with tris (2-carboxyethyl) phosphine (TCEP) and Iodoacetamide (IAA). Pressure Cycling Technology (PCT) assists in sample preparation. Lys-C and trypsin mixtures were used to digest the cleavage product and trifluoroacetic acid (TFA) was added to stop the reaction. Polypeptides from 39 samples to be tested, 6 randomly selected technical replicates (including 3 intra-and 3 inter-batch replicates) and 3 pooled samples were labeled using TMTpro 16plex according to manufacturer's instructions.
2) Experimental batch design was performed to minimize the effect of batch effects on proteomic data. For each batch of TMT samples, a Thermo Ultimate Dinex 3000 system (Thermo Fisher Scientific, san Jose, USA) and XBridge Peptide BEH C18 chromatography column (300A, 5 μm×4.6 mm ×250 mm) (Waters, milford, mass., USA) were used. Samples were separated by gradient in 10mM ammonia (ph=10.0) using 5% -35% Acetonitrile (ACN) at a flow rate of 1mL/min. The TMT-tagged peptides were separated by the system into 60 fractions which were further pooled into 30 fractions. After drying under vacuum, peptides were isolated with a gradient of 60 minutes using a Thermo ScientificTM UltiMateTM 3000 RSLCnano system and analyzed in Data Dependent Acquisition (DDA) mode using a Orbitrap Exploris mass spectrometer and FAIMS Pro interface. The mass spectrum data was analyzed using Proteome Discoverer (version 2.4.0.305,Thermo Fisher Scientific) and protein database (downloaded from UniProtKB).
3) The proteomic analysis results showed that: 7871 proteins were identified (FIG. 1A), and the median coefficient of variation between pooled samples was less than 0.2 (FIG. 1B), indicating that samples were more stable between batches; the median of the technique repetition coefficient of variation is below 0.2 (fig. 1C), indicating that the technique repetition is good.
Example 2: screening protein markers with differential diagnostic capabilities
1. Protein marker with differential diagnosis capability by utilizing machine learning algorithm Random Forest
1) Random forest analysis data mining of differentially expressed proteins using R-packet randomForest, 35 proteins with average precision drop greater than 3 were selected as input features to construct five-fold cross-validated 1000 trees.
2) Then 50 cross-validation iterations were performed to construct multiple classifiers using protein features with average accuracy degradation greater than 3 in each training set.
3) Diagnostic performance assessment was performed by mean accuracy in the subject's operating characteristics curve (ROC) and mean Area Under Curve (AUC). Iterations with accuracy > 0.9 and AUC > 0.9 were retained. 29 samples were randomly selected as the test set to evaluate the performance of these models (fig. 2A).
4) Finally, 35 candidate protein markers are obtained.
2. Screening protein markers with differential diagnosis capability by using machine learning algorithm SVM
1) The SVM classifier was constructed from the R-packet geNetClassifier according to default parameters based on data of all quantized proteins (fig. 2B).
2) Finally, 33 candidate protein markers are obtained.
3. Screening protein markers with differential diagnosis capability by using machine learning algorithm LASSO
1) The LASSO classifier was constructed after analysis of all quantized protein data using R-packet.
2) One standard error and the minimum lambda of 10 fold cross-validation were used to screen biomarkers, respectively (fig. 2C).
3) Finally, 14 candidate protein markers are obtained.
4. Synthesizing 3 machine learning algorithms to obtain final candidate protein markers
1) Taking intersection of candidate protein markers obtained by at least 2 algorithms.
2) A set of 10 candidate protein markers was determined: the primary group has high expression of CK7 and NARR, MLPH, S A14; the transfer groups were highly expressed with LYST, GLB1, OXNAD1, ATP1B3, CDH17, FABP1, see Venn diagram (FIG. 2D).
Example 3: immunohistochemical detection of expression of candidate protein markers in tumor tissue
1. Tissue to be tested
In example 1, 22 cases of primary lung adenocarcinoma with intestinal or mucous differentiation characteristics and 17 cases of lung metastatic colorectal cancer patients were subjected to surgical excision of tumor tissue for proteomic analysis, and paraffin-embedded sections were prepared as samples to be tested.
2. Immunohistochemical detection
1) The rectal cancer and lung adenocarcinoma tissues were used as controls on each section.
2) Dewaxing and hydrating paraffin sections: sequentially placing the slices into xylene I for 15min, xylene II for 15min, xylene III for 15min, absolute ethyl alcohol I for 5min, absolute ethyl alcohol II for 5min, 85% alcohol for 5min, 75% alcohol for 5min, and distilled water for washing.
3) Antigen retrieval: the sections were soaked in antigen buffer repair solution (Tris-EDTA), antigen was repaired at high pressure for 15min, washed with running water and cooled to room temperature, washed 3 times with PBS for 1min each time.
4) Blocking endogenous peroxidases: the sections were incubated in 3% hydrogen peroxide solution for 10min at room temperature in the dark, followed by 3 washes with PBS for 1min each.
5) Incubation resistance: the primary antibody working solution (prepared in a certain dilution ratio, see table 1 below) was added dropwise, incubated overnight at 4 ℃, left at room temperature for 30min, and then washed 3 times with PBS for 1min each.
6) Dripping secondary antibody: the enzyme-labeled anti-mouse/rabbit IgG polymer was added dropwise, left at room temperature for 30min, and washed 3 times with PBS for 1min each.
7) DAB color development: and (3) dripping freshly prepared DAB color development liquid into the slices after the slices are slightly dried, standing for 10min at room temperature, and flushing the slices with tap water to terminate color development.
8) Counterstaining the nuclei: hematoxylin counterstain for 1min, washing with tap water, hydrochloric acid alcohol differentiation for 8 seconds, washing with tap water, and washing with running water, wherein the blue returning liquid returns to blue for 25 seconds.
9) And (3) removing the water sealing piece: and (3) sequentially placing the slices into 75% alcohol and absolute alcohol, drying, and placing the slices into a sakura sealing machine to finish sealing.
10 Under-mirror interpretation: the immunohistochemical staining intensity (negative, weak positive, moderate positive, strong positive) and the respective percentages of each section are sequentially interpreted by a professional pathologist under a microscope according to the negative control and the positive control.
11 Calculating Hscore: hscore= Σ (pi×i) = (weak positive cell percentage×1) + (medium positive cell percentage×2) + (strong positive cell percentage×3), where pi represents the percentage of positive cell number to all cells in the slice, and i represents staining intensity.
TABLE 1 basic information of candidate protein marker antibodies
3. Drawing ROC curve, calculating area under curve AUC
1) For 9 candidate protein markers, whether Hscore of two groups of primary lung adenocarcinoma and lung metastatic colorectal cancer with intestinal or mucoid differentiation characteristics were different was compared.
2) And drawing an ROC curve, and calculating the area under the curve AUC. (FIG. 3)
3) The analysis results showed 5 indices (primary group high expression CK7, MLPH, NARR; the transfer group highly expresses CDH17 and FABP 1) still has good differential diagnosis capability at the level of immunohistochemistry.
Example 4: performing an immunohistochemical assay on candidate proteins in an independent validation queue
1. Independent validation queues
11 cases of primary lung adenocarcinoma with intestinal or mucus differentiation characteristics and 19 cases of colorectal cancer lung metastasis patients were screened, and tumor tissues were punctured and surgically excised, and paraffin embedding was performed to prepare sections.
2. Immunohistochemical detection
1) The rectal cancer and lung adenocarcinoma tissues were used as controls on each section.
2) Dewaxing and hydrating paraffin sections: sequentially placing the slices into xylene I for 15min, xylene II for 15min, xylene III for 15min, absolute ethyl alcohol I for 5min, absolute ethyl alcohol II for 5min, 85% alcohol for 5min, 75% alcohol for 5min, and distilled water for washing.
3) Antigen retrieval: the sections were soaked in 0.01M sodium citrate buffer, antigen was repaired at high pressure for 15min, washed with running water and cooled to room temperature, and washed 3 times with PBS for 1min each time.
4) Blocking endogenous peroxidases: the sections were incubated in 3% hydrogen peroxide solution for 10min at room temperature in the dark, followed by 3 washes with PBS for 1min each.
5) Incubation resistance: the primary antibody working solution (prepared in a certain dilution ratio, see table 1 above) was added dropwise, incubated overnight at 4 ℃, left at room temperature for 30min, and then washed 3 times with PBS for 1min each.
6) Dripping secondary antibody: HRP secondary antibody was added dropwise, left at room temperature for 30min, and washed 3 times with PBS for 1min each.
7) DAB color development: and (3) dripping freshly prepared DAB color development liquid into the slices after the slices are slightly dried, standing for 10min at room temperature, and flushing the slices with tap water to terminate color development.
8) Counterstaining the nuclei: hematoxylin counterstain for 1min, washing with tap water, hydrochloric acid alcohol differentiation for 8 seconds, washing with tap water, and washing with running water, wherein the blue returning liquid returns to blue for 25 seconds.
9) And (3) removing the water sealing piece: and (3) sequentially placing the slices into 75% alcohol and absolute alcohol, drying, and placing the slices into a sakura sealing machine to finish sealing.
10 Under-mirror interpretation: the immunohistochemical staining intensity (negative, weak positive, moderate positive, strong positive) and the respective percentages of each section are sequentially interpreted by a professional pathologist under a microscope according to the negative control and the positive control.
11 Calculating Hscore: hscore= Σ (pi×i) = (weak positive cell percentage×1) + (medium positive cell percentage×2) + (strong positive cell percentage×3), where pi represents the percentage of positive cell number to all cells in the slice, and i represents staining intensity.
3. Drawing ROC curve, calculating area under curve AUC
1) For 5 candidate protein markers, whether Hscore of two groups of primary lung adenocarcinoma and lung metastatic colorectal cancer with intestinal or mucoid differentiation characteristics were different was compared.
2) And drawing an ROC curve, and calculating the area under the curve AUC. (FIG. 4)
3) The analysis results showed 5 indices (primary group high expression CK7, MLPH, NARR; the transfer group highly expresses CDH17 and FABP 1) still has good differential diagnosis capability in an independent verification queue.
In summary, for the primary lung adenocarcinoma having intestinal or mucosal differentiation characteristics and the lung metastatic colorectal cancer which are difficult to identify, detection of CK7, CDH17, FABP1, MLPH, NARR can be performed, and diagnosis of the primary lung adenocarcinoma having intestinal or mucosal differentiation characteristics is supported when CK7, MLPH, NARR is highly expressed or immunohistochemical positive; diagnosis of lung metastatic colorectal cancer is supported when CDH17, FABP1 is highly expressed or immunohistochemically positive.
Claims (10)
1. Use of FABP1, MLPH or NARR as a biomarker for identifying primary lung adenocarcinoma with intestinal or mucoid differentiation characteristics and lung metastatic colorectal cancer.
2. The use according to claim 1, wherein when MLPH, NARR is highly expressed or immunohistochemical positive, it is determined as primary lung adenocarcinoma with intestinal or mucoid differentiation characteristics; when FABP1 is highly expressed or immunohistochemical positive, lung metastatic colorectal cancer is judged.
3. The use according to claim 1, wherein the primary lung adenocarcinoma characterized by intestinal or mucolytic differentiation is identified from lung metastatic colorectal cancer by detecting FABP1, MLPH or NARR of the sample to be tested, said detection comprising detecting the expression of FABP1, MLPH or NARR using one or several technical means selected from the group consisting of: protein immunoblotting (Western blot), enzyme-linked immunosorbent assay (ELISA), immunohistochemistry (IHC), immunocytochemistry (ICC), immunohistochemistry (IHF), immunocytofluorescence (ICF), immunoprecipitation (IP), flow cytometry, protein labeling, and mass spectrometry.
4. The use according to claim 3, wherein the sample to be tested is a tumor cell or a tumor tissue.
5. Use according to claim 3, wherein the sample to be tested is freshly frozen tissue of tumor tissue or paraffin embedded tissue.
6. A kit for identifying primary lung adenocarcinoma with intestinal or mucolytic differentiation characteristics from lung metastatic colorectal cancer, the kit comprising reagents for detecting FABP1, MLPH or NARR.
7. The kit according to claim 6, wherein the reagent for detecting FABP1, MLPH or NARR detects the expression of FABP1, MLPH or NARR at the protein level.
8. The kit of claim 6, wherein the kit comprises an immunohistochemical detection reagent comprising at least an antibody directed against FABP1, MLPH or NARR.
9. The kit of claim 8, wherein the kit further comprises an enzyme-labeled anti-mouse/rabbit IgG polymer.
10. The kit of claim 9, wherein the kit further comprises xylene, absolute ethanol, 85% ethanol, 75% ethanol, distilled water, antigen retrieval buffer, PBS buffer, 3% hydrogen peroxide solution, DAB color development solution, hematoxylin, hydrochloric acid alcohol, bluing solution.
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