Application of serum protein markers of APMAP, FGG, VWF, CEA and SCCA in preparation of esophageal precancerous lesion diagnosis kit

Abstract

The application relates to application of a serum protein marker combination based on quantitative proteomics in diagnosis of esophageal precancerous lesions, wherein the protein marker combination comprises APMAP, FGG, VWF, CEA and SCCA. Experiments prove that the protein marker combination has better diagnosis performance than a single protein marker combination, and has higher sensitivity and specificity.

Description

Technical Field

[0001] This invention relates to a combination of five serum protein biomarkers based on quantitative proteomics and its application in the diagnosis of precancerous lesions of the esophagus. Background Technology

[0002] Esophageal cancer is one of the most common malignant tumors of the digestive tract worldwide, ranking seventh in cancer incidence and sixth in cancer mortality. Timely detection and accurate diagnosis of esophageal squamous precancerous lesions (ESPL), such as low-grade intraepithelial neoplasia and high-grade intraepithelial neoplasia, are crucial for early intervention and preventing malignant progression. Currently, endoscopy and tissue biopsy remain the main methods for diagnosing precancerous lesions; however, these are invasive procedures that rely on physician experience and pathological interpretation, resulting in inconvenience, high cost, and limited accessibility for population screening. Therefore, developing a non-invasive, sensitive, and specific liquid biopsy method has become a key focus in clinical practice and research.

[0003] Previous studies have shown that the development and progression of esophageal cancer are accompanied by the abnormal expression of specific serum proteins, which provides a theoretical basis for non-invasive diagnosis using serum protein biomarkers. However, there are currently no serum biomarkers available for the diagnosis of esophageal cancer lesions (ESPL) in clinical practice, and single protein biomarkers often have limitations in sensitivity or specificity, making it difficult to meet the clinical requirements for accurate identification of precancerous lesions.

[0004] Therefore, there is an urgent need to integrate quantitative proteomics technology with bioinformatics analysis methods to screen and validate a combination of serum protein biomarkers with complementary diagnostic properties, in order to establish a highly accurate, reproducible, non-invasive diagnostic tool for the early identification and risk stratification of precancerous lesions of the esophagus, thereby making up for the shortcomings of existing clinical diagnostic methods and promoting the development of early prevention and control strategies for esophageal cancer. Summary of the Invention

[0005] The purpose of this invention is to provide a combination of five serum protein biomarkers based on quantitative proteomics with higher sensitivity and specificity and its application in the diagnosis of ESPL.

[0006] The present invention adopts the following technical solution:

[0007] Application of a combined five serum protein markers, APMAP, FGG, VWF, CEA, and SCCA, in the diagnosis of ESPL.

[0008] The amino acid sequence of the APMAP is shown in SEQ ID No. 1.

[0009] The amino acid sequence of FGG is shown in SEQ ID No. 2.

[0010] The amino acid sequence of the VWF is shown in SEQ ID No. 3.

[0011] The amino acid sequence of the CEA is shown in SEQ ID No. 4.

[0012] The amino acid sequence of the SCCA is shown in SEQ ID No. 5.

[0013] Application of a combination of five serum protein markers, APMAP, FGG, VWF, CEA, and SCCA, in the preparation of ESPL diagnostic reagents or diagnostic kits.

[0014] Furthermore, the diagnostic reagent is an ELISA diagnostic reagent.

[0015] Furthermore, the diagnostic kit is an ELISA diagnostic kit.

[0016] The beneficial effects of this invention are as follows: Based on quantitative proteomics screening of thousands of proteins in tissues and serum, this invention uses ELISA to detect the expression levels of APMAP, FGG, VWF, CEA, and SCCA in the serum proteins of patients in the validation cohort, obtaining accurate values ​​for ESPL diagnosis. This solves the problems of discomfort caused to patients by invasive examination methods and misdiagnosis due to inaccurate visual morphological judgment, laying an experimental foundation for further development of ESPL diagnostic detection products. Attached Figure Description

[0017] Figure 1 This is a roadmap for the overall research of this invention.

[0018] Figure 2 The image shows the results of quantitative proteomics analysis of precancerous lesions and adjacent normal tissues of the esophagus.

[0019] Figure 2 In the diagram, A is the protein expression density distribution map, B is the sample protein detection quantity map, and C is the differential protein volcano map.

[0020] Figure 3 The figure shows the results of screening and optimizing the combination model of serum diagnostic markers for esophageal precancerous lesions.

[0021] Figure 3 In the diagram, A is the protein expression density distribution map, B is the differential protein clustering heatmap, C is the Venn diagram of serum-tissue common differential proteins, D is the ROC curve of a single protein, E is the diagnostic efficacy table of the multi-protein combined model, and F is the ROC curve of the optimal combined biomarker.

[0022] Figure 4 The figure shows the verification results of the expression levels of ESPL-related serum protein markers in the cohort.

[0023] Figure 4 In the diagram, A is a scatter plot showing the expression of VWF in the serum of patients with precancerous esophageal lesions and healthy controls; B is a scatter plot showing the expression of FGG in the serum of patients with precancerous esophageal lesions and healthy controls; C is a scatter plot showing the expression of APMAP in the serum of patients with precancerous esophageal lesions and healthy controls; D is a scatter plot showing the expression of CEA in the serum of patients with precancerous esophageal lesions and healthy controls; and E is a scatter plot showing the expression of CSSA in the serum of patients with precancerous esophageal lesions and healthy controls.

[0024] Figure 5 ROC curve analysis of serum protein markers for precancerous esophageal lesions and their diagnostic efficacy in combination with a model.

[0025] Figure 5 In the table, A represents the ROC curve of the training set, B represents the ROC curve of the validation set, and C represents a summary table of diagnostic performance indicators for each cohort. Detailed Implementation

[0026] The present invention will now be described in detail with reference to specific embodiments.

[0027] The overall research process of this invention can be found in [link to research]. Figure 1 .

[0028] I. Sample Collection

[0029] Forty-seven pairs of esophageal precancerous lesion and adjacent normal tissue samples were collected from the Fourth Hospital of Hebei Medical University, and clinical cases were compiled. All tissues were primary and met the following criteria: 1) clearly identified as esophageal squamous intraepithelial neoplasia; 2) good quality of biopsy tissue; 3) consistent with endoscopic characteristics of precancerous lesions; and 4) sufficient clinical and pathological data.

[0030] Blood samples (0.5 mL) were collected from the Fourth Hospital of Hebei Medical University. The blood was collected into a coagulation tube and gently mixed. The tube was then placed upright at room temperature to ensure complete coagulation within 30–60 minutes. The coagulated sample was centrifuged at 2000 × g for 10 minutes at room temperature (18–25 °C) to separate the serum sample. The serum samples were aliquoted and stored at -80 °C or used immediately.

[0031] II. Protein extraction and proteomics analysis from tissues and serum

[0032] (1) Tissue lysis

[0033] 1) Pre-cool the tissue homogenizer and place the tissue sample plate at -80℃ for 30 min.

[0034] 2) Add protease inhibitor PI to PBS.

[0035] 3) Cut the tissue sample into small pieces, add two steel balls, add 500 μl of PBS, homogenize the tissue for 1 min, then add 500 μl of 2×SDT buffer (2% SDS, 0.1 M Tris / HCl, pH 7.6), and homogenize for 1 min.

[0036] 4) Transfer the supernatant after homogenization to an EP tube and boil in a water bath for 3 min.

[0037] 5) 40-50 W ultrasound, 5 seconds of ultrasound, 5 seconds of interval, for a total of 6 cycles.

[0038] 6) Boiling water bath for 2 min.

[0039] 7) Centrifuge at 12000×g for 15 min, collect the supernatant, and measure the protein concentration using the BCA method.

[0040] (2) Enzymatic digestion in ultrafiltration tube (Filter-aided sample preparation, FASP)

[0041] 1) Add 500 μl of ultrapure water to a 10K ultrafiltration tube, centrifuge at 8000×g for 5 min at room temperature, and discard the effluent.

[0042] 2) Add 200 μg of protein sample, centrifuge at 10000×g for 10 min at room temperature, and discard the effluent.

[0043] 3) Add 300 μl UA buffer (8M Urea in 0.1M Tris / HCl) and 15 μl DTT (1M), vortex, and let stand at 37℃ for 30 min.

[0044] 4) Centrifuge at 13000×g for 75 min at room temperature and discard the effluent.

[0045] 5) Add 300 μl UA buffer and 30 μl IAA (0.5 M), vortex, and react in the dark for 30 min.

[0046] 6) Centrifuge at 13000×g for 75 min at room temperature and discard the effluent.

[0047] 7) Replace with 300 μl UA buffer 3 times.

[0048] 8) Replace twice with 300 μl NH4HCO3 (50 mM).

[0049] 9) Replace with a new collection tube, resuspend the protein in 200 μl NH4HCO3, add trypsin (1:100, w / w), and incubate at 37°C with shaking for 18 h.

[0050] 10) Centrifuge at 13000×g for 15 min and collect the effluent. Wash twice with 150 μl of NH4HCO3 and combine the effluents.

[0051] 11) Acidify with 10% TFA to a final concentration of 0.4% to terminate the reaction.

[0052] (3) Desalting peptides at room temperature

[0053] 1) Prepare a C18 desalination column and desalination buffer A (0.2% TFA / H2O) and buffer B (80% ACN / 0.2% TFA).

[0054] 2) Methanol activation: Add 200 μl of methanol to the C18 desalting column, centrifuge at 1200×g for 10 min at room temperature, so that the liquid level of the remaining liquid is just above the C18 column bed, and discard the effluent.

[0055] 3) Recovery state: Add 200 μl buffer B, centrifuge at 4000×g for 2 min, discard the effluent, and repeat once more.

[0056] 4) Equilibration: Add 200 μl of buffer A, centrifuge at 6000×g for 2 min, discard the effluent, and repeat twice.

[0057] 5) Sample loading: Add 160 μl of sample, supplement with buffer A to 200 μl, centrifuge at 2000×g for 5 min, and discard the effluent.

[0058] 6) Washing: Add 200 μl of buffer A, centrifuge at 6000×g for 2 min, discard the effluent, repeat twice, and then replace with a new collection tube.

[0059] 7) Elution: Add 180 μl buffer B, centrifuge at 2000×g for 2 min, collect the eluent, repeat once, and combine the eluents.

[0060] 8) Repeat steps 3) to 7), and finally combine all the effluents and freeze-dry them in a vacuum centrifuge.

[0061] (4) Desalting peptides at room temperature

[0062] Using a high-pH reverse-phase peptide separation kit, follow the steps outlined in the instructions:

[0063] 1) Prepare elution solutions with triethylamine (0.1%) and ACN, with a total of 8 gradients and ACN concentrations of 5.0%, 7.5%, 10.0%, 12.5%, 15.0%, 17.5%, 20.0%, and 50%.

[0064] 2) Determine the peptide concentration by taking 80 μg of peptide and adding 0.1% TFA to make up to 300 μl.

[0065] 3) Place the fractionation column into a 2 ml sample tube, centrifuge at 5000×g for 2 min at room temperature, and discard the eluent.

[0066] 4) Add 300 μl of ACN, centrifuge at 5000×g for 2 min, discard the effluent, and repeat once more.

[0067] 5) Add 0.1% TFA, centrifuge at 5000×g for 2 min, discard the effluent, and repeat once more.

[0068] 6) Place the prepared fractionation column into a new 2 ml sample tube, add 300 μl of sample solution from step 2), centrifuge at 3000×g for 2 min, and label the effluent as F1.

[0069] 7) Place the fractionation column into a new 2 ml sample tube, add 300 μl of ultrapure water, centrifuge at 3000×g for 2 min, and label the effluent as F2.

[0070] 8) Place the fractionation column into a new 2ml sample tube, add the 8 gradient elution solutions from step 1) in sequence, centrifuge at 3000g for 2min, collect the eluents and label them as F3~F10 in sequence.

[0071] 9) Finally, F1~F10 were dispensed into four 600μl sample tubes and placed in a vacuum centrifuge to freeze dry.

[0072] (5) Mass spectrometry detection

[0073] The prepared samples were redissolved in 6 μl of solution A (0.1% formic acid solution), and the peaks were combined according to their composition: F1 and F7, F2 and F8, F3 and F9, and F4 and F10. After combining, each sample contained six components. Detection was performed using a Q-Exactive HF liquid chromatography-mass spectrometer equipped with an Easy-nLC 1200 high-performance liquid chromatography system. First, the sample was loaded onto a 2 cm pre-column (75 μm × 2 cm, nanoViper 2Pk) with solution A, and eluted on a 25 cm analytical column (75 μm × 25 cm, nanoViper) with a 60-min gradient (A: 0.1% formic acid solution, B: 0.1% formic acid / 80% acetonitrile solution) at a constant flow rate of 300 nl / min (0–60 min, 8% B 1 min; 28% B 45 min; 37% B 7 min; 100% B 4 min; 100% B 3 min). The peptide was ionized at 2.3 kV and introduced into the mass spectrometer. Mass spectrometry was performed in data-dependent acquisition mode (DDA). For the MS1 full scan, the Orbitrap mass spectrometer was used at a high resolution of 60,000 Hz, scanning ions in the m / z 350 ~ m / z 2000 range, with the AGC target set to 3E6 and a maximum ion implantation time of 20 ms. MS2 spectral acquisition was performed rapidly in quadrupole mode. Precursor ions were selected and fragmented using high-energy collisional dissociation (HCD) with an NCE of 27%. Fragment ions were analyzed using a quadrupole mass spectrometer with the AGC target set to 1E5, a maximum ion implantation time of 45 ms for MS2, and a dynamic exclusion time of 30 s.

[0074] (6) Protein identification and quantification

[0075] Mass spectrometry identification was performed in DDA mode. The resulting spectra were searched using MaxQuant with the Uniprot human protein database. Search parameters were set as follows: Q-Exactive HF-derived precursor ion massolerances of 20 ppm and daughter ion of 50 mmu; two missed cleavage sites were allowed; fixed modification was carbamidomethyl (C), and dynamic modification was protein acetyl (protein N-term) and oxidation (M); a 1% false positive rate (FDR) was set for peptide levels. Relative protein quantification in the whole-protein identification assay was performed using label-free quantification (LFQ). Protein levels calculated using the 1% FDR card were used for statistical analysis.

[0076] III. Screening differentially expressed proteins as candidate biomarkers for ESPL

[0077] Quantitative proteomics analysis was performed on 47 tissue samples from esophageal precancerous lesions and normal controls. The proteome of esophageal precancerous lesions (ESPL, dark blue) and adjacent normal controls (NE, green) both showed a unimodal distribution, passing the proteomics quality control procedure. Figure 2 A). Furthermore, the number of proteins identified in a single sample was significantly higher in the ESPL group than in the normal control group ( Figure 2 B). Next, we found that 1530 proteins in the ESPL tissue were significantly altered (Fold change > 1.5, FDR < 0.05), of which 1322 proteins were upregulated and 208 proteins were downregulated. Figure 2 C).

[0078] Currently, there are no serum biomarkers for the clinical diagnosis of ESPL. To construct a serum diagnostic model for the ESPL stage, we collected serum samples from 16 healthy controls and 16 ESPL patients. Both the proteomes of healthy controls (blue) and the proteomes of ESPL patients (green) showed a unimodal distribution, passing the proteomics data quality control procedure. Figure 3 A). Based on the criteria of Fold change > 1.5, P < 0.05, 12 proteins that were significantly elevated in the serum of ESPL patients compared with healthy controls were selected. Figure 3 B). Since serum proteins may originate from tumor tissue, protein biomarkers that are simultaneously overexpressed in both serum and tissue samples may better meet the criteria. To further improve the accuracy of diagnostic biomarker screening, 12 proteins elevated in ESPL serum and 1364 proteins elevated in ESPL tissue were integrated, resulting in five proteins: APMAP, FGG, TNC, CA2, and VWF (…). Figure 3 C). Next, we performed ROC curve analysis on these proteins. The results showed that three proteins met the criteria set for area under the curve (AUC) analysis (AUC ≥ 0.75), namely APMAP (AUC: 0.832), FGG (AUC: 0.828), and VWF (AUC: 0.844). Figure 3 Therefore, APMAP, FGG, and VWF may be serum biomarkers for the diagnosis of ESPL. These three proteins can be combined in four ways (D). Figure 3 E), ranked by score, the "APMAP, FGG, VWF" and "APMAP, FGG" models had the highest sensitivity and specificity. Figure 3F). Ultimately, APMAP (amino acid sequence as shown in SEQ ID No. 1), FGG (amino acid sequence as shown in SEQ ID No. 2), and VWF (amino acid sequence as shown in SEQ ID No. 3) were identified as candidate biomarkers for the diagnosis of ESPL.

[0079] IV. Multicenter Validation of Candidate Differential Proteins

[0080] To further validate the diagnostic efficacy of serum diagnostic markers for esophageal squamous cell carcinoma (ESPL), we collected serum samples from 150 ESPL patients and 110 healthy controls at the Hebei Center. The expression levels of APMAP, FGG, VWF, CEA, and SCCA in serum were detected using ELISA assays (all kits were commercially available). We found that, compared with healthy controls, the expression levels of APMAP, FGG, VWF, CEA (amino acid sequence as shown in SEQ ID No. 4), and SCCA (amino acid sequence as shown in SEQ ID No. 5) were significantly increased in ESPL patients at the Hebei Center, consistent with the results of quantitative proteomics. Figure 4 ).

[0081] V. Diagnostic Performance Evaluation of Candidate Differential Proteins

[0082] To investigate the diagnostic value of differentially expressed proteins for ESPL, we subsequently performed ROC curve analysis.

[0083] Figure 5 This demonstrates the best performance of an early diagnostic model constructed by combining candidate differentially expressed proteins with clinically used detection indicators (CEA and SCCA). Figure 5 As shown in Figure A, the area under the curve (AUC) of the five proteins in the training cohort ranged from 0.634 to 0.811, while the AUC of the combined diagnosis was 0.899. We further performed ROC curve analysis on the diagnostic model constructed by combining candidate differentially expressed proteins with CEA and SCCA in the validation cohort. The results showed that the AUC of the diagnostic model combining the five proteins in the validation cohort was 0.9 (…). Figure 5 BC).

[0084] Generally, an AUC ≥ 0.8 is considered strong evidence that a model or biomarker has good diagnostic function. Therefore, a combined diagnostic model of five proteins has better diagnostic performance than any single protein combined diagnostic model and can be used as a biomarker for diagnosing ESPL.

Claims

1. Application of a combination of five serum protein markers, APMAP, FGG, VWF, CEA and SCCA, in the diagnosis of precancerous lesions of the esophagus.

2. The application according to claim 1, characterized in that, The amino acid sequence of the APMAP is shown in SEQ ID No.

1.

3. The application according to claim 1, characterized in that, The amino acid sequence of the FGG is shown in SEQ ID No.

2.

4. The application according to claim 1, characterized in that, The amino acid sequence of the VWF is shown in SEQ ID No.

3.

5. The application according to claim 1, characterized in that, The amino acid sequence of the CEA is shown in SEQ ID No. 4, and the amino acid sequence of the SCCA is shown in SEQ ID No.

5.

6. The application of a combination of five serum protein markers, APMAP, FGG, VWF, CEA and SCCA, in the preparation of ESPL diagnostic reagents or diagnostic kits.

7. The application according to claim 6, characterized in that, The diagnostic reagent is an ELISA diagnostic reagent.

8. The application according to claim 6, characterized in that, The diagnostic kit is an ELISA diagnostic kit.

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