Application of the combination of FGGY, DSG1 and CUSTOS proteins in the prognosis of pancreatic neuroendocrine tumors

Abstract

The application of the combination of FGGY, DSG1 and CUSTOS proteins in the prognosis judgment of pancreatic neuroendocrine tumors belongs to the field of disease prognosis. The present invention first discovered that the abundance of the combination of FGGY, DSG1 and CUSTOS proteins can be used to judge the risk of postoperative recurrence and metastasis of PNETs patients. The prognosis of the high-risk group in the disease-free survival (DFS) curve is significantly worse than that of the low-risk group (P value<0.0001).

Description

Technical Field

[0001] The present invention belongs to the field of proteomics and clinical detection technology, and specifically relates to a marker combination consisting of three proteins, FGGY, DSG1 and CUSTOS, and an application of the marker combination in predicting the risk of recurrence and metastasis in pancreatic neuroendocrine tumors. Background Art

[0002] Pancreatic neuroendocrine tumors (PNETs) are the second most common pancreatic tumor. With the continuous improvement of detection methods in recent years, the detection rate of PNETs has continued to rise. Radical surgical resection is currently the only way to treat PNETs, ​​but many patients still experience in situ recurrence or distant metastasis after surgery, and the prognosis is poor.

[0003] Due to the large heterogeneity of the tumor itself, nearly one-third of PNETs patients who received surgical treatment had in situ recurrence or distant metastasis after surgery. Effective identification of high-risk postoperative recurrence populations can help to intervene as early as possible, provide reliable treatment options, and improve patient prognosis. At present, the identification of high-risk recurrence populations in clinical work focuses on clinical characteristics such as tumor size and grade, but the sensitivity is not high. The recurrence prediction model based on protein molecules has achieved considerable results in other tumors. Therefore, at the molecular level, it is extremely important to find reliable and effective biomarkers to distinguish the risk of postoperative recurrence in PNETs patients, build a postoperative predictive risk assessment model, accurately identify high-risk recurrence populations, and provide guidance for subsequent treatment. In current studies on pNET, it is common to use imaging omics, biomarkers (such as Ki-67, CgA, etc.), genomic data and clinical factors to evaluate prognosis. For example, the combination of imaging omics and other molecular markers is used to improve prognostic models. Summary of the invention

[0004] The purpose of the present invention is to provide a method for predicting the risk of postoperative recurrence and metastasis of PNETs patients.

[0005] The inventors of the present invention screened a combination of protein markers that can be used to judge the risk of postoperative recurrence and metastasis of PNETs patients in tumor tissue samples of PNETs patients through large-scale proteomics technology based on mass spectrometry. The protein marker combination includes the following three proteins, FGGY, DSG1 and CUSTOS. Thus, the technical solution of the present invention was obtained.

[0006] In a first aspect, the present invention provides a mass spectrometry-based system for judging the risk of recurrence and metastasis of PNETs patients after surgery, the system comprising a device for detecting FGGY, DSG1 and CUSTOS proteins; and a data processing device.

[0007] Furthermore, the apparatus for detecting FGGY, DSG1 and CUSTOS proteins includes but is not limited to signal reading apparatuses such as mass spectrometer, spectrophotometer, chemiluminometer, or antibody-based immunohistochemistry and ELISA quantitative apparatuses; the apparatus described can quantify the abundance of target proteins;

[0008] Furthermore, the protein detection device includes a mass spectrometer; preferably, the mass spectrometer is configured to include but not limited to the following modes: data independent acquisition (DIA), data dependent acquisition mode (DDA), mass spectrometry targeted quantification, and the above modes can quantify the abundance of target proteins;

[0009] Furthermore, the data processing device runs a discrimination criterion for predicting the risk of postoperative recurrence and metastasis of PNETs patients. This criterion calculates the relative contents of FGGY, DSG1 and CUSTOS proteins in tumor tissues of PNETs patients based on multivariate Cox regression, and the resulting judgment formula is: Score = -0.2854 × FGGY + 0.1844 × DSG1 + 0.7499 × CUSTOS. When the Score is greater than 3, preferably greater than 3.2, and more preferably greater than 3.286, the risk of postoperative recurrence and metastasis of PNETs patients is high; otherwise, the risk of postoperative recurrence and metastasis of PNETs patients is low; preferably, the relative content is defined as the abundance of FGGY, DSG1 and CUSTOS proteins in a sample measured using mass spectrometry or immunohistochemistry quantitative technology in quantitative proteomics analysis.

[0010] A second aspect of the present invention provides a marker combination, characterized in that the marker combination is FGGY, DSG1 and CUSTOS protein, and the marker combination is used to judge the risk of recurrence and metastasis of PNETs patients after surgery.

[0011] In a third aspect, the present invention provides a use of a substance for specifically detecting the marker combination described in the second aspect in the preparation of a preparation for assessing the risk of postoperative recurrence and metastasis of PNETs patients; characterized in that the substance for specific detection is suitable for mass spectrometry, and the use comprises the following steps:

[0012] S1. Obtain tumor tissue samples from patients with PNETs to be tested;

[0013] S2. Based on mass spectrometry, DIA proteomics method is used to perform qualitative and quantitative detection of proteins in the samples to be tested;

[0014] S3. The quantitative abundance values ​​of proteins were quantile normalized and log2 transformed. After filling the missing values ​​with the minimum value, the relative contents of FGGY, DSG1 and CUSTOS proteins were extracted and calculated using the following formula: Score = -0.2854 × FGGY + 0.1844 × DSG1 + 0.7499 × CUSTOS.

[0015] In a fourth aspect, the present invention provides use of a device for detecting FGGY, DSG1 and CUSTOS proteins in the preparation of a system for distinguishing the risk of postoperative recurrence and metastasis of PNETs patients.

[0016] Furthermore, the device for detecting FGGY, DSG1 and CUSTOS proteins is a device capable of qualitatively and quantitatively detecting FGGY, DSG1 and CUSTOS proteins in tumor tissue samples from PNETs patients, such as signal reading devices such as mass spectrometers, spectrophotometers, chemiluminometers, or antibody quantitative devices such as immunohistochemistry and ELISA.

[0017] The beneficial effects of the present invention include: through the above technical scheme, when the present invention judges the risk of postoperative recurrence and metastasis of PNETs patients, the prognosis in the disease-free survival (DFS) curve of the high-risk group is significantly worse than that of the low-risk group (P value < 0.0001). When the combination of FGGY, DSG1 and CUSTOS markers indicates a high risk, the median disease-free survival time of PNETs patients is 36 months; when the combination of FGGY, DSG1 and CUSTOS markers indicates a low risk, the median disease-free survival time of PNETs patients is 51.5 months.

[0018] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Kaplan–Meier curve of disease-free survival of the combined markers of FGGY, DSG1 and CUSTOS in patients with PNETs. DETAILED DESCRIPTION

[0020] The following is a further description of the concept of the present invention and the technical effects produced in conjunction with specific embodiments, so as to fully understand the purpose, features and effects of the present invention. The methods are conventional methods unless otherwise specified. The materials can be obtained from public commercial channels unless otherwise specified. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations of the present invention. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0021] Example 1 DIA non-label quantitative technology to detect the proteome of tumor tissues of PNETs patients

[0022] 1. Experimental Materials

[0023] The samples were from surgically removed tumor tissues of 140 PNETs patients, provided by the General Hospital of the Chinese People's Liberation Army.

[0024] 2. Experimental Methods

[0025] 1. Protein extraction: Formalin-fixed and paraffin-embedded samples (FFPE samples) of tumor tissue were dewaxed in xylene for 15 minutes, and then hydrated in 100%, 85%, 70%, and 50% ethanol for 5 minutes. After sampling, they were heated and lysed in 1% SDS extract for 30 minutes. After ultrasound, they were centrifuged at 16,000 g for 10 minutes, and the supernatant was taken for protein enzymatic hydrolysis.

[0026] 2. Protein enzymatic hydrolysis: Take 200μg of protein into a 30 kDa ultrafiltration tube and centrifuge at 14000g for 20 min. Add 200μl UA buffer (8 M urea, 100 mM Tris-Cl, pH 8.5) and centrifuge at 14000g for 15 min. Add 100μl 20 mM DTT, incubate at 37℃ for 4 h, and centrifuge at 14000g for 15 min. Add 100μl 50mM IAA, incubate in the dark for 30 min, and centrifuge at 14000g for 15 min. Add 100μl 20 mM DTT, incubate in the dark for 15 min, and centrifuge at 14000g for 15 min. After replacing the buffer with 50mM ammonium bicarbonate, add trypsin (Promega, 1 / 50 of the protein amount) and react for 12 hours. Centrifuge at 14000g for 15 min to collect peptides.

[0027] 3. Mass spectrometry data acquisition: The samples were analyzed by mass spectrometry in DIA mode using a timsTOF Pro mass spectrometer (Thermo Fisher Scientific) and a nano-HPLC chromatograph (UltiMate 3000 LC). The LC-MS parameters were as follows: mobile phase A was an aqueous solution of 0.1% formic acid, mobile phase B was an aqueous solution of 80% acetonitrile and 0.1% formic acid, the liquid phase gradient was 80 min, and the flow rate was 300 nl / min. The mass spectrometer was in diaPASEF scanning mode (m / z 300–1500), and the parent ions between 400 and 1200 m / z were characterized using 4 ion mobility steps in 16 PASEF scans. The isolation window for each step was 25 Th, and there was a 1 Da overlap between adjacent windows. In addition, 8 samples were collected in DDA mode to establish a reference library.

[0028] 4. Mass spectrometry data analysis: The spectra of the eight samples were compiled into a spectral library using Spectronaut (version 16.1, Biognosys). The FDR threshold was set to less than 0.01. The DIA raw data files were then integrated into Spectronaut to output protein identification and quantification results, with protein and peptide FDR less than 0.01.

[0029] The obtained protein quantification results were quantile normalized and log2 transformed. After the missing values ​​were filled with the minimum value, the relative quantitative values ​​of FGGY, DSG1 and CUSTOS proteins were extracted. The results are shown in Table 1.

[0030] Table 1. Relative abundance and prognostic information of FGGY, DSG1 and CUSTOS proteins in tumor tissues of PNETs patients

[0031]

[0032] The prognosis of PNETs patients was analyzed. The patients were divided into high-risk group and low-risk group according to the median score (3.286) of the combined markers FGGY, DSG1 and CUSTOS protein in the tumor tissue of PNETs patients. The Kaplan–Meier curve analysis of disease-free survival (DFS) was performed using GraphPadPrism 6 software, and the P value was calculated by log-rank test (e.g. Figure 1 The results showed that the disease-free survival time of the high-risk group scored by the combined markers was significantly shorter than that of the low-risk group (median disease-free survival time was 36 months vs. 51.5 months, P < 0.0001). The five-year recurrence rate of the high-risk group predicted by the combined markers was 31.4%, and that of the low-risk group was only 7.1%. Cox multivariate regression analysis of disease-free survival time found that the combined markers were independent predictors of disease-free survival in PNETs patients.

[0033] Table 2. Cox regression analysis of the relationship between clinical indicators and disease-free survival time of PNETs patients

[0034]

[0035] The above results indicate that by detecting the relative contents of FGGY, DSG1 and CUSTOS proteins in the tumor tissues of the PNETs patient group to be tested, the risk of postoperative recurrence and metastasis of PNETs patients can be evaluated or predicted. The postoperative disease-free survival time of the high-risk group of patients with combined marker scores is significantly shorter than that of the low-risk group of patients.

[0036] The high-risk group of combined markers has a score higher than the median value of the combined marker score of the PNETs patient group to be tested; the low-risk group of combined markers has a score not higher than the median value of the combined marker score of the PNETs patient group to be tested.

[0037] In the population of this embodiment, the median value of the combined marker score in the tumor tissues of 140 PNETs patients to be tested was 3.286.

[0038] The above embodiments specifically describe the present invention and are not intended to limit the present invention. Without departing from the principle of the present invention, modifications and alterations to the present invention should all be included in the protection scope of the present invention.

Claims

1. A system for judging the risk of recurrence and metastasis of PNETs patients after surgery, characterized in that: The system includes a device for detecting FGGY, DSG1 and CUSTOS proteins, and a data processing device; The device for detecting FGGY, DSG1 and CUSTOS proteins is a mass spectrometer; the mass spectrometer is configured in a data independent acquisition (DIA) mode, and the above mode can quantify the abundance of target proteins; The data processing device runs a criterion for predicting the risk of postoperative recurrence and metastasis of PNETs patients. This criterion calculates the relative contents of FGGY, DSG1 and CUSTOS proteins in tumor tissues of PNETs patients based on multivariate Cox regression, and the resulting judgment formula is: Score = -0.2854 × FGGY + 0.1844 × DSG1 + 0.7499 × CUSTOS. When Score>3, the risk of postoperative recurrence and metastasis of PNETs patients is high; otherwise, the risk of postoperative recurrence and metastasis of PNETs patients is low.

2. The system according to claim 1, characterized in that The relative content is defined as the abundance of FGGY, DSG1 and CUSTOS proteins in the sample measured using mass spectrometry-based quantitative technology in quantitative proteomics analysis.

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