Non-functional pancreatic neuroendocrine tumor prognostic marker and application thereof

Abstract

The invention provides a non-functional pancreatic neuroendocrine tumor prognostic marker and application thereof. The prognostic marker is one or more of GNAO1, INA and VCAN. Wherein the high expression of GNAO1 and INA is related to good prognosis, and the high expression of VCAN is related to poor prognosis. Three prognostic related proteins are combined into a multivariable prognostic marker, and the multivariable prognostic marker has excellent performance in the aspect of distinguishing high-risk patients from low-risk patients and has a good calibration effect in the aspect of estimating absolute risks, and the prediction error rate is low.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and particularly relates to a prognostic marker for non-functional pancreatic neuroendocrine tumors and an application thereof. Background Art

[0002] Pancreatic neuroendocrine tumors (PanNENs) are the second most common epithelial tumor of the pancreas, with a significant increase in incidence over the past four decades. According to the World Health Organization (WHO) classification, PNEs can be divided into well-differentiated PNEs (PanNETs) and poorly differentiated PNECs (PanNECs). PanNETs account for over 90% of all PNEs and are further divided into functional PNEs and nonfunctional PNEs (NF-PanNETs) based on the presence of a clinical hormone hypersecretion syndrome. NF-PanNETs account for approximately 90% of PNEs and exhibit a highly variable clinical presentation. Although most NF-PanNETs are clinically indolent, approximately half of patients have distant metastases (mostly liver metastases) at the time of diagnosis, resulting in a median survival of only 23 months, compared to 124 months for patients with localized disease. In clinical practice, for high-risk PanNETs patients, imaging surveillance is recommended every 3 to 6 months after radical resection, and adjuvant therapy can be considered within the framework of a clinical trial. However, there is currently no consensus on the optimal guidelines, and overtreatment of NF-PanNETs has been controversial in recent years. Some patients may be more suitable for an observational treatment strategy. Therefore, it is particularly urgent to develop a prognostic marker that can accurately risk stratify NF-PanNETs patients, thereby optimizing patient treatment options and disease management.

[0003] Currently, the prognosis prediction of NF-PanNETs mainly relies on clinicopathological variables, such as tumor size, WHO Grade classification, and lymph node metastasis status (N stage), and these clinical prognostic factors have obvious limitations in predictive performance and application. According to the guidelines of the National Comprehensive Cancer Network (NCCN), the European Neuroendocrine Tumor Society (ENETS), and the North American Neuroendocrine Tumor Society (NANETS), surgical resection is recommended for NF-PanNETs with a diameter greater than 2.0 cm, and efforts are made to achieve negative resection margins and regional lymph node dissection. For NF-PanNETs with a diameter of ≤2.0 cm, since they usually do not metastasize, selective surgical resection or observation alone can be considered. However, some NF-PanNETs with a diameter of ≤2.0 cm may still show invasiveness, and even some tumors classified as indolent may metastasize.

[0004] In addition, a small number of studies have begun exploring markers based on traditional molecular detection techniques (such as fluorescence in situ hybridization and immunohistochemistry), including alternative telomere elongation pathway (ALT) status, DAXX / ATRX expression levels, and ARX / PDX1 expression levels. However, published studies have shown inconsistent conclusions. For example, Jiao et al. found that patients with DAXX / ATRX loss of expression had improved overall survival compared with patients with wild-type NF-PanNETs; however, other studies have shown that DAXX / ATRX loss of expression is associated with tumor metastasis, shorter disease-free survival (DFS), and shorter disease-specific survival (DSS). This inconsistency may be due to differences in study populations, small sample sizes, and limitations of single-center studies. The performance of these potential biomarkers has not been fully evaluated, and validation in larger internal and multicenter external cohorts is lacking. Therefore, there is an urgent need to develop higher-resolution biomarkers that combine multi-omics molecular data to improve the prognostic classification and management of NF-PanNETs patients.

[0005] With the rapid development of high-throughput technologies such as second-generation sequencing and high-resolution mass spectrometry, multi-omics studies such as whole-genome, transcriptomics, and proteomics have been widely carried out in the field of oncology and are being applied to the development of molecular markers such as prognostic markers for cancer patients (such as disease recurrence, progression, and death). In addition, studies have found that compared with variations at the gene expression level, dysregulation of protein expression and its post-translational modification can better reflect changes in tumors at the pathological and physiological levels. This is of great significance for the development of molecular markers that are closer to the clinical manifestations of tumors.

[0006] Based on high-precision protein spectrometry data from a large clinical cohort of 108 NF-PanNETs patients, the present invention used the in-house developed ReProMSig analysis platform to develop a three-protein prognostic marker that can effectively distinguish high-risk from low-risk NF-PanNETs patients. The prognostic value of the marker and its constituent proteins was extensively validated in multiple internal and external independent cohorts (a total of 500 samples). In addition, using pancreatic cancer cell lines and cell line-derived xenograft (CDX) models, the effects of each marker protein on tumor cell proliferation were further clarified in vitro and in vivo. Summary of the Invention

[0007] In order to overcome the problems of the existing technology, the present invention aims to clarify high-resolution biomarkers related to the prognosis of NF-PanNETs and provide prompts to people at high risk of NF-PanNETs (poor prognosis), thereby proposing prognostic markers for non-functional pancreatic neuroendocrine tumors and their applications.

[0008] The object of the present invention is achieved like this:

[0009] A first aspect of the present invention provides a prognostic marker for non-functional pancreatic neuroendocrine tumors, wherein the prognostic marker is one or more of GNAO1, INA and VCAN.

[0010] Furthermore, the prognostic marker is RNA, or its reverse transcribed cDNA, or protein.

[0011] Furthermore, high expression of GNAO1 and INA was associated with a good prognosis, whereas high expression of VCAN was associated with a poor prognosis.

[0012] Furthermore, three prognosis-related proteins: GNAO1, INA, and VCAN were combined into a multivariate prognostic marker to calculate the prognostic risk score to determine the patient's prognosis:

[0013] A second aspect of the present invention provides use of a reagent for detecting a prognostic marker in the preparation of a product for evaluating the prognosis of non-functional pancreatic neuroendocrine tumors, wherein the prognostic marker is one or more of GNAO1, INA and VCAN.

[0014] Furthermore, the sample to be tested is a non-functional pancreatic neuroendocrine tumor tissue sample, and the reagent is any one of protein profiling, transcriptome sequencing, gene chip, NanoString, PCR, and immunohistochemistry related reagents.

[0015] A third aspect of the present invention provides a kit for assessing the prognosis risk of non-functional pancreatic neuroendocrine tumors, wherein the kit comprises a quantitative reagent for quantitatively detecting the expression amount of the prognostic marker as described in the first aspect.

[0016] The fourth aspect of the present invention provides the use of the prognostic marker described in the first aspect in risk stratification and precision treatment research for patients with non-functional pancreatic neuroendocrine tumors for non-therapeutic purposes.

[0017] A fifth aspect of the present invention provides a prognostic risk analysis system for patients with non-functional pancreatic neuroendocrine tumors using the prognostic markers described in the first aspect, the system comprising:

[0018] Target expression detection device: used to detect the expression level of the prognostic marker described in the first aspect in a sample; the detection sample is a patient's tumor tissue;

[0019] Prognostic risk analysis device: Based on the expression level of prognostic markers, the prognostic risk score is calculated to determine the patient's prognosis:

[0020] Result output device: used to output the results obtained by the prognostic risk analysis device, wherein the prognostic risk analysis includes predicting the possibility of adverse events (such as tumor recurrence and death, etc.) occurring in the patient.

[0021] In certain embodiments, when the data type reflecting the expression level of the marker is mass spectrometry (MS) quantitative data, the prognostic risk score is calculated using the following formula:

[0022] Risk score = -0.504*GNAO1-0.499*INA+0.32*VCAN.

[0023] In certain specific embodiments, the data types of marker applications also include but are not limited to: quantitative immunohistochemistry (IHC) marker data of patients.

[0024] A sixth aspect of the present invention provides use of a reagent for detecting VCAN (Versican) in the preparation of an early diagnostic reagent for non-functional pancreatic neuroendocrine tumors.

[0025] The advantages and beneficial effects of the present invention are:

[0026] 1. The present invention has for the first time identified GNAO1, INA, and VCAN, as prognostic markers in combination with the three, as being closely related to the prognosis of NF-PanNETs. This marker can be used to predict the prognostic risk of NF-PanNETs, that is, to predict the possible outcome information of cancer patients (e.g., cancer recurrence, progression, death, etc.). By performing mass spectrometry quantitative analysis on tumor tissues of NF-PanNETs patients, or detecting the expression of related molecules through immunohistochemistry, RT-qPCR, and whole transcriptome sequencing (RNA-seq), NF-PanNETs patients can be quantitatively risk stratified. The test results show that the prognostic markers of the present invention perform well in distinguishing high-risk patients from low-risk patients (5-year AUROC reaches 0.881), and show good calibration effects in estimating absolute risk, with a low prediction error rate. The test results can be used to optimize the patient's treatment plan selection, assist in clinical treatment decision-making, and design clinical trials.

[0027] 2. The present invention has determined that the secretory protein VCAN can be used as a non-invasive biomarker for the early screening of NF-PanNETs. By performing enzyme-linked immunosorbent assay (ELISA) on the peripheral blood of NF-PanNETs patients, the plasma level of VCAN protein can be detected, and the occurrence of NF-PanNETs can be assessed based on the plasma level of VCAN. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be further described below with reference to the accompanying drawings and examples.

[0029] Figure 1 Schematic diagram of the development and validation process of the NF-PanNETs prognostic marker of the present invention;

[0030] Figure 2 The figure shows the correlation between the expression levels of the three marker proteins and the risk scores predicted by the markers. Figure A shows the relative abundance of the marker proteins and the predicted risk scores for each sample in the discovery cohort, with the color depth proportional to the protein abundance. Figure B shows the immunohistochemistry (IHC) staining images of the marker proteins in representative patients in the high-risk and low-risk groups.

[0031] Figure 3 is the predictive performance evaluation result of the prognostic marker; A is the independence test of the marker in the discovery cohort; B is the independence test in the validation cohort; C is the discriminatory ability evaluation of the marker and its protein to distinguish high-risk and low-risk patients;

[0032] Figure 4 Comparative results of the predictive performance between prognostic markers and multiple clinicopathological variables related to the prognosis of NF-PanNETs, including time-dependent ROC curves at 1, 3, and 5 years;

[0033] Figure 5 Figure 3. Kaplan-Meier survival curves of high-risk and low-risk patient groups predicted by prognostic markers in the discovery cohort and validation cohort, reflecting the difference in survival probability between the two groups. A is the discovery cohort, and B is the validation cohort (DIA-MS).

[0034] Figure 6 To determine the difference in survival probability between high-risk and low-risk patient groups predicted by the prognostic markers in other validation cohorts (two internal TMA validation cohorts and one external validation cohort);

[0035] Figure 7 The difference in survival probability between the high and low expression groups of each marker protein in the discovery cohort and multiple internal and external validation cohorts;

[0036] Figure 8 Using pancreatic cancer cell lines and cell line-derived xenograft tumor CDX models, we validated the effects of various marker proteins on tumor cell proliferation in vitro and in vivo. Figure A represents the cell line experiment, and Figure B represents the CDX model experiment.

[0037] Figure 9is the expression level of the marker protein VCAN in the plasma of normal people and tumor patients, where A is the difference in VCAN plasma expression level between healthy people and tumor patients, B is the difference in VCAN plasma expression level between healthy people and stage I tumor patients, and C is the correlation between VCAN plasma expression level and VCAN immunohistochemical (IHC) marker expression in the corresponding tumor tissue. DETAILED DESCRIPTION

[0038] The examples are provided to better illustrate the present invention, but are not intended to limit the present invention to the examples. Therefore, non-essential improvements and adjustments to the embodiments made by those skilled in the art based on the above-mentioned invention still fall within the scope of protection of the present invention.

[0039] The endpoints of the ranges and any values disclosed herein are not limited to the precise ranges or values, and these ranges or values should be understood to include values close to these ranges or values. For numerical ranges, the endpoints of each range, the endpoints of each range and individual point values, and the individual point values can be combined with each other to obtain one or more new numerical ranges, which should be considered to be specifically disclosed herein.

[0040] The present invention will be described in detail below through examples. It should be understood that the following examples are only used to further explain and illustrate the contents of the present invention in detail, and are not intended to limit the present invention. Example 1: Screening and determination of NF-PanNETs prognostic markers

[0041] In this example, 144 pancreatic neuroendocrine tumor samples and their paired normal adjacent tissues (NATs) were collected. After excluding patients with neuroendocrine carcinoma or functional pancreatic neuroendocrine tumors, and patients who had received neoadjuvant therapy or lacked imaging data during follow-up, 108 untreated NF-PanNETs cases were finally included as the discovery cohort. The discovery cohort and the corresponding normal tissues were subjected to TMT-labeled whole-proteome mass spectrometry analysis. In addition, 51 NF-PanNETs cases were used as an independent validation cohort, and their tumor samples were analyzed by data-independent acquisition mass spectrometry (DIA-MS). The protein spectrum data of the discovery cohort and validation cohort were used to develop and validate the multi-protein markers provided by the present invention.

[0042] In the discovery cohort, this example identified 150 proteins that were significantly upregulated in NF-PanNETs tumor samples (compared to normal tissues from the same patients), including: ABCB11, ACTL6B, ADAMTSL2, ANKS4B, ARMCX4, ATP1A3, ATP6AP2, ATP9A, BASP1, BRAWNIN, BSN, CA11, CACNA2D2, CACNB2, CAMK2B, CD200, CDYL2, CELF3, CELF4, CELF5, CELF6, CERS6, CGREF1, CHGA, CHGB, CKB, CKMT1A, CLGN, COL22A1, C OL8A2,CPE,CPLX1,CPLX2,CRIP2,CRMP1,CRYBA2,CTHRC1,DYNC1I1,EEF1A2,ELAVL2,ELOVL5,ENO2,ENPP2,FAM155A,FAM169A,FBLL1,FIBIN,GDI1,GN AO1,GNAZ,GNG4,GPRIN1,GREM2,GRIA2,HAPLN1,HDGFL3,HMGB3,HMGN5,IGFBP3,IGFBP7,INA,KHDRBS2,KIF5C,KNDC1,LGALS3BP,LY6H,MAP1B,MAPK8IP 1,MARCKSL1,MARK1,MDK,MT3,MUC13,NAPB,NCAM1,NOVA1,OCC1,P4HTM,PAM,PCP4,PCSK1,PCSK1N,PFKP,PKIA,POSTN,PPP1R14C,PRRT1B,PTGDR2,PTM S,PTS,PVR,QPCT,RAB39A,RAB3A,RAB3B,RAB3C,RAI2,RAPGEF5,REEP2,RHBDD2,RIC3,RTN1,RUNDC3A,SCAMP5,SCD,SCG2,SCG3,SCGN,SDHAF4,SEPTIN3 ,SERPINE2,SFRP4,SH3GL2,SLC12A5,SLC7A14,SLC7A8,SMOC1,SNAP25,SNAP91,SOGA3,SRSF12,SSBP4,SSTR2,STMN3,STX1A,STXBP1,SV2A,SVOP,SYN 1,SYP,SYT5,SYT7,TCEAL2,TCEAL3,TCEAL5,TFF3,TM4SF4,TM4SF5,TMEM164,TMEM176B,TNC,TOX,TPPP3,UCHL1,UCN3,VCAN,VGF,VSTM2L,WNT4,XKR7.

[0043] like Figure 1 As shown, this example utilizes bioinformatics analysis and the ReProMSig platform to develop and evaluate multi-protein prognostic markers. ReProMSig uses a bootstrap strategy to screen robust proteins significantly associated with prognosis from the 150 NF-PanNETs upregulated proteins mentioned above. This strategy aims to correct for predictor selection bias and includes the following steps:

[0044] (i) The training set (i.e., discovery cohort) was bootstrapped 200 times to create multiple protein quantification matrices;

[0045] (ii) Supervised principal component analysis (SPCA) was performed on each matrix to identify a set of important predictors;

[0046] Finally, it was found that GNAO1, INA, and VCAN were selected by more than 75% of the models and were therefore determined to be the most robust predictors associated with prognosis. They were included in the final LASSO-penalized Cox regression model to obtain the NF-PanNETs prognostic markers of the present invention - GNAO1, INA, and VCAN. Figure 2 As shown in the results, high expression of GNAO1 and INA was associated with a good prognosis, while high expression of VCAN was associated with a poor prognosis.

[0047] Taking protein spectrometry data as an example, the three prognosis-related proteins mentioned above are combined into a multivariate prognostic marker. The risk score is calculated as follows: Risk score = -0.504*GNAO1 - 0.499*INA + 0.32*VCAN. By substituting the mass spectrometry quantitative values of these three proteins into the above formula, the patient's risk score can be calculated.

[0048] Example 2: Evaluation of the predictive performance of NF-PanNETs prognostic markers

[0049] In order to further improve the comprehensiveness of the prognostic marker evaluation, this example includes multiple cohorts to evaluate the predictive performance of the prognostic markers screened in Example 1: including an internal independent validation cohort (DIA-MS, containing 51 tumors); two tissue microarray (TMA) data sets (containing 76 and 98 tumors, respectively, IHC marker data); an internal independent cohort (containing 108 tumors and 79 healthy controls, ELISA test data); and an external validation cohort from Shanghai Changhai Hospital (containing 88 tumors). In the discovery cohort and the DIA-MS internal independent validation cohort, multivariate Cox regression analysis found that the multivariate prognostic marker composed of GNAO1, INA and VCAN was an independent prognostic risk factor ( Figure 3Its prognostic value was not related to common clinicopathological risk factors and DAXX / ATRX protein expression abundance.

[0050] By time-dependent ROC analysis, in the discovery cohort ( Figure 3 (C) It was found that the multivariate prognostic marker described in Example 1 performed well in distinguishing high-risk patients from low-risk patients (its 5-year AUROC reached 0.881), and showed good calibration effect in estimating absolute risk with a low prediction error rate.

[0051] In addition, this example directly compares the performance of the NF-PanNETs prognostic marker of the present invention and key clinical pathological risk factors (grade, tumor size, TNM stage and nerve invasion) in terms of discrimination ability (i.e., sensitivity and false positive rate). The higher the area under the ROC curve (AUROC), the more accurate the prognostic model. The ROC analysis results showed that the prognostic prediction performance of the NF-PanNETs marker of the present invention was better than all the above-mentioned clinical pathological variables, which was reflected in its prediction of 1-year, 3-year and 5-year survival probabilities, i.e., it had the highest AUROC value (see Figure 4 ; 1-year: 0.846; 3-year 0.844; 5-year 0.881).

[0052] At the same time, in the discovery queue ( Figure 5 Middle A) and DIA-MS internal independent validation cohort ( Figure 5 In Figure B), the multivariate prognostic markers described in Example 1 showed significant survival differences between the high-risk and low-risk groups, with 5-year survival probabilities of 51.4% and 97.8%, respectively. The prognostic value of this multivariate prognostic marker and its marker protein was also validated in other validation cohorts ( Figure 6 and Figure 7 For patients with high risk predicted by the multivariate prognostic markers, regular postoperative CT follow-up monitoring may be recommended to monitor for recurrence. The effects of each marker protein on tumor cell proliferation were verified in vitro and in vivo using pancreatic cancer cell lines and cell line-derived CDX models ( Figure 8 The expression level of secretory protein VCAN in the plasma of normal subjects and tumor patients was determined by ELISA technology, and it was determined that VCAN can be used as a non-invasive biomarker for early screening of NF-PanNETs ( Figure 9 ).

[0053] Finally, it should be noted that the above is only used to illustrate the technical solution of the present invention and is not limiting. Although the present invention is described in detail with reference to the preferred arrangement scheme, ordinary technicians in this field should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A prognostic marker for non-functional pancreatic neuroendocrine tumors, characterized in that: The prognostic marker is one or more of GNAO1, INA and VCAN.

2. The prognostic marker according to claim 1, characterized in that The prognostic marker is RNA, or its reverse transcribed cDNA, or protein.

3. The prognostic marker according to claim 1 or 2, characterized in that High expression of GNAO1 and INA was associated with a good prognosis, whereas high expression of VCAN was associated with a poor prognosis.

4. Use of a reagent for detecting a prognostic marker in the preparation of a product for evaluating the prognosis of non-functional pancreatic neuroendocrine tumors, characterized in that: The prognostic marker is one or more of GNAO1, INA and VCAN.

5. The use according to claim 4, characterized in that The sample to be tested is a non-functional pancreatic neuroendocrine tumor tissue sample, and the reagent is any one of protein spectrum, transcriptome sequencing, gene chip, NanoString, PCR, and immunohistochemistry related reagents.

6. A prognostic risk assessment kit for non-functional pancreatic neuroendocrine tumors, characterized in that: The kit comprises a quantitative reagent for quantitatively detecting the expression amount of the prognostic marker according to claim 1 or 2.

7. Use of the prognostic marker according to claim 1 or 2 in risk stratification and precision treatment research for patients with non-functional pancreatic neuroendocrine tumors for non-therapeutic purposes.

8. A prognostic risk analysis system for patients with non-functional pancreatic neuroendocrine tumors using the prognostic markers according to claim 1 or 2, characterized in that: The system comprises: Target expression detection device: used to detect the expression level of the prognostic marker according to claim 1 or 2 in a sample; the detection sample is a patient's tumor tissue; Prognostic risk analysis device: Based on the expression level of prognostic markers, the prognostic risk score is calculated to determine the patient's prognosis: Result output device: used to output the results obtained by the prognostic risk analysis device.

9. Use of a reagent for detecting VCAN in the preparation of an early diagnostic reagent for non-functional pancreatic neuroendocrine tumors.

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