Group of urine protein markers and application thereof in preparation of non-small cell lung cancer prognosis prediction products

Abstract

The invention belongs to the technical field of biology, and particularly relates to a group of urine protein markers and application thereof in preparation of non-small cell lung cancer prognosis prediction products. A group of urine proteins including AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein which are closely related to non-small cell lung cancer postoperative progress are screened out from urine of non-small cell lung cancer patients for the first time; a prognosis prediction model is constructed based on the six kinds of urine proteins, the constructed prognosis prediction model is verified to have high sensitivity and specificity on prognosis prediction of non-small cell lung cancer postoperative patients, the sensitivity and the specificity respectively reach 84.5% and 81.2%, and the prognosis prediction model has a good prognosis prediction effect on non-small cell lung cancer. Reliable guidance is provided for clinical targeted treatment, and important significance is achieved for improving prognosis survival conditions of non-small cell lung cancer patients.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and relates to a group of urinary protein markers and their application in the preparation of products for predicting the prognosis of lung cancer. More specifically, it relates to the application of this group of urinary protein markers in the preparation of products for predicting the prognosis of non-small cell lung cancer. Background Art

[0002] Non-small cell lung cancer is one of the common malignant tumors. The early symptoms of non-small cell lung cancer, such as cough, chest pain, persistent low fever, hoarseness, chest tightness and shortness of breath, are often mistaken by most people as caused by recent fatigue, and it is very difficult to associate these symptoms with non-small cell lung cancer. Although the current diagnostic and treatment level of non-small cell lung cancer has been greatly improved, most non-small cell lung cancer patients are already in the advanced stage of non-small cell lung cancer when they seek medical treatment, which greatly affects the prognosis of non-small cell lung cancer patients. At present, the 5-year survival rate of early non-small cell lung cancer patients after radical surgical treatment can reach more than 80%. However, for patients with middle and advanced non-small cell lung cancer, especially advanced patients, the 5-year survival rate is less than 5%. The prognosis and survival status of patients with middle and advanced non-small cell lung cancer are poor, and patients generally have a high postoperative drug resistance or recurrence and metastasis, etc., further leading to poor prognosis of advanced non-small cell lung cancer patients.

[0003] The prognosis grading of non-small cell lung cancer mainly takes the pathological grading of CT-guided percutaneous lung biopsy as the "gold standard", which has the risk of postoperative bleeding or concurrent pneumothorax, and the acceptability of patients is relatively low. At present, the sensitivity or specificity of the existing prognostic-related molecular markers is still relatively low, and the prognosis prediction of patients is not accurate enough, and they have not been clinically accepted and applied. The prognosis prediction of non-small cell lung cancer patients is still in the preliminary exploration stage. Therefore, exploring and establishing a prognostic prediction index with high sensitivity, strong specificity and high patient acceptability to accurately predict the prognosis of non-small cell lung cancer patients, as a basis for guiding treatment and judging prognosis, guiding clinical individualized treatment, and avoiding the situation of over-treatment or under-treatment, is of great significance for clinical treatment, follow-up and then improving the prognosis and survival situation of non-small cell lung cancer patients. Summary of the Invention

[0004] Aiming at the problems existing in the prior art, the present invention provides a group of urinary protein markers and their application in the preparation of products for predicting the prognosis of non-small cell lung cancer. The present invention first screens out urinary proteins closely related to the postoperative progression of non-small cell lung cancer from the urine of patients, and constructs a prognostic prediction model based on this. The constructed prognostic prediction model has been verified to have high sensitivity and specificity for the prognosis prediction of postoperative patients, providing strong guidance for the targeted treatment and prognostic efficacy evaluation of non-small cell lung cancer at different progression stages.

[0005] Based on the above purposes, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, the present invention provides the use of a set of urinary protein markers in the preparation of a prognostic prediction reagent for non-small cell lung cancer, wherein the urinary protein markers are a combination of AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein.

[0007] Preferably, the prognostic prediction reagent for non-small cell lung cancer is a molecular combination for detecting the expression of AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein.

[0008] Preferably, the molecular combination consists of an antibody specifically binding to AVIL protein, an antibody specifically binding to CXCR5 protein, an antibody specifically binding to ENPEP protein, an antibody specifically binding to DPEP1 protein, an antibody specifically binding to SNC66 protein and an antibody specifically binding to XPNPEP2 protein.

[0009] In a second aspect, the present invention provides an evaluation model for prognostic prediction of non-small cell lung cancer, wherein the evaluation model includes the above-mentioned urinary protein markers; the evaluation model calculates the risk score using the following regression equation:

[0010] Risk score Y = 0.215×X1 + 0.379×X2 + 0.294×X3 + 0.238×X4 + 0.257×X5 + 0.273×X6, where X1 is the expression level of AVIL protein; X2 is the expression level of CXCR5 protein; X3 is the expression level of ENPEP protein; X4 is the expression level of DPEP1 protein; X5 is the expression level of SNC66 protein; X6 is the expression level of XPNPEP2 protein;

[0011] When Y≥19.35, it is a high risk, and when Y<19.35, it is a low risk.

[0012] In a third aspect, the present invention provides a device or system for prognostic prediction of non-small cell lung cancer, including:

[0013] (1) A data acquisition module for acquiring the expression levels of the above-mentioned urinary protein markers in the urine sample of the subject to be tested;

[0014] (2) A prediction module for providing the expression levels of the urinary protein markers obtained by the data acquisition module as input data to a trained prediction model;

[0015] (3) A prediction result acquisition module for acquiring the output result of the prediction model in the above-mentioned prediction module to obtain the prognostic prediction result of the subject.

[0016] Preferably, the prediction model in the above-mentioned device or system for predicting the prognosis of non-small cell lung cancer is the aforementioned evaluation model.

[0017] Fourthly, the present invention provides a kit for predicting the prognosis of non-small cell lung cancer, including the aforementioned reagent for predicting the prognosis of non-small cell lung cancer; the evaluation model used by the kit for predicting the prognosis of non-small cell lung cancer patients based on the detected expression level of urinary protein markers is as shown above.

[0018] Preferably, the detection object of the kit for predicting the prognosis of non-small cell lung cancer is the urine of the subject.

[0019] Preferably, the urine of the subject needs to be pretreated by adsorption on a nitrocellulose membrane and eluted and precipitated before being detected by the kit.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0021] The present invention uses urine as a sample for collecting the prognosis prediction of non-small cell lung cancer, which is non-invasive. As a non-invasive sampling method, the acceptance of patients is relatively high.

[0022] The present invention firstly proposes a group of potentially feasible non-invasive biomarkers for predicting the prognosis of non-small cell lung cancer: AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein; it is found through experiments that compared with healthy people, the contents of these six urinary proteins are significantly increased in the urine of non-small cell lung cancer patients. At the same time, through analysis in combination with the progression of non-small cell lung cancer, it is found that the contents of these six urinary proteins are also closely related to the advanced stage of non-small cell lung cancer, showing an increasing trend with the aggravation of the non-small cell lung cancer process.

[0023] The present invention further conducts telephone follow-up on non-small cell lung cancer patients for survival analysis and finds that although the above six urinary proteins are closely related to the non-small cell lung cancer process, they cannot be used as independent prognosis prediction factors, while the non-small cell lung cancer prognosis prediction evaluation model obtained by COX regression processing based on the above six urinary proteins has a better prediction effect on non-small cell lung cancer, and there are significant differences in the overall survival rates of the high-risk group and the low-risk group divided.

[0024] The present invention further analyzed the prediction effect of the constructed non-small cell lung cancer prognosis prediction model through 110 verification sample sets. The results still showed a good prediction effect. There was a significant difference in the overall survival rates between the high-risk group and the low-risk group divided. The sensitivity and specificity of the prediction reached 84.5% and 81.2% respectively, further indicating that the non-small cell lung cancer prognosis prediction and evaluation model constructed by the present invention has a good prediction effect on non-small cell lung cancer, can provide effective guidance for targeted clinical treatment, and is of great significance for improving the prognosis and survival of non-small cell lung cancer patients. Description of the Drawings

[0025] Figure 1 It is a graph of the contents of AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein in the urine of non-small cell lung cancer patients (NSCLC) and the urine of healthy people (HL) group;

[0026] Figure 2 It is the contents of AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein corresponding to the stages of non-small cell lung cancer patients;

[0027] Figure 3 It is the Kaplan-Meier survival curve of postoperative non-small cell lung cancer patients corresponding to six urinary protein markers;

[0028] Figure 4 It is the Kaplan-Meier survival curve of postoperative non-small cell lung cancer patients (120 cases) judged by the non-small cell lung cancer prognosis prediction and evaluation model;

[0029] Figure 5 It is the Kaplan-Meier survival curve of postoperative non-small cell lung cancer patients (110 cases) corresponding to the prognosis prediction and evaluation model of the embodiment. Detailed Embodiments

[0030] To better illustrate the purpose, technical solution and advantages of the present invention, the present invention will be further described below in conjunction with specific embodiments. Those skilled in the art should understand that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. The test methods used in the embodiments are all conventional methods unless otherwise specified; the materials, reagents, etc. used, unless otherwise specified, can be obtained from commercial channels.

[0031] Embodiment 1

[0032] A total of 230 urine samples from patients with non-small cell lung cancer in China-Japan Friendship Hospital were collected in this study and stored frozen at -80°C. All patients with pathologically confirmed non-small cell lung cancer underwent radical surgical resection. Among the 230 patients, 125 were male and 105 were female, with an average age of 49.3 ± 4.6 years, and the age range was 35 to 65 years old. Among the 230 patients, 43 were pathologically staged as stage I, 88 as stage II, 71 as stage III, and 28 as stage IV by pathological section.

[0033] Meanwhile, 200 urine samples from healthy individuals in the physical examination center of China-Japan Friendship Hospital were collected and stored frozen at -80°C. There was no evidence of any tumors in the healthy population. Among the 200 individuals, 98 were male and 102 were female, with an average age of 45.1 ± 3.3 years, and the age range was 30 to 60 years old.

[0034] Urine samples from 120 patients with non-small cell lung cancer (the remaining 110 were used for subsequent model validation analysis) and 200 healthy individuals were selected. Among the 120 patients with non-small cell lung cancer, 20 were stage I, 45 were stage II, 42 were stage III, and 13 were stage IV.

[0035] The urine samples collected above were respectively pretreated by nitrocellulose membrane adsorption and urine protein elution. Among them, the steps for pretreating urine by nitrocellulose membrane adsorption are as follows:

[0036] (1) Centrifuge the urine at 4°C and 5000g for 40 minutes to remove intact cells and cell debris in the urine;

[0037] (2) Take 15 mL of the urine supernatant from the centrifuged urine sample, add 7.5 mL of phosphate buffer and mix well;

[0038] (3) Select a nitrocellulose membrane with a pore size of 0.22 μm. After wetting the nitrocellulose membrane of appropriate size with water, place it on a vacuum filtration flask and collect the filtrate by vacuum filtration;

[0039] (4) Remove the nitrocellulose membrane and dry it in an oven at 56°C. Put the dried nitrocellulose membrane adsorbed with urine protein into a self-sealing bag marked with the patient number and medical record number information, and store it in a -80°C refrigerator.

[0040] Among them, the urine protein elution treatment includes the following steps:

[0041] (1) Cut the nitrocellulose membrane adsorbed with urinary protein into small pieces as much as possible and place them in a 5 mL centrifuge tube. Then add 3.4 mL of acetone and 500 μL of 0.5% ammonium bicarbonate aqueous solution in sequence. Vortex the mixture strongly at room temperature for 20 s, place it in a drying oven at 55 °C and heat for 60 min, and stop heating and vortex strongly for 30 s every 20 min to ensure that the nitrocellulose membrane is fully mixed with the liquid;

[0042] (2) Precipitate the protein by gently shaking at 4 °C and 12 rpm for 2 h; then centrifuge at 12000 rpm for 15 min, discard the supernatant, and collect the precipitate;

[0043] (3) Add 300 μL of urine extraction buffer to the precipitate until the precipitate is completely dissolved and dispersed, and then transfer the solution to a 2 mL centrifuge tube and perform ultrasonic redissolution of the protein by ultrasonic treatment at 4 °C and 75 W for 3 min.

[0044] (4) Pipette the ultrasonically redissolved solution into a new 2 mL centrifuge tube, centrifuge at 4 °C, 12000 rpm for 15 min, take the supernatant, and store it frozen at -80 °C.

[0045] The supernatant after eluting and treating urinary protein was used to analyze the urinary proteins of non-small cell lung cancer patients and healthy controls by data-independent proteomic analysis. More than 30 proteins with significantly different expressions in the urine of non-small cell lung cancer patients and healthy controls were screened out. Among them, the contents of AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein in the urine of postoperative non-small cell lung cancer patients were significantly higher than those in the healthy population (as Figure 1 shown).

[0046] The content levels of AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein in urine also differed among different pathological stages. The contents of the above proteins in the urine of stage I non-small cell lung cancer patients were relatively lower than those of stage II, III and IV patients, and the contents of the above proteins showed an increasing trend with the progression of non-small cell lung cancer (as Figure 2 shown). It can be seen that AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein in urine are closely related to the pathological process of non-small cell lung cancer and can be used to indicate whether there is recurrence, progression and aggravation after surgery.

[0047] Further, telephone follow-up was conducted on non-small cell lung cancer patients for survival analysis. The average follow-up time for all patients was 40 months. Using the median of the AVIL protein content in the urine of 120 non-small cell lung cancer patients arranged from low to high as the cut-off value, those with AVIL protein content higher than this cut-off value were assigned to the high-risk group, and those with AVIL protein content lower than this cut-off value were assigned to the low-risk group for postoperative survival analysis; for the remaining proteins, postoperative survival analysis was carried out in the same way as for AVIL protein, using the median of their respective protein contents arranged from low to high as the cut-off value. The Kaplan-Meier survival curves of the postoperative patients are as Figure 3 shown. The survival analysis results showed that there was no significant difference between the high-risk group and the low-risk group divided according to the above urine protein content, indicating that the above urine protein could not be used as an independent prognostic factor for non-small cell lung cancer.

[0048] Based on the data of the abnormally highly expressed urine protein content in the aforementioned 120 non-small cell lung cancers, a prognostic prediction and evaluation model for non-small cell lung cancer was obtained through COX regression:

[0049] Y = 0.215×X1 + 0.379×X2 + 0.294×X3 + 0.238×X4 + 0.257×X5 + 0.273×X6, where

[0050] X1 is the expression level of AVIL protein; X2 is the expression level of CXCR5 protein; X3 is the expression level of ENPEP protein; X4 is the expression level of DPEP1 protein; X5 is the expression level of SNC66 protein; X6 is the expression level of XPNPEP2 protein; and according to the Y values of each specimen in the 120 cases arranged in ascending order, the median Y value is the cut-off value, that is, the cut-off value is 19.35; cases with Y values less than this cut-off value are in the low-risk group, and the postoperative three-year survival rate of non-small cell lung cancer patients in the low-risk group is relatively high, while cases with Y values higher than this cut-off value are in the high-risk group, and the postoperative three-year survival rate of non-small cell lung cancer patients in the high-risk group is relatively low.

[0051] Based on this model, Kaplan-Meier survival curve analysis was performed on the above 120 non-small cell lung cancer patients after surgery as Figure 4 shown. It can be seen that when the six proteins are jointly detected based on this evaluation model, the overall survival rates of both the low-risk group and the high-risk group gradually decrease, and the decrease in the high-risk group is more obvious, and there is a significant difference from the low-risk group, indicating that the above evaluation model has a high prognostic prediction value for non-small cell lung cancer.

[0052] Example 2

[0053] In this example, 110 of the remaining urine samples from 230 non-small cell lung cancer patients were used to verify the prognostic effect of the non-small cell lung cancer prognostic prediction model constructed in Example 1. Among the 110 non-small cell lung cancer cases, 23 were in stage I, 43 in stage II, 29 in stage III, and 15 in stage IV.

[0054] Refer to the method described in Example 1 to perform nitrocellulose membrane adsorption pretreatment and urinary protein elution treatment on 110 urine samples of non-small cell lung cancer, and then use a commercially available ELISA detection kit to measure the contents of AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein, and XPNPEP2 protein in the urine respectively.

[0055] Calculate the risk score of each patient sample according to the prognostic prediction evaluation model in Example 1 for the detected urinary protein content data, and divide the patient samples into high-risk group and low-risk group according to the cut-off value of 19.35, and then perform survival analysis, as Figure 5 shown. Compared with the low-risk group, the non-small cell lung cancer patients in the high-risk group had a shorter overall survival period, and the difference between the two groups was relatively significant, indicating that the non-small cell lung cancer prognostic prediction evaluation model constructed by the present invention can accurately distinguish the high-risk group and low-risk group of non-small cell lung cancer patients.

[0056] In addition, the sensitivity and specificity of predicting the three-year survival period for the 110 patients using the non-small cell lung cancer prognostic prediction evaluation model constructed by the above six protein content levels were 84.5% and 81.2% respectively. It can be seen that the prognostic prediction evaluation model of the present invention has better prediction sensitivity and specificity for the three-year survival period of non-small cell lung cancer patients.

[0057] In summary, the prognostic prediction model constructed by combining six protein markers of AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein, and XPNPEP2 protein in urine has higher predictive value for the prognosis of non-small cell lung cancer patients.

Claims

1. Use of a group of urine protein markers in the preparation of a reagent for predicting the prognosis of non-small cell lung cancer, characterized in that: The urine protein marker is a combination of AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein.

2. The use according to claim 1, characterized in that: The non-small cell lung cancer prognosis prediction reagent is a molecular combination for detecting whether AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein are expressed.

3. The use according to claim 2, characterized in that: The molecular combination consists of an antibody that specifically binds to AVIL protein, an antibody that specifically binds to CXCR5 protein, an antibody that specifically binds to ENPEP protein, an antibody that specifically binds to DPEP1 protein, an antibody that specifically binds to SNC66 protein, and an antibody that specifically binds to XPNPEP2 protein.

4. An evaluation model for predicting the prognosis of non-small cell lung cancer, characterized in that: The evaluation model includes the urine protein marker according to claim 1; the evaluation model uses the following regression equation to calculate the risk score: Risk score Y=0.215×X1+0.379×X2+0.294×X3+0.238×X4+0.257×X5+0.273×X6, where X1 is the expression level of AVIL protein; X2 is the expression level of CXCR5 protein; X3 is the expression level of ENPEP protein; X4 is the expression level of DPEP1 protein; X5 is the expression level of SNC66 protein; X6 is the expression level of XPNPEP2 protein; When Y≥19.35, it is high risk, and when Y<19.35, it is low risk.

5. A device or system for predicting the prognosis of non-small cell lung cancer, characterized in that: The device or system comprises: (1) a data acquisition module, used to obtain the expression level of the urine protein marker according to claim 1 in the urine sample of the test subject; (2) a prediction module, used to provide the expression level of the urine protein marker obtained by the data acquisition module as input data to the trained prediction model; (3) A prediction result acquisition module, used to obtain the output result of the prediction model in the prediction module to obtain the prognosis prediction result of the subject.

6. The device or system for predicting the prognosis of non-small cell lung cancer according to claim 5, characterized in that: The prediction model is the evaluation model described in claim 4.

7. A kit for predicting the prognosis of non-small cell lung cancer, characterized in that: The kit comprises the non-small cell lung cancer prognosis prediction reagent according to claim 2 or 3; the evaluation model used by the kit to predict the prognosis of non-small cell lung cancer patients based on the expression level of the detected urine protein marker is as shown in claim 4.

8. The kit for predicting the prognosis of non-small cell lung cancer according to claim 7, characterized in that: The detection object of the kit is the urine of the subject.

9. The kit for predicting the prognosis of non-small cell lung cancer according to claim 8, characterized in that: The subject's urine needs to be pre-treated by nitrocellulose membrane adsorption and protein elution precipitation before being tested by the kit.

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