阅读说明：本技术 一种用于检测癌症化疗敏感性的血浆蛋白分子、应用及试剂盒 (Plasma protein molecule for detecting cancer chemotherapy sensitivity, application and kit ) 是由 梁廷波 白雪莉 章琦 楼煜 叶茅 于 2019-09-27 设计创作，主要内容包括：本发明公开了一种用于检测癌症化疗敏感性的血浆蛋白分子、应用及试剂盒。一种用于检测癌症化疗敏感性的血浆蛋白分子,包括人血浆蛋白GSN、APOA4、IGHG1、Immunoglobulin mu heavy chain和FCN2中的至少一种。本发明研究发现上述人血浆蛋白浓度在不同化疗响应的胰腺癌患者中存在显著差异,作为肿瘤标记物单独使用时预测胰腺癌化疗响应的ROC曲线下面积可达到0.5550～0.7275；将这五种人血浆蛋白浓度组合作为肿瘤标记物联合患者年龄预测胰腺癌化疗响应的ROC曲线下面积可达0.915,可作为预测肿瘤化疗敏感性的肿瘤标记物,实现化疗敏感人群的有效筛选,大大提升临床获益。(The invention discloses a plasma protein molecule for detecting cancer chemotherapy sensitivity, application and a kit. A plasma protein molecule for detecting cancer chemotherapy sensitivity comprises at least one of human plasma proteins GSN, APOA4, IGHG1, immunologlobulin mu heavy chain and FCN 2. The research of the invention finds that the concentration of the human plasma protein has obvious difference in pancreatic cancer patients with different chemotherapy responses, and the area under the ROC curve for predicting the pancreatic cancer chemotherapy response can reach 0.5550-0.7275 when the human plasma protein is used as a tumor marker alone; the area under the ROC curve of the five human plasma protein concentration combinations used as tumor markers for predicting the pancreatic cancer chemotherapy response in combination with the age of a patient can reach 0.915, and the human plasma protein concentration combinations can be used as tumor markers for predicting the tumor chemotherapy sensitivity, so that the effective screening of chemotherapy-sensitive people is realized, and the clinical benefit is greatly improved.)

1. A plasma protein molecule for detecting cancer chemotherapy sensitivity, which comprises at least one of human plasma proteins GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain and FCN 2.

2. The plasma protein molecule of claim 1, comprising one of the following combinations of proteins: GAI, GAF, GAM, GIF, GIM, GFM, AIF, AFM, IFM, AIM, wherein G: GSN, A: APOA4, I: IGHG1, M: immunoglobulin mu heavy chain, F: FCN 2.

3. The plasma protein molecule of claim 2, comprising five proteins selected from the group consisting of human plasma proteins GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain and FCN 2.

4. Use of a plasma protein molecule according to any one of claims 1 to 3 as a target for detecting sensitivity to cancer chemotherapy.

5. The use of claim 4, wherein the cancer species is pancreatic cancer.

6. The use of claim 5, wherein the chemotherapeutic regimen is a FOLFIRINOX regimen.

7. The use of claim 6, comprising measuring the concentrations of human plasma proteins GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain and FCN2 in plasma, and inputting said 5 human plasma protein concentration data into a pre-generated random forest model for predictive assessment of the patient's chemotherapeutic response.

8. The application as claimed in claim 7, wherein the pre-generated random forest model is constructed by:

taking concentration data of human plasma proteins GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain and FCN2 in the plasma of patients with known FOLFIRINOX regimen sensitivity and resistance,

setting the seed number as 2019, randomly grouping according to the ratio of 2: 1 to construct a training set and a verification set, and importing concentration matrixes of 5 proteins, treatment response conditions and corresponding clinical information: and (3) constructing a model by using a random forest algorithm, repeating the operation for 100 times to obtain an ROC working curve for average prediction of the model, setting the number of trees to be 1000, setting the minimum observed data number of the peripheral nodes to be 1: 5, and fitting by using a anger function to obtain a final model.

9. A kit for detecting cancer chemotherapy susceptibility, comprising:

(1) reagents for extracting proteins from plasma;

(2) the reagent is used for detecting the concentrations of human plasma proteins GSN, APOA4, IGHG1, immunologlobulin muheavy chain and FCN 2.

Technical Field

The invention relates to the technical field of biotechnology detection, in particular to a plasma protein molecule for detecting cancer chemotherapy sensitivity, application and a kit.

Background

Tumors are one of the important diseases threatening human life and health. Pancreatic cancer is the most malignant tumor, and is called the king of cancer. The occult onset of the cancer is such that many patients often lose the chance of surgical operation during diagnosis, and the poor response rate of chemotherapy greatly limits the prognosis of pancreatic cancer patients.

With the research of multiple researches in recent years, some chemotherapy schemes such as FOLFIRINOX, nano albumin paclitaxel and gemcitabine are gradually separated out, and become a main chemotherapy strategy for treating pancreatic cancer. Unfortunately, however, these regimens remain very limited in their effectiveness in the pancreatic cancer population. Therefore, the search for effective tumor markers helps to accurately identify chemotherapy-benefited people, and has important significance for improving the prognosis of pancreatic cancer patients. CA19-9 is one of the most commonly used tumor markers in clinic, and has good sensitivity and specificity for pancreatic cancer diagnosis. Based on the expression pattern of CA19-9, it was found that the FOLFIRINOX regimen resulted in a higher objective sustained release rate (ORR, 44.0% vs. 22.9%) for patients with a more than 20% decrease in CA 19-9. However, CA19-9 is not expressed in all pancreatic cancer patients, nor is this strategy able to predict patient response prior to chemotherapy. To solve this problem, some studies have searched for markers of tumor circulating DNA and serum proteins, and found that biomarkers such as ctdna (kras), CEA, and sCD40L can predict the sensitivity of chemotherapeutic drugs to some extent. However, the sensitivity and specificity of prediction of these biomarkers, either alone or in combination, have been difficult to meet clinical needs. Therefore, the development of a new tumor marker has good application prospect in predicting drug sensitivity before chemotherapy.

With the development and maturation of technology, mass spectrometry-based proteomics strategies are gradually expanding in clinical detection and basic research. The technical characteristics of high flux, high sensitivity and high accuracy bring great convenience to the biomedical field, and the large-scale screening of the biomarkers becomes possible. Therefore, the strategy of utilizing omics big data is used for screening new markers and constructing a prediction model of multiple markers, or the accuracy of chemotherapy prediction can be improved to a new level.

Disclosure of Invention

The research of the invention finds that the plasma protein concentrations of free proteins GSN, APOA4, IGHG1, immunoglobinum muaviy chain (IgM heavy chain), FCN2 extracted from human peripheral blood show significant difference in pancreatic cancer patients with different chemotherapy responses, and further analysis can find that the high expression of serum GSN, APOA4, immunoglobinum heavy chain, FCN2 and the low expression of IGHG1 are related to the sensitivity of tumors to the FOLFIRINOX scheme. The protein molecule is used for constructing a pancreatic cancer chemotherapy response prediction model, the area under the working characteristic (ROC) curve of a subject can reach 0.88, and the area under the ROC curve for predicting pancreatic cancer chemotherapy response by combining the age of a patient can reach 0.915. Therefore, the prediction model constructed by combining the plasma protein marker molecules with the clinical information of the patient can be used as a tumor marker for predicting tumor chemotherapy response, and further helps to clinically screen chemotherapy-sensitive people.

A plasma protein molecule for detecting cancer chemotherapy sensitivity comprises at least one of human plasma proteins GSN, APOA4, IGHG1, immunologlobulin mu heavy chain and FCN 2.

Preferably, the plasma protein molecule comprises one of the following protein combinations: GAI, GAF, GAM, GIF, GIM, GFM, AIF, AFM, IFM, AIM, wherein G: GSN, A: APOA4, I: IGHG1, M: immunoglobin mu heavychain, F: FCN 2.

More preferably, the plasma protein molecules comprise human plasma proteins GSN, APOA4, IGHG1, immunologlulin mu heavy chain and FCN 2. The result is more accurate when the five human plasma proteins are combined together.

The invention also provides application of the plasma protein molecule in serving as a target for detecting cancer chemotherapy sensitivity. The application, the cancer type is pancreatic cancer. The chemotherapy is FOLFIRINOX.

The application comprises the steps of detecting the concentrations of human plasma proteins GSN, APOA4, IGHG1, immunologlulin Muheavy chain and FCN2 in plasma, and inputting the 5 kinds of human plasma protein concentration data into a pre-generated random forest model for prediction and evaluation of the chemotherapy response of a patient.

The construction method of the pre-generated random forest model comprises the following steps:

taking concentration data of human plasma proteins GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain and FCN2 in the plasma of patients with known FOLFIRINOX regimen sensitivity and resistance,

setting the seed number as 2019, randomly grouping according to the ratio of 2: 1 to construct a training set and a verification set, and importing concentration matrixes of 5 proteins, treatment response conditions and corresponding clinical information: and (3) constructing a model by using a random forest algorithm, repeating the operation for 100 times to obtain an ROC working curve for average prediction of the model, setting the number of trees to be 1000, setting the minimum observed data number of the peripheral nodes to be 1: 5, and fitting by using a anger function to obtain a final model.

The present invention also provides a kit for detecting cancer chemotherapy sensitivity, comprising:

(1) reagents for extracting proteins from plasma;

(2) the reagent is used for detecting the concentrations of human plasma proteins GSN, APOA4, IGHG1, immunologlobulin muheavy chain and FCN 2.

The research of the invention finds that the concentrations of human plasma proteins GSN, APOA4, IGHG1, immunologlulin muheavychain and FCN2 are obviously different in pancreatic cancer patients responding to different chemotherapies, and the area under the ROC curve for predicting the pancreatic cancer chemotherapies response can reach 0.5550-0.7275 when the human plasma proteins GSN, APOA4, IGHG1, immunologlulin muheavychain and FCN2 are used as tumor markers independently; the area under the ROC curve for predicting the pancreatic cancer chemotherapy response by using the five human plasma protein concentration combinations as tumor markers in combination with the patient age can reach 0.915. Therefore, based on the combination of plasma protein molecules GSN, APOA4, IGHG1, immunolobulin muheavy chain and FCN2 (including any combination of the five proteins and any combination of all parts of the five proteins) and clinical information of a patient, the tumor marker for predicting tumor chemotherapy sensitivity can be used as a tumor marker for assisting with a random forest model constructed in the early stage, so that the effective screening of chemotherapy-sensitive people is realized, and the clinical benefit is greatly improved.

Drawings

FIG. 1 is a graph of plasma protein concentrations versus results for chemotherapy resistant and sensitive pancreatic cancer patients.

FIG. 2 is a diagram of the decision tree results of one of the models extracted during model building in example 3.

FIG. 3 is a graph of the results of sensitivity and specificity analysis of plasma protein combinations in the prediction of pancreatic cancer chemotherapy response.

FIG. 4 is a graph of the results of a sensitivity and specificity analysis of the single protein marker model in the prediction of pancreatic cancer chemotherapy response.

FIG. 5 is a graph of the results of sensitivity and specificity analysis in response prediction of pancreatic cancer chemotherapy using a traditional three-protein marker combination model based on binary Logistic regression analysis.

Detailed Description

Sample source: plasma samples from pancreatic cancer patients were obtained from the first hospital affiliated with the university of Zhejiang medical college.

Ethical examination and approval: ethical review by ethical review committee of scientific research in first hospital affiliated to Zhejiang university medical college, lot number: (2019) research review quick review No. (622).