CN114778815B - Marker panel for predicting prognosis of gastric cancer - Google Patents
Marker panel for predicting prognosis of gastric cancer Download PDFInfo
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Abstract
The invention discloses a marker group for predicting prognosis of gastric cancer, which is prepared by detecting PDL1 concentration in sample plasma by ELISA method, wherein a Nomogram model constructed by combining the PDL1 concentration and CEA concentration can be used for predicting prognosis of gastric cancer patients, and ROC curve analysis results show that the area under the curve of the Nomogram model is 0.856. In the verification sequence, 68 patients with gastric cancer have the area under the curve of the Nomogram model of 0.910, and the survival of the patients with gastric cancer in 5 years can be well predicted. In terms of prognosis prediction of gastric cancer: the area under the curve of the Nomogram model constructed by the inventors was found to be 0.856, the area under the curve of CEA was 0.676, the area under the curve of PDL1 was 0.67, and the area under the curve of remote transfer was 0.647 by ROC curve analysis. The invention provides a new high-sensitivity and specificity detection index for prognosis prediction of gastric cancer, and provides a new approach for judging and predicting gastric cancer prognosis.
Description
Technical Field
The invention belongs to the field of medicine, and particularly relates to a marker group for predicting prognosis of gastric cancer.
Background
Gastric Cancer (GC) is the second most common cancer in china. According to the previous report, the GC prognosis is poor, and the GC prognosis has a great threat to human health.
The prognosis of GC is currently predicted mainly by the TNM staging system. However, prediction of patient prognosis based on TNM typing is far from adequate. CEA is used for GC screening, clinical prognosis diagnostic judgment, and recurrence monitoring. Carbohydrate antigen 19-9 (CA 19-9) is a good prognostic factor for gastric cancer patients. However, due to the low sensitivity and high false positive rate of these tumor markers, there has been a controversy over their use as independent prognostic factors.
Carcinoembryonic antigen (carcinoembryonic antigen, CEA) is a tumor-associated antigen first extracted from colon cancer and embryonic tissue by Gold and Freedman in 1965, is an acidic glycoprotein having the characteristics of human embryo antigens, exists on the surface of cancer cells differentiated from endodermal cells, and is a structural protein of cell membranes. Formed in the cytoplasm, secreted out of the cell through the cell membrane and then into the surrounding body fluids. Therefore, the sample can be detected from various body fluids and excretions such as serum, cerebrospinal fluid, milk, gastric juice, hydrothorax and ascites, urine, feces, and the like. Carcinoembryonic antigen is a broad-spectrum tumor marker, and can not be used as a specific index for diagnosing a certain malignant tumor, but has important clinical value in the aspects of differential diagnosis, disease monitoring, curative effect evaluation and the like of malignant tumors.
Cell apoptosis-ligand 1 (Programmed cell death ligand 1, PD-L1), also known as surface antigen cluster 274 (cluster of differentiation, CD 274) or B7 homolog (B7 homolog 1, B7-H1), is a protein in humans encoded by the CD274 gene. PD-L1 is a type I transmembrane protein of 40kDa and is believed to be involved in the suppression of the immune system in certain specific situations (e.g., pregnancy, tissue transplantation, autoimmune diseases, and certain diseases such as hepatitis). The immune system normally responds to foreign antigens that accumulate in the lymph nodes or spleen, triggering antigen-specific cytotoxic T cells (cd8+ Tcell proliferation). And the apoptosis receptor-1 (PD-1) is combined with the apoptosis-ligand 1 (PD-L1) to transmit an inhibitory signal and reduce the proliferation of the lymph node CD8+ T cells. Various antitumor drugs against PDL1 have been developed, however, the use of PDL1 levels in patient blood samples for predicting prognosis of patients has not been reported.
Disclosure of Invention
The present invention aims to overcome at least one of the deficiencies of the prior art and to provide a marker panel for predicting prognosis of gastric cancer.
The technical scheme adopted by the invention is as follows:
in a first aspect of the invention, there is provided:
a marker panel for predicting prognosis of gastric cancer, consisting of PDL1 and CEA.
In a second aspect of the invention, there is provided:
the application of a quantitative reagent in preparing a gastric cancer prognosis detection reagent can quantify the amounts of PDL1 and CEA in a sample.
The quantification reagent may be various commonly used in some application examples, selected from ELISA reagents, electrochemiluminescence kits.
In some examples of applications, the sample is a patient's blood or serum.
In a third aspect of the invention, there is provided:
a system for predicting prognosis of gastric cancer, comprising:
a quantifying means for determining the amounts of PDL1 and CEA in the sample;
a prognosis analysis device for determining prognosis of gastric cancer based on the result of the quantification device;
and the result output device is used for outputting the result obtained by the analysis of the prognosis analysis device.
In some examples of the system, the quantification device is selected from an ELISA detection device, a protein chip, an electrochemiluminescent protein detection device.
In some system examples, the prognosis is determined based on ROC curves or nomogram maps.
In some examples of the system, the sample is a patient's blood or serum.
The beneficial effects of the invention are as follows:
the inventor detects the PDL1 concentration in the sample plasma by using an ELISA method, and finds that a Nomogram model constructed by combining the PDL1 concentration and the CEA concentration can be used for predicting prognosis of gastric cancer patients, and the ROC curve analysis result shows that the area under the curve of the Nomogram model is 0.856. In the verification sequence, 68 patients with gastric cancer have the area under the curve of the Nomogram model of 0.910, and the survival of the patients with gastric cancer in 5 years can be well predicted.
In terms of prognosis prediction of gastric cancer: the area under the curve of the Nomogram model constructed by the inventors was found to be 0.856, the area under the curve of CEA was 0.676, the area under the curve of PDL1 was 0.67, and the area under the curve of remote transfer was 0.647 by ROC curve analysis.
The invention provides a new high-sensitivity and specific detection index for prognosis prediction of gastric cancer, a new way for judging and predicting gastric cancer prognosis and a corresponding basis for personalized treatment of gastric cancer.
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FIG. 1 is the result of ROC curve analysis of a Nomogram model;
FIG. 2 is a comparison of the Nomogram model with the ROC curve analysis results of CEA, PDL 1;
fig. 3 is a result of no Mo Tu verification (authentication and correction) of the Nomogram model.
Detailed Description
The technical scheme of the invention is further described below in conjunction with experiments.
Case screening
The inventors selected 247 patients as samples of patients initially diagnosed with gastric cancer, 34.4% (83) of which had gone out during follow-up treatment.
ELISA detection of PDL1
PDL1 levels in GC patient serum were detected using PDL1 ELISA kit (WEA 788 Hu-96T), purchased from cloud cloning corporation (CCC, chinese marchantia). The specimen collection is carried out by taking 3ml of blood by using a blood taking tube containing EDTA-K2 anticoagulant and centrifuging for 5min at room temperature at 3000 r/min. The ELISA test is to place the plasma specimen and ELISA test kit at room temperature in advance and operate strictly according to the ELISA test kit specification.
To evaluate the predictive ability of clinical and blood biochemical indices to prognosis for 247 patients, kaplan-Meier survival curves were used for analysis. The results show that: high CEA levels were significantly associated with a decrease in PFS (p <0.0001, fig. 1A) and OS (p=0.0002, fig. 1B) in GC patients. Patients with higher PDL1 levels were significantly worse PFS (p <0.0001, fig. 1C) and OS (p <0.0001, fig. 1D) than the low PDL1 group. Patients with stage M1 had PFS (p <0.0001, FIG. 1E) and OS (p <0.0001, FIG. 1F) worse than patients with stage M0. Clinical late PFS (p <0.0001, fig. 1G) and OS (p <0.0001, fig. 1H) were worse than early clinical.
Independent prognostic risk factors for PFS and OS in GC patients
To determine whether these prognostic factors affect PFS in GC patients, the inventors performed Cox single-and multi-factor analyses (table 1). Among the PFS-related factors analyzed in one-factor, 7 factors were significant, including tumor stage (HR: 4.404, p < 0.0001), lymph node stage (HR: 2.830, p=0.001), metastasis stage (HR: 2.830, p=0.001). 2.745, p < 0.0001), clinical stage (HR: 5.490, p < 0.0001), CEA (HR: 2.925, p < 0.0001), CA19-9 (HR: 1.589, p=0.027), PDL1 (HR: 2.599, p < 0.0001) levels. While other factors are not relevant. Then, in order to determine a good prognostic indicator, a multifactor analysis is performed. Multifactorial analysis showed that clinical stage (HR: 2.739, p=0.030), CEA (HR: 2.350, p < 0.0001) and PDL1 (HR: 1.762, p=0.016) levels were independent risk factors for PFS in GC patients.
And then, carrying out single-factor and multi-factor analysis on the clinical index and the blood biochemical index, and finding out the relation between the factors and the OS. In the single factor analysis, age (HR: 1.722, p=0.015), tumor stage (HR: 5.813, p < 0.0001), lymph node stage (HR: 2.738, p=0.005), metastasis stage (HR: 3.512, p < 0.0001), clinical stage (HR: 10.396, p < 0.0001), CEA (HR: 3.246, p < 0.0001), CA19-9 (HR: 1.596, p=0.041), PDL1 (HR: 3.293, p < 0.0001) were significantly worse correlated with total survival (Table 2).
To determine whether these 5 factors are independent prognostic factors for OS, a multifactorial analysis was performed. The results showed positive correlation between the transfer phase (HR: 1.774, p=0.029), clinical phase (HR: 6.832, p=0.005), CEA (HR: 2.154, p=0.003), PDL1 (HR: 2.133, p=0.003) and OS. Thus, the inventors' study results suggest that a nomogram gastric cancer patient OS prognosis risk model that combines metastasis stage, clinical stage, CEA level, and PDL1 level may be a new gastric cancer patient OS prognosis risk model.
Development and validation of predictive models
Next, the inventors further stratified the data according to clinical staging, separating early and late patients, and performing Cox univariate and multivariate analyses on the two groups of patients, respectively. Multifactorial analysis showed that advanced patient groups CEA, PDL1, were associated with PFS. The results of the one-factor analysis showed that age, metastasis stage, CEA, PDL1 were OS-related in the middle and late patient groups. Further multifactorial analysis showed that transfer, CEA, PDL1 is closely related to OS. These results indicate that the inventors' prognostic model may have better prognostic power for patients with advanced OS.
Next, the inventors constructed a novel nomogram that predicts 1 year, 3 years, 5 years of OS in gastric cancer patients in combination with metastasis stage, clinical stage, CEA level, and PDL1 level (fig. 2A). The verification of the nomogram includes authentication and correction. And judging by adopting a consistency index (C-index) and an ROC curve. Calibration is assessed by comparing the estimated predicted lifetime of the norgram with the observed lifetime. In the first queue, the c-index of the predicted OS is 0.783 (95% CI: 0.760-0.806). In the calibration plots of 1-, 3-, 5-year OS probabilities, the nomogram predictions most agree with the actual observations (FIG. 3A). 1. AUC (ROC curve) for survival at 3 and 5 years was 0.81, 0.82, 0.83, respectively (fig. 3B).
Next, the inventors applied nomogram to a validation cohort of 63 gastric cancer patients. The OS predicted c-index is 0.885 (95% CI: 0.848-0.922). 1. AUC (ROC curve) for survival at 3 and 4 years was 0.962, 0.913, 0.91, respectively (fig. 3C).
Taken together, experimental data indicate that the Nomogram model combined with PDL1 and CEA can more accurately predict prognosis of gastric cancer patients as a gastric cancer prognosis prediction risk model.
TABLE 1 PFS and patient characterization single and multiple factor analysis for gastric cancer patients
TABLE 2 single and multiple factor analysis of OS and patient characteristics for gastric cancer patients
The above description of the present invention is further illustrated in detail and should not be taken as limiting the practice of the present invention. It is within the scope of the present invention for those skilled in the art to make simple deductions or substitutions without departing from the concept of the present invention.
Claims (3)
1. A system for predicting prognosis of gastric cancer, comprising:
a quantifying means for determining the amounts of PDL1 and CEA in a sample, which is the blood or serum of a patient;
a prognosis analysis device for determining prognosis of gastric cancer based on the result of the quantification device in combination with the metastasis stage and the clinical stage;
and the result output device is used for outputting the result obtained by the analysis of the prognosis analysis device.
2. The system according to claim 1, wherein: the quantitative device is selected from ELISA detection device, protein chip and electrochemiluminescence protein detection device.
3. The system according to claim 1 or 2, characterized in that: the prognosis is determined based on ROC curves or nomogram.
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