CN117153252A - Prognosis biomarker for patients with diffuse large B cell lymphoma, and system and application thereof - Google Patents
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Abstract
The application belongs to the technical field of disease prognosis and molecular biology, and particularly relates to a biomarker for prognosis of patients with diffuse large B cell lymphoma, a system and application thereof. The application establishes TME immune scores based on immune cell components in DLBCL patients for the first time. TME immune scores are effective in predicting prognosis of DLBCL patients and are closely related to clinical characteristics of the patients. The differentially expressed genes of the different TME immune panels are enriched in biological processes associated with T cell immune responses, wherein CD2 can be a prognostic biomarker for DLBCL patients. In conclusion, the application provides a new DLBCL patient prognosis layering strategy, which lays a theoretical foundation for individual management of DLBCL patients, and therefore has good practical application value.
Description
Technical Field
The application belongs to the technical field of disease prognosis and molecular biology, and particularly relates to a biomarker for prognosis of patients with diffuse large B cell lymphoma, a system and application thereof.
Background
The disclosure of this background section is only intended to increase the understanding of the general background of the application and is not necessarily to be construed as an admission or any form of suggestion that this information forms the prior art already known to those of ordinary skill in the art.
Biological manifestations, clinical features and therapeutic responses of diffuse large B-cell lymphomas (DLBCL) are highly heterogeneous, which are closely related to genetic alterations and tumor microenvironment variation in patients. Although the use of novel therapeutic strategies such as rituximab has greatly improved survival in DLBCL patients, the clinical prognosis for advanced and relapsing patients remains undesirable. Thus, an in-depth analysis of disease heterogeneity would provide new opportunities for diagnosis and treatment of DLBCL.
Innovations in multiple sets of chemical techniques have helped identify different disease subtypes by analyzing the molecular characteristics of lymphoma cells. Recent studies have found that in addition to widely used IPI scores and Ann Arbor stages, the molecular characteristics of malignant cells are also significantly correlated with the prognosis of DLBCL patients. Cell origin (COO) typing distinguishes lymphomas into different origin types by their molecular characteristics, and patients with lymphomas of different origins have different clinical manifestations and prognosis. Furthermore, studies indicate that MYD88 mutated DLBCL patients have a worse prognosis, indicating the important value of cytogenetics in lymphoma typing. However, these classification systems only consider the molecular characteristics of malignant cells, but ignore the heterogeneity of non-malignant components in the tumor microenvironment, especially the effect of immune components on DLBCL patient prognosis. Thus, a novel scoring system constructed based on DLBCL tissue microenvironment immune components would help to further bridge the gap in existing clinical practice.
Tumor Microenvironment (TME) is a complex ecosystem around tumor cells, consisting mainly of immune cells and matrix components. Complex TMEs provide the underlying survival conditions for the growth of tumor cells. With the continued discovery of TME reprogramming in tumor progression, in-depth analysis of the immune components of TME is expected to further elucidate the effect of TME heterogeneity on clinical outcome and therapeutic response in DLBCL patients.
Disclosure of Invention
In order to overcome the defects of the prior art, the inventor provides a prognosis biomarker for patients with diffuse large B cell lymphoma, and a system and application thereof through long-term technical and practical exploration. The application analyzes TME immune components of a DLBCL patient based on mRNA microarray data, constructs a TME immune scoring model through lasso-cox regression, confirms key genes related to TME immune scoring based on connectivity and intermediacy centrality, and further verifies that gene CD2 has good prognostic value in the DLBCL patient. Based on the above results, the present application has been completed.
In order to achieve the technical purpose, the application adopts the following technical scheme:
in a first aspect of the application, there is provided a biomarker for prognosis of patients with diffuse large B-cell lymphoma, said biomarker comprising at least a key gene and/or TME immune cells;
the key genes comprise any one or more of CD2, CD3E, MMP, IFNG, GATA3, CCL5 and CCR 5;
the TME immune cells comprise: any one or more of activated NK cells, resting NK cells, plasma cells, naive B cells, memory B cells, CD4 naive T cells, activated CD4 memory T cells, resting CD4 memory T cells, CD 8T cells, γδ T cells, regulatory T cells, follicular helper T cells, monocytes, macrophages M1, macrophages M2, macrophages M0, activated dendritic cells, resting dendritic cells, activated mast cells, resting mast cells, neutrophils, and eosinophils.
Further, the biomarker is the gene CD2.
In a second aspect of the application, there is provided the use of a substance for detecting a biomarker as described above for the preparation of a prognostic product for patients suffering from diffuse large B-cell lymphoma.
Specifically, the substances for detecting the biomarkers include, but are not limited to, substances for detecting the key genes and/or TME immune cell content and proportion of the subject.
It should be noted that the substances for detecting the above-mentioned critical genes in the subject include, but are not limited to, substances for detecting transcription of the critical genes in the subject based on a gene sequencing method and/or based on a quantitative PCR method and/or based on an in situ hybridization method; or substances for detecting the expression condition of the key gene expression product of the subject based on an immunoassay method.
The key gene expression product may obviously be a protein encoded by the corresponding key gene, such as a CD2 molecule (sheep red blood cell receptor, LFA-2).
In a third aspect of the application, there is provided a prognostic system for diffuse large B-cell lymphoma patients, said system comprising:
an acquisition module configured to: obtaining the expression level of the marker in the subject;
an evaluation module configured to: predicting a risk score of the diffuse large B-cell lymphoma prognosis according to the expression level of the marker obtained by the obtaining unit, and outputting the risk score;
an output module configured to: and obtaining a prediction result according to the risk score.
Wherein the evaluation module at least comprises a diffuse large B cell lymphoma prognosis evaluation model, and the prognosis evaluation model is specifically a key gene prognosis scoring model and/or a TME immune scoring model;
the key gene prognosis scoring model can be an immunohistochemical scoring model, namely, the immunohistochemical scoring model is divided into strong positive cells, medium positive cells and weak positive cells according to the staining degree (antigen content) of cells, and the calculation formula of the immunohistochemical scoring model is (strong positive cell percentage multiplied by 3) + (medium positive cell percentage multiplied by 2) + (weak positive cell percentage multiplied by 1).
The TME immune scoring model is obtained by model training TME immune cells of a patient with diffuse large B cell lymphoma, which are collected in advance, by adopting an algorithm.
Specifically, the TME immune cell includes: activated NK cells, resting NK cells, plasma cells, naive B cells, memory B cells, CD4 naive T cells, activated CD4 memory T cells, resting CD4 memory T cells, CD 8T cells, γδ T cells, regulatory T cells, follicular helper T cells, monocytes, macrophage M1, macrophage M2, macrophage M0, activated dendritic cells, resting dendritic cells, activated mast cells, resting mast cells, neutrophils, and eosinophils.
In a fourth aspect of the application, there is provided a computer readable storage medium having stored thereon a program which when executed by a processor performs the functions of the system according to the fourth aspect of the application.
In a fifth aspect the present application provides an electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing the functions of the system according to the fourth aspect of the application when the program is executed.
In a sixth aspect of the application there is provided the use of a marker as described in the first aspect above as a target for screening for a medicament for the prophylaxis or treatment of diffuse large B-cell lymphoma.
Compared with the prior art, the one or more technical schemes have the following beneficial effects:
the above-described protocol first established an immune cell component-based TME immune score in DLBCL patients. TME immune scores are effective in predicting prognosis of DLBCL patients and are closely related to clinical characteristics of the patients. The differentially expressed genes of the different TME immune panels are enriched in biological processes associated with T cell immune responses. Further, of the 7 key genes associated with TME immune scores, CD2 is a key molecule for T cell receptor activation, confirming that CD2 has good prognostic value in DLBCL patients.
In conclusion, the technical scheme provides a novel prognosis layering strategy for DLBCL patients, and lays a theoretical foundation for individual management of DLBCL patients, so that the method has good practical application value.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application.
Fig. 1 is a graph showing the percentage of 22 immune cells in DLBCL patients in the training subset (n=946) in the examples of the present application.
FIG. 2 is a fitting process of a lasso-cox model in an embodiment of the application; 22 curves in A represent 22 immune cells, the curves showing the trend of each coefficient as a function of L1-norm (total absolute value of non-zero coefficients); the partial likelihood difference is shown in B as a function of lambda. The results indicate that the best fit is achieved when all 22 cells are included in the model.
FIG. 3 shows that in the training subset, DLBCL patients with low TME immune scores had longer overall survival, and the results indicate that TME immune scores were significantly correlated with overall survival of DLBCL patients.
FIG. 4 shows that TME immune scores of the examples of the application have better prognostic predictive efficacy in DLBCL patients. In fig. 4A, TME immune scores had better predictive accuracy (AUC > 0.7) for 3,5,10 year survival for DLBCL patients in the training subset. In fig. 4B, TME scores (red) predicted higher efficacy for 5-year survival than Ann Arbor stage (yellow) and COO subtype (blue) for DLBCL patients. The results indicate that TME immune scores are effective in predicting prognosis of DLBCL patients and that predictive efficacy is superior to traditional scoring systems.
FIG. 5 is a correlation between TME immune scores in training subsets and clinical characteristics of DLBCL patients in an embodiment of the present application. In A, DLBCL patients of stage III/IV had a higher TME immune score than Ann Arbor I/II patients. In B, non-GCB patients had a higher TME immune score than GCB patients. The results indicate that TME immune scores are significantly correlated with clinical features of DLBCL patients.
FIG. 6 is an enrichment result of differential genes significantly down-regulated in the high TME immune panel in the examples of the present application. The GO analysis results show that the differentially expressed genes are significantly enriched in immune-related biological processes including T cell differentiation and T cell activation. KEGG pathway analysis showed that differential genes were significantly enriched in immune-related pathways such as T cell receptor signaling pathways and T helper cell differentiation pathways. The results indicate that DLBCL patients with different TME immune scores may have different T cell immune response mechanisms.
FIG. 7 shows the process of identifying the core genes of TME immune scoring in an embodiment of the application. 7A shows a protein interaction network established from 81 significantly down-regulated genes. B shows the network connectivity and intermediacy of the differential gene. The core gene is a gene with a ligation of >10 and an intermediacy of > 0.05. The study found 7 TME immune scoring core genes that may play a key role in TME regulation in DLBCL patients.
Fig. 8 is a graph showing that a high TME immune score correlates with a poor prognosis in the validation cohort (n=230) in an embodiment of the application. The result shows that the prognosis prediction efficacy of TME immune score has better stability.
FIG. 9 shows the detection of mRNA expression levels of CD2 in DLBCL patients and their prognostic relevance in accordance with the examples of the present application. A shows that CD2 is expressed at lower mRNA levels in DLBCL patient tissue than in reactive lymph node tissue (GSE 32018). B shows that the prognosis for DLBCL patients with high expression of CD2 is significantly better than for patients with low expression (GSE 69051).
FIG. 10 is a graph showing the validation of prognostic predictive efficacy of CD2 in DLBCL patients in an example of the present application. A is a typical picture of CD2 immunohistochemical staining of DLBCL patients. B shows that CD2 positive and negative DLBCL patients have significant differences in OS. C shows a significant difference in PFS in CD2 positive and negative DLBCL patients.
Detailed Description
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof. In addition, the molecular biological methods not described in detail in the examples are all conventional in the art, and specific operations can be referred to the molecular biological guidelines or the product specifications.
In an exemplary embodiment of the application, a biomarker for prognosis of patients with diffuse large B-cell lymphoma is provided, said biomarker comprising at least a key gene and/or TME immune cells;
the key genes comprise any one or more of CD2, CD3E, MMP, IFNG, GATA3, CCL5 and CCR 5;
the TME immune cells comprise: any one or more of activated NK cells, resting NK cells, plasma cells, naive B cells, memory B cells, CD4 naive T cells, activated CD4 memory T cells, resting CD4 memory T cells, CD 8T cells, γδ T cells, regulatory T cells, follicular helper T cells, monocytes, macrophages M1, macrophages M2, macrophages M0, activated dendritic cells, resting dendritic cells, activated mast cells, resting mast cells, neutrophils, and eosinophils.
In yet another embodiment of the present application, the biomarker is the gene CD2.
In yet another embodiment of the application, there is provided the use of a substance for detecting a biomarker as described above for the preparation of a prognostic product for patients with diffuse large B-cell lymphoma.
In yet another embodiment of the present application, the means for detecting the above biomarkers includes, but is not limited to, means for detecting the above key genes and/or TME immune cell content and ratio in a subject.
It should be noted that the substances for detecting the above-mentioned critical genes in the subject include, but are not limited to, substances for detecting transcription of the critical genes in the subject based on a gene sequencing method and/or based on a quantitative PCR method and/or based on an in situ hybridization method; or substances for detecting the expression condition of the key gene expression product of the subject based on an immunoassay method.
The key gene expression product may obviously be a protein encoded by the corresponding key gene, such as a CD2 molecule (sheep red blood cell receptor, LFA-2).
The product may be a detection kit, a detection device or apparatus.
Wherein the prognosis comprises a prognostic assessment of survival (rate) of diffuse large B-cell lymphoma patients.
The lifetime includes a total lifetime (OS) and a progression free lifetime (PFS);
the survival rate comprises 3,5 and 10 years survival rate.
In yet another embodiment of the present application, there is provided a prognostic system for diffuse large B-cell lymphoma patients, comprising:
an acquisition module configured to: obtaining the expression level of the marker in the subject;
an evaluation module configured to: predicting a risk score of the diffuse large B-cell lymphoma prognosis according to the expression level of the marker obtained by the obtaining unit, and outputting the risk score;
an output module configured to: and obtaining a prediction result according to the risk score.
Wherein the evaluation module at least comprises a diffuse large B cell lymphoma prognosis evaluation model, and the prognosis evaluation model is specifically a key gene prognosis scoring model and/or a TME immune scoring model;
the key gene prognosis scoring model can be an immunohistochemical scoring model, namely, the immunohistochemical scoring model is divided into strong positive cells, medium positive cells and weak positive cells according to the positive staining degree (antigen content) of cells, and the calculation formula of the immunohistochemical scoring model is (strong positive cell percentage multiplied by 3) + (medium positive cell percentage multiplied by 2) + (weak positive cell percentage multiplied by 1).
The TME immune scoring model is obtained by model training TME immune cells of a patient with diffuse large B cell lymphoma, which are collected in advance, by adopting an algorithm.
Specifically, the TME immune cell includes: activated NK cells, resting NK cells, plasma cells, naive B cells, memory B cells, CD4 naive T cells, activated CD4 memory T cells, resting CD4 memory T cells, CD 8T cells, γδ T cells, regulatory T cells, follicular helper T cells, monocytes, macrophage M1, macrophage M2, macrophage M0, activated dendritic cells, resting dendritic cells, activated mast cells, resting mast cells, neutrophils, and eosinophils.
More specifically, the method for constructing the prognosis evaluation model comprises the following steps:
s1, obtaining TME immune cell data information of a diffuse large B cell lymphoma patient, and calculating 22 TME immune cell ratios of the diffuse large B cell lymphoma patient by using a CIBERSORT algorithm;
s2, according to the calculated prognosis related critical point of each TME immune cell, converting the proportion of each immune cell into 1 or 0 (greater than the critical point is assigned to 1 and less than the critical point is assigned to 0), and incorporating the converted immune cell proportion into lasso-cox to construct a regression model.
Further, in the step S2, a regression coefficient with minimum lambda is selected, and a TME immune score of the patient is calculated based on the regression model.
Wherein the prognosis comprises a prognostic assessment of survival (rate) of diffuse large B-cell lymphoma patients.
The lifetime includes a total lifetime (OS) and a progression free lifetime (PFS);
the survival rate comprises 3,5 and 10 years survival rate.
In yet another embodiment of the present application, a computer-readable storage medium is provided, on which a program is stored which, when executed by a processor, implements the functions of the system described above.
In yet another embodiment of the present application, an electronic device is provided that includes a memory, a processor, and a program stored on the memory and executable on the processor, the processor implementing the functions of the system as described above when the program is executed.
In a further embodiment of the application, there is provided the use of a marker as described in the first aspect above as a target for screening for a medicament for the prevention or treatment of diffuse large B-cell lymphoma.
In yet another embodiment of the application, the effect of the drug candidate on these biomarkers before and after use can be utilized to determine whether the drug candidate can be used to prevent or treat diffuse large B-cell lymphoma.
The application is further illustrated by the following examples, which are given for the purpose of illustration only and are not intended to be limiting. If experimental details are not specified in the examples, it is usually the case that the conditions are conventional or recommended by the sales company; the present application is not particularly limited and can be commercially available.
Examples
Experimental method
1. Data collection and processing
Clinical and microarray data were obtained from GEO databases and NCIGDC databases. 6 cohorts including GSE23501, GSE53786, GSE10846, GSE136971, GSE57611, and GSE32918 were included as training subsets and patients with incomplete prognostic information were excluded. The external validation cohort included 230 DLBCL patients from NCI cancer research center project (ncicr). CD2mRNA expression levels and prognostic relevance verification queues were from GSE32018 and GSE69051, respectively. The Sva package in R language is used to eliminate potential lot effects in different datasets.
TME immune score establishment
We used cibelsortx to evaluate the percentage of 22 immune cells in DLBCL patients. The surviviner software package in the R language was used to determine a prognostic-related cutoff for each cell type, from which the percentage of immune cells was converted from continuous variable to binary variable. All values below the threshold are designated 0 and values above the threshold are designated 1. To determine the regression coefficients for each immune cell we used lasso-cox regression. This is done through the glmnet package in the R language. The maximum number of passes for all lambda values is set to 1000.
3. Prognosis prediction efficiency assessment
Prognosis prediction specificity and sensitivity of TME immune scores were assessed by ROC when used. This is done by the timeROC software package in the R language. The immune score, the stroma score and the tumor purity score of the DLBCL patient are calculated by adopting an ESTIMATE algorithm, and the algorithm can ESTIMATE the proportion of immune cells and stroma components in the tumor by using an expression matrix.
4. Differential expression gene identification and functional enrichment
Differentially expressed genes in different TME immune scoring groups were identified using the limma software package in the R language. And using a clusterifier software package in the R language to perform function enrichment according to the KEGG and GO databases.
5. Construction of protein interaction networks
Protein interaction networks were established using sting, considering only edges with an interaction score greater than 0.400. The STRING PPI network was then further modified and analyzed in Cytoscape. Network Analyzer was used to calculate the intermediate centrality (BC) of genes in Cytoscape.
6. Immunohistochemical staining
FFPE tissue sections were taken from tumor tissues of 45 newly diagnosed DLBCL patients admitted during 2012 to 2019 of the shandong provincial hospital. Diagnostic criteria are based on the WHO classification of 2016. Immunohistochemical staining was performed using four micron sections of FFPE tissue. Tissue sections were deparaffinized and rehydrated in xylene and ethanol of different concentrations, respectively. EDTA buffer and 3% hydrogen peroxide were used for antigen retrieval and blocking endogenous peroxidase activity, respectively. The sections were incubated overnight at 4℃using CD2 primary antibody. After incubation of the sections with secondary antibodies for 1 hour at 37 ℃, the sections were developed using DAB detection system. The calculation formula of the immunohistochemical score is (strong positive cell percentage×3) + (medium positive cell percentage×2) + (weak positive cell percentage×1). The optimal cut-off point for the immunohistochemical scoring in the survival assay was determined by the maxstat package in R.
7. Statistical analysis
Statistical analysis was performed using R language 3.6.3 version and SPSS26 version. Results with P values less than 0.05 are considered statistically significant. Survival curves were established using Kaplan-Meier (K-M) analysis and statistically analyzed by Log-rank test. The size of our study samples was not statistically determined. The continuous variable is compared using t-test and non-parametric test.
Results
946 patients with complete total survival and mRNA expression data in GSE23501, GSE53786, GSE10846, GSE136971, GSE57611, GSE32918 cohorts were included as training cohorts in our study. The clinical characteristics of the patients are summarized in table 1. To capture the characteristics of the TME immune component of each sample, we calculated 22 TME immune cell ratios per patient using the cibert algorithm in the training subset, and found that the TME immune component of DLBCL patients had a high degree of heterogeneity (fig. 1).
Table 1 clinical characteristics of patients
We converted the ratio of each immune cell to 1 or 0 (greater than the threshold value being 1 and less than the threshold value being 0) based on the calculated prognostic correlation threshold for each cell, and incorporated the converted immune cell ratio into lasso-cox to construct a regression model. FIG. 2 shows the partial likelihood bias and immunocyte coefficient as a function of lambda during model construction. We selected the regression coefficients for lambda minimum and calculated TME immune scores for each DLBCL patient based on the model.
Next, to explore the prognostic predictive efficacy of TME immune scores, we split DLBCL patients in the training subset into two groups according to the median of TME immune scores. The K-M curve shows that in DLBCL patients, higher TME immune scores are clearly correlated with poorer prognosis (fig. 3, p < 0.0001). The prognostic evaluation efficacy of TME immune scores on DLBCL patients is demonstrated.
We used time dependent ROC to further estimate the predictive accuracy of TME immune scores. The results demonstrate that TME immune scores had good predictive performance for overall survival for 3,5,10 years in DLBCL patients (fig. 4A), and TME immune scores outperformed existing risk stratification methods including Ann Arbor staging and COO subtype in predicting 5-year survival in DLBCL patients (fig. 4B). Our results indicate that TME immune scores can be used as a novel prognostic stratification strategy for DLBCL patients. We further studied the correlation of TME immune scores with other clinical features of DLBCL patients, and the results showed that there was a clear difference in TME immune scores for Ann Arbor phase I/II and III/IV DLBCL patients, and that DLBCL patients not of the GCB subtype had higher TME immune scores (FIG. 5).
Next, to explore differences in DLBCL development molecular mechanisms between different TME immune panels, we screened two groups of differentially expressed genes. We identified 81 significant down-regulated genes and 3 up-regulated genes in the high TME immune panel. Further enrichment of gene function it was found that differentially expressed genes were significantly enriched in biological functions associated with T cell immune responses. KEGG pathway analysis showed that differentially expressed genes were significantly enriched in pathways such as T cell receptor signaling and T-helper cell differentiation (fig. 6). Furthermore, we constructed a protein interaction network and identified 7 key genes associated with TME immune scores, including CD2, CD3E, MMP9, IFNG, GATA3, CCL5 and CCR5 (fig. 7), based on connectivity and mediator centrality, which might play an important role in TME immune regulation in DLBCL patients. Finally, we calculated TME immune scores according to the same formula in an NCICCR cohort consisting of 230 patients, and calculated the best cutoff for TME immune scores by the maxstat package in the R language, thereby dividing the patients into two groups. The K-M curve indicates a poor prognosis for the high TME immune score group, which indicates the stability of TME immune scores to the predicted efficacy of prognosis for DLBCL patients (fig. 8). Finally, of the 7 key genes associated with TME immune scores, CD2 was the key molecule for T cell receptor activation, and we examined the differences in mRNA expression levels of CD2 in DLBCL patient tissue and reactive lymph node tissue and the correlation of CD2mRNA levels with DLBCL patient prognosis in GSE32018 and GSE69051, respectively (fig. 9). To further study the cell localization and prognostic value of CD2 in DLBCL, we performed immunohistochemical staining based on actual cases at home. The results show that high expression of CD2 correlates with better overall survival and progression free survival in DLBCL patients (fig. 10), confirming that CD2 has good prognostic value in DLBCL patients.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A biomarker for prognosis of patients with diffuse large B-cell lymphoma, characterized in that said biomarker comprises at least a key gene and/or TME immune cells;
the key genes comprise any one or more of CD2, CD3E, MMP, IFNG, GATA3, CCL5 and CCR 5;
the TME immune cells comprise: any one or more of activated NK cells, resting NK cells, plasma cells, naive B cells, memory B cells, CD4 naive T cells, activated CD4 memory T cells, resting CD4 memory T cells, CD 8T cells, γδ T cells, regulatory T cells, follicular helper T cells, monocytes, macrophages M1, macrophages M2, macrophages M0, activated dendritic cells, resting dendritic cells, activated mast cells, resting mast cells, neutrophils, and eosinophils.
2. The biomarker for prognosis of diffuse large B-cell lymphoma patient according to claim 1, wherein the biomarker is the gene CD2.
3. Use of a substance for detecting a biomarker according to claim 1 or 2, for the preparation of a product for predicting diffuse large B-cell lymphoma prognosis.
4. The use according to claim 3, wherein the substance for detecting a biomarker comprises a substance for detecting a key gene and/or TME immune cell content and proportion of a subject;
further, the substances for detecting the key genes of the subject include, but are not limited to, substances for detecting the transcription of the key genes of the subject based on a gene sequencing method and/or based on a quantitative PCR method and/or based on an in situ hybridization method; or detecting the expression of the key gene expression product of the subject based on an immunoassay;
the key gene expression product is protein coded by the corresponding key gene.
5. The use of claim 3, wherein the prognosis comprises a prognostic assessment of survival (rate) of patients with diffuse large B-cell lymphoma;
the lifetime includes a total lifetime and a progression-free lifetime;
the survival rate comprises 3,5 and 10 years survival rate.
6. The use according to claim 3, wherein the product is a test kit, a test device or an apparatus.
7. A prognostic system for diffuse large B-cell lymphoma patients, the system comprising:
an acquisition module configured to: obtaining the expression level of the biomarker of claim 1 or 2 in a subject;
an evaluation module configured to: predicting a risk score of the diffuse large B-cell lymphoma prognosis according to the expression level of the biomarker obtained by the obtaining unit, and outputting the risk score;
an output module configured to: obtaining a prediction result according to the risk score;
wherein the evaluation module at least comprises a diffuse large B cell lymphoma prognosis evaluation model, and the prognosis evaluation model is specifically a key gene prognosis scoring model and/or a TME immune scoring model;
the key gene prognosis scoring model can be an immunohistochemical scoring model, namely, the immunohistochemical scoring model is divided into strong positive cells, medium positive cells and weak positive cells according to the positive staining degree (antigen content) of cells, and the calculation formula of the immunohistochemical scoring model is (strong positive cell percentage multiplied by 3) + (medium positive cell percentage multiplied by 2) + (weak positive cell percentage multiplied by 1);
the TME immune scoring model is obtained by model training TME immune cells of a patient with diffuse large B cell lymphoma, which are collected in advance, by adopting an algorithm.
8. The system of claim 7, wherein the prognosis comprises a prognostic assessment of survival (rate) of diffuse large B-cell lymphoma patients;
the lifetime includes a total lifetime and a progression-free lifetime;
the survival rate comprises 3,5 and 10 years survival rate.
9. A computer readable storage medium, on which a program is stored, which program, when being executed by a processor, implements the functions of the system according to claim 7 or 8.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, characterized in that the processor implements the functions of the system of claim 7 or 8 when executing the program.
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