Application of immune-related polygenes in prognosis evaluation of diffuse large B-cell lymphoma

Abstract

The invention relates to the technical field of disease prognosis and molecular biology, in particular to application of immune-related polygenes in prognosis evaluation of diffuse large B-cell lymphoma. The biomarkers are DES, CHIT1, FGF14, CCR4, CTLA4 and VGF (Vertical Growth Factor). According to the method, core genes related to immunity are screened on the basis of diffuse large B cell lymphoma transcriptome data such as GEO and TCGA, and a multi-gene immune model: RS = (-0.0306) * DES + (-0.4683) * CHIT1 + (1.1418) * FGF14 + (0.1132) * CCR4 + (-0.07) * CTLA4 + (-0.3463) * VGF is constructed; according to the immune polygene immune model, diffuse large B-cell lymphoma patients can be divided into high and low RS groups, the survival prognosis of high-RS people is proved to be obviously lower than that of low-RS people, and the survival prognosis can be used as an independent prognosis index.

Description

Technical Field

[0001] This invention relates to the fields of disease prognosis and molecular biology, specifically to the application of immune-related multigenes in the prognostic assessment of diffuse large B-cell lymphoma. Background Technology

[0002] Diffuse large B-cell lymphoma (DLBCL) is a malignant and aggressive tumor originating from B-cell lymphocytes. It is a highly heterogeneous lymphoma characterized by variations in gene expression profiles and alterations in genetic information. Currently, some patients can be cured with combination therapy, but others remain ineligible for a cure. Furthermore, patients who do not respond to initial treatment or relapse after remission often have a poor prognosis. Therefore, to improve the prognosis of DLBCL patients, further research is needed to explore the underlying molecular mechanisms of DLBCL development and progression.

[0003] Currently, prognostic assessment of diffuse large B-cell lymphoma is a multifaceted process involving clinical characteristics, biomarkers, and molecular genetic information. These assessment methods help physicians develop personalized treatment plans, improving patient survival rates and quality of life. Therefore, this invention provides the application of immune-related multigenes in the prognostic assessment of diffuse large B-cell lymphoma. Summary of the Invention

[0004] The technical problem this invention aims to solve is to provide the application of immune-related multi-genes in the prognostic assessment of diffuse large B-cell lymphoma. The goal is to utilize biomedical big data combined with cutting-edge molecular pathology and bioinformatics analysis tools to comprehensively construct a prognostic risk assessment model for diffuse large B-cell lymphoma, which has positive significance for predicting patient survival.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: In a first aspect, a biomarker for prognostic assessment of diffuse large B-cell lymphoma, said biomarker being DES, CHIT1, FGF14, CCR4, CTLA4, and VGF.

[0006] Based on the above technical solution, the present invention can be further improved as follows.

[0007] Secondly, the application of a reagent for detecting the expression level of a biomarker used for prognostic assessment of diffuse large B-cell lymphoma in the preparation of the product.

[0008] Furthermore, the product is used for prognostic assessment of survival in patients with diffuse large B-cell lymphoma.

[0009] Thirdly, a product for assessing the prognostic survival of patients with diffuse large B-cell lymphoma, the product comprising a reagent for detecting the expression level of the biomarker for assessing the prognostic survival of patients with diffuse large B-cell lymphoma.

[0010] Furthermore, the product includes a reagent kit.

[0011] Fourthly, a multi-gene immune model for prognostic assessment of survival in patients with diffuse large B-cell lymphoma, wherein the multi-gene immune model is constructed based on the expression levels of the biomarker for prognostic assessment of diffuse large B-cell lymphoma. The multi-gene immune model is: RS=(-0.0306)*DES+(-0.4683)*CHIT1+(1.1418)*FGF14+(0.1132)*CCR4+(-0.07)*CTLA4+(-0.3463)*VGF, where DES represents the expression level of the DES gene, CHIT1 represents the expression level of the CHIT1 gene, FGF14 represents the expression level of the FGF14 gene, CCR4 represents the expression level of the CCR4 gene, CTLA4 represents the expression level of the CTLA4 gene, and VGF represents the expression level of the VGF gene.

[0012] Furthermore, the multi-gene immune model was constructed based on the expression levels of a biomarker for prognostic assessment of diffuse large B-cell lymphoma described in the TCGA database.

[0013] Furthermore, in the multi-gene immune model, RS expression is high when it is above a threshold, indicating a poor prognosis and short overall survival for the subject; RS expression is low when it is below a threshold, indicating a good prognosis and long overall survival for the subject; the threshold is the median critical value of the multi-gene immune model score.

[0014] Fifthly, a system for assessing the prognostic survival of patients with diffuse large B-cell lymphoma, the system comprising: (1) Analysis unit, the analysis unit comprising a reagent for detecting the expression level of the biomarker for prognostic assessment of diffuse large B-cell lymphoma; (2) An assessment unit comprising: assessing the survival prognosis of the subject based on the expression level of a biomarker for prognostic assessment of diffuse large B-cell lymphoma as determined in (1), and assessing the survival prognosis of the subject based on a multi-gene immune model for prognostic assessment of the survival of patients with diffuse large B-cell lymphoma.

[0015] The beneficial effects of this invention are: Based on transcriptome data from diffuse large B-cell lymphoma (DBL) such as GEO and TCGA, core immune-related genes were screened, and a multi-gene immune model was constructed: RS = (-0.0306)*DES + (-0.4683)*CHIT1 + (1.1418)*FGF14 + (0.1132)*CCR4 + (-0.07)*CTLA4 + (-0.3463)*VGF. According to this multi-gene immune model, DBL patients can be divided into high and low RS groups. The study confirmed that the survival prognosis of the high RS group was significantly lower than that of the low RS group, and that RS can serve as an independent prognostic indicator. Attached Figure Description

[0016] Figure 1 The grouping of differentially expressed genes in this invention is as follows: <2 times is orange, 2-4 times is red, 4-8 times is green, and >8 times is black. Figure 2 This is a diagram showing the intersection of genes from this invention (more than 4 times the number of genes) with the IMMPORT immune database. Figure 3 This is a PPI network interaction diagram of the present invention; Figure 4 A partial likelihood bias versus log(λ) relationship was plotted using the LASSO Cox regression model for this invention; where A represents the Riskscore and survival time / survival status, B represents the survival curve, C represents the scatter plot, D represents the expression heatmap containing the gene, and E represents the ROC curve. Figure 5 This invention uses TCGA data to evaluate the prediction accuracy of the ROC curve model. Detailed Implementation

[0017] The principles and features of this invention are described below. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention. Where specific techniques or conditions are not specified in the embodiments, they should be performed according to the techniques or conditions described in the literature in this field, or according to the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased through legitimate channels.

[0018] This invention utilizes biomedical big data combined with cutting-edge molecular pathology and bioinformatics analysis tools to comprehensively construct a prognostic risk assessment model for diffuse large B-cell lymphoma, which has positive significance for predicting patient prognosis and survival.

[0019] This embodiment relates to a biomarker for prognostic assessment of diffuse large B-cell lymphoma, wherein the biomarker is DES, CHIT1, FGF14, CCR4, CTLA4 and VGF.

[0020] This embodiment also relates to the application of a reagent for detecting the expression level of a biomarker used for prognostic assessment of diffuse large B-cell lymphoma in the preparation of the product.

[0021] Preferably, the product described in this embodiment is used for prognostic assessment of survival in patients with diffuse large B-cell lymphoma.

[0022] This embodiment also relates to a product for assessing the prognostic survival of patients with diffuse large B-cell lymphoma, the product comprising a reagent for detecting the expression level of the biomarker for assessing the prognostic survival of patients with diffuse large B-cell lymphoma.

[0023] Preferably, the product described in this embodiment includes a reagent kit.

[0024] This embodiment also relates to a multi-gene immune model for prognostic assessment of the survival of patients with diffuse large B-cell lymphoma, which is constructed based on the expression levels of the biomarker for prognostic assessment of diffuse large B-cell lymphoma. The multi-gene immune model is: RS=(-0.0306)*DES+(-0.4683)*CHIT1+(1.1418)*FGF14+(0.1132)*CCR4+(-0.07)*CTLA4+(-0.3463)*VGF, where DES represents the expression level of the DES gene, CHIT1 represents the expression level of the CHIT1 gene, FGF14 represents the expression level of the FGF14 gene, CCR4 represents the expression level of the CCR4 gene, CTLA4 represents the expression level of the CTLA4 gene, and VGF represents the expression level of the VGF gene.

[0025] Preferably, the multi-gene immune model of this embodiment is constructed based on the expression level of a biomarker for prognostic assessment of diffuse large B-cell lymphoma described in the TCGA database.

[0026] Preferably, in the multi-gene immune model of this embodiment, RS expression is high when it is above a threshold, indicating a poor prognosis and short overall survival for the subject; RS expression is low when it is below a threshold, indicating a good prognosis and long overall survival for the subject; the threshold is the median critical value of the multi-gene immune model score.

[0027] This embodiment also relates to a system for prognostic assessment of survival in patients with diffuse large B-cell lymphoma, the system comprising: (1) Analysis unit, the analysis unit comprising a reagent for detecting the expression level of the biomarker for prognostic assessment of diffuse large B-cell lymphoma; (2) An assessment unit comprising: assessing the survival prognosis of the subject based on the expression level of a biomarker for prognostic assessment of diffuse large B-cell lymphoma as determined in (1), and assessing the survival prognosis of the subject based on a multi-gene immune model for prognostic assessment of the survival of patients with diffuse large B-cell lymphoma.

[0028] Example 1. Study population and data collection.

[0029] A suitable dataset of diffuse large B-cell lymphomas was selected from the Gene Expression Omnibus (GEO) database (https: / / www.ncbi.nlm.nih.gov / gds / ). Patient inclusion criteria included: (a) histological diagnosis of diffuse large B-cell lymphoma; (b) complete gene expression data; and (c) complete survival information. Screening criteria: ① sample size > 10 cases; ② samples including those with varying prognostic outcomes.

[0030] 2. Research Methods.

[0031] (1) Analyze differentially expressed genes (DEGs) among different prognostic populations of diffuse large B-cell lymphoma and group all differentially expressed genes according to the fold change of the genes.

[0032] (2) Differentially expressed genes (DEGs) among different prognostic populations of GEO diffuse large B-cell lymphoma were grouped according to their fold change. The intersection of GEO differentially expressed genes with genes from the IMMPORT immune database was used, and a core gene cluster PPI network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) to identify core gene clusters potentially related to immune regulation. LASSO mathematical operations were performed on different permutations of the core gene clusters, and ROC curves were used to select key genes for constructing gene models.

[0033] (3) The correlation between the model and the prognosis of diffuse large B-cell lymphoma was verified by univariate and multivariate Cox analysis, KM analysis and ROC analysis.

[0034] 3. Research Results.

[0035] 3.1 Data Filtering: The dataset GSE178965 was selected, which includes 9 samples with poor prognosis and 10 samples with good prognosis. The clinical characteristics of GSE178965 are shown in Table 1.

[0036] Table 1 Clinical characteristics of GSE178965 3.2 Grouping of differentially expressed genes: Differentially expressed genes were divided into four groups: ① |log2FC|≤1.0 (fold change less than 2); ②1.0≤|log2FC|≤2.0 (fold change greater than 2 but less than 4); ③2.0≤|log2FC|≤3.0 (fold change greater than 4 but less than 8); ④3<|log2FC| (fold change greater than 8). Figure 1 110 genes with a fold change greater than 4 were selected and intersected with the IMMPORT immune database to obtain 10 immune-related genes. These 10 immune-related genes are: DES, CHIT1, FGF14, CCR4, CTLA4, ICOS, VGF, IL21, CD40LG, and IGHV3-7. Figure 2 The PPI network interaction diagram shows that 110 genes are interconnected and related to signal reception, tumorigenesis, and immune processes. Figure 3 ).

[0037] 3.3 Construction and validation of multi-gene immune models: Data and clinical information on diffuse large B-cell lymphoma were downloaded from the TCGA dataset library. Finally, samples with RNAseq data and clinical information were retained, and 47 samples were used for subsequent validation analysis. The 47 TCGA clinical features are shown in Table 2.

[0038] Table 2 Clinical characteristics of TCGA Log rank was used to test the survival differences between two or more groups in KM survival analysis, and timeROC analysis was performed to determine the accuracy of the predictive model. Lasso: Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, with 10-fold cross-validation. All analyses were performed using the glmnet package in R. Cox: A prognostic model was constructed using multivariate Cox regression analysis. All analyses were performed using the survival package in R. Step: First, multivariate Cox regression analysis was performed, followed by iterative analysis using the step function to select the optimal model as the final model.

[0039] For Kaplan-Meier curves, p-values ​​and hazard ratios (HRs) with 95% confidence intervals (CIs) were derived using logrank tests and univariate Cox regression. All analyses and R software packages were performed using R version 4.0.3. p < 0.05 was considered statistically significant.

[0040] The coefficients of the selected feature are displayed by the lambda parameter, with the horizontal axis representing the value of the independent variable lambda and the vertical axis representing the coefficients of the independent variable; using LASSO. The Cox regression model plotted the relationship between partial likelihood bias and log(λ), and selected 6 core genes from 10 candidate genes: DES, CHIT1, FGF14, CCR4, CTLA4, and VGF. These 6 genes were arranged in different combinations, and a multi-gene immune model was constructed based on their expression levels in the TCGA database: RS(Riskscore) = (-0.0306)*DES + (-0.4683)*CHIT1 + (1.1418)*FGF14 + (0.1132)*CCR4 + (-0.07)*CTLA4 + (-0.3463)*VGF, where DES represents the expression level of DES, CHIT1 represents the expression level of CHIT1, FGF14 represents the expression level of FGF14, CCR4 represents the expression level of CCR4, CTLA4 represents the expression level of CTLA4, VGF represents the expression level of VGF, and lambda.min = 0.0235.

[0041] Based on Riskscore, survival time, and survival status, patients with diffuse large B-cell lymphoma were further divided into high and low RS groups from the 47 samples in the selected TCGA dataset. The top plot represents a scatter plot of the Riskscore from low to high, with different colors representing different risk groups; the middle plot represents the scatter plot distribution of survival time and survival status corresponding to different sample Riskscores; the bottom plot represents the expression heatmap of genes included in the model; KM shows that the higher the model score, the greater the prognostic risk for the patient; the ROC curve and AUC of this risk model at different time points are also shown, with a higher AUC value indicating stronger predictive ability of the model. Figure 4 (A to E in the middle).

[0042] The model's predictive accuracy was evaluated using ROC curves based on TCGA data. The model's RS score was higher than that of the six single genes in the model, and the AUC value was >0.7, indicating good predictive performance. Figure 5 ).

[0043] 3.4 Independent prognostic value of gene prognostic models.

[0044] Univariate and multivariate Cox analyses revealed that this immune model can serve as an independent prognostic indicator.

[0045] Table 3 Cross Table with Age Table 4 shows the results of the chi-square test for age. In the table, a. the expected count for 0 cells (0.0%) is less than 5. The minimum expected base is 6.85. b. Calculations are performed only for 2×2 tables.

[0046] Table 5 Cross-tabulation with Gender Table 6. Results of the chi-square test for gender. In the table, a. the expected count for 0 cells (0.0%) is less than 5. The minimum expected base is 10.28. b. Calculations are performed only for 2×2 tables.

[0047] Table 7 Cross Table with BIM Table 8 shows the results of the BIM chi-square test. In the table, a. the expected count for 0 cells (0.0%) is less than 5. The minimum expected base is .98. b. Calculations are performed only for 2×2 tables.

[0048] As shown in Tables 3 to 8, the meaningless results related to gender, age, and BMI may be related to grouping (grouping can be further combined with the age range of disease incidence), but there was no significant difference in gender between patients in the high-risk and low-risk groups.

[0049] Table 9 Cross-reference with primary site Table 10 shows the results of the chi-square test with the primary site. In the table, a. the expected count for 0 cells (0.0%) is less than 5. The minimum expected base is 8.81. b. Calculations are performed only for 2×2 tables.

[0050] Table 11 Cross-tabulation with whether or not radiotherapy was administered Table 12 shows the results of the chi-square test for whether or not radiotherapy was received. In the table, a. the expected count for 0 cells (0.0%) is less than 5. The minimum expected base is 3.00. b. Calculations are performed only for 2×2 tables.

[0051] As shown in Tables 9 to 12, there was no significant difference in the distribution of patients in the high-risk and low-risk groups in terms of primary site and whether or not they received radiotherapy.

[0052] Table 13 Univariate Cox Regression Table 14 Multifactor Cox Regression In summary, based on GEO and TCGA transcriptome data of diffuse large B-cell lymphoma, core immune-related genes were screened, and a multi-gene immune model was constructed: RS = (-0.0306)*DES + (-0.4683)*CHIT1 + (1.1418)*FGF14 + (0.1132)*CCR4 + (-0.07)*CTLA4 + (-0.3463)*VGF. According to this multi-gene immune model, patients with diffuse large B-cell lymphoma can be divided into high and low RS groups, confirming that the survival prognosis of the high RS group is significantly lower than that of the low RS group, and that RS can serve as an independent prognostic indicator.

[0053] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A biomarker for the prognosis evaluation of diffuse large B-cell lymphoma, characterized in that, The biomarker is DES, CHIT1, FGF14, CCR4, CTLA4 and VGF.

2. Use of a reagent for detecting the expression level of a biomarker for evaluating the prognosis of diffuse large B-cell lymphoma in the preparation of a product.

3. Use according to claim 2, characterized in that, The product is used for evaluating the prognosis of the survival period of a patient with diffuse large B-cell lymphoma.

4. A product for the prognosis of survival in patients with diffuse large B-cell lymphoma, characterized in that, The product comprises a reagent for detecting the expression level of a biomarker for evaluating the prognosis of the survival period of a patient with diffuse large B-cell lymphoma.

5. The product for use in the prognostic evaluation of the survival of a patient with diffuse large B-cell lymphoma according to claim 4, characterized in that, The product comprises a kit.

6. A multi-gene immune model for the prognosis of survival in patients with diffuse large B-cell lymphoma, characterized in that, The multi-gene immune model is constructed based on the expression level of the biomarker for evaluating the prognosis of diffuse large B-cell lymphoma. The multi-gene immune model is: RS=(-0.0306)*DES+(-0.4683)*CHIT1+(1.1418)*FGF14+(0.1132)*CCR4+(-0.07)*CTLA4+(-0.3463)*VGF, wherein DES represents the expression level of the DES gene, CHIT1 represents the expression level of the CHIT1 gene, FGF14 represents the expression level of the FGF14 gene, CCR4 represents the expression level of the CCR4 gene, CTLA4 represents the expression level of the CTLA4 gene, and VGF represents the expression level of the VGF gene.

7. The multi-gene immunological model for the prognosis of the survival period of a patient with diffuse large B-cell lymphoma according to claim 6, characterized in that, The multi-gene immune model is constructed based on the expression level of the biomarker for evaluating the prognosis of diffuse large B-cell lymphoma in the TCGA database.

8. The multi-gene immunological model for the prognosis of the survival period of a patient with diffuse large B-cell lymphoma according to claim 6, characterized in that, When the RS of the multi-gene immune model is higher than the threshold value, it is high expression, indicating that the subject has a poor prognosis and a shorter overall survival period; when the RS of the multi-gene immune model is lower than the threshold value, it is low expression, indicating that the subject has a good prognosis and a longer overall survival period; and the threshold value is the median critical value of the score of the multi-gene immune model.

9. A system for the prognosis of survival in patients with diffuse large B-cell lymphoma, characterized by, The system comprises: (1) an analysis unit comprising a reagent for detecting the expression level of a biomarker for evaluating the prognosis of diffuse large B-cell lymphoma; (2) an evaluation unit comprising: based on the expression level of the biomarker for evaluating the prognosis of diffuse large B-cell lymphoma determined in (1), a multi-gene immune model for evaluating the prognosis of the survival period of a patient with diffuse large B-cell lymphoma according to any one of claims 6 to 8 is used to evaluate the survival prognosis of the subject.

Images