CN115572768B - Prognosis evaluation and combined treatment for diffuse large B cell lymphoma - Google Patents

Prognosis evaluation and combined treatment for diffuse large B cell lymphoma Download PDF

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CN115572768B
CN115572768B CN202211377902.8A CN202211377902A CN115572768B CN 115572768 B CN115572768 B CN 115572768B CN 202211377902 A CN202211377902 A CN 202211377902A CN 115572768 B CN115572768 B CN 115572768B
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diffuse large
cell lymphoma
dlbcl
prognosis
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CN115572768A (en
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周香香
王欣
吕烈媚
张玉
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Shandong Provincial Hospital Affiliated to Shandong First Medical University
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    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
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    • A61K31/33—Heterocyclic compounds
    • A61K31/395—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
    • A61K31/40—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having five-membered rings with one nitrogen as the only ring hetero atom, e.g. sulpiride, succinimide, tolmetin, buflomedil
    • A61K31/4025—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having five-membered rings with one nitrogen as the only ring hetero atom, e.g. sulpiride, succinimide, tolmetin, buflomedil not condensed and containing further heterocyclic rings, e.g. cromakalim
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Abstract

The invention belongs to the technical fields of biological medicine and molecular biology, and particularly relates to prognosis evaluation and combined treatment for diffuse large B cell lymphoma. The invention determines for the first time that the cell apoptosis is closely related to the occurrence and progress of diffuse large B cell lymphoma. Based on the characteristics of the gene related to cell apoptosis in diffuse large B cell lymphoma, a comprehensive prognosis model is established, and the prognosis of the diffuse large B cell lymphoma patient is accurately predicted. Furthermore, the invention specifically verifies the synergistic anti-tumor effect of the PD-L1 inhibitor BMS1166 and the cell scorch inhibitor DMXAA in diffuse large B cell lymphoma cells, and shows that the targeted cell scorch combined immunotherapy can become a promising treatment method for diffuse large B cell lymphoma, thereby having good practical application value.

Description

Prognosis evaluation and combined treatment for diffuse large B cell lymphoma
Technical Field
The invention belongs to the technical fields of biological medicine and molecular biology, and particularly relates to prognosis evaluation and combined treatment for diffuse large B cell lymphoma.
Background
The disclosure of this background section is only intended to increase the understanding of the general background of the invention 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.
Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid malignancy in adults, with a high degree of invasiveness and heterogeneity. DLBCL has made significant progress in therapy due to the advent of rituximab-based immunotherapeutic drugs. For most advanced cancer patients, the cure rate of the R-CHOP regimen (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) can reach over 60%. However, about one third of patients develop drug resistance or relapse, and it is difficult to achieve a desirable survival time. Thus, more studies are needed to stratify DLBCL patients and develop predictive models that can provide more accurate molecular subtypes to provide personalized therapies.
Apoptosis, also known as inflammatory necrosis, is a novel form of apoptosis (PCD). Characterized in that the cells continue to expand until the cell membrane ruptures, resulting in release of the cell contents and activation of the inflammatory response. Cell apoptosis plays an important role in anti-infective and immune defenses. Gasderm family members, including Gasderm A (GSDMA), gasderm E (GSDME) and Pejvakin (PJVK or DFNB 59) are the most important contributors to apoptosis. Under various microbial and endogenous stimuli, gasderm is cleaved by the cell-coke apoptosis caspase and the N-terminal domain of Gasderm is inserted into the membrane, initiating cell-coke apoptosis. More and more research has linked apoptosis to cancer and other malignancies, including lung, ovarian and colon cancer. Interestingly, cell apoptosis may play a dual role in the development of tumors. In one aspect, apoptosis, a form of cell death, inhibits tumor growth. Induction of tumor cell apoptosis may be a promising cancer treatment regimen. On the other hand, various signal pathways and inflammatory mediators released during the process of cell apoptosis create a good environment for the growth of tumor cells, thereby promoting tumor growth. For example, the release of IL-1 beta and IL-18 may promote the development and progression of cancer. Recent studies confirm the potential regulatory relationship that exists between apoptosis and the tumor immune microenvironment. GSDMA-mediated apoptosis of cell foci by increasing tumor-infiltrating NK cells and CD8 + T cells kill lymphocytes to inhibit tumor growth.
In recent years, the mechanism of action of cell apoptosis in the course of tumorigenesis and development is a hot spot. Cell apoptosis may have an important role in the development of DLBCL. However, the inventors found that little research has been done on the role of cell apoptosis in the development of DLBCL.
Disclosure of Invention
In order to overcome the defects in the prior art, the inventor provides a prognosis evaluation and a combined treatment for diffuse large B cell lymphoma through long-term technical and practical exploration. The invention determines that the cell apoptosis is closely related to the generation and the progress of DLBCL for the first time. Based on the characteristics of the cell apoptosis related genes (PRGs) in the DLBCL, a comprehensive prognosis model is established, and the prognosis of the DLBCL patient is accurately predicted. Further, the invention proves that the targeted cell apoptosis combined immunotherapy can be a promising treatment method of DLBCL through researches. Based on the above results, the present invention has been completed.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
in a first aspect of the invention, there is provided a biomarker for diffuse large B-cell lymphoma prognosis evaluation selected from any one or more of the following genes associated with apoptosis: SCAF11, CASP8, CASP9, NLRP1 and NLRP6.
The invention discovers that the gene related to cell apoptosis is obviously related to prognosis of patients with diffuse large B cell lymphoma, thus constructing a diffuse large B cell lymphoma prognosis evaluation model (named PRGs score) based on the biomarker, wherein the diffuse large B cell lymphoma prognosis evaluation model has a calculation formula of = (-1.971 x CASP8 exp.) + (-0.300 x CASP9 exp.) + (-0.340 x NLRP1 exp.) + (-0.124 x NLRP6 exp.) + (6.139 x SCAF11 exp.) + (-0.900 x TIRAP exp.).
In a second aspect of the invention, there is provided the use of a substance for detecting a biomarker as described above in the manufacture of a diffuse large B-cell lymphoma prognosis evaluation product.
Wherein the prognostic evaluation includes at least an evaluation of total survival (OS) of patients with diffuse large B-cell lymphoma.
In a third aspect of the invention, there is provided a system for diffuse large B-cell lymphoma prognosis evaluation, the system comprising:
a) An analysis module, the analysis module comprising: a detecting substance for determining the expression level of a gene associated with apoptosis selected from the above-mentioned genes in a sample to be tested of a subject, and;
b) An evaluation module, the analysis module comprising: performing a prognostic evaluation on said subject based on said apoptosis-related gene expression levels determined in a).
In a fourth aspect, the invention provides an application of a cell apoptosis inhibitor combined with a PD-1 inhibitor in preparing a diffuse large B cell lymphoma drug.
Wherein the inhibitor of apoptosis may be disulfiram and the inhibitor of PD-1 may be BMS1166.
Wherein, the Disulfiram (DSF) is a newly discovered drug (CAS number: 97-77-8) capable of inhibiting cell apoptosis, BMS1166 is a novel PD-1 inhibitor (CAS number: 1818314-88-3), and experiments prove that the Disulfiram (DSF) and the BMS have the effect of inhibiting proliferation of diffuse large B cell lymphoma cells, and the effect shows dose and time dependence; meanwhile, the two components are combined to generate good synergistic effect.
In a fifth aspect of the present invention, there is provided a pharmaceutical composition comprising at least a scorch inhibitor and a PD-1 inhibitor as active ingredients.
The active ingredients of the composition at least comprise disulfiram and BMS1166. The combination of the two can obviously inhibit the proliferation of DLBCL cells, thereby showing the good prospect of the targeted cell apoptosis combined immunotherapy in the treatment of DLBCL.
In a sixth aspect of the invention, there is provided a method of treating diffuse large B-cell lymphoma comprising administering to a patient the above-described pharmaceutical composition.
Compared with the prior art, the one or more technical schemes have the following beneficial effects:
the technical scheme constructs a prognosis model based on 6 cell coke death related genes SCAF11, CASP8, CASP9, NLRP1, NLRP6 and TIRAP, and divides DLBCL patients into high and low risk groups. Furthermore, qRT-PCR further confirmed the expression of the above genes in DLBCL cell lines. Survival analysis found that the mortality rate was lower and survival time was longer in the low risk group compared to the high risk group. Single and multiple factor Cox regression analysis showed that the PRGs-based risk scoring model was an independent prognostic factor (HR >1, p < 0.01) for DLBCL patients. Furthermore, the excellent predictive performance of the prognostic model was further verified by ROC curves and nomograms. The GO and KEGG enrichment demonstrates that these PRGs may be involved in cellular protein modification processes and in the regulation of the JAK-STAT signaling pathway. Notably, this risk score is closely related to DLBCL immune characteristics, and increased immune activity may contribute to the anti-tumor effects of DLBCL. The technical scheme further verifies the synergistic anti-tumor effect of the PD-L1 inhibitor BMS1166 and the cell apoptosis inhibitor DMXAA in DLBCL cells.
In sum, the technical scheme constructs a comprehensive prognosis model according to the characteristics of PRGs in DLBCL, and can accurately predict the prognosis of a DLBCL patient; and the targeted cell apoptosis combined immunotherapy can become a promising DLBCL alternative treatment method, so that the method has good practical application value.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 shows the relative expression of (A-C) PRGs in DLBCL samples and normal samples in an embodiment of the present invention. ns, no statistical significance; * p <0.05; * P <0.01; * P <0.001; * P <0.0001. (D) PPI networks show PRGs interactions.
FIG. 2 is a Kaplan-Meier survival analysis of six PRGs in examples (A-E) of the present invention; correlation analysis of (G-H) with 6 PRGs of risk score.
FIG. 3 shows an embodiment of the present invention(A-F) CASP8, CASP9, NLRP1, NLRP6, TIRAP and SCAF11 at CD19 + Relative mRNA expression in B cells and DLBCL cells.
FIG. 4 shows (A) univariate cox regression analysis with 9 PRGs (P < 0.05) associated with prognosis in the examples of the present invention; performing LASSO regression analysis; the situation is distributed in different queues based on risk scores, individual patient survival (low risk group: left side of dotted line; high risk group: right side of dotted line) and (D-E) CASP8, CASP9, NLRP1, NLRP6, SCAF11 and TIRAP expression heatmaps.
FIG. 5 is a diagram showing an example of an (A-B) Kaplan-Meier method for comparing OS between different queue high and low risk groups; (C-D) ROC curves evaluate the predictive efficiency of different queue risk scores.
FIG. 6 is a univariate analysis of different queues (stage: tumor differentiation, 1-4; ECOG, 0-4) in an example of the invention; (a-B) nomograms predict 1, 3 and 5 year overall survival of DLBCL patients (6C); a calibration curve (D) of the alignment chart; ROC curve analysis (E).
Fig. 7 is a comparison of ImmuneScore (A), stromalScore (B), estimateScore (C) and tumor purity (D) between high and low risk groups in an example of the invention. The enrichment scores of the low and high risk groups of 16 immune cells and 13 immune-related pathways in the training cohort (E-F) were compared. P values indicate ns is not significant, P <0.05, P <0.01, P <0.001.
FIG. 8 is a graph showing the correlation of (A) analysis risk score with immune checkpoint in an embodiment of the present invention. The scatter plot shows the correlation between risk scores and PDCD1 (B), LAG3 (C), CD40 (D) and CTLA4 (E).
FIG. 9 shows that BMS1166 and DSF both have an effect of inhibiting LY1 cell proliferation in the examples of the present invention, and the effect is dose and time dependent (A-B). BMS1166 and DMXAA combined treatment concentration ratio (BMS 1166) was 12. Mu.M: (DSF) 5. Mu.M significantly inhibited DLBCL cell proliferation (C-D).
Detailed Description
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present 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 example embodiments in accordance with 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 view of this, in one exemplary embodiment of the invention, a biomarker for diffuse large B-cell lymphoma prognosis evaluation is provided, selected from any one or more of the following genes associated with apoptosis: SCAF11, CASP8, CASP9, NLRP1 and NLRP6.
Further, the biomarker is a group consisting of SCAF11, CASP8, CASP9, NLRP1, and NLRP6.
The invention discovers that the gene related to cell apoptosis is obviously related to prognosis of patients with diffuse large B cell lymphoma, thus constructing a diffuse large B cell lymphoma prognosis evaluation model (named PRGs score) based on the biomarker, wherein the diffuse large B cell lymphoma prognosis evaluation model has a calculation formula of = (-1.971 x CASP8 exp.) + (-0.300 x CASP9 exp.) + (-0.340 x NLRP1 exp.) + (-0.124 x NLRP6 exp.) + (6.139 x SCAF11 exp.) + (-0.900 x TIRAP exp.).
In yet another embodiment of the invention, there is provided the use of a substance for detecting a biomarker as described above for the preparation of a diffuse large B-cell lymphoma prognosis evaluation product.
In particular, the substances for detecting the above biomarkers include, but are not limited to, substances for detecting the expression level of the biomarkers for RT-PCR, real-time quantitative PCR, in situ hybridization, gene chip and gene sequencing.
Such products include, but are not limited to, primers, probes, chips, nucleic acid membrane strips, formulations or kits, etc., for the expression level of the biomarker, and are not specifically limited herein.
In yet another embodiment of the present invention, the prognostic evaluation includes at least an assessment of total survival (OS) of patients with diffuse large B-cell lymphoma.
In yet another embodiment of the present invention, there is provided a system for diffuse large B-cell lymphoma prognosis evaluation, the system comprising:
a) An analysis module, the analysis module comprising: a detecting substance for determining the expression level of a gene associated with apoptosis selected from the above-mentioned genes in a sample to be tested of a subject, and;
b) An evaluation module, the analysis module comprising: performing a prognostic evaluation on said subject based on said cell apoptosis-related gene expression levels determined in a);
in yet another embodiment of the present invention, in the a) analysis module, the gene related to apoptosis includes any one or more selected from the group consisting of: SCAF11, CASP8, CASP9, NLRP1 and NLRP6.
Further, the biomarker is the group consisting of SCAF11, CASP8, CASP9, NLRP1 and NLRP6.
The specific evaluation flow of the evaluation module b) comprises the following steps: performing a prognosis evaluation for survival based on a prognosis evaluation model according to the level of gene expression associated with apoptosis determined in a);
in yet another embodiment of the present invention, the prognostic evaluation model has a calculation formula = (-1.971×casp8exp.) + (-0.300×casp9exp.) + (-0.340×nlrp1 exp.) + (-0.124×nlrp6 exp.) + (6.139×scaf1exp.) + (-0.900×tirrapexp.).
Wherein exp represents an exponential function based on a natural constant e.
In yet another embodiment of the present invention, a high expression of the subject prognostic model index above a threshold value indicates that the subject is experiencing poor prognosis (shorter overall survival, higher mortality);
a low expression of the subject prognosis model index below the threshold value indicates that the subject is better prognosis (longer overall survival, lower mortality);
in yet another embodiment of the present invention, the threshold may be a median risk score value for the prognostic model.
In yet another embodiment of the invention, there is provided the use of a cytostatic agent in combination with a PD-1 inhibitor in the manufacture of a medicament for treating diffuse large B-cell lymphomas.
Wherein the inhibitor of apoptosis may be disulfiram and the inhibitor of PD-1 may be BMS1166.
Wherein, the Disulfiram (DSF) is a recently discovered drug (CAS number: 97-77-8) capable of inhibiting cell apoptosis, BMS1166 is a novel PD-1 inhibitor (CAS number: 1818314-88-3), and experiments prove that both have the effect of inhibiting proliferation of diffuse large B-cell lymphoma cells, and the effect shows dose and time dependence; meanwhile, the two components are combined to generate good synergistic effect.
In yet another embodiment of the present invention, when combined, the BMS1166 and disulfiram are present in a molar ratio of 5-10:1-4; further 14:5.
In yet another embodiment of the present invention, a pharmaceutical composition is provided, the active ingredients of which comprise at least a cytostatic agent and a PD-1 inhibitor.
In yet another embodiment of the present invention, the pharmaceutical composition comprises at least disulfiram and BMS1166 as active ingredients. The combination of the two can obviously inhibit the proliferation of DLBCL cells, thereby showing the good prospect of the targeted cell apoptosis combined immunotherapy in the treatment of DLBCL.
In yet another embodiment of the present invention, the molar ratio of BMS1166 to disulfiram is 5-10:1-4; further 14:5.
The pharmaceutical composition may also include at least one pharmaceutically inactive ingredient.
The pharmaceutically inactive ingredients may be carriers, excipients, diluents and the like which are generally used in pharmacy. Further, the composition can be formulated into various dosage forms such as powders, granules, tablets, capsules, suspensions, emulsions, syrups, sprays, etc., for oral administration, external use, suppositories, and sterile injectable solutions according to a usual method.
The non-pharmaceutically active ingredients, such as carriers, excipients and diluents, which may be included, are well known in the art and can be determined by one of ordinary skill in the art to meet clinical criteria.
In yet another embodiment of the present invention, the carriers, excipients and diluents include, but are not limited to, lactose, dextrose, sucrose, sorbitol, mannitol, xylitol, erythritol, maltitol, starch, acacia, alginate, gelatin, calcium phosphate, calcium silicate, cellulose, methylcellulose, microcrystalline cellulose, polyvinylpyrrolidone, water, methyl hydroxybenzoate, propyl hydroxybenzoate, talc, magnesium stearate, mineral oil and the like.
In yet another embodiment of the invention, the medicament of the invention may be administered to the body in a known manner. Such as systemic delivery via veins. Alternatively via intravenous, transdermal, intranasal, mucosal or other delivery methods. Such administration may be via single or multiple doses. It will be appreciated by those skilled in the art that the actual dosage to be administered in the present invention may vary greatly depending on a variety of factors, such as the target cell, the type of organism or tissue thereof, the general condition of the subject to be treated, the route of administration, the mode of administration, and the like.
In yet another embodiment of the present invention, the subject to be administered can be human or non-human mammal, such as mouse, rat, guinea pig, rabbit, dog, monkey, gorilla, etc., preferably human.
In yet another embodiment of the present invention, there is provided a method of treating diffuse large B-cell lymphoma comprising administering to a patient the above-described pharmaceutical composition.
The invention 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 invention is not particularly limited and can be commercially available.
Examples
Materials and methods
Study population and data acquisition
Gene expression information in DLBCL patients and normal samples was derived from cancer genomic maps (TCGA) (https:// portal. Gdc. Cancer. Gov/projects/TCGA-PAAD /) and genomic tissue expression (GTEx) (https:// www.gtexportal.org /). The raw CEL data for GSE10846 and GSE53786 are extracted from the gene expression synthesis (GEO) database.
Recognition of differentially expressed PRGs
33 genes associated with apoptosis were obtained from previous literature. The gene expression profile of the patient in the TCGA and GTEx databases was analyzed and visualized using the R-package "ggplot 2". To analyze the relationship between PRGs, PPI networks were constructed using STRING online tool (http:// STRING-db. Org/cgi /), and interaction score >0.4 was used as a cut-off.
Construction and verification of PRGs prognostic model
To investigate the prognostic value of PRGs, a univariate Cox analysis was performed on training cohort GSE 53786. PRGs (P < 0.05) associated with prognosis were screened, LASSO-Cox regression analysis was performed in a training cohort, and a prognostic model was constructed. Risk score calculation formula: risk score = Σ7ixi×yi (X: coefficient, Y: gene expression level). Patients were divided into high and low risk groups according to median risk score, OS between subgroups were compared using Kaplan-Meier method, and ROC curves were plotted using the "timeROC" R package. GSE10846 is selected as a verification queue, and the analysis method is the same as the above.
Independent prognostic analysis of risk scores
Clinical information such as age, stage, chemotherapy regimen and eastern tumor co-operating group (ECOG) performance scores for DLBCL patients in GEO database were collected. Variables in the regression model were analyzed using univariate and multivariate Cox regression models in combination with risk scores. A nomogram comprising a plurality of independent indices is constructed for predicting prognosis of a patient. To evaluate the accuracy of the nomograms, calibration curves were applied to predict OS for 1, 3 and 5 years.
Immunoinfiltration characterization
Differentially Expressed Genes (DEGs) between high and low risk groups were screened and GO and KEGG enrichment analysis was performed on the DEGs. The "gsva" R package was used to calculate the score of infiltrating immune cells and evaluate the activity of immune-related pathways. In addition, differences in immune microenvironment were assessed using the "estimate" R package. The correlation of the expression of the common immune checkpoint with the risk score is analyzed by a correlation matrix.
Cell culture
IMDM complete medium containing 10% fetal bovine serum was prepared and human DLBCL cell lines LY1, LY3, LY8 and U2932 were resuspended in IMDM complete medium and cultured in a cell incubator containing 5% co2 at 37 ℃.
RNA extraction and real-time fluorescent quantitative PCR detection
Total RNA of risk PRGs was extracted using TRIzol reagent followed by reverse transcription into cDNA using reverse transcription reagent. Amplification reactions were performed on a LightCycler 480II real-time PCR system using SYBR Green. Real-time PCR for each sample was performed in triplicate and all primers used are listed in supplementary table S2.
Cell activity assay
Cell activity assays were used to explore the effect of BMS1166 and DSF on DLBCL cells. According to the pharmaceutical instructions, BMS1166 or disufiram powder was completely dissolved using DMSO to prepare a 5mM mother solution, and the mother solution was diluted into a working solution using IMDM medium according to a concentration conversion formula. Cells were resuspended using IMDM complete medium and seeded in 96-well plates at 1x10 4 Individual/ml cells. BMS1166, DSF, or BMS1166+ DSF were added, 3 duplicate wells were set for each concentration, and a control group was set, and after shaking uniformly, 96-well plates were placed in a 5% co2 incubator at 37 ℃. After 24 hours incubation, 10. Mu.l of CCK-8 reagent was added to each well and allowed to stand at 37℃for 4 hours in the dark. The absorbance at 450nm of each cell was measured using a microplate reader, and the cell viability was calculated from the results.
Statistical analysis
All statistical analyses were performed using R software (version 3.6.3) and Graphpad Prism 8. If not specified otherwise, statistical significance is considered to be P-value less than 0.05 (< 0.05 by P; P <0.01, P <0.001, P < 0.0001).
Results
Abnormal expression of various cell coke death related genes in DLBCL
To explore the profile of PRG expression in DLBCL, mRNA expression levels of PRGs in DLBCL and normal samples were assessed. The results show that most PRGs are deregulated in DLBCL samples (fig. 1A). Subsequently, 8 central genes were screened, including PJVK, TNF, CASP, CASP3, CASP5, CASP8, NLRP3 and IL18 (fig. 1B). Given the potential prognostic value of PRGs in DLBCL, the role of these risk PRGs in DLBCL prognosis was investigated. The results indicate that high expression of SCAF11 is associated with poor prognosis, while high expression of CASP8, CASP9, NLRP1, NLRP6 and TIRAP are protective factors (FIGS. 2A-F). SCAF11 correlated positively with risk score agreement, while other risk genes correlated negatively (fig. 2G-H). Furthermore, qRT-PCR results showed a significant increase in the expression of SCAF11 in the DLBCL cell line compared to normal cells. However, the mRNA expression levels of CASP8 and CASP9, NLRP1, NLRP6 and TIRAP were significantly reduced in the DLBCL cell line (FIGS. 3A-F).
Constructing a PRGs-based prognosis model in DLBCL: PRGs score
Univariate Cox regression analysis was applied to further investigate the prognostic value of PRGs in the training cohort. Overexpression of AIM2, CASP3, CASP5, CASP6, IL18 and SCAF11 was found to be associated with poor prognosis in DLBCL patients (fig. 4A). 19 prognostic genes in the training cohort were selected for lasso cox regression analysis to establish a prognostic model PRGs score (FIGS. 4B, C). The risk score is derived from the following formula: risk score= (-1.971×casp8exp.) + (-0.300×casp9exp.) + (-0.340×nlrp1 exp.) + (-0.124×nlrp6 exp.) + (6.139×scaf11 exp.) + (-0.900×tirrapexp.). Patients in the training cohort are classified into high and low risk groups according to median risk scores. The mortality rate and survival time were lower in the low risk group compared to the high risk group. The heat map revealed that SCAF11 was highly expressed in the high risk group, while CASP8, CASP9, NLRP1, NLRP6 and TIRAP were significantly up-regulated in the low risk group (fig. 4d, e).
Prognostic value of PRGs score in DLBCL
The actual application of the prognosis model is further verified. The Kaplan-Meier curve shows that the prognosis is significantly lower in the high risk group than in the low risk group at survival (fig. 5A). Similar results were obtained in the validation queue (fig. 5B). ROC analysis showed that the model showed satisfactory predictive effect in the training cohort (1 year auc= 0.787,2 years 0.848,3 years survival 0.849) (fig. 5C) and validation cohort auc=1 year 0.695,2 years 0.725,3 years survival 0.779) (fig. 5D). The high and low risk groups for pathology staging, chemotherapy regimens and stages were further analyzed and visualized using a Sankey diagram (fig. 5E). Consistently, most DLBCL patients derived from GCB belong to the low risk group. These evidences reveal the outstanding predictive efficacy and superiority of this model.
Construction and verification of predicted nomograms of DLBCL
Next, univariate and multivariate Cox regression analysis was applied to assess whether the risk score could be used as an independent prognostic marker for DLBCL patients. In a single factor Cox regression analysis, risk scores (P <0.001, hr=3.348, 95% ci:2.297-4.480, fig. 6A) are one potential risk factor. Multifactorial Cox regression analysis further showed that the risk score was still useable as a prognostic factor after adjustment for other confounding factors (p=0.0425, hr= 1.928, 95% CI:1.022-3.636, fig. 6B). To explore a reliable and quantifiable method, a novel prognostic nomogram was developed that can predict survival of DLBCL patients based on risk scores and clinical features (such as age, sex, stage, chemotherapy and ECOG performance scores). The alignment plots can effectively predict the probability of OS for DLBCL patients for 1, 3 and 5 years (fig. 6C). The calibration curve shows that the predicted OS is consistent with the observed OS (fig. 6D), with areas under ROC curves (AUC) of 0.794,0.847 and 0.849 for OS for 1, 3 and 5 years, respectively (fig. 6E).
PRGs socre is closely related to the immune status of DLBCL patients
The underlying biological roles and mechanisms by which PRGs pose different risks are further explored. To study differences in gene function and pathway between high and low risk groups, differentially Expressed Genes (DEGs) were extracted using the "limma" R package (|log2fc| >1, p < 0.05).
Based on these DEGs, GO and KEGG enrichment demonstrated that these PRGs may be involved in cellular protein modification processes and in the regulation of JAK-STAT signaling pathways. In view of the strong correlation of the JAK-STAT signaling pathway with the tumor immune environment, the immune status of the subgroup was analyzed. The Estimate algorithm showed that the immune scores were significantly higher in the low risk group, and there was no significant difference in tumor purity, estimated score, and matrix score between the high and low risk groups (FIGS. 7A-D). Subsequently, enrichment scores for 16 immune cell types and activities of 13 immune-related pathways were compared using ssGSEA. The low risk group was significantly more immune-infiltrated than the high risk group. In addition, the low risk group plasma cell-like dendritic cells (pDC), follicular helper T cells (Tfh), T helper 1 cells (Th 1) and Tumor Infiltrating Lymphocytes (TIL) were more active than the high risk group, while NK cells and T helper 2 cells (Th 2) were the opposite. In addition, the low risk group had higher immune pathway activity in APC co-suppression, T-co-stimulation, checkpoints and Human Leukocyte Antigens (HLA) (fig. 7e, f). Given the significant differences in checkpoint pathways between subgroups and the critical role of checkpoints in immunotherapy, correlations between risk scores and checkpoint expression were analyzed. Common checkpoints, such as PDCD1, LAG3, and CD40, are significantly correlated with risk scores in DLBCL (fig. 8A). CD40, LAG3 and PDCD1 were all inversely related to risk scores (fig. 8B-D), whereas CTLA4 was not (fig. 8C). Based on these findings, targeting cell apoptosis in combination with immunotherapy may provide potential therapeutic benefits.
Synergistic antitumor effect of cytostatic agents in combination with PD-1 inhibitors in DLBCL
The anti-tumor effect of PD-1 inhibitors in combination with cytostatic agents in DLBCL was further investigated. BMS1166 is a novel PD-1 inhibitor (CAS number 1818314-88-3), disulfiram (DSF) is a newly discovered drug that inhibits cell apoptosis (CAS number 97-77-8). Cell proliferation assay experiments showed that both BMS1166 and DSF had an effect on inhibition of LY1 cell proliferation, and that this effect was dose and time dependent (fig. 9A-B). The medicine combination index (CA) is less than or equal to 0.8 by using CompuSyn software, which shows that BMS1166 has better synergistic effect in combination with DSF. In addition, the ratio of the combined treatment concentration of BMS1166 and DMXAA was (BMS 1166) 12. Mu.M: (DSF) 5. Mu.M significantly inhibited DLBCL cell proliferation (FIGS. 9C-D). These results reveal good prospects for targeted cell apoptosis combined immunotherapy in DLBCL treatment.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims (1)

1. The application of the cell coke death inhibitor combined with the PD-1 inhibitor in preparing medicaments for treating diffuse large B cell lymphoma is characterized in that the cell coke death inhibitor is disulfiram, and the PD-1 inhibitor is BMS1166;
the BMS1166 to disulfiram molar ratio is 12:5.
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