Immune Microenvironment-related Gene Mapping Predicts Immunochemotherapy Response And Prognosis In Diffuse Large B-cell Lymphoma

Background and objectiveDiffuse large B-cell lymphoma(DLBCL)is the most common subtype of non-Hodgkin’s lymphoma.Currently,the rituximab-based R-CHOP immunochemotherapy regimen is the standard first-line treatment regimen for DLBCL,which leads to clinical cure in more than 50%of patients with advanced DLBCL.However,about one-third of patients are still resistant to the R-CHOP regimen or relapse after complete remission.For patients with drug resistance or relapse,second-line regimens have low overall efficiency.An increasing number of studies have shown that the tumor microenvironment plays an important role in the initiation,progression and drug responsiveness of DLBCL.The objective of this study was to explore the impact of tumor immune microenvironment on R-CHOP immunochemotherapy response and prognosis using gene expression profile data from public databases,and to mine a series of hub genes to explore the possibility of these genes as biomarkers for prognosis assessment and potential therapeutic targets.MethodsGSE31312 was selected from the GEO database as the training set for the study,and the gene expression profile data and clinical information of this dataset were downloaded to analyze the baseline clinical information of these patients.Patients with treatment response of CR and PR were defined as immunochemotherapy responders,while patients with SD and PD were defined as non-responders.The CIBERSORT algorithm was used to quantify the proportion of 22 types of immune cells in DLBCL tumor tissues and to identify immune cells that differentially infiltrated between immunochemotherapy responders and non-responders.Weighted gene co-expression network analysis(WGCNA)was performed for differentially expressed genes in responders and non-responders,gene co-expression modules were constructed,correlation coefficients between modules and immune cells were calculated,and the module with the highest correlation coefficient with the target immune cells was selected as the hub module.Functional enrichment analysis of hub modules was performed,and the STRING online database was used to construct protein-protein interaction(PPI)networks for hub modules and identify the hub genes connecting the networks.Survival analysis of these genes was performed to identify hub genes with prognostic significance and to analyze the relationship between these genes and clinical features and PD1 immune checkpoint.In addition,a cohort of DLBCL patients from the Southern Hospital of Southern Medical University was used as a validation set to validate our analysis results.Results1.The complete remission rate of R-CHOP standard treatment was 75.4%,partial remission rate was 15.3%,overall response rate was 90.7%,and non-response rate was only 9.3%.Compared with the responders,the non-responders had worse ECOG physical score(P=0.018),predominantly advanced tumor stage(P<0.001),mostly normal serum LDH level(P=0.015),higher IPI score(P=0.004),and lower survival rate(P<0.001).2.The cell abundance of activated memory CD4+ T cells(P=0.026)and y8 T cells(P=0.001)was significantly higher in the responders than in the non-responders.The proportion of these two types of cells gradually decreased as the treatment effect worsened.3.GO enrichment analysis showed that the proteins encoded by the hub module genes are mostly localized on the membrane and bind to upstream and downstream molecules to participate in the immune activation process.KEGG pathway enrichment results showed that the hub module genes are mainly involved in viral infection,NOD-like receptor signaling pathway,T cell receptor signaling pathway,PD-1/PDL1 immune checkpoint pathway,etc.4.High expression of CD3G(P<0.001),CD3D(P=0.036),GNB4(P=0.019),FCHO2(P<0.001),and GPR183(P=0.002)was positively correlated with overall survival time in DLBCL patients.Survival analysis of different subtype groups showed that CD3G(P=0.001),FCHO2(P=0.019)and GPR183(P=0.018)were prognostically significant in the GCB subtype,and FCHO2(P=0.012)and GPR183(P=0.002)were prognostically significant in the non-GCB subtype.5.The expression levels of CD3G(P=0.005),CD3D(P=0.0023)and GPR183(P=0.0008)gradually decreased with worsening treatment response.The expression levels of GNB4 and FCHO2 in different treatment response groups did not reach statistical differences.However,the expression levels of GNB4 and FCHO2 in the responders were significantly higher than those in the non-responders(P<0.05).GNB4(P=0.002)and GPR183(P=3e-05)were more highly expressed in the non-GCB subtype than in the GCB subtype.6.CD3G,CD3D,GNB4,FCHO2,and GPR183 were all negatively correlated with PD1.7.The validation cohort of our hospital showed that the immunochemotherapy response rate of patients with CD3-positive tumor tissue at the first diagnosis was significantly higher than that of CD3-negative patients(P=0.006).Conclusions1.Activated memory CD4+T cells and γδ T cells are closely associated with a good immunochemotherapy response in DLBCL,and the baseline immune status of the host tumor tissue affects the immunochemotherapy response.2.CD3G,CD3D,GNB4,FCHO2,and GPR183 are involved in the regulation of the immune microenvironment of DLBCL,and they can be used as biomarkers to predict the response to immunochemotherapy in DLBCL.3.As a marker to measure the level of tumor-infiltrating T cells,CD3 molecules can predict immunochemotherapy responses in patients with DLBCL.