Introduction

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid neoplasm, representing approximately 30% of non-Hodgkin lymphoma (NHL)1,2, and it is a highly heterogeneous, aggressive disease2,3. Currently, R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) is the standard first-line treatment for DLBCL2,4, however, about 30–40% of DLBCL patients remain resistant to RCHOP and are refractory or develop relapsed5,6, and only 10% of patients with refractory or relapsed DLBCL could be treated using salvage immunochemotherapy followed by autologous stem cell transplantation3,7, implying a significant unmet medical need8,9. The cell-of-origin (COO) classifications9,10 and the International Prognostic Index (IPI) score have often been considered the two most commonly prognostic factors for patients with DLBCL3,11,12. However, these prognostic factors do not fully explain risk stratification, clinical outcomes in DLBCL patients13,14, and there is an urgent need for valuable biomarkers to guide prognostic factors and therapeutic approaches for DLBCL.

Endoplasmic reticulum (ER) stress, a state in which the unfolded and misfolded protein accumulation affects the normal physiological function of cells, refers to the excessive stress caused by dysfunction of ER stress15,16. Notably, ER stress signaling is associated with the development of several cancers, including DLBCL15,16,17. The unfolded protein response (UPR), controlled general translation, misfolded protein degradation, and folding enzyme production are some of the adaptive responses that cells may initiate in response to ER stress. Protein kinase RNA (PKR)-like ER kinase (PERK), activating transcription factor 6 (ATF6), and inositol-requiring enzyme 1 (IRE1) are the three types of signal transducers evolved to be involved in the regulation of UPR network18,19. Malignant and stromal cells have their ER homeostasis disrupted by the hostile milieu created by a confluence of oncogenic, transcriptional, and metabolic aberrations in numerous tumor forms18,20,21. The alterations induce a state of persistent ER stress, which has been shown to regulate several protumor characteristics in cancer cells while dynamically altering the function of innate and adaptive immune cells. Generally, aberrant activation of ER stress sensors and their downstream signalling pathways have been identified as important regulators of cancer development, metastasis, and response to chemotherapy, targeted treatments, and immunotherapy19,20,21,22,23. But the significance of ER stress-related genes in the biological features and clinical prognosis of DLBCL has not been thoroughly investigated.

In our study, we aimed to better investigate and comprehend the predictive and prognostic value of ER stress-related genes in DLBCL using public databases. First, differentially expressed genes (DEGs) were compared in DLBCL patients with normal tissues. Second, the biological activities and potential pathways of ER stress-related DEGs were validated. Third, the prognostic model and immune infiltration assessment were performed. We explored and verified to illustrate the prognostic role and biological functions of ER stress-related genes in DLBCL at the bioinformatic and experimental levels.

Materials and methods

Data downloading and preprocessing

DLBCL datasets (GSE5631524, GSE1084625, and GSE1131826) were obtained from the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/). All three datasets were based on the GPL570 platform and came from Homo sapiens. There were 55 DLBCL patients and 33 noncancerous tissues (NCs) in the GSE56315 dataset, and all of them were included in this analysis. After removing patients with incomplete survival information, 414 DLBCL samples in the GSE10846 dataset and 200 DLBCL samples in the GSE11318 dataset were enrolled. And 48 DLBCL samples (TCGA_DLBC) in The Cancer Genome Atlas (TCGA) were downloaded from The University of California Santa Cruz (UCSC) Xena browser. A total of 295 ER stress-related geneset were obtained from the Molecular Signature Database v7.0 (MSigDB)27 after removing the overlapped genes (Supplementary Table S1).

DEGs screening and gene function analysis

DEGs had been screened between DLBCL samples and lymphocytal data from normal human tonsils, which came from the GSE56315 dataset, and were explored using the limma package28. Two thresholds were set to determine the degree of DEGs: the adjusted p value < 0.05 and |log2FC|> 1. And these DEGs were intersected with the ER stress-related genesets to extract the differentially ER stress-related DEGs. For the exploration of the function of the extracted genes, the gene ontology (GO) enrichment analysis29, and kyoto encyclopedia of genes and genomes (KEGG) pathway analysis30 were conducted using the clusterProfiler package31. In order to evaluate the gene expression level of potential signaling pathways and biological functions, the gene set enrichment analysis (GSEA)32 was conducted using the “c2.cp.kegg.v6.2.-symbols” geneset, and false discovery rate (FDR) q-value ≤ 0.25 was considered as statistically significant.

Protein–protein interaction network construction

For searching for the relationship between proteins of interest, a Protein–protein interaction (PPI) network was constructed through the Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org)33 online, and the combined score was greater than 0.4 considered statistically significant. Cytoscape (version 3.7.2, http://www.cytoscape.org)34 was used to visualize this PPI network, and the cytoHubba plug-in Cytoscape35 was used to calculate the hub nodes to select the top DEGs based on degree and the maximum correntropy criterion (MCC) algorithms.

Screening of prognosis-related genes and clinical correlation analysis

The GSE10846 dataset25, the GSE11318 dataset26, and the TCGA_DLBC databases were used to discover prognosis-related genes in DLBCL patients, and the clinical characteristics were extracted. For the investigation of the significant prognostic genes of ER stress-related DEGs, the samples of above the three datasets were divided into two groups based on the median value of differential ER stress-related DEGs expression and the intersection of them. The Kaplan–Meier (KM) curves were employed for survival analysis, and the log-rank method was used to compare the two groups, P < 0.05 was considered as significant difference. Simultaneously, the expression of ER stress-related DEGs in different COO and stages of DLBCL was analyzed by Kruskal Wallis test. In order to investigate the diagnostic role of ER stress-related DEGs in the GSE56315 dataset, the receiver operating characteristic (ROC) curve was drawn through the GSE56315 dataset using the pROC and plotROC packages36, and the area under the curve (AUC) was calculated. The experssion of ER stress-related DEGs in the TCGA_DLBC was also determined.

Gene expression quantification on cultured cell lines

In this study, we detected gene expression in cultured cell lines, there are four cell lines including HBL-1 (A gift from Fujian Research Institute of Haematology, China), SUDHL2 (A gift from School of Medcine, Southeast Universtiy, China), SUDHL4 (Meisen, China) and GM12878 (BeNa, China) enrolled, thess cell lines were cultured in Roswell Park Memorial Institute 1640 (RPMI‐1640) medium (Gibco, US), supplemented with 10% fetal bovine serum (FBS, Gibco, US) and 1% Penicillin/Streptomycin (Gibco, US), and incubated at 37℃ in a 5% CO2 incubator. Total RNA were isolated using TRIzol reagent (Invitrogen, US), then HiScript® Q RT SuperMix for qPCR (Vazyme, China) was used to transcribe the RNA into cDNA. The Quantitative real‐time polymerase chain reaction (qRT-PCR) was performed on the CFX Connect Real-Time PCR Detection System (BioRad, US) with an HQ SYBR qPCR Mix (Without ROX) (Zomen, China). The mRNA expressions were quantified with the 2−ΔΔCt method, and β-actin expression was used as an endogenous reference. The primer sequences of identified ER stress-related DEGs were purchased from Sunya, China (Supplementary Table S2).

Immune microenvironment assessment

For evaluating the amount and proportion of the expression of ER stress-related DEGs in immune cells, CIBERSORTx37,38, a suite of machine learning tools that used the deconvolution algorithm to evaluate the proportion of various immune cells from the expression profiles of tumor tissues, and leukocyte signature matrix (LM22) feature matrices were used to estimate the proportion of immune cell in the DLBCL samples in the GSE10846 and GSE11318 datasets using the ESTIMATE package39, which provides the algorithm to calculate the tumor purity, stromal score, immune score.

Construction and validation of the nomogram based on the prognostic model

A nomogram integrating the expression of ER stress-related DEGs and clinical characteristics of DLBCL patients, including tumor-node-metastasis (TNM) stage, Eastern Cooperative Oncology Group (ECOG) score, lactate dehydrogenase (LDH) ratio, and others, was constructed for prediction of prognosis in the GSE10846 as a training datasets. Furthermore, the nomogram’s prognosis prediction performance was confirmed in the GSE11318 datasets (validation dataset) by comparing the fitting degree between the observed and optimized values.

Statistical analysis

R programming (version 4.1) was used for all statistical analysis. We used the Student’s t-test or one-way analysis of variance (ANOVA) to compare normally distributed continuous variables between groups and Mann–Whitney test or Kruskal–Wallis test to examine non-normally distributed continuous variables. Two-sided P values less than 0.05 indicated statistical significance.

Results

Screening of ER stress-related DEGs

This study’s flowchart is depicted in Fig. 1. The clinical characteristics and standardized gene data were extracted from the GSE56315 dataset using GEOquery package40, and there are significant differences between DLBCL and NCs based on the principal component analysis (PCA), as shown in Fig. 2A. A total of 2179 DEGs were obtained through difference analysis using the limma package28, among which 1367 genes were upregulated and 812 genes were downregulated (Fig. 2B,C). After determining the overlap between ER stress-related genesets and DEGs through a Venn diagram, 26 ER stress-related DEGs were found (Fig. 2D).

Figure 1
figure 1

Workflow of this study.

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Figure 2
figure 2

Identification of ER stress-related DEGs. (A) PCA show the dimensionality reduction distribution of DLBCL samples and NCs in the GSE56315 dataset. (B)–(C) Heatmap plot and volcano plot of DEGs in DLBCL samples vs. NCs based on the GSE56315 dataset. (D) Venn diagram of ER stress-related genes and DEGs. DLBCL Diffuse Large B Cell Lymphoma, DEGs differentially expressed genes, ER endoplasmic reticulum, PCA principal component analysis.

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Functional enrichment analysis and PPI network construction

In order to find the enriched functions for the 26 ER stress-related DEGs, GO enrichment analysis were processed. The significantly enriched biological processes (BP) included response to ER stress, cellular response to stress, and response to stress. And the main top enrichment cellular component (CC) was ER (Fig. 3A). The KEGG pathway analysis showed that protein processing in ER, fluid shear stress, tumor necrosis factor (TNF) signaling pathway, atherosclerosis, and insulin resistance were enriched (Fig. 3B). More details of the top 50 significant items of GO enrichment analysis and KEGG pathway analysis (Ref: 231,102) could be shown in Supplementary Tables S3 and S4. The above results suggested that the metabolism and immune system were critical for DLBCL. We carried out a GSEA analysis to explore the metabolism and immune-related pathways; there were some pathways, including amino sugar and nucleotide sugar metabolism, diseases of metabolism reactome fatty acid metabolism, arachidonic acid metabolism, vitamin B12 metabolism were positively enriched in DLBCL patients (Fig. 3C). More details of the GSEA enrichment can be seen in Supplementary Table S5. For validation of the relationships among 26 ER stress-related DEGs, the STRING tool was used to assess with confidence (value ≥ 0.40), and there were 26 nodes and 15 edges with PPI network enrichment (Fig. 4A). The top ER stress-related DEGs based on MCC score were identified based on the cytoHubba plugin in Cytoscape software. There are 9 hub genes, including TP53, CCL2, CEBPB, NUPR1, TRIB3, CAV1, UBE4B, NPLOC4, and NRIH3 (Fig. 4B), indicating they might play a significant role in DLBCL.

Figure 3
figure 3

The functional enrichment analysis of ER stress-related DEGs. (A) GO enrichment analysis of ER stress-related DEGs. (B) KEGG pathway analysis of ER stress-related DEGs. (C) The enriched metabolic related pathways in DLBCL patients analyzed by GSEA. DEGs differentially expressed genes, ER endoplasmic reticulum, GO gene ontology, GSEA gene set enrichment analysis, KEGG kyoto encyclopedia of genes and genomes.

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Figure 4
figure 4

PPI network plot and hub genes of ER stress-related DEGs. (A) The PPI network of ER stress-related DEGs was visualized by Cytoscape software. (B) The hub genes were identified with the MCC score using cytoHubba plugin in Cytoscape software. PPI protein–protein interaction, ER endoplasmic reticulum, DEGs differentially expressed genes.

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Identification of significant prognostic gene

To identify the significant ER stress-related genes and survival data in DLBCL patients, we analyzed the prognostic value of ER stress-related genes using the GEO datasets (GSE10846 and GSE11318) and the TCGA-DLBC dataset, which the clinical characteristics are shown in Table 1. It was found that NUPR1 and TRIB3 had survival differences in the above 3 datasets (Fig. 5A) and were among 26 ER stress-related DEGs. Interestingly, according to the previous PPI results, NUPR1 and TRIB3 genes might have a direct interaction, and the connection score was 0.439 (Fig. 5B). The KM curve showed that there was a significant difference between high and low expression of NUPR1 and TRIB3 (P < 0.01) in the GSE10846 and GSE11318 datasets (Fig. 5C,D). In the TCGA-DLBC dataset, we found that NUPR1 and TRIB3 not only have survival differences in over survival (OS), but also have survival differences in progress free survival (PFS), disease specific survival (DSS), and disease free interval (DFI), as shown in Fig. 5E,F. These results showed that patients with high expression of NUPR1 and low expression of TRIB3 might have a better prognosis.

Table 1 Clinical characteristics of GSE10846, GSE11318, and TCGA_DLBC.
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Figure 5
figure 5

Prognosis-related genes and survival plot. (A) Venn diagram showed that 2 overlapped co-expressed prognosis-related genes were screened based on Log-rank test in the GSE10846, GSE11318, and TCGA_DLBC datasets. (B) Protein Interaction of NUPR1 and TRIB3 Genes. (C) The KM curves showed the OS of patients in the high-risk and low-risk groups in GSE10846 dataset. (D) The KM curves showed the OS of patients in the high-risk and low-risk groups in GSE11318 dataset. (E) The up-regulation NUPR1 might be associated with better OS, PFS, DSS and DFI in TCGA_DLBC dataset. (F) The up-regulation TRIB3 might be associated with poor OS, PFS, DSS in TCGA_DLBC dataset. DSS disease specific survival; DFI disease free interval; KM Kaplan–Meier, OS over survival, PFS progression free survival, TCGA the cancer genome atlas.

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Validation of the differential expression of NUPR1, TRIB3

We detected the expression of NUPR1 and TRIB3 in the GSE56315 dataset, the decreased expression of NUPR1 and increased expression of TRIB3 in DLBCL patients compared with NCs were showed, and the AUC was 0.791 and 0.807, respectively (Fig. 6A,B,D,E). Meanwhile, pan-cancer analysis based on the TIMER2 database41 (http://timer.cistrome.org) showed that NUPR1 had an inconsistent expression trend in different tumors, the expression of NUPR1 was decreased in bladder cancer, colon cancer, lung adenocarcinoma, prostate cancer, and other tumors. On the other hand, in breast cancer, renal clear cell carcinoma, and liver cancer, the expression of NUPR1 was increased (Fig. 6C). The upregulated TRIB3 might play an oncogenic role in tumors (Fig. 6F).

Figure 6
figure 6

The identification of NUPR1 and TRIB3 in DLBCL. (A) Comparison of NUPR1 expression level of DLBCL and NCs in GSE56315 dataset. (B) The ROC curves of NUPR1 in GSE56315 dataset. (C) Pan-cancer analysis of NUPR1 in cancer tissues and normal counterparts from TCGA-DLBC databases. D. Comparison of TRIB3 expression level of DLBCL and NCs in GSE56315 dataset. (E) The ROC curves of TRIB3 in GSE56315 dataset. (F) Pan-cancer analysis of TRIB3 in TCGA databases. DLBCL diffuse large B cell lymphoma; ROC receiver operating characteristic; TCGA the cancer genome atlas.

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Next, the correlation of NUPR1 and TRIB3 with the clinical characteristics of the GSE10864 and GSE11318 datasets was confirmed. The COO of DLBCL often identified three subgroups including activated B-cell-like (ABC), germinal-center B-cell-like (GCB), and unclassified (UNC). As shown in Fig. 7, The expression of NUPR1 in the ABC subtype was lower than in the GCB subtype both two GEO datasets (Fig. 7A,C), On the contrary, the expression of TRIB3 in the ABC subtype was higher than GCB subtype (Fig. 7E,G). However, the expression of NUPR1 and TRIB3 in differential stages was not significantly different based on the two GEO datasets (Fig. 7B,D,F,H). It showed that downregulated NUPR1 and upregulated TRIB3 might be identified as vital biomarkers for DLBCL.

Figure 7
figure 7

Differential expression of NUPR1 and TRIB3. (A)–(B). The expression of NUPR1 among different subtypes and stages in GSE10846 dataset; (C)–(D). The expression of NUPR1 among different subtypes and stages in GSE11318 dataset; (E)–(F). The expression of TRIB3 among different subtypes and stages in GSE10846 dataset; (G)–(H). The expression of TRIB3 among different subtypes and stages in GSE11318 dataset; The qRT-PCR validation, (I) The expression of NUPR1 of GM12878, HBL-1, SUDHL2 and SUDHL4; (J) The expression of TRIB3 of GM12878, HBL-1, SUDHL2 and SUDHL4. * P < 0.05; ** P < 0.01; *** P < 0.001. qRT-PCR: quantitative real‐time polymerase chain reaction.

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The expression of NUPR1 and TRIB3 in cultured cell lines

We also detected the expression of NUPR1 and TRIB3 in different cell lines via qRT-PCR (Fig. 7I,J). The expression in TRIB3 of DLBCL cell lines, including ABC subtype cell lines (HBL-1, SUDHL2) and GCB subtype cell lines (SUDHL4), were higher than normal B cell lines (GM12878) (P < 0.05), and ABC subtype cell lines were higher than GCB subtype (P < 0.05). But the expression of NUPR1 of HBL-1 had no significant difference in comparison with GM12878 (P = 0.085), the expression of NUPR1 in SUDHL2 and SUDHL4 was higher than GM12878 (P < 0.05). Both the expression of NUPR1 and TRIB3 in SUDHL2 than in SUDHL4 (P < 0.05).

Relationship between the expression of NUPR1, TRIB3 and immune infiltration

Based on our previous analysis, we speculated that NUPR1 might inhibit the progress of DLBCL and TRIB3 might promote it. Subsequently, the relationship between the expression of NUPR1, TRIB3, and immune infiltration was analyzed. As shown in Fig. 8, in the GSE10846 and GSE11318 datasets, NUPR1 was positively correlated with the estimate score, immune score, and matrix score, simultaneously, the expression of NUPR1 was positively correlated with M0 macrophages (Fig. 8A–D). However, TRIB3 was significantly negatively correlated with the estimate score, immune score, and matrix score (Fig. 8E–H), and had no significance with all immune cells in both two GEO datasets. These results suggested that NUPR1 might inhibit the progress of DLBCL by involving the immune response. But TRIB3 had no close relationship with immune cells through correlation with tumor purity.

Figure 8
figure 8

The correlation of prognostic genes with immune infiltration. (A) The relationship between the expression of NUPR1 with ESTIMATE score, immune score and matrix score in the GSE10846 dataset. (B) The relationship between the expression of NUPR1 and the different subsets of immune cell infiltrates in the GSE10846 dataset. (C) The relationship between the expression of TRIB3 with ESTIMATE score, immune score and matrix score in the GSE10846 dataset. (D) The relationship between the expression of TRIB3 and the different subsets of immune cell infiltrates in the GSE10846 dataset. (E) The relationship between the expression of NUPR1 with ESTIMATE score, immune score and matrix score in the GSE11318 dataset. (F) The relationship between the expression of NUPR1 and the different subsets of immune cell infiltrates in the GSE11318 dataset. (G) The relationship between the expression of TRIB3 with ESTIMATE score, immune score and matrix score in the GSE11318 dataset. (H) The relationship between the expression of TRIB3 and the different subsets of immune cell infiltrates in the GSE11318 dataset. |rho|> 0.4 and P < 0.05 were considered to be significantly correlated.

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Construction and validation of prognosis model

Based on the GSE10846 and GSE11318 datasets, a clinical prognosis model was developed using NUPR1 and TRIB3 as characteristic variables and its accuracy and generalizability assessed. By combining NUPR1 and TRIB3 with other clinical characteristics of DLBCL patients, such as gender, age, diagnosis type, stage, LDH ratio, number of extranodal lymph nodes, and ECOG score. We found that the above risk factors were statistically different in univariate analysis except for gender. Subsequently, multivariate Cox analysis found that age (≥ 60 years old), stage, LDH ratio, and the expression of TRIB3 were independent prognostic factors (Fig. 9A). Next, the nomogram for accurate patient prognosis and prediction was constructed based on the clinical characteristics of DLBCL patients and the expression of TRIB3 (Fig. 9B). In order to evaluate the accuracy of the model, the time-dependent ROC curve was drawn using the GSE10846 dataset as the training cohort and the GSE11318 dataset as the test cohort. The AUC corresponding to 1, 3, and 5 years of both the training cohort and test cohort were all above 0.75 (Fig. 9C–D), indicating the nomogram model provided a good predictive accuracy of DLBCL. Furthermore, the calibration curve suggested that the consistency of the model is good at 1-, 3-, and 5-year intervals (Fig. 9E). These above results indicated that combining the clinical characteristics of DLBCL with the expression of TRIB3 could predict the prognosis of patients with DLBCL in 1-, 3-, and 5-year intervals, which was expected to provide a more effective reference for clinicians and formulate effective intervention measures.

Figure 9
figure 9

Construction and validation of prognosis model. (A) Forest plot summary of the univariate and multivariable analyses of NUPR1, TRIB3 and other clinical characteristics. (B) Nomogram for predicting the probability of patient mortality at 1-, 3- and 5-year of OS. (C) The ROC for 1-, 2- and 3-year survival rate in the GSE10846 dataset, and the AUC was 0.785, 0.786 and 0.792, respectively. (D) The ROC for 1-, 2- and 3-year survival rate in the GSE11318 dataset, and the AUC was 0.754, 0.781 and 0.808, respectively. (E) 1-, 3-, and 5-year calibration curves of clinical prediction models for DLBCL patients, the model is validated by resampling with the bootstrap method, and the number of times was 1000. AUC area under curve, DLBCL diffuse large B cell lymphoma, OS overall survival.

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Discussion

Due to differences in gene expression profiles and genetic alterations, DLBCL is a highly diverse lymphoid neoplasm that exhibits a wide range of clinical outcomes and therapeutic responses2,3,9. There has been significant development in targeted treatment and immunotherapy for DLBCL in recent years3,5, however, innovative treatment approaches and targets are urgently required. ER stress has emerged as a focal-point and forward position field in a variety of human malignancies during the last decade22,23, and is involved in many biological processes, such as apoptosis, autophagy42, ferroptosis43, and hypoxia44. But there is a lack of in-depth knowledge about the role of ER stress in the clinical progress of DLBCL.

So far as we know, our study is the first research on the relationship between the ER stress-related gene and DLBCL. These findings should help researchers in the future learn more about how to predict prognosis and treat DLBCL patients individually in the clinic. Based on the outcomes of previous bioinformatic analysis, we have found nine genes may play essential roles in DLBCL. As previously described45,46,47, it had been proven that TP53, CCL2, and CEBPB were involved in the carcinogenesis and development of DLBCL. TP53 has been identified as one of the most frequently mutated genes and could be a valuable prognostic biomarker in both ABC and GCB DLBCL patients45,46. It was found that CEBPB and BCL2A1 can induce cell transformation and increase the survival of anaplastic large cell lymphomas cells47. However, no research has been reported about the role of NUPR1, TRIB3, CAV1, UBE4B, NPLOC4, and NRIH3 in DLBCL. After identifying the prognostic value of ER stress-related genes using three datasets, NUPR1 and TRIB3 may play important roles in DLBCL carcinogenesis and development. NUPR1 is located in the nucleus of various cells, including cancer cells48,49. Borrello et al. demonstrated the essential role of NUPR1 which participated in the regulation of UPR and more broadly in the integrated stress response by interacting with eIF2α, and protected the liver from metabolic distress by controlling lipid homeostasis50. Howerer, Liu et al. found that NUPR1 was upregulated in the bone marrow of patients with multiple myeloma (MM)51, downregulation of NUPR1 might significantly inhibit cell proliferation and promote autophagy-mediated apoptosis in MM52. NUPR1 could also active autophagy and bind to the promoter regions of some autophagy-related genes, such as BECN1, GREB1, RAB31, PGR, CYP1B1, thereby regulating breast cancer metastasis and transcription49,53. Histone methyltransferase Dot1L might inhibit pancreatic cancer cell apoptosis by targeting NUPR1, and overexpressed NUPR1 also inhibited pancreatic cancer cell apoptosis54. In summary, NUPR1 can play an important role in cell stress and stress‑related apoptosis48,55. We found the level of NUPR1 in DLBCL patients to be downregulated, and that in the GCB subtype was significantly higher than the ABC subtype in our study. We also found NUPR1 could involve in the immune response.

Compared with noncancerous tissues, TRIB3 expression was markedly increased in DLBCL patients, particularly in the ABC subtype and later stages, suggesting that TRIB3 may play a carcinogenic role in DLBCL56,57,58,59. As a member of the mammalian pseudokinase tribbles family, TRIB3 can interfere with a lot of proteins, such as kinase-dependent proteins, transcription factors, and ubiquitin ligases60,61. Evidences show that TRIB3 can regulate the downstream biological process of ER stress, alleviate cell stress, and promote cell survival58,62,63. Ohoka et al. demonstrated that certain ER stress inducers, including low glucose and hypoxia, may increase TRIB3 expression62,63,64. Twist Family BHLH Transcription Factor 1 (TWIST1) is stabilized in part by TRIB3 binding to its WR domain and blocking its ubiquitination, depletion of TRIB3 can boost TWIST1 degradation and increase sensitivity to all-trans retinoic acid (ATRA), consistent with its role in regulating carcinogenesis and progression63. TRIB3 overexpression in human gastric cancer was related to tumor angiogenesis and a poor prognosis65. Through analysis of lymphoma specimens, TRIB3 expression was positively correlated with MYC expression66, MYC is a transcription factor, and its alterations have been considered to be associated with aggressive clinical behavior in DLBCL67. Mechanistically, E3 ubiquitin ligase UBE3B-mediated MYC ubiquitination and degradation can be inhibited by TRIB3 bind to MYC66. These results are in agreement with our studies and imply that TRIB3 may be a critical regulatory factor in driving cancer cell proliferation, migration, and invasion57,58,68. Furthermore, our bioinformatic analysis indicated that TRIB3 could be a significant survival predictor and a potential therapeutic target for DLBCL.

Our bioinformatic analysis suggested a direct connection between NUPR1 and TRIB3. Methamphetamine (METH) exposed to rat and PC12 cell lines could increase the expression of NUPR1 accompanied by CHOP and TRIB3 upregulation and promote apoptosis and autophagy69, suggesting the NUPR1/CHOP/TRIB3 signal pathway plays a crucial function in the regulation of apoptosis and autophagy via ER stress69,70. There were shown the expression of NUPR1 and TRIB3 concurrently. Unfortunately, our study suggested that both the lower expression of NUPR1 and the higher expression of TRIB3 meant a worse clinical prognosis for DLBCL patients. Furthermore, the qRT-PCR test showed the expression of TRIB3 in HBL-1, SUDHL2, and SUDHL4 was higher than GM12878, and these results were consistent with our bioinformatic analysis, However, the expression of NUPR1 was not compatible with these analysis.

The clinical prognostic model was built based on the GSE10846 and GSE11318 datasets, using NUPR1 and TRIB3 as feature variables, and evaluated for accuracy and generalizability. There are some authors71,72 who have built the prognosis model that is similar to ours. Lv et al. showed that carcinoembryonic antigen, N stage, and surgical method were independent prognostic factors for overall survival in patients with obstructive colorectal cancer using a nomogram model71. For researching the role of ACK1-associated immunomodulators in non-small cell lung cancer (NSCLC), Zhu J et al. Established a multiple-gene risk prediction model, and the results showed the risk scores were an independent prognosis predictor in the TCGA lung cohorts72. It was suggested that ACK1 may be a potential immunotherapeutic target72. Numerous types of tumors may share similar genetic or molecular features, there are a lot of studies48,55,57,60,61 showed that NUPR1 and TRIB3 are involved in cancer biology, and we think this prognosis model that includes these genes may be applied to more types of tumors. However, due to the specificity and complexity of different types of cancer, further studies should verify and adjust the accuracy and reliability of these identical markers and explore the relationship between their biological functions and clinical applications in numerous types of tumors.

Some limitations existed in this study. First, the prognostic model was built and validated using publicly available data; hence, more prospective studies are needed to confirm its accuracy and utility. Second, by narrowing our focus to only two ER stress-related genes, NUPR1 and TRIB3, we may have missed other noteworthy prognostic genes for DLBCL. Third, only four cell lines were used to verify the expression of NUPR1 and TRIB3, and we will carry out more cell lines for research if the experimental conditions are confirmed.

Conclusion

In summary, our study clarified that downregulation the expression of NUPR1 and upregulation of TRIB3 in DLBCL patients. Further study has shown NUPR1 may inhibit the progress of DLBCL by involving the immune response, and TRIB3 should be a carcinogenic gene in DLBCL through regulating ER stress. Moreover, we found that combining the clinical characteristics of DLBCL by TRIB3 expression could better predict the prognosis of DLBCL patients, suggesting TRIB3 might serve as a potential therapeutic target and prognostic factor in DLBCL.