Lymphoma is a malignant tumor of the blood system (1). Lymphomas are classified as non-Hodgkin's lymphoma (NHL) and Hodgkin's lymphoma, according to their histological and cytological characteristics (2). Diffuse large B-cell lymphoma (DLBCL) is the most common form of NHL, accounting for 30–40% of all lymphomas. DLBCL is distinguished from other tumor types through high levels of heterogeneity, invasiveness, and diverse clinical, pathological and biological features (3). Notably, DLBCL develops as a result of the uncontrolled growth of malignant B cells, in the absence of external stimulation from the tumor microenvironment (TME) (4).

Proteins play key roles in numerous cellular activities and functions (5). Proteomics research focuses on the existence and activity patterns of all proteins in cells (6). Proteomic techniques are used to determine potential associations between changes in protein expression levels in DLBCL and disease progression (7); thus, offering a tool for DLBCL detection and management. Proteomics may also aid in identifying novel therapeutic targets and determining patient prognosis (8). Proteomics techniques include protein-separation technology based on two-dimensional gel electrophoresis, and protein identification using bioinformatics analysis and mass spectrometry (9). The strengths and limitations of proteomics techniques, including gel electrophoresis, mass spectrometry and microarray analysis are listed in Table I (10).

Proteomic techniques are used to identify and quantify changes in proteins (11), and determine potential associations between changes in protein expression and different stages of lymphoma development (12). Thus, proteomics provide valuable insights into alterations in the levels of proteins (13) and protein-related signaling molecules (14).

Ednersson et al (15) examined protein expression in formalin-fixed, paraffin-embedded tumor samples using quantitative proteomics in 202 patients with DLBCL. A total of 6,430 proteins were successfully identified. Of these proteins, a subset of 498 proteins were significantly differentially expressed between germinal center B-cell-like (GCB) and non-GCB cells. Notably, these proteins included guanylate-binding protein 1 (GBP1), CD64, CD85A and interferon-inducible protein with tetrapeptide repeat 2 and mixed lineage kinase domain-like protein (MLKL). In addition, immunohistochemical staining revealed the upregulation of GBP1 and MLKL protein expression in patients with DLBCL. Results of a previous clinical study demonstrated that human immunodeficiency virus (HIV)-related lymphoma is aggressive, with an increased incidence of drug resistance and a poor prognosis. Zhuang et al (16) used proteomics to screen 84 proteins that were differentially expressed between patients with AIDS and AIDS-NHL. Enrichment analysis of the differentially expressed proteins using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases indicated that the majority of proteins were closely associated with essential biological functions, including the humoral immune response and complement system activation. Protein-protein interaction analysis revealed extensive interactions among the proteins, including β₂-microglobulin, cathepsin D and various complement subunits. Collectively, these results highlighted the molecular changes occurring in patients with AIDS-NHL compared with patients with HIV infection alone; thus, demonstrating the differing molecular pathogenesis of AIDS-NHL.

Lymphomas are malignant tumors in which lymphocytes in the human body undergo different stages of development and differentiation (17). Lymphomas exhibit high levels of heterogeneity and a complex pathological classification, with different treatment responses among different pathological types. In addition, treatment responses may differ between patients with the same pathological type. Numerous factors, including cell proteomics and molecular features may impact the prognosis of patients (18). In clinical practice, patients with DLBCL often develop drug resistance (19), which is a barrier to treatment within clinical practice. Liu et al (20) examined samples from 14 patients with untreated DLBCL using mass spectrometry and two-dimensional (2D) gel electrophoresis, and quantitatively identified differentially expressed proteins between patients who were susceptible to CHOP treatment and those who were resistant. This approach allowed the comprehensive characterization of the proteomic landscape associated with chemotherapy response in DLBCL; thus, providing valuable insights into potential biomarkers and therapeutic targets for improving treatment outcomes. Results of the previous study demonstrated that the protein expression levels of histone H2A.2, S100A9, Ezrin and Pleckstrin were significantly increased. In addition, the protein expression levels of 61 kD protein, collagen alpha 1 (VI), glutathione S-transferase pi-1 and heat shock protein beta 1 were significantly lower in patients who were susceptible to CHOP treatment, compared with those that were resistant. Analyzing the protein network associated with resistance to CHOP chemotherapy may aid in identifying patients with DLBCL with CHOP resistance; thus, providing a novel theoretical basis for the identification of therapeutic targets.

Tumors form dynamic, complex and heterogeneous environments with various cells and surrounding components, known as the TME (21). The heterogeneity of DLBCL is associated with the types of cells in the TME (22), including matrix components, dendritic cells, macrophages, monocytes, fibroblasts and T cells (23). Notably, the extracellular matrix interacts with lymphoma cells (24), and high numbers of M2 macrophages, natural killer cells and plasma cells are associated with lower survival rates in patients with DLBCL (25). The TME plays a significant role in the initiation, development and treatment resistance of DLBCL, and these factors ultimately impact the prognosis of patients (19). In total, ~75% of patients with DLBCL possess aberrations in genes associated with immune escape (26), and the TME includes numerous inhibitory immune detection points (27). Liu et al (28) suggested that adaptor-related protein complex 2 subunit mu1 subunit may contribute to the resistance of DLBCL to chemotherapy and targeted medications through controlling the TME. Notably, results of previous studies highlighted that multiple components in the TME may impact the occurrence and development of DLBCL. Spatial proteomics analysis may provide location information for cells in the tissue (29), and this method may be used to explore the interaction between DLBCL cells and the TME (30).

Through the transcriptome analysis of 4,655 DLBCL microenvironments, Kotlov et al (31) identified four main types of lymphoma microenvironments. The composition of the DLBCL microenvironment was investigated using proteomics analysis and the establishment of a patient-driven tumor xenograft model. Results of these studies indicated that novel therapeutic options for the treatment of DLBCL should target tumor cells with specific genotypes, and consider the impact of different microenvironment types on lymphoma progression. Bouwstra et al (32) also used proteomics analysis, and results of the previous study demonstrated that the poor prognosis of patients with non-GCB type DLBCL following R-CHOP treatment may be associated with the upregulation of CD47. These results highlighted the occurrence of different DLBCL microenvironments derived from different cell sources that were regulated by intracellular genes and signal transduction. Different microenvironments may lead to the breakdown of homeostasis and microenvironment alterations in the tissue, ultimately resulting in lymphoma progression. Bram et al (15) used quantitative proteomics analysis to demonstrate that multiple proteins are involved in the development of DLBCL, including the upregulation of proteins in Activated B cell-like (ABC) DLBCL. Results of a cluster analysis demonstrated that the most common clusters contained proteins involved in the control of the immune system and TME, including MLKL. These clusters also included several damage-related molecular pattern proteins, including S-100A8, S100A9, fibrinogen-α and particulate lysin. Xu-Monette et al (33) investigated potential associations between MYC/BCL2 and microenvironment biomarkers in DLBCL isoforms. The results of the previous study revealed that the genotype, TME and high MYC/BCI2 double expression all played independent and interdependent roles in predicting the prognosis of DLBCL. Feng et al (34) used proteomics analysis to examine exosomes in the serum of patients with DLBCL, and the results demonstrated that chemotherapy-resistant DLBCL cells exhibited increased CA1 expression levels in exosomes, compared with chemotherapy-sensitive cells. In addition, results of the previous study demonstrated an association between the increased protein expression of CA1 in the TME and the prognosis of patients with DLBCL. Collectively, these results highlighted the potential of CA1 as a biomarker for assessing treatment efficacy and the prognosis of patients with DLBCL. In addition, further investigations are required to determine the specific role of the TME in the development, diagnosis, classification, treatment and prognosis of DLBCL.

The discovery and application of anti-CD20 monoclonal antibodies in the early 20th century led to a new era of DLBCL treatment (35). At present, global treatment guidelines recommend first-line therapy with R-CHOP, comprising rituximab with cyclophosphamide, doxorubicin, vincristine and prednisone (36), leading to a cure in ~60% of patients (37). However, a small number of patients continue to exhibit refractory disease or relapse following complete remission (38), and traditional salvage immunochemotherapy combined with autologous hematopoietic stem cell transplantation only achieves a cure in ~10% of these patients (39). Thus, the remaining 90% of patients exhibit poor treatment outcomes (Fig. 1). Thus, improving the prognosis of these patients is complex (40), and further proteomic analyses are required to determine the signaling pathways associated with the onset and progression of DLBCL (41). Novel developments in proteomics technology have led to the discovery of multiple drug resistance mechanisms in lymphoma (42); thus, strategies and methods that eliminate the drug resistance of lymphoma cells and improve the therapeutic effects are also required (43).

Proteomics includes the identification of differentially expressed proteins in DLBCL tissues (44), obtaining 2D electrophoresis profiles (45), and the use of mass spectrometry to identify associated proteins (46) (Fig. 2). Bioinformatics analysis is also used to identify differentially expressed proteins for further validation at the tissue level, which may provide a theoretical basis for subsequent experiments (47). In addition, further investigations are required to determine the specific mechanisms of DLBCL resistance and verify the feasibility of differentially expressed proteins as drug-resistance-related targets (48). Chen et al (49) carried out mRNA/protein analysis of clinicopathological samples, and the results demonstrated that inhibitors of bromodomain and extraterminal (BET) protein inhibited the progression of DLBCL. BET inhibition led to upregulation of GTPase regulatory protein (IQGAP3), which inhibited RAS protein activity in DLBCL cells, indicating that patients with DLBCL with low IQGAP3 expression levels exhibited a poor prognosis. In addition, BET inhibitors effectively controlled the progression of DLBCL. Collectively, these results provided a theoretical basis for targeting the BET protein (50) as a potential treatment strategy for DLBCL.

Advances in proteomics-associated technologies have demonstrated that the emergence of DLBCL chemoresistance is closely associated with signaling pathways (51), including the PI3K/Akt pathway. Akt promotes cell survival and proliferation (52), as well as dysregulation of key effectors controlling cell metabolism (53). A proteomics analysis conducted by Xu et al (52) revealed that removal of the PI3K/Akt signaling pathway antagonist, PTEN, led to inactivation of the PI3K/Akt pathway in GCB DLBCL. Thus, PI3K/Akt activation may play a key role in the development of GCB DLBCL, and these findings demonstrate the potential value of PTEN as a therapeutic target. PTEN acts as a lipoprotein phosphatase, dephosphorylating the 3′ position of phosphatidylinositol triphosphate, thereby reducing Akt activation (Fig. 3). Measurement of phosphorylated Akt levels indicated that PTEN expression was negatively correlated with PI3K/Akt activation in both a GCB DLBCL model and primary DLBCL samples (52). Bisserier and Wajapeyee (54) demonstrated that DLBCL cells resistant to Enhancer of Zeste Homolog 2 inhibitors exhibited activation of insulin-like growth factor I receptor, PI3K, and mitogen-activated protein kinase pathways. Feng et al (34) used proteomics technology to demonstrate the increased expression levels of exocrine carbonic anhydrase (CA)1, and the role of this protein as a biomarker for the prognosis of DLBCL. Notably, CA1 expression levels were also associated with an increased resistance to chemotherapy via the signal transducer and activator of transcription 3 signaling pathways and nuclear factor-κB.

Collectively, these results indicated that proteomic techniques exhibit potential in the differential and enrichment analyses of DLBCL-associated proteins for the subsequent discovery of novel therapeutic targets. Specific signaling pathway inhibitors also exhibit potential in highlighting the molecular mechanisms underlying drug resistance; thus, leading to the development of novel therapeutic options.

Proteomics technologies have improved the current understanding of the molecular changes associated with DLBCL (55,56). Storage of large amounts of proteomics data (57) is challenging; however, these often contain biologically significant results (7). Data storage may be aided through a combination of multiple omics techniques (58), and numerous biological techniques are used to examine lymphomas (59), including genomics (60), proteomics (61), epigenetics (62) and radiomics (63).

Fornecker et al (64) conducted a large-scale differential multi-group analysis of samples obtained from patients with DLBCL, with the main goal of identifying novel targets to overcome chemotherapeutic resistance and potential biomarkers for early recurrence risk. Through targeted RNA sequencing and non-labeled quantitative proteomics, results of the previous study revealed significant differences in the expression levels of 22 proteins and corresponding RNA between patients with typical DLBCL and patients with recurrent DLBCL. Notably, multiple key targets have successfully been identified using proteomics and transcriptomics techniques. Hexokinase 3 expression was significantly increased in patients with chemotherapeutic resistance, indicating that this protein may play a key role in the chemotherapeutic resistance of DLBCL. In addition, IDO1 is highly expressed in patients with chemotherapeutic resistance, and may exhibit potential as a novel immune checkpoint target. CXCL13 is overexpressed in patients with chemotherapeutic resistance and may play a crucial role in the microenvironment of DLBCL. The S100 protein is involved in regulating the proliferation, migration and invasion of cancer cells, and dysregulation of this protein is present in the majority of human cancers, such as breast, prostate, melanoma and colorectal (65). Results of a previous study demonstrated that the S100 protein may exhibit potential as a therapeutic target in R/R DLBCL. CD79B expression was significantly reduced at both protein and transcriptional levels; thus, a combination of transcriptome and proteome techniques (66) may aid in processing large datasets (67).

Moreover, results of previous studies revealed a regulatory role of interleukin-1 receptor-associated kinase (IRAK4) in lymphoma cell proliferation and inflammation through proteome and phosphorylation modifications (68). A series of targeted degradation agents of IRAK4 were used to study the effects of impaired IRAK4 function on the phosphorylation levels of downstream signaling proteins, and the results demonstrated that IRAK4 only partially participated in the regulation of ABC DLBCL cell proliferation and inflammatory signals (69). The survival of ABC DLBCL cells was not solely dependent on the function of IRAK4; thus, highlighting a requirement for the development of other drug targets in ABC DLBCL (70).

A combination of protein genomics, and proteome, transcriptome and genome data (71) has demonstrated potential in the discovery of novel biomarkers (72) and drug targets (73). A previous study used protein genomics to analyze the N-glycoprotein spectrum of 13 subtypes of lymphoma, spanning 32 cell lines (74). Using unsupervised clustering analysis, results of the previous study revealed that the N-glycoprotein spectrum categorized these cell lines according to lineage and cell origin. These conformed to the subtypes identified by the World Health Organization, and demonstrated that the N-glycoprotein spectrum of clinicopathological lymphoma samples may correspond with traditional pathological classification, providing a key theoretical basis for the discovery of novel drug targets (74). A computational biology tool; namely, Drug Combo Explorer, was developed to identify lymphoma signaling pathways. This tool integrated numerous existing DLBCL pharmacogenomics and proteomics data to provide effective and synergistic drug combinations for the treatment of lymphoma (75).

The integration of multi-omics technologies exhibits potential in the treatment of DLBCL (76). Proteomics may also be used in conjunction with other omics techniques, such as transcriptomics, metabolomics and genomics, to further the current understanding of the molecular landscape and mechanisms underlying DLBCL (77). This integrative approach exhibits potential in the discovery of novel biomarkers, therapeutic targets and personalized treatment strategies for patients with DLBCL.

In conclusion, proteomics techniques are widely established in the study of DLBCL (78), and proteomics have been used in investigating the pathogenesis, drug resistance and mechanisms of lymphoma, the evaluation of prognosis, and guiding treatment plans (79). Further developments in proteomics-associated technologies are required for the identification of novel drugs and drug targets for the treatment of DLBCL (80). For example, Maurer et al (81) found that DLBCL patients with elevated serum free light chain (sFLC) had a relatively poor prognosis using FREELITE analysis. Then, Witzig et al (82) conducted a 6-year monitoring of FLC concentrations in patients with DLBCL and found that patients with DLBCL belonged to the FLC monoclonal and polyclonal groups; and the results revealed that elevated FLC was an adverse factor in the poor prognosis of DLBCL patients, and the aforementioned study provides new ideas for the treatment of DLBCL (7). Spatial proteomics, also known as spatiomics technology, is advancing (83). This technique is used to examine biological components, such as RNA and proteins, and adds ‘location’ dimensional information to further the current understanding of the microenvironment (84). Spatial proteomics has been used in breast cancer research and treatment; Cords et al (85) used highly multiplexed imaging mass cytometry on breast cancer samples matched to single-cell RNA sequencing datasets to confirm their cancer-associated fibroblast phenotypes defined at the protein level, and used spatial proteomics to analyze their spatial distributions in tumors, which provided a new strategy for this treatment. Notably, spatial proteomics is being used in lymphoma research (86), and may exhibit potential in the treatment of DLBCL. Spatial proteomics involves analysis of the subcellular localization of proteins in a systematic and high-throughput manner (87), where proteins simultaneously exist in different subcellular locations (88) and travel between them (89). For example, spatial proteomics may be used to demonstrate the spatial profile of proteins in the liver of patients with obesity, and these results are compared with healthy individuals to determine the localization of hepatocytes. Thus, spatial proteomics may aid in the treatment of patients with liver disease (90). In an era of rapid advances in medical technology, the use of spatial proteomics for the analysis and precise treatment of DLBCL can help to develop personalized treatment plans for patients and improve the cure and survival rates of DLBCL patients. However, due to the limitations of research methods and research data, there is still a lot of space for the wide application of spatial proteomics.