The latest refined classification by the World Health Organization (WHO) categorizes large B-cell lymphoma as a heterogeneous group of B-cell lymphomas (Alaggio et al., 2022). DLBCL is the most prevalent type among them, accounting for around 30% of all non-Hodgkin lymphomas. DLBCL was initially classified by the Hans algorithm into two subtypes: germinal center B-cell-like (GCB) and non-GCB (Hans et al., 2004). Further differentiation has since identified three distinct gene-based subtypes: GCB, activated B-cell-like (ABC), and the unclassified category (Sehn and Salles, 2021). After undergoing R-CHOP chemotherapy, about 60% of patients achieve long-term remission; however, approximately 30% of patients experience relapse, resulting in poor prognosis and a considerable number of deaths from refractory lymphoma (Autio et al., 2021). Consequently, to explore the characteristics of DLBCL in detail is urgently needed for developing more effective therapy.

Solid tumor tissue comprises tumor cells and the surrounding stroma, which encompasses diverse types of matrix cells, immune cells, endothelial cells (Joyce and Pollard, 2009), etc. The tumor microenvironment (TME) is a complex and dynamic system that consists of the extracellular matrix and a variety of cellular components. Recent studies have unveiled multiple subgroups of immune cells within the microenvironment of DLBCL, including T cells, B cells, NK cells, monocytes/macrophages, dendritic cells, as well as the distribution of stromal cell components like fibroblasts and endothelial cells (Steen et al., 2021; Ciavarella et al., 2018). Despite the relatively limited composition of the TME in DLBCL, its role in tumor proliferation and evasion of the immune system should not be disregarded. The interaction between tumors and the microenvironment is a vital factor that impacts the development and prognosis of B-cell lymphoma (Ennishi et al., 2020). Nevertheless, the existing research on the influence of the TME on the prognosis of DLBCL patients is limited and lacks a consensus.

Moreover, the comprehensive investigation of non-immune cell components in the TME is still lacking. Previous research on stroma in DLBCL has predominantly indicated that a higher quantity of extracellular matrix is associated with a more favorable prognosis, while increased vascular density is associated with poorer prognosis (Miyawaki et al., 2022). Furthermore, higher stromal scores have been associated with an improved prognosis in DLBCL patients (Schmitz et al., 2018). Additionally, a fibrotic tumor microenvironment has been correlated with a better prognosis after DLBCL chemotherapy and immunotherapy (Lou et al., 2022). These research findings stem from computational analysis of stromal and immune scoring in gene databases and have not been experimentally validated as of yet.

Tumor purity quantifies the relative ratio of tumor cells to the surrounding stromal components in solid tumors, elucidating the dynamics between tumor cells and their microenvironment (Gong et al., 2020). It can partly reflect the characteristics of TME, namely, a higher tumor purity indicates a lower abundance of stromal components in TME. Tumor purity is associated with patients’ prognosis, and the strength of this association varies across different tumor types (Lou et al., 2021; Zhao et al., 2023; Zhang et al., 2017). Therefore, when investigating the influence of TME on the prognosis of DLBCL patients, it is crucial to analyze not only the immune cell components but also the significance of non-immune cell components.

This study utilized bioinformatic analysis to establish the relationship between immune and stromal components and the prognostic outcomes of DLBCL patients. We developed a novel immunohistochemical panel to assess prognostic outcomes and treatment sensitivity by detecting the expression of VCAN, CD3G, C1QB, CD68, CD4, and CD8 in both the TME and tumor cells of 190 DLBCL patients. We then explored their relationship with DLBCL clinicopathological features as well as overall survival (OS).

In this study, bioinformatics techniques were employed to identify three genes (VCAN, CD3G, C1QB) that exhibit associations with prognosis in both immune and stromal environments, thereby revealing their relationship with the prognosis of DLBCL patients. The findings indicate that higher expression of VCAN, increased infiltration of CD3G+ T cells, and decreased expression of C1QB are correlated with favorable prognostic outcomes. Conversely, a lower infiltration of CD68 + macrophages and lower infiltration of CD8 + T cells are associated with poorer prognosis. Furthermore, we investigated the relationship between risk genes related to tumor purity and treatment sensitivity and established a list of possible drugs that might be helpful for enhancing outcomes.

Previous studies have extensively investigated the VCAN gene in relation to tumorigenesis and metastasis (Baghy et al., 2016). VCAN, also known as versican, is a crucial component of extracellular matrix (Wight, 2017), and exists in several isoforms (Fujii et al., 2015). Research has shown that VCAN plays a multifaceted role in TME depending on the cell type expressing it. When expressed by myeloid cells, VCAN induces an anti-inflammatory and immunosuppressive microenvironment. Conversely, its expression by stromal cells typically leads to a pro-inflammatory response (Wight et al., 2020). In gastric cancer, high expression of VCAN has been associated with increased infiltration of fibroblasts, significant enrichment of stromal-associated signaling pathways, and poor prognosis (Song et al., 2022). In hepatocellular carcinoma, VCAN exhibits a strong association with immune checkpoint gene expression (Wang et al., 2022). Despite these findings in other tumor types, the role of VCAN in DLBCL has not been explored yet. Our study reveals that high expression of VCAN is actually associated with a more favorable prognosis. This suggests that VCAN may have different functions in different tumor types. One possible mechanism through which VCAN influences prognosis is that VCAN overexpression in DLBCL may also impact tumor cell proliferation. A study has shown that overexpression of VCAN V1 has an inhibitory effect on cell proliferation, partly due to its promotion of activation-induced cell death in lymphoid cell lines (Fujii et al., 2015). Hence, the high expression of VCAN in DLBCL could impact not only the TME but also tumor cell proliferation, suggesting a potential mechanism for the observed preferable prognosis.

C1q is synthesized in the tumor microenvironment and functions as an extracellular matrix protein, and C1QB is a component of C1q (Yang et al., 2022). Previous studies have provided insights into the diverse roles of C1q in cancer progression. However, the majority of these results, as observed in non-small cell lung carcinoma and gastric cancer, indicate that high C1q expression in TME is associated with a poor prognosis (Li et al., 2023; Mangogna et al., 2019; Jiang et al., 2020). Additionally, C1QB has been found to exert an impact on the TME and is positively associated with infiltration levels of CD8 + T cell, as well as with M1 and M2 macrophages in osteosarcoma (Chen et al., 2021a). Moreover, C1QB expression shows a positive correlation with predictive biomarkers for immunotherapy, such as PD-L1 expression and CD8 + T cell infiltration (Jiang et al., 2020). Furthermore, in malignant melanoma, C1QB promotes proliferation, migration, and invasion, while inhibiting cell apoptosis (Zheng et al., 2021), and the high-expression group exhibits significant enrichment of genes related to immune and apoptosis (Yang et al., 2022). In our study, we found that high expression of C1QB in DLBCL was associated with a worse prognosis and positively correlated with CD8 + T cells infiltration. Based on these findings, we propose that C1QB in DLBCL might share similarities with its functions in other tumor types, particularly regarding the promotion of recruitment and subsequent deactivation of CD8 + T cells within the TME through the induction of immune checkpoint effects. These results shed light on the intricate role of C1QB in TME and its potential significance as a prognostic marker in DLBCL.

CD3G is a member of the TCR/CD3 complex primarily expressed in lymphocytes subgroups. It plays a crucial role in initiating the activation of T cells (Wang et al., 2008). It is also involved in coupling antigen recognition (Chen et al., 2021b). It is reported to be associated with long-term OS and good prognosis in breast invasive carcinoma (Wang et al., 2019) as well as in head and neck squamous cell carcinoma (Wang et al., 2021). However, its role in DLBCL has not been fully explored. In our study, we revealed that high infiltration of CD3G+ T cells is correlated with good prognosis. The infiltration of CD3G+ T cells was found to be positively related to the infiltration of CD8+, CD4 +, and CD68+ cells. This indicates that CD3G+ T cells in DLBCL may enhance the tumor antigen recognition process and stimulate the infiltration of immune cells, leading to an increased abundance of immune cell infiltration in the TME. The presence of CD3G+ T cells in the TME may contribute to a favorable prognosis by facilitating the activation of immune responses against tumor cells.

Macrophages play a crucial role in TME, and CD68 is a surface marker specific to macrophages. Macrophages can be roughly classified into two types based on their functional features: M1 or M2. M1 macrophages exert anti-tumor effects, whereas M2 macrophages promote tumor growth and progression in TME (Zhang et al., 2021). A previous study found that low infiltration of CD68 + macrophages was associated with an inferior prognosis (Croci et al., 2021). Similarly, our study has yielded similar results, revealing a noteworthy correlation between a high proportion of CD68 + macrophages in the TME and improved prognosis. Additionally, by analyzing the datasets, we observed a higher proportion of M1 macrophages infiltration compared to M2 macrophages. This suggests that, within our DLBCL cohort, these macrophages may also exhibit the M1 phenotype and consequently play a protective role against tumor progression.

CD8 is widely recognized as a marker of CD8 + T cells, also known as cytotoxic T cells (Farhood et al., 2019). These cells are crucial for the immune response against tumors. However, in DLBCL, CD8 + T cells exhibits elevated levels of inhibitory molecules on their surface, such as PD-1, PD-L1, and TIM3. High expression of TIM3, an inhibitory immune checkpoint receptor, on CD8 + T cells has been associated with tumor progression and poor outcomes (Roussel et al., 2021; Xu-Monette et al., 2019). These inhibitory molecules may impair the function of CD8 + T cells and hinder their anti-tumor activity. Surprisingly, our study demonstrates a correlation between the infiltration of CD8 +T cells and favorable prognosis in DLBCL. Here, we propose a hypothesis that in our study, the observed high expression of VCAN might create a suppressive environment for PD-1 + CD8+ T cells (Wight et al., 2020; Hirani et al., 2021). Intriguingly, our study revealed a statistically significant correlation between VCAN expression, C1QB expression, and CD8 + T cell infiltration. VCAN has the potential to modulate immune infiltration by reducing the immunosuppressive phenotype of immune cells (Huang et al., 2021), thus enabling a more efficient anti-tumor response. This aspect is still worth of consideration.

Taken together, our findings underscore the significant roles of VCAN, CD3G, and C1QB, which influence both the TME and the behavior of tumor cells. The interaction between each component and the TME is rather complicated. To fully comprehend the underlying mechanisms and identify potential therapeutic targets in DLBCL, further investigation is required.

However, this study still has several limitations that should be addressed. First, the patients included in this study were from a single center, which may introduce biases into the results. Although we made efforts to minimize these biases, it is inevitable that some may persist. Second, we hypothesized that VCAN, CD3G, and C1QB could serve as continuous prognostic parameters, thereby eliminating the need for a cut-off. However, the methodology used in this study, which utilized IHC staining to assess the protein expression levels, may have potential limitations. While IHC is a widely used technique, additional validation is needed to confirm the prognostic value of VCAN, CD3G, and C1QB in DLBCL. Furthermore, due to the potential variability in interpreting IHC results across different centers, a standardized coefficient and formula have not been established to calculate the final prognostic index for patients with DLBCL. Developing a standardized approach would be beneficial in ensuring consistent and accurate interpretation of IHC results. To address these limitations and expand upon our findings, future studies should strive to incorporate a diverse range of patients from multiple centers. Additionally, it is crucial to employ rigorous experimental techniques to authenticate the prognostic significance of VCAN, CD3G, and C1QB in DLBCL.

Data from GEO are GSE53786 and GSE32918. Original human subjects data of CHCAMS cohort are provided as supplementary files.

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Author details

1. Zhenbang Ye

   Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft

   Contributed equally with

   Ning Huang

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3169-5023

2. Ning Huang

   Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Visualization, Methodology, Writing – original draft

   Contributed equally with

   Zhenbang Ye

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8874-2601

3. Yongliang Fu

   Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

4. Rongle Tian

   Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

5. Liming Wang

   Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

   Contribution

   Resources, Formal analysis, Supervision, Methodology

   For correspondence

   stewen_wang@sina.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0418-405X

6. Wenting Huang

   1. Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
   2. Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China

   Contribution

   Conceptualization, Resources, Supervision, Methodology, Writing – review and editing

   For correspondence

   huangwt@cicams.ac.cn

   Competing interests

   No competing interests declared

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