The molecular heterogeneity of DLBCL brings great challenges to precision therapy. It is generally accepted that the traditional IPI score cannot adequately predict the prognosis of DLBCL, and developing more reliable strategies for subtype identification and risk stratification is urgent [3, 19]. In this study, we identified two metabolism-associated molecular subtypes, and there were significant differences in prognosis and the immune microenvironment between these two subtypes. In addition, we developed a prognostic risk model based on 14 MAGs. We found that it was a powerful independent prognostic tool with better predictive performance than the IPI score and was closely related to the immunosuppressive microenvironment. Finally, we identified two hub genes among the model genes, and preliminarily verified them in our own TMA cohort using mIHC. Our results may contribute to the development of accurate immunotherapy for DLBCL that targets metabolic pathways.
Consensus clustering is an unsupervised clustering method that can identify different molecular subtypes according to a gene expression matrix . Using consensus clustering, we identified two metabolism-associated molecular subtypes, which had significant differences in prognosis and the immune microenvironment. Compared to cluster 2, the prognosis of the patients in cluster 1 was poor, accompanied by a high abundance of immunosuppressive cells and a general increase in the expression of immune checkpoints, indicating an immunosuppressive microenvironment. This is consistent with findings regarding other malignancies [6, 13, 14, 21]. As the consensus clustering was based on a MAG expression matrix, we inferred that the expression of MAGs was related to the prognosis and immunosuppressive microenvironment of DLBCL patients.
To further evaluate the prognostic value of the MAGs, we established a 14-gene risk model in the GEO training cohort by univariate Cox regression and LASSO regression. We then constructed a prognostic nomogram that integrated the risk score based on this model and all significant clinical features. The risk score effectively predicted prognosis in the GEO training cohort and was validated in a GEO internal validation cohort and a TCGA external validation cohort. ROC curve analysis confirmed that the risk score was superior to the traditional IPI score. Multiple validation methods indicated the robustness of the risk model, and it is reasonable to believe that this risk model will be broadly applicable for individualized risk management. As previously mentioned, in view of the close relationship between metabolic reprogramming and the tumor immune microenvironment, we performed multiple immune analyses (ESTIMATE, ssGSEA, and CIBERSORT) to explore the differences in the immune landscape between the high- and low- risk groups. As expected, the high-risk group had a poor prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints. The low-risk group showed the opposite trend, with a better prognosis and a relatively immunopositive microenvironment. This is also consistent with our immune analysis of metabolism-associated molecular subtypes. An increased risk score indicates a “cold tumor” , with attenuated immunotherapy effectiveness and an immunosuppressive tumor microenvironment caused by metabolic reprogramming, which is consistent with poor prognosis. These conclusions further indicated that MAGs might play important roles in the altered immune response in DLBCL.
Notably, in the two groups with poor prognosis (cluster 1 and the high-risk group), in addition to the increase in the abundance of immunosuppressive cells and the expression of immune checkpoints, there was a significant increase in the infiltration of resting and activated NK cells. This is consistent with the results of previous studies, that is, an increased abundance of activated NK cells is associated with poor prognosis . NK cell dysfunction is common in hematological cancer, and it is related to tumor immune escape . We also found that KIR2DL1 and KIR2DL3 , the common immune checkpoints on NK cells, were also significantly overexpressed in cluster 1 and the high-risk group. In the future, immunotherapy that blocks KIR2DL1/KIR2DL3 might reduce the abundance of activated NK cells.
Most of the MAGs in the risk model have been reported to be associated with cancer. To identify the most critical genes, i.e., the hub genes, among the 14 model genes for further experimental verification, we used the WGCNA algorithm to select key genes and then identified the overlapping genes among these genes and the model genes. As a result, we identified two hub genes: PLTP and PHKA1. The potential mechanisms of these two hub genes in DLBCL deserve further discussion.
Phospholipid transfer protein (PLTP) is a widely expressed lipid transfer protein that belongs to the lipopolysaccharide (LPS)-binding/lipid transfer gene family. PLTP can promote the transfer of a series of lipid molecules, including diacylglycerol, phosphatidic acid, sphingomyelin, phosphatidylcholine, phosphatidylglycerol, brain glycosides, and phosphatidylethanolamine. These transport functions play an important role in lipid and lipoprotein metabolism [26, 27]. PLTP is differentially expressed in many kinds of tumors, such as prostate cancer , ovarian cancer , breast cancer , lung cancer , gastric cancer  and glioma . Such a wide range of cancer types with differential expression of PLTP indicate that PLTP may be an important regulator of some common processes related to tumors.
The phosphorylase kinase regulatory subunit alpha 1 (PHKA1) gene encodes the muscle-type isoform of the PHK alpha subunit . PHKA1 plays a key role in glycogen metabolism  and PHKA1 mutations cause glycogen storage disease type 9D, also known as X-linked muscle glycogenosis . However, research on PHKA1 in tumors is still limited. Research has shown that PHKA1, as an important gene related to glycogen metabolism, is related to the metastasis of prostate cancer . In addition, the increased expression of PHKA1 was associated with younger ages of gastrointestinal stromal tumor patients .
We further preliminarily validated the two hub genes in our TMA cohort using mIHC, which can quantify immune cells in the tumor microenvironment more objectively than traditional semi-quantitative methods . Our verification results confirmed that the two hub genes were both overexpressed in DLBCL tissues. Thereafter, using X-tile (a valuable tool for outcome-based cutoff optimization)  and the 5-year OS of patients, we determined the optimal cutoff value for PLTP and PHKA1 expression. Based on each cutoff value, we subdivided the DLBCL patients into high- and low-expression groups, and further studied the differences in the tumor immune microenvironment between the pairs of groups. We found that the prognosis of the high-expression groups was poorer, accompanied by an immunosuppressive microenvironment characterized by higher abundances of immunosuppressive cells (M2 macrophages and TAMs) and higher expression of immune checkpoints (PD-L1 and PD-1). Finally, univariate and multivariate Cox regression analyses indicated that PLTP and PHKA1 were both independent prognostic factors in DLBCL. These experimental results showed that high expression of the hub genes was closely related to the prognosis and immunosuppressive microenvironment of DLBCL, which was consistent with our bioinformatics analyses, and further verified the stability and accuracy of the risk model.
Studies have shown that metabolic reprogramming is an important feature of immune cell activation. Immune cells have different metabolic characteristics, which affect their immune function [16, 18]. Macrophages, as the main immune-infiltrating cells in solid tumors, can polarize into inflammatory (M1) or immunosuppressive (M2) phenotypes based on external stimuli. M1 macrophages have pro-inflammatory and anti-tumor effects, while M2 macrophages have anti-inflammatory and pro-tumor effects . The metabolic reprogramming of tumors can affect the polarization process of macrophages [41, 42]. For example, hypoxia and lactic acid accumulation can promote the production of immunosuppressive M2 macrophages. The increase in tumor glycolysis produces a large amount of lactic acid, and the accumulation of lactic acid drives macrophages toward the M2 phenotype. M2 macrophages overexpress arginase 1 (ARG1). ARG1 consumes L-arginine, which is necessary for cytotoxic T lymphocytes to exert anti-tumor activity, and produces polyamines with strong immunosuppressive effects [18, 43]. Additionally, hypoxia promotes tumor development by inducing the production of angiogenic factors, mitogenic factors, and cytokines related to tumor metastasis in macrophages . Additionally, macrophages can undergo lipid-based metabolic reprogramming to promote tumor progression via increased membrane cholesterol efflux [44, 45]. Moreover, M2 macrophages up-regulate fatty acid oxidation, mitochondrial respiration, and angiogenesis, thereby promoting tumor progression [9, 46]. Our mIHC results also confirmed that M2 macrophages in DLBCL patients with high metabolic gene expression were significantly increased. Therefore, M2 macrophages may have potential as immunotherapy targets.
Our study also showed that the expression of most immune checkpoints significantly increased with increasing risk score, indicating an immunosuppressive microenvironment that was consistent with poor prognosis. Recent research has shown that checkpoint signals regulate metabolism . For example, PD-L1 in tumor cells can activate the PI3K-Akt-mTOR pathway, stimulate glycolysis, and enhance glucose uptake by the tumor cells . CD155-TIGIT signaling in T cells of human gastric cancer inhibits glucose uptake, lactic acid production, and glycolytic enzyme expression . Therefore, combining metabolic inhibitors with checkpoint inhibitors is expected to improve the efficacy of checkpoint blockade [49, 50]. Powell's team showed that a glutamine metabolism inhibitor not only improved the immunosuppressive microenvironment, but also effectively reversed PD-1 inhibitor resistance when combined with a PD-1 inhibitor .
Our research has some unique advantages. In this study, two metabolism-associated DLBCL subtypes were identified, and a risk model based on MAGs was constructed. We used multiple validation methods to evaluate the model: first, we tested the model in a GEO internal testing cohort, then in a TCGA external validation cohort, and finally we identified two hub genes and carried out preliminary verification in our own TMA cohort. Satisfactory results were obtained from the multiple validation methods, confirming the robustness and accuracy of the risk model. In addition, we not only studied the predictive performance of the risk model, but also explored the effect of MAG expression on the tumor immune microenvironment in DLBCL.