Identification of molecular subtypes and a novel prognostic model of diffuse large B-cell lymphoma based on a metabolism-associated gene signature

The GSE10846 Series Matrix File data were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (the annotation platform was GPL570). The data of 412 DLBCL patients with a complete mRNA expression profile and survival time > 0 was extracted from the GSE10846 dataset. Of the 412 patients in the GSE10846 dataset, 232 patients had undergone R-CHOP treatment, while 180 patients only received CHOP treatment. We randomly divided (4:1 ratio) the 412 cases extracted from the GSE10846 dataset into a training cohort (n = 330) and a testing cohort (n = 82). Additionally, data on DLBC were downloaded from The Cancer Genome Atlas (TCGA) database (https://​portal.​gdc.​cancer.​gov/​), and 47 DLBCL cases with a complete mRNA expression profile and survival time > 0 were obtained from the TCGA database. We used the TCGA dataset (n = 47) as an external validation cohort to evaluate the predictive efficacy and robustness of the prognosis-associated risk model. Relevant grouping information and clinicopathological features were shown in Table 1.

MAGs were obtained from the GeneCards database (https://​www.​genecards.​org/​). We identified 92 candidate prognosis-related MAGs in GSE10846 by univariate Cox regression. Based on the 92 MAGs, the 412 patients were divided into subgroups with different metabolic expression patterns by consensus clustering using the "ConsensusClusterPlus" R package, and unbiased and unsupervised outcomes were obtained.

The 92 candidate MAGs related to prognosis were selected, and a prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) regression. The risk score formula (based on the expression of each included gene weighted by its LASSO regression coefficient) was constructed using the following format: risk score = \({\sum }_{i=1}^{n}coef*gene expression\). Thereafter, the risk score of each patient was calculated. Using the median risk score as the cutoff, the training cohort was divided into low- and high-risk groups. Survival curves were generated by the Kaplan–Meier method and the two groups were compared using the log-rank test. A time-dependent receiver operating characteristic (ROC) curve analysis was used to study the model prediction accuracy. Cox regression was used to assess the independent prognostic value of the risk score and other clinicopathological features. To provide a reference for predicting the prognosis of DLBCL patients, we used the "rms" R package to construct a nomogram based on the risk score and clinicopathological features, and a calibration plot was used to assess the prognostic ability of the nomogram.

The Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) method was performed to calculate the stromal score, immune score, ESTIMATE score, and tumor purity. Next, the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm was used to analyze the RNA-Seq data of DLBCL patients in order to determine the relative proportions of 22 infiltrating immune cells. Furthermore, to quantify the immune cell infiltration in each sample, single-sample Gene Set Enrichment Analysis (ssGSEA) was used to assess the enrichment of 28 immune cells in the tumor samples. We then calculated the correlations between the risk score and immune regulatory genes, especially immune checkpoints.

Based on the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://​www.​cancerrxgene.​org/​), which is the largest pharmacogenomics database, we used the "pRRophetic" R package to predict the chemotherapy sensitivity of each tumor sample. The estimated half-maximal inhibitory concentration (IC50) value of each chemotherapy drug was obtained by regression, and the accuracy of regression and prediction was tested by cross-validation with GDSC training set for 10 times. All parameters were selected as default values, including "combat" for removing batch effect and the average value of repeated gene expression. Furthermore, we used FunRich (v3.1.3) and NPInter (v4.0) to construct a ceRNA network based on the model genes.

GSVA is a non-parametric and unsupervised method for evaluating the enrichment of gene sets in relation to mRNA expression data. In this study, gene sets were downloaded from the Molecular Signatures Database (v7.0). Each gene set was comprehensively scored by the GSVA algorithm, and the potential differences in biological functions between the high- and low-risk groups were evaluated. Additionally, to explore the functions of the prognosis-associated MAGs, the "ClusterProfiler" R package was used to annotate the genes with their predicted functions based on Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. GO terms and KEGG pathways with p and q values < 0.05 were deemed statistically significant.

To identify the hub genes among the 14 model genes, we used the WGCNA algorithm. After constructing a weighted gene co-expression network, the gene co-expression modules were identified, and the correlations between gene network and clinical phenotype were explored. The WGCNA-R package was used to construct the co-expression network of all genes in the GSE10846 dataset, and the genes with variance within the first 5000 were identified by the algorithm for subsequent analysis. The soft-threshold β was determined by the function "sft$powerEstimate". The weighted adjacency matrix was transformed into a topological overlap matrix (TOM) to estimate the network connectivity, with hierarchical clustering being used to construct the clustering tree structure of the TOM. Different branches of the clustering tree represented different gene modules, and different colors represented different modules. Tens of thousands of genes were classified into modules based on having similar expression patterns (using their weighted correlation coefficients).

The DLBCL TMA contained 104 DLBCL tissues and 28 reactive hyperplasia tissues (from cases with the same gender ratio and age range) collected from 2008 to 2015. It was prepared by the Department of Clinical Biobank of the Affiliated Hospital of Nantong University. Clinicopathological data, including gender, age, B symptoms, Ann Arbor stage, hemoglobin (Hb) level, LDH level, IPI score were collected. In addition, X-tile 3.6.1 software was performed to determine the optimal cutoff values for two hub genes expression. This study was a retrospective study, and the informed consent of all patients was obtained before the study. The Ethics Committee of the Affiliated Hospital of Nantong University approved this research.

The DLBCL TMA slides were stained with multiplex fluorescence by using the Opal 7-color Manual IHC Kit (PerkinElmer, MA). After dewaxing by xylene and rehydration by ethanol, slides were heated in a microwave with AR6 Buffer (AR600, AKOYA) and AR9 Buffer (AR900, AKOYA) for antigen retrieval. The slides were incubated with primary antibodies overnight at 4 °C and then incubated with secondary antibody for 10 min at room temperature. At last, we used 4',6-diamidino-2-phenylindole (DAPI; F6057, Sigma) to stain the nuclei and seal the slides. Imaging was achieved using the Vectra 3.0 Automated Quantitative Pathology Imaging System. Tumor and stroma images were captured at ×20 magnification. Finally, the staining was scored by inForm® Cell Analysis software based on the intensity and degree of staining. The degree of staining was compared using the Wilcoxon rank-sum test.

The primary antibodies used in this study were as follows: rabbit anti-PHKA1 (24279-1-AP, Proteintech), rabbit anti-PLTP (ab282456, Abcam), rabbit anti-CD163 (93498, Cell Signaling Technology), rabbit anti-CD68 (76437, Cell Signaling Technology), rabbit anti-CD11B (49420, Cell Signaling Technology), mouse anti-CD66b (ARG66287, Arigobio), rabbit anti-PD-1 (86163, Cell Signaling Technology) and rabbit anti-PD-L1 (13684, Cell Signaling Technology). The secondary antibody was Opal™ polymer HRP Ms + Rb (ARH1001EA, Perkin Elmer).

Survival curves were generated by the Kaplan–Meier method and compared using the log-rank test. Multivariate Cox proportional hazards regression was used to identify independent prognostic factors. Wilcoxon rank-sum test was applied to continuous variables with nonnormal distribution. All statistical analyses were performed in R software (v4.0). All statistical tests were two tailed, and p < 0.05 was considered statistically significant.

The whole study process is depicted in the flow chart in Additional file 1: Figure S1. First, we obtained 958 MAGs from the GeneCards database by using “metabolism” as the search term and setting the relevance score > 5. Second, DLBC mRNA expression data (Fragments Per Kilobase of transcript per Million mapped reads [FPKM]) in the GSE10846 dataset was downloaded, and 877 MAGs were extracted (based on the 958 MAGs identified using GeneCards). To identify the prognosis-associated genes among the 877 MAGs, we used prognostic data on DLBCL patients and obtained 92 prognosis-associated MAGs by univariate Cox regression (p < 0.001; Additional file 2: Figure S2).

Based on the expression patterns of 92 prognosis-associated MAGs, we used consensus clustering to cluster the 412 patients in the GSE10846 dataset into different metabolism-associated molecular subgroups. By increasing the clustering variable (k) from 2 to 5, we found that consensus clustering was most suitable when k = 2 (Fig. 1A). This indicated that DLBCL patients could be readily divided into two clusters, with 183 patients in cluster 1 and 229 patients in cluster 2. Heatmap visualization showed significant differences in the expression of the 92 MAGs between the two clusters (Fig. 1B). Survival analysis showed that the overall survival in the two clusters was different, with cluster 1 patients having a significantly worse prognosis than cluster 2 patients (follow-up time was 20 years in cluster 1 and 10 years in cluster 2) (Fig. 1C). In addition, the CIBERSORT algorithm was used to evaluate the differences in immune cell infiltration between the two clusters. The abundances of infiltrating immune cells between clusters are shown in Additional file 3: Figure S3, and the quantitative analysis demonstrated that there were significant differences. Cluster 1 had higher infiltration levels of Tregs, NK cells resting and mast cells activated, and lower infiltration levels of T cells gamma delta, T cells CD4 memory activated, monocytes and dendritic cells resting, while cluster 2 showed the opposite trends (Fig. 1D). Analysis of the differences in the expression of immune checkpoints between the two clusters showed that ADORA2A, CD244, CD274, CSF1R, CTLA4, HAVCR2, KIR2DL1, KIR2DL3, LAG3, LGALS9, PDCD1, TGFB1, TGFBR1, and VTCN1 expression levels were significantly higher in cluster 1 than in cluster 2 (Fig. 1E). Higher infiltration of immunosuppressive cells and increased expression of immune checkpoints in the cluster 1 indicated an immunosuppressive tumor microenvironment, which was consistent with the poor prognosis. These findings indicate that the expression of MAGs is related to the prognosis and the immunosuppressive microenvironment in DLBCL patients.

We further conducted functional enrichment analyses of the 92 prognosis-associated MAGs. GO analysis indicated that in biological processes (BP), 92 MAGs were mainly enriched in monosaccharide metabolic process, vitamin metabolic process, cofactor metabolic process, cofactor biosynthetic process and small molecule catabolic process. In cell component (CC), 92 MAGs were mainly enriched in cytochrome complex, respiratory chain complex, oxidoreductase complex, mitochondrial inner membrane and mitochondrial matrix. Regarding molecular function (MF), 92 MAGs were mainly enriched in fatty acid derivative binding, acyl-CoA dehydrogenase activity, amide binding, vitamin binding and coenzyme binding (Additional file 4: Figure S4A). Moreover, KEGG analysis revealed similar pathways, including biosynthesis of cofactors, starch and sucrose metabolism, central carbon metabolism in cancer, fatty acid degradation, and biosynthesis of amino acids (Additional file 4: Figure S4B). Combined with the results of GO and KEGG, we found that 92 MAGs were significantly enriched in many key biosynthetic and metabolic pathways, especially the biosynthesis and metabolism of cofactors, which might contribute to the hyperproliferation of tumor cells and dismal outcomes. Furthermore, we used Cytoscape software to construct a protein–protein interaction network of these prognostic MAGs (Additional file 4: Figure S4C).

Based on the MAGs identified in univariate Cox regression analyses, we selected 14 MAGs by LASSO regression in order to construct a metabolism-associated prognosis risk model (Additional file 5: Figure S5 and Table 2). The 412 GEO patients were randomly divided (4:1 ratio) into a training cohort (n = 330) and a testing cohort (n = 82). Because of random grouping, the expression patterns of 14 MAGs in the training cohort and the testing cohort were similar. After constructing the model, the risk score of each DLBCL patient in the training cohort was computed based on the following formula: risk score = NR3C1 × (− 0.285969433071478) + IGFBP3 × (− 0.16695536054869) + RARRES2 × (− 0.14291303044122) + F5 × (− 0.0961037965837689) + APOC1 × (− 0.0768487272489624) + CSF2RA × (− 0.056110125649913) + ENPP1 × (− 0.0221430402174049) + GYG1 × 0.0449620982512186 + PHKA1 × 0.0693510636252306 + CPT1A × 0.0752116074259248 + PDK4 × 0.0767229743647787 + CLOCK × 0.0858851468938173 + CTH × 0.108708800807851 + PLTP × 0.16013617077787. Patients were divided into high- and low-risk groups according to the median risk score. The distribution of risk score, survival status, and the expression of the 14 MAGs in the training cohort are depicted in Fig. 2A. Combining the hazard ratio and gene expression heatmap of 14 MAGs, we found that the expression of genes with hazard ratio > 1, such as GYG1, PHKA1, CPT1A, PDK4, CLOCK, CTH, and PLTP, was higher in the high-risk group, while the expression of genes with hazard ratio < 1, such as NR3C1, IGFBP3, RARRES2, F5, APOC1, CSF2RA, and ENPP1, was higher in the low-risk group. Additionally, Kaplan–Meier curves indicated that the DLBCL patients in the high-risk group had significantly worse overall survival (OS) (Fig. 2B). The area under the curve (AUC) values of the ROC curves for 1-, 2-, and 3-year OS were 0.79, 0.81, and 0.81, respectively (Fig. 2C), demonstrating the great predictive performance of the prognosis-associated risk model.

Next, we used the GEO testing cohort as an internal validation cohort and the TCGA dataset as an external validation cohort to evaluate the prediction performance and robustness of the prognosis-associated risk model. Using the same formula, we obtained consistent results in the testing cohort, which confirmed the robustness of the risk model. The risk score distribution and gene expression profiles are shown in Fig. 2D. OS was significantly worse in the high-risk group than the low-risk group (Fig. 2E). The AUCs for 1-, 2-, and 3-year OS were 0.79, 0.83 and 0.81, respectively (Fig. 2F). In addition, the TCGA results concurred with the GEO training cohort results. The risk score distribution and gene expression profile are shown in Fig. 2G. Patients with higher risk scores had worse OS (Fig. 2H). The AUCs for 1-, 2-, and 3-year OS were 0.78, 0.61, and 0.61, respectively, indicating that the risk model had a strong prognostic value for DLBCL patients in the TCGA validation cohort (Fig. 2I). In conclusion, these results confirmed that the risk model had a robust and accurate ability to predict OS.

Next, we further validated the clinical value of the risk score. Firstly, we investigated the applicability of the risk score to the immunotherapy cohort. Rituximab (an anti-CD20 monoclonal antibody) plus polychemotherapy (R-CHOP) is the standard of care in DLBCL. Among the 412 patients in GSE10846, only 232 received R-CHOP treatment. We selected these 232 patients as the immunotherapy cohort and compared the survival differences between the high- and low-risk groups in this cohort. The results showed that OS was significantly worse in the high-risk group than the low-risk group (Fig. 3A), indicating that the risk score also had a strong prognostic value for patients who have received immunotherapy. Secondly, to identify whether the risk score was related to clinicopathological features, we compared the risk scores between groups divided based on clinical features, as shown in box plots in Fig. 3B–H. Using Kruskal–Wallis rank sum tests, we found that the risk score significantly differed by age, ECOG status, stage, LDH level, and IPI score (Fig. 3B–F), but it was not related to gender or extranodal sites (Fig. 3G, H). These results indicated that the risk score had good clinical value for classifying DLBCL samples.

In addition, univariate and multivariate Cox regression indicated that risk score was an independent prognostic factor in DLBCL (Fig. 3I). Next, we constructed a prognostic nomogram that integrated the risk score and all significant clinical features. Nomograms can be used to quantitatively predict the prognosis of patients, providing a reference for clinical decision-making. The nomogram demonstrated that the risk score contributed the most to the prognosis, more than the IPI score and other clinical features (such as age, stage, ECOG status, and LDH level) (Fig. 3J). Additionally, the calibration curves for 3- and 5-year OS indicated high consistency between the nomogram predictions and actual observations (Fig. 3K). These findings confirmed that the risk score was a satisfactory prognostic tool for use in DLBCL. Instinctively, we would compare it with the current widely used scoring system, IPI score. Time-dependent ROC curve analysis was further used to determine which scoring system best predicted the OS of DLBCL patients. The AUC of the risk score for predicting 1-year OS (AUC = 0.799) was significantly higher than that of IPI score (AUC = 0.631) (Fig. 3L). This demonstrated that the risk score was superior to the IPI score regarding survival prediction accuracy in DLBCL.

The tumor immune microenvironment significantly affects the therapeutic effect and prognosis of tumor. We assessed the relationship between the risk score and the tumor immune microenvironment in DLBCL by multiple immune analyses. The ESTIMATE results indicated that the patients in the high-risk group had significantly lower stromal and ESTIMATE scores, and higher tumor purity, than those in the low-risk group (Fig. 4A). The ssGSEA was used to assess the immune status of the two groups, which suggesting that the DLBCL patients in the high-risk group had a relatively low immune status (Fig. 4B), which was consistent with the ESTIMATE results. In addition, the results of the CIBERSORT analysis revealed that there were significant differences in most infiltrating immune cells (Additional file 6: Figure S6A). The high-risk group was associated with significantly increased abundances of B cells naive, monocytes, macrophages M2, NK cells resting, NK cells activated, and significantly decreased abundances of T cells CD4 naive, T cells follicular helper, T cells gamma delta, macrophages M0 and dendritic cells resting, while the low-risk group showed the opposite trends (Fig. 4C). Higher abundance of immunosuppressive cells in the high-risk group indicated an immunosuppressive tumor microenvironment, which was consistent with the poor prognosis.

Additionally, we further explored the correlations between the risk score and immune cell content. The results showed that the risk score was positively correlated with macrophages M2, T cells CD4 naive, monocytes, B cells naïve, NK cells resting, and negatively correlated with T cells gamma delta, macrophages M0 and T cells follicular helper (Fig. 4D).

Thereafter, the immune-regulatory genes were further analyzed, and the differences in the expression levels of immune-related chemokines, immunosuppressants, immunostimulants, major histocompatibility complex (MHC) factors, and immune receptors between the high- and low-risk groups are shown in a heatmap (Additional file 6: Figure S6B). As immune cell dysfunction and immunosuppressive microenvironments are characterized by high expression of immune checkpoint-related transcripts, we subsequently focused on the differences in immune checkpoint expression levels and their correlations with the risk score. The results showed that the expression levels of ADORA2A, CD274, CSF1R, IL10, KIR2DL1, KIR2DL3, LGALS9, and TGFB1 were significantly higher in the high-risk group than the low-risk group (Fig. 4E). The correlations between the risk score and the expression of immune checkpoints showed that the risk score was positively correlated with the expression of CD274, CSF1R, KIR2DL1, KIR2DL3, LGALS9, and PVRL2, and negatively correlated with the expression of CD96, PDCD1LG2, TGFBR1, and TIGIT (Additional file 6: Figure S6C). The significantly increased expression of most immune checkpoints in the high-risk group further confirmed that the poor prognosis of high-risk patients was partly related to the immunosuppressive microenvironment. Based on these results, we can reasonably assume that the immunosuppressive microenvironments (characterized by low immune scores, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints) led to poor prognosis in the high-risk group. This is also consistent with our immune analysis of metabolism-associated molecular subtypes. Therefore, the risk model based on 14 MAGs is related to the immunosuppressive microenvironment of DLBCL, and abnormal immune cell infiltration and differential expression of immune checkpoints can be used as prognostic indicators and targets of immunotherapy, which is clinically significant.

Based on the drug sensitivity data from the GDSC database, we used the "pRRophetic" R package to predict the drug sensitivity of each patient. Gemcitabine, vinblastine, and metformin had lower IC50 values in the high-risk group, while cisplatin and etoposide had higher IC50 values in the high-risk group (Additional file 7: Figure S7A). Furthermore, to explore the discrepancies in signaling pathways between the high- and low-risk groups, GSVA was performed. As shown in Additional file 7: Figure S7B, the differentially enriched signaling pathways between the two groups mainly involved the unfolded protein response, xenobiotic metabolism, KRAS signaling, glycolysis, TGF beta signaling, epithelial–mesenchymal transition, and heme metabolism. The GSVA results suggest that disturbances in these signaling pathways may worsen the prognosis of DLBCL patients in the high-risk group compared to the low-risk group.

To further understand how the 14 MAGs in the risk model regulate mRNA expression by acting as miRNA sponges in DLBCL, we constructed a ceRNA network based on the 14 MAGs. Using FunRich to reverse predict miRNAs based on the 14 MAGs, 57 miRNAs and 130 mRNA-miRNA interactions were identified. Subsequently, using NPInter to reverse predict lncRNAs, 7395 mRNA-miRNA-lncRNA interactions were obtained. Finally, a ceRNA network related to the 14 genes was successfully constructed (Additional file 8: Figure S8). These data may provide clues for identifying the regulatory mechanism of the 14 MAGs in DLBCL.

To identify the most critical genes among the 14 model genes for further experimental verification, the WGCNA algorithm was applied. We constructed a weighted gene co-expression network based on the genes with variance within the first 5000 in the GSE10846 dataset (Additional file 9: Figure S9A). The soft-threshold was set to 1 to build a scale-free network (Additional file 9: Figure S9B). Next, we constructed an adjacency matrix and transformed it into a topological overlapping matrix (TOM). Based on the TOM, three gene modules were identified, namely blue (208), brown (151) and turquoise (4641) modules. Correlation analyses between the modules and clinical trait showed that the turquoise module had the highest correlation (cor = 0.4, p = 2e⁻¹⁷) (Additional file 9: Figure S9C). To identify the hub genes among the 14 model genes, we identified the overlapping genes between the turquoise module and the 14 model genes. This led to two hub genes being identified: PLTP and PHKA1 (Fig. 5A). Then, firstly, we explored the correlation between these two hub genes and infiltrating immune cells. We found that both hub genes were positively correlated with the infiltration of M2 macrophages and CD8 T cells (Fig. 5B). Secondly, we further analyzed the correlation between the two hub genes and immune checkpoints. As we expected, we found that PD-1, PD-L1 and LAG3, the common immune checkpoints on T cells, were also positively correlated with both hub genes, which indicated that the highly infiltrated CD8 T cells were dysfunctional (Fig. 5C). Thus, we speculated that these two hub genes were closely related to the immunosuppressive microenvironment and the potential mechanism of these two genes in DLBCL deserved further verification and discussion.

Next, we preliminarily verified the two hub genes using mIHC in our own TMA cohort. First, we compared DLBCL tissues with benign reactive hyperplasia tissues, and we found that the expression levels of PLTP and PHKA1 were both significantly increased in DLBCL patients (Fig. 5D, E). Next, X-tile analysis of 5-year OS was performed using the TMA cohort to determine the optimal cutoff values for PLTP and PHKA1 expression. According to the optimal cutoff value for each gene, we divided the 104 DLBCL samples in the TMA cohort into high- and low-expression groups (high-PLTP expression: n = 36, low-PLTP expression: n = 68; and high-PHKA1 expression: n = 40, low-PHKA1 expression: n = 64). Relevant grouping information and clinicopathological features are shown in Tables 3, 4. We then studied the differences in the tumor immune microenvironment between the pairs of groups. Regarding PLTP, the high-expression group had worse prognosis and higher infiltration of M2 macrophages and tumor-associated macrophages (TAMs) compared to the low-expression group (Fig. 6A–E). Moreover, PLTP expression was positively correlated with clinical stage (Table 3) and immune checkpoints PD-1 and PD-L1, but not correlated with LAG3 (Table 3 and Fig. 6F–I). Likewise, regarding PHKA1, the high-expression group had worse prognosis and higher infiltration of M2 macrophages and TAMs compared to the low-expression group (Fig. 7A–E). Moreover, PHKA1 expression was positively correlated with immune checkpoints PD-1 and PD-L1, but not correlated with clinical features and LAG3 (Table 4 and Fig. 7F–I). Finally, univariate and multivariate Cox regression analyses indicated that PLTP and PHKA1 were both independent prognostic factors for patients with DLBCL (Additional file 10: Figure S10). Our verification results confirmed that these two genes were both overexpressed in DLBCL tissues, and their expression levels were closely related to the prognosis and immunosuppressive microenvironment of DLBCL. The conclusions were basically consistent with the results of our bioinformatics analyses.