A novel lipid metabolism-based risk model associated with immunosuppressive mechanisms in diffuse large B-cell lymphoma

Abstract

Background: The molecular diversity exhibited by diffuse large B-cell lymphoma (DLBCL) is a significant obstacle facing current precision therapies. However, scoring using the International Prognostic Index (IPI) is inadequate when fully predicting the development of DLBCL. Reprogramming lipid metabolism is crucial for DLBCL carcinogenesis and expansion, while a predictive approach derived from lipid metabolism-associated genes (LMAGs) has not yet been recognized for DLBCL.

Methods: Gene expression profiles of DLBCL were generated using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The LASSO Cox regression was used to construct an effective predictive risk-scoring model for DLBCL patients. The Kaplan-Meier survival assessment was employed to compare a given risk score with the IPI score and its impact on the survival of DLBCL patients. Functional enrichment examination was performed utilizing the KEGG pathway. After identifying hub genes via single-sample GSEA (ssGSEA), immunohistochemical staining and immunofluorescence were performed on lymph node samples from control and DLBCL patients to confirm these identified genes.

Results: Sixteen lipid metabolism- and survival-associated genes were identified to construct a prognostic risk-scoring approach. This model demonstrated robust performance over various datasets and emerged as an autonomous risk factor for predicting the development of DLBCL patients. The risk score could significantly distinguish the development of DLBCL patients from the low-risk and elevated-risk IPI classes. Results from the inhibitory immune-related pathways and lower immune scores suggested an immunosuppressive phenotype within the elevated-risk group. Three hub genes, MECR, ARSK, and RAN, were identified to be negatively correlated with activated CD8 T cells and natural killer T cells in the elevated-risk score class. Ultimately, it was determined that these three genes were expressed by lymphoma cells but not by T cells in clinical samples from DLBCL patients.

Conclusion: The risk level model derived from 16 lipid metabolism-associated genes represents a prognostic biomarker for DLBCL that is novel, robust, and may have an immunosuppressive role. It can compensate for the limitations of the IPI score in predicting overall survival and has potential clinical application value.

Keywords: Diffuse large b-cell lymphoma; Immune infiltration; Lipid metabolism; Prognosis.

Fig. 1

Development of the lipid metabolism-based risk level approach for DLBCL patients. (A) Authentication of 523 lipid metabolism-related genes in three datasets (GSE181063, GSE10846, and NCICCR) using Venn diagrams. Changes in color denote differences in datasets. (B) LASSO coefficients of 16 obtained LMAGs over the 10-fold cross-validation approach. Vertical dotted lines denote the optimal values utilizing the minimum and 1-SE criteria. (C) Partial likelihood variance was uncovered using the LASSO regression model as well as the 10-fold cross-validation. Vertical dotted lines denote the optimal values utilizing the minimum and 1-SE criteria. (D) Forest plot of the linkages between the infiltrating levels of 16 prognostic molecules as well as the OS of the training cohort. The HR, 95% CI, and P-value were computed using univariate Cox regression analysis. (E) Coefficients for the 16 prognostic molecules within the Cox regression model. (F) The risk score distribution and survival levels of 16-gene signatures from the GSE181063 dataset. (G) Survival curves across the two risk groups from the GSE181063 dataset

Fig. 2

Robust confirmation of risk score approach in testing cohorts. Division of risk score and survival status of 16-gene signatures from the NCICCR dataset (A), GSE10846 R-CHOP dataset (C), GSE10846 CHOP dataset (E), and GSE11318 dataset (G). Survival curves across two risk classes in the NCICCR dataset (B), GSE10846 R-CHOP dataset (D), GSE10846 CHOP dataset (F), and GSE11318 dataset (H)

Fig. 3

Comparison of the lipid metabolism-based risk level and IPI score. (A-B) AUC values of risk score and IPI score over the course of 12 years from the GSE181063 and NCICCR datasets. (C-D) Kaplan-Meier curves denoting OS between the high- and low-risk groups from DLBCL patients possessing different IPI scores from the GSE181063 and NCICCR datasets

Fig. 4

Creation of a nomogram combining the risk score with the IPI score. (A) The 1-year, 3-year, and 5-year survivability of DLBCL patients was predicted by a nomogram based on their risk scores, IPI, and total points. (B-C) Time-dependent C-index chart for the nomogram as well as various clinical factors from the GSE181063 and NCICCR datasets. (D-E) Calibration plots used for prediction in DLBCL patients with 3-, 5-, and 7-year OS in the GSE181063 and NCICCR datasets. X-axis showed the nomogram-predicted survivability, while y-axis displayed the actual survivability

Fig. 5

Functional enrichment examination of the lipid metabolism-derived risk level approach. (A) GSVA examination of the biological pathways within the high- and low-risk score groups from the GSE181063, GSE10846 R-CHOP, GSE10846 CHOP, GSE11318, and NCICCR datasets. Orange and blue indicates the activation and inhibition of biological pathways, respectively. (B) GSEA indicates a significant increase in natural killer cell-mediated cytotoxicity, and T-cell receptor signaling pathways in the GSE181063 cohort. (C) Estimate score, immunity score, and stromal score across the high- and low-risk groups from the GSE181063 cohort. (D) ssGSEA comparison of the scores from various infiltrating immunity cells across DLBCL patients with high- and low-risk scores from the GSE181063 cohort. ***P < 0.001; **P < 0.01; *P < 0.05

Fig. 6

Identification of Immune-Independent Hub Genes. (A) Correlation analysis of the 16 genes associated with the T-cell receptor signaling pathway across multiple datasets, including GSE181063, GSE10846 R-CHOP, GSE10846 CHOP, GSE11318, and NCICCR. (B) Examination of the correlation between the 16 genes and natural killer cell-mediated cytotoxicity in the same datasets as in (A). (C) Pearson correlation analysis illustrating the relationship between the expression of the 5 lipid metabolism/survival-related genes and the levels of infiltrating immune cells in the GSE181063 dataset. (D) Box plots displaying the expression patterns of the 5 genes analyzed using the GEPIA website. DLBCL patients are represented in red, while normal controls are depicted in grey. *P < 0.05

Fig. 7

Clinical sample verification. (A) Microscopic images illustrating immunohistochemical staining for RAN, MECR, and ARSK in lymph node sections from both controls and DLBCL patients. Both images were captured at 400× magnification. The horizontal bar on the right demarcates the region displaying positive signal expression of RAN, MECR, and ARSK in the lymph node sections between these two groups. Scale bar corresponds to 200 pixels. ^***P < 0.001; ^**P < 0.01; ^*P < 0.05. (B-C) Selected immunofluorescent photograph depicting the expression of RAN, MECR, and ARSK alongside the marker for lymphoma cell (CD20 in B) or the T cell (CD3 in C) in lymph node sections obtained from DLBCL patients. DAPI was utilized for nuclear staining (bar = 500 pixels)