Analyzing the involvement of diverse cell death-related genes in diffuse large B-cell lymphoma using bioinformatics techniques

Abstract

Diffuse large B-cell lymphoma (DLBCL) stands as the most prevalent subtype of non-Hodgkin's lymphoma and exhibits significant heterogeneity. Various forms of programmed cell death (PCD) have been established to have close associations with tumor onset and progression. To this end, this study has compiled 16 PCD-related genes. The investigation delved into genes linked with prognosis, constructing risk models through consecutive application of univariate Cox regression analysis and Lasso-Cox regression analysis. Furthermore, we employed RT-qPCR to validate the mRNA expression levels of certain diagnosis-related genes. Subsequently, the models underwent validation through KM survival curves and ROC curves, respectively. Additionally, nomogram models were formulated employing prognosis-related genes and risk scores. Lastly, disparities in immune cell infiltration abundance and the expression of immune checkpoint-associated genes between high- and low-risk groups, as classified by risk models, were explored. These findings contribute to a more comprehensive understanding of the role played by the 16 PCD-associated genes in DLBCL, shedding light on potential novel therapeutic strategies for the condition.

Keywords: Biomarkers; Diffuse large B-Cell lymphoma; Prognosis; Programmed cell death; Risk model.

Fig. 1

The technological process diagram.

Fig. 2

Results of univariate Cox analysis and enrichment analysis of PCD genes. A is a forest plot obtained from univariate Cox regression analysis. B is the bar graph of metascape analysis of prognosis-related PCD genes. C and D are the circle chart and column chart of GO enrichment analysis.

Fig. 3

Risk models constructed with prognostic correlation genes and model performance validation. A shows the partial likelihood deviation plots. B is the distribution of LASSO coefficients. C and D are the KM survival curves for the risk models on the training and test sets, respectively. E and F are line plots illustrating the relationship between survival time and Concordance index in the training and testing datasets, respectively.

Fig. 4

Assessment of risk models. A and B are the expression heat maps of the risk genes constituting the risk model in the training and test sets, respectively. C and D are scatter plots reflecting the relationship between risk group assignment and risk scores in the training and testing datasets, respectively. E and F are scatter plots reflecting the relationship between risk scores and survival status in the training and testing datasets, respectively. G and H are visualizations after PCA reduction using the risk genes that make up the risk model on the training and test sets, respectively, with each point in the figure representing a sample.

Fig. 5

Results of independent prognostic analysis. A and B are forest plots obtained from univariate Cox regression analysis and multivariate Cox regression analysis for risk scores and clinical factors, respectively. C is the nomogram model constructed from clinical factors and risk scores. D is the result of the DCA analysis. E is the calibration curve of the nomogram model. F is the result of the ROC analysis of the nomogram model.

Fig. 6

GSEA analysis results. A and B are the upper five pathways obtained from GSEA analysis for the high- and low-risk groups, respectively.

Fig. 7

Results of ssGSEA analysis on the training set. A is a heatmap of the correlation between prognosis-related genes and the abundance of immune cell infiltration obtained from ssGSEA analysis. B–I are correlation analyses between risk scores and immune cells with significant correlation.

Fig. 8

Results of ssGSEA analysis on the test set. A is a heatmap of the correlation between prognosis-related genes and the abundance of immune cell infiltration obtained from ssGSEA analysis. B-L are the risk scores with significant correlation and the correlation analysis between immune cells.

Fig. 9

Analysis of immunological differences between high- and low-risk groups. A and B are differential box plots of the expression of genes associated with immune check loci and HLA gene expression for the high-and low-risk groups, respectively. C, D, and E are the results of the Estimate analysis (ESTIMATEScore, ImmuneScore, and StromalScore).

Fig. 10

Results of drug sensitivity analysis. A-L is a box plot of compounds with significantly different IC50 values between high- and low-risk subgroups.

Fig. 11

The mRNA expression levesl of AEN、DNAJC10、DNM1L、ELL3 and HIF1A.